Spaces:
Runtime error
Runtime error
File size: 3,955 Bytes
616dc83 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
data_aug_scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800]
data_aug_max_size = 1333
data_aug_scales2_resize = [400, 500, 600]
data_aug_scales2_crop = [384, 600]
data_aug_scale_overlap = None
batch_size = 2
modelname = 'groundingdino'
backbone = 'swin_B_384_22k'
position_embedding = 'sine'
pe_temperatureH = 20
pe_temperatureW = 20
return_interm_indices = [1, 2, 3]
enc_layers = 6
dec_layers = 6 # originally 6
pre_norm = False
dim_feedforward = 2048
hidden_dim = 256
dropout = 0.0
nheads = 8 # originally 8
num_queries = 900
query_dim = 4
num_patterns = 0
num_feature_levels = 4
enc_n_points = 4
dec_n_points = 4
two_stage_type = 'standard'
two_stage_bbox_embed_share = False
two_stage_class_embed_share = False
transformer_activation = 'relu'
dec_pred_bbox_embed_share = True
dn_box_noise_scale = 1.0
dn_label_noise_ratio = 0.5
dn_label_coef = 1.0
dn_bbox_coef = 1.0
embed_init_tgt = True
dn_labelbook_size = 91
max_text_len = 256
text_encoder_type = "bert-base-uncased"
use_text_enhancer = True
use_fusion_layer = True
use_checkpoint = True
use_transformer_ckpt = True
use_text_cross_attention = True
text_dropout = 0.0
fusion_dropout = 0.0
fusion_droppath = 0.1
sub_sentence_present = True
max_labels = 50 # pos + neg
lr = 0.001 #0.001 # base learning rate
backbone_freeze_keywords = None # only for gdino backbone
lora = True
trainable_keywords = ['transformer' , 'input_proj' , 'feat_map' , 'backbone.0' ] # for whole model, e.g. ['backbone.0', 'bert'] for freeze visual encoder and text encoder
lr_backbone = 1e-05 # specific learning rate
lr_backbone_names = ['backbone.0', 'bert']
lr_linear_proj_mult = 1e-05
lr_linear_proj_names = ['ref_point_head', 'sampling_offsets']
weight_decay = 0.001 #0.001
param_dict_type = 'ddetr_in_mmdet'
ddetr_lr_param = False
epochs = 50
lr_drop = 4
save_checkpoint_interval = 1
clip_max_norm = 0.1
onecyclelr = False
multi_step_lr = False
cosine_anneal = False
ReduceLROnPlateau = True
step_lr = False
gamma = 0.95
lr_drop_list = [2 , 5, 10 , 15 , 20 ]
frozen_weights = None
dilation = False
pdetr3_bbox_embed_diff_each_layer = False
pdetr3_refHW = -1
random_refpoints_xy = False
fix_refpoints_hw = -1
dabdetr_yolo_like_anchor_update = False
dabdetr_deformable_encoder = False
dabdetr_deformable_decoder = False
use_deformable_box_attn = False
box_attn_type = 'roi_align'
dec_layer_number = None
decoder_layer_noise = False
dln_xy_noise = 0.2
dln_hw_noise = 0.2
add_channel_attention = False
add_pos_value = False
two_stage_pat_embed = 0
two_stage_add_query_num = 0
two_stage_learn_wh = False
two_stage_default_hw = 0.05
two_stage_keep_all_tokens = False
num_select = 10
batch_norm_type = 'FrozenBatchNorm2d'
masks = False
aux_loss = True
set_cost_class = 1.0
set_cost_bbox = 5.0
set_cost_giou = 2.0
cls_loss_coef = 2.0 # originally 2.0
bbox_loss_coef = 5.0
giou_loss_coef = 2.0
enc_loss_coef = 1.0
interm_loss_coef = 1.0
no_interm_box_loss = False
mask_loss_coef = 1.0
dice_loss_coef = 1.0
focal_alpha = 0.25
focal_gamma = 2.0
decoder_sa_type = 'sa'
matcher_type = 'HungarianMatcher'
decoder_module_seq = ['sa', 'ca', 'ffn']
nms_iou_threshold = -1
dec_pred_class_embed_share = True
# label_list = [
# "airplane", "airport", "baseballfield", "basketballcourt", "bridge","chimney", "dam",
# "Expressway-Service-area", "Expressway-toll-station", "golffield",
# "groundtrackfield","harbor" , "overpass", "ship", "stadium", "storagetank",
# "tenniscourt", "trainstation", "vehicle" , "windmill"
# ] RSVGD
label_list = ["airplane","baseball diamond","basketball court","bridge","crossroad","ground track field","harbor","parking lot","ship","storage tank","swimming pool","tennis court","T junction","vehicle"]
match_unstable_error = True
use_ema = False
ema_decay = 0.9997
ema_epoch = 0
use_detached_boxes_dec_out = False
use_coco_eval = False
dn_scalar = 100 |