diff --git "a/downstream/det/retinanet.log" "b/downstream/det/retinanet.log" new file mode 100644--- /dev/null +++ "b/downstream/det/retinanet.log" @@ -0,0 +1,4111 @@ +2024/10/26 15:32:19 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 1576405368 + GPU 0,1,2,3: A100-SXM4-40GB + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.8, V11.8.89 + GCC: gcc (GCC) 7.3.1 20180303 (Red Hat 7.3.1-5) + PyTorch: 2.1.2+cu118 + PyTorch compiling details: PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.8 + - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_90,code=sm_90 + - CuDNN 8.7 + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.2, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.16.2+cu118 + OpenCV: 4.9.0 + MMEngine: 0.10.5 + +Runtime environment: + cudnn_benchmark: False + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 1576405368 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +2024/10/26 15:32:20 - mmengine - INFO - Config: +auto_scale_lr = dict(base_batch_size=16, enable=False) +backend_args = None +bs_ratio = 2 +data_root = 'data/coco/' +dataset_type = 'CocoDataset' +default_hooks = dict( + checkpoint=dict(interval=1, type='CheckpointHook'), + logger=dict(interval=50, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='DetVisualizationHook')) +default_scope = 'mmdet' +env_cfg = dict( + cudnn_benchmark=False, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_scales = [ + ( + 1333, + 800, + ), + ( + 666, + 400, + ), + ( + 2000, + 1200, + ), +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50) +max_epochs = 12 +model = dict( + backbone=dict( + depth=[ + 2, + 3, + 2, + ], + distillation=False, + down_ops=[ + [ + 'subsample', + 2, + ], + [ + 'subsample', + 2, + ], + [ + '', + ], + ], + drop_path=0.03, + embed_dim=[ + 200, + 376, + 448, + ], + forward_type='v052d', + frozen_stages=-1, + global_ratio=[ + 0.8, + 0.7, + 0.6, + ], + img_size=224, + in_chans=3, + kernels=[ + 7, + 5, + 3, + ], + local_ratio=[ + 0.2, + 0.2, + 0.3, + ], + norm_eval=True, + num_classes=80, + num_heads=[ + 4, + 4, + 4, + ], + out_indices=( + 1, + 2, + 3, + ), + patch_size=16, + pretrained= + '../../weights/MobileMamba_B1/mobilemamba_b1.pth', + ssm_ratio=2, + stages=[ + 's', + 's', + 's', + ], + sync_bn=False, + type='MobileMambaF', + window_size=[ + 7, + 7, + 7, + ]), + bbox_head=dict( + anchor_generator=dict( + octave_base_scale=4, + ratios=[ + 0.5, + 1.0, + 2.0, + ], + scales_per_octave=3, + strides=[ + 8, + 16, + 32, + 64, + 128, + ], + type='AnchorGenerator'), + bbox_coder=dict( + target_means=[ + 0.0, + 0.0, + 0.0, + 0.0, + ], + target_stds=[ + 1.0, + 1.0, + 1.0, + 1.0, + ], + type='DeltaXYWHBBoxCoder'), + feat_channels=256, + in_channels=256, + loss_bbox=dict(loss_weight=1.0, type='L1Loss'), + loss_cls=dict( + alpha=0.25, + gamma=2.0, + loss_weight=1.0, + type='FocalLoss', + use_sigmoid=True), + num_classes=80, + stacked_convs=4, + type='RetinaHead'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_size_divisor=32, + std=[ + 58.395, + 57.12, + 57.375, + ], + type='DetDataPreprocessor'), + neck=dict( + add_extra_convs='on_input', + in_channels=[ + 200, + 376, + 448, + ], + num_extra_trans_convs=1, + num_outs=5, + out_channels=256, + start_level=0, + type='EfficientViTFPN'), + test_cfg=dict( + max_per_img=100, + min_bbox_size=0, + nms=dict(iou_threshold=0.5, type='nms'), + nms_pre=1000, + score_thr=0.05), + train_cfg=dict( + allowed_border=-1, + assigner=dict( + ignore_iof_thr=-1, + min_pos_iou=0, + neg_iou_thr=0.4, + pos_iou_thr=0.5, + type='MaxIoUAssigner'), + debug=False, + pos_weight=-1), + type='RetinaNet') +optim_wrapper = dict( + clip_grad=dict(max_norm=0.1, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.0002, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + absolute_pos_embed=dict(decay_mult=0.0), + norm=dict(decay_mult=0.0), + relative_position_bias_table=dict(decay_mult=0.0))), + type='OptimWrapper') +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=500, start_factor=1e-05, type='LinearLR'), + dict( + T_max=6, + begin=6, + by_epoch=True, + end=12, + eta_min=0, + type='CosineAnnealingLR'), +] +ratio = 1 +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + ann_file='annotations/instances_val2017.json', + backend_args=None, + data_prefix=dict(img='val2017/'), + data_root='data/coco/', + pipeline=[ + dict(backend_args=None, type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 1333, + 800, + ), type='Resize'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + meta_keys=( + 'img_id', + 'img_path', + 'ori_shape', + 'img_shape', + 'scale_factor', + ), + type='PackDetInputs'), + ], + test_mode=True, + type='CocoDataset'), + drop_last=False, + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + ann_file='data/coco/annotations/instances_val2017.json', + backend_args=None, + format_only=False, + metric='bbox', + type='CocoMetric') +test_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 1333, + 800, + ), type='Resize'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + meta_keys=( + 'img_id', + 'img_path', + 'ori_shape', + 'img_shape', + 'scale_factor', + ), + type='PackDetInputs'), +] +train_cfg = dict(max_epochs=12, type='EpochBasedTrainLoop', val_interval=1) +train_dataloader = dict( + batch_sampler=dict(type='AspectRatioBatchSampler'), + batch_size=4, + dataset=dict( + ann_file='annotations/instances_train2017.json', + backend_args=None, + data_prefix=dict(img='train2017/'), + data_root='data/coco/', + filter_cfg=dict(filter_empty_gt=True, min_size=32), + pipeline=[ + dict(backend_args=None, type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(keep_ratio=True, scale=( + 1333, + 800, + ), type='Resize'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackDetInputs'), + ], + type='CocoDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='DefaultSampler')) +train_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(keep_ratio=True, scale=( + 1333, + 800, + ), type='Resize'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackDetInputs'), +] +tta_model = dict( + tta_cfg=dict(max_per_img=100, nms=dict(iou_threshold=0.5, type='nms')), + type='DetTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale=( + 1333, + 800, + ), type='Resize'), + dict(keep_ratio=True, scale=( + 666, + 400, + ), type='Resize'), + dict(keep_ratio=True, scale=( + 2000, + 1200, + ), type='Resize'), + ], + [ + dict(prob=1.0, type='RandomFlip'), + dict(prob=0.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations', with_bbox=True), + ], + [ + dict( + meta_keys=( + 'img_id', + 'img_path', + 'ori_shape', + 'img_shape', + 'scale_factor', + 'flip', + 'flip_direction', + ), + type='PackDetInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + ann_file='annotations/instances_val2017.json', + backend_args=None, + data_prefix=dict(img='val2017/'), + data_root='data/coco/', + pipeline=[ + dict(backend_args=None, type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 1333, + 800, + ), type='Resize'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + meta_keys=( + 'img_id', + 'img_path', + 'ori_shape', + 'img_shape', + 'scale_factor', + ), + type='PackDetInputs'), + ], + test_mode=True, + type='CocoDataset'), + drop_last=False, + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + ann_file='data/coco/annotations/instances_val2017.json', + backend_args=None, + format_only=False, + metric='bbox', + type='CocoMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='DetLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/retinanet_mobilemamba_b1_fpn_1x_coco' + +2024/10/26 15:33:41 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) DetVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) DetVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +2024/10/26 15:34:06 - mmengine - WARNING - backbone.blocks1.0.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False +2024/10/26 15:34:06 - mmengine - WARNING - backbone.blocks1.0.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.0002 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.0002 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0 +2024/10/26 15:34:06 - mmengine - WARNING - backbone.blocks1.1.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False +2024/10/26 15:34:06 - mmengine - WARNING - backbone.blocks1.1.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.0002 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.0002 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0 +2024/10/26 15:34:06 - mmengine - WARNING - backbone.blocks2.3.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False +2024/10/26 15:34:06 - mmengine - WARNING - backbone.blocks2.3.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.0002 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.0002 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0 +2024/10/26 15:34:06 - mmengine - WARNING - backbone.blocks2.4.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False +2024/10/26 15:34:06 - mmengine - WARNING - backbone.blocks2.4.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.0002 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.0002 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0 +2024/10/26 15:34:06 - mmengine - WARNING - backbone.blocks2.5.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False +2024/10/26 15:34:06 - mmengine - WARNING - backbone.blocks2.5.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.0002 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.0002 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0 +2024/10/26 15:34:06 - mmengine - WARNING - backbone.blocks3.3.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False +2024/10/26 15:34:06 - mmengine - WARNING - backbone.blocks3.3.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.0002 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.0002 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0 +2024/10/26 15:34:06 - mmengine - WARNING - backbone.blocks3.4.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False +2024/10/26 15:34:06 - mmengine - WARNING - backbone.blocks3.4.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.0002 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.0002 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0 +2024/10/26 15:34:06 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0 +Name of parameter - Initialization information + +backbone.patch_embed.0.c.weight - torch.Size([25, 3, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.patch_embed.0.bn.weight - torch.Size([25]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.patch_embed.0.bn.bias - torch.Size([25]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.patch_embed.2.c.weight - torch.Size([50, 25, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.patch_embed.2.bn.weight - torch.Size([50]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.patch_embed.2.bn.bias - torch.Size([50]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.patch_embed.4.c.weight - torch.Size([100, 50, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.patch_embed.4.bn.weight - torch.Size([100]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.patch_embed.4.bn.bias - torch.Size([100]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.patch_embed.6.c.weight - torch.Size([200, 100, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.patch_embed.6.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.patch_embed.6.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.dw0.m.c.weight - torch.Size([200, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.dw0.m.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.dw0.m.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.ffn0.m.pw1.c.weight - torch.Size([400, 200, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.ffn0.m.pw1.bn.weight - torch.Size([400]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.ffn0.m.pw1.bn.bias - torch.Size([400]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.ffn0.m.pw2.c.weight - torch.Size([200, 400, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.ffn0.m.pw2.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.ffn0.m.pw2.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.local_op.dwconv3x3.weight - torch.Size([40, 1, 7, 7]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.local_op.bn1.weight - torch.Size([40]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.local_op.bn1.bias - torch.Size([40]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.local_op.dwconv1x1.weight - torch.Size([40, 1, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.local_op.bn2.weight - torch.Size([40]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.local_op.bn2.bias - torch.Size([40]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.global_op.wt_filter - torch.Size([640, 1, 2, 2]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.global_op.iwt_filter - torch.Size([640, 1, 2, 2]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.x_proj_weight - torch.Size([2, 12, 320]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.Ds - torch.Size([640]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.A_logs - torch.Size([640, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.dt_projs_weight - torch.Size([2, 320, 10]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.dt_projs_bias - torch.Size([2, 320]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.weight - torch.Size([320]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.in_proj.c.weight - torch.Size([640, 160, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.in_proj.bn.weight - torch.Size([640]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.in_proj.bn.bias - torch.Size([640]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.conv2d.weight - torch.Size([320, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.conv2d.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_proj.c.weight - torch.Size([160, 320, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_proj.bn.weight - torch.Size([160]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_proj.bn.bias - torch.Size([160]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.global_op.base_scale.weight - torch.Size([1, 160, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.global_op.wavelet_convs.0.weight - torch.Size([640, 1, 7, 7]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.global_op.wavelet_scale.0.weight - torch.Size([1, 640, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.proj.1.c.weight - torch.Size([200, 200, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.proj.1.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.mixer.m.attn.proj.1.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.dw1.m.c.weight - torch.Size([200, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.dw1.m.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.dw1.m.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.ffn1.m.pw1.c.weight - torch.Size([400, 200, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.ffn1.m.pw1.bn.weight - torch.Size([400]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.ffn1.m.pw1.bn.bias - torch.Size([400]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.ffn1.m.pw2.c.weight - torch.Size([200, 400, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.ffn1.m.pw2.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.0.ffn1.m.pw2.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.dw0.m.c.weight - torch.Size([200, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.dw0.m.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.dw0.m.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.ffn0.m.pw1.c.weight - torch.Size([400, 200, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.ffn0.m.pw1.bn.weight - torch.Size([400]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.ffn0.m.pw1.bn.bias - torch.Size([400]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.ffn0.m.pw2.c.weight - torch.Size([200, 400, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.ffn0.m.pw2.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.ffn0.m.pw2.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.local_op.dwconv3x3.weight - torch.Size([40, 1, 7, 7]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.local_op.bn1.weight - torch.Size([40]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.local_op.bn1.bias - torch.Size([40]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.local_op.dwconv1x1.weight - torch.Size([40, 1, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.local_op.bn2.weight - torch.Size([40]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.local_op.bn2.bias - torch.Size([40]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.global_op.wt_filter - torch.Size([640, 1, 2, 2]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.global_op.iwt_filter - torch.Size([640, 1, 2, 2]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.x_proj_weight - torch.Size([2, 12, 320]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.Ds - torch.Size([640]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.A_logs - torch.Size([640, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.dt_projs_weight - torch.Size([2, 320, 10]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.dt_projs_bias - torch.Size([2, 320]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.weight - torch.Size([320]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.in_proj.c.weight - torch.Size([640, 160, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.in_proj.bn.weight - torch.Size([640]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.in_proj.bn.bias - torch.Size([640]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.conv2d.weight - torch.Size([320, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.conv2d.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_proj.c.weight - torch.Size([160, 320, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_proj.bn.weight - torch.Size([160]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_proj.bn.bias - torch.Size([160]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.global_op.base_scale.weight - torch.Size([1, 160, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.global_op.wavelet_convs.0.weight - torch.Size([640, 1, 7, 7]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.global_op.wavelet_scale.0.weight - torch.Size([1, 640, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.proj.1.c.weight - torch.Size([200, 200, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.proj.1.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.mixer.m.attn.proj.1.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.dw1.m.c.weight - torch.Size([200, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.dw1.m.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.dw1.m.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.ffn1.m.pw1.c.weight - torch.Size([400, 200, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.ffn1.m.pw1.bn.weight - torch.Size([400]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.ffn1.m.pw1.bn.bias - torch.Size([400]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.ffn1.m.pw2.c.weight - torch.Size([200, 400, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.ffn1.m.pw2.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks1.1.ffn1.m.pw2.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.0.0.m.c.weight - torch.Size([200, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.0.0.m.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.0.0.m.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.0.1.m.pw1.c.weight - torch.Size([400, 200, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.0.1.m.pw1.bn.weight - torch.Size([400]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.0.1.m.pw1.bn.bias - torch.Size([400]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.0.1.m.pw2.c.weight - torch.Size([200, 400, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.0.1.m.pw2.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.0.1.m.pw2.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.1.conv1.c.weight - torch.Size([800, 200, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.1.conv1.bn.weight - torch.Size([800]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.1.conv1.bn.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.1.conv2.c.weight - torch.Size([800, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.1.conv2.bn.weight - torch.Size([800]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.1.conv2.bn.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.1.se.fc1.weight - torch.Size([200, 800, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.1.se.fc1.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.1.se.fc2.weight - torch.Size([800, 200, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.1.se.fc2.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.1.conv3.c.weight - torch.Size([376, 800, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.1.conv3.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.1.conv3.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.2.0.m.c.weight - torch.Size([376, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.2.0.m.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.2.0.m.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.2.1.m.pw1.c.weight - torch.Size([752, 376, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.2.1.m.pw1.bn.weight - torch.Size([752]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.2.1.m.pw1.bn.bias - torch.Size([752]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.2.1.m.pw2.c.weight - torch.Size([376, 752, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.2.1.m.pw2.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.2.1.m.pw2.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.dw0.m.c.weight - torch.Size([376, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.dw0.m.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.dw0.m.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.ffn0.m.pw1.c.weight - torch.Size([752, 376, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.ffn0.m.pw1.bn.weight - torch.Size([752]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.ffn0.m.pw1.bn.bias - torch.Size([752]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.ffn0.m.pw2.c.weight - torch.Size([376, 752, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.ffn0.m.pw2.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.ffn0.m.pw2.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.local_op.dwconv3x3.weight - torch.Size([75, 1, 5, 5]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.local_op.bn1.weight - torch.Size([75]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.local_op.bn1.bias - torch.Size([75]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.local_op.dwconv1x1.weight - torch.Size([75, 1, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.local_op.bn2.weight - torch.Size([75]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.local_op.bn2.bias - torch.Size([75]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.global_op.wt_filter - torch.Size([1024, 1, 2, 2]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.global_op.iwt_filter - torch.Size([1024, 1, 2, 2]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.x_proj_weight - torch.Size([2, 18, 512]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.Ds - torch.Size([1024]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.A_logs - torch.Size([1024, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.dt_projs_weight - torch.Size([2, 512, 16]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.dt_projs_bias - torch.Size([2, 512]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.weight - torch.Size([512]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.bias - torch.Size([512]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.in_proj.c.weight - torch.Size([1024, 256, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.in_proj.bn.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.in_proj.bn.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.conv2d.weight - torch.Size([512, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.conv2d.bias - torch.Size([512]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_proj.c.weight - torch.Size([256, 512, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_proj.bn.weight - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_proj.bn.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.global_op.base_scale.weight - torch.Size([1, 256, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.global_op.wavelet_convs.0.weight - torch.Size([1024, 1, 5, 5]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.global_op.wavelet_scale.0.weight - torch.Size([1, 1024, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.proj.1.c.weight - torch.Size([376, 376, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.proj.1.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.mixer.m.attn.proj.1.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.dw1.m.c.weight - torch.Size([376, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.dw1.m.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.dw1.m.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.ffn1.m.pw1.c.weight - torch.Size([752, 376, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.ffn1.m.pw1.bn.weight - torch.Size([752]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.ffn1.m.pw1.bn.bias - torch.Size([752]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.ffn1.m.pw2.c.weight - torch.Size([376, 752, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.ffn1.m.pw2.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.3.ffn1.m.pw2.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.dw0.m.c.weight - torch.Size([376, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.dw0.m.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.dw0.m.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.ffn0.m.pw1.c.weight - torch.Size([752, 376, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.ffn0.m.pw1.bn.weight - torch.Size([752]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.ffn0.m.pw1.bn.bias - torch.Size([752]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.ffn0.m.pw2.c.weight - torch.Size([376, 752, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.ffn0.m.pw2.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.ffn0.m.pw2.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.local_op.dwconv3x3.weight - torch.Size([75, 1, 5, 5]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.local_op.bn1.weight - torch.Size([75]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.local_op.bn1.bias - torch.Size([75]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.local_op.dwconv1x1.weight - torch.Size([75, 1, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.local_op.bn2.weight - torch.Size([75]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.local_op.bn2.bias - torch.Size([75]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.global_op.wt_filter - torch.Size([1024, 1, 2, 2]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.global_op.iwt_filter - torch.Size([1024, 1, 2, 2]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.x_proj_weight - torch.Size([2, 18, 512]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.Ds - torch.Size([1024]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.A_logs - torch.Size([1024, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.dt_projs_weight - torch.Size([2, 512, 16]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.dt_projs_bias - torch.Size([2, 512]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.weight - torch.Size([512]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.bias - torch.Size([512]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.in_proj.c.weight - torch.Size([1024, 256, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.in_proj.bn.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.in_proj.bn.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.conv2d.weight - torch.Size([512, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.conv2d.bias - torch.Size([512]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_proj.c.weight - torch.Size([256, 512, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_proj.bn.weight - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_proj.bn.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.global_op.base_scale.weight - torch.Size([1, 256, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.global_op.wavelet_convs.0.weight - torch.Size([1024, 1, 5, 5]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.global_op.wavelet_scale.0.weight - torch.Size([1, 1024, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.proj.1.c.weight - torch.Size([376, 376, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.proj.1.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.mixer.m.attn.proj.1.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.dw1.m.c.weight - torch.Size([376, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.dw1.m.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.dw1.m.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.ffn1.m.pw1.c.weight - torch.Size([752, 376, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.ffn1.m.pw1.bn.weight - torch.Size([752]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.ffn1.m.pw1.bn.bias - torch.Size([752]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.ffn1.m.pw2.c.weight - torch.Size([376, 752, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.ffn1.m.pw2.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.4.ffn1.m.pw2.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.dw0.m.c.weight - torch.Size([376, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.dw0.m.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.dw0.m.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.ffn0.m.pw1.c.weight - torch.Size([752, 376, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.ffn0.m.pw1.bn.weight - torch.Size([752]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.ffn0.m.pw1.bn.bias - torch.Size([752]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.ffn0.m.pw2.c.weight - torch.Size([376, 752, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.ffn0.m.pw2.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.ffn0.m.pw2.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.local_op.dwconv3x3.weight - torch.Size([75, 1, 5, 5]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.local_op.bn1.weight - torch.Size([75]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.local_op.bn1.bias - torch.Size([75]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.local_op.dwconv1x1.weight - torch.Size([75, 1, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.local_op.bn2.weight - torch.Size([75]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.local_op.bn2.bias - torch.Size([75]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.global_op.wt_filter - torch.Size([1024, 1, 2, 2]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.global_op.iwt_filter - torch.Size([1024, 1, 2, 2]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.x_proj_weight - torch.Size([2, 18, 512]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.Ds - torch.Size([1024]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.A_logs - torch.Size([1024, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.dt_projs_weight - torch.Size([2, 512, 16]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.dt_projs_bias - torch.Size([2, 512]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.weight - torch.Size([512]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.bias - torch.Size([512]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.in_proj.c.weight - torch.Size([1024, 256, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.in_proj.bn.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.in_proj.bn.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.conv2d.weight - torch.Size([512, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.conv2d.bias - torch.Size([512]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_proj.c.weight - torch.Size([256, 512, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_proj.bn.weight - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_proj.bn.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.global_op.base_scale.weight - torch.Size([1, 256, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.global_op.wavelet_convs.0.weight - torch.Size([1024, 1, 5, 5]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.global_op.wavelet_scale.0.weight - torch.Size([1, 1024, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.proj.1.c.weight - torch.Size([376, 376, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.proj.1.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.mixer.m.attn.proj.1.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.dw1.m.c.weight - torch.Size([376, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.dw1.m.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.dw1.m.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.ffn1.m.pw1.c.weight - torch.Size([752, 376, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.ffn1.m.pw1.bn.weight - torch.Size([752]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.ffn1.m.pw1.bn.bias - torch.Size([752]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.ffn1.m.pw2.c.weight - torch.Size([376, 752, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.ffn1.m.pw2.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks2.5.ffn1.m.pw2.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.0.0.m.c.weight - torch.Size([376, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.0.0.m.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.0.0.m.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.0.1.m.pw1.c.weight - torch.Size([752, 376, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.0.1.m.pw1.bn.weight - torch.Size([752]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.0.1.m.pw1.bn.bias - torch.Size([752]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.0.1.m.pw2.c.weight - torch.Size([376, 752, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.0.1.m.pw2.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.0.1.m.pw2.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.1.conv1.c.weight - torch.Size([1504, 376, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.1.conv1.bn.weight - torch.Size([1504]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.1.conv1.bn.bias - torch.Size([1504]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.1.conv2.c.weight - torch.Size([1504, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.1.conv2.bn.weight - torch.Size([1504]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.1.conv2.bn.bias - torch.Size([1504]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.1.se.fc1.weight - torch.Size([376, 1504, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.1.se.fc1.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.1.se.fc2.weight - torch.Size([1504, 376, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.1.se.fc2.bias - torch.Size([1504]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.1.conv3.c.weight - torch.Size([448, 1504, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.1.conv3.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.1.conv3.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.2.0.m.c.weight - torch.Size([448, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.2.0.m.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.2.0.m.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.2.1.m.pw1.c.weight - torch.Size([896, 448, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.2.1.m.pw1.bn.weight - torch.Size([896]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.2.1.m.pw1.bn.bias - torch.Size([896]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.2.1.m.pw2.c.weight - torch.Size([448, 896, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.2.1.m.pw2.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.2.1.m.pw2.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.dw0.m.c.weight - torch.Size([448, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.dw0.m.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.dw0.m.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.ffn0.m.pw1.c.weight - torch.Size([896, 448, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.ffn0.m.pw1.bn.weight - torch.Size([896]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.ffn0.m.pw1.bn.bias - torch.Size([896]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.ffn0.m.pw2.c.weight - torch.Size([448, 896, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.ffn0.m.pw2.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.ffn0.m.pw2.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.local_op.dwconv3x3.weight - torch.Size([134, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.local_op.bn1.weight - torch.Size([134]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.local_op.bn1.bias - torch.Size([134]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.local_op.dwconv1x1.weight - torch.Size([134, 1, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.local_op.bn2.weight - torch.Size([134]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.local_op.bn2.bias - torch.Size([134]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.global_op.wt_filter - torch.Size([1088, 1, 2, 2]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.global_op.iwt_filter - torch.Size([1088, 1, 2, 2]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.x_proj_weight - torch.Size([2, 19, 544]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.Ds - torch.Size([1088]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.A_logs - torch.Size([1088, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.dt_projs_weight - torch.Size([2, 544, 17]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.dt_projs_bias - torch.Size([2, 544]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.weight - torch.Size([544]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.bias - torch.Size([544]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.in_proj.c.weight - torch.Size([1088, 272, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.in_proj.bn.weight - torch.Size([1088]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.in_proj.bn.bias - torch.Size([1088]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.conv2d.weight - torch.Size([544, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.conv2d.bias - torch.Size([544]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_proj.c.weight - torch.Size([272, 544, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_proj.bn.weight - torch.Size([272]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_proj.bn.bias - torch.Size([272]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.global_op.base_scale.weight - torch.Size([1, 272, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.global_op.wavelet_convs.0.weight - torch.Size([1088, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.global_op.wavelet_scale.0.weight - torch.Size([1, 1088, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.proj.1.c.weight - torch.Size([448, 448, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.proj.1.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.mixer.m.attn.proj.1.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.dw1.m.c.weight - torch.Size([448, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.dw1.m.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.dw1.m.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.ffn1.m.pw1.c.weight - torch.Size([896, 448, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.ffn1.m.pw1.bn.weight - torch.Size([896]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.ffn1.m.pw1.bn.bias - torch.Size([896]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.ffn1.m.pw2.c.weight - torch.Size([448, 896, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.ffn1.m.pw2.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.3.ffn1.m.pw2.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.dw0.m.c.weight - torch.Size([448, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.dw0.m.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.dw0.m.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.ffn0.m.pw1.c.weight - torch.Size([896, 448, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.ffn0.m.pw1.bn.weight - torch.Size([896]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.ffn0.m.pw1.bn.bias - torch.Size([896]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.ffn0.m.pw2.c.weight - torch.Size([448, 896, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.ffn0.m.pw2.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.ffn0.m.pw2.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.local_op.dwconv3x3.weight - torch.Size([134, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.local_op.bn1.weight - torch.Size([134]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.local_op.bn1.bias - torch.Size([134]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.local_op.dwconv1x1.weight - torch.Size([134, 1, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.local_op.bn2.weight - torch.Size([134]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.local_op.bn2.bias - torch.Size([134]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.global_op.wt_filter - torch.Size([1088, 1, 2, 2]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.global_op.iwt_filter - torch.Size([1088, 1, 2, 2]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.x_proj_weight - torch.Size([2, 19, 544]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.Ds - torch.Size([1088]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.A_logs - torch.Size([1088, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.dt_projs_weight - torch.Size([2, 544, 17]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.dt_projs_bias - torch.Size([2, 544]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.weight - torch.Size([544]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.bias - torch.Size([544]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.in_proj.c.weight - torch.Size([1088, 272, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.in_proj.bn.weight - torch.Size([1088]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.in_proj.bn.bias - torch.Size([1088]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.conv2d.weight - torch.Size([544, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.conv2d.bias - torch.Size([544]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_proj.c.weight - torch.Size([272, 544, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_proj.bn.weight - torch.Size([272]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_proj.bn.bias - torch.Size([272]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.global_op.base_scale.weight - torch.Size([1, 272, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.global_op.wavelet_convs.0.weight - torch.Size([1088, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.global_op.wavelet_scale.0.weight - torch.Size([1, 1088, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.proj.1.c.weight - torch.Size([448, 448, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.proj.1.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.mixer.m.attn.proj.1.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.dw1.m.c.weight - torch.Size([448, 1, 3, 3]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.dw1.m.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.dw1.m.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.ffn1.m.pw1.c.weight - torch.Size([896, 448, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.ffn1.m.pw1.bn.weight - torch.Size([896]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.ffn1.m.pw1.bn.bias - torch.Size([896]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.ffn1.m.pw2.c.weight - torch.Size([448, 896, 1, 1]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.ffn1.m.pw2.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +backbone.blocks3.4.ffn1.m.pw2.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of RetinaNet + +neck.lateral_convs.0.conv.weight - torch.Size([256, 200, 1, 1]): +Initialized by user-defined `init_weights` in EfficientViTFPN + +neck.lateral_convs.0.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +neck.lateral_convs.1.conv.weight - torch.Size([256, 376, 1, 1]): +Initialized by user-defined `init_weights` in EfficientViTFPN + +neck.lateral_convs.1.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +neck.lateral_convs.2.conv.weight - torch.Size([256, 448, 1, 1]): +Initialized by user-defined `init_weights` in EfficientViTFPN + +neck.lateral_convs.2.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +neck.fpn_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): +Initialized by user-defined `init_weights` in EfficientViTFPN + +neck.fpn_convs.0.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +neck.fpn_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): +Initialized by user-defined `init_weights` in EfficientViTFPN + +neck.fpn_convs.1.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +neck.fpn_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): +Initialized by user-defined `init_weights` in EfficientViTFPN + +neck.fpn_convs.2.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +neck.fpn_convs.3.conv.weight - torch.Size([256, 448, 3, 3]): +Initialized by user-defined `init_weights` in EfficientViTFPN + +neck.fpn_convs.3.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +neck.extra_trans_convs.0.conv.weight - torch.Size([256, 256, 2, 2]): +The value is the same before and after calling `init_weights` of RetinaNet + +neck.extra_trans_convs.0.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +neck.extra_fpn_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): +Initialized by user-defined `init_weights` in EfficientViTFPN + +neck.extra_fpn_convs.0.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +bbox_head.cls_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): +NormalInit: mean=0, std=0.01, bias=0 + +bbox_head.cls_convs.0.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +bbox_head.cls_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): +NormalInit: mean=0, std=0.01, bias=0 + +bbox_head.cls_convs.1.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +bbox_head.cls_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): +NormalInit: mean=0, std=0.01, bias=0 + +bbox_head.cls_convs.2.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +bbox_head.cls_convs.3.conv.weight - torch.Size([256, 256, 3, 3]): +NormalInit: mean=0, std=0.01, bias=0 + +bbox_head.cls_convs.3.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +bbox_head.reg_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): +NormalInit: mean=0, std=0.01, bias=0 + +bbox_head.reg_convs.0.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +bbox_head.reg_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): +NormalInit: mean=0, std=0.01, bias=0 + +bbox_head.reg_convs.1.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +bbox_head.reg_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): +NormalInit: mean=0, std=0.01, bias=0 + +bbox_head.reg_convs.2.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +bbox_head.reg_convs.3.conv.weight - torch.Size([256, 256, 3, 3]): +NormalInit: mean=0, std=0.01, bias=0 + +bbox_head.reg_convs.3.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of RetinaNet + +bbox_head.retina_cls.weight - torch.Size([720, 256, 3, 3]): +NormalInit: mean=0, std=0.01, bias=-4.59511985013459 + +bbox_head.retina_cls.bias - torch.Size([720]): +NormalInit: mean=0, std=0.01, bias=-4.59511985013459 + +bbox_head.retina_reg.weight - torch.Size([36, 256, 3, 3]): +NormalInit: mean=0, std=0.01, bias=0 + +bbox_head.retina_reg.bias - torch.Size([36]): +NormalInit: mean=0, std=0.01, bias=0 +2024/10/26 15:34:09 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +2024/10/26 15:34:09 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +2024/10/26 15:34:09 - mmengine - INFO - Checkpoints will be saved to work_dirs/retinanet_mobilemamba_b1_fpn_1x_coco. +2024/10/26 15:35:32 - mmengine - INFO - Epoch(train) [1][ 50/7330] base_lr: 1.9641e-05 lr: 1.9641e-05 eta: 1 day, 16:21:13 time: 1.6525 data_time: 0.0113 memory: 5133 grad_norm: 2.7793 loss: 1.8913 loss_cls: 1.1821 loss_bbox: 0.7092 +2024/10/26 15:36:16 - mmengine - INFO - Epoch(train) [1][ 100/7330] base_lr: 3.9681e-05 lr: 3.9681e-05 eta: 1 day, 7:01:54 time: 0.8905 data_time: 0.0116 memory: 5132 grad_norm: 5.7435 loss: 1.7002 loss_cls: 1.0200 loss_bbox: 0.6802 +2024/10/26 15:36:53 - mmengine - INFO - Epoch(train) [1][ 150/7330] base_lr: 5.9721e-05 lr: 5.9721e-05 eta: 1 day, 2:36:36 time: 0.7298 data_time: 0.0100 memory: 5133 grad_norm: 5.4334 loss: 1.4867 loss_cls: 0.8559 loss_bbox: 0.6308 +2024/10/26 15:37:23 - mmengine - INFO - Epoch(train) [1][ 200/7330] base_lr: 7.9761e-05 lr: 7.9761e-05 eta: 23:42:57 time: 0.6185 data_time: 0.0097 memory: 5131 grad_norm: 6.2940 loss: 1.3785 loss_cls: 0.7738 loss_bbox: 0.6047 +2024/10/26 15:37:57 - mmengine - INFO - Epoch(train) [1][ 250/7330] base_lr: 9.9801e-05 lr: 9.9801e-05 eta: 22:12:52 time: 0.6675 data_time: 0.0141 memory: 5132 grad_norm: 6.7343 loss: 1.2741 loss_cls: 0.7291 loss_bbox: 0.5450 +2024/10/26 15:38:24 - mmengine - INFO - Epoch(train) [1][ 300/7330] base_lr: 1.1984e-04 lr: 1.1984e-04 eta: 20:40:02 time: 0.5336 data_time: 0.0113 memory: 5135 grad_norm: 6.7806 loss: 1.2372 loss_cls: 0.7108 loss_bbox: 0.5265 +2024/10/26 15:38:56 - mmengine - INFO - Epoch(train) [1][ 350/7330] base_lr: 1.3988e-04 lr: 1.3988e-04 eta: 19:57:04 time: 0.6462 data_time: 0.0092 memory: 5133 grad_norm: 6.6307 loss: 1.1967 loss_cls: 0.7009 loss_bbox: 0.4958 +2024/10/26 15:39:26 - mmengine - INFO - Epoch(train) [1][ 400/7330] base_lr: 1.5992e-04 lr: 1.5992e-04 eta: 19:16:24 time: 0.6006 data_time: 0.0100 memory: 5132 grad_norm: 6.9838 loss: 1.1935 loss_cls: 0.7090 loss_bbox: 0.4845 +2024/10/26 15:39:57 - mmengine - INFO - Epoch(train) [1][ 450/7330] base_lr: 1.7996e-04 lr: 1.7996e-04 eta: 18:48:14 time: 0.6227 data_time: 0.0101 memory: 5134 grad_norm: 5.4095 loss: 1.1288 loss_cls: 0.6541 loss_bbox: 0.4747 +2024/10/26 15:40:23 - mmengine - INFO - Epoch(train) [1][ 500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 18:09:23 time: 0.5114 data_time: 0.0104 memory: 5134 grad_norm: 6.2494 loss: 1.1437 loss_cls: 0.6707 loss_bbox: 0.4730 +2024/10/26 15:40:55 - mmengine - INFO - Epoch(train) [1][ 550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 17:54:52 time: 0.6424 data_time: 0.0131 memory: 5134 grad_norm: 5.6067 loss: 1.1184 loss_cls: 0.6720 loss_bbox: 0.4464 +2024/10/26 15:41:23 - mmengine - INFO - Epoch(train) [1][ 600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 17:32:23 time: 0.5576 data_time: 0.0093 memory: 5133 grad_norm: 5.5646 loss: 1.0822 loss_cls: 0.6271 loss_bbox: 0.4551 +2024/10/26 15:41:56 - mmengine - INFO - Epoch(train) [1][ 650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 17:25:24 time: 0.6658 data_time: 0.0083 memory: 5132 grad_norm: 5.8095 loss: 1.0532 loss_cls: 0.6076 loss_bbox: 0.4456 +2024/10/26 15:42:24 - mmengine - INFO - Epoch(train) [1][ 700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 17:09:00 time: 0.5662 data_time: 0.0086 memory: 5134 grad_norm: 5.6770 loss: 1.0764 loss_cls: 0.6348 loss_bbox: 0.4416 +2024/10/26 15:42:57 - mmengine - INFO - Epoch(train) [1][ 750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 17:02:53 time: 0.6506 data_time: 0.0082 memory: 5133 grad_norm: 5.8658 loss: 1.0266 loss_cls: 0.5897 loss_bbox: 0.4369 +2024/10/26 15:43:22 - mmengine - INFO - Epoch(train) [1][ 800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 16:44:00 time: 0.5021 data_time: 0.0080 memory: 5133 grad_norm: 5.2311 loss: 1.0519 loss_cls: 0.5958 loss_bbox: 0.4561 +2024/10/26 15:43:58 - mmengine - INFO - Epoch(train) [1][ 850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 16:45:57 time: 0.7208 data_time: 0.0083 memory: 5134 grad_norm: 5.5578 loss: 1.0015 loss_cls: 0.5713 loss_bbox: 0.4303 +2024/10/26 15:44:26 - mmengine - INFO - Epoch(train) [1][ 900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 16:35:33 time: 0.5709 data_time: 0.0084 memory: 5133 grad_norm: 5.6059 loss: 1.0403 loss_cls: 0.5980 loss_bbox: 0.4422 +2024/10/26 15:44:59 - mmengine - INFO - Epoch(train) [1][ 950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 16:31:42 time: 0.6432 data_time: 0.0107 memory: 5134 grad_norm: 5.3457 loss: 1.0145 loss_cls: 0.5835 loss_bbox: 0.4310 +2024/10/26 15:45:28 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 15:45:28 - mmengine - INFO - Epoch(train) [1][1000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 16:24:06 time: 0.5869 data_time: 0.0085 memory: 5132 grad_norm: 5.1624 loss: 0.9750 loss_cls: 0.5562 loss_bbox: 0.4188 +2024/10/26 15:45:58 - mmengine - INFO - Epoch(train) [1][1050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 16:18:45 time: 0.6097 data_time: 0.0085 memory: 5132 grad_norm: 5.6795 loss: 0.9568 loss_cls: 0.5397 loss_bbox: 0.4171 +2024/10/26 15:46:27 - mmengine - INFO - Epoch(train) [1][1100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 16:11:54 time: 0.5802 data_time: 0.0085 memory: 5133 grad_norm: 5.1388 loss: 0.9465 loss_cls: 0.5324 loss_bbox: 0.4141 +2024/10/26 15:46:59 - mmengine - INFO - Epoch(train) [1][1150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 16:08:42 time: 0.6293 data_time: 0.0142 memory: 5134 grad_norm: 4.7514 loss: 0.9258 loss_cls: 0.5097 loss_bbox: 0.4161 +2024/10/26 15:47:28 - mmengine - INFO - Epoch(train) [1][1200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 16:02:38 time: 0.5782 data_time: 0.0085 memory: 5133 grad_norm: 5.6162 loss: 0.9798 loss_cls: 0.5459 loss_bbox: 0.4339 +2024/10/26 15:47:59 - mmengine - INFO - Epoch(train) [1][1250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:59:54 time: 0.6281 data_time: 0.0084 memory: 5134 grad_norm: 4.9197 loss: 0.9234 loss_cls: 0.5207 loss_bbox: 0.4028 +2024/10/26 15:48:28 - mmengine - INFO - Epoch(train) [1][1300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:54:47 time: 0.5819 data_time: 0.0095 memory: 5136 grad_norm: 4.8152 loss: 0.9110 loss_cls: 0.5098 loss_bbox: 0.4013 +2024/10/26 15:49:00 - mmengine - INFO - Epoch(train) [1][1350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:52:20 time: 0.6258 data_time: 0.0093 memory: 5134 grad_norm: 4.8579 loss: 0.9334 loss_cls: 0.5380 loss_bbox: 0.3954 +2024/10/26 15:49:28 - mmengine - INFO - Epoch(train) [1][1400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:47:16 time: 0.5720 data_time: 0.0087 memory: 5135 grad_norm: 4.8930 loss: 0.9316 loss_cls: 0.5119 loss_bbox: 0.4197 +2024/10/26 15:50:00 - mmengine - INFO - Epoch(train) [1][1450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:46:00 time: 0.6423 data_time: 0.0095 memory: 5134 grad_norm: 7.0140 loss: 0.9051 loss_cls: 0.5091 loss_bbox: 0.3960 +2024/10/26 15:50:28 - mmengine - INFO - Epoch(train) [1][1500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:40:50 time: 0.5599 data_time: 0.0088 memory: 5132 grad_norm: 5.0606 loss: 0.8961 loss_cls: 0.5041 loss_bbox: 0.3920 +2024/10/26 15:51:01 - mmengine - INFO - Epoch(train) [1][1550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:40:11 time: 0.6504 data_time: 0.0129 memory: 5134 grad_norm: 4.9115 loss: 0.8752 loss_cls: 0.4899 loss_bbox: 0.3853 +2024/10/26 15:51:30 - mmengine - INFO - Epoch(train) [1][1600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:36:06 time: 0.5741 data_time: 0.0088 memory: 5135 grad_norm: 4.7450 loss: 0.9172 loss_cls: 0.4874 loss_bbox: 0.4298 +2024/10/26 15:52:01 - mmengine - INFO - Epoch(train) [1][1650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:34:26 time: 0.6247 data_time: 0.0084 memory: 5134 grad_norm: 4.9174 loss: 0.8801 loss_cls: 0.4889 loss_bbox: 0.3912 +2024/10/26 15:52:29 - mmengine - INFO - Epoch(train) [1][1700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:30:29 time: 0.5691 data_time: 0.0088 memory: 5132 grad_norm: 4.7188 loss: 0.8790 loss_cls: 0.4790 loss_bbox: 0.4000 +2024/10/26 15:53:02 - mmengine - INFO - Epoch(train) [1][1750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:29:55 time: 0.6464 data_time: 0.0087 memory: 5132 grad_norm: 5.1116 loss: 0.9123 loss_cls: 0.5133 loss_bbox: 0.3990 +2024/10/26 15:53:32 - mmengine - INFO - Epoch(train) [1][1800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:28:10 time: 0.6170 data_time: 0.0866 memory: 5133 grad_norm: 4.8869 loss: 0.8854 loss_cls: 0.4861 loss_bbox: 0.3993 +2024/10/26 15:54:03 - mmengine - INFO - Epoch(train) [1][1850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:26:16 time: 0.6112 data_time: 0.0085 memory: 5133 grad_norm: 4.7310 loss: 0.8821 loss_cls: 0.4890 loss_bbox: 0.3931 +2024/10/26 15:54:38 - mmengine - INFO - Epoch(train) [1][1900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:27:47 time: 0.6998 data_time: 0.0624 memory: 5133 grad_norm: 4.5298 loss: 0.8551 loss_cls: 0.4686 loss_bbox: 0.3865 +2024/10/26 15:55:09 - mmengine - INFO - Epoch(train) [1][1950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:26:05 time: 0.6154 data_time: 0.0136 memory: 5132 grad_norm: 4.2085 loss: 0.8581 loss_cls: 0.4648 loss_bbox: 0.3933 +2024/10/26 15:55:36 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 15:55:36 - mmengine - INFO - Epoch(train) [1][2000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:21:55 time: 0.5446 data_time: 0.0103 memory: 5134 grad_norm: 4.8781 loss: 0.8912 loss_cls: 0.5060 loss_bbox: 0.3852 +2024/10/26 15:56:08 - mmengine - INFO - Epoch(train) [1][2050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:21:06 time: 0.6354 data_time: 0.0198 memory: 5132 grad_norm: 4.6055 loss: 0.8735 loss_cls: 0.4774 loss_bbox: 0.3960 +2024/10/26 15:56:37 - mmengine - INFO - Epoch(train) [1][2100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:18:35 time: 0.5851 data_time: 0.0151 memory: 5132 grad_norm: 4.7268 loss: 0.8634 loss_cls: 0.4706 loss_bbox: 0.3928 +2024/10/26 15:57:07 - mmengine - INFO - Epoch(train) [1][2150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:16:39 time: 0.6002 data_time: 0.0169 memory: 5133 grad_norm: 5.0001 loss: 0.8299 loss_cls: 0.4596 loss_bbox: 0.3704 +2024/10/26 15:57:37 - mmengine - INFO - Epoch(train) [1][2200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:14:36 time: 0.5943 data_time: 0.0103 memory: 5133 grad_norm: 4.5543 loss: 0.8491 loss_cls: 0.4636 loss_bbox: 0.3855 +2024/10/26 15:58:10 - mmengine - INFO - Epoch(train) [1][2250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:14:48 time: 0.6626 data_time: 0.0092 memory: 5135 grad_norm: 4.5703 loss: 0.8689 loss_cls: 0.4758 loss_bbox: 0.3930 +2024/10/26 15:58:40 - mmengine - INFO - Epoch(train) [1][2300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:13:09 time: 0.6048 data_time: 0.0083 memory: 5133 grad_norm: 4.6845 loss: 0.8391 loss_cls: 0.4579 loss_bbox: 0.3812 +2024/10/26 15:59:11 - mmengine - INFO - Epoch(train) [1][2350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:11:49 time: 0.6131 data_time: 0.0082 memory: 5136 grad_norm: 4.8129 loss: 0.8489 loss_cls: 0.4657 loss_bbox: 0.3832 +2024/10/26 15:59:37 - mmengine - INFO - Epoch(train) [1][2400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:07:56 time: 0.5264 data_time: 0.0087 memory: 5135 grad_norm: 4.5880 loss: 0.8529 loss_cls: 0.4728 loss_bbox: 0.3802 +2024/10/26 16:00:07 - mmengine - INFO - Epoch(train) [1][2450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:06:10 time: 0.5941 data_time: 0.0088 memory: 5136 grad_norm: 4.6811 loss: 0.8133 loss_cls: 0.4464 loss_bbox: 0.3668 +2024/10/26 16:00:39 - mmengine - INFO - Epoch(train) [1][2500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:06:00 time: 0.6486 data_time: 0.0087 memory: 5135 grad_norm: 5.0957 loss: 0.8559 loss_cls: 0.4763 loss_bbox: 0.3797 +2024/10/26 16:01:09 - mmengine - INFO - Epoch(train) [1][2550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:04:19 time: 0.5947 data_time: 0.0087 memory: 5132 grad_norm: 4.4386 loss: 0.8449 loss_cls: 0.4574 loss_bbox: 0.3875 +2024/10/26 16:01:40 - mmengine - INFO - Epoch(train) [1][2600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:03:21 time: 0.6194 data_time: 0.0086 memory: 5135 grad_norm: 4.3796 loss: 0.8327 loss_cls: 0.4607 loss_bbox: 0.3720 +2024/10/26 16:02:10 - mmengine - INFO - Epoch(train) [1][2650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:01:49 time: 0.5974 data_time: 0.0091 memory: 5133 grad_norm: 5.0632 loss: 0.8406 loss_cls: 0.4658 loss_bbox: 0.3748 +2024/10/26 16:02:41 - mmengine - INFO - Epoch(train) [1][2700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 15:00:56 time: 0.6209 data_time: 0.0090 memory: 5131 grad_norm: 4.3657 loss: 0.8095 loss_cls: 0.4256 loss_bbox: 0.3839 +2024/10/26 16:03:11 - mmengine - INFO - Epoch(train) [1][2750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:59:45 time: 0.6085 data_time: 0.0085 memory: 5137 grad_norm: 4.7597 loss: 0.8348 loss_cls: 0.4433 loss_bbox: 0.3916 +2024/10/26 16:03:40 - mmengine - INFO - Epoch(train) [1][2800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:57:34 time: 0.5681 data_time: 0.0086 memory: 5136 grad_norm: 5.4514 loss: 0.8272 loss_cls: 0.4583 loss_bbox: 0.3689 +2024/10/26 16:04:08 - mmengine - INFO - Epoch(train) [1][2850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:55:36 time: 0.5751 data_time: 0.0083 memory: 5133 grad_norm: 4.8203 loss: 0.7875 loss_cls: 0.4412 loss_bbox: 0.3463 +2024/10/26 16:04:38 - mmengine - INFO - Epoch(train) [1][2900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:54:18 time: 0.5995 data_time: 0.0089 memory: 5134 grad_norm: 5.0638 loss: 0.8273 loss_cls: 0.4572 loss_bbox: 0.3701 +2024/10/26 16:05:10 - mmengine - INFO - Epoch(train) [1][2950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:53:36 time: 0.6233 data_time: 0.0089 memory: 5135 grad_norm: 4.9544 loss: 0.7901 loss_cls: 0.4259 loss_bbox: 0.3643 +2024/10/26 16:05:37 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 16:05:37 - mmengine - INFO - Epoch(train) [1][3000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:51:18 time: 0.5560 data_time: 0.0104 memory: 5135 grad_norm: 4.3952 loss: 0.8191 loss_cls: 0.4437 loss_bbox: 0.3754 +2024/10/26 16:06:09 - mmengine - INFO - Epoch(train) [1][3050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:50:51 time: 0.6322 data_time: 0.0098 memory: 5135 grad_norm: 4.6601 loss: 0.8146 loss_cls: 0.4497 loss_bbox: 0.3649 +2024/10/26 16:06:42 - mmengine - INFO - Epoch(train) [1][3100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:50:49 time: 0.6514 data_time: 0.0104 memory: 5133 grad_norm: 4.2481 loss: 0.8033 loss_cls: 0.4427 loss_bbox: 0.3606 +2024/10/26 16:07:09 - mmengine - INFO - Epoch(train) [1][3150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:48:38 time: 0.5557 data_time: 0.0089 memory: 5133 grad_norm: 4.1514 loss: 0.7767 loss_cls: 0.4241 loss_bbox: 0.3526 +2024/10/26 16:07:39 - mmengine - INFO - Epoch(train) [1][3200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:47:10 time: 0.5860 data_time: 0.0093 memory: 5134 grad_norm: 4.2818 loss: 0.8200 loss_cls: 0.4410 loss_bbox: 0.3790 +2024/10/26 16:08:08 - mmengine - INFO - Epoch(train) [1][3250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:45:58 time: 0.5971 data_time: 0.0088 memory: 5134 grad_norm: 4.6354 loss: 0.8332 loss_cls: 0.4538 loss_bbox: 0.3794 +2024/10/26 16:08:39 - mmengine - INFO - Epoch(train) [1][3300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:45:02 time: 0.6083 data_time: 0.0109 memory: 5133 grad_norm: 4.4173 loss: 0.7844 loss_cls: 0.4241 loss_bbox: 0.3603 +2024/10/26 16:09:10 - mmengine - INFO - Epoch(train) [1][3350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:44:20 time: 0.6184 data_time: 0.0136 memory: 5134 grad_norm: 4.4835 loss: 0.8056 loss_cls: 0.4345 loss_bbox: 0.3710 +2024/10/26 16:09:41 - mmengine - INFO - Epoch(train) [1][3400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:43:40 time: 0.6204 data_time: 0.0084 memory: 5133 grad_norm: 4.9421 loss: 0.7635 loss_cls: 0.4085 loss_bbox: 0.3550 +2024/10/26 16:10:09 - mmengine - INFO - Epoch(train) [1][3450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:41:41 time: 0.5556 data_time: 0.0083 memory: 5132 grad_norm: 4.4597 loss: 0.7693 loss_cls: 0.4153 loss_bbox: 0.3540 +2024/10/26 16:10:36 - mmengine - INFO - Epoch(train) [1][3500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:39:47 time: 0.5569 data_time: 0.0078 memory: 5134 grad_norm: 4.7181 loss: 0.8034 loss_cls: 0.4396 loss_bbox: 0.3638 +2024/10/26 16:11:07 - mmengine - INFO - Epoch(train) [1][3550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:39:02 time: 0.6138 data_time: 0.0088 memory: 5133 grad_norm: 4.4565 loss: 0.8065 loss_cls: 0.4434 loss_bbox: 0.3632 +2024/10/26 16:11:35 - mmengine - INFO - Epoch(train) [1][3600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:37:11 time: 0.5570 data_time: 0.0081 memory: 5136 grad_norm: 4.4875 loss: 0.7736 loss_cls: 0.4204 loss_bbox: 0.3532 +2024/10/26 16:12:08 - mmengine - INFO - Epoch(train) [1][3650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:37:15 time: 0.6542 data_time: 0.0181 memory: 5134 grad_norm: 4.6036 loss: 0.7708 loss_cls: 0.4068 loss_bbox: 0.3641 +2024/10/26 16:12:37 - mmengine - INFO - Epoch(train) [1][3700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:35:49 time: 0.5764 data_time: 0.0078 memory: 5134 grad_norm: 4.8774 loss: 0.7805 loss_cls: 0.4161 loss_bbox: 0.3645 +2024/10/26 16:13:08 - mmengine - INFO - Epoch(train) [1][3750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:35:20 time: 0.6255 data_time: 0.0103 memory: 5132 grad_norm: 4.3279 loss: 0.7507 loss_cls: 0.4099 loss_bbox: 0.3408 +2024/10/26 16:13:35 - mmengine - INFO - Epoch(train) [1][3800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:33:25 time: 0.5477 data_time: 0.0078 memory: 5135 grad_norm: 4.7507 loss: 0.7804 loss_cls: 0.4163 loss_bbox: 0.3640 +2024/10/26 16:14:07 - mmengine - INFO - Epoch(train) [1][3850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:33:00 time: 0.6288 data_time: 0.0084 memory: 5133 grad_norm: 4.7937 loss: 0.7780 loss_cls: 0.4236 loss_bbox: 0.3544 +2024/10/26 16:14:34 - mmengine - INFO - Epoch(train) [1][3900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:31:01 time: 0.5406 data_time: 0.0082 memory: 5133 grad_norm: 4.5488 loss: 0.8003 loss_cls: 0.4353 loss_bbox: 0.3650 +2024/10/26 16:15:04 - mmengine - INFO - Epoch(train) [1][3950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:30:20 time: 0.6130 data_time: 0.0078 memory: 5133 grad_norm: 4.9893 loss: 0.7844 loss_cls: 0.4389 loss_bbox: 0.3455 +2024/10/26 16:15:35 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 16:15:35 - mmengine - INFO - Epoch(train) [1][4000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:29:50 time: 0.6226 data_time: 0.0559 memory: 5136 grad_norm: 4.3412 loss: 0.7622 loss_cls: 0.4020 loss_bbox: 0.3602 +2024/10/26 16:16:06 - mmengine - INFO - Epoch(train) [1][4050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:29:13 time: 0.6153 data_time: 0.0076 memory: 5134 grad_norm: 4.6144 loss: 0.7483 loss_cls: 0.4035 loss_bbox: 0.3449 +2024/10/26 16:16:40 - mmengine - INFO - Epoch(train) [1][4100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:29:33 time: 0.6717 data_time: 0.0283 memory: 5134 grad_norm: 4.7839 loss: 0.7681 loss_cls: 0.4231 loss_bbox: 0.3451 +2024/10/26 16:17:08 - mmengine - INFO - Epoch(train) [1][4150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:28:07 time: 0.5679 data_time: 0.0136 memory: 5132 grad_norm: 4.6842 loss: 0.7621 loss_cls: 0.3983 loss_bbox: 0.3638 +2024/10/26 16:17:38 - mmengine - INFO - Epoch(train) [1][4200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:27:12 time: 0.5973 data_time: 0.0084 memory: 5134 grad_norm: 4.6198 loss: 0.7730 loss_cls: 0.4151 loss_bbox: 0.3578 +2024/10/26 16:18:11 - mmengine - INFO - Epoch(train) [1][4250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:27:17 time: 0.6577 data_time: 0.0110 memory: 5134 grad_norm: 4.3323 loss: 0.7895 loss_cls: 0.4308 loss_bbox: 0.3587 +2024/10/26 16:18:43 - mmengine - INFO - Epoch(train) [1][4300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:27:01 time: 0.6370 data_time: 0.0159 memory: 5135 grad_norm: 4.8194 loss: 0.7840 loss_cls: 0.4259 loss_bbox: 0.3581 +2024/10/26 16:19:13 - mmengine - INFO - Epoch(train) [1][4350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:26:09 time: 0.6008 data_time: 0.0078 memory: 5134 grad_norm: 4.5223 loss: 0.7549 loss_cls: 0.4087 loss_bbox: 0.3462 +2024/10/26 16:19:42 - mmengine - INFO - Epoch(train) [1][4400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:25:05 time: 0.5867 data_time: 0.0076 memory: 5133 grad_norm: 4.6296 loss: 0.7873 loss_cls: 0.4265 loss_bbox: 0.3608 +2024/10/26 16:20:07 - mmengine - INFO - Epoch(train) [1][4450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:22:32 time: 0.4912 data_time: 0.0078 memory: 5134 grad_norm: 4.4552 loss: 0.7942 loss_cls: 0.4295 loss_bbox: 0.3647 +2024/10/26 16:20:35 - mmengine - INFO - Epoch(train) [1][4500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:21:12 time: 0.5672 data_time: 0.0080 memory: 5134 grad_norm: 4.7784 loss: 0.7728 loss_cls: 0.4209 loss_bbox: 0.3519 +2024/10/26 16:21:06 - mmengine - INFO - Epoch(train) [1][4550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:20:34 time: 0.6110 data_time: 0.0149 memory: 5133 grad_norm: 4.8442 loss: 0.7961 loss_cls: 0.4340 loss_bbox: 0.3621 +2024/10/26 16:21:34 - mmengine - INFO - Epoch(train) [1][4600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:19:13 time: 0.5636 data_time: 0.0078 memory: 5135 grad_norm: 4.5449 loss: 0.7725 loss_cls: 0.4119 loss_bbox: 0.3605 +2024/10/26 16:22:04 - mmengine - INFO - Epoch(train) [1][4650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:18:35 time: 0.6114 data_time: 0.0079 memory: 5135 grad_norm: 4.4468 loss: 0.8011 loss_cls: 0.4336 loss_bbox: 0.3675 +2024/10/26 16:22:41 - mmengine - INFO - Epoch(train) [1][4700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:19:51 time: 0.7390 data_time: 0.0205 memory: 5133 grad_norm: 4.4947 loss: 0.7756 loss_cls: 0.4236 loss_bbox: 0.3520 +2024/10/26 16:23:11 - mmengine - INFO - Epoch(train) [1][4750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:19:00 time: 0.5962 data_time: 0.0081 memory: 5136 grad_norm: 4.4649 loss: 0.7422 loss_cls: 0.3989 loss_bbox: 0.3432 +2024/10/26 16:23:39 - mmengine - INFO - Epoch(train) [1][4800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:17:29 time: 0.5500 data_time: 0.0075 memory: 5133 grad_norm: 4.2234 loss: 0.7833 loss_cls: 0.4304 loss_bbox: 0.3529 +2024/10/26 16:24:10 - mmengine - INFO - Epoch(train) [1][4850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:17:06 time: 0.6278 data_time: 0.0087 memory: 5133 grad_norm: 4.4292 loss: 0.7706 loss_cls: 0.4143 loss_bbox: 0.3564 +2024/10/26 16:24:41 - mmengine - INFO - Epoch(train) [1][4900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:16:38 time: 0.6232 data_time: 0.0079 memory: 5133 grad_norm: 4.1082 loss: 0.7602 loss_cls: 0.4057 loss_bbox: 0.3544 +2024/10/26 16:25:11 - mmengine - INFO - Epoch(train) [1][4950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:15:53 time: 0.6014 data_time: 0.0126 memory: 5134 grad_norm: 4.4816 loss: 0.7797 loss_cls: 0.4322 loss_bbox: 0.3474 +2024/10/26 16:25:40 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 16:25:40 - mmengine - INFO - Epoch(train) [1][5000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:14:40 time: 0.5676 data_time: 0.0108 memory: 5132 grad_norm: 4.5590 loss: 0.7622 loss_cls: 0.4030 loss_bbox: 0.3592 +2024/10/26 16:26:08 - mmengine - INFO - Epoch(train) [1][5050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:13:20 time: 0.5592 data_time: 0.0114 memory: 5132 grad_norm: 4.2307 loss: 0.7517 loss_cls: 0.4064 loss_bbox: 0.3453 +2024/10/26 16:26:39 - mmengine - INFO - Epoch(train) [1][5100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:12:56 time: 0.6261 data_time: 0.0104 memory: 5136 grad_norm: 4.7811 loss: 0.7731 loss_cls: 0.4136 loss_bbox: 0.3595 +2024/10/26 16:27:09 - mmengine - INFO - Epoch(train) [1][5150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:12:15 time: 0.6044 data_time: 0.0133 memory: 5133 grad_norm: 4.5473 loss: 0.7358 loss_cls: 0.3854 loss_bbox: 0.3504 +2024/10/26 16:27:40 - mmengine - INFO - Epoch(train) [1][5200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:11:43 time: 0.6156 data_time: 0.0085 memory: 5134 grad_norm: 4.4963 loss: 0.7616 loss_cls: 0.4036 loss_bbox: 0.3580 +2024/10/26 16:28:11 - mmengine - INFO - Epoch(train) [1][5250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:11:22 time: 0.6306 data_time: 0.0082 memory: 5136 grad_norm: 4.4703 loss: 0.7658 loss_cls: 0.4038 loss_bbox: 0.3620 +2024/10/26 16:28:39 - mmengine - INFO - Epoch(train) [1][5300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:10:05 time: 0.5585 data_time: 0.0078 memory: 5134 grad_norm: 4.0832 loss: 0.7337 loss_cls: 0.3934 loss_bbox: 0.3403 +2024/10/26 16:29:10 - mmengine - INFO - Epoch(train) [1][5350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:09:29 time: 0.6105 data_time: 0.0078 memory: 5134 grad_norm: 4.4127 loss: 0.7353 loss_cls: 0.3928 loss_bbox: 0.3424 +2024/10/26 16:29:41 - mmengine - INFO - Epoch(train) [1][5400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:09:10 time: 0.6321 data_time: 0.0080 memory: 5132 grad_norm: 4.3296 loss: 0.7456 loss_cls: 0.3932 loss_bbox: 0.3524 +2024/10/26 16:30:12 - mmengine - INFO - Epoch(train) [1][5450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:08:29 time: 0.6037 data_time: 0.0080 memory: 5133 grad_norm: 4.4640 loss: 0.7669 loss_cls: 0.4156 loss_bbox: 0.3513 +2024/10/26 16:30:45 - mmengine - INFO - Epoch(train) [1][5500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:08:28 time: 0.6572 data_time: 0.0079 memory: 5133 grad_norm: 4.5971 loss: 0.7142 loss_cls: 0.3838 loss_bbox: 0.3304 +2024/10/26 16:31:17 - mmengine - INFO - Epoch(train) [1][5550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:08:23 time: 0.6518 data_time: 0.0086 memory: 5132 grad_norm: 4.2103 loss: 0.7416 loss_cls: 0.3831 loss_bbox: 0.3586 +2024/10/26 16:31:50 - mmengine - INFO - Epoch(train) [1][5600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:08:15 time: 0.6491 data_time: 0.0075 memory: 5134 grad_norm: 4.5929 loss: 0.7360 loss_cls: 0.3929 loss_bbox: 0.3431 +2024/10/26 16:32:16 - mmengine - INFO - Epoch(train) [1][5650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:06:44 time: 0.5358 data_time: 0.0092 memory: 5134 grad_norm: 4.4227 loss: 0.7407 loss_cls: 0.3910 loss_bbox: 0.3497 +2024/10/26 16:32:49 - mmengine - INFO - Epoch(train) [1][5700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:06:32 time: 0.6431 data_time: 0.0088 memory: 5135 grad_norm: 4.3953 loss: 0.7685 loss_cls: 0.4122 loss_bbox: 0.3563 +2024/10/26 16:33:17 - mmengine - INFO - Epoch(train) [1][5750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:05:33 time: 0.5777 data_time: 0.0077 memory: 5136 grad_norm: 4.4043 loss: 0.7363 loss_cls: 0.4038 loss_bbox: 0.3325 +2024/10/26 16:33:50 - mmengine - INFO - Epoch(train) [1][5800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:05:22 time: 0.6452 data_time: 0.0077 memory: 5134 grad_norm: 4.0302 loss: 0.7418 loss_cls: 0.3986 loss_bbox: 0.3431 +2024/10/26 16:34:18 - mmengine - INFO - Epoch(train) [1][5850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:04:13 time: 0.5639 data_time: 0.0087 memory: 5132 grad_norm: 4.6208 loss: 0.7455 loss_cls: 0.4061 loss_bbox: 0.3394 +2024/10/26 16:34:48 - mmengine - INFO - Epoch(train) [1][5900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:03:35 time: 0.6061 data_time: 0.0081 memory: 5134 grad_norm: 3.9686 loss: 0.7586 loss_cls: 0.4062 loss_bbox: 0.3524 +2024/10/26 16:35:14 - mmengine - INFO - Epoch(train) [1][5950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:01:52 time: 0.5122 data_time: 0.0093 memory: 5134 grad_norm: 4.4637 loss: 0.7150 loss_cls: 0.3734 loss_bbox: 0.3416 +2024/10/26 16:35:46 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 16:35:46 - mmengine - INFO - Epoch(train) [1][6000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:01:45 time: 0.6511 data_time: 0.0083 memory: 5132 grad_norm: 4.5159 loss: 0.7348 loss_cls: 0.3960 loss_bbox: 0.3388 +2024/10/26 16:36:19 - mmengine - INFO - Epoch(train) [1][6050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:01:34 time: 0.6445 data_time: 0.0127 memory: 5133 grad_norm: 4.3068 loss: 0.7334 loss_cls: 0.3942 loss_bbox: 0.3392 +2024/10/26 16:36:52 - mmengine - INFO - Epoch(train) [1][6100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:01:40 time: 0.6723 data_time: 0.0085 memory: 5134 grad_norm: 4.6603 loss: 0.7371 loss_cls: 0.3953 loss_bbox: 0.3418 +2024/10/26 16:37:25 - mmengine - INFO - Epoch(train) [1][6150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:01:35 time: 0.6548 data_time: 0.0137 memory: 5134 grad_norm: 4.4383 loss: 0.7250 loss_cls: 0.3928 loss_bbox: 0.3322 +2024/10/26 16:37:57 - mmengine - INFO - Epoch(train) [1][6200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:01:20 time: 0.6425 data_time: 0.0099 memory: 5133 grad_norm: 4.1498 loss: 0.7554 loss_cls: 0.4001 loss_bbox: 0.3554 +2024/10/26 16:38:26 - mmengine - INFO - Epoch(train) [1][6250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:00:23 time: 0.5772 data_time: 0.0078 memory: 5134 grad_norm: 4.1396 loss: 0.7562 loss_cls: 0.4163 loss_bbox: 0.3399 +2024/10/26 16:38:58 - mmengine - INFO - Epoch(train) [1][6300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 14:00:08 time: 0.6408 data_time: 0.0083 memory: 5135 grad_norm: 4.2316 loss: 0.7552 loss_cls: 0.4028 loss_bbox: 0.3524 +2024/10/26 16:39:24 - mmengine - INFO - Epoch(train) [1][6350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:58:33 time: 0.5175 data_time: 0.0075 memory: 5135 grad_norm: 4.2205 loss: 0.7281 loss_cls: 0.3834 loss_bbox: 0.3448 +2024/10/26 16:39:55 - mmengine - INFO - Epoch(train) [1][6400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:58:12 time: 0.6326 data_time: 0.0076 memory: 5132 grad_norm: 4.2298 loss: 0.7243 loss_cls: 0.3845 loss_bbox: 0.3398 +2024/10/26 16:40:20 - mmengine - INFO - Epoch(train) [1][6450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:56:28 time: 0.5004 data_time: 0.0079 memory: 5132 grad_norm: 4.5198 loss: 0.7108 loss_cls: 0.3811 loss_bbox: 0.3296 +2024/10/26 16:40:52 - mmengine - INFO - Epoch(train) [1][6500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:56:09 time: 0.6348 data_time: 0.0081 memory: 5134 grad_norm: 4.5464 loss: 0.7330 loss_cls: 0.3980 loss_bbox: 0.3350 +2024/10/26 16:41:23 - mmengine - INFO - Epoch(train) [1][6550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:55:39 time: 0.6170 data_time: 0.0079 memory: 5133 grad_norm: 4.3252 loss: 0.7280 loss_cls: 0.3943 loss_bbox: 0.3337 +2024/10/26 16:41:54 - mmengine - INFO - Epoch(train) [1][6600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:55:13 time: 0.6228 data_time: 0.0084 memory: 5133 grad_norm: 4.0556 loss: 0.7484 loss_cls: 0.4051 loss_bbox: 0.3433 +2024/10/26 16:42:27 - mmengine - INFO - Epoch(train) [1][6650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:55:01 time: 0.6480 data_time: 0.0074 memory: 5136 grad_norm: 3.8684 loss: 0.7190 loss_cls: 0.3751 loss_bbox: 0.3438 +2024/10/26 16:42:58 - mmengine - INFO - Epoch(train) [1][6700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:54:32 time: 0.6182 data_time: 0.0076 memory: 5134 grad_norm: 4.1037 loss: 0.7129 loss_cls: 0.3732 loss_bbox: 0.3397 +2024/10/26 16:43:26 - mmengine - INFO - Epoch(train) [1][6750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:53:36 time: 0.5746 data_time: 0.0123 memory: 5133 grad_norm: 4.4785 loss: 0.7758 loss_cls: 0.4136 loss_bbox: 0.3622 +2024/10/26 16:43:58 - mmengine - INFO - Epoch(train) [1][6800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:53:12 time: 0.6268 data_time: 0.0150 memory: 5136 grad_norm: 4.2693 loss: 0.7476 loss_cls: 0.3994 loss_bbox: 0.3482 +2024/10/26 16:44:21 - mmengine - INFO - Epoch(train) [1][6850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:51:14 time: 0.4691 data_time: 0.0077 memory: 5136 grad_norm: 4.2634 loss: 0.7040 loss_cls: 0.3642 loss_bbox: 0.3398 +2024/10/26 16:44:54 - mmengine - INFO - Epoch(train) [1][6900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:51:05 time: 0.6515 data_time: 0.0084 memory: 5135 grad_norm: 4.2429 loss: 0.7525 loss_cls: 0.4049 loss_bbox: 0.3477 +2024/10/26 16:45:21 - mmengine - INFO - Epoch(train) [1][6950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:49:55 time: 0.5482 data_time: 0.0087 memory: 5134 grad_norm: 4.3022 loss: 0.7164 loss_cls: 0.3742 loss_bbox: 0.3422 +2024/10/26 16:45:52 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 16:45:52 - mmengine - INFO - Epoch(train) [1][7000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:49:24 time: 0.6147 data_time: 0.0074 memory: 5134 grad_norm: 4.5005 loss: 0.7126 loss_cls: 0.3751 loss_bbox: 0.3375 +2024/10/26 16:46:19 - mmengine - INFO - Epoch(train) [1][7050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:48:09 time: 0.5370 data_time: 0.0115 memory: 5133 grad_norm: 4.6478 loss: 0.7394 loss_cls: 0.3943 loss_bbox: 0.3451 +2024/10/26 16:46:51 - mmengine - INFO - Epoch(train) [1][7100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:47:59 time: 0.6504 data_time: 0.0078 memory: 5133 grad_norm: 3.9967 loss: 0.7235 loss_cls: 0.3756 loss_bbox: 0.3478 +2024/10/26 16:47:18 - mmengine - INFO - Epoch(train) [1][7150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:46:45 time: 0.5379 data_time: 0.0080 memory: 5134 grad_norm: 4.1276 loss: 0.7079 loss_cls: 0.3708 loss_bbox: 0.3371 +2024/10/26 16:47:50 - mmengine - INFO - Epoch(train) [1][7200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:46:34 time: 0.6487 data_time: 0.0077 memory: 5133 grad_norm: 4.3169 loss: 0.7293 loss_cls: 0.3860 loss_bbox: 0.3433 +2024/10/26 16:48:20 - mmengine - INFO - Epoch(train) [1][7250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:45:49 time: 0.5893 data_time: 0.0077 memory: 5135 grad_norm: 4.4055 loss: 0.7246 loss_cls: 0.3891 loss_bbox: 0.3355 +2024/10/26 16:48:54 - mmengine - INFO - Epoch(train) [1][7300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:45:55 time: 0.6797 data_time: 0.0078 memory: 5136 grad_norm: 4.6916 loss: 0.7520 loss_cls: 0.4044 loss_bbox: 0.3476 +2024/10/26 16:49:18 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 16:49:18 - mmengine - INFO - Saving checkpoint at 1 epochs +2024/10/26 16:49:27 - mmengine - INFO - Epoch(val) [1][ 50/1250] eta: 0:01:52 time: 0.0936 data_time: 0.0068 memory: 5133 +2024/10/26 16:49:44 - mmengine - INFO - Epoch(val) [1][ 100/1250] eta: 0:04:16 time: 0.3516 data_time: 0.0015 memory: 630 +2024/10/26 16:49:50 - mmengine - INFO - Epoch(val) [1][ 150/1250] eta: 0:03:23 time: 0.1094 data_time: 0.0015 memory: 634 +2024/10/26 16:49:55 - mmengine - INFO - Epoch(val) [1][ 200/1250] eta: 0:02:50 time: 0.0949 data_time: 0.0015 memory: 635 +2024/10/26 16:50:00 - mmengine - INFO - Epoch(val) [1][ 250/1250] eta: 0:02:30 time: 0.1033 data_time: 0.0016 memory: 626 +2024/10/26 16:50:18 - mmengine - INFO - Epoch(val) [1][ 300/1250] eta: 0:02:57 time: 0.3660 data_time: 0.0016 memory: 626 +2024/10/26 16:50:22 - mmengine - INFO - Epoch(val) [1][ 350/1250] eta: 0:02:35 time: 0.0876 data_time: 0.0015 memory: 626 +2024/10/26 16:50:27 - mmengine - INFO - Epoch(val) [1][ 400/1250] eta: 0:02:18 time: 0.0986 data_time: 0.0015 memory: 615 +2024/10/26 16:50:32 - mmengine - INFO - Epoch(val) [1][ 450/1250] eta: 0:02:03 time: 0.0896 data_time: 0.0015 memory: 634 +2024/10/26 16:50:36 - mmengine - INFO - Epoch(val) [1][ 500/1250] eta: 0:01:51 time: 0.0879 data_time: 0.0015 memory: 634 +2024/10/26 16:50:42 - mmengine - INFO - Epoch(val) [1][ 550/1250] eta: 0:01:41 time: 0.1094 data_time: 0.0015 memory: 615 +2024/10/26 16:50:50 - mmengine - INFO - Epoch(val) [1][ 600/1250] eta: 0:01:34 time: 0.1582 data_time: 0.0015 memory: 626 +2024/10/26 16:50:57 - mmengine - INFO - Epoch(val) [1][ 650/1250] eta: 0:01:27 time: 0.1414 data_time: 0.0016 memory: 626 +2024/10/26 16:51:02 - mmengine - INFO - Epoch(val) [1][ 700/1250] eta: 0:01:18 time: 0.0983 data_time: 0.0016 memory: 630 +2024/10/26 16:51:05 - mmengine - INFO - Epoch(val) [1][ 750/1250] eta: 0:01:08 time: 0.0764 data_time: 0.0016 memory: 630 +2024/10/26 16:51:10 - mmengine - INFO - Epoch(val) [1][ 800/1250] eta: 0:01:00 time: 0.0967 data_time: 0.0016 memory: 634 +2024/10/26 16:51:14 - mmengine - INFO - Epoch(val) [1][ 850/1250] eta: 0:00:52 time: 0.0734 data_time: 0.0016 memory: 634 +2024/10/26 16:51:18 - mmengine - INFO - Epoch(val) [1][ 900/1250] eta: 0:00:45 time: 0.0855 data_time: 0.0016 memory: 634 +2024/10/26 16:51:23 - mmengine - INFO - Epoch(val) [1][ 950/1250] eta: 0:00:38 time: 0.0865 data_time: 0.0016 memory: 626 +2024/10/26 16:51:32 - mmengine - INFO - Epoch(val) [1][1000/1250] eta: 0:00:32 time: 0.1807 data_time: 0.0016 memory: 626 +2024/10/26 16:51:37 - mmengine - INFO - Epoch(val) [1][1050/1250] eta: 0:00:25 time: 0.1129 data_time: 0.0016 memory: 630 +2024/10/26 16:51:42 - mmengine - INFO - Epoch(val) [1][1100/1250] eta: 0:00:19 time: 0.0962 data_time: 0.0014 memory: 635 +2024/10/26 16:51:47 - mmengine - INFO - Epoch(val) [1][1150/1250] eta: 0:00:12 time: 0.0954 data_time: 0.0014 memory: 630 +2024/10/26 16:51:53 - mmengine - INFO - Epoch(val) [1][1200/1250] eta: 0:00:06 time: 0.1158 data_time: 0.0014 memory: 630 +2024/10/26 16:52:00 - mmengine - INFO - Epoch(val) [1][1250/1250] eta: 0:00:00 time: 0.1502 data_time: 0.0014 memory: 635 +2024/10/26 16:52:14 - mmengine - INFO - Evaluating bbox... +2024/10/26 16:53:04 - mmengine - INFO - bbox_mAP_copypaste: 0.214 0.360 0.224 0.099 0.230 0.300 +2024/10/26 16:53:05 - mmengine - INFO - Epoch(val) [1][1250/1250] coco/bbox_mAP: 0.2140 coco/bbox_mAP_50: 0.3600 coco/bbox_mAP_75: 0.2240 coco/bbox_mAP_s: 0.0990 coco/bbox_mAP_m: 0.2300 coco/bbox_mAP_l: 0.3000 data_time: 0.0017 time: 0.1264 +2024/10/26 16:53:33 - mmengine - INFO - Epoch(train) [2][ 50/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:45:26 time: 0.5465 data_time: 0.0079 memory: 5134 grad_norm: 4.4623 loss: 0.7012 loss_cls: 0.3684 loss_bbox: 0.3328 +2024/10/26 16:54:04 - mmengine - INFO - Epoch(train) [2][ 100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:44:58 time: 0.6182 data_time: 0.0082 memory: 5132 grad_norm: 3.9901 loss: 0.7246 loss_cls: 0.3798 loss_bbox: 0.3448 +2024/10/26 16:54:35 - mmengine - INFO - Epoch(train) [2][ 150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:44:30 time: 0.6212 data_time: 0.0301 memory: 5136 grad_norm: 4.0853 loss: 0.6771 loss_cls: 0.3624 loss_bbox: 0.3146 +2024/10/26 16:55:05 - mmengine - INFO - Epoch(train) [2][ 200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:43:48 time: 0.5923 data_time: 0.0080 memory: 5135 grad_norm: 4.4016 loss: 0.6988 loss_cls: 0.3730 loss_bbox: 0.3259 +2024/10/26 16:55:33 - mmengine - INFO - Epoch(train) [2][ 250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:42:46 time: 0.5568 data_time: 0.0085 memory: 5135 grad_norm: 4.3484 loss: 0.6767 loss_cls: 0.3613 loss_bbox: 0.3154 +2024/10/26 16:56:06 - mmengine - INFO - Epoch(train) [2][ 300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:42:38 time: 0.6577 data_time: 0.0082 memory: 5133 grad_norm: 4.2176 loss: 0.6687 loss_cls: 0.3437 loss_bbox: 0.3250 +2024/10/26 16:56:35 - mmengine - INFO - Epoch(train) [2][ 350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:41:56 time: 0.5917 data_time: 0.0080 memory: 5133 grad_norm: 4.2500 loss: 0.6776 loss_cls: 0.3495 loss_bbox: 0.3281 +2024/10/26 16:57:07 - mmengine - INFO - Epoch(train) [2][ 400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:41:37 time: 0.6381 data_time: 0.0133 memory: 5133 grad_norm: 4.1129 loss: 0.6803 loss_cls: 0.3535 loss_bbox: 0.3267 +2024/10/26 16:57:37 - mmengine - INFO - Epoch(train) [2][ 450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:41:02 time: 0.6044 data_time: 0.0080 memory: 5133 grad_norm: 4.4165 loss: 0.6679 loss_cls: 0.3411 loss_bbox: 0.3268 +2024/10/26 16:58:06 - mmengine - INFO - Epoch(train) [2][ 500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:40:11 time: 0.5749 data_time: 0.0082 memory: 5134 grad_norm: 3.8853 loss: 0.7306 loss_cls: 0.3969 loss_bbox: 0.3337 +2024/10/26 16:58:33 - mmengine - INFO - Epoch(train) [2][ 550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:39:02 time: 0.5395 data_time: 0.0112 memory: 5132 grad_norm: 4.1111 loss: 0.6783 loss_cls: 0.3597 loss_bbox: 0.3186 +2024/10/26 16:59:04 - mmengine - INFO - Epoch(train) [2][ 600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:38:36 time: 0.6226 data_time: 0.0081 memory: 5134 grad_norm: 4.0617 loss: 0.7177 loss_cls: 0.3766 loss_bbox: 0.3411 +2024/10/26 16:59:35 - mmengine - INFO - Epoch(train) [2][ 650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:38:11 time: 0.6259 data_time: 0.0266 memory: 5133 grad_norm: 4.1550 loss: 0.7296 loss_cls: 0.3941 loss_bbox: 0.3355 +2024/10/26 16:59:48 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 17:00:07 - mmengine - INFO - Epoch(train) [2][ 700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:37:47 time: 0.6276 data_time: 0.0124 memory: 5132 grad_norm: 4.1618 loss: 0.6997 loss_cls: 0.3687 loss_bbox: 0.3310 +2024/10/26 17:00:39 - mmengine - INFO - Epoch(train) [2][ 750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:37:30 time: 0.6398 data_time: 0.0082 memory: 5133 grad_norm: 4.1715 loss: 0.6910 loss_cls: 0.3682 loss_bbox: 0.3228 +2024/10/26 17:01:10 - mmengine - INFO - Epoch(train) [2][ 800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:37:00 time: 0.6167 data_time: 0.0080 memory: 5133 grad_norm: 4.1784 loss: 0.6689 loss_cls: 0.3511 loss_bbox: 0.3178 +2024/10/26 17:01:40 - mmengine - INFO - Epoch(train) [2][ 850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:36:27 time: 0.6100 data_time: 0.0092 memory: 5134 grad_norm: 3.7726 loss: 0.7116 loss_cls: 0.3756 loss_bbox: 0.3360 +2024/10/26 17:02:10 - mmengine - INFO - Epoch(train) [2][ 900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:35:49 time: 0.5970 data_time: 0.0083 memory: 5135 grad_norm: 4.0665 loss: 0.6761 loss_cls: 0.3531 loss_bbox: 0.3230 +2024/10/26 17:02:41 - mmengine - INFO - Epoch(train) [2][ 950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:35:16 time: 0.6105 data_time: 0.0109 memory: 5135 grad_norm: 4.0095 loss: 0.6709 loss_cls: 0.3485 loss_bbox: 0.3224 +2024/10/26 17:03:13 - mmengine - INFO - Epoch(train) [2][1000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:34:59 time: 0.6424 data_time: 0.0109 memory: 5135 grad_norm: 3.8026 loss: 0.7056 loss_cls: 0.3713 loss_bbox: 0.3343 +2024/10/26 17:03:44 - mmengine - INFO - Epoch(train) [2][1050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:34:36 time: 0.6301 data_time: 0.0080 memory: 5134 grad_norm: 3.9850 loss: 0.6914 loss_cls: 0.3629 loss_bbox: 0.3286 +2024/10/26 17:04:13 - mmengine - INFO - Epoch(train) [2][1100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:33:52 time: 0.5856 data_time: 0.0089 memory: 5133 grad_norm: 4.2168 loss: 0.7097 loss_cls: 0.3830 loss_bbox: 0.3267 +2024/10/26 17:04:46 - mmengine - INFO - Epoch(train) [2][1150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:33:37 time: 0.6487 data_time: 0.0081 memory: 5134 grad_norm: 4.0780 loss: 0.7300 loss_cls: 0.3914 loss_bbox: 0.3386 +2024/10/26 17:05:17 - mmengine - INFO - Epoch(train) [2][1200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:33:11 time: 0.6232 data_time: 0.0083 memory: 5132 grad_norm: 4.0103 loss: 0.6568 loss_cls: 0.3464 loss_bbox: 0.3104 +2024/10/26 17:05:51 - mmengine - INFO - Epoch(train) [2][1250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:33:08 time: 0.6745 data_time: 0.0083 memory: 5135 grad_norm: 4.3718 loss: 0.6946 loss_cls: 0.3699 loss_bbox: 0.3247 +2024/10/26 17:06:22 - mmengine - INFO - Epoch(train) [2][1300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:32:41 time: 0.6228 data_time: 0.0135 memory: 5132 grad_norm: 4.0215 loss: 0.6976 loss_cls: 0.3714 loss_bbox: 0.3262 +2024/10/26 17:06:53 - mmengine - INFO - Epoch(train) [2][1350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:32:13 time: 0.6213 data_time: 0.0090 memory: 5133 grad_norm: 3.8749 loss: 0.6832 loss_cls: 0.3518 loss_bbox: 0.3313 +2024/10/26 17:07:18 - mmengine - INFO - Epoch(train) [2][1400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:30:50 time: 0.4984 data_time: 0.0123 memory: 5135 grad_norm: 3.9631 loss: 0.7238 loss_cls: 0.3869 loss_bbox: 0.3370 +2024/10/26 17:07:52 - mmengine - INFO - Epoch(train) [2][1450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:30:46 time: 0.6728 data_time: 0.0129 memory: 5134 grad_norm: 3.9136 loss: 0.7088 loss_cls: 0.3726 loss_bbox: 0.3362 +2024/10/26 17:08:18 - mmengine - INFO - Epoch(train) [2][1500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:29:40 time: 0.5369 data_time: 0.0100 memory: 5133 grad_norm: 4.0942 loss: 0.7130 loss_cls: 0.3680 loss_bbox: 0.3450 +2024/10/26 17:08:50 - mmengine - INFO - Epoch(train) [2][1550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:29:18 time: 0.6326 data_time: 0.0101 memory: 5134 grad_norm: 3.9839 loss: 0.6942 loss_cls: 0.3573 loss_bbox: 0.3369 +2024/10/26 17:09:18 - mmengine - INFO - Epoch(train) [2][1600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:28:19 time: 0.5506 data_time: 0.0113 memory: 5131 grad_norm: 4.0989 loss: 0.7092 loss_cls: 0.3715 loss_bbox: 0.3377 +2024/10/26 17:09:51 - mmengine - INFO - Epoch(train) [2][1650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:28:09 time: 0.6598 data_time: 0.0132 memory: 5134 grad_norm: 3.7929 loss: 0.6630 loss_cls: 0.3390 loss_bbox: 0.3240 +2024/10/26 17:10:04 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 17:10:21 - mmengine - INFO - Epoch(train) [2][1700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:27:33 time: 0.6029 data_time: 0.0108 memory: 5133 grad_norm: 3.8560 loss: 0.7046 loss_cls: 0.3684 loss_bbox: 0.3362 +2024/10/26 17:10:53 - mmengine - INFO - Epoch(train) [2][1750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:27:19 time: 0.6513 data_time: 0.0113 memory: 5136 grad_norm: 4.1538 loss: 0.6715 loss_cls: 0.3542 loss_bbox: 0.3173 +2024/10/26 17:11:23 - mmengine - INFO - Epoch(train) [2][1800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:26:40 time: 0.5962 data_time: 0.0108 memory: 5133 grad_norm: 4.2593 loss: 0.7080 loss_cls: 0.3728 loss_bbox: 0.3351 +2024/10/26 17:11:55 - mmengine - INFO - Epoch(train) [2][1850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:26:19 time: 0.6361 data_time: 0.0111 memory: 5133 grad_norm: 4.0607 loss: 0.6825 loss_cls: 0.3537 loss_bbox: 0.3288 +2024/10/26 17:12:26 - mmengine - INFO - Epoch(train) [2][1900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:25:49 time: 0.6152 data_time: 0.0104 memory: 5134 grad_norm: 4.0699 loss: 0.6775 loss_cls: 0.3530 loss_bbox: 0.3246 +2024/10/26 17:12:58 - mmengine - INFO - Epoch(train) [2][1950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:25:30 time: 0.6428 data_time: 0.0123 memory: 5134 grad_norm: 4.0404 loss: 0.6750 loss_cls: 0.3520 loss_bbox: 0.3230 +2024/10/26 17:13:29 - mmengine - INFO - Epoch(train) [2][2000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:25:00 time: 0.6165 data_time: 0.0102 memory: 5131 grad_norm: 3.9053 loss: 0.6902 loss_cls: 0.3589 loss_bbox: 0.3313 +2024/10/26 17:14:00 - mmengine - INFO - Epoch(train) [2][2050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:24:37 time: 0.6309 data_time: 0.0115 memory: 5135 grad_norm: 4.1040 loss: 0.7091 loss_cls: 0.3677 loss_bbox: 0.3414 +2024/10/26 17:14:31 - mmengine - INFO - Epoch(train) [2][2100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:24:08 time: 0.6194 data_time: 0.0104 memory: 5132 grad_norm: 3.8404 loss: 0.6738 loss_cls: 0.3534 loss_bbox: 0.3204 +2024/10/26 17:15:04 - mmengine - INFO - Epoch(train) [2][2150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:23:53 time: 0.6534 data_time: 0.0100 memory: 5134 grad_norm: 4.1630 loss: 0.6909 loss_cls: 0.3598 loss_bbox: 0.3311 +2024/10/26 17:15:33 - mmengine - INFO - Epoch(train) [2][2200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:23:14 time: 0.5933 data_time: 0.0465 memory: 5133 grad_norm: 4.0253 loss: 0.6805 loss_cls: 0.3557 loss_bbox: 0.3248 +2024/10/26 17:16:05 - mmengine - INFO - Epoch(train) [2][2250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:22:50 time: 0.6303 data_time: 0.0109 memory: 5133 grad_norm: 4.2110 loss: 0.6965 loss_cls: 0.3653 loss_bbox: 0.3312 +2024/10/26 17:16:31 - mmengine - INFO - Epoch(train) [2][2300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:21:39 time: 0.5161 data_time: 0.0105 memory: 5134 grad_norm: 4.1108 loss: 0.6825 loss_cls: 0.3736 loss_bbox: 0.3089 +2024/10/26 17:17:03 - mmengine - INFO - Epoch(train) [2][2350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:21:24 time: 0.6528 data_time: 0.0104 memory: 5134 grad_norm: 4.3778 loss: 0.6954 loss_cls: 0.3647 loss_bbox: 0.3307 +2024/10/26 17:17:36 - mmengine - INFO - Epoch(train) [2][2400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:21:09 time: 0.6532 data_time: 0.0444 memory: 5135 grad_norm: 4.0436 loss: 0.6593 loss_cls: 0.3451 loss_bbox: 0.3142 +2024/10/26 17:18:07 - mmengine - INFO - Epoch(train) [2][2450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:20:39 time: 0.6172 data_time: 0.0109 memory: 5134 grad_norm: 3.6685 loss: 0.6769 loss_cls: 0.3525 loss_bbox: 0.3243 +2024/10/26 17:18:38 - mmengine - INFO - Epoch(train) [2][2500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:20:15 time: 0.6303 data_time: 0.0117 memory: 5135 grad_norm: 4.0558 loss: 0.7151 loss_cls: 0.3847 loss_bbox: 0.3304 +2024/10/26 17:19:10 - mmengine - INFO - Epoch(train) [2][2550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:19:48 time: 0.6241 data_time: 0.0108 memory: 5135 grad_norm: 4.1136 loss: 0.6947 loss_cls: 0.3695 loss_bbox: 0.3253 +2024/10/26 17:19:39 - mmengine - INFO - Epoch(train) [2][2600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:19:04 time: 0.5823 data_time: 0.0101 memory: 5134 grad_norm: 3.8386 loss: 0.6683 loss_cls: 0.3433 loss_bbox: 0.3250 +2024/10/26 17:20:04 - mmengine - INFO - Epoch(train) [2][2650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:17:53 time: 0.5099 data_time: 0.0104 memory: 5135 grad_norm: 4.1559 loss: 0.7085 loss_cls: 0.3707 loss_bbox: 0.3378 +2024/10/26 17:20:16 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 17:20:30 - mmengine - INFO - Epoch(train) [2][2700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:16:46 time: 0.5210 data_time: 0.0384 memory: 5134 grad_norm: 3.8932 loss: 0.7257 loss_cls: 0.3859 loss_bbox: 0.3398 +2024/10/26 17:20:43 - mmengine - INFO - Epoch(train) [2][2750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:13:58 time: 0.2577 data_time: 0.0150 memory: 5136 grad_norm: 3.8407 loss: 0.6965 loss_cls: 0.3761 loss_bbox: 0.3204 +2024/10/26 17:20:56 - mmengine - INFO - Epoch(train) [2][2800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:11:10 time: 0.2543 data_time: 0.0104 memory: 5134 grad_norm: 3.7779 loss: 0.7152 loss_cls: 0.3829 loss_bbox: 0.3323 +2024/10/26 17:21:09 - mmengine - INFO - Epoch(train) [2][2850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:08:24 time: 0.2553 data_time: 0.0108 memory: 5132 grad_norm: 4.1966 loss: 0.6915 loss_cls: 0.3699 loss_bbox: 0.3216 +2024/10/26 17:21:21 - mmengine - INFO - Epoch(train) [2][2900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:05:40 time: 0.2555 data_time: 0.0101 memory: 5134 grad_norm: 4.0571 loss: 0.6941 loss_cls: 0.3536 loss_bbox: 0.3406 +2024/10/26 17:21:34 - mmengine - INFO - Epoch(train) [2][2950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:02:58 time: 0.2600 data_time: 0.0104 memory: 5133 grad_norm: 3.9706 loss: 0.6960 loss_cls: 0.3640 loss_bbox: 0.3319 +2024/10/26 17:21:48 - mmengine - INFO - Epoch(train) [2][3000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 13:00:20 time: 0.2632 data_time: 0.0100 memory: 5135 grad_norm: 3.6501 loss: 0.6593 loss_cls: 0.3351 loss_bbox: 0.3243 +2024/10/26 17:22:00 - mmengine - INFO - Epoch(train) [2][3050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:57:39 time: 0.2545 data_time: 0.0102 memory: 5135 grad_norm: 3.8491 loss: 0.7203 loss_cls: 0.3747 loss_bbox: 0.3456 +2024/10/26 17:22:13 - mmengine - INFO - Epoch(train) [2][3100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:55:01 time: 0.2555 data_time: 0.0102 memory: 5136 grad_norm: 3.9822 loss: 0.6748 loss_cls: 0.3545 loss_bbox: 0.3203 +2024/10/26 17:22:26 - mmengine - INFO - Epoch(train) [2][3150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:52:22 time: 0.2523 data_time: 0.0096 memory: 5133 grad_norm: 3.9705 loss: 0.6731 loss_cls: 0.3442 loss_bbox: 0.3289 +2024/10/26 17:22:38 - mmengine - INFO - Epoch(train) [2][3200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:49:45 time: 0.2524 data_time: 0.0097 memory: 5133 grad_norm: 3.9126 loss: 0.6556 loss_cls: 0.3426 loss_bbox: 0.3131 +2024/10/26 17:22:51 - mmengine - INFO - Epoch(train) [2][3250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:47:13 time: 0.2603 data_time: 0.0095 memory: 5135 grad_norm: 3.9834 loss: 0.6950 loss_cls: 0.3667 loss_bbox: 0.3283 +2024/10/26 17:23:04 - mmengine - INFO - Epoch(train) [2][3300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:44:39 time: 0.2543 data_time: 0.0104 memory: 5133 grad_norm: 4.0033 loss: 0.7228 loss_cls: 0.3809 loss_bbox: 0.3419 +2024/10/26 17:23:18 - mmengine - INFO - Epoch(train) [2][3350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:42:16 time: 0.2806 data_time: 0.0382 memory: 5133 grad_norm: 3.9397 loss: 0.6782 loss_cls: 0.3590 loss_bbox: 0.3192 +2024/10/26 17:23:31 - mmengine - INFO - Epoch(train) [2][3400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:39:45 time: 0.2536 data_time: 0.0101 memory: 5133 grad_norm: 3.9213 loss: 0.6567 loss_cls: 0.3449 loss_bbox: 0.3118 +2024/10/26 17:23:44 - mmengine - INFO - Epoch(train) [2][3450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:37:15 time: 0.2541 data_time: 0.0097 memory: 5133 grad_norm: 3.8841 loss: 0.6842 loss_cls: 0.3583 loss_bbox: 0.3259 +2024/10/26 17:23:56 - mmengine - INFO - Epoch(train) [2][3500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:34:46 time: 0.2535 data_time: 0.0098 memory: 5134 grad_norm: 3.8888 loss: 0.6849 loss_cls: 0.3639 loss_bbox: 0.3210 +2024/10/26 17:24:09 - mmengine - INFO - Epoch(train) [2][3550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:32:20 time: 0.2566 data_time: 0.0102 memory: 5135 grad_norm: 3.7135 loss: 0.6618 loss_cls: 0.3439 loss_bbox: 0.3179 +2024/10/26 17:24:22 - mmengine - INFO - Epoch(train) [2][3600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:29:53 time: 0.2528 data_time: 0.0096 memory: 5134 grad_norm: 4.0493 loss: 0.7128 loss_cls: 0.3712 loss_bbox: 0.3416 +2024/10/26 17:24:34 - mmengine - INFO - Epoch(train) [2][3650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:27:28 time: 0.2536 data_time: 0.0103 memory: 5133 grad_norm: 4.0008 loss: 0.6908 loss_cls: 0.3506 loss_bbox: 0.3402 +2024/10/26 17:24:40 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 17:24:47 - mmengine - INFO - Epoch(train) [2][3700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:25:06 time: 0.2579 data_time: 0.0152 memory: 5134 grad_norm: 4.2049 loss: 0.6873 loss_cls: 0.3637 loss_bbox: 0.3236 +2024/10/26 17:25:00 - mmengine - INFO - Epoch(train) [2][3750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:22:43 time: 0.2535 data_time: 0.0101 memory: 5134 grad_norm: 4.2105 loss: 0.6978 loss_cls: 0.3605 loss_bbox: 0.3372 +2024/10/26 17:25:13 - mmengine - INFO - Epoch(train) [2][3800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:20:26 time: 0.2654 data_time: 0.0098 memory: 5133 grad_norm: 3.6789 loss: 0.6677 loss_cls: 0.3613 loss_bbox: 0.3064 +2024/10/26 17:25:26 - mmengine - INFO - Epoch(train) [2][3850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:18:05 time: 0.2524 data_time: 0.0102 memory: 5134 grad_norm: 3.9614 loss: 0.7013 loss_cls: 0.3698 loss_bbox: 0.3315 +2024/10/26 17:25:39 - mmengine - INFO - Epoch(train) [2][3900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:15:48 time: 0.2605 data_time: 0.0128 memory: 5134 grad_norm: 3.9355 loss: 0.6986 loss_cls: 0.3572 loss_bbox: 0.3414 +2024/10/26 17:25:52 - mmengine - INFO - Epoch(train) [2][3950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:13:30 time: 0.2534 data_time: 0.0101 memory: 5134 grad_norm: 4.0231 loss: 0.6797 loss_cls: 0.3540 loss_bbox: 0.3257 +2024/10/26 17:26:04 - mmengine - INFO - Epoch(train) [2][4000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:11:13 time: 0.2546 data_time: 0.0104 memory: 5134 grad_norm: 3.8361 loss: 0.6696 loss_cls: 0.3566 loss_bbox: 0.3130 +2024/10/26 17:26:18 - mmengine - INFO - Epoch(train) [2][4050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:09:05 time: 0.2758 data_time: 0.0319 memory: 5137 grad_norm: 3.7497 loss: 0.6927 loss_cls: 0.3595 loss_bbox: 0.3332 +2024/10/26 17:26:31 - mmengine - INFO - Epoch(train) [2][4100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:06:54 time: 0.2672 data_time: 0.0146 memory: 5136 grad_norm: 3.9314 loss: 0.6930 loss_cls: 0.3604 loss_bbox: 0.3326 +2024/10/26 17:26:44 - mmengine - INFO - Epoch(train) [2][4150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:04:42 time: 0.2598 data_time: 0.0168 memory: 5134 grad_norm: 3.6030 loss: 0.6451 loss_cls: 0.3315 loss_bbox: 0.3136 +2024/10/26 17:26:58 - mmengine - INFO - Epoch(train) [2][4200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:02:33 time: 0.2633 data_time: 0.0203 memory: 5133 grad_norm: 3.8106 loss: 0.7268 loss_cls: 0.3747 loss_bbox: 0.3521 +2024/10/26 17:27:10 - mmengine - INFO - Epoch(train) [2][4250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 12:00:22 time: 0.2555 data_time: 0.0108 memory: 5134 grad_norm: 3.7940 loss: 0.6467 loss_cls: 0.3332 loss_bbox: 0.3135 +2024/10/26 17:27:23 - mmengine - INFO - Epoch(train) [2][4300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:58:10 time: 0.2521 data_time: 0.0102 memory: 5133 grad_norm: 4.0086 loss: 0.6743 loss_cls: 0.3486 loss_bbox: 0.3257 +2024/10/26 17:27:36 - mmengine - INFO - Epoch(train) [2][4350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:56:01 time: 0.2536 data_time: 0.0101 memory: 5133 grad_norm: 3.8220 loss: 0.6817 loss_cls: 0.3568 loss_bbox: 0.3249 +2024/10/26 17:27:48 - mmengine - INFO - Epoch(train) [2][4400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:53:52 time: 0.2545 data_time: 0.0135 memory: 5133 grad_norm: 4.1269 loss: 0.6792 loss_cls: 0.3598 loss_bbox: 0.3194 +2024/10/26 17:28:01 - mmengine - INFO - Epoch(train) [2][4450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:51:44 time: 0.2513 data_time: 0.0099 memory: 5133 grad_norm: 3.7530 loss: 0.6500 loss_cls: 0.3427 loss_bbox: 0.3072 +2024/10/26 17:28:18 - mmengine - INFO - Epoch(train) [2][4500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:50:04 time: 0.3379 data_time: 0.0951 memory: 5133 grad_norm: 3.7852 loss: 0.6744 loss_cls: 0.3579 loss_bbox: 0.3165 +2024/10/26 17:28:30 - mmengine - INFO - Epoch(train) [2][4550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:47:57 time: 0.2510 data_time: 0.0097 memory: 5133 grad_norm: 4.0559 loss: 0.6804 loss_cls: 0.3517 loss_bbox: 0.3286 +2024/10/26 17:28:43 - mmengine - INFO - Epoch(train) [2][4600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:45:51 time: 0.2511 data_time: 0.0097 memory: 5134 grad_norm: 3.6103 loss: 0.6775 loss_cls: 0.3502 loss_bbox: 0.3273 +2024/10/26 17:28:56 - mmengine - INFO - Epoch(train) [2][4650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:43:47 time: 0.2516 data_time: 0.0112 memory: 5135 grad_norm: 3.6416 loss: 0.6772 loss_cls: 0.3546 loss_bbox: 0.3227 +2024/10/26 17:29:01 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 17:29:08 - mmengine - INFO - Epoch(train) [2][4700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:41:44 time: 0.2569 data_time: 0.0106 memory: 5133 grad_norm: 3.9381 loss: 0.7007 loss_cls: 0.3704 loss_bbox: 0.3303 +2024/10/26 17:29:21 - mmengine - INFO - Epoch(train) [2][4750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:39:44 time: 0.2605 data_time: 0.0103 memory: 5135 grad_norm: 3.9754 loss: 0.6748 loss_cls: 0.3434 loss_bbox: 0.3314 +2024/10/26 17:29:34 - mmengine - INFO - Epoch(train) [2][4800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:37:44 time: 0.2581 data_time: 0.0113 memory: 5134 grad_norm: 3.8020 loss: 0.6795 loss_cls: 0.3544 loss_bbox: 0.3250 +2024/10/26 17:29:47 - mmengine - INFO - Epoch(train) [2][4850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:35:45 time: 0.2560 data_time: 0.0119 memory: 5135 grad_norm: 3.8459 loss: 0.6850 loss_cls: 0.3619 loss_bbox: 0.3231 +2024/10/26 17:30:00 - mmengine - INFO - Epoch(train) [2][4900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:33:46 time: 0.2555 data_time: 0.0101 memory: 5135 grad_norm: 3.7094 loss: 0.6799 loss_cls: 0.3534 loss_bbox: 0.3264 +2024/10/26 17:30:13 - mmengine - INFO - Epoch(train) [2][4950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:31:49 time: 0.2600 data_time: 0.0138 memory: 5134 grad_norm: 3.6571 loss: 0.6653 loss_cls: 0.3422 loss_bbox: 0.3231 +2024/10/26 17:30:26 - mmengine - INFO - Epoch(train) [2][5000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:29:52 time: 0.2549 data_time: 0.0101 memory: 5133 grad_norm: 3.6782 loss: 0.6725 loss_cls: 0.3504 loss_bbox: 0.3221 +2024/10/26 17:30:39 - mmengine - INFO - Epoch(train) [2][5050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:27:57 time: 0.2628 data_time: 0.0100 memory: 5134 grad_norm: 3.9950 loss: 0.7028 loss_cls: 0.3649 loss_bbox: 0.3379 +2024/10/26 17:30:51 - mmengine - INFO - Epoch(train) [2][5100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:26:01 time: 0.2533 data_time: 0.0102 memory: 5135 grad_norm: 3.8002 loss: 0.6733 loss_cls: 0.3459 loss_bbox: 0.3274 +2024/10/26 17:31:04 - mmengine - INFO - Epoch(train) [2][5150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:24:06 time: 0.2538 data_time: 0.0101 memory: 5134 grad_norm: 3.6222 loss: 0.6794 loss_cls: 0.3424 loss_bbox: 0.3370 +2024/10/26 17:31:18 - mmengine - INFO - Epoch(train) [2][5200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:22:18 time: 0.2761 data_time: 0.0314 memory: 5134 grad_norm: 4.1590 loss: 0.6974 loss_cls: 0.3698 loss_bbox: 0.3276 +2024/10/26 17:31:31 - mmengine - INFO - Epoch(train) [2][5250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:20:25 time: 0.2551 data_time: 0.0102 memory: 5136 grad_norm: 3.8090 loss: 0.6618 loss_cls: 0.3399 loss_bbox: 0.3219 +2024/10/26 17:31:43 - mmengine - INFO - Epoch(train) [2][5300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:18:32 time: 0.2545 data_time: 0.0100 memory: 5134 grad_norm: 3.7868 loss: 0.6915 loss_cls: 0.3621 loss_bbox: 0.3293 +2024/10/26 17:31:56 - mmengine - INFO - Epoch(train) [2][5350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:16:41 time: 0.2559 data_time: 0.0109 memory: 5133 grad_norm: 3.4855 loss: 0.6587 loss_cls: 0.3424 loss_bbox: 0.3162 +2024/10/26 17:32:09 - mmengine - INFO - Epoch(train) [2][5400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:14:49 time: 0.2534 data_time: 0.0103 memory: 5134 grad_norm: 3.8263 loss: 0.6945 loss_cls: 0.3596 loss_bbox: 0.3349 +2024/10/26 17:32:22 - mmengine - INFO - Epoch(train) [2][5450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:12:59 time: 0.2543 data_time: 0.0100 memory: 5134 grad_norm: 4.0006 loss: 0.6703 loss_cls: 0.3448 loss_bbox: 0.3255 +2024/10/26 17:32:34 - mmengine - INFO - Epoch(train) [2][5500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:11:09 time: 0.2539 data_time: 0.0104 memory: 5135 grad_norm: 4.0312 loss: 0.6758 loss_cls: 0.3569 loss_bbox: 0.3188 +2024/10/26 17:32:48 - mmengine - INFO - Epoch(train) [2][5550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:09:23 time: 0.2639 data_time: 0.0101 memory: 5134 grad_norm: 3.8024 loss: 0.6796 loss_cls: 0.3510 loss_bbox: 0.3286 +2024/10/26 17:33:00 - mmengine - INFO - Epoch(train) [2][5600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:07:34 time: 0.2524 data_time: 0.0098 memory: 5132 grad_norm: 3.7581 loss: 0.7044 loss_cls: 0.3729 loss_bbox: 0.3315 +2024/10/26 17:33:13 - mmengine - INFO - Epoch(train) [2][5650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:05:50 time: 0.2649 data_time: 0.0134 memory: 5132 grad_norm: 3.7205 loss: 0.6531 loss_cls: 0.3377 loss_bbox: 0.3154 +2024/10/26 17:33:19 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 17:33:26 - mmengine - INFO - Epoch(train) [2][5700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:04:05 time: 0.2600 data_time: 0.0134 memory: 5134 grad_norm: 3.6285 loss: 0.6721 loss_cls: 0.3557 loss_bbox: 0.3164 +2024/10/26 17:33:39 - mmengine - INFO - Epoch(train) [2][5750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:02:18 time: 0.2536 data_time: 0.0101 memory: 5132 grad_norm: 3.9092 loss: 0.6925 loss_cls: 0.3585 loss_bbox: 0.3340 +2024/10/26 17:33:52 - mmengine - INFO - Epoch(train) [2][5800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 11:00:33 time: 0.2555 data_time: 0.0126 memory: 5133 grad_norm: 3.7498 loss: 0.6829 loss_cls: 0.3590 loss_bbox: 0.3239 +2024/10/26 17:34:05 - mmengine - INFO - Epoch(train) [2][5850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:58:51 time: 0.2620 data_time: 0.0098 memory: 5134 grad_norm: 3.7285 loss: 0.6615 loss_cls: 0.3390 loss_bbox: 0.3225 +2024/10/26 17:34:18 - mmengine - INFO - Epoch(train) [2][5900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:57:10 time: 0.2649 data_time: 0.0212 memory: 5135 grad_norm: 4.0899 loss: 0.6849 loss_cls: 0.3591 loss_bbox: 0.3258 +2024/10/26 17:34:31 - mmengine - INFO - Epoch(train) [2][5950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:55:28 time: 0.2567 data_time: 0.0128 memory: 5133 grad_norm: 3.6312 loss: 0.6749 loss_cls: 0.3469 loss_bbox: 0.3280 +2024/10/26 17:34:44 - mmengine - INFO - Epoch(train) [2][6000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:53:45 time: 0.2544 data_time: 0.0100 memory: 5132 grad_norm: 3.9107 loss: 0.6900 loss_cls: 0.3610 loss_bbox: 0.3291 +2024/10/26 17:34:56 - mmengine - INFO - Epoch(train) [2][6050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:52:03 time: 0.2532 data_time: 0.0103 memory: 5134 grad_norm: 3.6828 loss: 0.6611 loss_cls: 0.3417 loss_bbox: 0.3194 +2024/10/26 17:35:09 - mmengine - INFO - Epoch(train) [2][6100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:50:21 time: 0.2522 data_time: 0.0098 memory: 5133 grad_norm: 4.0721 loss: 0.6792 loss_cls: 0.3574 loss_bbox: 0.3218 +2024/10/26 17:35:22 - mmengine - INFO - Epoch(train) [2][6150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:48:40 time: 0.2522 data_time: 0.0099 memory: 5133 grad_norm: 3.7853 loss: 0.6480 loss_cls: 0.3448 loss_bbox: 0.3032 +2024/10/26 17:35:34 - mmengine - INFO - Epoch(train) [2][6200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:47:00 time: 0.2547 data_time: 0.0103 memory: 5135 grad_norm: 3.7961 loss: 0.7113 loss_cls: 0.3738 loss_bbox: 0.3375 +2024/10/26 17:35:47 - mmengine - INFO - Epoch(train) [2][6250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:45:20 time: 0.2532 data_time: 0.0101 memory: 5134 grad_norm: 3.6662 loss: 0.6688 loss_cls: 0.3458 loss_bbox: 0.3230 +2024/10/26 17:36:00 - mmengine - INFO - Epoch(train) [2][6300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:43:41 time: 0.2520 data_time: 0.0098 memory: 5135 grad_norm: 3.9288 loss: 0.6321 loss_cls: 0.3252 loss_bbox: 0.3069 +2024/10/26 17:36:12 - mmengine - INFO - Epoch(train) [2][6350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:42:03 time: 0.2527 data_time: 0.0100 memory: 5133 grad_norm: 3.6827 loss: 0.6620 loss_cls: 0.3428 loss_bbox: 0.3192 +2024/10/26 17:36:25 - mmengine - INFO - Epoch(train) [2][6400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:40:25 time: 0.2529 data_time: 0.0110 memory: 5136 grad_norm: 3.9101 loss: 0.6650 loss_cls: 0.3469 loss_bbox: 0.3181 +2024/10/26 17:36:38 - mmengine - INFO - Epoch(train) [2][6450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:38:50 time: 0.2613 data_time: 0.0154 memory: 5135 grad_norm: 3.7616 loss: 0.6409 loss_cls: 0.3305 loss_bbox: 0.3103 +2024/10/26 17:36:51 - mmengine - INFO - Epoch(train) [2][6500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:37:18 time: 0.2695 data_time: 0.0168 memory: 5133 grad_norm: 3.6214 loss: 0.6606 loss_cls: 0.3311 loss_bbox: 0.3295 +2024/10/26 17:37:04 - mmengine - INFO - Epoch(train) [2][6550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:35:42 time: 0.2537 data_time: 0.0115 memory: 5135 grad_norm: 3.7719 loss: 0.6673 loss_cls: 0.3476 loss_bbox: 0.3197 +2024/10/26 17:37:18 - mmengine - INFO - Epoch(train) [2][6600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:34:13 time: 0.2741 data_time: 0.0304 memory: 5132 grad_norm: 3.7967 loss: 0.6534 loss_cls: 0.3299 loss_bbox: 0.3235 +2024/10/26 17:37:31 - mmengine - INFO - Epoch(train) [2][6650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:32:38 time: 0.2535 data_time: 0.0105 memory: 5135 grad_norm: 3.7972 loss: 0.6728 loss_cls: 0.3413 loss_bbox: 0.3315 +2024/10/26 17:37:36 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 17:37:43 - mmengine - INFO - Epoch(train) [2][6700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:31:04 time: 0.2543 data_time: 0.0112 memory: 5134 grad_norm: 3.8257 loss: 0.6930 loss_cls: 0.3654 loss_bbox: 0.3275 +2024/10/26 17:37:56 - mmengine - INFO - Epoch(train) [2][6750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:29:33 time: 0.2631 data_time: 0.0173 memory: 5134 grad_norm: 3.7795 loss: 0.6522 loss_cls: 0.3378 loss_bbox: 0.3144 +2024/10/26 17:38:10 - mmengine - INFO - Epoch(train) [2][6800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:28:03 time: 0.2655 data_time: 0.0110 memory: 5135 grad_norm: 3.9380 loss: 0.6590 loss_cls: 0.3424 loss_bbox: 0.3166 +2024/10/26 17:38:22 - mmengine - INFO - Epoch(train) [2][6850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:26:31 time: 0.2548 data_time: 0.0102 memory: 5133 grad_norm: 3.5359 loss: 0.6707 loss_cls: 0.3469 loss_bbox: 0.3239 +2024/10/26 17:38:35 - mmengine - INFO - Epoch(train) [2][6900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:24:59 time: 0.2531 data_time: 0.0104 memory: 5135 grad_norm: 3.8063 loss: 0.6464 loss_cls: 0.3406 loss_bbox: 0.3058 +2024/10/26 17:38:48 - mmengine - INFO - Epoch(train) [2][6950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:23:30 time: 0.2606 data_time: 0.0167 memory: 5134 grad_norm: 3.9933 loss: 0.6166 loss_cls: 0.3167 loss_bbox: 0.2999 +2024/10/26 17:39:01 - mmengine - INFO - Epoch(train) [2][7000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:21:59 time: 0.2526 data_time: 0.0104 memory: 5134 grad_norm: 3.6990 loss: 0.6568 loss_cls: 0.3461 loss_bbox: 0.3106 +2024/10/26 17:39:14 - mmengine - INFO - Epoch(train) [2][7050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:20:30 time: 0.2564 data_time: 0.0105 memory: 5134 grad_norm: 3.7273 loss: 0.6652 loss_cls: 0.3395 loss_bbox: 0.3257 +2024/10/26 17:39:26 - mmengine - INFO - Epoch(train) [2][7100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:19:00 time: 0.2551 data_time: 0.0104 memory: 5135 grad_norm: 3.9859 loss: 0.6695 loss_cls: 0.3576 loss_bbox: 0.3119 +2024/10/26 17:39:39 - mmengine - INFO - Epoch(train) [2][7150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:17:32 time: 0.2563 data_time: 0.0108 memory: 5133 grad_norm: 3.7597 loss: 0.6528 loss_cls: 0.3314 loss_bbox: 0.3214 +2024/10/26 17:39:52 - mmengine - INFO - Epoch(train) [2][7200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:16:04 time: 0.2540 data_time: 0.0106 memory: 5134 grad_norm: 3.4481 loss: 0.6819 loss_cls: 0.3681 loss_bbox: 0.3138 +2024/10/26 17:40:05 - mmengine - INFO - Epoch(train) [2][7250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:14:36 time: 0.2570 data_time: 0.0120 memory: 5133 grad_norm: 3.7885 loss: 0.6636 loss_cls: 0.3464 loss_bbox: 0.3173 +2024/10/26 17:40:18 - mmengine - INFO - Epoch(train) [2][7300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:13 time: 0.2687 data_time: 0.0185 memory: 5134 grad_norm: 3.9103 loss: 0.6777 loss_cls: 0.3538 loss_bbox: 0.3238 +2024/10/26 17:40:33 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 17:40:33 - mmengine - INFO - Saving checkpoint at 2 epochs +2024/10/26 17:40:41 - mmengine - INFO - Epoch(val) [2][ 50/1250] eta: 0:01:22 time: 0.0691 data_time: 0.0024 memory: 6858 +2024/10/26 17:40:44 - mmengine - INFO - Epoch(val) [2][ 100/1250] eta: 0:01:17 time: 0.0663 data_time: 0.0017 memory: 629 +2024/10/26 17:40:47 - mmengine - INFO - Epoch(val) [2][ 150/1250] eta: 0:01:13 time: 0.0657 data_time: 0.0018 memory: 634 +2024/10/26 17:40:51 - mmengine - INFO - Epoch(val) [2][ 200/1250] eta: 0:01:09 time: 0.0649 data_time: 0.0018 memory: 634 +2024/10/26 17:40:54 - mmengine - INFO - Epoch(val) [2][ 250/1250] eta: 0:01:06 time: 0.0660 data_time: 0.0017 memory: 625 +2024/10/26 17:40:57 - mmengine - INFO - Epoch(val) [2][ 300/1250] eta: 0:01:03 time: 0.0682 data_time: 0.0019 memory: 625 +2024/10/26 17:41:01 - mmengine - INFO - Epoch(val) [2][ 350/1250] eta: 0:01:00 time: 0.0697 data_time: 0.0020 memory: 625 +2024/10/26 17:41:04 - mmengine - INFO - Epoch(val) [2][ 400/1250] eta: 0:00:57 time: 0.0668 data_time: 0.0017 memory: 614 +2024/10/26 17:41:07 - mmengine - INFO - Epoch(val) [2][ 450/1250] eta: 0:00:53 time: 0.0682 data_time: 0.0017 memory: 634 +2024/10/26 17:41:11 - mmengine - INFO - Epoch(val) [2][ 500/1250] eta: 0:00:50 time: 0.0676 data_time: 0.0018 memory: 634 +2024/10/26 17:41:14 - mmengine - INFO - Epoch(val) [2][ 550/1250] eta: 0:00:47 time: 0.0722 data_time: 0.0020 memory: 615 +2024/10/26 17:41:18 - mmengine - INFO - Epoch(val) [2][ 600/1250] eta: 0:00:44 time: 0.0736 data_time: 0.0019 memory: 625 +2024/10/26 17:41:22 - mmengine - INFO - Epoch(val) [2][ 650/1250] eta: 0:00:41 time: 0.0719 data_time: 0.0018 memory: 625 +2024/10/26 17:41:25 - mmengine - INFO - Epoch(val) [2][ 700/1250] eta: 0:00:37 time: 0.0668 data_time: 0.0017 memory: 629 +2024/10/26 17:41:29 - mmengine - INFO - Epoch(val) [2][ 750/1250] eta: 0:00:34 time: 0.0694 data_time: 0.0018 memory: 629 +2024/10/26 17:41:32 - mmengine - INFO - Epoch(val) [2][ 800/1250] eta: 0:00:30 time: 0.0685 data_time: 0.0019 memory: 634 +2024/10/26 17:41:36 - mmengine - INFO - Epoch(val) [2][ 850/1250] eta: 0:00:27 time: 0.0713 data_time: 0.0019 memory: 634 +2024/10/26 17:41:39 - mmengine - INFO - Epoch(val) [2][ 900/1250] eta: 0:00:24 time: 0.0703 data_time: 0.0018 memory: 634 +2024/10/26 17:41:43 - mmengine - INFO - Epoch(val) [2][ 950/1250] eta: 0:00:20 time: 0.0712 data_time: 0.0018 memory: 626 +2024/10/26 17:41:46 - mmengine - INFO - Epoch(val) [2][1000/1250] eta: 0:00:17 time: 0.0731 data_time: 0.0017 memory: 626 +2024/10/26 17:41:50 - mmengine - INFO - Epoch(val) [2][1050/1250] eta: 0:00:13 time: 0.0755 data_time: 0.0020 memory: 629 +2024/10/26 17:41:54 - mmengine - INFO - Epoch(val) [2][1100/1250] eta: 0:00:10 time: 0.0732 data_time: 0.0018 memory: 634 +2024/10/26 17:41:57 - mmengine - INFO - Epoch(val) [2][1150/1250] eta: 0:00:06 time: 0.0723 data_time: 0.0018 memory: 629 +2024/10/26 17:42:01 - mmengine - INFO - Epoch(val) [2][1200/1250] eta: 0:00:03 time: 0.0722 data_time: 0.0017 memory: 629 +2024/10/26 17:42:05 - mmengine - INFO - Epoch(val) [2][1250/1250] eta: 0:00:00 time: 0.0737 data_time: 0.0018 memory: 635 +2024/10/26 17:42:14 - mmengine - INFO - Evaluating bbox... +2024/10/26 17:43:14 - mmengine - INFO - bbox_mAP_copypaste: 0.264 0.431 0.278 0.124 0.292 0.361 +2024/10/26 17:43:15 - mmengine - INFO - Epoch(val) [2][1250/1250] coco/bbox_mAP: 0.2640 coco/bbox_mAP_50: 0.4310 coco/bbox_mAP_75: 0.2780 coco/bbox_mAP_s: 0.1240 coco/bbox_mAP_m: 0.2920 coco/bbox_mAP_l: 0.3610 data_time: 0.0018 time: 0.0699 +2024/10/26 17:43:48 - mmengine - INFO - Epoch(train) [3][ 50/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:11 time: 0.6606 data_time: 0.0095 memory: 5136 grad_norm: 3.6501 loss: 0.6208 loss_cls: 0.3087 loss_bbox: 0.3121 +2024/10/26 17:44:18 - mmengine - INFO - Epoch(train) [3][ 100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:08 time: 0.5901 data_time: 0.0136 memory: 5133 grad_norm: 3.6752 loss: 0.6007 loss_cls: 0.3089 loss_bbox: 0.2918 +2024/10/26 17:44:50 - mmengine - INFO - Epoch(train) [3][ 150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:19 time: 0.6490 data_time: 0.0084 memory: 5132 grad_norm: 3.5479 loss: 0.6630 loss_cls: 0.3511 loss_bbox: 0.3120 +2024/10/26 17:45:21 - mmengine - INFO - Epoch(train) [3][ 200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:18 time: 0.6025 data_time: 0.0081 memory: 5136 grad_norm: 3.5698 loss: 0.6432 loss_cls: 0.3303 loss_bbox: 0.3129 +2024/10/26 17:45:53 - mmengine - INFO - Epoch(train) [3][ 250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:31 time: 0.6555 data_time: 0.0088 memory: 5133 grad_norm: 3.4694 loss: 0.6282 loss_cls: 0.3229 loss_bbox: 0.3053 +2024/10/26 17:46:21 - mmengine - INFO - Epoch(train) [3][ 300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:19 time: 0.5598 data_time: 0.0082 memory: 5135 grad_norm: 3.4165 loss: 0.6415 loss_cls: 0.3164 loss_bbox: 0.3251 +2024/10/26 17:46:47 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 17:46:54 - mmengine - INFO - Epoch(train) [3][ 350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:30 time: 0.6534 data_time: 0.0084 memory: 5134 grad_norm: 3.5303 loss: 0.6358 loss_cls: 0.3276 loss_bbox: 0.3082 +2024/10/26 17:47:23 - mmengine - INFO - Epoch(train) [3][ 400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:23 time: 0.5785 data_time: 0.0097 memory: 5135 grad_norm: 3.7217 loss: 0.6342 loss_cls: 0.3295 loss_bbox: 0.3047 +2024/10/26 17:47:57 - mmengine - INFO - Epoch(train) [3][ 450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:38 time: 0.6747 data_time: 0.0095 memory: 5136 grad_norm: 3.5521 loss: 0.6356 loss_cls: 0.3314 loss_bbox: 0.3042 +2024/10/26 17:48:26 - mmengine - INFO - Epoch(train) [3][ 500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:34 time: 0.5912 data_time: 0.0097 memory: 5135 grad_norm: 3.6626 loss: 0.6535 loss_cls: 0.3371 loss_bbox: 0.3164 +2024/10/26 17:48:59 - mmengine - INFO - Epoch(train) [3][ 550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:44 time: 0.6536 data_time: 0.0113 memory: 5135 grad_norm: 3.8607 loss: 0.6805 loss_cls: 0.3508 loss_bbox: 0.3298 +2024/10/26 17:49:31 - mmengine - INFO - Epoch(train) [3][ 600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:50 time: 0.6393 data_time: 0.0113 memory: 5135 grad_norm: 3.8056 loss: 0.6151 loss_cls: 0.3177 loss_bbox: 0.2974 +2024/10/26 17:50:04 - mmengine - INFO - Epoch(train) [3][ 650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:14:03 time: 0.6661 data_time: 0.0108 memory: 5135 grad_norm: 3.7162 loss: 0.6305 loss_cls: 0.3235 loss_bbox: 0.3070 +2024/10/26 17:50:37 - mmengine - INFO - Epoch(train) [3][ 700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:14:15 time: 0.6663 data_time: 0.0436 memory: 5135 grad_norm: 3.6223 loss: 0.6254 loss_cls: 0.3241 loss_bbox: 0.3012 +2024/10/26 17:51:08 - mmengine - INFO - Epoch(train) [3][ 750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:14:16 time: 0.6206 data_time: 0.0109 memory: 5135 grad_norm: 3.9476 loss: 0.6575 loss_cls: 0.3496 loss_bbox: 0.3079 +2024/10/26 17:51:41 - mmengine - INFO - Epoch(train) [3][ 800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:14:23 time: 0.6453 data_time: 0.0109 memory: 5133 grad_norm: 3.4619 loss: 0.6215 loss_cls: 0.3119 loss_bbox: 0.3096 +2024/10/26 17:52:10 - mmengine - INFO - Epoch(train) [3][ 850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:14:13 time: 0.5758 data_time: 0.0108 memory: 5134 grad_norm: 3.3372 loss: 0.6170 loss_cls: 0.3167 loss_bbox: 0.3003 +2024/10/26 17:52:42 - mmengine - INFO - Epoch(train) [3][ 900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:14:23 time: 0.6585 data_time: 0.0115 memory: 5133 grad_norm: 3.8222 loss: 0.6556 loss_cls: 0.3346 loss_bbox: 0.3210 +2024/10/26 17:53:13 - mmengine - INFO - Epoch(train) [3][ 950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:14:18 time: 0.6002 data_time: 0.0161 memory: 5136 grad_norm: 3.9983 loss: 0.6725 loss_cls: 0.3535 loss_bbox: 0.3190 +2024/10/26 17:53:44 - mmengine - INFO - Epoch(train) [3][1000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:14:20 time: 0.6277 data_time: 0.0143 memory: 5134 grad_norm: 3.6604 loss: 0.6195 loss_cls: 0.3163 loss_bbox: 0.3032 +2024/10/26 17:54:16 - mmengine - INFO - Epoch(train) [3][1050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:14:26 time: 0.6479 data_time: 0.0105 memory: 5134 grad_norm: 3.6626 loss: 0.6571 loss_cls: 0.3453 loss_bbox: 0.3117 +2024/10/26 17:54:48 - mmengine - INFO - Epoch(train) [3][1100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:14:30 time: 0.6379 data_time: 0.0101 memory: 5133 grad_norm: 3.4029 loss: 0.6408 loss_cls: 0.3243 loss_bbox: 0.3166 +2024/10/26 17:55:17 - mmengine - INFO - Epoch(train) [3][1150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:14:20 time: 0.5771 data_time: 0.0109 memory: 5135 grad_norm: 3.6303 loss: 0.6513 loss_cls: 0.3371 loss_bbox: 0.3142 +2024/10/26 17:55:49 - mmengine - INFO - Epoch(train) [3][1200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:14:25 time: 0.6446 data_time: 0.0132 memory: 5135 grad_norm: 3.5795 loss: 0.6426 loss_cls: 0.3306 loss_bbox: 0.3120 +2024/10/26 17:56:20 - mmengine - INFO - Epoch(train) [3][1250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:14:21 time: 0.6077 data_time: 0.0104 memory: 5134 grad_norm: 3.4171 loss: 0.6331 loss_cls: 0.3313 loss_bbox: 0.3018 +2024/10/26 17:56:53 - mmengine - INFO - Epoch(train) [3][1300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:14:28 time: 0.6575 data_time: 0.0124 memory: 5136 grad_norm: 3.5150 loss: 0.6490 loss_cls: 0.3326 loss_bbox: 0.3164 +2024/10/26 17:57:17 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 17:57:23 - mmengine - INFO - Epoch(train) [3][1350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:14:26 time: 0.6181 data_time: 0.0105 memory: 5132 grad_norm: 3.6071 loss: 0.6499 loss_cls: 0.3357 loss_bbox: 0.3143 +2024/10/26 17:57:56 - mmengine - INFO - Epoch(train) [3][1400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:14:34 time: 0.6598 data_time: 0.0099 memory: 5135 grad_norm: 3.5765 loss: 0.6269 loss_cls: 0.3201 loss_bbox: 0.3069 +2024/10/26 17:58:22 - mmengine - INFO - Epoch(train) [3][1450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:14:09 time: 0.5157 data_time: 0.0105 memory: 5135 grad_norm: 3.5463 loss: 0.6668 loss_cls: 0.3486 loss_bbox: 0.3182 +2024/10/26 17:58:54 - mmengine - INFO - Epoch(train) [3][1500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:14:09 time: 0.6291 data_time: 0.0176 memory: 5136 grad_norm: 3.4563 loss: 0.6176 loss_cls: 0.3222 loss_bbox: 0.2954 +2024/10/26 17:59:23 - mmengine - INFO - Epoch(train) [3][1550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:58 time: 0.5790 data_time: 0.0116 memory: 5134 grad_norm: 3.7380 loss: 0.6601 loss_cls: 0.3364 loss_bbox: 0.3237 +2024/10/26 17:59:55 - mmengine - INFO - Epoch(train) [3][1600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:14:02 time: 0.6485 data_time: 0.0106 memory: 5136 grad_norm: 3.7050 loss: 0.6265 loss_cls: 0.3234 loss_bbox: 0.3032 +2024/10/26 18:00:25 - mmengine - INFO - Epoch(train) [3][1650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:53 time: 0.5921 data_time: 0.0116 memory: 5133 grad_norm: 3.7629 loss: 0.6172 loss_cls: 0.3192 loss_bbox: 0.2980 +2024/10/26 18:00:57 - mmengine - INFO - Epoch(train) [3][1700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:55 time: 0.6408 data_time: 0.0116 memory: 5134 grad_norm: 3.7794 loss: 0.6310 loss_cls: 0.3210 loss_bbox: 0.3100 +2024/10/26 18:01:25 - mmengine - INFO - Epoch(train) [3][1750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:41 time: 0.5657 data_time: 0.0102 memory: 5134 grad_norm: 3.4591 loss: 0.6230 loss_cls: 0.3213 loss_bbox: 0.3017 +2024/10/26 18:01:58 - mmengine - INFO - Epoch(train) [3][1800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:47 time: 0.6601 data_time: 0.0107 memory: 5133 grad_norm: 3.6933 loss: 0.6655 loss_cls: 0.3437 loss_bbox: 0.3218 +2024/10/26 18:02:25 - mmengine - INFO - Epoch(train) [3][1850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:27 time: 0.5420 data_time: 0.0105 memory: 5133 grad_norm: 3.3046 loss: 0.6380 loss_cls: 0.3273 loss_bbox: 0.3107 +2024/10/26 18:02:58 - mmengine - INFO - Epoch(train) [3][1900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:32 time: 0.6585 data_time: 0.0102 memory: 5134 grad_norm: 3.6817 loss: 0.6309 loss_cls: 0.3153 loss_bbox: 0.3156 +2024/10/26 18:03:26 - mmengine - INFO - Epoch(train) [3][1950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:16 time: 0.5630 data_time: 0.0100 memory: 5134 grad_norm: 3.5672 loss: 0.6199 loss_cls: 0.3127 loss_bbox: 0.3072 +2024/10/26 18:03:59 - mmengine - INFO - Epoch(train) [3][2000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:20 time: 0.6534 data_time: 0.0124 memory: 5134 grad_norm: 3.6664 loss: 0.6372 loss_cls: 0.3299 loss_bbox: 0.3073 +2024/10/26 18:04:29 - mmengine - INFO - Epoch(train) [3][2050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:15 time: 0.6119 data_time: 0.0097 memory: 5134 grad_norm: 3.4894 loss: 0.6387 loss_cls: 0.3245 loss_bbox: 0.3142 +2024/10/26 18:05:02 - mmengine - INFO - Epoch(train) [3][2100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:19 time: 0.6578 data_time: 0.0097 memory: 5135 grad_norm: 3.5321 loss: 0.5961 loss_cls: 0.3019 loss_bbox: 0.2942 +2024/10/26 18:05:36 - mmengine - INFO - Epoch(train) [3][2150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:26 time: 0.6748 data_time: 0.0786 memory: 5134 grad_norm: 3.8248 loss: 0.6796 loss_cls: 0.3517 loss_bbox: 0.3279 +2024/10/26 18:06:07 - mmengine - INFO - Epoch(train) [3][2200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:24 time: 0.6280 data_time: 0.0146 memory: 5135 grad_norm: 3.4231 loss: 0.6164 loss_cls: 0.3175 loss_bbox: 0.2989 +2024/10/26 18:06:39 - mmengine - INFO - Epoch(train) [3][2250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:21 time: 0.6254 data_time: 0.0097 memory: 5135 grad_norm: 3.3345 loss: 0.6378 loss_cls: 0.3269 loss_bbox: 0.3110 +2024/10/26 18:07:09 - mmengine - INFO - Epoch(train) [3][2300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:12 time: 0.5986 data_time: 0.0109 memory: 5135 grad_norm: 3.6958 loss: 0.6300 loss_cls: 0.3263 loss_bbox: 0.3037 +2024/10/26 18:07:33 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 18:07:40 - mmengine - INFO - Epoch(train) [3][2350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:07 time: 0.6205 data_time: 0.0252 memory: 5134 grad_norm: 3.4696 loss: 0.6074 loss_cls: 0.3141 loss_bbox: 0.2933 +2024/10/26 18:08:12 - mmengine - INFO - Epoch(train) [3][2400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:07 time: 0.6412 data_time: 0.0095 memory: 5135 grad_norm: 3.8686 loss: 0.6385 loss_cls: 0.3336 loss_bbox: 0.3049 +2024/10/26 18:08:43 - mmengine - INFO - Epoch(train) [3][2450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:13:04 time: 0.6305 data_time: 0.0101 memory: 5136 grad_norm: 3.8803 loss: 0.6443 loss_cls: 0.3325 loss_bbox: 0.3119 +2024/10/26 18:09:11 - mmengine - INFO - Epoch(train) [3][2500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:12:45 time: 0.5530 data_time: 0.0149 memory: 5135 grad_norm: 3.6226 loss: 0.6327 loss_cls: 0.3252 loss_bbox: 0.3075 +2024/10/26 18:09:43 - mmengine - INFO - Epoch(train) [3][2550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:12:45 time: 0.6467 data_time: 0.0095 memory: 5133 grad_norm: 3.6313 loss: 0.6584 loss_cls: 0.3423 loss_bbox: 0.3160 +2024/10/26 18:10:13 - mmengine - INFO - Epoch(train) [3][2600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:12:33 time: 0.5892 data_time: 0.0099 memory: 5134 grad_norm: 3.5143 loss: 0.6792 loss_cls: 0.3625 loss_bbox: 0.3167 +2024/10/26 18:10:44 - mmengine - INFO - Epoch(train) [3][2650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:12:30 time: 0.6313 data_time: 0.0116 memory: 5133 grad_norm: 3.2992 loss: 0.6212 loss_cls: 0.3242 loss_bbox: 0.2970 +2024/10/26 18:11:12 - mmengine - INFO - Epoch(train) [3][2700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:12:10 time: 0.5514 data_time: 0.0151 memory: 5135 grad_norm: 3.6503 loss: 0.6524 loss_cls: 0.3372 loss_bbox: 0.3152 +2024/10/26 18:11:44 - mmengine - INFO - Epoch(train) [3][2750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:12:10 time: 0.6481 data_time: 0.0102 memory: 5135 grad_norm: 3.4620 loss: 0.6203 loss_cls: 0.3138 loss_bbox: 0.3065 +2024/10/26 18:12:14 - mmengine - INFO - Epoch(train) [3][2800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:11:57 time: 0.5845 data_time: 0.0106 memory: 5130 grad_norm: 3.7030 loss: 0.6401 loss_cls: 0.3278 loss_bbox: 0.3124 +2024/10/26 18:12:47 - mmengine - INFO - Epoch(train) [3][2850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:12:02 time: 0.6758 data_time: 0.0101 memory: 5134 grad_norm: 3.5535 loss: 0.6056 loss_cls: 0.3058 loss_bbox: 0.2998 +2024/10/26 18:13:18 - mmengine - INFO - Epoch(train) [3][2900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:11:55 time: 0.6123 data_time: 0.0141 memory: 5134 grad_norm: 3.2382 loss: 0.6342 loss_cls: 0.3227 loss_bbox: 0.3115 +2024/10/26 18:13:51 - mmengine - INFO - Epoch(train) [3][2950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:11:55 time: 0.6544 data_time: 0.0141 memory: 5136 grad_norm: 3.5790 loss: 0.6413 loss_cls: 0.3405 loss_bbox: 0.3008 +2024/10/26 18:14:21 - mmengine - INFO - Epoch(train) [3][3000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:11:45 time: 0.6050 data_time: 0.0097 memory: 5134 grad_norm: 3.4140 loss: 0.6179 loss_cls: 0.3168 loss_bbox: 0.3011 +2024/10/26 18:14:53 - mmengine - INFO - Epoch(train) [3][3050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:11:42 time: 0.6362 data_time: 0.0098 memory: 5134 grad_norm: 3.6930 loss: 0.6196 loss_cls: 0.3158 loss_bbox: 0.3038 +2024/10/26 18:15:20 - mmengine - INFO - Epoch(train) [3][3100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:11:22 time: 0.5547 data_time: 0.0110 memory: 5136 grad_norm: 3.5681 loss: 0.6742 loss_cls: 0.3593 loss_bbox: 0.3150 +2024/10/26 18:15:52 - mmengine - INFO - Epoch(train) [3][3150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:11:18 time: 0.6325 data_time: 0.0125 memory: 5134 grad_norm: 3.8172 loss: 0.6777 loss_cls: 0.3429 loss_bbox: 0.3347 +2024/10/26 18:16:23 - mmengine - INFO - Epoch(train) [3][3200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:11:10 time: 0.6161 data_time: 0.0137 memory: 5134 grad_norm: 3.3877 loss: 0.6455 loss_cls: 0.3260 loss_bbox: 0.3195 +2024/10/26 18:16:56 - mmengine - INFO - Epoch(train) [3][3250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:11:10 time: 0.6591 data_time: 0.0105 memory: 5132 grad_norm: 3.5650 loss: 0.6704 loss_cls: 0.3522 loss_bbox: 0.3182 +2024/10/26 18:17:24 - mmengine - INFO - Epoch(train) [3][3300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:10:54 time: 0.5716 data_time: 0.0108 memory: 5134 grad_norm: 3.4914 loss: 0.6373 loss_cls: 0.3236 loss_bbox: 0.3138 +2024/10/26 18:17:50 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 18:17:57 - mmengine - INFO - Epoch(train) [3][3350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:10:51 time: 0.6431 data_time: 0.0162 memory: 5133 grad_norm: 3.6052 loss: 0.6312 loss_cls: 0.3293 loss_bbox: 0.3019 +2024/10/26 18:18:27 - mmengine - INFO - Epoch(train) [3][3400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:10:41 time: 0.6115 data_time: 0.0156 memory: 5136 grad_norm: 3.5315 loss: 0.6406 loss_cls: 0.3297 loss_bbox: 0.3109 +2024/10/26 18:19:00 - mmengine - INFO - Epoch(train) [3][3450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:10:39 time: 0.6476 data_time: 0.0104 memory: 5133 grad_norm: 3.5522 loss: 0.6696 loss_cls: 0.3371 loss_bbox: 0.3325 +2024/10/26 18:19:28 - mmengine - INFO - Epoch(train) [3][3500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:10:21 time: 0.5664 data_time: 0.0107 memory: 5134 grad_norm: 3.4949 loss: 0.6519 loss_cls: 0.3382 loss_bbox: 0.3137 +2024/10/26 18:20:00 - mmengine - INFO - Epoch(train) [3][3550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:10:19 time: 0.6505 data_time: 0.0101 memory: 5136 grad_norm: 3.5186 loss: 0.6470 loss_cls: 0.3326 loss_bbox: 0.3145 +2024/10/26 18:20:29 - mmengine - INFO - Epoch(train) [3][3600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:09:59 time: 0.5626 data_time: 0.0152 memory: 5135 grad_norm: 3.7179 loss: 0.6675 loss_cls: 0.3521 loss_bbox: 0.3154 +2024/10/26 18:21:01 - mmengine - INFO - Epoch(train) [3][3650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:09:58 time: 0.6554 data_time: 0.0090 memory: 5135 grad_norm: 3.7541 loss: 0.6320 loss_cls: 0.3325 loss_bbox: 0.2995 +2024/10/26 18:21:27 - mmengine - INFO - Epoch(train) [3][3700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:09:30 time: 0.5146 data_time: 0.0088 memory: 5134 grad_norm: 3.5625 loss: 0.6405 loss_cls: 0.3346 loss_bbox: 0.3059 +2024/10/26 18:22:00 - mmengine - INFO - Epoch(train) [3][3750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:09:29 time: 0.6602 data_time: 0.0120 memory: 5134 grad_norm: 3.6230 loss: 0.6410 loss_cls: 0.3293 loss_bbox: 0.3117 +2024/10/26 18:22:29 - mmengine - INFO - Epoch(train) [3][3800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:09:12 time: 0.5737 data_time: 0.0153 memory: 5136 grad_norm: 3.5597 loss: 0.6496 loss_cls: 0.3430 loss_bbox: 0.3065 +2024/10/26 18:23:02 - mmengine - INFO - Epoch(train) [3][3850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:09:11 time: 0.6634 data_time: 0.0105 memory: 5134 grad_norm: 3.4634 loss: 0.6156 loss_cls: 0.3156 loss_bbox: 0.3000 +2024/10/26 18:23:36 - mmengine - INFO - Epoch(train) [3][3900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:09:15 time: 0.6881 data_time: 0.0843 memory: 5137 grad_norm: 3.5722 loss: 0.6378 loss_cls: 0.3306 loss_bbox: 0.3071 +2024/10/26 18:24:07 - mmengine - INFO - Epoch(train) [3][3950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:09:06 time: 0.6220 data_time: 0.0165 memory: 5135 grad_norm: 3.4117 loss: 0.6530 loss_cls: 0.3386 loss_bbox: 0.3144 +2024/10/26 18:24:37 - mmengine - INFO - Epoch(train) [3][4000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:08:51 time: 0.5860 data_time: 0.0105 memory: 5134 grad_norm: 3.5416 loss: 0.6148 loss_cls: 0.3202 loss_bbox: 0.2947 +2024/10/26 18:25:07 - mmengine - INFO - Epoch(train) [3][4050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:08:38 time: 0.6008 data_time: 0.0133 memory: 5135 grad_norm: 3.6408 loss: 0.6002 loss_cls: 0.3020 loss_bbox: 0.2982 +2024/10/26 18:25:38 - mmengine - INFO - Epoch(train) [3][4100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:08:31 time: 0.6328 data_time: 0.0097 memory: 5133 grad_norm: 3.3966 loss: 0.6632 loss_cls: 0.3459 loss_bbox: 0.3174 +2024/10/26 18:26:10 - mmengine - INFO - Epoch(train) [3][4150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:08:23 time: 0.6238 data_time: 0.0117 memory: 5132 grad_norm: 3.5699 loss: 0.6407 loss_cls: 0.3300 loss_bbox: 0.3107 +2024/10/26 18:26:40 - mmengine - INFO - Epoch(train) [3][4200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:08:10 time: 0.6042 data_time: 0.0101 memory: 5134 grad_norm: 3.5369 loss: 0.6414 loss_cls: 0.3296 loss_bbox: 0.3117 +2024/10/26 18:27:11 - mmengine - INFO - Epoch(train) [3][4250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:08:00 time: 0.6146 data_time: 0.0098 memory: 5135 grad_norm: 3.4581 loss: 0.6433 loss_cls: 0.3253 loss_bbox: 0.3180 +2024/10/26 18:27:41 - mmengine - INFO - Epoch(train) [3][4300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:07:48 time: 0.6109 data_time: 0.0099 memory: 5131 grad_norm: 3.2519 loss: 0.6547 loss_cls: 0.3337 loss_bbox: 0.3209 +2024/10/26 18:28:07 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 18:28:12 - mmengine - INFO - Epoch(train) [3][4350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:07:37 time: 0.6122 data_time: 0.0144 memory: 5139 grad_norm: 3.5827 loss: 0.6716 loss_cls: 0.3476 loss_bbox: 0.3241 +2024/10/26 18:28:45 - mmengine - INFO - Epoch(train) [3][4400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:07:35 time: 0.6640 data_time: 0.0165 memory: 5135 grad_norm: 3.9914 loss: 0.6908 loss_cls: 0.3659 loss_bbox: 0.3249 +2024/10/26 18:29:15 - mmengine - INFO - Epoch(train) [3][4450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:07:20 time: 0.5930 data_time: 0.0159 memory: 5139 grad_norm: 3.6383 loss: 0.6426 loss_cls: 0.3353 loss_bbox: 0.3073 +2024/10/26 18:29:48 - mmengine - INFO - Epoch(train) [3][4500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:07:17 time: 0.6587 data_time: 0.0096 memory: 5136 grad_norm: 3.5740 loss: 0.6348 loss_cls: 0.3273 loss_bbox: 0.3076 +2024/10/26 18:30:19 - mmengine - INFO - Epoch(train) [3][4550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:07:10 time: 0.6374 data_time: 0.0098 memory: 5134 grad_norm: 3.3902 loss: 0.6319 loss_cls: 0.3226 loss_bbox: 0.3092 +2024/10/26 18:30:51 - mmengine - INFO - Epoch(train) [3][4600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:07:00 time: 0.6225 data_time: 0.0107 memory: 5136 grad_norm: 3.5154 loss: 0.6283 loss_cls: 0.3262 loss_bbox: 0.3021 +2024/10/26 18:31:18 - mmengine - INFO - Epoch(train) [3][4650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:06:36 time: 0.5444 data_time: 0.0098 memory: 5134 grad_norm: 3.5768 loss: 0.6451 loss_cls: 0.3350 loss_bbox: 0.3101 +2024/10/26 18:31:51 - mmengine - INFO - Epoch(train) [3][4700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:06:34 time: 0.6691 data_time: 0.0083 memory: 5132 grad_norm: 3.4930 loss: 0.6104 loss_cls: 0.3062 loss_bbox: 0.3043 +2024/10/26 18:32:18 - mmengine - INFO - Epoch(train) [3][4750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:06:08 time: 0.5364 data_time: 0.0083 memory: 5135 grad_norm: 3.3351 loss: 0.6280 loss_cls: 0.3105 loss_bbox: 0.3175 +2024/10/26 18:32:50 - mmengine - INFO - Epoch(train) [3][4800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:06:03 time: 0.6489 data_time: 0.0088 memory: 5134 grad_norm: 3.5688 loss: 0.6483 loss_cls: 0.3349 loss_bbox: 0.3135 +2024/10/26 18:33:21 - mmengine - INFO - Epoch(train) [3][4850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:05:52 time: 0.6196 data_time: 0.0088 memory: 5136 grad_norm: 3.7323 loss: 0.6407 loss_cls: 0.3298 loss_bbox: 0.3109 +2024/10/26 18:33:53 - mmengine - INFO - Epoch(train) [3][4900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:05:42 time: 0.6292 data_time: 0.0153 memory: 5136 grad_norm: 3.4172 loss: 0.6455 loss_cls: 0.3366 loss_bbox: 0.3089 +2024/10/26 18:34:21 - mmengine - INFO - Epoch(train) [3][4950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:05:21 time: 0.5594 data_time: 0.0093 memory: 5135 grad_norm: 3.4478 loss: 0.6242 loss_cls: 0.3228 loss_bbox: 0.3014 +2024/10/26 18:34:52 - mmengine - INFO - Epoch(train) [3][5000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:05:10 time: 0.6251 data_time: 0.0133 memory: 5134 grad_norm: 3.6022 loss: 0.6084 loss_cls: 0.3132 loss_bbox: 0.2952 +2024/10/26 18:35:23 - mmengine - INFO - Epoch(train) [3][5050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:04:57 time: 0.6078 data_time: 0.0087 memory: 5134 grad_norm: 3.3474 loss: 0.6651 loss_cls: 0.3335 loss_bbox: 0.3316 +2024/10/26 18:35:54 - mmengine - INFO - Epoch(train) [3][5100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:04:48 time: 0.6352 data_time: 0.0108 memory: 5135 grad_norm: 3.6243 loss: 0.6145 loss_cls: 0.3104 loss_bbox: 0.3040 +2024/10/26 18:36:24 - mmengine - INFO - Epoch(train) [3][5150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:04:32 time: 0.5919 data_time: 0.0123 memory: 5133 grad_norm: 3.2927 loss: 0.6364 loss_cls: 0.3213 loss_bbox: 0.3151 +2024/10/26 18:36:56 - mmengine - INFO - Epoch(train) [3][5200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:04:26 time: 0.6504 data_time: 0.0134 memory: 5133 grad_norm: 3.6174 loss: 0.6400 loss_cls: 0.3303 loss_bbox: 0.3097 +2024/10/26 18:37:24 - mmengine - INFO - Epoch(train) [3][5250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:04:04 time: 0.5616 data_time: 0.0081 memory: 5134 grad_norm: 3.4554 loss: 0.6578 loss_cls: 0.3293 loss_bbox: 0.3286 +2024/10/26 18:37:57 - mmengine - INFO - Epoch(train) [3][5300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:03:57 time: 0.6484 data_time: 0.0140 memory: 5134 grad_norm: 3.5363 loss: 0.6696 loss_cls: 0.3433 loss_bbox: 0.3263 +2024/10/26 18:38:20 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 18:38:27 - mmengine - INFO - Epoch(train) [3][5350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:03:41 time: 0.5975 data_time: 0.0081 memory: 5134 grad_norm: 3.1584 loss: 0.6393 loss_cls: 0.3297 loss_bbox: 0.3096 +2024/10/26 18:39:00 - mmengine - INFO - Epoch(train) [3][5400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:03:37 time: 0.6633 data_time: 0.0081 memory: 5135 grad_norm: 3.1151 loss: 0.6300 loss_cls: 0.3193 loss_bbox: 0.3107 +2024/10/26 18:39:32 - mmengine - INFO - Epoch(train) [3][5450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:03:27 time: 0.6337 data_time: 0.0088 memory: 5134 grad_norm: 3.7528 loss: 0.6356 loss_cls: 0.3235 loss_bbox: 0.3120 +2024/10/26 18:40:04 - mmengine - INFO - Epoch(train) [3][5500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:03:21 time: 0.6554 data_time: 0.0084 memory: 5133 grad_norm: 3.6039 loss: 0.6265 loss_cls: 0.3147 loss_bbox: 0.3118 +2024/10/26 18:40:37 - mmengine - INFO - Epoch(train) [3][5550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:03:12 time: 0.6439 data_time: 0.0140 memory: 5133 grad_norm: 3.5438 loss: 0.6419 loss_cls: 0.3226 loss_bbox: 0.3193 +2024/10/26 18:41:09 - mmengine - INFO - Epoch(train) [3][5600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:03:06 time: 0.6529 data_time: 0.0080 memory: 5134 grad_norm: 3.3149 loss: 0.6223 loss_cls: 0.3227 loss_bbox: 0.2996 +2024/10/26 18:41:38 - mmengine - INFO - Epoch(train) [3][5650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:02:46 time: 0.5804 data_time: 0.0080 memory: 5134 grad_norm: 3.4586 loss: 0.6348 loss_cls: 0.3253 loss_bbox: 0.3095 +2024/10/26 18:42:11 - mmengine - INFO - Epoch(train) [3][5700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:02:39 time: 0.6509 data_time: 0.0130 memory: 5135 grad_norm: 3.4285 loss: 0.6440 loss_cls: 0.3220 loss_bbox: 0.3221 +2024/10/26 18:42:42 - mmengine - INFO - Epoch(train) [3][5750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:02:27 time: 0.6244 data_time: 0.0093 memory: 5138 grad_norm: 3.5580 loss: 0.6406 loss_cls: 0.3216 loss_bbox: 0.3190 +2024/10/26 18:43:12 - mmengine - INFO - Epoch(train) [3][5800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:02:12 time: 0.6046 data_time: 0.0097 memory: 5133 grad_norm: 3.4102 loss: 0.6257 loss_cls: 0.3219 loss_bbox: 0.3038 +2024/10/26 18:43:44 - mmengine - INFO - Epoch(train) [3][5850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:02:00 time: 0.6292 data_time: 0.0086 memory: 5133 grad_norm: 3.4173 loss: 0.6222 loss_cls: 0.3202 loss_bbox: 0.3020 +2024/10/26 18:44:12 - mmengine - INFO - Epoch(train) [3][5900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:01:37 time: 0.5568 data_time: 0.0088 memory: 5134 grad_norm: 3.4192 loss: 0.6345 loss_cls: 0.3289 loss_bbox: 0.3056 +2024/10/26 18:44:45 - mmengine - INFO - Epoch(train) [3][5950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:01:33 time: 0.6710 data_time: 0.0112 memory: 5135 grad_norm: 3.4893 loss: 0.6303 loss_cls: 0.3237 loss_bbox: 0.3066 +2024/10/26 18:45:16 - mmengine - INFO - Epoch(train) [3][6000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:01:19 time: 0.6148 data_time: 0.0163 memory: 5133 grad_norm: 3.5161 loss: 0.6169 loss_cls: 0.3178 loss_bbox: 0.2991 +2024/10/26 18:45:49 - mmengine - INFO - Epoch(train) [3][6050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:01:11 time: 0.6562 data_time: 0.0085 memory: 5136 grad_norm: 3.4754 loss: 0.6356 loss_cls: 0.3153 loss_bbox: 0.3203 +2024/10/26 18:46:18 - mmengine - INFO - Epoch(train) [3][6100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:00:53 time: 0.5906 data_time: 0.0083 memory: 5132 grad_norm: 3.3604 loss: 0.6411 loss_cls: 0.3300 loss_bbox: 0.3111 +2024/10/26 18:46:52 - mmengine - INFO - Epoch(train) [3][6150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:00:48 time: 0.6675 data_time: 0.0084 memory: 5134 grad_norm: 3.7232 loss: 0.6495 loss_cls: 0.3335 loss_bbox: 0.3160 +2024/10/26 18:47:20 - mmengine - INFO - Epoch(train) [3][6200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:00:26 time: 0.5700 data_time: 0.0091 memory: 5135 grad_norm: 3.4084 loss: 0.6628 loss_cls: 0.3462 loss_bbox: 0.3166 +2024/10/26 18:47:52 - mmengine - INFO - Epoch(train) [3][6250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 10:00:14 time: 0.6308 data_time: 0.0082 memory: 5135 grad_norm: 3.2243 loss: 0.5911 loss_cls: 0.2903 loss_bbox: 0.3008 +2024/10/26 18:48:21 - mmengine - INFO - Epoch(train) [3][6300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:59:57 time: 0.5958 data_time: 0.0086 memory: 5134 grad_norm: 3.1950 loss: 0.6222 loss_cls: 0.3187 loss_bbox: 0.3034 +2024/10/26 18:48:48 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 18:48:54 - mmengine - INFO - Epoch(train) [3][6350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:59:49 time: 0.6560 data_time: 0.0082 memory: 5133 grad_norm: 3.5162 loss: 0.6338 loss_cls: 0.3277 loss_bbox: 0.3061 +2024/10/26 18:49:23 - mmengine - INFO - Epoch(train) [3][6400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:59:27 time: 0.5671 data_time: 0.0081 memory: 5133 grad_norm: 3.5298 loss: 0.6344 loss_cls: 0.3293 loss_bbox: 0.3051 +2024/10/26 18:49:55 - mmengine - INFO - Epoch(train) [3][6450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:59:19 time: 0.6579 data_time: 0.0101 memory: 5134 grad_norm: 3.5711 loss: 0.6051 loss_cls: 0.3053 loss_bbox: 0.2998 +2024/10/26 18:50:24 - mmengine - INFO - Epoch(train) [3][6500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:58:57 time: 0.5697 data_time: 0.0126 memory: 5136 grad_norm: 3.2772 loss: 0.6146 loss_cls: 0.3137 loss_bbox: 0.3009 +2024/10/26 18:50:56 - mmengine - INFO - Epoch(train) [3][6550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:58:46 time: 0.6387 data_time: 0.0097 memory: 5136 grad_norm: 3.4566 loss: 0.6152 loss_cls: 0.3168 loss_bbox: 0.2984 +2024/10/26 18:51:26 - mmengine - INFO - Epoch(train) [3][6600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:58:29 time: 0.6015 data_time: 0.0120 memory: 5134 grad_norm: 3.3415 loss: 0.6370 loss_cls: 0.3235 loss_bbox: 0.3135 +2024/10/26 18:51:59 - mmengine - INFO - Epoch(train) [3][6650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:58:20 time: 0.6562 data_time: 0.0084 memory: 5134 grad_norm: 3.5975 loss: 0.6191 loss_cls: 0.3235 loss_bbox: 0.2955 +2024/10/26 18:52:29 - mmengine - INFO - Epoch(train) [3][6700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:58:04 time: 0.6044 data_time: 0.0082 memory: 5135 grad_norm: 3.2175 loss: 0.6176 loss_cls: 0.3125 loss_bbox: 0.3052 +2024/10/26 18:53:02 - mmengine - INFO - Epoch(train) [3][6750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:57:56 time: 0.6613 data_time: 0.0089 memory: 5135 grad_norm: 3.3603 loss: 0.6265 loss_cls: 0.3166 loss_bbox: 0.3099 +2024/10/26 18:53:34 - mmengine - INFO - Epoch(train) [3][6800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:57:45 time: 0.6431 data_time: 0.0822 memory: 5133 grad_norm: 3.5604 loss: 0.6506 loss_cls: 0.3393 loss_bbox: 0.3113 +2024/10/26 18:54:06 - mmengine - INFO - Epoch(train) [3][6850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:57:32 time: 0.6294 data_time: 0.0082 memory: 5136 grad_norm: 3.3876 loss: 0.6429 loss_cls: 0.3339 loss_bbox: 0.3090 +2024/10/26 18:54:37 - mmengine - INFO - Epoch(train) [3][6900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:57:18 time: 0.6220 data_time: 0.0082 memory: 5134 grad_norm: 3.4189 loss: 0.6427 loss_cls: 0.3285 loss_bbox: 0.3142 +2024/10/26 18:55:09 - mmengine - INFO - Epoch(train) [3][6950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:57:05 time: 0.6351 data_time: 0.0085 memory: 5134 grad_norm: 3.4436 loss: 0.6569 loss_cls: 0.3353 loss_bbox: 0.3216 +2024/10/26 18:55:40 - mmengine - INFO - Epoch(train) [3][7000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:56:51 time: 0.6230 data_time: 0.0081 memory: 5138 grad_norm: 3.3921 loss: 0.6548 loss_cls: 0.3336 loss_bbox: 0.3212 +2024/10/26 18:56:11 - mmengine - INFO - Epoch(train) [3][7050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:56:36 time: 0.6208 data_time: 0.0115 memory: 5137 grad_norm: 3.3553 loss: 0.6305 loss_cls: 0.3277 loss_bbox: 0.3028 +2024/10/26 18:56:44 - mmengine - INFO - Epoch(train) [3][7100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:56:28 time: 0.6608 data_time: 0.0155 memory: 5134 grad_norm: 3.3574 loss: 0.6486 loss_cls: 0.3316 loss_bbox: 0.3170 +2024/10/26 18:57:13 - mmengine - INFO - Epoch(train) [3][7150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:56:09 time: 0.5931 data_time: 0.0083 memory: 5133 grad_norm: 3.2905 loss: 0.5930 loss_cls: 0.3021 loss_bbox: 0.2909 +2024/10/26 18:57:45 - mmengine - INFO - Epoch(train) [3][7200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:55:56 time: 0.6348 data_time: 0.0082 memory: 5132 grad_norm: 3.4471 loss: 0.6110 loss_cls: 0.3023 loss_bbox: 0.3087 +2024/10/26 18:58:15 - mmengine - INFO - Epoch(train) [3][7250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:55:37 time: 0.5945 data_time: 0.0080 memory: 5133 grad_norm: 3.6679 loss: 0.6658 loss_cls: 0.3485 loss_bbox: 0.3173 +2024/10/26 18:58:48 - mmengine - INFO - Epoch(train) [3][7300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:55:29 time: 0.6689 data_time: 0.0083 memory: 5134 grad_norm: 3.3412 loss: 0.6589 loss_cls: 0.3450 loss_bbox: 0.3139 +2024/10/26 18:59:14 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 18:59:14 - mmengine - INFO - Saving checkpoint at 3 epochs +2024/10/26 18:59:23 - mmengine - INFO - Epoch(val) [3][ 50/1250] eta: 0:02:14 time: 0.1119 data_time: 0.0019 memory: 5137 +2024/10/26 18:59:29 - mmengine - INFO - Epoch(val) [3][ 100/1250] eta: 0:02:06 time: 0.1082 data_time: 0.0016 memory: 630 +2024/10/26 18:59:34 - mmengine - INFO - Epoch(val) [3][ 150/1250] eta: 0:01:58 time: 0.1045 data_time: 0.0016 memory: 635 +2024/10/26 18:59:39 - mmengine - INFO - Epoch(val) [3][ 200/1250] eta: 0:01:54 time: 0.1100 data_time: 0.0016 memory: 635 +2024/10/26 18:59:45 - mmengine - INFO - Epoch(val) [3][ 250/1250] eta: 0:01:48 time: 0.1103 data_time: 0.0016 memory: 625 +2024/10/26 18:59:50 - mmengine - INFO - Epoch(val) [3][ 300/1250] eta: 0:01:43 time: 0.1093 data_time: 0.0017 memory: 625 +2024/10/26 18:59:56 - mmengine - INFO - Epoch(val) [3][ 350/1250] eta: 0:01:38 time: 0.1126 data_time: 0.0017 memory: 625 +2024/10/26 19:00:01 - mmengine - INFO - Epoch(val) [3][ 400/1250] eta: 0:01:32 time: 0.1076 data_time: 0.0016 memory: 614 +2024/10/26 19:00:07 - mmengine - INFO - Epoch(val) [3][ 450/1250] eta: 0:01:27 time: 0.1071 data_time: 0.0016 memory: 635 +2024/10/26 19:00:13 - mmengine - INFO - Epoch(val) [3][ 500/1250] eta: 0:01:23 time: 0.1257 data_time: 0.0016 memory: 635 +2024/10/26 19:00:19 - mmengine - INFO - Epoch(val) [3][ 550/1250] eta: 0:01:18 time: 0.1205 data_time: 0.0017 memory: 614 +2024/10/26 19:00:24 - mmengine - INFO - Epoch(val) [3][ 600/1250] eta: 0:01:12 time: 0.1074 data_time: 0.0016 memory: 625 +2024/10/26 19:00:30 - mmengine - INFO - Epoch(val) [3][ 650/1250] eta: 0:01:06 time: 0.1067 data_time: 0.0017 memory: 625 +2024/10/26 19:00:35 - mmengine - INFO - Epoch(val) [3][ 700/1250] eta: 0:01:01 time: 0.1115 data_time: 0.0016 memory: 630 +2024/10/26 19:00:41 - mmengine - INFO - Epoch(val) [3][ 750/1250] eta: 0:00:55 time: 0.1114 data_time: 0.0016 memory: 630 +2024/10/26 19:00:46 - mmengine - INFO - Epoch(val) [3][ 800/1250] eta: 0:00:49 time: 0.1096 data_time: 0.0015 memory: 635 +2024/10/26 19:00:52 - mmengine - INFO - Epoch(val) [3][ 850/1250] eta: 0:00:44 time: 0.1098 data_time: 0.0015 memory: 635 +2024/10/26 19:00:57 - mmengine - INFO - Epoch(val) [3][ 900/1250] eta: 0:00:38 time: 0.1125 data_time: 0.0015 memory: 635 +2024/10/26 19:01:03 - mmengine - INFO - Epoch(val) [3][ 950/1250] eta: 0:00:33 time: 0.1127 data_time: 0.0016 memory: 626 +2024/10/26 19:01:08 - mmengine - INFO - Epoch(val) [3][1000/1250] eta: 0:00:27 time: 0.0985 data_time: 0.0015 memory: 626 +2024/10/26 19:01:13 - mmengine - INFO - Epoch(val) [3][1050/1250] eta: 0:00:21 time: 0.0989 data_time: 0.0016 memory: 630 +2024/10/26 19:01:19 - mmengine - INFO - Epoch(val) [3][1100/1250] eta: 0:00:16 time: 0.1209 data_time: 0.0015 memory: 635 +2024/10/26 19:01:25 - mmengine - INFO - Epoch(val) [3][1150/1250] eta: 0:00:11 time: 0.1150 data_time: 0.0014 memory: 629 +2024/10/26 19:01:30 - mmengine - INFO - Epoch(val) [3][1200/1250] eta: 0:00:05 time: 0.1044 data_time: 0.0015 memory: 630 +2024/10/26 19:01:36 - mmengine - INFO - Epoch(val) [3][1250/1250] eta: 0:00:00 time: 0.1121 data_time: 0.0015 memory: 636 +2024/10/26 19:01:44 - mmengine - INFO - Evaluating bbox... +2024/10/26 19:02:36 - mmengine - INFO - bbox_mAP_copypaste: 0.282 0.454 0.296 0.142 0.320 0.376 +2024/10/26 19:02:37 - mmengine - INFO - Epoch(val) [3][1250/1250] coco/bbox_mAP: 0.2820 coco/bbox_mAP_50: 0.4540 coco/bbox_mAP_75: 0.2960 coco/bbox_mAP_s: 0.1420 coco/bbox_mAP_m: 0.3200 coco/bbox_mAP_l: 0.3760 data_time: 0.0016 time: 0.1103 +2024/10/26 19:02:42 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 19:03:06 - mmengine - INFO - Epoch(train) [4][ 50/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:55:17 time: 0.5714 data_time: 0.0077 memory: 5136 grad_norm: 3.2735 loss: 0.6067 loss_cls: 0.3042 loss_bbox: 0.3025 +2024/10/26 19:03:36 - mmengine - INFO - Epoch(train) [4][ 100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:54:59 time: 0.6000 data_time: 0.0082 memory: 5134 grad_norm: 3.3901 loss: 0.5805 loss_cls: 0.2967 loss_bbox: 0.2837 +2024/10/26 19:04:07 - mmengine - INFO - Epoch(train) [4][ 150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:54:44 time: 0.6291 data_time: 0.0103 memory: 5142 grad_norm: 3.4534 loss: 0.5790 loss_cls: 0.2916 loss_bbox: 0.2874 +2024/10/26 19:04:38 - mmengine - INFO - Epoch(train) [4][ 200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:54:28 time: 0.6156 data_time: 0.0085 memory: 5133 grad_norm: 3.4547 loss: 0.5983 loss_cls: 0.3002 loss_bbox: 0.2981 +2024/10/26 19:05:07 - mmengine - INFO - Epoch(train) [4][ 250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:54:07 time: 0.5804 data_time: 0.0082 memory: 5135 grad_norm: 3.0823 loss: 0.5699 loss_cls: 0.2830 loss_bbox: 0.2870 +2024/10/26 19:05:37 - mmengine - INFO - Epoch(train) [4][ 300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:53:47 time: 0.5912 data_time: 0.0080 memory: 5134 grad_norm: 3.5229 loss: 0.6189 loss_cls: 0.3166 loss_bbox: 0.3023 +2024/10/26 19:06:08 - mmengine - INFO - Epoch(train) [4][ 350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:53:34 time: 0.6372 data_time: 0.0085 memory: 5134 grad_norm: 3.7106 loss: 0.5949 loss_cls: 0.2980 loss_bbox: 0.2969 +2024/10/26 19:06:39 - mmengine - INFO - Epoch(train) [4][ 400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:53:16 time: 0.6103 data_time: 0.0082 memory: 5134 grad_norm: 3.1760 loss: 0.5944 loss_cls: 0.3069 loss_bbox: 0.2875 +2024/10/26 19:07:10 - mmengine - INFO - Epoch(train) [4][ 450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:53:00 time: 0.6177 data_time: 0.0084 memory: 5134 grad_norm: 3.3884 loss: 0.5981 loss_cls: 0.3014 loss_bbox: 0.2967 +2024/10/26 19:07:40 - mmengine - INFO - Epoch(train) [4][ 500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:52:42 time: 0.6067 data_time: 0.0082 memory: 5135 grad_norm: 3.4337 loss: 0.6324 loss_cls: 0.3256 loss_bbox: 0.3068 +2024/10/26 19:08:11 - mmengine - INFO - Epoch(train) [4][ 550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:52:25 time: 0.6117 data_time: 0.0104 memory: 5134 grad_norm: 3.3347 loss: 0.5882 loss_cls: 0.3037 loss_bbox: 0.2845 +2024/10/26 19:08:40 - mmengine - INFO - Epoch(train) [4][ 600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:52:05 time: 0.5918 data_time: 0.0087 memory: 5134 grad_norm: 3.4592 loss: 0.6191 loss_cls: 0.3147 loss_bbox: 0.3044 +2024/10/26 19:09:09 - mmengine - INFO - Epoch(train) [4][ 650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:51:41 time: 0.5635 data_time: 0.0083 memory: 5133 grad_norm: 3.2802 loss: 0.5918 loss_cls: 0.3006 loss_bbox: 0.2912 +2024/10/26 19:09:37 - mmengine - INFO - Epoch(train) [4][ 700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:51:17 time: 0.5676 data_time: 0.0083 memory: 5136 grad_norm: 3.5182 loss: 0.5762 loss_cls: 0.2854 loss_bbox: 0.2907 +2024/10/26 19:10:07 - mmengine - INFO - Epoch(train) [4][ 750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:50:57 time: 0.5931 data_time: 0.0086 memory: 5133 grad_norm: 3.3817 loss: 0.6444 loss_cls: 0.3314 loss_bbox: 0.3130 +2024/10/26 19:10:34 - mmengine - INFO - Epoch(train) [4][ 800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:50:30 time: 0.5447 data_time: 0.0088 memory: 5131 grad_norm: 3.3917 loss: 0.5807 loss_cls: 0.2974 loss_bbox: 0.2832 +2024/10/26 19:11:05 - mmengine - INFO - Epoch(train) [4][ 850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:50:13 time: 0.6184 data_time: 0.0090 memory: 5135 grad_norm: 3.3320 loss: 0.6012 loss_cls: 0.2914 loss_bbox: 0.3098 +2024/10/26 19:11:35 - mmengine - INFO - Epoch(train) [4][ 900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:49:53 time: 0.5946 data_time: 0.0090 memory: 5135 grad_norm: 3.2414 loss: 0.6084 loss_cls: 0.3115 loss_bbox: 0.2969 +2024/10/26 19:12:05 - mmengine - INFO - Epoch(train) [4][ 950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:49:36 time: 0.6156 data_time: 0.0132 memory: 5133 grad_norm: 3.2228 loss: 0.6213 loss_cls: 0.3145 loss_bbox: 0.3068 +2024/10/26 19:12:35 - mmengine - INFO - Epoch(train) [4][1000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:49:16 time: 0.5901 data_time: 0.0084 memory: 5133 grad_norm: 3.2989 loss: 0.5835 loss_cls: 0.2885 loss_bbox: 0.2951 +2024/10/26 19:12:41 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 19:13:06 - mmengine - INFO - Epoch(train) [4][1050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:49:01 time: 0.6335 data_time: 0.0085 memory: 5136 grad_norm: 3.3447 loss: 0.6357 loss_cls: 0.3173 loss_bbox: 0.3184 +2024/10/26 19:13:37 - mmengine - INFO - Epoch(train) [4][1100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:48:44 time: 0.6175 data_time: 0.0268 memory: 5132 grad_norm: 3.5968 loss: 0.6303 loss_cls: 0.3313 loss_bbox: 0.2991 +2024/10/26 19:14:10 - mmengine - INFO - Epoch(train) [4][1150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:48:31 time: 0.6472 data_time: 0.0082 memory: 5136 grad_norm: 3.3042 loss: 0.6163 loss_cls: 0.3149 loss_bbox: 0.3015 +2024/10/26 19:14:41 - mmengine - INFO - Epoch(train) [4][1200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:48:16 time: 0.6298 data_time: 0.0135 memory: 5135 grad_norm: 3.3662 loss: 0.6239 loss_cls: 0.3228 loss_bbox: 0.3011 +2024/10/26 19:15:12 - mmengine - INFO - Epoch(train) [4][1250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:47:59 time: 0.6195 data_time: 0.0082 memory: 5134 grad_norm: 3.3602 loss: 0.5989 loss_cls: 0.3113 loss_bbox: 0.2876 +2024/10/26 19:15:43 - mmengine - INFO - Epoch(train) [4][1300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:47:41 time: 0.6123 data_time: 0.0085 memory: 5133 grad_norm: 3.4132 loss: 0.5895 loss_cls: 0.3021 loss_bbox: 0.2874 +2024/10/26 19:16:13 - mmengine - INFO - Epoch(train) [4][1350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:47:21 time: 0.5977 data_time: 0.0087 memory: 5137 grad_norm: 3.5349 loss: 0.6143 loss_cls: 0.3113 loss_bbox: 0.3030 +2024/10/26 19:16:44 - mmengine - INFO - Epoch(train) [4][1400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:47:06 time: 0.6343 data_time: 0.0099 memory: 5134 grad_norm: 3.1976 loss: 0.5991 loss_cls: 0.3067 loss_bbox: 0.2924 +2024/10/26 19:17:15 - mmengine - INFO - Epoch(train) [4][1450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:46:47 time: 0.6063 data_time: 0.0087 memory: 5132 grad_norm: 3.4104 loss: 0.5960 loss_cls: 0.2979 loss_bbox: 0.2981 +2024/10/26 19:17:48 - mmengine - INFO - Epoch(train) [4][1500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:46:36 time: 0.6669 data_time: 0.0134 memory: 5135 grad_norm: 3.4043 loss: 0.5959 loss_cls: 0.3067 loss_bbox: 0.2892 +2024/10/26 19:18:18 - mmengine - INFO - Epoch(train) [4][1550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:46:16 time: 0.5977 data_time: 0.0083 memory: 5134 grad_norm: 3.1949 loss: 0.6206 loss_cls: 0.3265 loss_bbox: 0.2941 +2024/10/26 19:18:48 - mmengine - INFO - Epoch(train) [4][1600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:45:58 time: 0.6109 data_time: 0.0078 memory: 5135 grad_norm: 3.3187 loss: 0.6127 loss_cls: 0.3139 loss_bbox: 0.2988 +2024/10/26 19:19:15 - mmengine - INFO - Epoch(train) [4][1650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:45:28 time: 0.5252 data_time: 0.0082 memory: 5132 grad_norm: 3.3319 loss: 0.6355 loss_cls: 0.3280 loss_bbox: 0.3075 +2024/10/26 19:19:47 - mmengine - INFO - Epoch(train) [4][1700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:45:13 time: 0.6389 data_time: 0.0083 memory: 5135 grad_norm: 3.1057 loss: 0.6180 loss_cls: 0.3211 loss_bbox: 0.2969 +2024/10/26 19:20:17 - mmengine - INFO - Epoch(train) [4][1750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:44:54 time: 0.6054 data_time: 0.0084 memory: 5136 grad_norm: 3.3429 loss: 0.6205 loss_cls: 0.3144 loss_bbox: 0.3061 +2024/10/26 19:20:49 - mmengine - INFO - Epoch(train) [4][1800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:44:39 time: 0.6422 data_time: 0.0131 memory: 5137 grad_norm: 3.2642 loss: 0.5760 loss_cls: 0.2865 loss_bbox: 0.2895 +2024/10/26 19:21:17 - mmengine - INFO - Epoch(train) [4][1850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:44:13 time: 0.5568 data_time: 0.0083 memory: 5136 grad_norm: 3.2698 loss: 0.6233 loss_cls: 0.3233 loss_bbox: 0.3000 +2024/10/26 19:21:50 - mmengine - INFO - Epoch(train) [4][1900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:44:00 time: 0.6518 data_time: 0.0083 memory: 5134 grad_norm: 3.4349 loss: 0.6213 loss_cls: 0.3100 loss_bbox: 0.3113 +2024/10/26 19:22:18 - mmengine - INFO - Epoch(train) [4][1950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:43:37 time: 0.5776 data_time: 0.0084 memory: 5135 grad_norm: 3.3110 loss: 0.6190 loss_cls: 0.3102 loss_bbox: 0.3088 +2024/10/26 19:22:51 - mmengine - INFO - Epoch(train) [4][2000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:43:23 time: 0.6510 data_time: 0.0096 memory: 5135 grad_norm: 3.3631 loss: 0.6041 loss_cls: 0.3080 loss_bbox: 0.2962 +2024/10/26 19:22:57 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 19:23:19 - mmengine - INFO - Epoch(train) [4][2050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:42:57 time: 0.5533 data_time: 0.0085 memory: 5134 grad_norm: 3.3164 loss: 0.5791 loss_cls: 0.2927 loss_bbox: 0.2864 +2024/10/26 19:23:51 - mmengine - INFO - Epoch(train) [4][2100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:42:43 time: 0.6529 data_time: 0.0076 memory: 5134 grad_norm: 3.2143 loss: 0.6419 loss_cls: 0.3189 loss_bbox: 0.3230 +2024/10/26 19:24:22 - mmengine - INFO - Epoch(train) [4][2150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:42:25 time: 0.6196 data_time: 0.0080 memory: 5133 grad_norm: 3.1466 loss: 0.5827 loss_cls: 0.2987 loss_bbox: 0.2840 +2024/10/26 19:24:55 - mmengine - INFO - Epoch(train) [4][2200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:42:12 time: 0.6527 data_time: 0.0083 memory: 5135 grad_norm: 3.4145 loss: 0.6002 loss_cls: 0.3097 loss_bbox: 0.2904 +2024/10/26 19:25:21 - mmengine - INFO - Epoch(train) [4][2250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:41:41 time: 0.5195 data_time: 0.0080 memory: 5137 grad_norm: 3.3268 loss: 0.6185 loss_cls: 0.3118 loss_bbox: 0.3068 +2024/10/26 19:25:54 - mmengine - INFO - Epoch(train) [4][2300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:41:28 time: 0.6597 data_time: 0.0163 memory: 5137 grad_norm: 3.4912 loss: 0.6123 loss_cls: 0.3109 loss_bbox: 0.3014 +2024/10/26 19:26:23 - mmengine - INFO - Epoch(train) [4][2350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:41:05 time: 0.5802 data_time: 0.0084 memory: 5134 grad_norm: 3.3019 loss: 0.5804 loss_cls: 0.2889 loss_bbox: 0.2914 +2024/10/26 19:26:56 - mmengine - INFO - Epoch(train) [4][2400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:40:53 time: 0.6657 data_time: 0.0080 memory: 5133 grad_norm: 3.2577 loss: 0.6236 loss_cls: 0.3122 loss_bbox: 0.3114 +2024/10/26 19:27:24 - mmengine - INFO - Epoch(train) [4][2450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:40:26 time: 0.5522 data_time: 0.0190 memory: 5135 grad_norm: 3.5260 loss: 0.6249 loss_cls: 0.3245 loss_bbox: 0.3004 +2024/10/26 19:27:56 - mmengine - INFO - Epoch(train) [4][2500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:40:12 time: 0.6487 data_time: 0.0082 memory: 5134 grad_norm: 3.2118 loss: 0.5789 loss_cls: 0.2964 loss_bbox: 0.2825 +2024/10/26 19:28:24 - mmengine - INFO - Epoch(train) [4][2550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:39:45 time: 0.5585 data_time: 0.0082 memory: 5135 grad_norm: 3.3901 loss: 0.6341 loss_cls: 0.3332 loss_bbox: 0.3010 +2024/10/26 19:28:57 - mmengine - INFO - Epoch(train) [4][2600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:39:32 time: 0.6553 data_time: 0.0142 memory: 5136 grad_norm: 3.2664 loss: 0.6255 loss_cls: 0.3106 loss_bbox: 0.3149 +2024/10/26 19:29:25 - mmengine - INFO - Epoch(train) [4][2650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:39:05 time: 0.5534 data_time: 0.0085 memory: 5137 grad_norm: 3.4871 loss: 0.6043 loss_cls: 0.3099 loss_bbox: 0.2945 +2024/10/26 19:29:57 - mmengine - INFO - Epoch(train) [4][2700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:38:50 time: 0.6457 data_time: 0.0092 memory: 5134 grad_norm: 3.4194 loss: 0.6616 loss_cls: 0.3468 loss_bbox: 0.3148 +2024/10/26 19:30:24 - mmengine - INFO - Epoch(train) [4][2750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:38:21 time: 0.5414 data_time: 0.0091 memory: 5136 grad_norm: 3.4098 loss: 0.5870 loss_cls: 0.3036 loss_bbox: 0.2834 +2024/10/26 19:30:56 - mmengine - INFO - Epoch(train) [4][2800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:38:07 time: 0.6495 data_time: 0.0083 memory: 5135 grad_norm: 3.4642 loss: 0.6248 loss_cls: 0.3207 loss_bbox: 0.3041 +2024/10/26 19:31:27 - mmengine - INFO - Epoch(train) [4][2850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:37:47 time: 0.6090 data_time: 0.0086 memory: 5135 grad_norm: 3.2185 loss: 0.6201 loss_cls: 0.3168 loss_bbox: 0.3034 +2024/10/26 19:31:59 - mmengine - INFO - Epoch(train) [4][2900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:37:32 time: 0.6505 data_time: 0.0153 memory: 5134 grad_norm: 3.1565 loss: 0.5906 loss_cls: 0.3039 loss_bbox: 0.2868 +2024/10/26 19:32:29 - mmengine - INFO - Epoch(train) [4][2950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:37:10 time: 0.5922 data_time: 0.0086 memory: 5135 grad_norm: 3.2711 loss: 0.6207 loss_cls: 0.3181 loss_bbox: 0.3026 +2024/10/26 19:33:01 - mmengine - INFO - Epoch(train) [4][3000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:36:55 time: 0.6433 data_time: 0.0086 memory: 5135 grad_norm: 3.2806 loss: 0.6141 loss_cls: 0.3017 loss_bbox: 0.3124 +2024/10/26 19:33:05 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 19:33:29 - mmengine - INFO - Epoch(train) [4][3050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:36:27 time: 0.5506 data_time: 0.0084 memory: 5135 grad_norm: 3.3960 loss: 0.6283 loss_cls: 0.3168 loss_bbox: 0.3114 +2024/10/26 19:34:01 - mmengine - INFO - Epoch(train) [4][3100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:36:12 time: 0.6463 data_time: 0.0084 memory: 5134 grad_norm: 3.2265 loss: 0.6472 loss_cls: 0.3337 loss_bbox: 0.3135 +2024/10/26 19:34:35 - mmengine - INFO - Epoch(train) [4][3150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:36:02 time: 0.6891 data_time: 0.0810 memory: 5134 grad_norm: 3.2411 loss: 0.5866 loss_cls: 0.3002 loss_bbox: 0.2864 +2024/10/26 19:35:07 - mmengine - INFO - Epoch(train) [4][3200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:35:44 time: 0.6297 data_time: 0.0084 memory: 5137 grad_norm: 3.2619 loss: 0.6181 loss_cls: 0.3155 loss_bbox: 0.3027 +2024/10/26 19:35:35 - mmengine - INFO - Epoch(train) [4][3250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:35:17 time: 0.5560 data_time: 0.0079 memory: 5134 grad_norm: 3.0870 loss: 0.5949 loss_cls: 0.3051 loss_bbox: 0.2897 +2024/10/26 19:36:07 - mmengine - INFO - Epoch(train) [4][3300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:35:01 time: 0.6384 data_time: 0.0084 memory: 5132 grad_norm: 3.0539 loss: 0.6091 loss_cls: 0.3014 loss_bbox: 0.3076 +2024/10/26 19:36:37 - mmengine - INFO - Epoch(train) [4][3350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:34:40 time: 0.6018 data_time: 0.0080 memory: 5137 grad_norm: 3.2623 loss: 0.5977 loss_cls: 0.2996 loss_bbox: 0.2981 +2024/10/26 19:37:08 - mmengine - INFO - Epoch(train) [4][3400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:34:21 time: 0.6248 data_time: 0.0082 memory: 5135 grad_norm: 3.2246 loss: 0.5959 loss_cls: 0.2945 loss_bbox: 0.3013 +2024/10/26 19:37:39 - mmengine - INFO - Epoch(train) [4][3450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:34:02 time: 0.6171 data_time: 0.0086 memory: 5138 grad_norm: 3.1907 loss: 0.6017 loss_cls: 0.3057 loss_bbox: 0.2960 +2024/10/26 19:38:08 - mmengine - INFO - Epoch(train) [4][3500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:33:38 time: 0.5785 data_time: 0.0099 memory: 5134 grad_norm: 3.2362 loss: 0.6437 loss_cls: 0.3277 loss_bbox: 0.3160 +2024/10/26 19:38:37 - mmengine - INFO - Epoch(train) [4][3550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:33:14 time: 0.5791 data_time: 0.0100 memory: 5134 grad_norm: 3.2128 loss: 0.6236 loss_cls: 0.3177 loss_bbox: 0.3059 +2024/10/26 19:39:08 - mmengine - INFO - Epoch(train) [4][3600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:32:55 time: 0.6277 data_time: 0.0108 memory: 5135 grad_norm: 3.3183 loss: 0.6117 loss_cls: 0.3160 loss_bbox: 0.2956 +2024/10/26 19:39:37 - mmengine - INFO - Epoch(train) [4][3650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:32:32 time: 0.5867 data_time: 0.0159 memory: 5136 grad_norm: 3.1291 loss: 0.6357 loss_cls: 0.3250 loss_bbox: 0.3107 +2024/10/26 19:40:09 - mmengine - INFO - Epoch(train) [4][3700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:32:14 time: 0.6282 data_time: 0.0125 memory: 5135 grad_norm: 3.1963 loss: 0.5977 loss_cls: 0.2980 loss_bbox: 0.2997 +2024/10/26 19:40:40 - mmengine - INFO - Epoch(train) [4][3750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:31:56 time: 0.6277 data_time: 0.0103 memory: 5134 grad_norm: 3.1936 loss: 0.6042 loss_cls: 0.3065 loss_bbox: 0.2977 +2024/10/26 19:41:09 - mmengine - INFO - Epoch(train) [4][3800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:31:31 time: 0.5796 data_time: 0.0100 memory: 5134 grad_norm: 3.0367 loss: 0.6430 loss_cls: 0.3339 loss_bbox: 0.3091 +2024/10/26 19:41:39 - mmengine - INFO - Epoch(train) [4][3850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:31:08 time: 0.5871 data_time: 0.0102 memory: 5134 grad_norm: 3.2171 loss: 0.6409 loss_cls: 0.3178 loss_bbox: 0.3231 +2024/10/26 19:42:10 - mmengine - INFO - Epoch(train) [4][3900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:30:51 time: 0.6369 data_time: 0.0095 memory: 5135 grad_norm: 3.1512 loss: 0.6219 loss_cls: 0.3188 loss_bbox: 0.3031 +2024/10/26 19:42:40 - mmengine - INFO - Epoch(train) [4][3950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:30:29 time: 0.5991 data_time: 0.0085 memory: 5133 grad_norm: 3.2222 loss: 0.5971 loss_cls: 0.3032 loss_bbox: 0.2939 +2024/10/26 19:43:09 - mmengine - INFO - Epoch(train) [4][4000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:30:04 time: 0.5740 data_time: 0.0137 memory: 5132 grad_norm: 3.2165 loss: 0.6217 loss_cls: 0.3156 loss_bbox: 0.3062 +2024/10/26 19:43:14 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 19:43:40 - mmengine - INFO - Epoch(train) [4][4050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:29:45 time: 0.6265 data_time: 0.0083 memory: 5135 grad_norm: 3.2612 loss: 0.6234 loss_cls: 0.3124 loss_bbox: 0.3110 +2024/10/26 19:44:11 - mmengine - INFO - Epoch(train) [4][4100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:29:24 time: 0.6044 data_time: 0.0083 memory: 5135 grad_norm: 3.1788 loss: 0.6015 loss_cls: 0.3003 loss_bbox: 0.3012 +2024/10/26 19:44:42 - mmengine - INFO - Epoch(train) [4][4150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:29:06 time: 0.6352 data_time: 0.0086 memory: 5134 grad_norm: 3.5904 loss: 0.6302 loss_cls: 0.3236 loss_bbox: 0.3066 +2024/10/26 19:45:14 - mmengine - INFO - Epoch(train) [4][4200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:28:47 time: 0.6230 data_time: 0.0091 memory: 5134 grad_norm: 3.2135 loss: 0.5649 loss_cls: 0.2868 loss_bbox: 0.2782 +2024/10/26 19:45:45 - mmengine - INFO - Epoch(train) [4][4250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:28:27 time: 0.6231 data_time: 0.0103 memory: 5131 grad_norm: 3.2690 loss: 0.6359 loss_cls: 0.3224 loss_bbox: 0.3134 +2024/10/26 19:46:15 - mmengine - INFO - Epoch(train) [4][4300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:28:05 time: 0.5996 data_time: 0.0085 memory: 5132 grad_norm: 3.2552 loss: 0.6050 loss_cls: 0.3087 loss_bbox: 0.2963 +2024/10/26 19:46:48 - mmengine - INFO - Epoch(train) [4][4350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:27:51 time: 0.6646 data_time: 0.0087 memory: 5136 grad_norm: 3.0860 loss: 0.6186 loss_cls: 0.3198 loss_bbox: 0.2988 +2024/10/26 19:47:16 - mmengine - INFO - Epoch(train) [4][4400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:27:23 time: 0.5535 data_time: 0.0188 memory: 5136 grad_norm: 3.1468 loss: 0.6278 loss_cls: 0.3196 loss_bbox: 0.3082 +2024/10/26 19:47:48 - mmengine - INFO - Epoch(train) [4][4450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:27:06 time: 0.6462 data_time: 0.0149 memory: 5136 grad_norm: 3.2642 loss: 0.5889 loss_cls: 0.2886 loss_bbox: 0.3003 +2024/10/26 19:48:19 - mmengine - INFO - Epoch(train) [4][4500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:26:47 time: 0.6244 data_time: 0.0084 memory: 5135 grad_norm: 3.3763 loss: 0.6154 loss_cls: 0.3169 loss_bbox: 0.2985 +2024/10/26 19:48:50 - mmengine - INFO - Epoch(train) [4][4550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:26:26 time: 0.6144 data_time: 0.0157 memory: 5132 grad_norm: 3.3643 loss: 0.6053 loss_cls: 0.3138 loss_bbox: 0.2914 +2024/10/26 19:49:20 - mmengine - INFO - Epoch(train) [4][4600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:26:04 time: 0.5963 data_time: 0.0081 memory: 5135 grad_norm: 3.2754 loss: 0.6288 loss_cls: 0.3197 loss_bbox: 0.3091 +2024/10/26 19:49:49 - mmengine - INFO - Epoch(train) [4][4650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:25:40 time: 0.5939 data_time: 0.0126 memory: 5134 grad_norm: 3.4712 loss: 0.6078 loss_cls: 0.3157 loss_bbox: 0.2922 +2024/10/26 19:50:19 - mmengine - INFO - Epoch(train) [4][4700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:25:17 time: 0.5882 data_time: 0.0098 memory: 5136 grad_norm: 3.3542 loss: 0.5739 loss_cls: 0.2859 loss_bbox: 0.2881 +2024/10/26 19:50:51 - mmengine - INFO - Epoch(train) [4][4750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:25:00 time: 0.6526 data_time: 0.0134 memory: 5133 grad_norm: 3.4219 loss: 0.6282 loss_cls: 0.3229 loss_bbox: 0.3053 +2024/10/26 19:51:19 - mmengine - INFO - Epoch(train) [4][4800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:24:32 time: 0.5492 data_time: 0.0100 memory: 5135 grad_norm: 3.4619 loss: 0.6502 loss_cls: 0.3347 loss_bbox: 0.3154 +2024/10/26 19:51:52 - mmengine - INFO - Epoch(train) [4][4850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:24:16 time: 0.6549 data_time: 0.0102 memory: 5133 grad_norm: 3.1083 loss: 0.5934 loss_cls: 0.3027 loss_bbox: 0.2907 +2024/10/26 19:52:21 - mmengine - INFO - Epoch(train) [4][4900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:23:51 time: 0.5809 data_time: 0.0105 memory: 5136 grad_norm: 3.2373 loss: 0.6176 loss_cls: 0.3168 loss_bbox: 0.3008 +2024/10/26 19:52:53 - mmengine - INFO - Epoch(train) [4][4950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:23:35 time: 0.6493 data_time: 0.0118 memory: 5134 grad_norm: 3.2801 loss: 0.5812 loss_cls: 0.3028 loss_bbox: 0.2784 +2024/10/26 19:53:24 - mmengine - INFO - Epoch(train) [4][5000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:23:13 time: 0.6097 data_time: 0.0128 memory: 5137 grad_norm: 3.3480 loss: 0.6041 loss_cls: 0.3146 loss_bbox: 0.2895 +2024/10/26 19:53:30 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 19:53:57 - mmengine - INFO - Epoch(train) [4][5050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:22:57 time: 0.6591 data_time: 0.0106 memory: 5135 grad_norm: 3.2318 loss: 0.6191 loss_cls: 0.3222 loss_bbox: 0.2970 +2024/10/26 19:54:25 - mmengine - INFO - Epoch(train) [4][5100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:22:32 time: 0.5735 data_time: 0.0117 memory: 5134 grad_norm: 3.1016 loss: 0.6062 loss_cls: 0.3119 loss_bbox: 0.2943 +2024/10/26 19:54:58 - mmengine - INFO - Epoch(train) [4][5150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:22:16 time: 0.6604 data_time: 0.0111 memory: 5135 grad_norm: 3.1567 loss: 0.6328 loss_cls: 0.3314 loss_bbox: 0.3014 +2024/10/26 19:55:29 - mmengine - INFO - Epoch(train) [4][5200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:21:53 time: 0.6030 data_time: 0.0102 memory: 5133 grad_norm: 3.1009 loss: 0.6077 loss_cls: 0.3086 loss_bbox: 0.2991 +2024/10/26 19:56:02 - mmengine - INFO - Epoch(train) [4][5250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:21:38 time: 0.6674 data_time: 0.0134 memory: 5134 grad_norm: 3.2849 loss: 0.6333 loss_cls: 0.3247 loss_bbox: 0.3087 +2024/10/26 19:56:37 - mmengine - INFO - Epoch(train) [4][5300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:21:27 time: 0.7076 data_time: 0.0798 memory: 5136 grad_norm: 3.0087 loss: 0.6332 loss_cls: 0.3212 loss_bbox: 0.3119 +2024/10/26 19:57:07 - mmengine - INFO - Epoch(train) [4][5350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:21:04 time: 0.5939 data_time: 0.0109 memory: 5136 grad_norm: 3.2378 loss: 0.5787 loss_cls: 0.2927 loss_bbox: 0.2860 +2024/10/26 19:57:37 - mmengine - INFO - Epoch(train) [4][5400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:20:42 time: 0.6098 data_time: 0.0098 memory: 5133 grad_norm: 3.0084 loss: 0.6146 loss_cls: 0.3103 loss_bbox: 0.3042 +2024/10/26 19:58:10 - mmengine - INFO - Epoch(train) [4][5450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:20:24 time: 0.6440 data_time: 0.0101 memory: 5134 grad_norm: 3.1622 loss: 0.6040 loss_cls: 0.3024 loss_bbox: 0.3016 +2024/10/26 19:58:42 - mmengine - INFO - Epoch(train) [4][5500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:20:06 time: 0.6423 data_time: 0.0100 memory: 5133 grad_norm: 3.1419 loss: 0.6399 loss_cls: 0.3260 loss_bbox: 0.3139 +2024/10/26 19:59:10 - mmengine - INFO - Epoch(train) [4][5550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:19:40 time: 0.5694 data_time: 0.0101 memory: 5134 grad_norm: 3.2001 loss: 0.6245 loss_cls: 0.3172 loss_bbox: 0.3073 +2024/10/26 19:59:43 - mmengine - INFO - Epoch(train) [4][5600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:19:22 time: 0.6460 data_time: 0.0098 memory: 5133 grad_norm: 3.1199 loss: 0.5911 loss_cls: 0.3050 loss_bbox: 0.2862 +2024/10/26 20:00:12 - mmengine - INFO - Epoch(train) [4][5650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:18:57 time: 0.5877 data_time: 0.0109 memory: 5134 grad_norm: 2.9016 loss: 0.5964 loss_cls: 0.2948 loss_bbox: 0.3017 +2024/10/26 20:00:45 - mmengine - INFO - Epoch(train) [4][5700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:18:40 time: 0.6541 data_time: 0.0101 memory: 5135 grad_norm: 3.1104 loss: 0.6217 loss_cls: 0.3030 loss_bbox: 0.3187 +2024/10/26 20:01:14 - mmengine - INFO - Epoch(train) [4][5750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:18:16 time: 0.5880 data_time: 0.0102 memory: 5133 grad_norm: 3.1437 loss: 0.6504 loss_cls: 0.3281 loss_bbox: 0.3223 +2024/10/26 20:01:43 - mmengine - INFO - Epoch(train) [4][5800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:17:50 time: 0.5736 data_time: 0.0109 memory: 5133 grad_norm: 3.3545 loss: 0.6096 loss_cls: 0.3147 loss_bbox: 0.2949 +2024/10/26 20:02:13 - mmengine - INFO - Epoch(train) [4][5850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:17:26 time: 0.5956 data_time: 0.0099 memory: 5133 grad_norm: 3.5069 loss: 0.6300 loss_cls: 0.3301 loss_bbox: 0.2999 +2024/10/26 20:02:45 - mmengine - INFO - Epoch(train) [4][5900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:17:09 time: 0.6521 data_time: 0.0097 memory: 5135 grad_norm: 3.3523 loss: 0.6056 loss_cls: 0.3135 loss_bbox: 0.2921 +2024/10/26 20:03:16 - mmengine - INFO - Epoch(train) [4][5950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:16:47 time: 0.6098 data_time: 0.0097 memory: 5135 grad_norm: 3.0817 loss: 0.6240 loss_cls: 0.3160 loss_bbox: 0.3080 +2024/10/26 20:03:49 - mmengine - INFO - Epoch(train) [4][6000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:16:31 time: 0.6637 data_time: 0.0110 memory: 5134 grad_norm: 3.2889 loss: 0.5697 loss_cls: 0.2867 loss_bbox: 0.2829 +2024/10/26 20:03:55 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 20:04:19 - mmengine - INFO - Epoch(train) [4][6050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:16:09 time: 0.6126 data_time: 0.0097 memory: 5133 grad_norm: 3.0980 loss: 0.5986 loss_cls: 0.2963 loss_bbox: 0.3023 +2024/10/26 20:04:52 - mmengine - INFO - Epoch(train) [4][6100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:15:52 time: 0.6609 data_time: 0.0096 memory: 5134 grad_norm: 3.5064 loss: 0.6384 loss_cls: 0.3209 loss_bbox: 0.3175 +2024/10/26 20:05:21 - mmengine - INFO - Epoch(train) [4][6150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:15:26 time: 0.5723 data_time: 0.0098 memory: 5133 grad_norm: 3.3180 loss: 0.6191 loss_cls: 0.3162 loss_bbox: 0.3029 +2024/10/26 20:05:54 - mmengine - INFO - Epoch(train) [4][6200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:15:09 time: 0.6609 data_time: 0.0172 memory: 5135 grad_norm: 3.1306 loss: 0.6138 loss_cls: 0.3167 loss_bbox: 0.2971 +2024/10/26 20:06:24 - mmengine - INFO - Epoch(train) [4][6250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:14:45 time: 0.5932 data_time: 0.0100 memory: 5133 grad_norm: 3.0588 loss: 0.6241 loss_cls: 0.3172 loss_bbox: 0.3070 +2024/10/26 20:06:57 - mmengine - INFO - Epoch(train) [4][6300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:14:28 time: 0.6641 data_time: 0.0118 memory: 5137 grad_norm: 3.1690 loss: 0.6119 loss_cls: 0.3103 loss_bbox: 0.3015 +2024/10/26 20:07:25 - mmengine - INFO - Epoch(train) [4][6350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:14:00 time: 0.5537 data_time: 0.0142 memory: 5135 grad_norm: 3.1773 loss: 0.6108 loss_cls: 0.3108 loss_bbox: 0.3000 +2024/10/26 20:07:57 - mmengine - INFO - Epoch(train) [4][6400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:13:41 time: 0.6461 data_time: 0.0105 memory: 5136 grad_norm: 3.1626 loss: 0.6076 loss_cls: 0.3163 loss_bbox: 0.2913 +2024/10/26 20:08:25 - mmengine - INFO - Epoch(train) [4][6450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:13:13 time: 0.5549 data_time: 0.0127 memory: 5135 grad_norm: 3.2516 loss: 0.6057 loss_cls: 0.2977 loss_bbox: 0.3080 +2024/10/26 20:08:58 - mmengine - INFO - Epoch(train) [4][6500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:12:55 time: 0.6544 data_time: 0.0177 memory: 5135 grad_norm: 3.2821 loss: 0.6042 loss_cls: 0.3041 loss_bbox: 0.3001 +2024/10/26 20:09:28 - mmengine - INFO - Epoch(train) [4][6550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:12:33 time: 0.6108 data_time: 0.0109 memory: 5135 grad_norm: 3.2838 loss: 0.6358 loss_cls: 0.3325 loss_bbox: 0.3033 +2024/10/26 20:10:00 - mmengine - INFO - Epoch(train) [4][6600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:12:13 time: 0.6354 data_time: 0.0092 memory: 5132 grad_norm: 3.2949 loss: 0.6189 loss_cls: 0.3075 loss_bbox: 0.3113 +2024/10/26 20:10:30 - mmengine - INFO - Epoch(train) [4][6650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:11:49 time: 0.5988 data_time: 0.0097 memory: 5134 grad_norm: 3.1946 loss: 0.6133 loss_cls: 0.3126 loss_bbox: 0.3007 +2024/10/26 20:11:02 - mmengine - INFO - Epoch(train) [4][6700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:11:30 time: 0.6371 data_time: 0.0094 memory: 5134 grad_norm: 3.1129 loss: 0.6324 loss_cls: 0.3244 loss_bbox: 0.3081 +2024/10/26 20:11:35 - mmengine - INFO - Epoch(train) [4][6750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:11:12 time: 0.6587 data_time: 0.0800 memory: 5134 grad_norm: 3.1312 loss: 0.5878 loss_cls: 0.2915 loss_bbox: 0.2963 +2024/10/26 20:12:07 - mmengine - INFO - Epoch(train) [4][6800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:10:53 time: 0.6404 data_time: 0.0091 memory: 5135 grad_norm: 3.0719 loss: 0.6198 loss_cls: 0.3171 loss_bbox: 0.3027 +2024/10/26 20:12:36 - mmengine - INFO - Epoch(train) [4][6850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:10:28 time: 0.5879 data_time: 0.0091 memory: 5134 grad_norm: 3.2577 loss: 0.6170 loss_cls: 0.3089 loss_bbox: 0.3080 +2024/10/26 20:13:06 - mmengine - INFO - Epoch(train) [4][6900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:10:03 time: 0.5910 data_time: 0.0153 memory: 5134 grad_norm: 3.1825 loss: 0.5929 loss_cls: 0.3118 loss_bbox: 0.2811 +2024/10/26 20:13:34 - mmengine - INFO - Epoch(train) [4][6950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:09:36 time: 0.5633 data_time: 0.0090 memory: 5133 grad_norm: 3.2744 loss: 0.6146 loss_cls: 0.3168 loss_bbox: 0.2978 +2024/10/26 20:14:06 - mmengine - INFO - Epoch(train) [4][7000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:09:16 time: 0.6437 data_time: 0.0092 memory: 5134 grad_norm: 3.0828 loss: 0.6111 loss_cls: 0.3030 loss_bbox: 0.3081 +2024/10/26 20:14:11 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 20:14:34 - mmengine - INFO - Epoch(train) [4][7050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:08:49 time: 0.5633 data_time: 0.0089 memory: 5135 grad_norm: 3.1242 loss: 0.6324 loss_cls: 0.3276 loss_bbox: 0.3047 +2024/10/26 20:15:05 - mmengine - INFO - Epoch(train) [4][7100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:08:28 time: 0.6264 data_time: 0.0089 memory: 5132 grad_norm: 3.1484 loss: 0.6261 loss_cls: 0.3212 loss_bbox: 0.3050 +2024/10/26 20:15:34 - mmengine - INFO - Epoch(train) [4][7150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:08:00 time: 0.5647 data_time: 0.0089 memory: 5134 grad_norm: 3.1998 loss: 0.5939 loss_cls: 0.2988 loss_bbox: 0.2952 +2024/10/26 20:16:06 - mmengine - INFO - Epoch(train) [4][7200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:07:41 time: 0.6462 data_time: 0.0089 memory: 5133 grad_norm: 3.0555 loss: 0.5727 loss_cls: 0.2790 loss_bbox: 0.2936 +2024/10/26 20:16:35 - mmengine - INFO - Epoch(train) [4][7250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:07:15 time: 0.5772 data_time: 0.0091 memory: 5134 grad_norm: 3.0979 loss: 0.6075 loss_cls: 0.3150 loss_bbox: 0.2925 +2024/10/26 20:17:07 - mmengine - INFO - Epoch(train) [4][7300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:06:56 time: 0.6458 data_time: 0.0104 memory: 5135 grad_norm: 3.0276 loss: 0.6184 loss_cls: 0.3084 loss_bbox: 0.3100 +2024/10/26 20:17:31 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 20:17:31 - mmengine - INFO - Saving checkpoint at 4 epochs +2024/10/26 20:17:41 - mmengine - INFO - Epoch(val) [4][ 50/1250] eta: 0:02:09 time: 0.1079 data_time: 0.0021 memory: 5135 +2024/10/26 20:17:47 - mmengine - INFO - Epoch(val) [4][ 100/1250] eta: 0:02:06 time: 0.1116 data_time: 0.0017 memory: 629 +2024/10/26 20:17:52 - mmengine - INFO - Epoch(val) [4][ 150/1250] eta: 0:02:01 time: 0.1114 data_time: 0.0018 memory: 635 +2024/10/26 20:17:58 - mmengine - INFO - Epoch(val) [4][ 200/1250] eta: 0:01:55 time: 0.1104 data_time: 0.0018 memory: 635 +2024/10/26 20:18:03 - mmengine - INFO - Epoch(val) [4][ 250/1250] eta: 0:01:49 time: 0.1075 data_time: 0.0017 memory: 626 +2024/10/26 20:18:09 - mmengine - INFO - Epoch(val) [4][ 300/1250] eta: 0:01:43 time: 0.1057 data_time: 0.0018 memory: 626 +2024/10/26 20:18:15 - mmengine - INFO - Epoch(val) [4][ 350/1250] eta: 0:01:39 time: 0.1198 data_time: 0.0018 memory: 626 +2024/10/26 20:18:20 - mmengine - INFO - Epoch(val) [4][ 400/1250] eta: 0:01:34 time: 0.1179 data_time: 0.0018 memory: 614 +2024/10/26 20:18:26 - mmengine - INFO - Epoch(val) [4][ 450/1250] eta: 0:01:28 time: 0.1065 data_time: 0.0017 memory: 635 +2024/10/26 20:18:31 - mmengine - INFO - Epoch(val) [4][ 500/1250] eta: 0:01:23 time: 0.1091 data_time: 0.0018 memory: 635 +2024/10/26 20:18:37 - mmengine - INFO - Epoch(val) [4][ 550/1250] eta: 0:01:17 time: 0.1053 data_time: 0.0017 memory: 616 +2024/10/26 20:18:42 - mmengine - INFO - Epoch(val) [4][ 600/1250] eta: 0:01:11 time: 0.1084 data_time: 0.0018 memory: 626 +2024/10/26 20:18:47 - mmengine - INFO - Epoch(val) [4][ 650/1250] eta: 0:01:06 time: 0.1090 data_time: 0.0016 memory: 626 +2024/10/26 20:18:53 - mmengine - INFO - Epoch(val) [4][ 700/1250] eta: 0:01:00 time: 0.1119 data_time: 0.0017 memory: 630 +2024/10/26 20:18:58 - mmengine - INFO - Epoch(val) [4][ 750/1250] eta: 0:00:55 time: 0.1095 data_time: 0.0017 memory: 629 +2024/10/26 20:19:04 - mmengine - INFO - Epoch(val) [4][ 800/1250] eta: 0:00:49 time: 0.1103 data_time: 0.0017 memory: 635 +2024/10/26 20:19:09 - mmengine - INFO - Epoch(val) [4][ 850/1250] eta: 0:00:43 time: 0.0992 data_time: 0.0018 memory: 635 +2024/10/26 20:19:15 - mmengine - INFO - Epoch(val) [4][ 900/1250] eta: 0:00:38 time: 0.1113 data_time: 0.0017 memory: 635 +2024/10/26 20:19:19 - mmengine - INFO - Epoch(val) [4][ 950/1250] eta: 0:00:32 time: 0.0999 data_time: 0.0018 memory: 627 +2024/10/26 20:19:25 - mmengine - INFO - Epoch(val) [4][1000/1250] eta: 0:00:27 time: 0.1113 data_time: 0.0017 memory: 626 +2024/10/26 20:19:31 - mmengine - INFO - Epoch(val) [4][1050/1250] eta: 0:00:21 time: 0.1131 data_time: 0.0018 memory: 629 +2024/10/26 20:19:36 - mmengine - INFO - Epoch(val) [4][1100/1250] eta: 0:00:16 time: 0.1073 data_time: 0.0017 memory: 635 +2024/10/26 20:19:41 - mmengine - INFO - Epoch(val) [4][1150/1250] eta: 0:00:10 time: 0.1075 data_time: 0.0017 memory: 629 +2024/10/26 20:19:47 - mmengine - INFO - Epoch(val) [4][1200/1250] eta: 0:00:05 time: 0.1087 data_time: 0.0017 memory: 629 +2024/10/26 20:19:52 - mmengine - INFO - Epoch(val) [4][1250/1250] eta: 0:00:00 time: 0.1112 data_time: 0.0018 memory: 635 +2024/10/26 20:20:02 - mmengine - INFO - Evaluating bbox... +2024/10/26 20:21:02 - mmengine - INFO - bbox_mAP_copypaste: 0.292 0.472 0.309 0.154 0.316 0.392 +2024/10/26 20:21:04 - mmengine - INFO - Epoch(val) [4][1250/1250] coco/bbox_mAP: 0.2920 coco/bbox_mAP_50: 0.4720 coco/bbox_mAP_75: 0.3090 coco/bbox_mAP_s: 0.1540 coco/bbox_mAP_m: 0.3160 coco/bbox_mAP_l: 0.3920 data_time: 0.0018 time: 0.1093 +2024/10/26 20:21:19 - mmengine - INFO - Epoch(train) [5][ 50/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:05:55 time: 0.2545 data_time: 0.0089 memory: 5132 grad_norm: 3.1876 loss: 0.5899 loss_cls: 0.3021 loss_bbox: 0.2878 +2024/10/26 20:21:31 - mmengine - INFO - Epoch(train) [5][ 100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:04:57 time: 0.2543 data_time: 0.0089 memory: 5134 grad_norm: 3.0372 loss: 0.5651 loss_cls: 0.2787 loss_bbox: 0.2865 +2024/10/26 20:21:44 - mmengine - INFO - Epoch(train) [5][ 150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:03:59 time: 0.2584 data_time: 0.0089 memory: 5134 grad_norm: 3.0805 loss: 0.5927 loss_cls: 0.2954 loss_bbox: 0.2973 +2024/10/26 20:21:57 - mmengine - INFO - Epoch(train) [5][ 200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:03:01 time: 0.2535 data_time: 0.0092 memory: 5134 grad_norm: 3.0937 loss: 0.5928 loss_cls: 0.3023 loss_bbox: 0.2906 +2024/10/26 20:22:10 - mmengine - INFO - Epoch(train) [5][ 250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:02:04 time: 0.2618 data_time: 0.0092 memory: 5133 grad_norm: 3.1471 loss: 0.5878 loss_cls: 0.2886 loss_bbox: 0.2992 +2024/10/26 20:22:23 - mmengine - INFO - Epoch(train) [5][ 300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:01:06 time: 0.2529 data_time: 0.0091 memory: 5136 grad_norm: 3.0239 loss: 0.5891 loss_cls: 0.2933 loss_bbox: 0.2958 +2024/10/26 20:22:35 - mmengine - INFO - Epoch(train) [5][ 350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 9:00:08 time: 0.2543 data_time: 0.0110 memory: 5135 grad_norm: 3.0801 loss: 0.5985 loss_cls: 0.2995 loss_bbox: 0.2990 +2024/10/26 20:22:48 - mmengine - INFO - Epoch(train) [5][ 400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:59:11 time: 0.2524 data_time: 0.0086 memory: 5133 grad_norm: 3.0317 loss: 0.5877 loss_cls: 0.2912 loss_bbox: 0.2965 +2024/10/26 20:23:01 - mmengine - INFO - Epoch(train) [5][ 450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:58:13 time: 0.2511 data_time: 0.0084 memory: 5136 grad_norm: 3.1569 loss: 0.5830 loss_cls: 0.2902 loss_bbox: 0.2928 +2024/10/26 20:23:13 - mmengine - INFO - Epoch(train) [5][ 500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:57:16 time: 0.2547 data_time: 0.0093 memory: 5133 grad_norm: 3.2189 loss: 0.5871 loss_cls: 0.2922 loss_bbox: 0.2949 +2024/10/26 20:23:26 - mmengine - INFO - Epoch(train) [5][ 550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:56:20 time: 0.2554 data_time: 0.0106 memory: 5136 grad_norm: 3.2582 loss: 0.5949 loss_cls: 0.2983 loss_bbox: 0.2966 +2024/10/26 20:23:39 - mmengine - INFO - Epoch(train) [5][ 600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:55:23 time: 0.2577 data_time: 0.0128 memory: 5137 grad_norm: 3.0852 loss: 0.5738 loss_cls: 0.2931 loss_bbox: 0.2807 +2024/10/26 20:23:52 - mmengine - INFO - Epoch(train) [5][ 650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:54:27 time: 0.2544 data_time: 0.0098 memory: 5135 grad_norm: 3.0351 loss: 0.5958 loss_cls: 0.3099 loss_bbox: 0.2859 +2024/10/26 20:23:59 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 20:24:05 - mmengine - INFO - Epoch(train) [5][ 700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:53:30 time: 0.2584 data_time: 0.0098 memory: 5134 grad_norm: 3.3001 loss: 0.5942 loss_cls: 0.3072 loss_bbox: 0.2869 +2024/10/26 20:24:18 - mmengine - INFO - Epoch(train) [5][ 750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:52:36 time: 0.2733 data_time: 0.0264 memory: 5137 grad_norm: 3.1313 loss: 0.5839 loss_cls: 0.2958 loss_bbox: 0.2881 +2024/10/26 20:24:32 - mmengine - INFO - Epoch(train) [5][ 800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:51:41 time: 0.2673 data_time: 0.0204 memory: 5135 grad_norm: 2.8853 loss: 0.5915 loss_cls: 0.2847 loss_bbox: 0.3068 +2024/10/26 20:24:45 - mmengine - INFO - Epoch(train) [5][ 850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:50:47 time: 0.2765 data_time: 0.0201 memory: 5137 grad_norm: 2.8786 loss: 0.6030 loss_cls: 0.3133 loss_bbox: 0.2897 +2024/10/26 20:24:58 - mmengine - INFO - Epoch(train) [5][ 900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:49:51 time: 0.2563 data_time: 0.0109 memory: 5134 grad_norm: 3.0561 loss: 0.5929 loss_cls: 0.2894 loss_bbox: 0.3035 +2024/10/26 20:25:11 - mmengine - INFO - Epoch(train) [5][ 950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:48:56 time: 0.2630 data_time: 0.0097 memory: 5134 grad_norm: 3.1091 loss: 0.5733 loss_cls: 0.2822 loss_bbox: 0.2911 +2024/10/26 20:25:24 - mmengine - INFO - Epoch(train) [5][1000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:48:01 time: 0.2595 data_time: 0.0114 memory: 5134 grad_norm: 3.1921 loss: 0.5716 loss_cls: 0.2922 loss_bbox: 0.2794 +2024/10/26 20:25:37 - mmengine - INFO - Epoch(train) [5][1050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:47:06 time: 0.2563 data_time: 0.0105 memory: 5135 grad_norm: 3.1640 loss: 0.5763 loss_cls: 0.2872 loss_bbox: 0.2891 +2024/10/26 20:25:50 - mmengine - INFO - Epoch(train) [5][1100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:46:11 time: 0.2561 data_time: 0.0100 memory: 5135 grad_norm: 2.9548 loss: 0.5895 loss_cls: 0.3014 loss_bbox: 0.2881 +2024/10/26 20:26:03 - mmengine - INFO - Epoch(train) [5][1150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:45:16 time: 0.2619 data_time: 0.0111 memory: 5135 grad_norm: 3.0960 loss: 0.6261 loss_cls: 0.3169 loss_bbox: 0.3093 +2024/10/26 20:26:18 - mmengine - INFO - Epoch(train) [5][1200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:44:25 time: 0.2963 data_time: 0.0484 memory: 5135 grad_norm: 2.9770 loss: 0.5782 loss_cls: 0.2848 loss_bbox: 0.2934 +2024/10/26 20:26:31 - mmengine - INFO - Epoch(train) [5][1250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:43:31 time: 0.2576 data_time: 0.0097 memory: 5135 grad_norm: 3.1799 loss: 0.5701 loss_cls: 0.2830 loss_bbox: 0.2872 +2024/10/26 20:26:44 - mmengine - INFO - Epoch(train) [5][1300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:42:37 time: 0.2640 data_time: 0.0167 memory: 5135 grad_norm: 2.8197 loss: 0.5796 loss_cls: 0.2868 loss_bbox: 0.2927 +2024/10/26 20:26:58 - mmengine - INFO - Epoch(train) [5][1350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:41:43 time: 0.2695 data_time: 0.0123 memory: 5133 grad_norm: 3.1256 loss: 0.5866 loss_cls: 0.2902 loss_bbox: 0.2964 +2024/10/26 20:27:11 - mmengine - INFO - Epoch(train) [5][1400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:40:49 time: 0.2600 data_time: 0.0114 memory: 5136 grad_norm: 2.9570 loss: 0.5756 loss_cls: 0.2947 loss_bbox: 0.2809 +2024/10/26 20:27:23 - mmengine - INFO - Epoch(train) [5][1450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:39:55 time: 0.2577 data_time: 0.0101 memory: 5134 grad_norm: 3.1953 loss: 0.5804 loss_cls: 0.3003 loss_bbox: 0.2802 +2024/10/26 20:27:36 - mmengine - INFO - Epoch(train) [5][1500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:39:01 time: 0.2572 data_time: 0.0103 memory: 5134 grad_norm: 3.1737 loss: 0.6067 loss_cls: 0.3158 loss_bbox: 0.2909 +2024/10/26 20:27:50 - mmengine - INFO - Epoch(train) [5][1550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:38:10 time: 0.2788 data_time: 0.0317 memory: 5134 grad_norm: 3.1706 loss: 0.5714 loss_cls: 0.2844 loss_bbox: 0.2870 +2024/10/26 20:28:03 - mmengine - INFO - Epoch(train) [5][1600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:37:16 time: 0.2540 data_time: 0.0098 memory: 5135 grad_norm: 3.2880 loss: 0.5788 loss_cls: 0.2961 loss_bbox: 0.2827 +2024/10/26 20:28:18 - mmengine - INFO - Epoch(train) [5][1650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:36:25 time: 0.2945 data_time: 0.0475 memory: 5137 grad_norm: 3.0373 loss: 0.6151 loss_cls: 0.3143 loss_bbox: 0.3007 +2024/10/26 20:28:25 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 20:28:30 - mmengine - INFO - Epoch(train) [5][1700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:35:32 time: 0.2551 data_time: 0.0095 memory: 5134 grad_norm: 3.4087 loss: 0.6050 loss_cls: 0.3056 loss_bbox: 0.2994 +2024/10/26 20:28:43 - mmengine - INFO - Epoch(train) [5][1750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:34:38 time: 0.2569 data_time: 0.0101 memory: 5135 grad_norm: 3.0737 loss: 0.5694 loss_cls: 0.2833 loss_bbox: 0.2861 +2024/10/26 20:28:56 - mmengine - INFO - Epoch(train) [5][1800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:33:46 time: 0.2625 data_time: 0.0096 memory: 5134 grad_norm: 3.2151 loss: 0.5506 loss_cls: 0.2700 loss_bbox: 0.2806 +2024/10/26 20:29:09 - mmengine - INFO - Epoch(train) [5][1850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:32:52 time: 0.2556 data_time: 0.0095 memory: 5132 grad_norm: 3.1203 loss: 0.6192 loss_cls: 0.3180 loss_bbox: 0.3012 +2024/10/26 20:29:22 - mmengine - INFO - Epoch(train) [5][1900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:31:59 time: 0.2586 data_time: 0.0104 memory: 5137 grad_norm: 2.9926 loss: 0.6058 loss_cls: 0.3021 loss_bbox: 0.3037 +2024/10/26 20:29:35 - mmengine - INFO - Epoch(train) [5][1950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:31:06 time: 0.2538 data_time: 0.0086 memory: 5137 grad_norm: 3.2532 loss: 0.5810 loss_cls: 0.2952 loss_bbox: 0.2858 +2024/10/26 20:29:48 - mmengine - INFO - Epoch(train) [5][2000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:30:15 time: 0.2671 data_time: 0.0131 memory: 5133 grad_norm: 3.0295 loss: 0.5820 loss_cls: 0.2924 loss_bbox: 0.2896 +2024/10/26 20:30:01 - mmengine - INFO - Epoch(train) [5][2050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:29:22 time: 0.2537 data_time: 0.0079 memory: 5134 grad_norm: 2.9974 loss: 0.5740 loss_cls: 0.2820 loss_bbox: 0.2920 +2024/10/26 20:30:14 - mmengine - INFO - Epoch(train) [5][2100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:28:29 time: 0.2556 data_time: 0.0077 memory: 5134 grad_norm: 2.9935 loss: 0.5934 loss_cls: 0.3017 loss_bbox: 0.2917 +2024/10/26 20:30:26 - mmengine - INFO - Epoch(train) [5][2150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:27:36 time: 0.2538 data_time: 0.0086 memory: 5133 grad_norm: 3.0932 loss: 0.5619 loss_cls: 0.2778 loss_bbox: 0.2841 +2024/10/26 20:30:39 - mmengine - INFO - Epoch(train) [5][2200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:26:44 time: 0.2525 data_time: 0.0081 memory: 5134 grad_norm: 2.9693 loss: 0.6271 loss_cls: 0.3215 loss_bbox: 0.3056 +2024/10/26 20:30:52 - mmengine - INFO - Epoch(train) [5][2250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:25:52 time: 0.2585 data_time: 0.0123 memory: 5134 grad_norm: 3.0546 loss: 0.6091 loss_cls: 0.3023 loss_bbox: 0.3067 +2024/10/26 20:31:05 - mmengine - INFO - Epoch(train) [5][2300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:25:00 time: 0.2597 data_time: 0.0078 memory: 5134 grad_norm: 3.2960 loss: 0.5760 loss_cls: 0.2828 loss_bbox: 0.2932 +2024/10/26 20:31:18 - mmengine - INFO - Epoch(train) [5][2350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:24:08 time: 0.2597 data_time: 0.0144 memory: 5132 grad_norm: 2.9955 loss: 0.5944 loss_cls: 0.2963 loss_bbox: 0.2981 +2024/10/26 20:31:31 - mmengine - INFO - Epoch(train) [5][2400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:23:16 time: 0.2541 data_time: 0.0078 memory: 5135 grad_norm: 3.0584 loss: 0.5950 loss_cls: 0.2943 loss_bbox: 0.3007 +2024/10/26 20:31:43 - mmengine - INFO - Epoch(train) [5][2450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:22:25 time: 0.2537 data_time: 0.0087 memory: 5133 grad_norm: 3.0957 loss: 0.5876 loss_cls: 0.2925 loss_bbox: 0.2951 +2024/10/26 20:31:56 - mmengine - INFO - Epoch(train) [5][2500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:21:33 time: 0.2515 data_time: 0.0075 memory: 5137 grad_norm: 3.0646 loss: 0.5766 loss_cls: 0.2846 loss_bbox: 0.2920 +2024/10/26 20:32:09 - mmengine - INFO - Epoch(train) [5][2550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:20:41 time: 0.2547 data_time: 0.0079 memory: 5133 grad_norm: 3.0514 loss: 0.5730 loss_cls: 0.2888 loss_bbox: 0.2841 +2024/10/26 20:32:22 - mmengine - INFO - Epoch(train) [5][2600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:19:50 time: 0.2622 data_time: 0.0078 memory: 5135 grad_norm: 3.0296 loss: 0.5998 loss_cls: 0.3075 loss_bbox: 0.2922 +2024/10/26 20:32:35 - mmengine - INFO - Epoch(train) [5][2650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:18:59 time: 0.2589 data_time: 0.0137 memory: 5134 grad_norm: 3.1182 loss: 0.6225 loss_cls: 0.3183 loss_bbox: 0.3042 +2024/10/26 20:32:43 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 20:32:48 - mmengine - INFO - Epoch(train) [5][2700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:18:09 time: 0.2601 data_time: 0.0077 memory: 5134 grad_norm: 3.0909 loss: 0.5723 loss_cls: 0.2892 loss_bbox: 0.2830 +2024/10/26 20:33:00 - mmengine - INFO - Epoch(train) [5][2750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:17:17 time: 0.2537 data_time: 0.0075 memory: 5134 grad_norm: 3.0872 loss: 0.6073 loss_cls: 0.3079 loss_bbox: 0.2993 +2024/10/26 20:33:13 - mmengine - INFO - Epoch(train) [5][2800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:16:27 time: 0.2566 data_time: 0.0077 memory: 5135 grad_norm: 3.0579 loss: 0.5994 loss_cls: 0.2973 loss_bbox: 0.3021 +2024/10/26 20:33:26 - mmengine - INFO - Epoch(train) [5][2850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:15:36 time: 0.2563 data_time: 0.0074 memory: 5138 grad_norm: 3.1210 loss: 0.5914 loss_cls: 0.3015 loss_bbox: 0.2899 +2024/10/26 20:33:39 - mmengine - INFO - Epoch(train) [5][2900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:14:46 time: 0.2651 data_time: 0.0168 memory: 5134 grad_norm: 2.8846 loss: 0.6149 loss_cls: 0.3162 loss_bbox: 0.2987 +2024/10/26 20:33:52 - mmengine - INFO - Epoch(train) [5][2950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:13:56 time: 0.2655 data_time: 0.0080 memory: 5135 grad_norm: 3.0004 loss: 0.5926 loss_cls: 0.2964 loss_bbox: 0.2962 +2024/10/26 20:34:05 - mmengine - INFO - Epoch(train) [5][3000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:13:06 time: 0.2582 data_time: 0.0095 memory: 5135 grad_norm: 3.0645 loss: 0.5782 loss_cls: 0.2881 loss_bbox: 0.2901 +2024/10/26 20:34:19 - mmengine - INFO - Epoch(train) [5][3050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:12:17 time: 0.2712 data_time: 0.0256 memory: 5136 grad_norm: 3.0649 loss: 0.6172 loss_cls: 0.3072 loss_bbox: 0.3100 +2024/10/26 20:34:32 - mmengine - INFO - Epoch(train) [5][3100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:11:27 time: 0.2571 data_time: 0.0111 memory: 5134 grad_norm: 3.1376 loss: 0.6100 loss_cls: 0.3147 loss_bbox: 0.2953 +2024/10/26 20:34:45 - mmengine - INFO - Epoch(train) [5][3150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:10:38 time: 0.2599 data_time: 0.0077 memory: 5133 grad_norm: 3.2026 loss: 0.5830 loss_cls: 0.2917 loss_bbox: 0.2913 +2024/10/26 20:34:57 - mmengine - INFO - Epoch(train) [5][3200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:09:47 time: 0.2525 data_time: 0.0076 memory: 5132 grad_norm: 3.2628 loss: 0.5863 loss_cls: 0.2823 loss_bbox: 0.3040 +2024/10/26 20:35:10 - mmengine - INFO - Epoch(train) [5][3250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:08:57 time: 0.2549 data_time: 0.0077 memory: 5135 grad_norm: 3.2827 loss: 0.5943 loss_cls: 0.3035 loss_bbox: 0.2908 +2024/10/26 20:35:23 - mmengine - INFO - Epoch(train) [5][3300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:08:08 time: 0.2538 data_time: 0.0093 memory: 5135 grad_norm: 2.9692 loss: 0.6161 loss_cls: 0.3110 loss_bbox: 0.3051 +2024/10/26 20:35:36 - mmengine - INFO - Epoch(train) [5][3350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:07:18 time: 0.2558 data_time: 0.0116 memory: 5133 grad_norm: 3.0386 loss: 0.5958 loss_cls: 0.2957 loss_bbox: 0.3001 +2024/10/26 20:35:48 - mmengine - INFO - Epoch(train) [5][3400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:06:28 time: 0.2540 data_time: 0.0087 memory: 5135 grad_norm: 2.9525 loss: 0.5753 loss_cls: 0.2963 loss_bbox: 0.2790 +2024/10/26 20:36:01 - mmengine - INFO - Epoch(train) [5][3450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:05:39 time: 0.2542 data_time: 0.0091 memory: 5133 grad_norm: 2.9879 loss: 0.6110 loss_cls: 0.3096 loss_bbox: 0.3014 +2024/10/26 20:36:14 - mmengine - INFO - Epoch(train) [5][3500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:04:49 time: 0.2530 data_time: 0.0082 memory: 5134 grad_norm: 3.1542 loss: 0.5720 loss_cls: 0.2887 loss_bbox: 0.2833 +2024/10/26 20:36:27 - mmengine - INFO - Epoch(train) [5][3550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:04:01 time: 0.2608 data_time: 0.0079 memory: 5134 grad_norm: 3.1189 loss: 0.5914 loss_cls: 0.3085 loss_bbox: 0.2829 +2024/10/26 20:36:39 - mmengine - INFO - Epoch(train) [5][3600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:03:11 time: 0.2523 data_time: 0.0089 memory: 5136 grad_norm: 3.1575 loss: 0.5786 loss_cls: 0.2846 loss_bbox: 0.2941 +2024/10/26 20:36:52 - mmengine - INFO - Epoch(train) [5][3650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:02:22 time: 0.2584 data_time: 0.0084 memory: 5134 grad_norm: 3.0666 loss: 0.6095 loss_cls: 0.3091 loss_bbox: 0.3004 +2024/10/26 20:37:00 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 20:37:05 - mmengine - INFO - Epoch(train) [5][3700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:01:33 time: 0.2512 data_time: 0.0084 memory: 5134 grad_norm: 3.2064 loss: 0.6077 loss_cls: 0.3061 loss_bbox: 0.3016 +2024/10/26 20:37:18 - mmengine - INFO - Epoch(train) [5][3750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 8:00:46 time: 0.2701 data_time: 0.0183 memory: 5134 grad_norm: 3.0271 loss: 0.5894 loss_cls: 0.2978 loss_bbox: 0.2916 +2024/10/26 20:37:31 - mmengine - INFO - Epoch(train) [5][3800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:59:57 time: 0.2539 data_time: 0.0104 memory: 5133 grad_norm: 3.0956 loss: 0.5944 loss_cls: 0.2936 loss_bbox: 0.3008 +2024/10/26 20:37:44 - mmengine - INFO - Epoch(train) [5][3850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:59:08 time: 0.2517 data_time: 0.0081 memory: 5136 grad_norm: 3.0097 loss: 0.5955 loss_cls: 0.2938 loss_bbox: 0.3017 +2024/10/26 20:37:56 - mmengine - INFO - Epoch(train) [5][3900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:58:20 time: 0.2537 data_time: 0.0100 memory: 5133 grad_norm: 3.1162 loss: 0.6196 loss_cls: 0.3140 loss_bbox: 0.3057 +2024/10/26 20:38:09 - mmengine - INFO - Epoch(train) [5][3950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:57:31 time: 0.2568 data_time: 0.0113 memory: 5134 grad_norm: 2.9136 loss: 0.6036 loss_cls: 0.3020 loss_bbox: 0.3017 +2024/10/26 20:38:22 - mmengine - INFO - Epoch(train) [5][4000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:56:44 time: 0.2631 data_time: 0.0189 memory: 5134 grad_norm: 2.9425 loss: 0.6035 loss_cls: 0.3125 loss_bbox: 0.2910 +2024/10/26 20:38:35 - mmengine - INFO - Epoch(train) [5][4050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:55:56 time: 0.2578 data_time: 0.0085 memory: 5134 grad_norm: 3.3699 loss: 0.5745 loss_cls: 0.2982 loss_bbox: 0.2763 +2024/10/26 20:38:48 - mmengine - INFO - Epoch(train) [5][4100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:55:08 time: 0.2522 data_time: 0.0080 memory: 5134 grad_norm: 2.9753 loss: 0.5903 loss_cls: 0.2992 loss_bbox: 0.2911 +2024/10/26 20:39:01 - mmengine - INFO - Epoch(train) [5][4150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:54:20 time: 0.2625 data_time: 0.0181 memory: 5133 grad_norm: 3.3480 loss: 0.6105 loss_cls: 0.3045 loss_bbox: 0.3060 +2024/10/26 20:39:17 - mmengine - INFO - Epoch(train) [5][4200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:53:38 time: 0.3283 data_time: 0.0828 memory: 5134 grad_norm: 3.0390 loss: 0.5943 loss_cls: 0.2994 loss_bbox: 0.2949 +2024/10/26 20:39:30 - mmengine - INFO - Epoch(train) [5][4250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:52:50 time: 0.2521 data_time: 0.0082 memory: 5135 grad_norm: 3.0046 loss: 0.5751 loss_cls: 0.2843 loss_bbox: 0.2908 +2024/10/26 20:39:43 - mmengine - INFO - Epoch(train) [5][4300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:52:03 time: 0.2546 data_time: 0.0104 memory: 5136 grad_norm: 2.9936 loss: 0.5993 loss_cls: 0.2966 loss_bbox: 0.3026 +2024/10/26 20:39:56 - mmengine - INFO - Epoch(train) [5][4350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:51:16 time: 0.2605 data_time: 0.0082 memory: 5134 grad_norm: 3.0460 loss: 0.5951 loss_cls: 0.3081 loss_bbox: 0.2869 +2024/10/26 20:40:08 - mmengine - INFO - Epoch(train) [5][4400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:50:28 time: 0.2540 data_time: 0.0098 memory: 5132 grad_norm: 3.3925 loss: 0.5873 loss_cls: 0.3004 loss_bbox: 0.2869 +2024/10/26 20:40:21 - mmengine - INFO - Epoch(train) [5][4450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:49:41 time: 0.2577 data_time: 0.0081 memory: 5133 grad_norm: 3.1226 loss: 0.5698 loss_cls: 0.2808 loss_bbox: 0.2890 +2024/10/26 20:40:34 - mmengine - INFO - Epoch(train) [5][4500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:48:54 time: 0.2528 data_time: 0.0082 memory: 5134 grad_norm: 3.1884 loss: 0.5945 loss_cls: 0.2984 loss_bbox: 0.2961 +2024/10/26 20:40:47 - mmengine - INFO - Epoch(train) [5][4550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:48:07 time: 0.2575 data_time: 0.0137 memory: 5134 grad_norm: 3.1427 loss: 0.6079 loss_cls: 0.3097 loss_bbox: 0.2981 +2024/10/26 20:41:00 - mmengine - INFO - Epoch(train) [5][4600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:47:20 time: 0.2527 data_time: 0.0079 memory: 5133 grad_norm: 3.2729 loss: 0.5910 loss_cls: 0.2960 loss_bbox: 0.2950 +2024/10/26 20:41:12 - mmengine - INFO - Epoch(train) [5][4650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:46:33 time: 0.2545 data_time: 0.0085 memory: 5136 grad_norm: 3.1021 loss: 0.5749 loss_cls: 0.2854 loss_bbox: 0.2895 +2024/10/26 20:41:20 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 20:41:25 - mmengine - INFO - Epoch(train) [5][4700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:45:46 time: 0.2626 data_time: 0.0116 memory: 5133 grad_norm: 3.2237 loss: 0.6042 loss_cls: 0.3132 loss_bbox: 0.2910 +2024/10/26 20:41:38 - mmengine - INFO - Epoch(train) [5][4750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:45:00 time: 0.2543 data_time: 0.0100 memory: 5133 grad_norm: 2.9739 loss: 0.5997 loss_cls: 0.3010 loss_bbox: 0.2988 +2024/10/26 20:41:51 - mmengine - INFO - Epoch(train) [5][4800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:44:13 time: 0.2530 data_time: 0.0081 memory: 5134 grad_norm: 3.0645 loss: 0.6074 loss_cls: 0.3032 loss_bbox: 0.3041 +2024/10/26 20:42:03 - mmengine - INFO - Epoch(train) [5][4850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:43:26 time: 0.2533 data_time: 0.0083 memory: 5135 grad_norm: 2.8472 loss: 0.5716 loss_cls: 0.2883 loss_bbox: 0.2833 +2024/10/26 20:42:19 - mmengine - INFO - Epoch(train) [5][4900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:42:44 time: 0.3102 data_time: 0.0596 memory: 5136 grad_norm: 3.0354 loss: 0.5908 loss_cls: 0.2953 loss_bbox: 0.2954 +2024/10/26 20:42:32 - mmengine - INFO - Epoch(train) [5][4950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:41:58 time: 0.2552 data_time: 0.0085 memory: 5135 grad_norm: 3.0394 loss: 0.6129 loss_cls: 0.3108 loss_bbox: 0.3021 +2024/10/26 20:42:44 - mmengine - INFO - Epoch(train) [5][5000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:41:12 time: 0.2542 data_time: 0.0088 memory: 5134 grad_norm: 3.0546 loss: 0.5996 loss_cls: 0.2985 loss_bbox: 0.3011 +2024/10/26 20:42:57 - mmengine - INFO - Epoch(train) [5][5050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:40:25 time: 0.2538 data_time: 0.0081 memory: 5137 grad_norm: 3.1742 loss: 0.5926 loss_cls: 0.2921 loss_bbox: 0.3004 +2024/10/26 20:43:10 - mmengine - INFO - Epoch(train) [5][5100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:39:39 time: 0.2554 data_time: 0.0084 memory: 5135 grad_norm: 2.8645 loss: 0.5872 loss_cls: 0.2926 loss_bbox: 0.2946 +2024/10/26 20:43:23 - mmengine - INFO - Epoch(train) [5][5150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:38:53 time: 0.2557 data_time: 0.0098 memory: 5134 grad_norm: 3.0549 loss: 0.6072 loss_cls: 0.3146 loss_bbox: 0.2926 +2024/10/26 20:43:35 - mmengine - INFO - Epoch(train) [5][5200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:38:08 time: 0.2543 data_time: 0.0080 memory: 5136 grad_norm: 3.0656 loss: 0.6094 loss_cls: 0.3057 loss_bbox: 0.3037 +2024/10/26 20:43:49 - mmengine - INFO - Epoch(train) [5][5250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:37:23 time: 0.2647 data_time: 0.0087 memory: 5137 grad_norm: 2.9548 loss: 0.5979 loss_cls: 0.3027 loss_bbox: 0.2951 +2024/10/26 20:44:01 - mmengine - INFO - Epoch(train) [5][5300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:36:37 time: 0.2540 data_time: 0.0084 memory: 5134 grad_norm: 2.8645 loss: 0.6003 loss_cls: 0.3045 loss_bbox: 0.2958 +2024/10/26 20:44:14 - mmengine - INFO - Epoch(train) [5][5350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:35:51 time: 0.2568 data_time: 0.0083 memory: 5135 grad_norm: 3.0817 loss: 0.6044 loss_cls: 0.3070 loss_bbox: 0.2974 +2024/10/26 20:44:27 - mmengine - INFO - Epoch(train) [5][5400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:35:06 time: 0.2567 data_time: 0.0084 memory: 5135 grad_norm: 2.8754 loss: 0.6038 loss_cls: 0.3042 loss_bbox: 0.2996 +2024/10/26 20:44:40 - mmengine - INFO - Epoch(train) [5][5450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:34:21 time: 0.2550 data_time: 0.0092 memory: 5134 grad_norm: 3.1476 loss: 0.5910 loss_cls: 0.3045 loss_bbox: 0.2865 +2024/10/26 20:44:53 - mmengine - INFO - Epoch(train) [5][5500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:33:36 time: 0.2606 data_time: 0.0093 memory: 5137 grad_norm: 3.2022 loss: 0.5760 loss_cls: 0.2829 loss_bbox: 0.2931 +2024/10/26 20:45:06 - mmengine - INFO - Epoch(train) [5][5550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:32:51 time: 0.2629 data_time: 0.0160 memory: 5134 grad_norm: 3.0634 loss: 0.5889 loss_cls: 0.2936 loss_bbox: 0.2953 +2024/10/26 20:45:19 - mmengine - INFO - Epoch(train) [5][5600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:32:06 time: 0.2590 data_time: 0.0126 memory: 5135 grad_norm: 2.8934 loss: 0.5905 loss_cls: 0.2936 loss_bbox: 0.2969 +2024/10/26 20:45:32 - mmengine - INFO - Epoch(train) [5][5650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:31:21 time: 0.2545 data_time: 0.0089 memory: 5132 grad_norm: 2.9370 loss: 0.6143 loss_cls: 0.3100 loss_bbox: 0.3043 +2024/10/26 20:45:39 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 20:45:44 - mmengine - INFO - Epoch(train) [5][5700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:30:37 time: 0.2559 data_time: 0.0105 memory: 5136 grad_norm: 2.8547 loss: 0.5776 loss_cls: 0.2908 loss_bbox: 0.2868 +2024/10/26 20:45:58 - mmengine - INFO - Epoch(train) [5][5750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:29:53 time: 0.2656 data_time: 0.0197 memory: 5138 grad_norm: 3.1837 loss: 0.6346 loss_cls: 0.3239 loss_bbox: 0.3107 +2024/10/26 20:46:11 - mmengine - INFO - Epoch(train) [5][5800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:29:09 time: 0.2648 data_time: 0.0145 memory: 5135 grad_norm: 2.9890 loss: 0.5879 loss_cls: 0.2922 loss_bbox: 0.2956 +2024/10/26 20:46:24 - mmengine - INFO - Epoch(train) [5][5850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:28:24 time: 0.2590 data_time: 0.0146 memory: 5135 grad_norm: 3.0174 loss: 0.6070 loss_cls: 0.2997 loss_bbox: 0.3073 +2024/10/26 20:46:37 - mmengine - INFO - Epoch(train) [5][5900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:27:41 time: 0.2659 data_time: 0.0198 memory: 5135 grad_norm: 2.9937 loss: 0.6045 loss_cls: 0.3085 loss_bbox: 0.2960 +2024/10/26 20:46:50 - mmengine - INFO - Epoch(train) [5][5950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:26:56 time: 0.2583 data_time: 0.0123 memory: 5135 grad_norm: 3.0878 loss: 0.5951 loss_cls: 0.3005 loss_bbox: 0.2945 +2024/10/26 20:47:03 - mmengine - INFO - Epoch(train) [5][6000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:26:12 time: 0.2546 data_time: 0.0083 memory: 5132 grad_norm: 2.9248 loss: 0.5908 loss_cls: 0.2971 loss_bbox: 0.2937 +2024/10/26 20:47:18 - mmengine - INFO - Epoch(train) [5][6050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:25:31 time: 0.2974 data_time: 0.0500 memory: 5134 grad_norm: 3.0923 loss: 0.5846 loss_cls: 0.2995 loss_bbox: 0.2850 +2024/10/26 20:47:31 - mmengine - INFO - Epoch(train) [5][6100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:24:47 time: 0.2655 data_time: 0.0101 memory: 5132 grad_norm: 3.1851 loss: 0.5786 loss_cls: 0.2996 loss_bbox: 0.2791 +2024/10/26 20:47:44 - mmengine - INFO - Epoch(train) [5][6150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:24:03 time: 0.2559 data_time: 0.0084 memory: 5134 grad_norm: 2.9650 loss: 0.5668 loss_cls: 0.2769 loss_bbox: 0.2900 +2024/10/26 20:47:57 - mmengine - INFO - Epoch(train) [5][6200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:23:20 time: 0.2618 data_time: 0.0089 memory: 5134 grad_norm: 3.2263 loss: 0.6314 loss_cls: 0.3240 loss_bbox: 0.3074 +2024/10/26 20:48:10 - mmengine - INFO - Epoch(train) [5][6250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:22:36 time: 0.2563 data_time: 0.0090 memory: 5135 grad_norm: 2.9823 loss: 0.5748 loss_cls: 0.2906 loss_bbox: 0.2842 +2024/10/26 20:48:23 - mmengine - INFO - Epoch(train) [5][6300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:21:53 time: 0.2661 data_time: 0.0204 memory: 5133 grad_norm: 2.8310 loss: 0.5867 loss_cls: 0.2914 loss_bbox: 0.2953 +2024/10/26 20:48:36 - mmengine - INFO - Epoch(train) [5][6350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:21:09 time: 0.2547 data_time: 0.0084 memory: 5134 grad_norm: 3.0179 loss: 0.5674 loss_cls: 0.2787 loss_bbox: 0.2887 +2024/10/26 20:48:48 - mmengine - INFO - Epoch(train) [5][6400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:20:25 time: 0.2543 data_time: 0.0076 memory: 5135 grad_norm: 2.9672 loss: 0.6029 loss_cls: 0.3070 loss_bbox: 0.2959 +2024/10/26 20:49:02 - mmengine - INFO - Epoch(train) [5][6450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:19:42 time: 0.2625 data_time: 0.0106 memory: 5137 grad_norm: 3.1812 loss: 0.6075 loss_cls: 0.3170 loss_bbox: 0.2905 +2024/10/26 20:49:18 - mmengine - INFO - Epoch(train) [5][6500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:19:05 time: 0.3361 data_time: 0.0895 memory: 5134 grad_norm: 3.3179 loss: 0.6084 loss_cls: 0.3130 loss_bbox: 0.2954 +2024/10/26 20:49:31 - mmengine - INFO - Epoch(train) [5][6550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:18:22 time: 0.2603 data_time: 0.0158 memory: 5136 grad_norm: 3.0319 loss: 0.5745 loss_cls: 0.2913 loss_bbox: 0.2832 +2024/10/26 20:49:44 - mmengine - INFO - Epoch(train) [5][6600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:17:38 time: 0.2557 data_time: 0.0078 memory: 5133 grad_norm: 2.9072 loss: 0.5843 loss_cls: 0.2912 loss_bbox: 0.2931 +2024/10/26 20:49:57 - mmengine - INFO - Epoch(train) [5][6650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:16:55 time: 0.2597 data_time: 0.0080 memory: 5136 grad_norm: 3.3571 loss: 0.5914 loss_cls: 0.3026 loss_bbox: 0.2888 +2024/10/26 20:50:05 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 20:50:10 - mmengine - INFO - Epoch(train) [5][6700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:16:13 time: 0.2661 data_time: 0.0177 memory: 5135 grad_norm: 2.9474 loss: 0.6101 loss_cls: 0.3105 loss_bbox: 0.2996 +2024/10/26 20:50:23 - mmengine - INFO - Epoch(train) [5][6750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:15:30 time: 0.2554 data_time: 0.0080 memory: 5135 grad_norm: 3.0077 loss: 0.5964 loss_cls: 0.3077 loss_bbox: 0.2886 +2024/10/26 20:50:36 - mmengine - INFO - Epoch(train) [5][6800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:14:47 time: 0.2536 data_time: 0.0081 memory: 5137 grad_norm: 2.9221 loss: 0.5699 loss_cls: 0.2931 loss_bbox: 0.2767 +2024/10/26 20:50:49 - mmengine - INFO - Epoch(train) [5][6850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:14:04 time: 0.2547 data_time: 0.0088 memory: 5134 grad_norm: 3.0999 loss: 0.5587 loss_cls: 0.2862 loss_bbox: 0.2725 +2024/10/26 20:51:01 - mmengine - INFO - Epoch(train) [5][6900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:13:21 time: 0.2544 data_time: 0.0078 memory: 5136 grad_norm: 3.0435 loss: 0.5910 loss_cls: 0.2961 loss_bbox: 0.2950 +2024/10/26 20:51:18 - mmengine - INFO - Epoch(train) [5][6950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:12:44 time: 0.3370 data_time: 0.0906 memory: 5136 grad_norm: 2.9517 loss: 0.6169 loss_cls: 0.3208 loss_bbox: 0.2961 +2024/10/26 20:51:31 - mmengine - INFO - Epoch(train) [5][7000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:12:01 time: 0.2555 data_time: 0.0082 memory: 5132 grad_norm: 3.0252 loss: 0.6029 loss_cls: 0.3078 loss_bbox: 0.2950 +2024/10/26 20:51:44 - mmengine - INFO - Epoch(train) [5][7050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:11:19 time: 0.2620 data_time: 0.0077 memory: 5135 grad_norm: 2.9712 loss: 0.6047 loss_cls: 0.3096 loss_bbox: 0.2951 +2024/10/26 20:51:57 - mmengine - INFO - Epoch(train) [5][7100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:10:37 time: 0.2543 data_time: 0.0081 memory: 5134 grad_norm: 3.2608 loss: 0.5891 loss_cls: 0.3071 loss_bbox: 0.2820 +2024/10/26 20:52:10 - mmengine - INFO - Epoch(train) [5][7150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:09:55 time: 0.2601 data_time: 0.0077 memory: 5134 grad_norm: 2.9400 loss: 0.6217 loss_cls: 0.3151 loss_bbox: 0.3065 +2024/10/26 20:52:23 - mmengine - INFO - Epoch(train) [5][7200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:09:12 time: 0.2578 data_time: 0.0104 memory: 5133 grad_norm: 2.8695 loss: 0.6109 loss_cls: 0.3146 loss_bbox: 0.2963 +2024/10/26 20:52:36 - mmengine - INFO - Epoch(train) [5][7250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:08:31 time: 0.2598 data_time: 0.0077 memory: 5135 grad_norm: 3.1549 loss: 0.5602 loss_cls: 0.2772 loss_bbox: 0.2830 +2024/10/26 20:52:49 - mmengine - INFO - Epoch(train) [5][7300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:07:49 time: 0.2576 data_time: 0.0105 memory: 5134 grad_norm: 2.9706 loss: 0.5852 loss_cls: 0.2858 loss_bbox: 0.2994 +2024/10/26 20:53:02 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 20:53:02 - mmengine - INFO - Saving checkpoint at 5 epochs +2024/10/26 20:53:12 - mmengine - INFO - Epoch(val) [5][ 50/1250] eta: 0:01:20 time: 0.0674 data_time: 0.0018 memory: 5136 +2024/10/26 20:53:15 - mmengine - INFO - Epoch(val) [5][ 100/1250] eta: 0:01:17 time: 0.0674 data_time: 0.0015 memory: 631 +2024/10/26 20:53:18 - mmengine - INFO - Epoch(val) [5][ 150/1250] eta: 0:01:13 time: 0.0666 data_time: 0.0015 memory: 636 +2024/10/26 20:53:22 - mmengine - INFO - Epoch(val) [5][ 200/1250] eta: 0:01:10 time: 0.0654 data_time: 0.0014 memory: 636 +2024/10/26 20:53:25 - mmengine - INFO - Epoch(val) [5][ 250/1250] eta: 0:01:06 time: 0.0661 data_time: 0.0014 memory: 627 +2024/10/26 20:53:28 - mmengine - INFO - Epoch(val) [5][ 300/1250] eta: 0:01:03 time: 0.0661 data_time: 0.0014 memory: 626 +2024/10/26 20:53:32 - mmengine - INFO - Epoch(val) [5][ 350/1250] eta: 0:00:59 time: 0.0655 data_time: 0.0014 memory: 626 +2024/10/26 20:53:35 - mmengine - INFO - Epoch(val) [5][ 400/1250] eta: 0:00:56 time: 0.0660 data_time: 0.0015 memory: 615 +2024/10/26 20:53:38 - mmengine - INFO - Epoch(val) [5][ 450/1250] eta: 0:00:52 time: 0.0652 data_time: 0.0014 memory: 636 +2024/10/26 20:53:41 - mmengine - INFO - Epoch(val) [5][ 500/1250] eta: 0:00:49 time: 0.0654 data_time: 0.0015 memory: 636 +2024/10/26 20:53:45 - mmengine - INFO - Epoch(val) [5][ 550/1250] eta: 0:00:46 time: 0.0653 data_time: 0.0015 memory: 616 +2024/10/26 20:53:48 - mmengine - INFO - Epoch(val) [5][ 600/1250] eta: 0:00:42 time: 0.0655 data_time: 0.0016 memory: 626 +2024/10/26 20:53:51 - mmengine - INFO - Epoch(val) [5][ 650/1250] eta: 0:00:39 time: 0.0639 data_time: 0.0015 memory: 626 +2024/10/26 20:53:54 - mmengine - INFO - Epoch(val) [5][ 700/1250] eta: 0:00:36 time: 0.0668 data_time: 0.0015 memory: 631 +2024/10/26 20:53:58 - mmengine - INFO - Epoch(val) [5][ 750/1250] eta: 0:00:32 time: 0.0673 data_time: 0.0015 memory: 631 +2024/10/26 20:54:01 - mmengine - INFO - Epoch(val) [5][ 800/1250] eta: 0:00:29 time: 0.0663 data_time: 0.0014 memory: 636 +2024/10/26 20:54:04 - mmengine - INFO - Epoch(val) [5][ 850/1250] eta: 0:00:26 time: 0.0668 data_time: 0.0015 memory: 636 +2024/10/26 20:54:08 - mmengine - INFO - Epoch(val) [5][ 900/1250] eta: 0:00:23 time: 0.0663 data_time: 0.0014 memory: 636 +2024/10/26 20:54:11 - mmengine - INFO - Epoch(val) [5][ 950/1250] eta: 0:00:19 time: 0.0654 data_time: 0.0014 memory: 626 +2024/10/26 20:54:14 - mmengine - INFO - Epoch(val) [5][1000/1250] eta: 0:00:16 time: 0.0661 data_time: 0.0014 memory: 627 +2024/10/26 20:54:18 - mmengine - INFO - Epoch(val) [5][1050/1250] eta: 0:00:13 time: 0.0669 data_time: 0.0015 memory: 631 +2024/10/26 20:54:21 - mmengine - INFO - Epoch(val) [5][1100/1250] eta: 0:00:09 time: 0.0658 data_time: 0.0014 memory: 636 +2024/10/26 20:54:24 - mmengine - INFO - Epoch(val) [5][1150/1250] eta: 0:00:06 time: 0.0660 data_time: 0.0014 memory: 631 +2024/10/26 20:54:28 - mmengine - INFO - Epoch(val) [5][1200/1250] eta: 0:00:03 time: 0.0657 data_time: 0.0014 memory: 631 +2024/10/26 20:54:31 - mmengine - INFO - Epoch(val) [5][1250/1250] eta: 0:00:00 time: 0.0659 data_time: 0.0015 memory: 636 +2024/10/26 20:54:39 - mmengine - INFO - Evaluating bbox... +2024/10/26 20:55:29 - mmengine - INFO - bbox_mAP_copypaste: 0.297 0.475 0.319 0.154 0.330 0.404 +2024/10/26 20:55:30 - mmengine - INFO - Epoch(val) [5][1250/1250] coco/bbox_mAP: 0.2970 coco/bbox_mAP_50: 0.4750 coco/bbox_mAP_75: 0.3190 coco/bbox_mAP_s: 0.1540 coco/bbox_mAP_m: 0.3300 coco/bbox_mAP_l: 0.4040 data_time: 0.0015 time: 0.0661 +2024/10/26 20:55:43 - mmengine - INFO - Epoch(train) [6][ 50/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:06:50 time: 0.2582 data_time: 0.0073 memory: 5136 grad_norm: 3.0315 loss: 0.5981 loss_cls: 0.3026 loss_bbox: 0.2955 +2024/10/26 20:56:12 - mmengine - INFO - Epoch(train) [6][ 100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:06:30 time: 0.5740 data_time: 0.0080 memory: 5133 grad_norm: 3.0116 loss: 0.5876 loss_cls: 0.2942 loss_bbox: 0.2933 +2024/10/26 20:56:45 - mmengine - INFO - Epoch(train) [6][ 150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:06:16 time: 0.6536 data_time: 0.0153 memory: 5136 grad_norm: 2.8460 loss: 0.5645 loss_cls: 0.2911 loss_bbox: 0.2734 +2024/10/26 20:57:14 - mmengine - INFO - Epoch(train) [6][ 200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:05:56 time: 0.5779 data_time: 0.0089 memory: 5132 grad_norm: 2.7249 loss: 0.5767 loss_cls: 0.2795 loss_bbox: 0.2972 +2024/10/26 20:57:46 - mmengine - INFO - Epoch(train) [6][ 250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:05:41 time: 0.6490 data_time: 0.0080 memory: 5135 grad_norm: 2.8382 loss: 0.5846 loss_cls: 0.2867 loss_bbox: 0.2979 +2024/10/26 20:58:14 - mmengine - INFO - Epoch(train) [6][ 300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:05:21 time: 0.5643 data_time: 0.0103 memory: 5135 grad_norm: 2.8363 loss: 0.5805 loss_cls: 0.2922 loss_bbox: 0.2883 +2024/10/26 20:58:45 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 20:58:45 - mmengine - INFO - Epoch(train) [6][ 350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:05:04 time: 0.6233 data_time: 0.0086 memory: 5135 grad_norm: 2.9666 loss: 0.5783 loss_cls: 0.2923 loss_bbox: 0.2861 +2024/10/26 20:59:14 - mmengine - INFO - Epoch(train) [6][ 400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:04:44 time: 0.5680 data_time: 0.0150 memory: 5135 grad_norm: 2.8440 loss: 0.5622 loss_cls: 0.2748 loss_bbox: 0.2874 +2024/10/26 20:59:45 - mmengine - INFO - Epoch(train) [6][ 450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:04:27 time: 0.6245 data_time: 0.0155 memory: 5134 grad_norm: 2.8458 loss: 0.5620 loss_cls: 0.2796 loss_bbox: 0.2824 +2024/10/26 21:00:15 - mmengine - INFO - Epoch(train) [6][ 500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:04:09 time: 0.5934 data_time: 0.0107 memory: 5136 grad_norm: 3.0385 loss: 0.5832 loss_cls: 0.2892 loss_bbox: 0.2940 +2024/10/26 21:00:48 - mmengine - INFO - Epoch(train) [6][ 550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:03:54 time: 0.6570 data_time: 0.0129 memory: 5133 grad_norm: 3.1203 loss: 0.5918 loss_cls: 0.2970 loss_bbox: 0.2948 +2024/10/26 21:01:14 - mmengine - INFO - Epoch(train) [6][ 600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:03:32 time: 0.5371 data_time: 0.0084 memory: 5134 grad_norm: 2.9375 loss: 0.5580 loss_cls: 0.2781 loss_bbox: 0.2799 +2024/10/26 21:01:48 - mmengine - INFO - Epoch(train) [6][ 650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:03:18 time: 0.6627 data_time: 0.0122 memory: 5135 grad_norm: 3.0704 loss: 0.5377 loss_cls: 0.2630 loss_bbox: 0.2747 +2024/10/26 21:02:16 - mmengine - INFO - Epoch(train) [6][ 700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:02:57 time: 0.5701 data_time: 0.0083 memory: 5134 grad_norm: 2.9628 loss: 0.5451 loss_cls: 0.2762 loss_bbox: 0.2689 +2024/10/26 21:02:48 - mmengine - INFO - Epoch(train) [6][ 750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:02:41 time: 0.6314 data_time: 0.0082 memory: 5137 grad_norm: 3.0441 loss: 0.5726 loss_cls: 0.2842 loss_bbox: 0.2885 +2024/10/26 21:03:18 - mmengine - INFO - Epoch(train) [6][ 800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:02:23 time: 0.6022 data_time: 0.0083 memory: 5134 grad_norm: 2.9269 loss: 0.5901 loss_cls: 0.3043 loss_bbox: 0.2857 +2024/10/26 21:03:50 - mmengine - INFO - Epoch(train) [6][ 850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:02:07 time: 0.6462 data_time: 0.0085 memory: 5136 grad_norm: 3.1245 loss: 0.5633 loss_cls: 0.2744 loss_bbox: 0.2889 +2024/10/26 21:04:18 - mmengine - INFO - Epoch(train) [6][ 900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:01:47 time: 0.5689 data_time: 0.0200 memory: 5134 grad_norm: 3.0486 loss: 0.5811 loss_cls: 0.2922 loss_bbox: 0.2889 +2024/10/26 21:04:50 - mmengine - INFO - Epoch(train) [6][ 950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:01:30 time: 0.6310 data_time: 0.0085 memory: 5134 grad_norm: 2.8379 loss: 0.5804 loss_cls: 0.2944 loss_bbox: 0.2860 +2024/10/26 21:05:21 - mmengine - INFO - Epoch(train) [6][1000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:01:12 time: 0.6108 data_time: 0.0133 memory: 5133 grad_norm: 2.7367 loss: 0.5523 loss_cls: 0.2691 loss_bbox: 0.2831 +2024/10/26 21:05:53 - mmengine - INFO - Epoch(train) [6][1050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:00:57 time: 0.6482 data_time: 0.0088 memory: 5132 grad_norm: 2.9303 loss: 0.5837 loss_cls: 0.2986 loss_bbox: 0.2852 +2024/10/26 21:06:24 - mmengine - INFO - Epoch(train) [6][1100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:00:40 time: 0.6194 data_time: 0.0110 memory: 5134 grad_norm: 2.8570 loss: 0.5631 loss_cls: 0.2836 loss_bbox: 0.2795 +2024/10/26 21:06:57 - mmengine - INFO - Epoch(train) [6][1150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:00:25 time: 0.6687 data_time: 0.0083 memory: 5134 grad_norm: 3.0348 loss: 0.5725 loss_cls: 0.2852 loss_bbox: 0.2873 +2024/10/26 21:07:29 - mmengine - INFO - Epoch(train) [6][1200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 7:00:08 time: 0.6232 data_time: 0.0223 memory: 5135 grad_norm: 2.7856 loss: 0.5563 loss_cls: 0.2776 loss_bbox: 0.2787 +2024/10/26 21:08:02 - mmengine - INFO - Epoch(train) [6][1250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:59:54 time: 0.6637 data_time: 0.0087 memory: 5134 grad_norm: 3.3538 loss: 0.6177 loss_cls: 0.3181 loss_bbox: 0.2995 +2024/10/26 21:08:37 - mmengine - INFO - Epoch(train) [6][1300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:59:42 time: 0.7012 data_time: 0.0839 memory: 5132 grad_norm: 2.9783 loss: 0.5863 loss_cls: 0.2946 loss_bbox: 0.2918 +2024/10/26 21:09:08 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 21:09:08 - mmengine - INFO - Epoch(train) [6][1350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:59:25 time: 0.6306 data_time: 0.0090 memory: 5133 grad_norm: 3.0516 loss: 0.5774 loss_cls: 0.2810 loss_bbox: 0.2964 +2024/10/26 21:09:36 - mmengine - INFO - Epoch(train) [6][1400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:59:03 time: 0.5610 data_time: 0.0090 memory: 5136 grad_norm: 2.8033 loss: 0.5611 loss_cls: 0.2777 loss_bbox: 0.2834 +2024/10/26 21:10:07 - mmengine - INFO - Epoch(train) [6][1450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:58:45 time: 0.6136 data_time: 0.0089 memory: 5134 grad_norm: 2.8991 loss: 0.5760 loss_cls: 0.2897 loss_bbox: 0.2863 +2024/10/26 21:10:39 - mmengine - INFO - Epoch(train) [6][1500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:58:29 time: 0.6356 data_time: 0.0081 memory: 5134 grad_norm: 2.8895 loss: 0.5683 loss_cls: 0.2822 loss_bbox: 0.2861 +2024/10/26 21:11:11 - mmengine - INFO - Epoch(train) [6][1550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:58:12 time: 0.6335 data_time: 0.0083 memory: 5134 grad_norm: 2.8784 loss: 0.5928 loss_cls: 0.2993 loss_bbox: 0.2935 +2024/10/26 21:11:40 - mmengine - INFO - Epoch(train) [6][1600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:57:53 time: 0.5931 data_time: 0.0084 memory: 5137 grad_norm: 2.9061 loss: 0.5765 loss_cls: 0.2863 loss_bbox: 0.2903 +2024/10/26 21:12:10 - mmengine - INFO - Epoch(train) [6][1650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:57:33 time: 0.5941 data_time: 0.0088 memory: 5133 grad_norm: 3.0701 loss: 0.5914 loss_cls: 0.3023 loss_bbox: 0.2891 +2024/10/26 21:12:42 - mmengine - INFO - Epoch(train) [6][1700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:57:17 time: 0.6385 data_time: 0.0162 memory: 5134 grad_norm: 2.8541 loss: 0.5979 loss_cls: 0.3040 loss_bbox: 0.2938 +2024/10/26 21:13:12 - mmengine - INFO - Epoch(train) [6][1750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:56:58 time: 0.6128 data_time: 0.0085 memory: 5133 grad_norm: 2.8755 loss: 0.5905 loss_cls: 0.2944 loss_bbox: 0.2961 +2024/10/26 21:13:45 - mmengine - INFO - Epoch(train) [6][1800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:56:43 time: 0.6557 data_time: 0.0086 memory: 5134 grad_norm: 3.0232 loss: 0.5621 loss_cls: 0.2817 loss_bbox: 0.2804 +2024/10/26 21:14:15 - mmengine - INFO - Epoch(train) [6][1850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:56:23 time: 0.5957 data_time: 0.0117 memory: 5134 grad_norm: 3.0257 loss: 0.5467 loss_cls: 0.2743 loss_bbox: 0.2724 +2024/10/26 21:14:48 - mmengine - INFO - Epoch(train) [6][1900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:56:08 time: 0.6592 data_time: 0.0190 memory: 5135 grad_norm: 3.0244 loss: 0.6004 loss_cls: 0.3049 loss_bbox: 0.2954 +2024/10/26 21:15:17 - mmengine - INFO - Epoch(train) [6][1950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:55:47 time: 0.5737 data_time: 0.0090 memory: 5134 grad_norm: 3.0273 loss: 0.5621 loss_cls: 0.2831 loss_bbox: 0.2790 +2024/10/26 21:15:48 - mmengine - INFO - Epoch(train) [6][2000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:55:30 time: 0.6350 data_time: 0.0089 memory: 5136 grad_norm: 2.9653 loss: 0.5644 loss_cls: 0.2835 loss_bbox: 0.2810 +2024/10/26 21:16:19 - mmengine - INFO - Epoch(train) [6][2050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:55:12 time: 0.6129 data_time: 0.0086 memory: 5134 grad_norm: 2.8597 loss: 0.5564 loss_cls: 0.2747 loss_bbox: 0.2817 +2024/10/26 21:16:51 - mmengine - INFO - Epoch(train) [6][2100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:54:55 time: 0.6384 data_time: 0.0101 memory: 5136 grad_norm: 2.8983 loss: 0.5710 loss_cls: 0.2860 loss_bbox: 0.2850 +2024/10/26 21:17:21 - mmengine - INFO - Epoch(train) [6][2150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:54:36 time: 0.6022 data_time: 0.0091 memory: 5136 grad_norm: 2.7435 loss: 0.5637 loss_cls: 0.2718 loss_bbox: 0.2919 +2024/10/26 21:17:53 - mmengine - INFO - Epoch(train) [6][2200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:54:18 time: 0.6305 data_time: 0.0155 memory: 5135 grad_norm: 2.8567 loss: 0.5819 loss_cls: 0.2940 loss_bbox: 0.2879 +2024/10/26 21:18:22 - mmengine - INFO - Epoch(train) [6][2250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:53:58 time: 0.5894 data_time: 0.0090 memory: 5137 grad_norm: 3.4348 loss: 0.5723 loss_cls: 0.2930 loss_bbox: 0.2793 +2024/10/26 21:18:54 - mmengine - INFO - Epoch(train) [6][2300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:53:40 time: 0.6293 data_time: 0.0186 memory: 5134 grad_norm: 3.1261 loss: 0.5624 loss_cls: 0.2836 loss_bbox: 0.2788 +2024/10/26 21:19:22 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 21:19:22 - mmengine - INFO - Epoch(train) [6][2350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:53:19 time: 0.5625 data_time: 0.0106 memory: 5134 grad_norm: 3.0033 loss: 0.5767 loss_cls: 0.2825 loss_bbox: 0.2942 +2024/10/26 21:19:55 - mmengine - INFO - Epoch(train) [6][2400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:53:03 time: 0.6636 data_time: 0.0089 memory: 5134 grad_norm: 2.7960 loss: 0.5702 loss_cls: 0.2885 loss_bbox: 0.2817 +2024/10/26 21:20:25 - mmengine - INFO - Epoch(train) [6][2450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:52:44 time: 0.6006 data_time: 0.0093 memory: 5135 grad_norm: 3.0180 loss: 0.6065 loss_cls: 0.3047 loss_bbox: 0.3018 +2024/10/26 21:20:57 - mmengine - INFO - Epoch(train) [6][2500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:52:27 time: 0.6451 data_time: 0.0114 memory: 5136 grad_norm: 3.1571 loss: 0.5949 loss_cls: 0.2936 loss_bbox: 0.3013 +2024/10/26 21:21:26 - mmengine - INFO - Epoch(train) [6][2550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:52:06 time: 0.5696 data_time: 0.0091 memory: 5134 grad_norm: 2.9501 loss: 0.5785 loss_cls: 0.2891 loss_bbox: 0.2894 +2024/10/26 21:21:59 - mmengine - INFO - Epoch(train) [6][2600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:51:50 time: 0.6609 data_time: 0.0087 memory: 5134 grad_norm: 2.9161 loss: 0.5703 loss_cls: 0.2844 loss_bbox: 0.2859 +2024/10/26 21:22:32 - mmengine - INFO - Epoch(train) [6][2650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:51:34 time: 0.6570 data_time: 0.0088 memory: 5136 grad_norm: 2.9121 loss: 0.5607 loss_cls: 0.2735 loss_bbox: 0.2872 +2024/10/26 21:23:04 - mmengine - INFO - Epoch(train) [6][2700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:51:17 time: 0.6475 data_time: 0.0090 memory: 5133 grad_norm: 3.0940 loss: 0.5892 loss_cls: 0.2904 loss_bbox: 0.2988 +2024/10/26 21:23:35 - mmengine - INFO - Epoch(train) [6][2750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:50:59 time: 0.6302 data_time: 0.0353 memory: 5134 grad_norm: 2.9082 loss: 0.5743 loss_cls: 0.2925 loss_bbox: 0.2818 +2024/10/26 21:24:07 - mmengine - INFO - Epoch(train) [6][2800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:50:42 time: 0.6390 data_time: 0.0085 memory: 5134 grad_norm: 2.8013 loss: 0.5869 loss_cls: 0.2891 loss_bbox: 0.2978 +2024/10/26 21:24:39 - mmengine - INFO - Epoch(train) [6][2850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:50:24 time: 0.6259 data_time: 0.0087 memory: 5135 grad_norm: 2.9395 loss: 0.5931 loss_cls: 0.2951 loss_bbox: 0.2980 +2024/10/26 21:25:10 - mmengine - INFO - Epoch(train) [6][2900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:50:05 time: 0.6248 data_time: 0.0084 memory: 5134 grad_norm: 3.1315 loss: 0.5689 loss_cls: 0.2807 loss_bbox: 0.2882 +2024/10/26 21:25:43 - mmengine - INFO - Epoch(train) [6][2950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:49:49 time: 0.6558 data_time: 0.0086 memory: 5136 grad_norm: 2.9509 loss: 0.5731 loss_cls: 0.2845 loss_bbox: 0.2886 +2024/10/26 21:26:13 - mmengine - INFO - Epoch(train) [6][3000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:49:29 time: 0.6001 data_time: 0.0086 memory: 5134 grad_norm: 2.8183 loss: 0.5685 loss_cls: 0.2824 loss_bbox: 0.2861 +2024/10/26 21:26:46 - mmengine - INFO - Epoch(train) [6][3050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:49:13 time: 0.6668 data_time: 0.0087 memory: 5135 grad_norm: 2.8130 loss: 0.5895 loss_cls: 0.3069 loss_bbox: 0.2826 +2024/10/26 21:27:15 - mmengine - INFO - Epoch(train) [6][3100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:48:52 time: 0.5795 data_time: 0.0086 memory: 5137 grad_norm: 2.8867 loss: 0.5570 loss_cls: 0.2806 loss_bbox: 0.2764 +2024/10/26 21:27:47 - mmengine - INFO - Epoch(train) [6][3150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:48:35 time: 0.6442 data_time: 0.0097 memory: 5134 grad_norm: 2.8036 loss: 0.5593 loss_cls: 0.2772 loss_bbox: 0.2821 +2024/10/26 21:28:17 - mmengine - INFO - Epoch(train) [6][3200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:48:14 time: 0.5840 data_time: 0.0091 memory: 5134 grad_norm: 2.9299 loss: 0.6188 loss_cls: 0.3175 loss_bbox: 0.3012 +2024/10/26 21:28:47 - mmengine - INFO - Epoch(train) [6][3250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:47:54 time: 0.6011 data_time: 0.0087 memory: 5134 grad_norm: 2.8263 loss: 0.5829 loss_cls: 0.2872 loss_bbox: 0.2957 +2024/10/26 21:29:14 - mmengine - INFO - Epoch(train) [6][3300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:47:31 time: 0.5532 data_time: 0.0090 memory: 5133 grad_norm: 2.8666 loss: 0.6069 loss_cls: 0.3042 loss_bbox: 0.3028 +2024/10/26 21:29:47 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 21:29:47 - mmengine - INFO - Epoch(train) [6][3350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:47:15 time: 0.6566 data_time: 0.0085 memory: 5137 grad_norm: 2.7816 loss: 0.5571 loss_cls: 0.2662 loss_bbox: 0.2909 +2024/10/26 21:30:15 - mmengine - INFO - Epoch(train) [6][3400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:46:52 time: 0.5529 data_time: 0.0089 memory: 5134 grad_norm: 2.9203 loss: 0.5911 loss_cls: 0.3024 loss_bbox: 0.2887 +2024/10/26 21:30:46 - mmengine - INFO - Epoch(train) [6][3450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:46:34 time: 0.6355 data_time: 0.0230 memory: 5135 grad_norm: 2.7597 loss: 0.5484 loss_cls: 0.2692 loss_bbox: 0.2792 +2024/10/26 21:31:17 - mmengine - INFO - Epoch(train) [6][3500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:46:14 time: 0.6026 data_time: 0.0087 memory: 5134 grad_norm: 3.0030 loss: 0.5816 loss_cls: 0.2884 loss_bbox: 0.2933 +2024/10/26 21:31:50 - mmengine - INFO - Epoch(train) [6][3550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:45:57 time: 0.6599 data_time: 0.0084 memory: 5130 grad_norm: 3.1239 loss: 0.5561 loss_cls: 0.2810 loss_bbox: 0.2751 +2024/10/26 21:32:20 - mmengine - INFO - Epoch(train) [6][3600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:45:38 time: 0.6099 data_time: 0.0087 memory: 5136 grad_norm: 2.8471 loss: 0.5900 loss_cls: 0.2898 loss_bbox: 0.3002 +2024/10/26 21:32:53 - mmengine - INFO - Epoch(train) [6][3650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:45:21 time: 0.6571 data_time: 0.0087 memory: 5137 grad_norm: 2.9700 loss: 0.5919 loss_cls: 0.3020 loss_bbox: 0.2899 +2024/10/26 21:33:22 - mmengine - INFO - Epoch(train) [6][3700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:44:59 time: 0.5722 data_time: 0.0102 memory: 5136 grad_norm: 2.8496 loss: 0.5723 loss_cls: 0.2890 loss_bbox: 0.2833 +2024/10/26 21:33:52 - mmengine - INFO - Epoch(train) [6][3750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:44:39 time: 0.6100 data_time: 0.0093 memory: 5135 grad_norm: 2.8042 loss: 0.5696 loss_cls: 0.2789 loss_bbox: 0.2907 +2024/10/26 21:34:22 - mmengine - INFO - Epoch(train) [6][3800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:44:18 time: 0.5897 data_time: 0.0096 memory: 5134 grad_norm: 2.8219 loss: 0.5971 loss_cls: 0.3041 loss_bbox: 0.2930 +2024/10/26 21:34:55 - mmengine - INFO - Epoch(train) [6][3850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:44:02 time: 0.6583 data_time: 0.0091 memory: 5134 grad_norm: 3.1319 loss: 0.5674 loss_cls: 0.2746 loss_bbox: 0.2928 +2024/10/26 21:35:23 - mmengine - INFO - Epoch(train) [6][3900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:43:39 time: 0.5615 data_time: 0.0101 memory: 5132 grad_norm: 2.8602 loss: 0.5792 loss_cls: 0.2911 loss_bbox: 0.2882 +2024/10/26 21:35:55 - mmengine - INFO - Epoch(train) [6][3950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:43:21 time: 0.6407 data_time: 0.0093 memory: 5134 grad_norm: 2.9632 loss: 0.5441 loss_cls: 0.2604 loss_bbox: 0.2836 +2024/10/26 21:36:26 - mmengine - INFO - Epoch(train) [6][4000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:43:02 time: 0.6234 data_time: 0.0093 memory: 5133 grad_norm: 2.7964 loss: 0.5685 loss_cls: 0.2871 loss_bbox: 0.2814 +2024/10/26 21:36:59 - mmengine - INFO - Epoch(train) [6][4050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:42:45 time: 0.6595 data_time: 0.0097 memory: 5135 grad_norm: 2.8380 loss: 0.5783 loss_cls: 0.2942 loss_bbox: 0.2840 +2024/10/26 21:37:27 - mmengine - INFO - Epoch(train) [6][4100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:42:22 time: 0.5580 data_time: 0.0098 memory: 5140 grad_norm: 2.8794 loss: 0.5917 loss_cls: 0.2999 loss_bbox: 0.2918 +2024/10/26 21:37:59 - mmengine - INFO - Epoch(train) [6][4150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:42:05 time: 0.6544 data_time: 0.0088 memory: 5136 grad_norm: 3.1379 loss: 0.6089 loss_cls: 0.3066 loss_bbox: 0.3023 +2024/10/26 21:38:30 - mmengine - INFO - Epoch(train) [6][4200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:41:45 time: 0.6068 data_time: 0.0089 memory: 5133 grad_norm: 2.8780 loss: 0.5777 loss_cls: 0.2907 loss_bbox: 0.2870 +2024/10/26 21:39:02 - mmengine - INFO - Epoch(train) [6][4250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:41:27 time: 0.6473 data_time: 0.0089 memory: 5136 grad_norm: 2.8657 loss: 0.5540 loss_cls: 0.2728 loss_bbox: 0.2812 +2024/10/26 21:39:36 - mmengine - INFO - Epoch(train) [6][4300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:41:11 time: 0.6858 data_time: 0.0928 memory: 5134 grad_norm: 3.0204 loss: 0.5761 loss_cls: 0.2883 loss_bbox: 0.2878 +2024/10/26 21:40:09 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 21:40:09 - mmengine - INFO - Epoch(train) [6][4350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:40:54 time: 0.6492 data_time: 0.0101 memory: 5133 grad_norm: 3.0050 loss: 0.5957 loss_cls: 0.3000 loss_bbox: 0.2957 +2024/10/26 21:40:41 - mmengine - INFO - Epoch(train) [6][4400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:40:36 time: 0.6519 data_time: 0.0108 memory: 5134 grad_norm: 3.0275 loss: 0.6049 loss_cls: 0.3050 loss_bbox: 0.2999 +2024/10/26 21:41:12 - mmengine - INFO - Epoch(train) [6][4450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:40:16 time: 0.6146 data_time: 0.0085 memory: 5134 grad_norm: 2.9940 loss: 0.5691 loss_cls: 0.2783 loss_bbox: 0.2908 +2024/10/26 21:41:43 - mmengine - INFO - Epoch(train) [6][4500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:39:56 time: 0.6109 data_time: 0.0097 memory: 5138 grad_norm: 2.7592 loss: 0.5782 loss_cls: 0.2785 loss_bbox: 0.2997 +2024/10/26 21:42:12 - mmengine - INFO - Epoch(train) [6][4550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:39:34 time: 0.5759 data_time: 0.0087 memory: 5132 grad_norm: 2.8409 loss: 0.5953 loss_cls: 0.3104 loss_bbox: 0.2849 +2024/10/26 21:42:45 - mmengine - INFO - Epoch(train) [6][4600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:39:17 time: 0.6718 data_time: 0.0086 memory: 5137 grad_norm: 3.0260 loss: 0.6030 loss_cls: 0.3126 loss_bbox: 0.2904 +2024/10/26 21:43:15 - mmengine - INFO - Epoch(train) [6][4650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:38:56 time: 0.5934 data_time: 0.0159 memory: 5134 grad_norm: 3.0201 loss: 0.5962 loss_cls: 0.3024 loss_bbox: 0.2939 +2024/10/26 21:43:48 - mmengine - INFO - Epoch(train) [6][4700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:38:39 time: 0.6648 data_time: 0.0185 memory: 5135 grad_norm: 2.8168 loss: 0.5813 loss_cls: 0.2934 loss_bbox: 0.2880 +2024/10/26 21:44:17 - mmengine - INFO - Epoch(train) [6][4750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:38:17 time: 0.5763 data_time: 0.0085 memory: 5135 grad_norm: 3.0144 loss: 0.5647 loss_cls: 0.2794 loss_bbox: 0.2853 +2024/10/26 21:44:50 - mmengine - INFO - Epoch(train) [6][4800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:38:00 time: 0.6610 data_time: 0.0084 memory: 5133 grad_norm: 2.9746 loss: 0.5711 loss_cls: 0.2860 loss_bbox: 0.2851 +2024/10/26 21:45:16 - mmengine - INFO - Epoch(train) [6][4850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:37:34 time: 0.5117 data_time: 0.0086 memory: 5133 grad_norm: 2.8194 loss: 0.5797 loss_cls: 0.2857 loss_bbox: 0.2940 +2024/10/26 21:45:47 - mmengine - INFO - Epoch(train) [6][4900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:37:15 time: 0.6282 data_time: 0.0086 memory: 5135 grad_norm: 2.9209 loss: 0.5439 loss_cls: 0.2678 loss_bbox: 0.2762 +2024/10/26 21:46:16 - mmengine - INFO - Epoch(train) [6][4950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:36:53 time: 0.5850 data_time: 0.0089 memory: 5133 grad_norm: 2.8447 loss: 0.5509 loss_cls: 0.2691 loss_bbox: 0.2818 +2024/10/26 21:46:49 - mmengine - INFO - Epoch(train) [6][5000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:36:35 time: 0.6544 data_time: 0.0116 memory: 5135 grad_norm: 2.9520 loss: 0.5964 loss_cls: 0.2999 loss_bbox: 0.2965 +2024/10/26 21:47:18 - mmengine - INFO - Epoch(train) [6][5050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:36:13 time: 0.5789 data_time: 0.0131 memory: 5132 grad_norm: 2.8886 loss: 0.5959 loss_cls: 0.3022 loss_bbox: 0.2936 +2024/10/26 21:47:50 - mmengine - INFO - Epoch(train) [6][5100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:35:55 time: 0.6476 data_time: 0.0236 memory: 5135 grad_norm: 2.9737 loss: 0.5770 loss_cls: 0.2878 loss_bbox: 0.2892 +2024/10/26 21:48:19 - mmengine - INFO - Epoch(train) [6][5150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:35:32 time: 0.5673 data_time: 0.0104 memory: 5135 grad_norm: 2.7465 loss: 0.5903 loss_cls: 0.2971 loss_bbox: 0.2932 +2024/10/26 21:48:50 - mmengine - INFO - Epoch(train) [6][5200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:35:13 time: 0.6281 data_time: 0.0164 memory: 5136 grad_norm: 2.6336 loss: 0.5309 loss_cls: 0.2614 loss_bbox: 0.2695 +2024/10/26 21:49:19 - mmengine - INFO - Epoch(train) [6][5250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:34:50 time: 0.5779 data_time: 0.0108 memory: 5135 grad_norm: 2.9335 loss: 0.5956 loss_cls: 0.2983 loss_bbox: 0.2973 +2024/10/26 21:49:51 - mmengine - INFO - Epoch(train) [6][5300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:34:31 time: 0.6341 data_time: 0.0134 memory: 5134 grad_norm: 2.8508 loss: 0.5594 loss_cls: 0.2758 loss_bbox: 0.2836 +2024/10/26 21:50:21 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 21:50:21 - mmengine - INFO - Epoch(train) [6][5350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:34:10 time: 0.5995 data_time: 0.0155 memory: 5136 grad_norm: 2.8886 loss: 0.5996 loss_cls: 0.3040 loss_bbox: 0.2956 +2024/10/26 21:50:53 - mmengine - INFO - Epoch(train) [6][5400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:33:52 time: 0.6516 data_time: 0.0112 memory: 5134 grad_norm: 2.7093 loss: 0.5732 loss_cls: 0.2818 loss_bbox: 0.2914 +2024/10/26 21:51:23 - mmengine - INFO - Epoch(train) [6][5450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:33:31 time: 0.6054 data_time: 0.0136 memory: 5133 grad_norm: 3.0044 loss: 0.5960 loss_cls: 0.2945 loss_bbox: 0.3015 +2024/10/26 21:51:57 - mmengine - INFO - Epoch(train) [6][5500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:33:14 time: 0.6788 data_time: 0.0109 memory: 5137 grad_norm: 3.1273 loss: 0.5756 loss_cls: 0.2831 loss_bbox: 0.2924 +2024/10/26 21:52:26 - mmengine - INFO - Epoch(train) [6][5550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:32:52 time: 0.5792 data_time: 0.0212 memory: 5135 grad_norm: 2.8772 loss: 0.5812 loss_cls: 0.2947 loss_bbox: 0.2866 +2024/10/26 21:52:59 - mmengine - INFO - Epoch(train) [6][5600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:32:34 time: 0.6576 data_time: 0.0113 memory: 5133 grad_norm: 3.0868 loss: 0.5598 loss_cls: 0.2820 loss_bbox: 0.2778 +2024/10/26 21:53:27 - mmengine - INFO - Epoch(train) [6][5650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:32:11 time: 0.5636 data_time: 0.0099 memory: 5134 grad_norm: 2.9835 loss: 0.5881 loss_cls: 0.3019 loss_bbox: 0.2862 +2024/10/26 21:54:00 - mmengine - INFO - Epoch(train) [6][5700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:31:53 time: 0.6557 data_time: 0.0131 memory: 5136 grad_norm: 3.0035 loss: 0.5622 loss_cls: 0.2824 loss_bbox: 0.2798 +2024/10/26 21:54:28 - mmengine - INFO - Epoch(train) [6][5750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:31:29 time: 0.5649 data_time: 0.0083 memory: 5134 grad_norm: 2.9634 loss: 0.5730 loss_cls: 0.2862 loss_bbox: 0.2869 +2024/10/26 21:55:01 - mmengine - INFO - Epoch(train) [6][5800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:31:10 time: 0.6414 data_time: 0.0094 memory: 5135 grad_norm: 2.7946 loss: 0.5523 loss_cls: 0.2751 loss_bbox: 0.2772 +2024/10/26 21:55:31 - mmengine - INFO - Epoch(train) [6][5850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:30:49 time: 0.6048 data_time: 0.0101 memory: 5134 grad_norm: 2.7109 loss: 0.5815 loss_cls: 0.2958 loss_bbox: 0.2857 +2024/10/26 21:56:03 - mmengine - INFO - Epoch(train) [6][5900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:30:30 time: 0.6352 data_time: 0.0113 memory: 5134 grad_norm: 2.8466 loss: 0.5871 loss_cls: 0.2868 loss_bbox: 0.3003 +2024/10/26 21:56:35 - mmengine - INFO - Epoch(train) [6][5950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:30:11 time: 0.6424 data_time: 0.0778 memory: 5133 grad_norm: 2.9583 loss: 0.5783 loss_cls: 0.2910 loss_bbox: 0.2873 +2024/10/26 21:57:07 - mmengine - INFO - Epoch(train) [6][6000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:29:52 time: 0.6466 data_time: 0.0091 memory: 5135 grad_norm: 2.9322 loss: 0.5867 loss_cls: 0.2916 loss_bbox: 0.2952 +2024/10/26 21:57:37 - mmengine - INFO - Epoch(train) [6][6050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:29:31 time: 0.5999 data_time: 0.0087 memory: 5135 grad_norm: 2.8280 loss: 0.5757 loss_cls: 0.2891 loss_bbox: 0.2866 +2024/10/26 21:58:08 - mmengine - INFO - Epoch(train) [6][6100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:29:10 time: 0.6109 data_time: 0.0087 memory: 5136 grad_norm: 2.8692 loss: 0.5819 loss_cls: 0.2911 loss_bbox: 0.2909 +2024/10/26 21:58:39 - mmengine - INFO - Epoch(train) [6][6150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:28:50 time: 0.6236 data_time: 0.0154 memory: 5134 grad_norm: 2.9763 loss: 0.5784 loss_cls: 0.2926 loss_bbox: 0.2858 +2024/10/26 21:59:10 - mmengine - INFO - Epoch(train) [6][6200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:28:29 time: 0.6208 data_time: 0.0130 memory: 5134 grad_norm: 2.7273 loss: 0.5818 loss_cls: 0.2932 loss_bbox: 0.2886 +2024/10/26 21:59:41 - mmengine - INFO - Epoch(train) [6][6250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:28:09 time: 0.6201 data_time: 0.0087 memory: 5133 grad_norm: 2.7967 loss: 0.5672 loss_cls: 0.2822 loss_bbox: 0.2850 +2024/10/26 22:00:12 - mmengine - INFO - Epoch(train) [6][6300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:27:48 time: 0.6173 data_time: 0.0105 memory: 5135 grad_norm: 2.8074 loss: 0.5860 loss_cls: 0.2959 loss_bbox: 0.2901 +2024/10/26 22:00:45 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 22:00:45 - mmengine - INFO - Epoch(train) [6][6350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:27:30 time: 0.6571 data_time: 0.0101 memory: 5135 grad_norm: 2.7382 loss: 0.5982 loss_cls: 0.2962 loss_bbox: 0.3019 +2024/10/26 22:01:14 - mmengine - INFO - Epoch(train) [6][6400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:27:08 time: 0.5851 data_time: 0.0105 memory: 5136 grad_norm: 2.9278 loss: 0.5717 loss_cls: 0.2918 loss_bbox: 0.2799 +2024/10/26 22:01:46 - mmengine - INFO - Epoch(train) [6][6450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:26:48 time: 0.6466 data_time: 0.0086 memory: 5137 grad_norm: 2.7783 loss: 0.6031 loss_cls: 0.3059 loss_bbox: 0.2972 +2024/10/26 22:02:15 - mmengine - INFO - Epoch(train) [6][6500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:26:26 time: 0.5809 data_time: 0.0131 memory: 5134 grad_norm: 2.8892 loss: 0.5923 loss_cls: 0.2950 loss_bbox: 0.2973 +2024/10/26 22:02:47 - mmengine - INFO - Epoch(train) [6][6550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:26:06 time: 0.6289 data_time: 0.0135 memory: 5134 grad_norm: 2.9588 loss: 0.5667 loss_cls: 0.2784 loss_bbox: 0.2883 +2024/10/26 22:03:16 - mmengine - INFO - Epoch(train) [6][6600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:25:43 time: 0.5801 data_time: 0.0090 memory: 5137 grad_norm: 3.0137 loss: 0.5988 loss_cls: 0.3023 loss_bbox: 0.2965 +2024/10/26 22:03:48 - mmengine - INFO - Epoch(train) [6][6650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:25:23 time: 0.6394 data_time: 0.0131 memory: 5135 grad_norm: 2.8753 loss: 0.5898 loss_cls: 0.2994 loss_bbox: 0.2903 +2024/10/26 22:04:17 - mmengine - INFO - Epoch(train) [6][6700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:25:02 time: 0.5948 data_time: 0.0129 memory: 5136 grad_norm: 2.8723 loss: 0.5473 loss_cls: 0.2692 loss_bbox: 0.2781 +2024/10/26 22:04:49 - mmengine - INFO - Epoch(train) [6][6750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:24:41 time: 0.6269 data_time: 0.0090 memory: 5135 grad_norm: 2.8323 loss: 0.6032 loss_cls: 0.3023 loss_bbox: 0.3010 +2024/10/26 22:05:18 - mmengine - INFO - Epoch(train) [6][6800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:24:19 time: 0.5918 data_time: 0.0097 memory: 5134 grad_norm: 2.9542 loss: 0.5735 loss_cls: 0.2964 loss_bbox: 0.2772 +2024/10/26 22:05:52 - mmengine - INFO - Epoch(train) [6][6850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:24:01 time: 0.6696 data_time: 0.0122 memory: 5135 grad_norm: 2.8746 loss: 0.5872 loss_cls: 0.3001 loss_bbox: 0.2871 +2024/10/26 22:06:22 - mmengine - INFO - Epoch(train) [6][6900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:23:40 time: 0.6067 data_time: 0.0139 memory: 5136 grad_norm: 2.7996 loss: 0.6114 loss_cls: 0.3184 loss_bbox: 0.2931 +2024/10/26 22:06:53 - mmengine - INFO - Epoch(train) [6][6950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:23:19 time: 0.6135 data_time: 0.0091 memory: 5134 grad_norm: 2.7501 loss: 0.5971 loss_cls: 0.3002 loss_bbox: 0.2969 +2024/10/26 22:07:20 - mmengine - INFO - Epoch(train) [6][7000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:22:53 time: 0.5352 data_time: 0.0146 memory: 5132 grad_norm: 2.7585 loss: 0.5798 loss_cls: 0.2985 loss_bbox: 0.2812 +2024/10/26 22:07:52 - mmengine - INFO - Epoch(train) [6][7050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:22:34 time: 0.6432 data_time: 0.0204 memory: 5133 grad_norm: 2.7753 loss: 0.5517 loss_cls: 0.2728 loss_bbox: 0.2789 +2024/10/26 22:08:22 - mmengine - INFO - Epoch(train) [6][7100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:22:12 time: 0.6023 data_time: 0.0104 memory: 5138 grad_norm: 2.8481 loss: 0.5694 loss_cls: 0.2788 loss_bbox: 0.2907 +2024/10/26 22:08:55 - mmengine - INFO - Epoch(train) [6][7150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:21:53 time: 0.6571 data_time: 0.0105 memory: 5135 grad_norm: 2.9155 loss: 0.5791 loss_cls: 0.2975 loss_bbox: 0.2816 +2024/10/26 22:09:20 - mmengine - INFO - Epoch(train) [6][7200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:21:26 time: 0.4993 data_time: 0.0106 memory: 5136 grad_norm: 2.7368 loss: 0.5830 loss_cls: 0.3007 loss_bbox: 0.2823 +2024/10/26 22:09:51 - mmengine - INFO - Epoch(train) [6][7250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:21:06 time: 0.6240 data_time: 0.0100 memory: 5134 grad_norm: 2.9499 loss: 0.5880 loss_cls: 0.3015 loss_bbox: 0.2865 +2024/10/26 22:10:20 - mmengine - INFO - Epoch(train) [6][7300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:20:43 time: 0.5862 data_time: 0.0095 memory: 5133 grad_norm: 2.7559 loss: 0.6091 loss_cls: 0.3099 loss_bbox: 0.2992 +2024/10/26 22:10:46 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 22:10:46 - mmengine - INFO - Saving checkpoint at 6 epochs +2024/10/26 22:10:57 - mmengine - INFO - Epoch(val) [6][ 50/1250] eta: 0:02:11 time: 0.1098 data_time: 0.0018 memory: 5133 +2024/10/26 22:11:02 - mmengine - INFO - Epoch(val) [6][ 100/1250] eta: 0:02:07 time: 0.1116 data_time: 0.0016 memory: 629 +2024/10/26 22:11:07 - mmengine - INFO - Epoch(val) [6][ 150/1250] eta: 0:01:59 time: 0.1054 data_time: 0.0015 memory: 635 +2024/10/26 22:11:12 - mmengine - INFO - Epoch(val) [6][ 200/1250] eta: 0:01:50 time: 0.0938 data_time: 0.0015 memory: 635 +2024/10/26 22:11:18 - mmengine - INFO - Epoch(val) [6][ 250/1250] eta: 0:01:46 time: 0.1096 data_time: 0.0015 memory: 626 +2024/10/26 22:11:23 - mmengine - INFO - Epoch(val) [6][ 300/1250] eta: 0:01:41 time: 0.1128 data_time: 0.0016 memory: 626 +2024/10/26 22:11:29 - mmengine - INFO - Epoch(val) [6][ 350/1250] eta: 0:01:36 time: 0.1111 data_time: 0.0015 memory: 626 +2024/10/26 22:11:35 - mmengine - INFO - Epoch(val) [6][ 400/1250] eta: 0:01:32 time: 0.1140 data_time: 0.0015 memory: 615 +2024/10/26 22:11:40 - mmengine - INFO - Epoch(val) [6][ 450/1250] eta: 0:01:27 time: 0.1162 data_time: 0.0015 memory: 635 +2024/10/26 22:11:46 - mmengine - INFO - Epoch(val) [6][ 500/1250] eta: 0:01:21 time: 0.1089 data_time: 0.0016 memory: 635 +2024/10/26 22:11:51 - mmengine - INFO - Epoch(val) [6][ 550/1250] eta: 0:01:16 time: 0.1062 data_time: 0.0016 memory: 615 +2024/10/26 22:11:57 - mmengine - INFO - Epoch(val) [6][ 600/1250] eta: 0:01:10 time: 0.1111 data_time: 0.0015 memory: 626 +2024/10/26 22:12:02 - mmengine - INFO - Epoch(val) [6][ 650/1250] eta: 0:01:05 time: 0.1130 data_time: 0.0015 memory: 626 +2024/10/26 22:12:07 - mmengine - INFO - Epoch(val) [6][ 700/1250] eta: 0:00:59 time: 0.1030 data_time: 0.0016 memory: 629 +2024/10/26 22:12:12 - mmengine - INFO - Epoch(val) [6][ 750/1250] eta: 0:00:54 time: 0.0960 data_time: 0.0016 memory: 629 +2024/10/26 22:12:18 - mmengine - INFO - Epoch(val) [6][ 800/1250] eta: 0:00:48 time: 0.1100 data_time: 0.0015 memory: 635 +2024/10/26 22:12:24 - mmengine - INFO - Epoch(val) [6][ 850/1250] eta: 0:00:43 time: 0.1146 data_time: 0.0015 memory: 635 +2024/10/26 22:12:29 - mmengine - INFO - Epoch(val) [6][ 900/1250] eta: 0:00:38 time: 0.1081 data_time: 0.0016 memory: 635 +2024/10/26 22:12:35 - mmengine - INFO - Epoch(val) [6][ 950/1250] eta: 0:00:32 time: 0.1131 data_time: 0.0016 memory: 626 +2024/10/26 22:12:40 - mmengine - INFO - Epoch(val) [6][1000/1250] eta: 0:00:27 time: 0.1080 data_time: 0.0016 memory: 626 +2024/10/26 22:12:45 - mmengine - INFO - Epoch(val) [6][1050/1250] eta: 0:00:21 time: 0.1102 data_time: 0.0017 memory: 629 +2024/10/26 22:12:51 - mmengine - INFO - Epoch(val) [6][1100/1250] eta: 0:00:16 time: 0.1068 data_time: 0.0016 memory: 635 +2024/10/26 22:12:56 - mmengine - INFO - Epoch(val) [6][1150/1250] eta: 0:00:10 time: 0.1131 data_time: 0.0016 memory: 629 +2024/10/26 22:13:02 - mmengine - INFO - Epoch(val) [6][1200/1250] eta: 0:00:05 time: 0.1022 data_time: 0.0015 memory: 629 +2024/10/26 22:13:07 - mmengine - INFO - Epoch(val) [6][1250/1250] eta: 0:00:00 time: 0.1077 data_time: 0.0018 memory: 635 +2024/10/26 22:13:15 - mmengine - INFO - Evaluating bbox... +2024/10/26 22:14:05 - mmengine - INFO - bbox_mAP_copypaste: 0.306 0.492 0.324 0.163 0.335 0.420 +2024/10/26 22:14:06 - mmengine - INFO - Epoch(val) [6][1250/1250] coco/bbox_mAP: 0.3060 coco/bbox_mAP_50: 0.4920 coco/bbox_mAP_75: 0.3240 coco/bbox_mAP_s: 0.1630 coco/bbox_mAP_m: 0.3350 coco/bbox_mAP_l: 0.4200 data_time: 0.0016 time: 0.1086 +2024/10/26 22:14:19 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 22:14:39 - mmengine - INFO - Epoch(train) [7][ 50/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:20:19 time: 0.6517 data_time: 0.0077 memory: 5134 grad_norm: 2.8035 loss: 0.5514 loss_cls: 0.2698 loss_bbox: 0.2816 +2024/10/26 22:15:10 - mmengine - INFO - Epoch(train) [7][ 100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:19:58 time: 0.6326 data_time: 0.0108 memory: 5136 grad_norm: 2.6183 loss: 0.5471 loss_cls: 0.2661 loss_bbox: 0.2809 +2024/10/26 22:15:43 - mmengine - INFO - Epoch(train) [7][ 150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:19:38 time: 0.6418 data_time: 0.0105 memory: 5137 grad_norm: 2.8306 loss: 0.5451 loss_cls: 0.2737 loss_bbox: 0.2714 +2024/10/26 22:16:13 - mmengine - INFO - Epoch(train) [7][ 200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:19:17 time: 0.6159 data_time: 0.0113 memory: 5133 grad_norm: 2.7449 loss: 0.5104 loss_cls: 0.2440 loss_bbox: 0.2664 +2024/10/26 22:16:43 - mmengine - INFO - Epoch(train) [7][ 250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:18:55 time: 0.5916 data_time: 0.0099 memory: 5136 grad_norm: 2.7390 loss: 0.5297 loss_cls: 0.2658 loss_bbox: 0.2639 +2024/10/26 22:17:13 - mmengine - INFO - Epoch(train) [7][ 300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:18:33 time: 0.6052 data_time: 0.0093 memory: 5134 grad_norm: 3.0643 loss: 0.5596 loss_cls: 0.2705 loss_bbox: 0.2890 +2024/10/26 22:17:44 - mmengine - INFO - Epoch(train) [7][ 350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:18:12 time: 0.6213 data_time: 0.0086 memory: 5135 grad_norm: 2.7283 loss: 0.5271 loss_cls: 0.2554 loss_bbox: 0.2717 +2024/10/26 22:18:15 - mmengine - INFO - Epoch(train) [7][ 400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:17:50 time: 0.6076 data_time: 0.0178 memory: 5137 grad_norm: 2.8401 loss: 0.5655 loss_cls: 0.2778 loss_bbox: 0.2876 +2024/10/26 22:18:46 - mmengine - INFO - Epoch(train) [7][ 450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:17:29 time: 0.6183 data_time: 0.0081 memory: 5136 grad_norm: 2.7111 loss: 0.5241 loss_cls: 0.2570 loss_bbox: 0.2670 +2024/10/26 22:19:15 - mmengine - INFO - Epoch(train) [7][ 500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:17:06 time: 0.5894 data_time: 0.0088 memory: 5135 grad_norm: 2.6855 loss: 0.5420 loss_cls: 0.2761 loss_bbox: 0.2659 +2024/10/26 22:19:48 - mmengine - INFO - Epoch(train) [7][ 550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:16:47 time: 0.6539 data_time: 0.0096 memory: 5136 grad_norm: 2.6527 loss: 0.5446 loss_cls: 0.2643 loss_bbox: 0.2803 +2024/10/26 22:20:16 - mmengine - INFO - Epoch(train) [7][ 600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:16:23 time: 0.5578 data_time: 0.0111 memory: 5134 grad_norm: 2.6236 loss: 0.4999 loss_cls: 0.2362 loss_bbox: 0.2637 +2024/10/26 22:20:47 - mmengine - INFO - Epoch(train) [7][ 650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:16:02 time: 0.6358 data_time: 0.0080 memory: 5135 grad_norm: 2.8015 loss: 0.5859 loss_cls: 0.2944 loss_bbox: 0.2916 +2024/10/26 22:21:16 - mmengine - INFO - Epoch(train) [7][ 700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:15:39 time: 0.5726 data_time: 0.0088 memory: 5135 grad_norm: 2.9655 loss: 0.5293 loss_cls: 0.2627 loss_bbox: 0.2666 +2024/10/26 22:21:48 - mmengine - INFO - Epoch(train) [7][ 750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:15:18 time: 0.6328 data_time: 0.0082 memory: 5134 grad_norm: 2.6626 loss: 0.5836 loss_cls: 0.2968 loss_bbox: 0.2869 +2024/10/26 22:22:17 - mmengine - INFO - Epoch(train) [7][ 800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:14:55 time: 0.5922 data_time: 0.0087 memory: 5136 grad_norm: 2.8233 loss: 0.5278 loss_cls: 0.2577 loss_bbox: 0.2701 +2024/10/26 22:22:50 - mmengine - INFO - Epoch(train) [7][ 850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:14:36 time: 0.6533 data_time: 0.0080 memory: 5133 grad_norm: 2.9081 loss: 0.5408 loss_cls: 0.2725 loss_bbox: 0.2683 +2024/10/26 22:23:20 - mmengine - INFO - Epoch(train) [7][ 900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:14:14 time: 0.6029 data_time: 0.0154 memory: 5135 grad_norm: 2.9894 loss: 0.5690 loss_cls: 0.2762 loss_bbox: 0.2928 +2024/10/26 22:23:53 - mmengine - INFO - Epoch(train) [7][ 950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:13:54 time: 0.6633 data_time: 0.0093 memory: 5135 grad_norm: 2.9251 loss: 0.5453 loss_cls: 0.2585 loss_bbox: 0.2868 +2024/10/26 22:24:19 - mmengine - INFO - Epoch(train) [7][1000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:13:28 time: 0.5129 data_time: 0.0146 memory: 5137 grad_norm: 2.9561 loss: 0.5771 loss_cls: 0.2948 loss_bbox: 0.2824 +2024/10/26 22:24:31 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 22:24:51 - mmengine - INFO - Epoch(train) [7][1050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:13:08 time: 0.6451 data_time: 0.0128 memory: 5134 grad_norm: 2.8089 loss: 0.5522 loss_cls: 0.2748 loss_bbox: 0.2773 +2024/10/26 22:25:17 - mmengine - INFO - Epoch(train) [7][1100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:12:41 time: 0.5089 data_time: 0.0083 memory: 5134 grad_norm: 2.7122 loss: 0.5284 loss_cls: 0.2591 loss_bbox: 0.2693 +2024/10/26 22:25:48 - mmengine - INFO - Epoch(train) [7][1150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:12:20 time: 0.6298 data_time: 0.0080 memory: 5134 grad_norm: 2.8748 loss: 0.5617 loss_cls: 0.2800 loss_bbox: 0.2817 +2024/10/26 22:26:18 - mmengine - INFO - Epoch(train) [7][1200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:11:58 time: 0.6032 data_time: 0.0082 memory: 5135 grad_norm: 2.6794 loss: 0.5584 loss_cls: 0.2680 loss_bbox: 0.2904 +2024/10/26 22:26:51 - mmengine - INFO - Epoch(train) [7][1250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:11:38 time: 0.6607 data_time: 0.0100 memory: 5133 grad_norm: 2.8533 loss: 0.5640 loss_cls: 0.2826 loss_bbox: 0.2814 +2024/10/26 22:27:21 - mmengine - INFO - Epoch(train) [7][1300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:11:16 time: 0.5915 data_time: 0.0151 memory: 5134 grad_norm: 2.7396 loss: 0.5607 loss_cls: 0.2834 loss_bbox: 0.2773 +2024/10/26 22:27:54 - mmengine - INFO - Epoch(train) [7][1350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:10:56 time: 0.6614 data_time: 0.0089 memory: 5134 grad_norm: 2.7112 loss: 0.5731 loss_cls: 0.2818 loss_bbox: 0.2913 +2024/10/26 22:28:27 - mmengine - INFO - Epoch(train) [7][1400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:10:36 time: 0.6538 data_time: 0.0105 memory: 5135 grad_norm: 3.1384 loss: 0.5669 loss_cls: 0.2773 loss_bbox: 0.2897 +2024/10/26 22:28:59 - mmengine - INFO - Epoch(train) [7][1450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:10:15 time: 0.6403 data_time: 0.0088 memory: 5133 grad_norm: 2.9524 loss: 0.5428 loss_cls: 0.2679 loss_bbox: 0.2749 +2024/10/26 22:29:30 - mmengine - INFO - Epoch(train) [7][1500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:09:54 time: 0.6241 data_time: 0.0177 memory: 5136 grad_norm: 3.1813 loss: 0.5621 loss_cls: 0.2792 loss_bbox: 0.2829 +2024/10/26 22:30:03 - mmengine - INFO - Epoch(train) [7][1550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:09:34 time: 0.6618 data_time: 0.0096 memory: 5134 grad_norm: 2.8360 loss: 0.5445 loss_cls: 0.2646 loss_bbox: 0.2798 +2024/10/26 22:30:35 - mmengine - INFO - Epoch(train) [7][1600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:09:14 time: 0.6431 data_time: 0.0416 memory: 5137 grad_norm: 2.9563 loss: 0.6031 loss_cls: 0.3186 loss_bbox: 0.2845 +2024/10/26 22:31:05 - mmengine - INFO - Epoch(train) [7][1650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:08:51 time: 0.5996 data_time: 0.0121 memory: 5135 grad_norm: 2.8789 loss: 0.5477 loss_cls: 0.2667 loss_bbox: 0.2810 +2024/10/26 22:31:36 - mmengine - INFO - Epoch(train) [7][1700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:08:30 time: 0.6170 data_time: 0.0103 memory: 5135 grad_norm: 2.8872 loss: 0.6003 loss_cls: 0.3004 loss_bbox: 0.2999 +2024/10/26 22:32:07 - mmengine - INFO - Epoch(train) [7][1750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:08:08 time: 0.6188 data_time: 0.0146 memory: 5134 grad_norm: 2.8486 loss: 0.5654 loss_cls: 0.2820 loss_bbox: 0.2835 +2024/10/26 22:32:38 - mmengine - INFO - Epoch(train) [7][1800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:07:47 time: 0.6289 data_time: 0.0101 memory: 5134 grad_norm: 8.2891 loss: 0.5899 loss_cls: 0.2985 loss_bbox: 0.2914 +2024/10/26 22:33:10 - mmengine - INFO - Epoch(train) [7][1850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:07:26 time: 0.6340 data_time: 0.0105 memory: 5135 grad_norm: 2.7413 loss: 0.5739 loss_cls: 0.2870 loss_bbox: 0.2869 +2024/10/26 22:33:42 - mmengine - INFO - Epoch(train) [7][1900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:07:05 time: 0.6459 data_time: 0.0106 memory: 5134 grad_norm: 2.9265 loss: 0.5621 loss_cls: 0.2810 loss_bbox: 0.2811 +2024/10/26 22:34:13 - mmengine - INFO - Epoch(train) [7][1950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:06:43 time: 0.6086 data_time: 0.0106 memory: 5136 grad_norm: 2.6750 loss: 0.5668 loss_cls: 0.2745 loss_bbox: 0.2924 +2024/10/26 22:34:46 - mmengine - INFO - Epoch(train) [7][2000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:06:23 time: 0.6573 data_time: 0.0167 memory: 5134 grad_norm: 2.9360 loss: 0.5583 loss_cls: 0.2710 loss_bbox: 0.2873 +2024/10/26 22:34:59 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 22:35:15 - mmengine - INFO - Epoch(train) [7][2050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:05:59 time: 0.5777 data_time: 0.0196 memory: 5135 grad_norm: 2.7766 loss: 0.5588 loss_cls: 0.2818 loss_bbox: 0.2771 +2024/10/26 22:35:46 - mmengine - INFO - Epoch(train) [7][2100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:05:38 time: 0.6283 data_time: 0.0110 memory: 5135 grad_norm: 2.6637 loss: 0.5615 loss_cls: 0.2788 loss_bbox: 0.2827 +2024/10/26 22:36:17 - mmengine - INFO - Epoch(train) [7][2150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:05:15 time: 0.6147 data_time: 0.0110 memory: 5136 grad_norm: 2.7548 loss: 0.5762 loss_cls: 0.2879 loss_bbox: 0.2883 +2024/10/26 22:36:49 - mmengine - INFO - Epoch(train) [7][2200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:04:55 time: 0.6418 data_time: 0.0203 memory: 5134 grad_norm: 2.8607 loss: 0.5656 loss_cls: 0.2796 loss_bbox: 0.2861 +2024/10/26 22:37:18 - mmengine - INFO - Epoch(train) [7][2250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:04:31 time: 0.5784 data_time: 0.0118 memory: 5134 grad_norm: 2.8688 loss: 0.5732 loss_cls: 0.2788 loss_bbox: 0.2943 +2024/10/26 22:37:50 - mmengine - INFO - Epoch(train) [7][2300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:04:10 time: 0.6409 data_time: 0.0112 memory: 5135 grad_norm: 2.8465 loss: 0.5466 loss_cls: 0.2697 loss_bbox: 0.2769 +2024/10/26 22:38:19 - mmengine - INFO - Epoch(train) [7][2350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:03:46 time: 0.5789 data_time: 0.0161 memory: 5135 grad_norm: 2.6932 loss: 0.5893 loss_cls: 0.2949 loss_bbox: 0.2944 +2024/10/26 22:38:52 - mmengine - INFO - Epoch(train) [7][2400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:03:26 time: 0.6601 data_time: 0.0128 memory: 5134 grad_norm: 2.8264 loss: 0.5626 loss_cls: 0.2850 loss_bbox: 0.2776 +2024/10/26 22:39:22 - mmengine - INFO - Epoch(train) [7][2450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:03:04 time: 0.6087 data_time: 0.0191 memory: 5135 grad_norm: 2.7137 loss: 0.5718 loss_cls: 0.2868 loss_bbox: 0.2850 +2024/10/26 22:39:56 - mmengine - INFO - Epoch(train) [7][2500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:02:44 time: 0.6649 data_time: 0.0099 memory: 5136 grad_norm: 2.8695 loss: 0.5488 loss_cls: 0.2737 loss_bbox: 0.2751 +2024/10/26 22:40:28 - mmengine - INFO - Epoch(train) [7][2550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:02:23 time: 0.6498 data_time: 0.0100 memory: 5136 grad_norm: 2.6749 loss: 0.5923 loss_cls: 0.2928 loss_bbox: 0.2996 +2024/10/26 22:41:01 - mmengine - INFO - Epoch(train) [7][2600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:02:02 time: 0.6504 data_time: 0.0113 memory: 5134 grad_norm: 2.8568 loss: 0.5399 loss_cls: 0.2756 loss_bbox: 0.2643 +2024/10/26 22:41:28 - mmengine - INFO - Epoch(train) [7][2650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:01:37 time: 0.5483 data_time: 0.0098 memory: 5134 grad_norm: 2.8773 loss: 0.5737 loss_cls: 0.2860 loss_bbox: 0.2877 +2024/10/26 22:42:01 - mmengine - INFO - Epoch(train) [7][2700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:01:16 time: 0.6538 data_time: 0.0104 memory: 5135 grad_norm: 2.7369 loss: 0.5420 loss_cls: 0.2716 loss_bbox: 0.2705 +2024/10/26 22:42:31 - mmengine - INFO - Epoch(train) [7][2750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:00:54 time: 0.6103 data_time: 0.0103 memory: 5136 grad_norm: 2.6283 loss: 0.5539 loss_cls: 0.2636 loss_bbox: 0.2903 +2024/10/26 22:43:04 - mmengine - INFO - Epoch(train) [7][2800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:00:34 time: 0.6579 data_time: 0.0102 memory: 5135 grad_norm: 2.7465 loss: 0.5372 loss_cls: 0.2655 loss_bbox: 0.2718 +2024/10/26 22:43:35 - mmengine - INFO - Epoch(train) [7][2850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 6:00:12 time: 0.6235 data_time: 0.0350 memory: 5133 grad_norm: 2.7710 loss: 0.5665 loss_cls: 0.2861 loss_bbox: 0.2805 +2024/10/26 22:44:07 - mmengine - INFO - Epoch(train) [7][2900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:59:50 time: 0.6336 data_time: 0.0105 memory: 5134 grad_norm: 2.8351 loss: 0.5255 loss_cls: 0.2536 loss_bbox: 0.2719 +2024/10/26 22:44:39 - mmengine - INFO - Epoch(train) [7][2950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:59:28 time: 0.6329 data_time: 0.0113 memory: 5135 grad_norm: 2.9735 loss: 0.5495 loss_cls: 0.2740 loss_bbox: 0.2755 +2024/10/26 22:45:10 - mmengine - INFO - Epoch(train) [7][3000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:59:07 time: 0.6350 data_time: 0.0102 memory: 5133 grad_norm: 2.6820 loss: 0.5441 loss_cls: 0.2704 loss_bbox: 0.2737 +2024/10/26 22:45:23 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 22:45:43 - mmengine - INFO - Epoch(train) [7][3050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:58:46 time: 0.6543 data_time: 0.0126 memory: 5139 grad_norm: 2.6978 loss: 0.5338 loss_cls: 0.2618 loss_bbox: 0.2720 +2024/10/26 22:46:14 - mmengine - INFO - Epoch(train) [7][3100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:58:24 time: 0.6236 data_time: 0.0097 memory: 5136 grad_norm: 3.0744 loss: 0.5619 loss_cls: 0.2816 loss_bbox: 0.2803 +2024/10/26 22:46:46 - mmengine - INFO - Epoch(train) [7][3150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:58:03 time: 0.6371 data_time: 0.0118 memory: 5135 grad_norm: 2.8317 loss: 0.5366 loss_cls: 0.2612 loss_bbox: 0.2754 +2024/10/26 22:47:16 - mmengine - INFO - Epoch(train) [7][3200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:57:39 time: 0.5888 data_time: 0.0135 memory: 5135 grad_norm: 2.8831 loss: 0.5742 loss_cls: 0.2946 loss_bbox: 0.2795 +2024/10/26 22:47:46 - mmengine - INFO - Epoch(train) [7][3250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:57:16 time: 0.6062 data_time: 0.0109 memory: 5133 grad_norm: 2.7586 loss: 0.5506 loss_cls: 0.2787 loss_bbox: 0.2719 +2024/10/26 22:48:15 - mmengine - INFO - Epoch(train) [7][3300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:56:53 time: 0.5882 data_time: 0.0098 memory: 5133 grad_norm: 2.7990 loss: 0.5437 loss_cls: 0.2686 loss_bbox: 0.2751 +2024/10/26 22:48:48 - mmengine - INFO - Epoch(train) [7][3350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:56:31 time: 0.6454 data_time: 0.0094 memory: 5135 grad_norm: 2.8977 loss: 0.5494 loss_cls: 0.2755 loss_bbox: 0.2739 +2024/10/26 22:49:16 - mmengine - INFO - Epoch(train) [7][3400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:56:07 time: 0.5723 data_time: 0.0088 memory: 5135 grad_norm: 2.7893 loss: 0.5306 loss_cls: 0.2682 loss_bbox: 0.2624 +2024/10/26 22:49:48 - mmengine - INFO - Epoch(train) [7][3450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:55:45 time: 0.6369 data_time: 0.0089 memory: 5133 grad_norm: 2.8409 loss: 0.5728 loss_cls: 0.2740 loss_bbox: 0.2989 +2024/10/26 22:50:18 - mmengine - INFO - Epoch(train) [7][3500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:55:22 time: 0.6055 data_time: 0.0088 memory: 5134 grad_norm: 2.5991 loss: 0.5507 loss_cls: 0.2739 loss_bbox: 0.2768 +2024/10/26 22:50:51 - mmengine - INFO - Epoch(train) [7][3550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:55:01 time: 0.6524 data_time: 0.0136 memory: 5134 grad_norm: 2.7373 loss: 0.5828 loss_cls: 0.2924 loss_bbox: 0.2904 +2024/10/26 22:51:21 - mmengine - INFO - Epoch(train) [7][3600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:54:38 time: 0.6058 data_time: 0.0104 memory: 5133 grad_norm: 2.6075 loss: 0.5606 loss_cls: 0.2789 loss_bbox: 0.2817 +2024/10/26 22:51:53 - mmengine - INFO - Epoch(train) [7][3650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:54:17 time: 0.6396 data_time: 0.0100 memory: 5135 grad_norm: 2.7464 loss: 0.5672 loss_cls: 0.2835 loss_bbox: 0.2837 +2024/10/26 22:52:24 - mmengine - INFO - Epoch(train) [7][3700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:53:54 time: 0.6129 data_time: 0.0139 memory: 5134 grad_norm: 2.7206 loss: 0.5829 loss_cls: 0.2931 loss_bbox: 0.2898 +2024/10/26 22:52:57 - mmengine - INFO - Epoch(train) [7][3750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:53:33 time: 0.6573 data_time: 0.0097 memory: 5135 grad_norm: 2.6674 loss: 0.5781 loss_cls: 0.2840 loss_bbox: 0.2941 +2024/10/26 22:53:26 - mmengine - INFO - Epoch(train) [7][3800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:53:10 time: 0.5927 data_time: 0.0149 memory: 5134 grad_norm: 2.7655 loss: 0.5589 loss_cls: 0.2726 loss_bbox: 0.2863 +2024/10/26 22:53:59 - mmengine - INFO - Epoch(train) [7][3850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:52:49 time: 0.6593 data_time: 0.0091 memory: 5133 grad_norm: 2.7467 loss: 0.5444 loss_cls: 0.2759 loss_bbox: 0.2685 +2024/10/26 22:54:27 - mmengine - INFO - Epoch(train) [7][3900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:52:23 time: 0.5517 data_time: 0.0168 memory: 5136 grad_norm: 2.7055 loss: 0.5822 loss_cls: 0.2919 loss_bbox: 0.2904 +2024/10/26 22:54:59 - mmengine - INFO - Epoch(train) [7][3950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:52:02 time: 0.6480 data_time: 0.0125 memory: 5133 grad_norm: 2.7313 loss: 0.5537 loss_cls: 0.2726 loss_bbox: 0.2811 +2024/10/26 22:55:27 - mmengine - INFO - Epoch(train) [7][4000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:51:37 time: 0.5567 data_time: 0.0093 memory: 5135 grad_norm: 2.8001 loss: 0.5683 loss_cls: 0.2819 loss_bbox: 0.2864 +2024/10/26 22:55:40 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 22:55:59 - mmengine - INFO - Epoch(train) [7][4050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:51:15 time: 0.6451 data_time: 0.0092 memory: 5133 grad_norm: 2.8184 loss: 0.5737 loss_cls: 0.2830 loss_bbox: 0.2907 +2024/10/26 22:56:29 - mmengine - INFO - Epoch(train) [7][4100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:50:52 time: 0.5887 data_time: 0.0100 memory: 5132 grad_norm: 2.7912 loss: 0.5660 loss_cls: 0.2760 loss_bbox: 0.2900 +2024/10/26 22:57:02 - mmengine - INFO - Epoch(train) [7][4150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:50:31 time: 0.6632 data_time: 0.0094 memory: 5132 grad_norm: 2.9427 loss: 0.5420 loss_cls: 0.2614 loss_bbox: 0.2806 +2024/10/26 22:57:34 - mmengine - INFO - Epoch(train) [7][4200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:50:09 time: 0.6354 data_time: 0.0665 memory: 5134 grad_norm: 2.7655 loss: 0.5315 loss_cls: 0.2696 loss_bbox: 0.2619 +2024/10/26 22:58:05 - mmengine - INFO - Epoch(train) [7][4250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:49:46 time: 0.6256 data_time: 0.0094 memory: 5134 grad_norm: 2.7319 loss: 0.5633 loss_cls: 0.2868 loss_bbox: 0.2764 +2024/10/26 22:58:35 - mmengine - INFO - Epoch(train) [7][4300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:49:23 time: 0.6043 data_time: 0.0157 memory: 5135 grad_norm: 2.6747 loss: 0.5211 loss_cls: 0.2585 loss_bbox: 0.2627 +2024/10/26 22:59:07 - mmengine - INFO - Epoch(train) [7][4350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:49:01 time: 0.6266 data_time: 0.0154 memory: 5133 grad_norm: 2.8700 loss: 0.5581 loss_cls: 0.2792 loss_bbox: 0.2789 +2024/10/26 22:59:37 - mmengine - INFO - Epoch(train) [7][4400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:48:38 time: 0.6157 data_time: 0.0092 memory: 5133 grad_norm: 2.7043 loss: 0.5611 loss_cls: 0.2756 loss_bbox: 0.2856 +2024/10/26 23:00:09 - mmengine - INFO - Epoch(train) [7][4450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:48:16 time: 0.6394 data_time: 0.0094 memory: 5134 grad_norm: 2.6474 loss: 0.5749 loss_cls: 0.2884 loss_bbox: 0.2866 +2024/10/26 23:00:43 - mmengine - INFO - Epoch(train) [7][4500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:47:55 time: 0.6725 data_time: 0.0161 memory: 5135 grad_norm: 2.6623 loss: 0.5531 loss_cls: 0.2739 loss_bbox: 0.2792 +2024/10/26 23:01:13 - mmengine - INFO - Epoch(train) [7][4550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:47:31 time: 0.5907 data_time: 0.0104 memory: 5136 grad_norm: 2.7735 loss: 0.5730 loss_cls: 0.2864 loss_bbox: 0.2866 +2024/10/26 23:01:44 - mmengine - INFO - Epoch(train) [7][4600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:47:09 time: 0.6272 data_time: 0.0107 memory: 5134 grad_norm: 2.6791 loss: 0.5596 loss_cls: 0.2741 loss_bbox: 0.2856 +2024/10/26 23:02:13 - mmengine - INFO - Epoch(train) [7][4650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:46:45 time: 0.5846 data_time: 0.0095 memory: 5135 grad_norm: 2.7025 loss: 0.5832 loss_cls: 0.2918 loss_bbox: 0.2914 +2024/10/26 23:02:44 - mmengine - INFO - Epoch(train) [7][4700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:46:22 time: 0.6244 data_time: 0.0093 memory: 5136 grad_norm: 2.6582 loss: 0.5777 loss_cls: 0.2821 loss_bbox: 0.2956 +2024/10/26 23:03:15 - mmengine - INFO - Epoch(train) [7][4750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:45:59 time: 0.6031 data_time: 0.0160 memory: 5133 grad_norm: 2.7165 loss: 0.5687 loss_cls: 0.2810 loss_bbox: 0.2877 +2024/10/26 23:03:48 - mmengine - INFO - Epoch(train) [7][4800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:45:38 time: 0.6625 data_time: 0.0145 memory: 5133 grad_norm: 2.6984 loss: 0.5692 loss_cls: 0.2862 loss_bbox: 0.2830 +2024/10/26 23:04:17 - mmengine - INFO - Epoch(train) [7][4850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:45:14 time: 0.5887 data_time: 0.0090 memory: 5134 grad_norm: 2.5808 loss: 0.5417 loss_cls: 0.2635 loss_bbox: 0.2782 +2024/10/26 23:04:48 - mmengine - INFO - Epoch(train) [7][4900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:44:51 time: 0.6199 data_time: 0.0150 memory: 5137 grad_norm: 2.8115 loss: 0.5775 loss_cls: 0.2874 loss_bbox: 0.2901 +2024/10/26 23:05:19 - mmengine - INFO - Epoch(train) [7][4950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:44:27 time: 0.6073 data_time: 0.0161 memory: 5134 grad_norm: 2.7793 loss: 0.5459 loss_cls: 0.2677 loss_bbox: 0.2781 +2024/10/26 23:05:51 - mmengine - INFO - Epoch(train) [7][5000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:44:05 time: 0.6426 data_time: 0.0162 memory: 5134 grad_norm: 3.0848 loss: 0.5820 loss_cls: 0.2859 loss_bbox: 0.2961 +2024/10/26 23:06:04 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 23:06:21 - mmengine - INFO - Epoch(train) [7][5050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:43:42 time: 0.6157 data_time: 0.0140 memory: 5133 grad_norm: 2.8480 loss: 0.5844 loss_cls: 0.2948 loss_bbox: 0.2897 +2024/10/26 23:06:53 - mmengine - INFO - Epoch(train) [7][5100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:43:20 time: 0.6308 data_time: 0.0096 memory: 5135 grad_norm: 2.9035 loss: 0.5622 loss_cls: 0.2864 loss_bbox: 0.2758 +2024/10/26 23:07:22 - mmengine - INFO - Epoch(train) [7][5150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:42:56 time: 0.5899 data_time: 0.0155 memory: 5137 grad_norm: 2.9348 loss: 0.5493 loss_cls: 0.2848 loss_bbox: 0.2645 +2024/10/26 23:07:56 - mmengine - INFO - Epoch(train) [7][5200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:42:35 time: 0.6733 data_time: 0.0112 memory: 5136 grad_norm: 2.6826 loss: 0.5737 loss_cls: 0.2840 loss_bbox: 0.2897 +2024/10/26 23:08:27 - mmengine - INFO - Epoch(train) [7][5250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:42:12 time: 0.6115 data_time: 0.0092 memory: 5135 grad_norm: 2.7345 loss: 0.5349 loss_cls: 0.2637 loss_bbox: 0.2712 +2024/10/26 23:09:00 - mmengine - INFO - Epoch(train) [7][5300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:41:50 time: 0.6626 data_time: 0.0130 memory: 5134 grad_norm: 2.7547 loss: 0.5722 loss_cls: 0.2903 loss_bbox: 0.2819 +2024/10/26 23:09:30 - mmengine - INFO - Epoch(train) [7][5350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:41:27 time: 0.6113 data_time: 0.0094 memory: 5132 grad_norm: 2.6021 loss: 0.5728 loss_cls: 0.2770 loss_bbox: 0.2957 +2024/10/26 23:10:03 - mmengine - INFO - Epoch(train) [7][5400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:41:05 time: 0.6521 data_time: 0.0093 memory: 5137 grad_norm: 2.6954 loss: 0.5613 loss_cls: 0.2863 loss_bbox: 0.2750 +2024/10/26 23:10:34 - mmengine - INFO - Epoch(train) [7][5450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:40:42 time: 0.6210 data_time: 0.0497 memory: 5134 grad_norm: 2.7330 loss: 0.5773 loss_cls: 0.2810 loss_bbox: 0.2963 +2024/10/26 23:11:06 - mmengine - INFO - Epoch(train) [7][5500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:40:20 time: 0.6330 data_time: 0.0092 memory: 5138 grad_norm: 2.7876 loss: 0.5739 loss_cls: 0.2833 loss_bbox: 0.2905 +2024/10/26 23:11:37 - mmengine - INFO - Epoch(train) [7][5550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:39:57 time: 0.6222 data_time: 0.0174 memory: 5134 grad_norm: 2.8087 loss: 0.5702 loss_cls: 0.2851 loss_bbox: 0.2851 +2024/10/26 23:12:07 - mmengine - INFO - Epoch(train) [7][5600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:39:33 time: 0.6054 data_time: 0.0095 memory: 5133 grad_norm: 2.6825 loss: 0.5504 loss_cls: 0.2631 loss_bbox: 0.2873 +2024/10/26 23:12:37 - mmengine - INFO - Epoch(train) [7][5650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:39:09 time: 0.6028 data_time: 0.0092 memory: 5136 grad_norm: 2.7302 loss: 0.5676 loss_cls: 0.2818 loss_bbox: 0.2859 +2024/10/26 23:13:10 - mmengine - INFO - Epoch(train) [7][5700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:38:47 time: 0.6445 data_time: 0.0092 memory: 5134 grad_norm: 2.8447 loss: 0.5756 loss_cls: 0.2885 loss_bbox: 0.2871 +2024/10/26 23:13:39 - mmengine - INFO - Epoch(train) [7][5750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:38:22 time: 0.5802 data_time: 0.0092 memory: 5133 grad_norm: 2.6215 loss: 0.5558 loss_cls: 0.2721 loss_bbox: 0.2837 +2024/10/26 23:14:10 - mmengine - INFO - Epoch(train) [7][5800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:37:59 time: 0.6262 data_time: 0.0091 memory: 5133 grad_norm: 2.7983 loss: 0.5637 loss_cls: 0.2865 loss_bbox: 0.2772 +2024/10/26 23:14:42 - mmengine - INFO - Epoch(train) [7][5850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:37:37 time: 0.6378 data_time: 0.0090 memory: 5134 grad_norm: 2.9259 loss: 0.5510 loss_cls: 0.2751 loss_bbox: 0.2759 +2024/10/26 23:15:12 - mmengine - INFO - Epoch(train) [7][5900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:37:13 time: 0.6081 data_time: 0.0097 memory: 5135 grad_norm: 2.9166 loss: 0.5316 loss_cls: 0.2544 loss_bbox: 0.2772 +2024/10/26 23:15:44 - mmengine - INFO - Epoch(train) [7][5950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:36:51 time: 0.6414 data_time: 0.0094 memory: 5133 grad_norm: 2.8302 loss: 0.5224 loss_cls: 0.2549 loss_bbox: 0.2675 +2024/10/26 23:16:13 - mmengine - INFO - Epoch(train) [7][6000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:36:26 time: 0.5830 data_time: 0.0097 memory: 5133 grad_norm: 2.9540 loss: 0.5798 loss_cls: 0.2888 loss_bbox: 0.2909 +2024/10/26 23:16:24 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 23:16:43 - mmengine - INFO - Epoch(train) [7][6050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:36:02 time: 0.6008 data_time: 0.0091 memory: 5133 grad_norm: 2.7582 loss: 0.5965 loss_cls: 0.3023 loss_bbox: 0.2942 +2024/10/26 23:17:12 - mmengine - INFO - Epoch(train) [7][6100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:35:37 time: 0.5667 data_time: 0.0093 memory: 5132 grad_norm: 2.5516 loss: 0.5700 loss_cls: 0.2840 loss_bbox: 0.2860 +2024/10/26 23:17:42 - mmengine - INFO - Epoch(train) [7][6150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:35:13 time: 0.6033 data_time: 0.0100 memory: 5135 grad_norm: 2.6603 loss: 0.5820 loss_cls: 0.2954 loss_bbox: 0.2866 +2024/10/26 23:18:12 - mmengine - INFO - Epoch(train) [7][6200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:34:49 time: 0.6054 data_time: 0.0099 memory: 5136 grad_norm: 2.5472 loss: 0.5657 loss_cls: 0.2822 loss_bbox: 0.2835 +2024/10/26 23:18:45 - mmengine - INFO - Epoch(train) [7][6250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:34:27 time: 0.6486 data_time: 0.0101 memory: 5134 grad_norm: 2.9000 loss: 0.5361 loss_cls: 0.2660 loss_bbox: 0.2701 +2024/10/26 23:19:14 - mmengine - INFO - Epoch(train) [7][6300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:34:03 time: 0.5914 data_time: 0.0098 memory: 5136 grad_norm: 2.6409 loss: 0.5853 loss_cls: 0.2896 loss_bbox: 0.2957 +2024/10/26 23:19:45 - mmengine - INFO - Epoch(train) [7][6350/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:33:40 time: 0.6189 data_time: 0.0093 memory: 5135 grad_norm: 2.7367 loss: 0.5350 loss_cls: 0.2663 loss_bbox: 0.2687 +2024/10/26 23:20:14 - mmengine - INFO - Epoch(train) [7][6400/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:33:15 time: 0.5858 data_time: 0.0150 memory: 5133 grad_norm: 2.5154 loss: 0.5603 loss_cls: 0.2802 loss_bbox: 0.2801 +2024/10/26 23:20:46 - mmengine - INFO - Epoch(train) [7][6450/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:32:52 time: 0.6314 data_time: 0.0142 memory: 5135 grad_norm: 2.6791 loss: 0.5647 loss_cls: 0.2780 loss_bbox: 0.2867 +2024/10/26 23:21:00 - mmengine - INFO - Epoch(train) [7][6500/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:32:16 time: 0.2892 data_time: 0.0156 memory: 5134 grad_norm: 2.6810 loss: 0.5653 loss_cls: 0.2837 loss_bbox: 0.2816 +2024/10/26 23:21:14 - mmengine - INFO - Epoch(train) [7][6550/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:31:40 time: 0.2692 data_time: 0.0085 memory: 5133 grad_norm: 2.6945 loss: 0.5614 loss_cls: 0.2828 loss_bbox: 0.2786 +2024/10/26 23:21:27 - mmengine - INFO - Epoch(train) [7][6600/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:31:04 time: 0.2671 data_time: 0.0085 memory: 5135 grad_norm: 2.9218 loss: 0.5683 loss_cls: 0.2779 loss_bbox: 0.2904 +2024/10/26 23:21:40 - mmengine - INFO - Epoch(train) [7][6650/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:30:27 time: 0.2637 data_time: 0.0091 memory: 5133 grad_norm: 3.1216 loss: 0.5432 loss_cls: 0.2637 loss_bbox: 0.2795 +2024/10/26 23:21:54 - mmengine - INFO - Epoch(train) [7][6700/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:29:51 time: 0.2617 data_time: 0.0108 memory: 5134 grad_norm: 2.6244 loss: 0.5832 loss_cls: 0.2920 loss_bbox: 0.2912 +2024/10/26 23:22:07 - mmengine - INFO - Epoch(train) [7][6750/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:29:14 time: 0.2633 data_time: 0.0090 memory: 5135 grad_norm: 2.6223 loss: 0.5609 loss_cls: 0.2737 loss_bbox: 0.2872 +2024/10/26 23:22:20 - mmengine - INFO - Epoch(train) [7][6800/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:28:38 time: 0.2607 data_time: 0.0096 memory: 5134 grad_norm: 2.7235 loss: 0.5649 loss_cls: 0.2890 loss_bbox: 0.2759 +2024/10/26 23:22:33 - mmengine - INFO - Epoch(train) [7][6850/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:28:02 time: 0.2628 data_time: 0.0088 memory: 5136 grad_norm: 2.7421 loss: 0.5372 loss_cls: 0.2607 loss_bbox: 0.2766 +2024/10/26 23:22:46 - mmengine - INFO - Epoch(train) [7][6900/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:27:25 time: 0.2612 data_time: 0.0091 memory: 5135 grad_norm: 2.7022 loss: 0.5256 loss_cls: 0.2591 loss_bbox: 0.2665 +2024/10/26 23:22:59 - mmengine - INFO - Epoch(train) [7][6950/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:26:49 time: 0.2673 data_time: 0.0148 memory: 5134 grad_norm: 2.7787 loss: 0.5881 loss_cls: 0.2900 loss_bbox: 0.2980 +2024/10/26 23:23:12 - mmengine - INFO - Epoch(train) [7][7000/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:26:13 time: 0.2626 data_time: 0.0096 memory: 5135 grad_norm: 2.6316 loss: 0.5662 loss_cls: 0.2805 loss_bbox: 0.2856 +2024/10/26 23:23:18 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 23:23:26 - mmengine - INFO - Epoch(train) [7][7050/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:25:37 time: 0.2625 data_time: 0.0094 memory: 5135 grad_norm: 2.7826 loss: 0.6059 loss_cls: 0.3082 loss_bbox: 0.2977 +2024/10/26 23:23:39 - mmengine - INFO - Epoch(train) [7][7100/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:25:01 time: 0.2628 data_time: 0.0092 memory: 5135 grad_norm: 2.6300 loss: 0.5649 loss_cls: 0.2830 loss_bbox: 0.2819 +2024/10/26 23:23:52 - mmengine - INFO - Epoch(train) [7][7150/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:24:25 time: 0.2670 data_time: 0.0094 memory: 5135 grad_norm: 2.7144 loss: 0.5685 loss_cls: 0.2758 loss_bbox: 0.2927 +2024/10/26 23:24:06 - mmengine - INFO - Epoch(train) [7][7200/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:23:50 time: 0.2714 data_time: 0.0096 memory: 5134 grad_norm: 2.7772 loss: 0.5741 loss_cls: 0.2865 loss_bbox: 0.2877 +2024/10/26 23:24:19 - mmengine - INFO - Epoch(train) [7][7250/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:23:14 time: 0.2704 data_time: 0.0096 memory: 5135 grad_norm: 2.7246 loss: 0.5606 loss_cls: 0.2758 loss_bbox: 0.2847 +2024/10/26 23:24:32 - mmengine - INFO - Epoch(train) [7][7300/7330] base_lr: 2.0000e-04 lr: 2.0000e-04 eta: 5:22:38 time: 0.2605 data_time: 0.0094 memory: 5135 grad_norm: 2.7569 loss: 0.5751 loss_cls: 0.2863 loss_bbox: 0.2888 +2024/10/26 23:24:41 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 23:24:41 - mmengine - INFO - Saving checkpoint at 7 epochs +2024/10/26 23:24:48 - mmengine - INFO - Epoch(val) [7][ 50/1250] eta: 0:01:18 time: 0.0657 data_time: 0.0018 memory: 5134 +2024/10/26 23:24:51 - mmengine - INFO - Epoch(val) [7][ 100/1250] eta: 0:01:15 time: 0.0657 data_time: 0.0015 memory: 628 +2024/10/26 23:24:54 - mmengine - INFO - Epoch(val) [7][ 150/1250] eta: 0:01:12 time: 0.0669 data_time: 0.0017 memory: 634 +2024/10/26 23:24:58 - mmengine - INFO - Epoch(val) [7][ 200/1250] eta: 0:01:09 time: 0.0670 data_time: 0.0016 memory: 634 +2024/10/26 23:25:01 - mmengine - INFO - Epoch(val) [7][ 250/1250] eta: 0:01:06 time: 0.0667 data_time: 0.0016 memory: 625 +2024/10/26 23:25:04 - mmengine - INFO - Epoch(val) [7][ 300/1250] eta: 0:01:03 time: 0.0668 data_time: 0.0016 memory: 624 +2024/10/26 23:25:08 - mmengine - INFO - Epoch(val) [7][ 350/1250] eta: 0:00:59 time: 0.0655 data_time: 0.0015 memory: 624 +2024/10/26 23:25:11 - mmengine - INFO - Epoch(val) [7][ 400/1250] eta: 0:00:56 time: 0.0656 data_time: 0.0015 memory: 612 +2024/10/26 23:25:14 - mmengine - INFO - Epoch(val) [7][ 450/1250] eta: 0:00:52 time: 0.0659 data_time: 0.0015 memory: 634 +2024/10/26 23:25:18 - mmengine - INFO - Epoch(val) [7][ 500/1250] eta: 0:00:49 time: 0.0662 data_time: 0.0015 memory: 634 +2024/10/26 23:25:21 - mmengine - INFO - Epoch(val) [7][ 550/1250] eta: 0:00:46 time: 0.0661 data_time: 0.0015 memory: 612 +2024/10/26 23:25:24 - mmengine - INFO - Epoch(val) [7][ 600/1250] eta: 0:00:43 time: 0.0659 data_time: 0.0015 memory: 624 +2024/10/26 23:25:27 - mmengine - INFO - Epoch(val) [7][ 650/1250] eta: 0:00:39 time: 0.0650 data_time: 0.0015 memory: 624 +2024/10/26 23:25:31 - mmengine - INFO - Epoch(val) [7][ 700/1250] eta: 0:00:36 time: 0.0660 data_time: 0.0016 memory: 628 +2024/10/26 23:25:34 - mmengine - INFO - Epoch(val) [7][ 750/1250] eta: 0:00:33 time: 0.0660 data_time: 0.0016 memory: 628 +2024/10/26 23:25:37 - mmengine - INFO - Epoch(val) [7][ 800/1250] eta: 0:00:29 time: 0.0660 data_time: 0.0015 memory: 634 +2024/10/26 23:25:41 - mmengine - INFO - Epoch(val) [7][ 850/1250] eta: 0:00:26 time: 0.0662 data_time: 0.0015 memory: 634 +2024/10/26 23:25:44 - mmengine - INFO - Epoch(val) [7][ 900/1250] eta: 0:00:23 time: 0.0663 data_time: 0.0015 memory: 634 +2024/10/26 23:25:47 - mmengine - INFO - Epoch(val) [7][ 950/1250] eta: 0:00:19 time: 0.0661 data_time: 0.0015 memory: 625 +2024/10/26 23:25:51 - mmengine - INFO - Epoch(val) [7][1000/1250] eta: 0:00:16 time: 0.0660 data_time: 0.0015 memory: 625 +2024/10/26 23:25:54 - mmengine - INFO - Epoch(val) [7][1050/1250] eta: 0:00:13 time: 0.0656 data_time: 0.0015 memory: 628 +2024/10/26 23:25:57 - mmengine - INFO - Epoch(val) [7][1100/1250] eta: 0:00:09 time: 0.0661 data_time: 0.0015 memory: 634 +2024/10/26 23:26:00 - mmengine - INFO - Epoch(val) [7][1150/1250] eta: 0:00:06 time: 0.0664 data_time: 0.0015 memory: 628 +2024/10/26 23:26:04 - mmengine - INFO - Epoch(val) [7][1200/1250] eta: 0:00:03 time: 0.0675 data_time: 0.0016 memory: 628 +2024/10/26 23:26:07 - mmengine - INFO - Epoch(val) [7][1250/1250] eta: 0:00:00 time: 0.0664 data_time: 0.0014 memory: 634 +2024/10/26 23:26:15 - mmengine - INFO - Evaluating bbox... +2024/10/26 23:27:05 - mmengine - INFO - bbox_mAP_copypaste: 0.319 0.500 0.338 0.167 0.354 0.448 +2024/10/26 23:27:06 - mmengine - INFO - Epoch(val) [7][1250/1250] coco/bbox_mAP: 0.3190 coco/bbox_mAP_50: 0.5000 coco/bbox_mAP_75: 0.3380 coco/bbox_mAP_s: 0.1670 coco/bbox_mAP_m: 0.3540 coco/bbox_mAP_l: 0.4480 data_time: 0.0015 time: 0.0662 +2024/10/26 23:27:27 - mmengine - INFO - Epoch(train) [8][ 50/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:21:47 time: 0.4309 data_time: 0.0085 memory: 5134 grad_norm: 2.6116 loss: 0.5779 loss_cls: 0.2804 loss_bbox: 0.2975 +2024/10/26 23:28:00 - mmengine - INFO - Epoch(train) [8][ 100/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:21:25 time: 0.6561 data_time: 0.0146 memory: 5134 grad_norm: 2.6790 loss: 0.5392 loss_cls: 0.2701 loss_bbox: 0.2691 +2024/10/26 23:28:29 - mmengine - INFO - Epoch(train) [8][ 150/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:21:01 time: 0.5801 data_time: 0.0088 memory: 5132 grad_norm: 2.6885 loss: 0.5422 loss_cls: 0.2671 loss_bbox: 0.2751 +2024/10/26 23:29:01 - mmengine - INFO - Epoch(train) [8][ 200/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:20:38 time: 0.6363 data_time: 0.0118 memory: 5133 grad_norm: 2.5498 loss: 0.5082 loss_cls: 0.2465 loss_bbox: 0.2617 +2024/10/26 23:29:33 - mmengine - INFO - Epoch(train) [8][ 250/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:20:16 time: 0.6487 data_time: 0.0743 memory: 5134 grad_norm: 2.5555 loss: 0.5418 loss_cls: 0.2658 loss_bbox: 0.2760 +2024/10/26 23:30:06 - mmengine - INFO - Epoch(train) [8][ 300/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:19:54 time: 0.6437 data_time: 0.0086 memory: 5137 grad_norm: 2.5606 loss: 0.5390 loss_cls: 0.2603 loss_bbox: 0.2788 +2024/10/26 23:30:34 - mmengine - INFO - Epoch(train) [8][ 350/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:19:29 time: 0.5722 data_time: 0.0101 memory: 5135 grad_norm: 2.6908 loss: 0.5199 loss_cls: 0.2508 loss_bbox: 0.2691 +2024/10/26 23:31:07 - mmengine - INFO - Epoch(train) [8][ 400/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:19:06 time: 0.6460 data_time: 0.0197 memory: 5136 grad_norm: 2.6606 loss: 0.5485 loss_cls: 0.2723 loss_bbox: 0.2761 +2024/10/26 23:31:34 - mmengine - INFO - Epoch(train) [8][ 450/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:18:41 time: 0.5553 data_time: 0.0090 memory: 5131 grad_norm: 2.6648 loss: 0.5307 loss_cls: 0.2578 loss_bbox: 0.2729 +2024/10/26 23:32:07 - mmengine - INFO - Epoch(train) [8][ 500/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:18:19 time: 0.6581 data_time: 0.0088 memory: 5132 grad_norm: 2.6908 loss: 0.5380 loss_cls: 0.2604 loss_bbox: 0.2776 +2024/10/26 23:32:36 - mmengine - INFO - Epoch(train) [8][ 550/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:17:54 time: 0.5737 data_time: 0.0089 memory: 5135 grad_norm: 2.7745 loss: 0.5475 loss_cls: 0.2750 loss_bbox: 0.2725 +2024/10/26 23:33:06 - mmengine - INFO - Epoch(train) [8][ 600/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:17:31 time: 0.6069 data_time: 0.0090 memory: 5135 grad_norm: 2.8246 loss: 0.5385 loss_cls: 0.2671 loss_bbox: 0.2715 +2024/10/26 23:33:37 - mmengine - INFO - Epoch(train) [8][ 650/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:17:07 time: 0.6110 data_time: 0.0098 memory: 5133 grad_norm: 2.4251 loss: 0.5367 loss_cls: 0.2584 loss_bbox: 0.2783 +2024/10/26 23:34:03 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 23:34:10 - mmengine - INFO - Epoch(train) [8][ 700/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:16:45 time: 0.6542 data_time: 0.0132 memory: 5134 grad_norm: 2.6872 loss: 0.5495 loss_cls: 0.2678 loss_bbox: 0.2817 +2024/10/26 23:34:41 - mmengine - INFO - Epoch(train) [8][ 750/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:16:22 time: 0.6204 data_time: 0.0086 memory: 5133 grad_norm: 2.6375 loss: 0.5184 loss_cls: 0.2570 loss_bbox: 0.2613 +2024/10/26 23:35:10 - mmengine - INFO - Epoch(train) [8][ 800/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:15:57 time: 0.5897 data_time: 0.0124 memory: 5135 grad_norm: 2.6223 loss: 0.5143 loss_cls: 0.2504 loss_bbox: 0.2639 +2024/10/26 23:35:42 - mmengine - INFO - Epoch(train) [8][ 850/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:15:35 time: 0.6357 data_time: 0.0089 memory: 5134 grad_norm: 2.8327 loss: 0.5396 loss_cls: 0.2654 loss_bbox: 0.2742 +2024/10/26 23:36:13 - mmengine - INFO - Epoch(train) [8][ 900/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:15:11 time: 0.6216 data_time: 0.0090 memory: 5135 grad_norm: 2.9006 loss: 0.5362 loss_cls: 0.2631 loss_bbox: 0.2731 +2024/10/26 23:36:45 - mmengine - INFO - Epoch(train) [8][ 950/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:14:49 time: 0.6436 data_time: 0.0094 memory: 5136 grad_norm: 2.5424 loss: 0.5593 loss_cls: 0.2779 loss_bbox: 0.2813 +2024/10/26 23:37:16 - mmengine - INFO - Epoch(train) [8][1000/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:14:25 time: 0.6154 data_time: 0.0097 memory: 5133 grad_norm: 2.4606 loss: 0.5333 loss_cls: 0.2588 loss_bbox: 0.2746 +2024/10/26 23:37:48 - mmengine - INFO - Epoch(train) [8][1050/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:14:03 time: 0.6487 data_time: 0.0089 memory: 5134 grad_norm: 2.6898 loss: 0.5025 loss_cls: 0.2457 loss_bbox: 0.2568 +2024/10/26 23:38:16 - mmengine - INFO - Epoch(train) [8][1100/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:13:37 time: 0.5589 data_time: 0.0092 memory: 5133 grad_norm: 2.5222 loss: 0.5507 loss_cls: 0.2585 loss_bbox: 0.2922 +2024/10/26 23:38:49 - mmengine - INFO - Epoch(train) [8][1150/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:13:15 time: 0.6482 data_time: 0.0094 memory: 5137 grad_norm: 2.6421 loss: 0.5417 loss_cls: 0.2700 loss_bbox: 0.2717 +2024/10/26 23:39:19 - mmengine - INFO - Epoch(train) [8][1200/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:12:51 time: 0.6036 data_time: 0.0097 memory: 5134 grad_norm: 2.8672 loss: 0.5629 loss_cls: 0.2872 loss_bbox: 0.2757 +2024/10/26 23:39:51 - mmengine - INFO - Epoch(train) [8][1250/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:12:28 time: 0.6479 data_time: 0.0091 memory: 5135 grad_norm: 2.6188 loss: 0.5468 loss_cls: 0.2691 loss_bbox: 0.2777 +2024/10/26 23:40:21 - mmengine - INFO - Epoch(train) [8][1300/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:12:04 time: 0.5891 data_time: 0.0174 memory: 5134 grad_norm: 2.5228 loss: 0.5301 loss_cls: 0.2618 loss_bbox: 0.2682 +2024/10/26 23:40:53 - mmengine - INFO - Epoch(train) [8][1350/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:11:41 time: 0.6473 data_time: 0.0094 memory: 5137 grad_norm: 2.6753 loss: 0.5501 loss_cls: 0.2601 loss_bbox: 0.2900 +2024/10/26 23:41:21 - mmengine - INFO - Epoch(train) [8][1400/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:11:16 time: 0.5541 data_time: 0.0242 memory: 5134 grad_norm: 2.6926 loss: 0.5406 loss_cls: 0.2677 loss_bbox: 0.2728 +2024/10/26 23:41:54 - mmengine - INFO - Epoch(train) [8][1450/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:10:54 time: 0.6626 data_time: 0.0101 memory: 5137 grad_norm: 2.4403 loss: 0.5168 loss_cls: 0.2449 loss_bbox: 0.2719 +2024/10/26 23:42:21 - mmengine - INFO - Epoch(train) [8][1500/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:10:28 time: 0.5431 data_time: 0.0091 memory: 5133 grad_norm: 2.5904 loss: 0.5380 loss_cls: 0.2536 loss_bbox: 0.2844 +2024/10/26 23:42:53 - mmengine - INFO - Epoch(train) [8][1550/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:10:05 time: 0.6329 data_time: 0.0088 memory: 5136 grad_norm: 2.6196 loss: 0.5278 loss_cls: 0.2585 loss_bbox: 0.2693 +2024/10/26 23:43:20 - mmengine - INFO - Epoch(train) [8][1600/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:09:39 time: 0.5443 data_time: 0.0086 memory: 5135 grad_norm: 2.4012 loss: 0.5270 loss_cls: 0.2560 loss_bbox: 0.2710 +2024/10/26 23:43:53 - mmengine - INFO - Epoch(train) [8][1650/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:09:16 time: 0.6521 data_time: 0.0092 memory: 5134 grad_norm: 2.6500 loss: 0.5118 loss_cls: 0.2479 loss_bbox: 0.2639 +2024/10/26 23:44:16 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 23:44:23 - mmengine - INFO - Epoch(train) [8][1700/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:08:52 time: 0.6003 data_time: 0.0087 memory: 5135 grad_norm: 2.7793 loss: 0.5075 loss_cls: 0.2368 loss_bbox: 0.2706 +2024/10/26 23:44:55 - mmengine - INFO - Epoch(train) [8][1750/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:08:29 time: 0.6383 data_time: 0.0092 memory: 5133 grad_norm: 2.9206 loss: 0.5580 loss_cls: 0.2739 loss_bbox: 0.2841 +2024/10/26 23:45:22 - mmengine - INFO - Epoch(train) [8][1800/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:08:03 time: 0.5446 data_time: 0.0095 memory: 5132 grad_norm: 2.6963 loss: 0.5117 loss_cls: 0.2437 loss_bbox: 0.2680 +2024/10/26 23:45:54 - mmengine - INFO - Epoch(train) [8][1850/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:07:40 time: 0.6543 data_time: 0.0095 memory: 5135 grad_norm: 2.5659 loss: 0.5326 loss_cls: 0.2524 loss_bbox: 0.2803 +2024/10/26 23:46:24 - mmengine - INFO - Epoch(train) [8][1900/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:07:16 time: 0.5843 data_time: 0.0088 memory: 5136 grad_norm: 2.5490 loss: 0.5266 loss_cls: 0.2595 loss_bbox: 0.2671 +2024/10/26 23:46:56 - mmengine - INFO - Epoch(train) [8][1950/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:06:53 time: 0.6506 data_time: 0.0090 memory: 5135 grad_norm: 2.7584 loss: 0.5652 loss_cls: 0.2686 loss_bbox: 0.2966 +2024/10/26 23:47:26 - mmengine - INFO - Epoch(train) [8][2000/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:06:29 time: 0.5931 data_time: 0.0090 memory: 5132 grad_norm: 3.0067 loss: 0.5397 loss_cls: 0.2652 loss_bbox: 0.2745 +2024/10/26 23:47:59 - mmengine - INFO - Epoch(train) [8][2050/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:06:06 time: 0.6544 data_time: 0.0092 memory: 5134 grad_norm: 2.7494 loss: 0.5600 loss_cls: 0.2824 loss_bbox: 0.2776 +2024/10/26 23:48:24 - mmengine - INFO - Epoch(train) [8][2100/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:05:39 time: 0.5100 data_time: 0.0111 memory: 5134 grad_norm: 2.8703 loss: 0.5512 loss_cls: 0.2730 loss_bbox: 0.2782 +2024/10/26 23:48:56 - mmengine - INFO - Epoch(train) [8][2150/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:05:16 time: 0.6350 data_time: 0.0197 memory: 5134 grad_norm: 2.5668 loss: 0.5281 loss_cls: 0.2525 loss_bbox: 0.2756 +2024/10/26 23:49:26 - mmengine - INFO - Epoch(train) [8][2200/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:04:51 time: 0.6038 data_time: 0.0101 memory: 5134 grad_norm: 2.5459 loss: 0.5477 loss_cls: 0.2701 loss_bbox: 0.2776 +2024/10/26 23:49:58 - mmengine - INFO - Epoch(train) [8][2250/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:04:29 time: 0.6450 data_time: 0.0087 memory: 5135 grad_norm: 2.6765 loss: 0.5524 loss_cls: 0.2725 loss_bbox: 0.2798 +2024/10/26 23:50:28 - mmengine - INFO - Epoch(train) [8][2300/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:04:04 time: 0.5978 data_time: 0.0099 memory: 5132 grad_norm: 2.7200 loss: 0.5400 loss_cls: 0.2670 loss_bbox: 0.2731 +2024/10/26 23:51:00 - mmengine - INFO - Epoch(train) [8][2350/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:03:41 time: 0.6424 data_time: 0.0095 memory: 5134 grad_norm: 2.6329 loss: 0.5411 loss_cls: 0.2647 loss_bbox: 0.2764 +2024/10/26 23:51:29 - mmengine - INFO - Epoch(train) [8][2400/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:03:16 time: 0.5636 data_time: 0.0091 memory: 5133 grad_norm: 2.6926 loss: 0.5686 loss_cls: 0.2724 loss_bbox: 0.2961 +2024/10/26 23:52:01 - mmengine - INFO - Epoch(train) [8][2450/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:02:53 time: 0.6495 data_time: 0.0091 memory: 5135 grad_norm: 2.7277 loss: 0.5116 loss_cls: 0.2450 loss_bbox: 0.2667 +2024/10/26 23:52:36 - mmengine - INFO - Epoch(train) [8][2500/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:02:31 time: 0.6961 data_time: 0.0938 memory: 5136 grad_norm: 2.6776 loss: 0.5595 loss_cls: 0.2843 loss_bbox: 0.2752 +2024/10/26 23:53:05 - mmengine - INFO - Epoch(train) [8][2550/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:02:07 time: 0.5891 data_time: 0.0087 memory: 5134 grad_norm: 2.6044 loss: 0.5432 loss_cls: 0.2650 loss_bbox: 0.2782 +2024/10/26 23:53:38 - mmengine - INFO - Epoch(train) [8][2600/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:01:44 time: 0.6600 data_time: 0.0189 memory: 5135 grad_norm: 2.8322 loss: 0.5304 loss_cls: 0.2618 loss_bbox: 0.2686 +2024/10/26 23:54:10 - mmengine - INFO - Epoch(train) [8][2650/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:01:21 time: 0.6351 data_time: 0.0096 memory: 5134 grad_norm: 2.6562 loss: 0.5324 loss_cls: 0.2631 loss_bbox: 0.2693 +2024/10/26 23:54:34 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/26 23:54:41 - mmengine - INFO - Epoch(train) [8][2700/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:00:57 time: 0.6146 data_time: 0.0173 memory: 5136 grad_norm: 2.5183 loss: 0.5365 loss_cls: 0.2557 loss_bbox: 0.2808 +2024/10/26 23:55:11 - mmengine - INFO - Epoch(train) [8][2750/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:00:33 time: 0.6065 data_time: 0.0089 memory: 5134 grad_norm: 2.6721 loss: 0.5030 loss_cls: 0.2388 loss_bbox: 0.2642 +2024/10/26 23:55:44 - mmengine - INFO - Epoch(train) [8][2800/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 5:00:10 time: 0.6567 data_time: 0.0109 memory: 5136 grad_norm: 2.9587 loss: 0.5591 loss_cls: 0.2791 loss_bbox: 0.2800 +2024/10/26 23:56:13 - mmengine - INFO - Epoch(train) [8][2850/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:59:45 time: 0.5895 data_time: 0.0093 memory: 5135 grad_norm: 2.6749 loss: 0.5551 loss_cls: 0.2706 loss_bbox: 0.2844 +2024/10/26 23:56:44 - mmengine - INFO - Epoch(train) [8][2900/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:59:21 time: 0.6096 data_time: 0.0096 memory: 5134 grad_norm: 2.6972 loss: 0.5618 loss_cls: 0.2759 loss_bbox: 0.2859 +2024/10/26 23:57:12 - mmengine - INFO - Epoch(train) [8][2950/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:58:55 time: 0.5686 data_time: 0.0106 memory: 5136 grad_norm: 2.6066 loss: 0.5191 loss_cls: 0.2576 loss_bbox: 0.2615 +2024/10/26 23:57:42 - mmengine - INFO - Epoch(train) [8][3000/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:58:30 time: 0.5847 data_time: 0.0088 memory: 5134 grad_norm: 2.6341 loss: 0.5092 loss_cls: 0.2504 loss_bbox: 0.2587 +2024/10/26 23:58:13 - mmengine - INFO - Epoch(train) [8][3050/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:58:07 time: 0.6273 data_time: 0.0093 memory: 5137 grad_norm: 2.5330 loss: 0.5141 loss_cls: 0.2597 loss_bbox: 0.2544 +2024/10/26 23:58:43 - mmengine - INFO - Epoch(train) [8][3100/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:57:42 time: 0.5956 data_time: 0.0087 memory: 5135 grad_norm: 2.6461 loss: 0.5327 loss_cls: 0.2607 loss_bbox: 0.2719 +2024/10/26 23:59:14 - mmengine - INFO - Epoch(train) [8][3150/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:57:19 time: 0.6350 data_time: 0.0089 memory: 5137 grad_norm: 2.5362 loss: 0.5544 loss_cls: 0.2786 loss_bbox: 0.2758 +2024/10/26 23:59:45 - mmengine - INFO - Epoch(train) [8][3200/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:56:54 time: 0.6140 data_time: 0.0089 memory: 5137 grad_norm: 2.4905 loss: 0.5233 loss_cls: 0.2539 loss_bbox: 0.2694 +2024/10/27 00:00:15 - mmengine - INFO - Epoch(train) [8][3250/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:56:30 time: 0.5896 data_time: 0.0108 memory: 5134 grad_norm: 2.6190 loss: 0.5538 loss_cls: 0.2635 loss_bbox: 0.2903 +2024/10/27 00:00:47 - mmengine - INFO - Epoch(train) [8][3300/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:56:06 time: 0.6451 data_time: 0.0092 memory: 5135 grad_norm: 2.6923 loss: 0.5681 loss_cls: 0.2827 loss_bbox: 0.2854 +2024/10/27 00:01:17 - mmengine - INFO - Epoch(train) [8][3350/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:55:42 time: 0.5988 data_time: 0.0089 memory: 5132 grad_norm: 2.5310 loss: 0.5471 loss_cls: 0.2691 loss_bbox: 0.2780 +2024/10/27 00:01:51 - mmengine - INFO - Epoch(train) [8][3400/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:55:19 time: 0.6728 data_time: 0.0090 memory: 5137 grad_norm: 3.0222 loss: 0.5707 loss_cls: 0.2798 loss_bbox: 0.2908 +2024/10/27 00:02:20 - mmengine - INFO - Epoch(train) [8][3450/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:54:55 time: 0.5963 data_time: 0.0091 memory: 5135 grad_norm: 2.7525 loss: 0.5419 loss_cls: 0.2619 loss_bbox: 0.2800 +2024/10/27 00:02:54 - mmengine - INFO - Epoch(train) [8][3500/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:54:32 time: 0.6703 data_time: 0.0168 memory: 5136 grad_norm: 2.7010 loss: 0.5491 loss_cls: 0.2715 loss_bbox: 0.2776 +2024/10/27 00:03:24 - mmengine - INFO - Epoch(train) [8][3550/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:54:08 time: 0.6119 data_time: 0.0091 memory: 5135 grad_norm: 2.6539 loss: 0.5393 loss_cls: 0.2641 loss_bbox: 0.2753 +2024/10/27 00:03:57 - mmengine - INFO - Epoch(train) [8][3600/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:53:45 time: 0.6464 data_time: 0.0109 memory: 5135 grad_norm: 2.6490 loss: 0.5792 loss_cls: 0.2841 loss_bbox: 0.2951 +2024/10/27 00:04:26 - mmengine - INFO - Epoch(train) [8][3650/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:53:19 time: 0.5822 data_time: 0.0109 memory: 5135 grad_norm: 2.6674 loss: 0.5482 loss_cls: 0.2717 loss_bbox: 0.2765 +2024/10/27 00:04:52 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 00:04:59 - mmengine - INFO - Epoch(train) [8][3700/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:52:57 time: 0.6626 data_time: 0.0096 memory: 5132 grad_norm: 2.5539 loss: 0.5535 loss_cls: 0.2657 loss_bbox: 0.2879 +2024/10/27 00:05:29 - mmengine - INFO - Epoch(train) [8][3750/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:52:32 time: 0.6075 data_time: 0.0087 memory: 5134 grad_norm: 3.2175 loss: 0.5596 loss_cls: 0.2817 loss_bbox: 0.2779 +2024/10/27 00:06:02 - mmengine - INFO - Epoch(train) [8][3800/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:52:09 time: 0.6483 data_time: 0.0085 memory: 5133 grad_norm: 2.9736 loss: 0.5630 loss_cls: 0.2789 loss_bbox: 0.2841 +2024/10/27 00:06:34 - mmengine - INFO - Epoch(train) [8][3850/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:51:45 time: 0.6428 data_time: 0.0937 memory: 5136 grad_norm: 2.8539 loss: 0.5395 loss_cls: 0.2585 loss_bbox: 0.2809 +2024/10/27 00:07:05 - mmengine - INFO - Epoch(train) [8][3900/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:51:21 time: 0.6152 data_time: 0.0112 memory: 5136 grad_norm: 2.5671 loss: 0.5442 loss_cls: 0.2739 loss_bbox: 0.2703 +2024/10/27 00:07:35 - mmengine - INFO - Epoch(train) [8][3950/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:50:57 time: 0.6111 data_time: 0.0173 memory: 5137 grad_norm: 2.5839 loss: 0.5518 loss_cls: 0.2680 loss_bbox: 0.2838 +2024/10/27 00:08:06 - mmengine - INFO - Epoch(train) [8][4000/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:50:33 time: 0.6213 data_time: 0.0095 memory: 5133 grad_norm: 2.8433 loss: 0.5312 loss_cls: 0.2588 loss_bbox: 0.2724 +2024/10/27 00:08:35 - mmengine - INFO - Epoch(train) [8][4050/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:50:07 time: 0.5828 data_time: 0.0088 memory: 5133 grad_norm: 2.6131 loss: 0.5449 loss_cls: 0.2724 loss_bbox: 0.2725 +2024/10/27 00:09:06 - mmengine - INFO - Epoch(train) [8][4100/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:49:43 time: 0.6036 data_time: 0.0087 memory: 5135 grad_norm: 2.7202 loss: 0.5467 loss_cls: 0.2647 loss_bbox: 0.2820 +2024/10/27 00:09:32 - mmengine - INFO - Epoch(train) [8][4150/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:49:16 time: 0.5291 data_time: 0.0090 memory: 5134 grad_norm: 2.5125 loss: 0.5699 loss_cls: 0.2844 loss_bbox: 0.2855 +2024/10/27 00:10:05 - mmengine - INFO - Epoch(train) [8][4200/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:48:53 time: 0.6533 data_time: 0.0094 memory: 5134 grad_norm: 2.6035 loss: 0.5385 loss_cls: 0.2613 loss_bbox: 0.2771 +2024/10/27 00:10:35 - mmengine - INFO - Epoch(train) [8][4250/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:48:28 time: 0.6003 data_time: 0.0186 memory: 5135 grad_norm: 2.7662 loss: 0.5315 loss_cls: 0.2539 loss_bbox: 0.2776 +2024/10/27 00:11:05 - mmengine - INFO - Epoch(train) [8][4300/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:48:04 time: 0.6130 data_time: 0.0089 memory: 5135 grad_norm: 2.8210 loss: 0.5537 loss_cls: 0.2788 loss_bbox: 0.2749 +2024/10/27 00:11:37 - mmengine - INFO - Epoch(train) [8][4350/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:47:40 time: 0.6367 data_time: 0.0149 memory: 5133 grad_norm: 2.6016 loss: 0.5460 loss_cls: 0.2615 loss_bbox: 0.2845 +2024/10/27 00:12:08 - mmengine - INFO - Epoch(train) [8][4400/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:47:16 time: 0.6228 data_time: 0.0092 memory: 5134 grad_norm: 2.7367 loss: 0.5460 loss_cls: 0.2723 loss_bbox: 0.2736 +2024/10/27 00:12:41 - mmengine - INFO - Epoch(train) [8][4450/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:46:52 time: 0.6412 data_time: 0.0095 memory: 5135 grad_norm: 2.8108 loss: 0.5654 loss_cls: 0.2817 loss_bbox: 0.2836 +2024/10/27 00:13:09 - mmengine - INFO - Epoch(train) [8][4500/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:46:26 time: 0.5730 data_time: 0.0121 memory: 5135 grad_norm: 2.6768 loss: 0.5319 loss_cls: 0.2625 loss_bbox: 0.2694 +2024/10/27 00:13:41 - mmengine - INFO - Epoch(train) [8][4550/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:46:02 time: 0.6270 data_time: 0.0097 memory: 5135 grad_norm: 2.6931 loss: 0.5343 loss_cls: 0.2723 loss_bbox: 0.2619 +2024/10/27 00:14:09 - mmengine - INFO - Epoch(train) [8][4600/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:45:37 time: 0.5695 data_time: 0.0117 memory: 5135 grad_norm: 2.6913 loss: 0.5439 loss_cls: 0.2697 loss_bbox: 0.2742 +2024/10/27 00:14:40 - mmengine - INFO - Epoch(train) [8][4650/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:45:12 time: 0.6150 data_time: 0.0144 memory: 5134 grad_norm: 2.5610 loss: 0.5361 loss_cls: 0.2670 loss_bbox: 0.2692 +2024/10/27 00:15:05 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 00:15:11 - mmengine - INFO - Epoch(train) [8][4700/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:44:48 time: 0.6224 data_time: 0.0097 memory: 5136 grad_norm: 2.6799 loss: 0.5353 loss_cls: 0.2611 loss_bbox: 0.2742 +2024/10/27 00:15:40 - mmengine - INFO - Epoch(train) [8][4750/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:44:23 time: 0.5846 data_time: 0.0092 memory: 5136 grad_norm: 2.6440 loss: 0.5145 loss_cls: 0.2498 loss_bbox: 0.2648 +2024/10/27 00:16:12 - mmengine - INFO - Epoch(train) [8][4800/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:43:58 time: 0.6285 data_time: 0.0151 memory: 5134 grad_norm: 2.6835 loss: 0.5344 loss_cls: 0.2595 loss_bbox: 0.2750 +2024/10/27 00:16:43 - mmengine - INFO - Epoch(train) [8][4850/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:43:35 time: 0.6367 data_time: 0.0099 memory: 5140 grad_norm: 2.6434 loss: 0.5377 loss_cls: 0.2579 loss_bbox: 0.2799 +2024/10/27 00:17:13 - mmengine - INFO - Epoch(train) [8][4900/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:43:10 time: 0.6007 data_time: 0.0157 memory: 5134 grad_norm: 2.7583 loss: 0.5331 loss_cls: 0.2632 loss_bbox: 0.2699 +2024/10/27 00:17:46 - mmengine - INFO - Epoch(train) [8][4950/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:42:46 time: 0.6605 data_time: 0.0156 memory: 5134 grad_norm: 2.6226 loss: 0.5480 loss_cls: 0.2672 loss_bbox: 0.2808 +2024/10/27 00:18:17 - mmengine - INFO - Epoch(train) [8][5000/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:42:22 time: 0.6067 data_time: 0.0090 memory: 5136 grad_norm: 2.7499 loss: 0.5275 loss_cls: 0.2514 loss_bbox: 0.2761 +2024/10/27 00:18:49 - mmengine - INFO - Epoch(train) [8][5050/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:41:58 time: 0.6532 data_time: 0.0093 memory: 5134 grad_norm: 2.7477 loss: 0.5647 loss_cls: 0.2800 loss_bbox: 0.2847 +2024/10/27 00:19:17 - mmengine - INFO - Epoch(train) [8][5100/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:41:32 time: 0.5564 data_time: 0.0119 memory: 5135 grad_norm: 2.5408 loss: 0.5407 loss_cls: 0.2643 loss_bbox: 0.2763 +2024/10/27 00:19:50 - mmengine - INFO - Epoch(train) [8][5150/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:41:08 time: 0.6439 data_time: 0.0088 memory: 5137 grad_norm: 2.6175 loss: 0.5280 loss_cls: 0.2582 loss_bbox: 0.2698 +2024/10/27 00:20:20 - mmengine - INFO - Epoch(train) [8][5200/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:40:44 time: 0.6147 data_time: 0.0089 memory: 5133 grad_norm: 2.5356 loss: 0.5400 loss_cls: 0.2538 loss_bbox: 0.2862 +2024/10/27 00:20:52 - mmengine - INFO - Epoch(train) [8][5250/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:40:20 time: 0.6316 data_time: 0.0093 memory: 5135 grad_norm: 2.7959 loss: 0.5577 loss_cls: 0.2831 loss_bbox: 0.2746 +2024/10/27 00:21:19 - mmengine - INFO - Epoch(train) [8][5300/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:39:53 time: 0.5426 data_time: 0.0093 memory: 5133 grad_norm: 2.7591 loss: 0.5588 loss_cls: 0.2753 loss_bbox: 0.2835 +2024/10/27 00:21:52 - mmengine - INFO - Epoch(train) [8][5350/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:39:30 time: 0.6602 data_time: 0.0086 memory: 5136 grad_norm: 2.8857 loss: 0.5581 loss_cls: 0.2706 loss_bbox: 0.2874 +2024/10/27 00:22:23 - mmengine - INFO - Epoch(train) [8][5400/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:39:05 time: 0.6130 data_time: 0.0108 memory: 5136 grad_norm: 2.5007 loss: 0.5647 loss_cls: 0.2727 loss_bbox: 0.2921 +2024/10/27 00:22:54 - mmengine - INFO - Epoch(train) [8][5450/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:38:41 time: 0.6337 data_time: 0.0086 memory: 5133 grad_norm: 2.5825 loss: 0.5475 loss_cls: 0.2664 loss_bbox: 0.2810 +2024/10/27 00:23:23 - mmengine - INFO - Epoch(train) [8][5500/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:38:15 time: 0.5763 data_time: 0.0094 memory: 5133 grad_norm: 2.4575 loss: 0.5456 loss_cls: 0.2678 loss_bbox: 0.2778 +2024/10/27 00:23:56 - mmengine - INFO - Epoch(train) [8][5550/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:37:51 time: 0.6473 data_time: 0.0100 memory: 5131 grad_norm: 2.4713 loss: 0.5571 loss_cls: 0.2768 loss_bbox: 0.2803 +2024/10/27 00:24:23 - mmengine - INFO - Epoch(train) [8][5600/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:37:25 time: 0.5525 data_time: 0.0200 memory: 5136 grad_norm: 2.5491 loss: 0.5358 loss_cls: 0.2633 loss_bbox: 0.2725 +2024/10/27 00:24:56 - mmengine - INFO - Epoch(train) [8][5650/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:37:02 time: 0.6569 data_time: 0.0149 memory: 5135 grad_norm: 2.5916 loss: 0.5251 loss_cls: 0.2563 loss_bbox: 0.2688 +2024/10/27 00:25:19 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 00:25:26 - mmengine - INFO - Epoch(train) [8][5700/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:36:37 time: 0.5996 data_time: 0.0100 memory: 5134 grad_norm: 2.7691 loss: 0.5536 loss_cls: 0.2738 loss_bbox: 0.2798 +2024/10/27 00:25:57 - mmengine - INFO - Epoch(train) [8][5750/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:36:12 time: 0.6215 data_time: 0.0110 memory: 5136 grad_norm: 2.5030 loss: 0.5263 loss_cls: 0.2560 loss_bbox: 0.2703 +2024/10/27 00:26:27 - mmengine - INFO - Epoch(train) [8][5800/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:35:47 time: 0.5981 data_time: 0.0099 memory: 5133 grad_norm: 2.8644 loss: 0.5669 loss_cls: 0.2786 loss_bbox: 0.2883 +2024/10/27 00:26:58 - mmengine - INFO - Epoch(train) [8][5850/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:35:22 time: 0.6145 data_time: 0.0101 memory: 5135 grad_norm: 2.6607 loss: 0.5314 loss_cls: 0.2566 loss_bbox: 0.2748 +2024/10/27 00:27:26 - mmengine - INFO - Epoch(train) [8][5900/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:34:56 time: 0.5625 data_time: 0.0127 memory: 5134 grad_norm: 2.6426 loss: 0.5788 loss_cls: 0.2937 loss_bbox: 0.2852 +2024/10/27 00:27:58 - mmengine - INFO - Epoch(train) [8][5950/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:34:32 time: 0.6520 data_time: 0.0171 memory: 5134 grad_norm: 2.5455 loss: 0.5851 loss_cls: 0.2912 loss_bbox: 0.2939 +2024/10/27 00:28:29 - mmengine - INFO - Epoch(train) [8][6000/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:34:07 time: 0.6070 data_time: 0.0107 memory: 5136 grad_norm: 2.6634 loss: 0.5441 loss_cls: 0.2748 loss_bbox: 0.2694 +2024/10/27 00:29:01 - mmengine - INFO - Epoch(train) [8][6050/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:33:43 time: 0.6389 data_time: 0.0100 memory: 5134 grad_norm: 2.7431 loss: 0.5225 loss_cls: 0.2524 loss_bbox: 0.2701 +2024/10/27 00:29:30 - mmengine - INFO - Epoch(train) [8][6100/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:33:18 time: 0.5802 data_time: 0.0103 memory: 5133 grad_norm: 2.4929 loss: 0.5208 loss_cls: 0.2576 loss_bbox: 0.2632 +2024/10/27 00:30:03 - mmengine - INFO - Epoch(train) [8][6150/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:32:54 time: 0.6633 data_time: 0.0141 memory: 5134 grad_norm: 2.7103 loss: 0.5599 loss_cls: 0.2708 loss_bbox: 0.2891 +2024/10/27 00:30:34 - mmengine - INFO - Epoch(train) [8][6200/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:32:30 time: 0.6257 data_time: 0.0553 memory: 5135 grad_norm: 2.5616 loss: 0.5546 loss_cls: 0.2738 loss_bbox: 0.2808 +2024/10/27 00:31:07 - mmengine - INFO - Epoch(train) [8][6250/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:32:06 time: 0.6449 data_time: 0.0103 memory: 5134 grad_norm: 2.5138 loss: 0.5504 loss_cls: 0.2765 loss_bbox: 0.2739 +2024/10/27 00:31:38 - mmengine - INFO - Epoch(train) [8][6300/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:31:41 time: 0.6301 data_time: 0.0134 memory: 5137 grad_norm: 2.5903 loss: 0.5211 loss_cls: 0.2516 loss_bbox: 0.2695 +2024/10/27 00:32:10 - mmengine - INFO - Epoch(train) [8][6350/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:31:17 time: 0.6383 data_time: 0.0104 memory: 5136 grad_norm: 2.5735 loss: 0.5256 loss_cls: 0.2531 loss_bbox: 0.2725 +2024/10/27 00:32:41 - mmengine - INFO - Epoch(train) [8][6400/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:30:53 time: 0.6283 data_time: 0.0103 memory: 5137 grad_norm: 2.6649 loss: 0.5403 loss_cls: 0.2657 loss_bbox: 0.2746 +2024/10/27 00:33:12 - mmengine - INFO - Epoch(train) [8][6450/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:30:28 time: 0.6080 data_time: 0.0182 memory: 5132 grad_norm: 2.6884 loss: 0.5488 loss_cls: 0.2698 loss_bbox: 0.2790 +2024/10/27 00:33:44 - mmengine - INFO - Epoch(train) [8][6500/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:30:03 time: 0.6384 data_time: 0.0099 memory: 5134 grad_norm: 2.5934 loss: 0.5550 loss_cls: 0.2741 loss_bbox: 0.2808 +2024/10/27 00:34:13 - mmengine - INFO - Epoch(train) [8][6550/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:29:38 time: 0.5829 data_time: 0.0092 memory: 5136 grad_norm: 2.7570 loss: 0.5428 loss_cls: 0.2653 loss_bbox: 0.2776 +2024/10/27 00:34:46 - mmengine - INFO - Epoch(train) [8][6600/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:29:14 time: 0.6551 data_time: 0.0096 memory: 5134 grad_norm: 2.6384 loss: 0.5292 loss_cls: 0.2690 loss_bbox: 0.2602 +2024/10/27 00:35:15 - mmengine - INFO - Epoch(train) [8][6650/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:28:48 time: 0.5898 data_time: 0.0095 memory: 5133 grad_norm: 2.6358 loss: 0.5016 loss_cls: 0.2486 loss_bbox: 0.2530 +2024/10/27 00:35:39 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 00:35:46 - mmengine - INFO - Epoch(train) [8][6700/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:28:23 time: 0.6142 data_time: 0.0095 memory: 5133 grad_norm: 2.6100 loss: 0.5539 loss_cls: 0.2732 loss_bbox: 0.2807 +2024/10/27 00:36:15 - mmengine - INFO - Epoch(train) [8][6750/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:27:58 time: 0.5796 data_time: 0.0093 memory: 5136 grad_norm: 2.5718 loss: 0.5526 loss_cls: 0.2798 loss_bbox: 0.2728 +2024/10/27 00:36:48 - mmengine - INFO - Epoch(train) [8][6800/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:27:34 time: 0.6685 data_time: 0.0100 memory: 5134 grad_norm: 2.5260 loss: 0.5184 loss_cls: 0.2516 loss_bbox: 0.2668 +2024/10/27 00:37:17 - mmengine - INFO - Epoch(train) [8][6850/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:27:08 time: 0.5715 data_time: 0.0091 memory: 5135 grad_norm: 2.6328 loss: 0.5289 loss_cls: 0.2618 loss_bbox: 0.2671 +2024/10/27 00:37:49 - mmengine - INFO - Epoch(train) [8][6900/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:26:44 time: 0.6398 data_time: 0.0091 memory: 5137 grad_norm: 2.6841 loss: 0.5353 loss_cls: 0.2654 loss_bbox: 0.2699 +2024/10/27 00:38:20 - mmengine - INFO - Epoch(train) [8][6950/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:26:19 time: 0.6146 data_time: 0.0087 memory: 5135 grad_norm: 2.6070 loss: 0.5182 loss_cls: 0.2489 loss_bbox: 0.2693 +2024/10/27 00:38:51 - mmengine - INFO - Epoch(train) [8][7000/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:25:54 time: 0.6339 data_time: 0.0085 memory: 5134 grad_norm: 2.5119 loss: 0.5469 loss_cls: 0.2680 loss_bbox: 0.2789 +2024/10/27 00:39:19 - mmengine - INFO - Epoch(train) [8][7050/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:25:28 time: 0.5644 data_time: 0.0092 memory: 5133 grad_norm: 2.4247 loss: 0.5599 loss_cls: 0.2797 loss_bbox: 0.2802 +2024/10/27 00:39:52 - mmengine - INFO - Epoch(train) [8][7100/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:25:04 time: 0.6426 data_time: 0.0089 memory: 5135 grad_norm: 2.4612 loss: 0.5389 loss_cls: 0.2658 loss_bbox: 0.2731 +2024/10/27 00:40:21 - mmengine - INFO - Epoch(train) [8][7150/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:24:38 time: 0.5883 data_time: 0.0089 memory: 5136 grad_norm: 2.6270 loss: 0.5348 loss_cls: 0.2651 loss_bbox: 0.2697 +2024/10/27 00:40:53 - mmengine - INFO - Epoch(train) [8][7200/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:24:14 time: 0.6379 data_time: 0.0092 memory: 5135 grad_norm: 2.6569 loss: 0.5536 loss_cls: 0.2739 loss_bbox: 0.2797 +2024/10/27 00:41:23 - mmengine - INFO - Epoch(train) [8][7250/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:23:48 time: 0.5975 data_time: 0.0089 memory: 5134 grad_norm: 2.6060 loss: 0.5178 loss_cls: 0.2653 loss_bbox: 0.2525 +2024/10/27 00:41:56 - mmengine - INFO - Epoch(train) [8][7300/7330] base_lr: 1.8660e-04 lr: 1.8660e-04 eta: 4:23:25 time: 0.6648 data_time: 0.0093 memory: 5136 grad_norm: 2.6058 loss: 0.5459 loss_cls: 0.2733 loss_bbox: 0.2726 +2024/10/27 00:42:17 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 00:42:17 - mmengine - INFO - Saving checkpoint at 8 epochs +2024/10/27 00:42:28 - mmengine - INFO - Epoch(val) [8][ 50/1250] eta: 0:02:17 time: 0.1146 data_time: 0.0018 memory: 5134 +2024/10/27 00:42:33 - mmengine - INFO - Epoch(val) [8][ 100/1250] eta: 0:02:07 time: 0.1068 data_time: 0.0016 memory: 629 +2024/10/27 00:42:39 - mmengine - INFO - Epoch(val) [8][ 150/1250] eta: 0:02:02 time: 0.1127 data_time: 0.0015 memory: 634 +2024/10/27 00:42:44 - mmengine - INFO - Epoch(val) [8][ 200/1250] eta: 0:01:55 time: 0.1061 data_time: 0.0016 memory: 634 +2024/10/27 00:42:49 - mmengine - INFO - Epoch(val) [8][ 250/1250] eta: 0:01:49 time: 0.1071 data_time: 0.0016 memory: 624 +2024/10/27 00:42:55 - mmengine - INFO - Epoch(val) [8][ 300/1250] eta: 0:01:43 time: 0.1069 data_time: 0.0016 memory: 624 +2024/10/27 00:43:00 - mmengine - INFO - Epoch(val) [8][ 350/1250] eta: 0:01:38 time: 0.1111 data_time: 0.0017 memory: 624 +2024/10/27 00:43:05 - mmengine - INFO - Epoch(val) [8][ 400/1250] eta: 0:01:32 time: 0.1050 data_time: 0.0017 memory: 614 +2024/10/27 00:43:11 - mmengine - INFO - Epoch(val) [8][ 450/1250] eta: 0:01:26 time: 0.1033 data_time: 0.0016 memory: 634 +2024/10/27 00:43:15 - mmengine - INFO - Epoch(val) [8][ 500/1250] eta: 0:01:20 time: 0.0961 data_time: 0.0016 memory: 634 +2024/10/27 00:43:21 - mmengine - INFO - Epoch(val) [8][ 550/1250] eta: 0:01:15 time: 0.1181 data_time: 0.0016 memory: 614 +2024/10/27 00:43:27 - mmengine - INFO - Epoch(val) [8][ 600/1250] eta: 0:01:10 time: 0.1112 data_time: 0.0016 memory: 624 +2024/10/27 00:43:32 - mmengine - INFO - Epoch(val) [8][ 650/1250] eta: 0:01:05 time: 0.1111 data_time: 0.0016 memory: 624 +2024/10/27 00:43:38 - mmengine - INFO - Epoch(val) [8][ 700/1250] eta: 0:00:59 time: 0.1112 data_time: 0.0016 memory: 629 +2024/10/27 00:43:43 - mmengine - INFO - Epoch(val) [8][ 750/1250] eta: 0:00:54 time: 0.1094 data_time: 0.0014 memory: 629 +2024/10/27 00:43:49 - mmengine - INFO - Epoch(val) [8][ 800/1250] eta: 0:00:49 time: 0.1120 data_time: 0.0015 memory: 634 +2024/10/27 00:43:55 - mmengine - INFO - Epoch(val) [8][ 850/1250] eta: 0:00:43 time: 0.1145 data_time: 0.0015 memory: 634 +2024/10/27 00:44:00 - mmengine - INFO - Epoch(val) [8][ 900/1250] eta: 0:00:38 time: 0.1087 data_time: 0.0015 memory: 634 +2024/10/27 00:44:05 - mmengine - INFO - Epoch(val) [8][ 950/1250] eta: 0:00:32 time: 0.0943 data_time: 0.0015 memory: 625 +2024/10/27 00:44:10 - mmengine - INFO - Epoch(val) [8][1000/1250] eta: 0:00:27 time: 0.1065 data_time: 0.0015 memory: 625 +2024/10/27 00:44:16 - mmengine - INFO - Epoch(val) [8][1050/1250] eta: 0:00:21 time: 0.1133 data_time: 0.0015 memory: 629 +2024/10/27 00:44:21 - mmengine - INFO - Epoch(val) [8][1100/1250] eta: 0:00:16 time: 0.1077 data_time: 0.0015 memory: 634 +2024/10/27 00:44:27 - mmengine - INFO - Epoch(val) [8][1150/1250] eta: 0:00:10 time: 0.1123 data_time: 0.0016 memory: 629 +2024/10/27 00:44:32 - mmengine - INFO - Epoch(val) [8][1200/1250] eta: 0:00:05 time: 0.1089 data_time: 0.0015 memory: 629 +2024/10/27 00:44:38 - mmengine - INFO - Epoch(val) [8][1250/1250] eta: 0:00:00 time: 0.1065 data_time: 0.0014 memory: 635 +2024/10/27 00:44:46 - mmengine - INFO - Evaluating bbox... +2024/10/27 00:45:37 - mmengine - INFO - bbox_mAP_copypaste: 0.331 0.516 0.355 0.180 0.359 0.457 +2024/10/27 00:45:38 - mmengine - INFO - Epoch(val) [8][1250/1250] coco/bbox_mAP: 0.3310 coco/bbox_mAP_50: 0.5160 coco/bbox_mAP_75: 0.3550 coco/bbox_mAP_s: 0.1800 coco/bbox_mAP_m: 0.3590 coco/bbox_mAP_l: 0.4570 data_time: 0.0016 time: 0.1086 +2024/10/27 00:46:09 - mmengine - INFO - Epoch(train) [9][ 50/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:22:46 time: 0.6180 data_time: 0.0084 memory: 5133 grad_norm: 2.6179 loss: 0.5036 loss_cls: 0.2410 loss_bbox: 0.2626 +2024/10/27 00:46:42 - mmengine - INFO - Epoch(train) [9][ 100/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:22:22 time: 0.6580 data_time: 0.0120 memory: 5135 grad_norm: 2.5879 loss: 0.4890 loss_cls: 0.2321 loss_bbox: 0.2570 +2024/10/27 00:47:12 - mmengine - INFO - Epoch(train) [9][ 150/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:21:57 time: 0.6021 data_time: 0.0089 memory: 5137 grad_norm: 2.7118 loss: 0.4871 loss_cls: 0.2262 loss_bbox: 0.2608 +2024/10/27 00:47:42 - mmengine - INFO - Epoch(train) [9][ 200/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:21:31 time: 0.5860 data_time: 0.0086 memory: 5136 grad_norm: 2.6593 loss: 0.4919 loss_cls: 0.2355 loss_bbox: 0.2564 +2024/10/27 00:48:10 - mmengine - INFO - Epoch(train) [9][ 250/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:21:05 time: 0.5642 data_time: 0.0088 memory: 5137 grad_norm: 2.6029 loss: 0.4643 loss_cls: 0.2228 loss_bbox: 0.2415 +2024/10/27 00:48:42 - mmengine - INFO - Epoch(train) [9][ 300/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:20:40 time: 0.6461 data_time: 0.0087 memory: 5134 grad_norm: 2.4173 loss: 0.5010 loss_cls: 0.2411 loss_bbox: 0.2599 +2024/10/27 00:49:10 - mmengine - INFO - Epoch(train) [9][ 350/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:20:14 time: 0.5636 data_time: 0.0088 memory: 5133 grad_norm: 2.3379 loss: 0.4991 loss_cls: 0.2373 loss_bbox: 0.2618 +2024/10/27 00:49:17 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 00:49:43 - mmengine - INFO - Epoch(train) [9][ 400/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:19:50 time: 0.6415 data_time: 0.0092 memory: 5136 grad_norm: 2.4629 loss: 0.5118 loss_cls: 0.2426 loss_bbox: 0.2692 +2024/10/27 00:50:13 - mmengine - INFO - Epoch(train) [9][ 450/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:19:24 time: 0.6037 data_time: 0.0089 memory: 5133 grad_norm: 2.5086 loss: 0.5078 loss_cls: 0.2424 loss_bbox: 0.2654 +2024/10/27 00:50:46 - mmengine - INFO - Epoch(train) [9][ 500/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:19:00 time: 0.6701 data_time: 0.0088 memory: 5135 grad_norm: 2.6146 loss: 0.5103 loss_cls: 0.2385 loss_bbox: 0.2718 +2024/10/27 00:51:15 - mmengine - INFO - Epoch(train) [9][ 550/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:18:34 time: 0.5660 data_time: 0.0094 memory: 5135 grad_norm: 2.4387 loss: 0.5162 loss_cls: 0.2449 loss_bbox: 0.2713 +2024/10/27 00:51:45 - mmengine - INFO - Epoch(train) [9][ 600/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:18:09 time: 0.6095 data_time: 0.0178 memory: 5134 grad_norm: 2.6734 loss: 0.5188 loss_cls: 0.2534 loss_bbox: 0.2654 +2024/10/27 00:52:16 - mmengine - INFO - Epoch(train) [9][ 650/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:17:44 time: 0.6103 data_time: 0.0097 memory: 5135 grad_norm: 2.5732 loss: 0.5085 loss_cls: 0.2511 loss_bbox: 0.2574 +2024/10/27 00:52:49 - mmengine - INFO - Epoch(train) [9][ 700/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:17:20 time: 0.6650 data_time: 0.0138 memory: 5134 grad_norm: 2.5097 loss: 0.5143 loss_cls: 0.2454 loss_bbox: 0.2689 +2024/10/27 00:53:17 - mmengine - INFO - Epoch(train) [9][ 750/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:16:53 time: 0.5740 data_time: 0.0098 memory: 5135 grad_norm: 2.4455 loss: 0.5007 loss_cls: 0.2422 loss_bbox: 0.2585 +2024/10/27 00:53:50 - mmengine - INFO - Epoch(train) [9][ 800/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:16:29 time: 0.6600 data_time: 0.0092 memory: 5133 grad_norm: 2.5236 loss: 0.4834 loss_cls: 0.2251 loss_bbox: 0.2583 +2024/10/27 00:54:20 - mmengine - INFO - Epoch(train) [9][ 850/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:16:03 time: 0.5826 data_time: 0.0096 memory: 5136 grad_norm: 2.4610 loss: 0.5255 loss_cls: 0.2590 loss_bbox: 0.2665 +2024/10/27 00:54:54 - mmengine - INFO - Epoch(train) [9][ 900/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:15:40 time: 0.6780 data_time: 0.0108 memory: 5133 grad_norm: 2.8672 loss: 0.5200 loss_cls: 0.2407 loss_bbox: 0.2794 +2024/10/27 00:55:23 - mmengine - INFO - Epoch(train) [9][ 950/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:15:14 time: 0.5884 data_time: 0.0090 memory: 5135 grad_norm: 2.3219 loss: 0.5179 loss_cls: 0.2459 loss_bbox: 0.2720 +2024/10/27 00:55:55 - mmengine - INFO - Epoch(train) [9][1000/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:14:50 time: 0.6484 data_time: 0.0099 memory: 5134 grad_norm: 2.3754 loss: 0.5206 loss_cls: 0.2522 loss_bbox: 0.2684 +2024/10/27 00:56:23 - mmengine - INFO - Epoch(train) [9][1050/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:14:23 time: 0.5570 data_time: 0.0091 memory: 5135 grad_norm: 2.4775 loss: 0.5062 loss_cls: 0.2402 loss_bbox: 0.2660 +2024/10/27 00:56:56 - mmengine - INFO - Epoch(train) [9][1100/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:13:59 time: 0.6627 data_time: 0.0088 memory: 5136 grad_norm: 2.4333 loss: 0.5023 loss_cls: 0.2420 loss_bbox: 0.2604 +2024/10/27 00:57:25 - mmengine - INFO - Epoch(train) [9][1150/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:13:33 time: 0.5738 data_time: 0.0092 memory: 5135 grad_norm: 2.4917 loss: 0.4969 loss_cls: 0.2297 loss_bbox: 0.2672 +2024/10/27 00:57:57 - mmengine - INFO - Epoch(train) [9][1200/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:13:08 time: 0.6483 data_time: 0.0117 memory: 5132 grad_norm: 2.4258 loss: 0.4970 loss_cls: 0.2341 loss_bbox: 0.2629 +2024/10/27 00:58:25 - mmengine - INFO - Epoch(train) [9][1250/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:12:42 time: 0.5565 data_time: 0.0088 memory: 5133 grad_norm: 2.5537 loss: 0.5016 loss_cls: 0.2462 loss_bbox: 0.2555 +2024/10/27 00:58:57 - mmengine - INFO - Epoch(train) [9][1300/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:12:17 time: 0.6403 data_time: 0.0092 memory: 5136 grad_norm: 2.4394 loss: 0.4938 loss_cls: 0.2399 loss_bbox: 0.2539 +2024/10/27 00:59:26 - mmengine - INFO - Epoch(train) [9][1350/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:11:51 time: 0.5825 data_time: 0.0087 memory: 5134 grad_norm: 2.7113 loss: 0.5232 loss_cls: 0.2545 loss_bbox: 0.2687 +2024/10/27 00:59:33 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 00:59:59 - mmengine - INFO - Epoch(train) [9][1400/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:11:26 time: 0.6497 data_time: 0.0097 memory: 5136 grad_norm: 2.5647 loss: 0.4892 loss_cls: 0.2408 loss_bbox: 0.2484 +2024/10/27 01:00:28 - mmengine - INFO - Epoch(train) [9][1450/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:11:00 time: 0.5752 data_time: 0.0117 memory: 5136 grad_norm: 2.4631 loss: 0.5174 loss_cls: 0.2500 loss_bbox: 0.2674 +2024/10/27 01:01:01 - mmengine - INFO - Epoch(train) [9][1500/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:10:36 time: 0.6668 data_time: 0.0082 memory: 5135 grad_norm: 2.3958 loss: 0.5027 loss_cls: 0.2403 loss_bbox: 0.2624 +2024/10/27 01:01:34 - mmengine - INFO - Epoch(train) [9][1550/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:10:12 time: 0.6547 data_time: 0.0928 memory: 5134 grad_norm: 2.3800 loss: 0.4939 loss_cls: 0.2400 loss_bbox: 0.2539 +2024/10/27 01:02:04 - mmengine - INFO - Epoch(train) [9][1600/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:09:46 time: 0.6065 data_time: 0.0125 memory: 5136 grad_norm: 2.5519 loss: 0.5119 loss_cls: 0.2536 loss_bbox: 0.2583 +2024/10/27 01:02:36 - mmengine - INFO - Epoch(train) [9][1650/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:09:21 time: 0.6400 data_time: 0.0340 memory: 5134 grad_norm: 2.3997 loss: 0.4882 loss_cls: 0.2319 loss_bbox: 0.2563 +2024/10/27 01:03:08 - mmengine - INFO - Epoch(train) [9][1700/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:08:57 time: 0.6373 data_time: 0.0090 memory: 5136 grad_norm: 2.4881 loss: 0.5018 loss_cls: 0.2424 loss_bbox: 0.2594 +2024/10/27 01:03:40 - mmengine - INFO - Epoch(train) [9][1750/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:08:32 time: 0.6302 data_time: 0.0125 memory: 5134 grad_norm: 2.4594 loss: 0.4750 loss_cls: 0.2227 loss_bbox: 0.2523 +2024/10/27 01:04:12 - mmengine - INFO - Epoch(train) [9][1800/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:08:07 time: 0.6422 data_time: 0.0084 memory: 5136 grad_norm: 2.3976 loss: 0.4965 loss_cls: 0.2386 loss_bbox: 0.2579 +2024/10/27 01:04:41 - mmengine - INFO - Epoch(train) [9][1850/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:07:41 time: 0.5959 data_time: 0.0090 memory: 5135 grad_norm: 2.4884 loss: 0.4790 loss_cls: 0.2307 loss_bbox: 0.2483 +2024/10/27 01:05:11 - mmengine - INFO - Epoch(train) [9][1900/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:07:15 time: 0.5838 data_time: 0.0193 memory: 5136 grad_norm: 2.4483 loss: 0.4829 loss_cls: 0.2285 loss_bbox: 0.2544 +2024/10/27 01:05:42 - mmengine - INFO - Epoch(train) [9][1950/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:06:50 time: 0.6315 data_time: 0.0109 memory: 5135 grad_norm: 2.4653 loss: 0.5258 loss_cls: 0.2539 loss_bbox: 0.2718 +2024/10/27 01:06:11 - mmengine - INFO - Epoch(train) [9][2000/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:06:24 time: 0.5776 data_time: 0.0097 memory: 5135 grad_norm: 2.4071 loss: 0.5233 loss_cls: 0.2556 loss_bbox: 0.2677 +2024/10/27 01:06:44 - mmengine - INFO - Epoch(train) [9][2050/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:05:59 time: 0.6583 data_time: 0.0099 memory: 5134 grad_norm: 2.6530 loss: 0.5237 loss_cls: 0.2619 loss_bbox: 0.2618 +2024/10/27 01:07:13 - mmengine - INFO - Epoch(train) [9][2100/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:05:33 time: 0.5834 data_time: 0.0089 memory: 5134 grad_norm: 2.5348 loss: 0.4858 loss_cls: 0.2408 loss_bbox: 0.2449 +2024/10/27 01:07:47 - mmengine - INFO - Epoch(train) [9][2150/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:05:09 time: 0.6715 data_time: 0.0089 memory: 5136 grad_norm: 2.3353 loss: 0.5082 loss_cls: 0.2377 loss_bbox: 0.2705 +2024/10/27 01:08:18 - mmengine - INFO - Epoch(train) [9][2200/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:04:44 time: 0.6290 data_time: 0.0089 memory: 5135 grad_norm: 2.4640 loss: 0.5438 loss_cls: 0.2640 loss_bbox: 0.2798 +2024/10/27 01:08:50 - mmengine - INFO - Epoch(train) [9][2250/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:04:19 time: 0.6271 data_time: 0.0095 memory: 5136 grad_norm: 2.4916 loss: 0.5022 loss_cls: 0.2452 loss_bbox: 0.2570 +2024/10/27 01:09:15 - mmengine - INFO - Epoch(train) [9][2300/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:03:51 time: 0.5182 data_time: 0.0094 memory: 5132 grad_norm: 2.6340 loss: 0.4936 loss_cls: 0.2420 loss_bbox: 0.2516 +2024/10/27 01:09:48 - mmengine - INFO - Epoch(train) [9][2350/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:03:26 time: 0.6479 data_time: 0.0102 memory: 5133 grad_norm: 2.3940 loss: 0.4965 loss_cls: 0.2338 loss_bbox: 0.2627 +2024/10/27 01:09:55 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 01:10:19 - mmengine - INFO - Epoch(train) [9][2400/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:03:01 time: 0.6162 data_time: 0.0085 memory: 5134 grad_norm: 2.4074 loss: 0.5020 loss_cls: 0.2416 loss_bbox: 0.2604 +2024/10/27 01:10:50 - mmengine - INFO - Epoch(train) [9][2450/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:02:36 time: 0.6248 data_time: 0.0092 memory: 5136 grad_norm: 2.5986 loss: 0.4990 loss_cls: 0.2412 loss_bbox: 0.2579 +2024/10/27 01:11:20 - mmengine - INFO - Epoch(train) [9][2500/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:02:10 time: 0.5980 data_time: 0.0088 memory: 5134 grad_norm: 2.4162 loss: 0.4948 loss_cls: 0.2360 loss_bbox: 0.2588 +2024/10/27 01:11:53 - mmengine - INFO - Epoch(train) [9][2550/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:01:46 time: 0.6655 data_time: 0.0087 memory: 5134 grad_norm: 2.5006 loss: 0.4912 loss_cls: 0.2313 loss_bbox: 0.2599 +2024/10/27 01:12:21 - mmengine - INFO - Epoch(train) [9][2600/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:01:19 time: 0.5577 data_time: 0.0093 memory: 5134 grad_norm: 2.6117 loss: 0.5024 loss_cls: 0.2419 loss_bbox: 0.2605 +2024/10/27 01:12:54 - mmengine - INFO - Epoch(train) [9][2650/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:00:54 time: 0.6691 data_time: 0.0090 memory: 5134 grad_norm: 2.4284 loss: 0.4965 loss_cls: 0.2361 loss_bbox: 0.2604 +2024/10/27 01:13:22 - mmengine - INFO - Epoch(train) [9][2700/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:00:28 time: 0.5501 data_time: 0.0096 memory: 5133 grad_norm: 2.3806 loss: 0.5065 loss_cls: 0.2437 loss_bbox: 0.2629 +2024/10/27 01:13:54 - mmengine - INFO - Epoch(train) [9][2750/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 4:00:03 time: 0.6397 data_time: 0.0102 memory: 5135 grad_norm: 2.6213 loss: 0.5125 loss_cls: 0.2467 loss_bbox: 0.2658 +2024/10/27 01:14:25 - mmengine - INFO - Epoch(train) [9][2800/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:59:37 time: 0.6133 data_time: 0.0096 memory: 5136 grad_norm: 2.5437 loss: 0.4942 loss_cls: 0.2453 loss_bbox: 0.2489 +2024/10/27 01:14:56 - mmengine - INFO - Epoch(train) [9][2850/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:59:12 time: 0.6361 data_time: 0.0097 memory: 5134 grad_norm: 2.5186 loss: 0.5173 loss_cls: 0.2503 loss_bbox: 0.2671 +2024/10/27 01:15:27 - mmengine - INFO - Epoch(train) [9][2900/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:58:46 time: 0.6120 data_time: 0.0094 memory: 5139 grad_norm: 2.6608 loss: 0.4724 loss_cls: 0.2229 loss_bbox: 0.2494 +2024/10/27 01:16:00 - mmengine - INFO - Epoch(train) [9][2950/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:58:22 time: 0.6624 data_time: 0.0089 memory: 5136 grad_norm: 2.3993 loss: 0.5204 loss_cls: 0.2578 loss_bbox: 0.2626 +2024/10/27 01:16:31 - mmengine - INFO - Epoch(train) [9][3000/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:57:56 time: 0.6071 data_time: 0.0093 memory: 5134 grad_norm: 2.4447 loss: 0.4784 loss_cls: 0.2284 loss_bbox: 0.2500 +2024/10/27 01:17:04 - mmengine - INFO - Epoch(train) [9][3050/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:57:31 time: 0.6597 data_time: 0.0097 memory: 5135 grad_norm: 2.4315 loss: 0.5001 loss_cls: 0.2419 loss_bbox: 0.2582 +2024/10/27 01:17:37 - mmengine - INFO - Epoch(train) [9][3100/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:57:07 time: 0.6741 data_time: 0.0670 memory: 5135 grad_norm: 2.5121 loss: 0.5028 loss_cls: 0.2404 loss_bbox: 0.2624 +2024/10/27 01:18:09 - mmengine - INFO - Epoch(train) [9][3150/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:56:42 time: 0.6286 data_time: 0.0087 memory: 5137 grad_norm: 2.5889 loss: 0.5114 loss_cls: 0.2503 loss_bbox: 0.2610 +2024/10/27 01:18:40 - mmengine - INFO - Epoch(train) [9][3200/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:56:16 time: 0.6293 data_time: 0.0095 memory: 5134 grad_norm: 2.3292 loss: 0.5179 loss_cls: 0.2496 loss_bbox: 0.2683 +2024/10/27 01:19:11 - mmengine - INFO - Epoch(train) [9][3250/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:55:51 time: 0.6229 data_time: 0.0172 memory: 5133 grad_norm: 2.4487 loss: 0.5025 loss_cls: 0.2442 loss_bbox: 0.2583 +2024/10/27 01:19:43 - mmengine - INFO - Epoch(train) [9][3300/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:55:26 time: 0.6367 data_time: 0.0090 memory: 5134 grad_norm: 2.4299 loss: 0.5076 loss_cls: 0.2444 loss_bbox: 0.2632 +2024/10/27 01:20:13 - mmengine - INFO - Epoch(train) [9][3350/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:55:00 time: 0.5925 data_time: 0.0139 memory: 5132 grad_norm: 2.4624 loss: 0.4866 loss_cls: 0.2351 loss_bbox: 0.2516 +2024/10/27 01:20:18 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 01:20:45 - mmengine - INFO - Epoch(train) [9][3400/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:54:35 time: 0.6468 data_time: 0.0107 memory: 5136 grad_norm: 2.5435 loss: 0.5346 loss_cls: 0.2654 loss_bbox: 0.2691 +2024/10/27 01:21:15 - mmengine - INFO - Epoch(train) [9][3450/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:54:09 time: 0.5908 data_time: 0.0093 memory: 5136 grad_norm: 2.3885 loss: 0.5149 loss_cls: 0.2507 loss_bbox: 0.2643 +2024/10/27 01:21:47 - mmengine - INFO - Epoch(train) [9][3500/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:53:44 time: 0.6524 data_time: 0.0142 memory: 5135 grad_norm: 2.5045 loss: 0.5007 loss_cls: 0.2431 loss_bbox: 0.2575 +2024/10/27 01:22:18 - mmengine - INFO - Epoch(train) [9][3550/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:53:18 time: 0.6200 data_time: 0.0170 memory: 5136 grad_norm: 2.4932 loss: 0.5520 loss_cls: 0.2698 loss_bbox: 0.2822 +2024/10/27 01:22:50 - mmengine - INFO - Epoch(train) [9][3600/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:52:53 time: 0.6311 data_time: 0.0095 memory: 5136 grad_norm: 2.6591 loss: 0.5183 loss_cls: 0.2481 loss_bbox: 0.2702 +2024/10/27 01:23:21 - mmengine - INFO - Epoch(train) [9][3650/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:52:27 time: 0.6217 data_time: 0.0145 memory: 5134 grad_norm: 2.6994 loss: 0.5279 loss_cls: 0.2570 loss_bbox: 0.2709 +2024/10/27 01:23:54 - mmengine - INFO - Epoch(train) [9][3700/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:52:03 time: 0.6667 data_time: 0.0096 memory: 5134 grad_norm: 2.4404 loss: 0.5096 loss_cls: 0.2401 loss_bbox: 0.2694 +2024/10/27 01:24:22 - mmengine - INFO - Epoch(train) [9][3750/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:51:35 time: 0.5470 data_time: 0.0091 memory: 5134 grad_norm: 2.3458 loss: 0.4839 loss_cls: 0.2341 loss_bbox: 0.2498 +2024/10/27 01:24:54 - mmengine - INFO - Epoch(train) [9][3800/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:51:10 time: 0.6455 data_time: 0.0096 memory: 5134 grad_norm: 2.4510 loss: 0.4880 loss_cls: 0.2265 loss_bbox: 0.2615 +2024/10/27 01:25:25 - mmengine - INFO - Epoch(train) [9][3850/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:50:45 time: 0.6299 data_time: 0.0092 memory: 5132 grad_norm: 2.5362 loss: 0.5077 loss_cls: 0.2454 loss_bbox: 0.2623 +2024/10/27 01:25:59 - mmengine - INFO - Epoch(train) [9][3900/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:50:20 time: 0.6711 data_time: 0.0096 memory: 5133 grad_norm: 2.4297 loss: 0.4711 loss_cls: 0.2204 loss_bbox: 0.2507 +2024/10/27 01:26:28 - mmengine - INFO - Epoch(train) [9][3950/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:49:54 time: 0.5766 data_time: 0.0231 memory: 5136 grad_norm: 2.5607 loss: 0.5148 loss_cls: 0.2462 loss_bbox: 0.2687 +2024/10/27 01:27:00 - mmengine - INFO - Epoch(train) [9][4000/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:49:29 time: 0.6454 data_time: 0.0107 memory: 5134 grad_norm: 2.5017 loss: 0.5108 loss_cls: 0.2467 loss_bbox: 0.2641 +2024/10/27 01:27:29 - mmengine - INFO - Epoch(train) [9][4050/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:49:02 time: 0.5815 data_time: 0.0111 memory: 5133 grad_norm: 2.5288 loss: 0.5025 loss_cls: 0.2368 loss_bbox: 0.2657 +2024/10/27 01:28:02 - mmengine - INFO - Epoch(train) [9][4100/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:48:37 time: 0.6534 data_time: 0.0174 memory: 5134 grad_norm: 2.4709 loss: 0.4756 loss_cls: 0.2265 loss_bbox: 0.2491 +2024/10/27 01:28:37 - mmengine - INFO - Epoch(train) [9][4150/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:48:13 time: 0.6939 data_time: 0.0917 memory: 5135 grad_norm: 2.3349 loss: 0.5104 loss_cls: 0.2386 loss_bbox: 0.2718 +2024/10/27 01:29:08 - mmengine - INFO - Epoch(train) [9][4200/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:47:48 time: 0.6240 data_time: 0.0097 memory: 5136 grad_norm: 2.4741 loss: 0.5201 loss_cls: 0.2550 loss_bbox: 0.2651 +2024/10/27 01:29:38 - mmengine - INFO - Epoch(train) [9][4250/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:47:22 time: 0.6140 data_time: 0.0098 memory: 5133 grad_norm: 2.5545 loss: 0.5148 loss_cls: 0.2490 loss_bbox: 0.2657 +2024/10/27 01:30:10 - mmengine - INFO - Epoch(train) [9][4300/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:46:56 time: 0.6252 data_time: 0.0127 memory: 5133 grad_norm: 2.5042 loss: 0.5202 loss_cls: 0.2548 loss_bbox: 0.2653 +2024/10/27 01:30:39 - mmengine - INFO - Epoch(train) [9][4350/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:46:30 time: 0.5871 data_time: 0.0114 memory: 5135 grad_norm: 2.4302 loss: 0.4917 loss_cls: 0.2352 loss_bbox: 0.2565 +2024/10/27 01:30:45 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 01:31:10 - mmengine - INFO - Epoch(train) [9][4400/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:46:04 time: 0.6222 data_time: 0.0174 memory: 5135 grad_norm: 2.4798 loss: 0.4661 loss_cls: 0.2186 loss_bbox: 0.2475 +2024/10/27 01:31:43 - mmengine - INFO - Epoch(train) [9][4450/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:45:39 time: 0.6480 data_time: 0.0128 memory: 5136 grad_norm: 2.7208 loss: 0.4873 loss_cls: 0.2391 loss_bbox: 0.2482 +2024/10/27 01:32:13 - mmengine - INFO - Epoch(train) [9][4500/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:45:13 time: 0.6063 data_time: 0.0109 memory: 5134 grad_norm: 2.6133 loss: 0.5308 loss_cls: 0.2564 loss_bbox: 0.2745 +2024/10/27 01:32:47 - mmengine - INFO - Epoch(train) [9][4550/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:44:48 time: 0.6795 data_time: 0.0104 memory: 5133 grad_norm: 2.4892 loss: 0.5349 loss_cls: 0.2689 loss_bbox: 0.2660 +2024/10/27 01:33:17 - mmengine - INFO - Epoch(train) [9][4600/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:44:22 time: 0.5962 data_time: 0.0098 memory: 5135 grad_norm: 2.4296 loss: 0.5158 loss_cls: 0.2514 loss_bbox: 0.2644 +2024/10/27 01:33:48 - mmengine - INFO - Epoch(train) [9][4650/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:43:57 time: 0.6303 data_time: 0.0095 memory: 5135 grad_norm: 2.5946 loss: 0.4906 loss_cls: 0.2256 loss_bbox: 0.2650 +2024/10/27 01:34:19 - mmengine - INFO - Epoch(train) [9][4700/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:43:31 time: 0.6099 data_time: 0.0091 memory: 5137 grad_norm: 2.5125 loss: 0.5053 loss_cls: 0.2388 loss_bbox: 0.2665 +2024/10/27 01:34:51 - mmengine - INFO - Epoch(train) [9][4750/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:43:05 time: 0.6386 data_time: 0.0091 memory: 5134 grad_norm: 2.4563 loss: 0.5089 loss_cls: 0.2468 loss_bbox: 0.2621 +2024/10/27 01:35:22 - mmengine - INFO - Epoch(train) [9][4800/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:42:39 time: 0.6228 data_time: 0.0097 memory: 5134 grad_norm: 2.4478 loss: 0.5167 loss_cls: 0.2487 loss_bbox: 0.2681 +2024/10/27 01:35:55 - mmengine - INFO - Epoch(train) [9][4850/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:42:14 time: 0.6577 data_time: 0.0105 memory: 5134 grad_norm: 2.4416 loss: 0.4988 loss_cls: 0.2364 loss_bbox: 0.2624 +2024/10/27 01:36:26 - mmengine - INFO - Epoch(train) [9][4900/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:41:49 time: 0.6278 data_time: 0.0101 memory: 5133 grad_norm: 2.5254 loss: 0.5447 loss_cls: 0.2648 loss_bbox: 0.2800 +2024/10/27 01:36:59 - mmengine - INFO - Epoch(train) [9][4950/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:41:24 time: 0.6562 data_time: 0.0152 memory: 5132 grad_norm: 2.4694 loss: 0.4925 loss_cls: 0.2416 loss_bbox: 0.2510 +2024/10/27 01:37:29 - mmengine - INFO - Epoch(train) [9][5000/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:40:58 time: 0.6071 data_time: 0.0110 memory: 5135 grad_norm: 2.5594 loss: 0.5138 loss_cls: 0.2588 loss_bbox: 0.2551 +2024/10/27 01:38:02 - mmengine - INFO - Epoch(train) [9][5050/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:40:32 time: 0.6621 data_time: 0.0098 memory: 5133 grad_norm: 2.5202 loss: 0.5064 loss_cls: 0.2399 loss_bbox: 0.2666 +2024/10/27 01:38:36 - mmengine - INFO - Epoch(train) [9][5100/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:40:08 time: 0.6676 data_time: 0.0642 memory: 5137 grad_norm: 2.7261 loss: 0.5045 loss_cls: 0.2439 loss_bbox: 0.2606 +2024/10/27 01:39:09 - mmengine - INFO - Epoch(train) [9][5150/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:39:42 time: 0.6565 data_time: 0.0142 memory: 5133 grad_norm: 2.4910 loss: 0.5172 loss_cls: 0.2472 loss_bbox: 0.2700 +2024/10/27 01:39:38 - mmengine - INFO - Epoch(train) [9][5200/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:39:16 time: 0.5821 data_time: 0.0117 memory: 5135 grad_norm: 2.6139 loss: 0.5320 loss_cls: 0.2601 loss_bbox: 0.2719 +2024/10/27 01:40:10 - mmengine - INFO - Epoch(train) [9][5250/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:38:50 time: 0.6461 data_time: 0.0096 memory: 5133 grad_norm: 2.3640 loss: 0.5065 loss_cls: 0.2469 loss_bbox: 0.2596 +2024/10/27 01:40:42 - mmengine - INFO - Epoch(train) [9][5300/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:38:25 time: 0.6389 data_time: 0.0101 memory: 5134 grad_norm: 2.4574 loss: 0.4805 loss_cls: 0.2288 loss_bbox: 0.2517 +2024/10/27 01:41:11 - mmengine - INFO - Epoch(train) [9][5350/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:37:58 time: 0.5745 data_time: 0.0102 memory: 5135 grad_norm: 2.5415 loss: 0.5064 loss_cls: 0.2482 loss_bbox: 0.2582 +2024/10/27 01:41:17 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 01:41:43 - mmengine - INFO - Epoch(train) [9][5400/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:37:33 time: 0.6410 data_time: 0.0098 memory: 5133 grad_norm: 2.4536 loss: 0.5162 loss_cls: 0.2546 loss_bbox: 0.2615 +2024/10/27 01:42:13 - mmengine - INFO - Epoch(train) [9][5450/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:37:06 time: 0.5996 data_time: 0.0097 memory: 5134 grad_norm: 2.4991 loss: 0.5227 loss_cls: 0.2593 loss_bbox: 0.2634 +2024/10/27 01:42:46 - mmengine - INFO - Epoch(train) [9][5500/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:36:41 time: 0.6596 data_time: 0.0092 memory: 5134 grad_norm: 2.4762 loss: 0.4819 loss_cls: 0.2312 loss_bbox: 0.2507 +2024/10/27 01:43:17 - mmengine - INFO - Epoch(train) [9][5550/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:36:15 time: 0.6152 data_time: 0.0092 memory: 5134 grad_norm: 2.5183 loss: 0.5034 loss_cls: 0.2285 loss_bbox: 0.2749 +2024/10/27 01:43:49 - mmengine - INFO - Epoch(train) [9][5600/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:35:50 time: 0.6537 data_time: 0.0122 memory: 5131 grad_norm: 2.5883 loss: 0.5321 loss_cls: 0.2535 loss_bbox: 0.2786 +2024/10/27 01:44:19 - mmengine - INFO - Epoch(train) [9][5650/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:35:23 time: 0.5931 data_time: 0.0111 memory: 5135 grad_norm: 2.4152 loss: 0.5027 loss_cls: 0.2446 loss_bbox: 0.2581 +2024/10/27 01:44:52 - mmengine - INFO - Epoch(train) [9][5700/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:34:58 time: 0.6541 data_time: 0.0103 memory: 5137 grad_norm: 2.5259 loss: 0.5190 loss_cls: 0.2472 loss_bbox: 0.2718 +2024/10/27 01:45:22 - mmengine - INFO - Epoch(train) [9][5750/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:34:32 time: 0.6067 data_time: 0.0149 memory: 5134 grad_norm: 2.4246 loss: 0.5090 loss_cls: 0.2449 loss_bbox: 0.2640 +2024/10/27 01:45:55 - mmengine - INFO - Epoch(train) [9][5800/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:34:07 time: 0.6631 data_time: 0.0104 memory: 5133 grad_norm: 2.5110 loss: 0.5259 loss_cls: 0.2566 loss_bbox: 0.2693 +2024/10/27 01:46:27 - mmengine - INFO - Epoch(train) [9][5850/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:33:41 time: 0.6399 data_time: 0.0103 memory: 5134 grad_norm: 2.5265 loss: 0.5074 loss_cls: 0.2440 loss_bbox: 0.2634 +2024/10/27 01:47:01 - mmengine - INFO - Epoch(train) [9][5900/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:33:16 time: 0.6757 data_time: 0.0177 memory: 5132 grad_norm: 2.4563 loss: 0.5074 loss_cls: 0.2396 loss_bbox: 0.2679 +2024/10/27 01:47:31 - mmengine - INFO - Epoch(train) [9][5950/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:32:50 time: 0.6090 data_time: 0.0090 memory: 5135 grad_norm: 2.4519 loss: 0.5116 loss_cls: 0.2452 loss_bbox: 0.2664 +2024/10/27 01:48:04 - mmengine - INFO - Epoch(train) [9][6000/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:32:25 time: 0.6567 data_time: 0.0099 memory: 5132 grad_norm: 2.4428 loss: 0.5087 loss_cls: 0.2441 loss_bbox: 0.2646 +2024/10/27 01:48:35 - mmengine - INFO - Epoch(train) [9][6050/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:31:59 time: 0.6247 data_time: 0.0236 memory: 5134 grad_norm: 2.5075 loss: 0.5206 loss_cls: 0.2522 loss_bbox: 0.2684 +2024/10/27 01:49:07 - mmengine - INFO - Epoch(train) [9][6100/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:31:33 time: 0.6317 data_time: 0.0097 memory: 5134 grad_norm: 2.5170 loss: 0.5216 loss_cls: 0.2519 loss_bbox: 0.2697 +2024/10/27 01:49:39 - mmengine - INFO - Epoch(train) [9][6150/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:31:07 time: 0.6422 data_time: 0.0136 memory: 5134 grad_norm: 2.3830 loss: 0.5337 loss_cls: 0.2565 loss_bbox: 0.2772 +2024/10/27 01:50:11 - mmengine - INFO - Epoch(train) [9][6200/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:30:42 time: 0.6341 data_time: 0.0089 memory: 5138 grad_norm: 2.4462 loss: 0.5267 loss_cls: 0.2578 loss_bbox: 0.2689 +2024/10/27 01:50:42 - mmengine - INFO - Epoch(train) [9][6250/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:30:16 time: 0.6280 data_time: 0.0091 memory: 5134 grad_norm: 2.5658 loss: 0.4957 loss_cls: 0.2427 loss_bbox: 0.2530 +2024/10/27 01:51:13 - mmengine - INFO - Epoch(train) [9][6300/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:29:50 time: 0.6209 data_time: 0.0094 memory: 5134 grad_norm: 3.0468 loss: 0.5167 loss_cls: 0.2432 loss_bbox: 0.2734 +2024/10/27 01:51:46 - mmengine - INFO - Epoch(train) [9][6350/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:29:24 time: 0.6585 data_time: 0.0105 memory: 5135 grad_norm: 2.3730 loss: 0.4953 loss_cls: 0.2373 loss_bbox: 0.2579 +2024/10/27 01:51:53 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 01:52:18 - mmengine - INFO - Epoch(train) [9][6400/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:28:59 time: 0.6435 data_time: 0.0097 memory: 5136 grad_norm: 2.3798 loss: 0.4960 loss_cls: 0.2425 loss_bbox: 0.2535 +2024/10/27 01:52:49 - mmengine - INFO - Epoch(train) [9][6450/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:28:32 time: 0.6178 data_time: 0.0094 memory: 5135 grad_norm: 2.3970 loss: 0.4972 loss_cls: 0.2345 loss_bbox: 0.2627 +2024/10/27 01:53:19 - mmengine - INFO - Epoch(train) [9][6500/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:28:06 time: 0.5873 data_time: 0.0092 memory: 5136 grad_norm: 2.6851 loss: 0.5010 loss_cls: 0.2468 loss_bbox: 0.2542 +2024/10/27 01:53:52 - mmengine - INFO - Epoch(train) [9][6550/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:27:41 time: 0.6661 data_time: 0.0095 memory: 5135 grad_norm: 2.6992 loss: 0.5350 loss_cls: 0.2563 loss_bbox: 0.2787 +2024/10/27 01:54:20 - mmengine - INFO - Epoch(train) [9][6600/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:27:13 time: 0.5628 data_time: 0.0098 memory: 5135 grad_norm: 2.4956 loss: 0.5046 loss_cls: 0.2501 loss_bbox: 0.2545 +2024/10/27 01:54:53 - mmengine - INFO - Epoch(train) [9][6650/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:26:48 time: 0.6567 data_time: 0.0100 memory: 5134 grad_norm: 2.5150 loss: 0.5080 loss_cls: 0.2576 loss_bbox: 0.2503 +2024/10/27 01:55:23 - mmengine - INFO - Epoch(train) [9][6700/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:26:22 time: 0.6000 data_time: 0.0102 memory: 5135 grad_norm: 2.6999 loss: 0.4851 loss_cls: 0.2359 loss_bbox: 0.2493 +2024/10/27 01:55:56 - mmengine - INFO - Epoch(train) [9][6750/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:25:56 time: 0.6579 data_time: 0.0100 memory: 5134 grad_norm: 2.5437 loss: 0.4976 loss_cls: 0.2385 loss_bbox: 0.2591 +2024/10/27 01:56:25 - mmengine - INFO - Epoch(train) [9][6800/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:25:29 time: 0.5821 data_time: 0.0098 memory: 5135 grad_norm: 2.2611 loss: 0.5027 loss_cls: 0.2405 loss_bbox: 0.2622 +2024/10/27 01:56:58 - mmengine - INFO - Epoch(train) [9][6850/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:25:04 time: 0.6603 data_time: 0.0095 memory: 5134 grad_norm: 2.4342 loss: 0.5062 loss_cls: 0.2439 loss_bbox: 0.2623 +2024/10/27 01:57:29 - mmengine - INFO - Epoch(train) [9][6900/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:24:38 time: 0.6264 data_time: 0.0171 memory: 5134 grad_norm: 2.4524 loss: 0.5269 loss_cls: 0.2534 loss_bbox: 0.2735 +2024/10/27 01:58:03 - mmengine - INFO - Epoch(train) [9][6950/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:24:12 time: 0.6639 data_time: 0.0101 memory: 5135 grad_norm: 2.4116 loss: 0.5078 loss_cls: 0.2454 loss_bbox: 0.2623 +2024/10/27 01:58:35 - mmengine - INFO - Epoch(train) [9][7000/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:23:47 time: 0.6484 data_time: 0.0613 memory: 5133 grad_norm: 2.3931 loss: 0.5143 loss_cls: 0.2470 loss_bbox: 0.2673 +2024/10/27 01:59:07 - mmengine - INFO - Epoch(train) [9][7050/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:23:21 time: 0.6350 data_time: 0.0130 memory: 5138 grad_norm: 2.4991 loss: 0.4876 loss_cls: 0.2409 loss_bbox: 0.2467 +2024/10/27 01:59:39 - mmengine - INFO - Epoch(train) [9][7100/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:22:55 time: 0.6351 data_time: 0.0098 memory: 5134 grad_norm: 2.4146 loss: 0.4942 loss_cls: 0.2394 loss_bbox: 0.2548 +2024/10/27 02:00:09 - mmengine - INFO - Epoch(train) [9][7150/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:22:28 time: 0.6035 data_time: 0.0229 memory: 5137 grad_norm: 2.5792 loss: 0.5071 loss_cls: 0.2478 loss_bbox: 0.2593 +2024/10/27 02:00:38 - mmengine - INFO - Epoch(train) [9][7200/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:22:02 time: 0.5854 data_time: 0.0152 memory: 5133 grad_norm: 2.4599 loss: 0.5201 loss_cls: 0.2460 loss_bbox: 0.2740 +2024/10/27 02:01:08 - mmengine - INFO - Epoch(train) [9][7250/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:21:35 time: 0.5900 data_time: 0.0098 memory: 5134 grad_norm: 2.5750 loss: 0.5103 loss_cls: 0.2390 loss_bbox: 0.2713 +2024/10/27 02:01:40 - mmengine - INFO - Epoch(train) [9][7300/7330] base_lr: 1.5000e-04 lr: 1.5000e-04 eta: 3:21:09 time: 0.6440 data_time: 0.0092 memory: 5135 grad_norm: 2.4948 loss: 0.5244 loss_cls: 0.2573 loss_bbox: 0.2670 +2024/10/27 02:01:59 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 02:01:59 - mmengine - INFO - Saving checkpoint at 9 epochs +2024/10/27 02:02:12 - mmengine - INFO - Epoch(val) [9][ 50/1250] eta: 0:02:00 time: 0.1001 data_time: 0.0022 memory: 5133 +2024/10/27 02:02:18 - mmengine - INFO - Epoch(val) [9][ 100/1250] eta: 0:02:07 time: 0.1211 data_time: 0.0021 memory: 630 +2024/10/27 02:02:24 - mmengine - INFO - Epoch(val) [9][ 150/1250] eta: 0:02:01 time: 0.1098 data_time: 0.0018 memory: 635 +2024/10/27 02:02:29 - mmengine - INFO - Epoch(val) [9][ 200/1250] eta: 0:01:56 time: 0.1132 data_time: 0.0020 memory: 635 +2024/10/27 02:02:35 - mmengine - INFO - Epoch(val) [9][ 250/1250] eta: 0:01:51 time: 0.1113 data_time: 0.0018 memory: 625 +2024/10/27 02:02:40 - mmengine - INFO - Epoch(val) [9][ 300/1250] eta: 0:01:44 time: 0.1025 data_time: 0.0019 memory: 625 +2024/10/27 02:02:46 - mmengine - INFO - Epoch(val) [9][ 350/1250] eta: 0:01:39 time: 0.1135 data_time: 0.0019 memory: 625 +2024/10/27 02:02:51 - mmengine - INFO - Epoch(val) [9][ 400/1250] eta: 0:01:33 time: 0.1071 data_time: 0.0018 memory: 613 +2024/10/27 02:02:57 - mmengine - INFO - Epoch(val) [9][ 450/1250] eta: 0:01:28 time: 0.1115 data_time: 0.0018 memory: 635 +2024/10/27 02:03:02 - mmengine - INFO - Epoch(val) [9][ 500/1250] eta: 0:01:22 time: 0.1151 data_time: 0.0020 memory: 635 +2024/10/27 02:03:08 - mmengine - INFO - Epoch(val) [9][ 550/1250] eta: 0:01:17 time: 0.1083 data_time: 0.0019 memory: 614 +2024/10/27 02:03:13 - mmengine - INFO - Epoch(val) [9][ 600/1250] eta: 0:01:11 time: 0.1070 data_time: 0.0020 memory: 625 +2024/10/27 02:03:19 - mmengine - INFO - Epoch(val) [9][ 650/1250] eta: 0:01:06 time: 0.1233 data_time: 0.0020 memory: 625 +2024/10/27 02:03:25 - mmengine - INFO - Epoch(val) [9][ 700/1250] eta: 0:01:00 time: 0.1074 data_time: 0.0020 memory: 630 +2024/10/27 02:03:30 - mmengine - INFO - Epoch(val) [9][ 750/1250] eta: 0:00:55 time: 0.1144 data_time: 0.0021 memory: 629 +2024/10/27 02:03:36 - mmengine - INFO - Epoch(val) [9][ 800/1250] eta: 0:00:49 time: 0.1113 data_time: 0.0019 memory: 635 +2024/10/27 02:03:41 - mmengine - INFO - Epoch(val) [9][ 850/1250] eta: 0:00:44 time: 0.0974 data_time: 0.0018 memory: 635 +2024/10/27 02:03:46 - mmengine - INFO - Epoch(val) [9][ 900/1250] eta: 0:00:38 time: 0.1131 data_time: 0.0020 memory: 635 +2024/10/27 02:03:52 - mmengine - INFO - Epoch(val) [9][ 950/1250] eta: 0:00:33 time: 0.1082 data_time: 0.0021 memory: 625 +2024/10/27 02:03:57 - mmengine - INFO - Epoch(val) [9][1000/1250] eta: 0:00:27 time: 0.1062 data_time: 0.0019 memory: 626 +2024/10/27 02:04:03 - mmengine - INFO - Epoch(val) [9][1050/1250] eta: 0:00:22 time: 0.1133 data_time: 0.0020 memory: 630 +2024/10/27 02:04:08 - mmengine - INFO - Epoch(val) [9][1100/1250] eta: 0:00:16 time: 0.1048 data_time: 0.0019 memory: 635 +2024/10/27 02:04:14 - mmengine - INFO - Epoch(val) [9][1150/1250] eta: 0:00:11 time: 0.1208 data_time: 0.0020 memory: 629 +2024/10/27 02:04:20 - mmengine - INFO - Epoch(val) [9][1200/1250] eta: 0:00:05 time: 0.1233 data_time: 0.0019 memory: 630 +2024/10/27 02:04:25 - mmengine - INFO - Epoch(val) [9][1250/1250] eta: 0:00:00 time: 0.0989 data_time: 0.0019 memory: 635 +2024/10/27 02:04:35 - mmengine - INFO - Evaluating bbox... +2024/10/27 02:05:37 - mmengine - INFO - bbox_mAP_copypaste: 0.346 0.541 0.366 0.181 0.379 0.481 +2024/10/27 02:05:38 - mmengine - INFO - Epoch(val) [9][1250/1250] coco/bbox_mAP: 0.3460 coco/bbox_mAP_50: 0.5410 coco/bbox_mAP_75: 0.3660 coco/bbox_mAP_s: 0.1810 coco/bbox_mAP_m: 0.3790 coco/bbox_mAP_l: 0.4810 data_time: 0.0019 time: 0.1105 +2024/10/27 02:05:57 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 02:06:09 - mmengine - INFO - Epoch(train) [10][ 50/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:20:27 time: 0.6116 data_time: 0.0095 memory: 5136 grad_norm: 2.3258 loss: 0.4654 loss_cls: 0.2120 loss_bbox: 0.2535 +2024/10/27 02:06:41 - mmengine - INFO - Epoch(train) [10][ 100/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:20:01 time: 0.6364 data_time: 0.0113 memory: 5135 grad_norm: 2.3681 loss: 0.4583 loss_cls: 0.2119 loss_bbox: 0.2464 +2024/10/27 02:07:11 - mmengine - INFO - Epoch(train) [10][ 150/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:19:35 time: 0.6096 data_time: 0.0112 memory: 5134 grad_norm: 2.3719 loss: 0.4518 loss_cls: 0.2040 loss_bbox: 0.2478 +2024/10/27 02:07:42 - mmengine - INFO - Epoch(train) [10][ 200/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:19:09 time: 0.6217 data_time: 0.0135 memory: 5137 grad_norm: 2.1565 loss: 0.4769 loss_cls: 0.2225 loss_bbox: 0.2544 +2024/10/27 02:08:13 - mmengine - INFO - Epoch(train) [10][ 250/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:18:42 time: 0.6135 data_time: 0.0106 memory: 5135 grad_norm: 2.3963 loss: 0.4592 loss_cls: 0.2104 loss_bbox: 0.2488 +2024/10/27 02:08:44 - mmengine - INFO - Epoch(train) [10][ 300/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:18:16 time: 0.6140 data_time: 0.0099 memory: 5137 grad_norm: 2.2822 loss: 0.4705 loss_cls: 0.2129 loss_bbox: 0.2576 +2024/10/27 02:09:14 - mmengine - INFO - Epoch(train) [10][ 350/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:17:50 time: 0.5973 data_time: 0.0114 memory: 5135 grad_norm: 2.2964 loss: 0.4464 loss_cls: 0.2091 loss_bbox: 0.2373 +2024/10/27 02:09:46 - mmengine - INFO - Epoch(train) [10][ 400/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:17:24 time: 0.6386 data_time: 0.0110 memory: 5134 grad_norm: 2.3126 loss: 0.4705 loss_cls: 0.2164 loss_bbox: 0.2541 +2024/10/27 02:10:17 - mmengine - INFO - Epoch(train) [10][ 450/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:16:57 time: 0.6334 data_time: 0.0107 memory: 5136 grad_norm: 2.1916 loss: 0.4735 loss_cls: 0.2268 loss_bbox: 0.2467 +2024/10/27 02:10:48 - mmengine - INFO - Epoch(train) [10][ 500/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:16:31 time: 0.6227 data_time: 0.0113 memory: 5134 grad_norm: 2.2636 loss: 0.4853 loss_cls: 0.2377 loss_bbox: 0.2476 +2024/10/27 02:11:18 - mmengine - INFO - Epoch(train) [10][ 550/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:16:04 time: 0.5836 data_time: 0.0098 memory: 5133 grad_norm: 2.2525 loss: 0.4484 loss_cls: 0.2074 loss_bbox: 0.2411 +2024/10/27 02:11:51 - mmengine - INFO - Epoch(train) [10][ 600/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:15:39 time: 0.6607 data_time: 0.0100 memory: 5133 grad_norm: 2.3930 loss: 0.4740 loss_cls: 0.2207 loss_bbox: 0.2533 +2024/10/27 02:12:19 - mmengine - INFO - Epoch(train) [10][ 650/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:15:12 time: 0.5716 data_time: 0.0101 memory: 5133 grad_norm: 2.2899 loss: 0.4849 loss_cls: 0.2315 loss_bbox: 0.2533 +2024/10/27 02:12:52 - mmengine - INFO - Epoch(train) [10][ 700/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:14:46 time: 0.6597 data_time: 0.0101 memory: 5131 grad_norm: 2.3621 loss: 0.4692 loss_cls: 0.2247 loss_bbox: 0.2446 +2024/10/27 02:13:23 - mmengine - INFO - Epoch(train) [10][ 750/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:14:20 time: 0.6136 data_time: 0.0115 memory: 5133 grad_norm: 2.3290 loss: 0.4512 loss_cls: 0.2170 loss_bbox: 0.2342 +2024/10/27 02:13:54 - mmengine - INFO - Epoch(train) [10][ 800/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:13:53 time: 0.6152 data_time: 0.0099 memory: 5134 grad_norm: 2.3228 loss: 0.4437 loss_cls: 0.2041 loss_bbox: 0.2395 +2024/10/27 02:14:24 - mmengine - INFO - Epoch(train) [10][ 850/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:13:27 time: 0.6087 data_time: 0.0101 memory: 5137 grad_norm: 2.4462 loss: 0.4741 loss_cls: 0.2207 loss_bbox: 0.2534 +2024/10/27 02:14:57 - mmengine - INFO - Epoch(train) [10][ 900/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:13:01 time: 0.6577 data_time: 0.0103 memory: 5134 grad_norm: 2.4116 loss: 0.4543 loss_cls: 0.2094 loss_bbox: 0.2449 +2024/10/27 02:15:27 - mmengine - INFO - Epoch(train) [10][ 950/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:12:34 time: 0.6064 data_time: 0.0156 memory: 5133 grad_norm: 2.2335 loss: 0.4567 loss_cls: 0.2083 loss_bbox: 0.2484 +2024/10/27 02:15:59 - mmengine - INFO - Epoch(train) [10][1000/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:12:08 time: 0.6379 data_time: 0.0103 memory: 5132 grad_norm: 2.1888 loss: 0.4549 loss_cls: 0.2166 loss_bbox: 0.2384 +2024/10/27 02:16:17 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 02:16:30 - mmengine - INFO - Epoch(train) [10][1050/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:11:42 time: 0.6162 data_time: 0.0097 memory: 5134 grad_norm: 2.4269 loss: 0.4760 loss_cls: 0.2278 loss_bbox: 0.2482 +2024/10/27 02:17:01 - mmengine - INFO - Epoch(train) [10][1100/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:11:15 time: 0.6183 data_time: 0.0100 memory: 5136 grad_norm: 2.2647 loss: 0.4444 loss_cls: 0.2114 loss_bbox: 0.2330 +2024/10/27 02:17:27 - mmengine - INFO - Epoch(train) [10][1150/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:10:48 time: 0.5158 data_time: 0.0102 memory: 5133 grad_norm: 2.2680 loss: 0.4094 loss_cls: 0.1836 loss_bbox: 0.2258 +2024/10/27 02:18:00 - mmengine - INFO - Epoch(train) [10][1200/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:10:22 time: 0.6565 data_time: 0.0156 memory: 5133 grad_norm: 2.5717 loss: 0.4593 loss_cls: 0.2143 loss_bbox: 0.2450 +2024/10/27 02:18:31 - mmengine - INFO - Epoch(train) [10][1250/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:09:55 time: 0.6193 data_time: 0.0189 memory: 5132 grad_norm: 2.2489 loss: 0.4656 loss_cls: 0.2227 loss_bbox: 0.2429 +2024/10/27 02:19:03 - mmengine - INFO - Epoch(train) [10][1300/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:09:29 time: 0.6498 data_time: 0.0096 memory: 5134 grad_norm: 2.3294 loss: 0.4833 loss_cls: 0.2302 loss_bbox: 0.2531 +2024/10/27 02:19:36 - mmengine - INFO - Epoch(train) [10][1350/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:09:04 time: 0.6541 data_time: 0.0547 memory: 5133 grad_norm: 2.3378 loss: 0.4940 loss_cls: 0.2322 loss_bbox: 0.2618 +2024/10/27 02:20:07 - mmengine - INFO - Epoch(train) [10][1400/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:08:37 time: 0.6154 data_time: 0.0111 memory: 5135 grad_norm: 2.2964 loss: 0.4498 loss_cls: 0.2074 loss_bbox: 0.2424 +2024/10/27 02:20:35 - mmengine - INFO - Epoch(train) [10][1450/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:08:10 time: 0.5727 data_time: 0.0096 memory: 5135 grad_norm: 2.4311 loss: 0.4621 loss_cls: 0.2159 loss_bbox: 0.2463 +2024/10/27 02:21:01 - mmengine - INFO - Epoch(train) [10][1500/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:07:42 time: 0.5086 data_time: 0.0112 memory: 5134 grad_norm: 2.2900 loss: 0.4673 loss_cls: 0.2212 loss_bbox: 0.2461 +2024/10/27 02:21:15 - mmengine - INFO - Epoch(train) [10][1550/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:07:10 time: 0.2778 data_time: 0.0167 memory: 5134 grad_norm: 2.4209 loss: 0.4752 loss_cls: 0.2225 loss_bbox: 0.2527 +2024/10/27 02:21:28 - mmengine - INFO - Epoch(train) [10][1600/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:06:38 time: 0.2695 data_time: 0.0096 memory: 5135 grad_norm: 2.2811 loss: 0.4634 loss_cls: 0.2159 loss_bbox: 0.2475 +2024/10/27 02:21:41 - mmengine - INFO - Epoch(train) [10][1650/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:06:07 time: 0.2681 data_time: 0.0093 memory: 5134 grad_norm: 2.1952 loss: 0.4396 loss_cls: 0.2070 loss_bbox: 0.2326 +2024/10/27 02:21:55 - mmengine - INFO - Epoch(train) [10][1700/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:05:35 time: 0.2678 data_time: 0.0095 memory: 5135 grad_norm: 2.3328 loss: 0.4471 loss_cls: 0.2076 loss_bbox: 0.2394 +2024/10/27 02:22:08 - mmengine - INFO - Epoch(train) [10][1750/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:05:03 time: 0.2673 data_time: 0.0096 memory: 5136 grad_norm: 2.2945 loss: 0.4577 loss_cls: 0.2143 loss_bbox: 0.2434 +2024/10/27 02:22:23 - mmengine - INFO - Epoch(train) [10][1800/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:04:32 time: 0.2879 data_time: 0.0095 memory: 5133 grad_norm: 2.2866 loss: 0.4605 loss_cls: 0.2141 loss_bbox: 0.2464 +2024/10/27 02:22:36 - mmengine - INFO - Epoch(train) [10][1850/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:04:01 time: 0.2762 data_time: 0.0102 memory: 5134 grad_norm: 2.3320 loss: 0.4547 loss_cls: 0.2137 loss_bbox: 0.2409 +2024/10/27 02:22:50 - mmengine - INFO - Epoch(train) [10][1900/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:03:29 time: 0.2734 data_time: 0.0099 memory: 5133 grad_norm: 2.3749 loss: 0.4778 loss_cls: 0.2255 loss_bbox: 0.2524 +2024/10/27 02:23:04 - mmengine - INFO - Epoch(train) [10][1950/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:02:58 time: 0.2731 data_time: 0.0095 memory: 5136 grad_norm: 2.3127 loss: 0.4536 loss_cls: 0.2126 loss_bbox: 0.2410 +2024/10/27 02:23:19 - mmengine - INFO - Epoch(train) [10][2000/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:02:27 time: 0.3029 data_time: 0.0410 memory: 5136 grad_norm: 2.3481 loss: 0.4822 loss_cls: 0.2335 loss_bbox: 0.2486 +2024/10/27 02:23:27 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 02:23:33 - mmengine - INFO - Epoch(train) [10][2050/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:01:55 time: 0.2724 data_time: 0.0089 memory: 5133 grad_norm: 2.4866 loss: 0.4613 loss_cls: 0.2234 loss_bbox: 0.2378 +2024/10/27 02:23:46 - mmengine - INFO - Epoch(train) [10][2100/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:01:24 time: 0.2717 data_time: 0.0147 memory: 5135 grad_norm: 2.4682 loss: 0.4504 loss_cls: 0.2170 loss_bbox: 0.2334 +2024/10/27 02:23:59 - mmengine - INFO - Epoch(train) [10][2150/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:00:52 time: 0.2652 data_time: 0.0099 memory: 5135 grad_norm: 2.5018 loss: 0.4550 loss_cls: 0.2180 loss_bbox: 0.2370 +2024/10/27 02:24:13 - mmengine - INFO - Epoch(train) [10][2200/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 3:00:21 time: 0.2770 data_time: 0.0140 memory: 5133 grad_norm: 2.4534 loss: 0.4593 loss_cls: 0.2193 loss_bbox: 0.2400 +2024/10/27 02:24:27 - mmengine - INFO - Epoch(train) [10][2250/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:59:50 time: 0.2680 data_time: 0.0098 memory: 5135 grad_norm: 2.3201 loss: 0.4486 loss_cls: 0.2108 loss_bbox: 0.2378 +2024/10/27 02:24:40 - mmengine - INFO - Epoch(train) [10][2300/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:59:19 time: 0.2709 data_time: 0.0106 memory: 5133 grad_norm: 2.3601 loss: 0.4461 loss_cls: 0.1978 loss_bbox: 0.2483 +2024/10/27 02:24:54 - mmengine - INFO - Epoch(train) [10][2350/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:58:47 time: 0.2702 data_time: 0.0099 memory: 5132 grad_norm: 2.1383 loss: 0.4649 loss_cls: 0.2167 loss_bbox: 0.2482 +2024/10/27 02:25:07 - mmengine - INFO - Epoch(train) [10][2400/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:58:16 time: 0.2735 data_time: 0.0126 memory: 5133 grad_norm: 2.3694 loss: 0.4570 loss_cls: 0.2081 loss_bbox: 0.2489 +2024/10/27 02:25:21 - mmengine - INFO - Epoch(train) [10][2450/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:57:45 time: 0.2794 data_time: 0.0173 memory: 5133 grad_norm: 2.4250 loss: 0.4235 loss_cls: 0.1991 loss_bbox: 0.2244 +2024/10/27 02:25:35 - mmengine - INFO - Epoch(train) [10][2500/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:57:14 time: 0.2690 data_time: 0.0110 memory: 5134 grad_norm: 2.3715 loss: 0.4733 loss_cls: 0.2239 loss_bbox: 0.2495 +2024/10/27 02:25:48 - mmengine - INFO - Epoch(train) [10][2550/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:56:43 time: 0.2720 data_time: 0.0101 memory: 5137 grad_norm: 2.4015 loss: 0.4723 loss_cls: 0.2246 loss_bbox: 0.2477 +2024/10/27 02:26:03 - mmengine - INFO - Epoch(train) [10][2600/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:56:12 time: 0.2867 data_time: 0.0110 memory: 5135 grad_norm: 2.3626 loss: 0.4752 loss_cls: 0.2270 loss_bbox: 0.2482 +2024/10/27 02:26:19 - mmengine - INFO - Epoch(train) [10][2650/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:55:41 time: 0.3189 data_time: 0.0543 memory: 5134 grad_norm: 2.4189 loss: 0.4723 loss_cls: 0.2216 loss_bbox: 0.2506 +2024/10/27 02:26:32 - mmengine - INFO - Epoch(train) [10][2700/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:55:10 time: 0.2724 data_time: 0.0101 memory: 5136 grad_norm: 2.3580 loss: 0.4504 loss_cls: 0.2123 loss_bbox: 0.2381 +2024/10/27 02:26:46 - mmengine - INFO - Epoch(train) [10][2750/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:54:39 time: 0.2731 data_time: 0.0097 memory: 5135 grad_norm: 2.2700 loss: 0.4646 loss_cls: 0.2201 loss_bbox: 0.2445 +2024/10/27 02:27:00 - mmengine - INFO - Epoch(train) [10][2800/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:54:08 time: 0.2733 data_time: 0.0093 memory: 5134 grad_norm: 2.6879 loss: 0.4880 loss_cls: 0.2307 loss_bbox: 0.2573 +2024/10/27 02:27:14 - mmengine - INFO - Epoch(train) [10][2850/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:53:37 time: 0.2797 data_time: 0.0097 memory: 5136 grad_norm: 2.3753 loss: 0.4645 loss_cls: 0.2215 loss_bbox: 0.2430 +2024/10/27 02:27:27 - mmengine - INFO - Epoch(train) [10][2900/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:53:06 time: 0.2720 data_time: 0.0096 memory: 5135 grad_norm: 2.3466 loss: 0.4357 loss_cls: 0.1950 loss_bbox: 0.2407 +2024/10/27 02:27:41 - mmengine - INFO - Epoch(train) [10][2950/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:52:35 time: 0.2755 data_time: 0.0132 memory: 5133 grad_norm: 2.1786 loss: 0.4645 loss_cls: 0.2190 loss_bbox: 0.2455 +2024/10/27 02:27:55 - mmengine - INFO - Epoch(train) [10][3000/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:52:04 time: 0.2715 data_time: 0.0102 memory: 5136 grad_norm: 2.3304 loss: 0.4502 loss_cls: 0.2026 loss_bbox: 0.2477 +2024/10/27 02:28:03 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 02:28:08 - mmengine - INFO - Epoch(train) [10][3050/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:51:33 time: 0.2757 data_time: 0.0108 memory: 5135 grad_norm: 2.4710 loss: 0.4661 loss_cls: 0.2096 loss_bbox: 0.2565 +2024/10/27 02:28:22 - mmengine - INFO - Epoch(train) [10][3100/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:51:03 time: 0.2739 data_time: 0.0096 memory: 5133 grad_norm: 2.3475 loss: 0.4622 loss_cls: 0.2150 loss_bbox: 0.2473 +2024/10/27 02:28:36 - mmengine - INFO - Epoch(train) [10][3150/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:50:32 time: 0.2756 data_time: 0.0119 memory: 5133 grad_norm: 2.3741 loss: 0.4521 loss_cls: 0.2139 loss_bbox: 0.2382 +2024/10/27 02:28:50 - mmengine - INFO - Epoch(train) [10][3200/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:50:01 time: 0.2744 data_time: 0.0103 memory: 5134 grad_norm: 2.2756 loss: 0.4525 loss_cls: 0.2023 loss_bbox: 0.2502 +2024/10/27 02:29:04 - mmengine - INFO - Epoch(train) [10][3250/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:49:30 time: 0.2766 data_time: 0.0119 memory: 5135 grad_norm: 2.3836 loss: 0.4351 loss_cls: 0.2013 loss_bbox: 0.2338 +2024/10/27 02:29:20 - mmengine - INFO - Epoch(train) [10][3300/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:49:00 time: 0.3204 data_time: 0.0569 memory: 5134 grad_norm: 2.4453 loss: 0.4356 loss_cls: 0.2024 loss_bbox: 0.2332 +2024/10/27 02:29:33 - mmengine - INFO - Epoch(train) [10][3350/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:48:29 time: 0.2731 data_time: 0.0094 memory: 5133 grad_norm: 2.4288 loss: 0.4561 loss_cls: 0.2104 loss_bbox: 0.2457 +2024/10/27 02:29:47 - mmengine - INFO - Epoch(train) [10][3400/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:47:59 time: 0.2709 data_time: 0.0091 memory: 5134 grad_norm: 2.4929 loss: 0.4580 loss_cls: 0.2140 loss_bbox: 0.2440 +2024/10/27 02:30:00 - mmengine - INFO - Epoch(train) [10][3450/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:47:28 time: 0.2689 data_time: 0.0106 memory: 5134 grad_norm: 2.4360 loss: 0.4896 loss_cls: 0.2352 loss_bbox: 0.2544 +2024/10/27 02:30:14 - mmengine - INFO - Epoch(train) [10][3500/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:46:57 time: 0.2710 data_time: 0.0094 memory: 5134 grad_norm: 2.3111 loss: 0.4480 loss_cls: 0.2099 loss_bbox: 0.2381 +2024/10/27 02:30:28 - mmengine - INFO - Epoch(train) [10][3550/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:46:27 time: 0.2828 data_time: 0.0091 memory: 5135 grad_norm: 2.3112 loss: 0.4552 loss_cls: 0.2134 loss_bbox: 0.2418 +2024/10/27 02:30:41 - mmengine - INFO - Epoch(train) [10][3600/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:45:56 time: 0.2703 data_time: 0.0090 memory: 5135 grad_norm: 2.4216 loss: 0.4421 loss_cls: 0.2038 loss_bbox: 0.2383 +2024/10/27 02:30:55 - mmengine - INFO - Epoch(train) [10][3650/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:45:25 time: 0.2706 data_time: 0.0093 memory: 5132 grad_norm: 2.1554 loss: 0.4698 loss_cls: 0.2115 loss_bbox: 0.2583 +2024/10/27 02:31:09 - mmengine - INFO - Epoch(train) [10][3700/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:44:55 time: 0.2737 data_time: 0.0095 memory: 5137 grad_norm: 2.4052 loss: 0.4333 loss_cls: 0.1987 loss_bbox: 0.2346 +2024/10/27 02:31:23 - mmengine - INFO - Epoch(train) [10][3750/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:44:24 time: 0.2822 data_time: 0.0152 memory: 5133 grad_norm: 2.6047 loss: 0.4901 loss_cls: 0.2326 loss_bbox: 0.2576 +2024/10/27 02:31:37 - mmengine - INFO - Epoch(train) [10][3800/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:43:54 time: 0.2916 data_time: 0.0171 memory: 5134 grad_norm: 2.3163 loss: 0.4678 loss_cls: 0.2124 loss_bbox: 0.2554 +2024/10/27 02:31:51 - mmengine - INFO - Epoch(train) [10][3850/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:43:23 time: 0.2711 data_time: 0.0093 memory: 5134 grad_norm: 2.4227 loss: 0.4819 loss_cls: 0.2227 loss_bbox: 0.2592 +2024/10/27 02:32:05 - mmengine - INFO - Epoch(train) [10][3900/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:42:53 time: 0.2723 data_time: 0.0101 memory: 5135 grad_norm: 2.3326 loss: 0.4568 loss_cls: 0.2155 loss_bbox: 0.2413 +2024/10/27 02:32:19 - mmengine - INFO - Epoch(train) [10][3950/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:42:23 time: 0.2795 data_time: 0.0140 memory: 5133 grad_norm: 2.3526 loss: 0.4657 loss_cls: 0.2251 loss_bbox: 0.2406 +2024/10/27 02:32:32 - mmengine - INFO - Epoch(train) [10][4000/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:41:52 time: 0.2718 data_time: 0.0101 memory: 5134 grad_norm: 2.3491 loss: 0.4597 loss_cls: 0.2176 loss_bbox: 0.2421 +2024/10/27 02:32:40 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 02:32:46 - mmengine - INFO - Epoch(train) [10][4050/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:41:22 time: 0.2744 data_time: 0.0104 memory: 5133 grad_norm: 2.3832 loss: 0.4411 loss_cls: 0.2109 loss_bbox: 0.2303 +2024/10/27 02:33:00 - mmengine - INFO - Epoch(train) [10][4100/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:40:51 time: 0.2745 data_time: 0.0098 memory: 5135 grad_norm: 2.3119 loss: 0.4877 loss_cls: 0.2308 loss_bbox: 0.2570 +2024/10/27 02:33:14 - mmengine - INFO - Epoch(train) [10][4150/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:40:21 time: 0.2792 data_time: 0.0136 memory: 5135 grad_norm: 2.4112 loss: 0.4780 loss_cls: 0.2289 loss_bbox: 0.2490 +2024/10/27 02:33:27 - mmengine - INFO - Epoch(train) [10][4200/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:39:51 time: 0.2769 data_time: 0.0155 memory: 5134 grad_norm: 2.2554 loss: 0.4458 loss_cls: 0.2088 loss_bbox: 0.2370 +2024/10/27 02:33:41 - mmengine - INFO - Epoch(train) [10][4250/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:39:20 time: 0.2736 data_time: 0.0135 memory: 5134 grad_norm: 2.1892 loss: 0.4846 loss_cls: 0.2265 loss_bbox: 0.2581 +2024/10/27 02:33:55 - mmengine - INFO - Epoch(train) [10][4300/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:38:50 time: 0.2768 data_time: 0.0115 memory: 5134 grad_norm: 2.3753 loss: 0.4484 loss_cls: 0.2100 loss_bbox: 0.2384 +2024/10/27 02:34:10 - mmengine - INFO - Epoch(train) [10][4350/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:38:20 time: 0.2933 data_time: 0.0180 memory: 5135 grad_norm: 2.4440 loss: 0.4493 loss_cls: 0.2119 loss_bbox: 0.2374 +2024/10/27 02:34:23 - mmengine - INFO - Epoch(train) [10][4400/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:37:50 time: 0.2693 data_time: 0.0106 memory: 5133 grad_norm: 2.2667 loss: 0.4470 loss_cls: 0.2166 loss_bbox: 0.2304 +2024/10/27 02:34:37 - mmengine - INFO - Epoch(train) [10][4450/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:37:20 time: 0.2760 data_time: 0.0153 memory: 5135 grad_norm: 2.3210 loss: 0.4599 loss_cls: 0.2036 loss_bbox: 0.2563 +2024/10/27 02:34:50 - mmengine - INFO - Epoch(train) [10][4500/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:36:49 time: 0.2678 data_time: 0.0091 memory: 5133 grad_norm: 2.2360 loss: 0.4330 loss_cls: 0.1994 loss_bbox: 0.2336 +2024/10/27 02:35:04 - mmengine - INFO - Epoch(train) [10][4550/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:36:19 time: 0.2713 data_time: 0.0094 memory: 5133 grad_norm: 2.1755 loss: 0.4752 loss_cls: 0.2226 loss_bbox: 0.2526 +2024/10/27 02:35:19 - mmengine - INFO - Epoch(train) [10][4600/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:35:49 time: 0.3002 data_time: 0.0319 memory: 5136 grad_norm: 2.5243 loss: 0.4581 loss_cls: 0.2124 loss_bbox: 0.2457 +2024/10/27 02:35:32 - mmengine - INFO - Epoch(train) [10][4650/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:35:19 time: 0.2724 data_time: 0.0102 memory: 5134 grad_norm: 2.4790 loss: 0.4976 loss_cls: 0.2354 loss_bbox: 0.2621 +2024/10/27 02:35:46 - mmengine - INFO - Epoch(train) [10][4700/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:34:49 time: 0.2748 data_time: 0.0099 memory: 5135 grad_norm: 2.2279 loss: 0.4764 loss_cls: 0.2230 loss_bbox: 0.2535 +2024/10/27 02:36:00 - mmengine - INFO - Epoch(train) [10][4750/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:34:19 time: 0.2746 data_time: 0.0113 memory: 5132 grad_norm: 2.3164 loss: 0.4625 loss_cls: 0.2150 loss_bbox: 0.2475 +2024/10/27 02:36:14 - mmengine - INFO - Epoch(train) [10][4800/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:33:49 time: 0.2766 data_time: 0.0116 memory: 5133 grad_norm: 2.3334 loss: 0.4576 loss_cls: 0.2125 loss_bbox: 0.2451 +2024/10/27 02:36:28 - mmengine - INFO - Epoch(train) [10][4850/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:33:19 time: 0.2756 data_time: 0.0096 memory: 5136 grad_norm: 2.3038 loss: 0.4743 loss_cls: 0.2235 loss_bbox: 0.2508 +2024/10/27 02:36:41 - mmengine - INFO - Epoch(train) [10][4900/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:32:49 time: 0.2760 data_time: 0.0099 memory: 5135 grad_norm: 2.2462 loss: 0.4476 loss_cls: 0.2084 loss_bbox: 0.2392 +2024/10/27 02:36:55 - mmengine - INFO - Epoch(train) [10][4950/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:32:19 time: 0.2743 data_time: 0.0095 memory: 5132 grad_norm: 2.4106 loss: 0.4829 loss_cls: 0.2273 loss_bbox: 0.2556 +2024/10/27 02:37:09 - mmengine - INFO - Epoch(train) [10][5000/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:31:49 time: 0.2743 data_time: 0.0098 memory: 5134 grad_norm: 2.3409 loss: 0.4366 loss_cls: 0.2042 loss_bbox: 0.2324 +2024/10/27 02:37:17 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 02:37:23 - mmengine - INFO - Epoch(train) [10][5050/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:31:19 time: 0.2808 data_time: 0.0142 memory: 5134 grad_norm: 2.3792 loss: 0.4143 loss_cls: 0.1881 loss_bbox: 0.2262 +2024/10/27 02:37:37 - mmengine - INFO - Epoch(train) [10][5100/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:30:49 time: 0.2740 data_time: 0.0098 memory: 5137 grad_norm: 2.2591 loss: 0.4735 loss_cls: 0.2189 loss_bbox: 0.2546 +2024/10/27 02:37:50 - mmengine - INFO - Epoch(train) [10][5150/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:30:20 time: 0.2760 data_time: 0.0111 memory: 5132 grad_norm: 2.3402 loss: 0.4555 loss_cls: 0.2179 loss_bbox: 0.2376 +2024/10/27 02:38:04 - mmengine - INFO - Epoch(train) [10][5200/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:29:50 time: 0.2787 data_time: 0.0091 memory: 5132 grad_norm: 2.3009 loss: 0.4404 loss_cls: 0.2075 loss_bbox: 0.2329 +2024/10/27 02:38:19 - mmengine - INFO - Epoch(train) [10][5250/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:29:20 time: 0.2964 data_time: 0.0352 memory: 5133 grad_norm: 2.2972 loss: 0.4825 loss_cls: 0.2284 loss_bbox: 0.2542 +2024/10/27 02:38:33 - mmengine - INFO - Epoch(train) [10][5300/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:28:50 time: 0.2839 data_time: 0.0089 memory: 5135 grad_norm: 2.3416 loss: 0.4101 loss_cls: 0.1857 loss_bbox: 0.2243 +2024/10/27 02:38:47 - mmengine - INFO - Epoch(train) [10][5350/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:28:21 time: 0.2728 data_time: 0.0088 memory: 5135 grad_norm: 2.2660 loss: 0.4310 loss_cls: 0.2020 loss_bbox: 0.2290 +2024/10/27 02:39:01 - mmengine - INFO - Epoch(train) [10][5400/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:27:51 time: 0.2743 data_time: 0.0123 memory: 5134 grad_norm: 2.2224 loss: 0.4542 loss_cls: 0.2093 loss_bbox: 0.2449 +2024/10/27 02:39:15 - mmengine - INFO - Epoch(train) [10][5450/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:27:21 time: 0.2799 data_time: 0.0149 memory: 5132 grad_norm: 2.3071 loss: 0.4669 loss_cls: 0.2160 loss_bbox: 0.2509 +2024/10/27 02:39:28 - mmengine - INFO - Epoch(train) [10][5500/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:26:51 time: 0.2737 data_time: 0.0106 memory: 5133 grad_norm: 2.3777 loss: 0.4890 loss_cls: 0.2339 loss_bbox: 0.2551 +2024/10/27 02:39:43 - mmengine - INFO - Epoch(train) [10][5550/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:26:22 time: 0.2835 data_time: 0.0152 memory: 5134 grad_norm: 2.3628 loss: 0.4502 loss_cls: 0.2075 loss_bbox: 0.2428 +2024/10/27 02:39:56 - mmengine - INFO - Epoch(train) [10][5600/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:25:52 time: 0.2771 data_time: 0.0143 memory: 5134 grad_norm: 2.2909 loss: 0.4912 loss_cls: 0.2292 loss_bbox: 0.2621 +2024/10/27 02:40:10 - mmengine - INFO - Epoch(train) [10][5650/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:25:22 time: 0.2744 data_time: 0.0093 memory: 5135 grad_norm: 2.3258 loss: 0.4914 loss_cls: 0.2366 loss_bbox: 0.2548 +2024/10/27 02:40:24 - mmengine - INFO - Epoch(train) [10][5700/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:24:53 time: 0.2727 data_time: 0.0095 memory: 5133 grad_norm: 2.1574 loss: 0.4626 loss_cls: 0.2183 loss_bbox: 0.2442 +2024/10/27 02:40:38 - mmengine - INFO - Epoch(train) [10][5750/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:24:23 time: 0.2746 data_time: 0.0110 memory: 5134 grad_norm: 2.4304 loss: 0.4599 loss_cls: 0.2118 loss_bbox: 0.2481 +2024/10/27 02:40:51 - mmengine - INFO - Epoch(train) [10][5800/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:23:54 time: 0.2746 data_time: 0.0092 memory: 5134 grad_norm: 2.2625 loss: 0.4961 loss_cls: 0.2259 loss_bbox: 0.2702 +2024/10/27 02:41:05 - mmengine - INFO - Epoch(train) [10][5850/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:23:24 time: 0.2726 data_time: 0.0097 memory: 5137 grad_norm: 2.3145 loss: 0.4510 loss_cls: 0.2087 loss_bbox: 0.2423 +2024/10/27 02:41:19 - mmengine - INFO - Epoch(train) [10][5900/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:22:55 time: 0.2827 data_time: 0.0194 memory: 5133 grad_norm: 2.5715 loss: 0.4741 loss_cls: 0.2230 loss_bbox: 0.2511 +2024/10/27 02:41:33 - mmengine - INFO - Epoch(train) [10][5950/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:22:25 time: 0.2738 data_time: 0.0104 memory: 5135 grad_norm: 2.3586 loss: 0.4728 loss_cls: 0.2239 loss_bbox: 0.2489 +2024/10/27 02:41:47 - mmengine - INFO - Epoch(train) [10][6000/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:21:56 time: 0.2750 data_time: 0.0106 memory: 5135 grad_norm: 2.4489 loss: 0.4634 loss_cls: 0.2178 loss_bbox: 0.2456 +2024/10/27 02:41:55 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 02:42:00 - mmengine - INFO - Epoch(train) [10][6050/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:21:26 time: 0.2790 data_time: 0.0093 memory: 5133 grad_norm: 2.5593 loss: 0.4924 loss_cls: 0.2391 loss_bbox: 0.2533 +2024/10/27 02:42:14 - mmengine - INFO - Epoch(train) [10][6100/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:20:57 time: 0.2733 data_time: 0.0099 memory: 5134 grad_norm: 2.4411 loss: 0.4703 loss_cls: 0.2245 loss_bbox: 0.2457 +2024/10/27 02:42:28 - mmengine - INFO - Epoch(train) [10][6150/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:20:27 time: 0.2866 data_time: 0.0100 memory: 5136 grad_norm: 2.4343 loss: 0.4760 loss_cls: 0.2243 loss_bbox: 0.2517 +2024/10/27 02:42:42 - mmengine - INFO - Epoch(train) [10][6200/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:19:58 time: 0.2714 data_time: 0.0094 memory: 5134 grad_norm: 2.2909 loss: 0.4583 loss_cls: 0.2138 loss_bbox: 0.2445 +2024/10/27 02:42:56 - mmengine - INFO - Epoch(train) [10][6250/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:19:28 time: 0.2739 data_time: 0.0102 memory: 5134 grad_norm: 2.3113 loss: 0.4774 loss_cls: 0.2275 loss_bbox: 0.2499 +2024/10/27 02:43:10 - mmengine - INFO - Epoch(train) [10][6300/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:18:59 time: 0.2750 data_time: 0.0096 memory: 5135 grad_norm: 2.2937 loss: 0.4641 loss_cls: 0.2213 loss_bbox: 0.2428 +2024/10/27 02:43:24 - mmengine - INFO - Epoch(train) [10][6350/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:18:30 time: 0.2856 data_time: 0.0150 memory: 5137 grad_norm: 2.3155 loss: 0.4550 loss_cls: 0.2115 loss_bbox: 0.2435 +2024/10/27 02:43:37 - mmengine - INFO - Epoch(train) [10][6400/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:18:00 time: 0.2723 data_time: 0.0088 memory: 5133 grad_norm: 2.3664 loss: 0.4763 loss_cls: 0.2284 loss_bbox: 0.2479 +2024/10/27 02:43:51 - mmengine - INFO - Epoch(train) [10][6450/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:17:31 time: 0.2757 data_time: 0.0101 memory: 5134 grad_norm: 2.4047 loss: 0.4673 loss_cls: 0.2227 loss_bbox: 0.2446 +2024/10/27 02:44:05 - mmengine - INFO - Epoch(train) [10][6500/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:17:02 time: 0.2782 data_time: 0.0129 memory: 5137 grad_norm: 2.2558 loss: 0.4512 loss_cls: 0.2126 loss_bbox: 0.2386 +2024/10/27 02:44:19 - mmengine - INFO - Epoch(train) [10][6550/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:16:33 time: 0.2744 data_time: 0.0096 memory: 5136 grad_norm: 2.3809 loss: 0.4831 loss_cls: 0.2288 loss_bbox: 0.2544 +2024/10/27 02:44:33 - mmengine - INFO - Epoch(train) [10][6600/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:16:03 time: 0.2756 data_time: 0.0103 memory: 5133 grad_norm: 2.5281 loss: 0.4596 loss_cls: 0.2185 loss_bbox: 0.2411 +2024/10/27 02:44:46 - mmengine - INFO - Epoch(train) [10][6650/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:15:34 time: 0.2768 data_time: 0.0117 memory: 5133 grad_norm: 2.3384 loss: 0.4475 loss_cls: 0.2059 loss_bbox: 0.2416 +2024/10/27 02:45:00 - mmengine - INFO - Epoch(train) [10][6700/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:15:05 time: 0.2720 data_time: 0.0093 memory: 5138 grad_norm: 2.3041 loss: 0.4726 loss_cls: 0.2181 loss_bbox: 0.2546 +2024/10/27 02:45:14 - mmengine - INFO - Epoch(train) [10][6750/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:14:36 time: 0.2769 data_time: 0.0105 memory: 5136 grad_norm: 2.1991 loss: 0.4615 loss_cls: 0.2152 loss_bbox: 0.2462 +2024/10/27 02:45:28 - mmengine - INFO - Epoch(train) [10][6800/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:14:07 time: 0.2794 data_time: 0.0138 memory: 5133 grad_norm: 2.2780 loss: 0.4602 loss_cls: 0.2169 loss_bbox: 0.2433 +2024/10/27 02:45:42 - mmengine - INFO - Epoch(train) [10][6850/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:13:38 time: 0.2725 data_time: 0.0090 memory: 5134 grad_norm: 2.3712 loss: 0.4811 loss_cls: 0.2300 loss_bbox: 0.2511 +2024/10/27 02:45:55 - mmengine - INFO - Epoch(train) [10][6900/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:13:08 time: 0.2742 data_time: 0.0089 memory: 5135 grad_norm: 2.1609 loss: 0.4671 loss_cls: 0.2150 loss_bbox: 0.2521 +2024/10/27 02:46:09 - mmengine - INFO - Epoch(train) [10][6950/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:12:39 time: 0.2784 data_time: 0.0095 memory: 5136 grad_norm: 2.3058 loss: 0.4597 loss_cls: 0.2157 loss_bbox: 0.2439 +2024/10/27 02:46:23 - mmengine - INFO - Epoch(train) [10][7000/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:12:10 time: 0.2777 data_time: 0.0125 memory: 5135 grad_norm: 2.3133 loss: 0.4175 loss_cls: 0.1979 loss_bbox: 0.2195 +2024/10/27 02:46:31 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 02:46:37 - mmengine - INFO - Epoch(train) [10][7050/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:11:41 time: 0.2846 data_time: 0.0097 memory: 5136 grad_norm: 2.3440 loss: 0.4839 loss_cls: 0.2248 loss_bbox: 0.2591 +2024/10/27 02:46:51 - mmengine - INFO - Epoch(train) [10][7100/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:11:12 time: 0.2772 data_time: 0.0136 memory: 5134 grad_norm: 2.3882 loss: 0.4576 loss_cls: 0.2217 loss_bbox: 0.2359 +2024/10/27 02:47:05 - mmengine - INFO - Epoch(train) [10][7150/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:10:43 time: 0.2724 data_time: 0.0097 memory: 5136 grad_norm: 2.2250 loss: 0.4633 loss_cls: 0.2250 loss_bbox: 0.2384 +2024/10/27 02:47:19 - mmengine - INFO - Epoch(train) [10][7200/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:10:14 time: 0.2776 data_time: 0.0139 memory: 5138 grad_norm: 2.2544 loss: 0.4611 loss_cls: 0.2122 loss_bbox: 0.2489 +2024/10/27 02:47:33 - mmengine - INFO - Epoch(train) [10][7250/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:09:45 time: 0.2774 data_time: 0.0106 memory: 5134 grad_norm: 2.3600 loss: 0.4815 loss_cls: 0.2318 loss_bbox: 0.2497 +2024/10/27 02:47:47 - mmengine - INFO - Epoch(train) [10][7300/7330] base_lr: 1.0000e-04 lr: 1.0000e-04 eta: 2:09:17 time: 0.2803 data_time: 0.0093 memory: 5136 grad_norm: 2.2914 loss: 0.4555 loss_cls: 0.2162 loss_bbox: 0.2393 +2024/10/27 02:47:56 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 02:47:56 - mmengine - INFO - Saving checkpoint at 10 epochs +2024/10/27 02:48:03 - mmengine - INFO - Epoch(val) [10][ 50/1250] eta: 0:01:19 time: 0.0662 data_time: 0.0019 memory: 6856 +2024/10/27 02:48:06 - mmengine - INFO - Epoch(val) [10][ 100/1250] eta: 0:01:14 time: 0.0638 data_time: 0.0015 memory: 630 +2024/10/27 02:48:09 - mmengine - INFO - Epoch(val) [10][ 150/1250] eta: 0:01:11 time: 0.0642 data_time: 0.0015 memory: 635 +2024/10/27 02:48:13 - mmengine - INFO - Epoch(val) [10][ 200/1250] eta: 0:01:08 time: 0.0658 data_time: 0.0015 memory: 635 +2024/10/27 02:48:16 - mmengine - INFO - Epoch(val) [10][ 250/1250] eta: 0:01:05 time: 0.0657 data_time: 0.0015 memory: 625 +2024/10/27 02:48:19 - mmengine - INFO - Epoch(val) [10][ 300/1250] eta: 0:01:01 time: 0.0647 data_time: 0.0016 memory: 625 +2024/10/27 02:48:22 - mmengine - INFO - Epoch(val) [10][ 350/1250] eta: 0:00:58 time: 0.0631 data_time: 0.0015 memory: 625 +2024/10/27 02:48:26 - mmengine - INFO - Epoch(val) [10][ 400/1250] eta: 0:00:55 time: 0.0649 data_time: 0.0015 memory: 614 +2024/10/27 02:48:29 - mmengine - INFO - Epoch(val) [10][ 450/1250] eta: 0:00:51 time: 0.0646 data_time: 0.0015 memory: 635 +2024/10/27 02:48:32 - mmengine - INFO - Epoch(val) [10][ 500/1250] eta: 0:00:48 time: 0.0660 data_time: 0.0017 memory: 635 +2024/10/27 02:48:35 - mmengine - INFO - Epoch(val) [10][ 550/1250] eta: 0:00:45 time: 0.0652 data_time: 0.0017 memory: 615 +2024/10/27 02:48:39 - mmengine - INFO - Epoch(val) [10][ 600/1250] eta: 0:00:42 time: 0.0647 data_time: 0.0016 memory: 625 +2024/10/27 02:48:42 - mmengine - INFO - Epoch(val) [10][ 650/1250] eta: 0:00:38 time: 0.0635 data_time: 0.0017 memory: 625 +2024/10/27 02:48:45 - mmengine - INFO - Epoch(val) [10][ 700/1250] eta: 0:00:35 time: 0.0650 data_time: 0.0017 memory: 630 +2024/10/27 02:48:48 - mmengine - INFO - Epoch(val) [10][ 750/1250] eta: 0:00:32 time: 0.0644 data_time: 0.0017 memory: 629 +2024/10/27 02:48:51 - mmengine - INFO - Epoch(val) [10][ 800/1250] eta: 0:00:29 time: 0.0645 data_time: 0.0018 memory: 635 +2024/10/27 02:48:55 - mmengine - INFO - Epoch(val) [10][ 850/1250] eta: 0:00:25 time: 0.0640 data_time: 0.0017 memory: 635 +2024/10/27 02:48:58 - mmengine - INFO - Epoch(val) [10][ 900/1250] eta: 0:00:22 time: 0.0636 data_time: 0.0018 memory: 635 +2024/10/27 02:49:01 - mmengine - INFO - Epoch(val) [10][ 950/1250] eta: 0:00:19 time: 0.0631 data_time: 0.0017 memory: 625 +2024/10/27 02:49:04 - mmengine - INFO - Epoch(val) [10][1000/1250] eta: 0:00:16 time: 0.0634 data_time: 0.0017 memory: 625 +2024/10/27 02:49:07 - mmengine - INFO - Epoch(val) [10][1050/1250] eta: 0:00:12 time: 0.0636 data_time: 0.0018 memory: 630 +2024/10/27 02:49:11 - mmengine - INFO - Epoch(val) [10][1100/1250] eta: 0:00:09 time: 0.0636 data_time: 0.0018 memory: 635 +2024/10/27 02:49:14 - mmengine - INFO - Epoch(val) [10][1150/1250] eta: 0:00:06 time: 0.0634 data_time: 0.0016 memory: 629 +2024/10/27 02:49:17 - mmengine - INFO - Epoch(val) [10][1200/1250] eta: 0:00:03 time: 0.0630 data_time: 0.0017 memory: 630 +2024/10/27 02:49:20 - mmengine - INFO - Epoch(val) [10][1250/1250] eta: 0:00:00 time: 0.0633 data_time: 0.0017 memory: 635 +2024/10/27 02:49:31 - mmengine - INFO - Evaluating bbox... +2024/10/27 02:50:20 - mmengine - INFO - bbox_mAP_copypaste: 0.368 0.564 0.390 0.191 0.399 0.507 +2024/10/27 02:50:21 - mmengine - INFO - Epoch(val) [10][1250/1250] coco/bbox_mAP: 0.3680 coco/bbox_mAP_50: 0.5640 coco/bbox_mAP_75: 0.3900 coco/bbox_mAP_s: 0.1910 coco/bbox_mAP_m: 0.3990 coco/bbox_mAP_l: 0.5070 data_time: 0.0017 time: 0.0643 +2024/10/27 02:50:35 - mmengine - INFO - Epoch(train) [11][ 50/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 2:08:30 time: 0.2729 data_time: 0.0088 memory: 5132 grad_norm: 2.2017 loss: 0.4361 loss_cls: 0.2042 loss_bbox: 0.2319 +2024/10/27 02:50:48 - mmengine - INFO - Epoch(train) [11][ 100/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 2:08:01 time: 0.2721 data_time: 0.0091 memory: 5135 grad_norm: 2.1862 loss: 0.4286 loss_cls: 0.1938 loss_bbox: 0.2348 +2024/10/27 02:51:02 - mmengine - INFO - Epoch(train) [11][ 150/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 2:07:33 time: 0.2721 data_time: 0.0091 memory: 5137 grad_norm: 2.1089 loss: 0.4118 loss_cls: 0.1785 loss_bbox: 0.2333 +2024/10/27 02:51:19 - mmengine - INFO - Epoch(train) [11][ 200/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 2:07:04 time: 0.3528 data_time: 0.0896 memory: 5135 grad_norm: 2.1113 loss: 0.4445 loss_cls: 0.2027 loss_bbox: 0.2417 +2024/10/27 02:51:33 - mmengine - INFO - Epoch(train) [11][ 250/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 2:06:36 time: 0.2719 data_time: 0.0100 memory: 5134 grad_norm: 2.0870 loss: 0.4350 loss_cls: 0.2000 loss_bbox: 0.2351 +2024/10/27 02:51:47 - mmengine - INFO - Epoch(train) [11][ 300/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 2:06:07 time: 0.2719 data_time: 0.0095 memory: 5135 grad_norm: 2.1741 loss: 0.4344 loss_cls: 0.1991 loss_bbox: 0.2353 +2024/10/27 02:52:00 - mmengine - INFO - Epoch(train) [11][ 350/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 2:05:38 time: 0.2730 data_time: 0.0090 memory: 5137 grad_norm: 2.1211 loss: 0.4228 loss_cls: 0.1960 loss_bbox: 0.2268 +2024/10/27 02:52:14 - mmengine - INFO - Epoch(train) [11][ 400/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 2:05:09 time: 0.2745 data_time: 0.0122 memory: 5133 grad_norm: 2.1653 loss: 0.4048 loss_cls: 0.1841 loss_bbox: 0.2207 +2024/10/27 02:52:28 - mmengine - INFO - Epoch(train) [11][ 450/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 2:04:40 time: 0.2737 data_time: 0.0105 memory: 5134 grad_norm: 2.1977 loss: 0.4046 loss_cls: 0.1816 loss_bbox: 0.2230 +2024/10/27 02:52:42 - mmengine - INFO - Epoch(train) [11][ 500/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 2:04:12 time: 0.2913 data_time: 0.0136 memory: 5134 grad_norm: 2.0707 loss: 0.4011 loss_cls: 0.1850 loss_bbox: 0.2160 +2024/10/27 02:52:56 - mmengine - INFO - Epoch(train) [11][ 550/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 2:03:43 time: 0.2750 data_time: 0.0095 memory: 5133 grad_norm: 2.2490 loss: 0.4303 loss_cls: 0.1946 loss_bbox: 0.2357 +2024/10/27 02:53:10 - mmengine - INFO - Epoch(train) [11][ 600/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 2:03:14 time: 0.2812 data_time: 0.0140 memory: 5134 grad_norm: 2.1266 loss: 0.4175 loss_cls: 0.1893 loss_bbox: 0.2282 +2024/10/27 02:53:24 - mmengine - INFO - Epoch(train) [11][ 650/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 2:02:46 time: 0.2737 data_time: 0.0095 memory: 5135 grad_norm: 2.2814 loss: 0.4324 loss_cls: 0.1920 loss_bbox: 0.2403 +2024/10/27 02:53:38 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 02:53:38 - mmengine - INFO - Epoch(train) [11][ 700/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 2:02:17 time: 0.2750 data_time: 0.0121 memory: 5134 grad_norm: 2.0282 loss: 0.4345 loss_cls: 0.1977 loss_bbox: 0.2368 +2024/10/27 02:53:52 - mmengine - INFO - Epoch(train) [11][ 750/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 2:01:49 time: 0.2791 data_time: 0.0093 memory: 5134 grad_norm: 2.1980 loss: 0.4109 loss_cls: 0.1889 loss_bbox: 0.2220 +2024/10/27 02:54:05 - mmengine - INFO - Epoch(train) [11][ 800/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 2:01:20 time: 0.2719 data_time: 0.0094 memory: 5135 grad_norm: 2.1244 loss: 0.4113 loss_cls: 0.1902 loss_bbox: 0.2211 +2024/10/27 02:54:19 - mmengine - INFO - Epoch(train) [11][ 850/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 2:00:51 time: 0.2762 data_time: 0.0146 memory: 5136 grad_norm: 2.0445 loss: 0.3974 loss_cls: 0.1795 loss_bbox: 0.2179 +2024/10/27 02:54:33 - mmengine - INFO - Epoch(train) [11][ 900/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 2:00:23 time: 0.2729 data_time: 0.0097 memory: 5133 grad_norm: 2.0648 loss: 0.4170 loss_cls: 0.1852 loss_bbox: 0.2319 +2024/10/27 02:54:46 - mmengine - INFO - Epoch(train) [11][ 950/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:59:54 time: 0.2721 data_time: 0.0094 memory: 5134 grad_norm: 2.1486 loss: 0.4107 loss_cls: 0.1870 loss_bbox: 0.2237 +2024/10/27 02:55:00 - mmengine - INFO - Epoch(train) [11][1000/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:59:26 time: 0.2709 data_time: 0.0094 memory: 5135 grad_norm: 2.1016 loss: 0.4233 loss_cls: 0.1943 loss_bbox: 0.2289 +2024/10/27 02:55:13 - mmengine - INFO - Epoch(train) [11][1050/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:58:57 time: 0.2735 data_time: 0.0097 memory: 5135 grad_norm: 2.0802 loss: 0.4118 loss_cls: 0.1846 loss_bbox: 0.2272 +2024/10/27 02:55:27 - mmengine - INFO - Epoch(train) [11][1100/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:58:28 time: 0.2723 data_time: 0.0100 memory: 5135 grad_norm: 2.0610 loss: 0.4217 loss_cls: 0.1840 loss_bbox: 0.2377 +2024/10/27 02:55:41 - mmengine - INFO - Epoch(train) [11][1150/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:58:00 time: 0.2762 data_time: 0.0140 memory: 5135 grad_norm: 2.1489 loss: 0.4616 loss_cls: 0.2105 loss_bbox: 0.2511 +2024/10/27 02:55:54 - mmengine - INFO - Epoch(train) [11][1200/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:57:32 time: 0.2714 data_time: 0.0096 memory: 5135 grad_norm: 2.0051 loss: 0.3883 loss_cls: 0.1780 loss_bbox: 0.2103 +2024/10/27 02:56:08 - mmengine - INFO - Epoch(train) [11][1250/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:57:03 time: 0.2708 data_time: 0.0093 memory: 5136 grad_norm: 2.2269 loss: 0.4122 loss_cls: 0.1823 loss_bbox: 0.2298 +2024/10/27 02:56:22 - mmengine - INFO - Epoch(train) [11][1300/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:56:35 time: 0.2722 data_time: 0.0092 memory: 5134 grad_norm: 2.1424 loss: 0.4228 loss_cls: 0.1915 loss_bbox: 0.2314 +2024/10/27 02:56:35 - mmengine - INFO - Epoch(train) [11][1350/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:56:06 time: 0.2716 data_time: 0.0093 memory: 5135 grad_norm: 2.1497 loss: 0.4352 loss_cls: 0.1991 loss_bbox: 0.2361 +2024/10/27 02:56:49 - mmengine - INFO - Epoch(train) [11][1400/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:55:38 time: 0.2716 data_time: 0.0094 memory: 5133 grad_norm: 2.1724 loss: 0.4289 loss_cls: 0.1950 loss_bbox: 0.2339 +2024/10/27 02:57:03 - mmengine - INFO - Epoch(train) [11][1450/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:55:09 time: 0.2795 data_time: 0.0095 memory: 5135 grad_norm: 2.1485 loss: 0.3963 loss_cls: 0.1835 loss_bbox: 0.2127 +2024/10/27 02:57:19 - mmengine - INFO - Epoch(train) [11][1500/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:54:42 time: 0.3217 data_time: 0.0635 memory: 5134 grad_norm: 2.1800 loss: 0.4058 loss_cls: 0.1808 loss_bbox: 0.2250 +2024/10/27 02:57:32 - mmengine - INFO - Epoch(train) [11][1550/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:54:13 time: 0.2670 data_time: 0.0096 memory: 5134 grad_norm: 2.0619 loss: 0.4106 loss_cls: 0.1851 loss_bbox: 0.2254 +2024/10/27 02:57:46 - mmengine - INFO - Epoch(train) [11][1600/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:53:45 time: 0.2671 data_time: 0.0094 memory: 5136 grad_norm: 2.1789 loss: 0.4137 loss_cls: 0.1876 loss_bbox: 0.2261 +2024/10/27 02:57:59 - mmengine - INFO - Epoch(train) [11][1650/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:53:16 time: 0.2682 data_time: 0.0094 memory: 5133 grad_norm: 2.1334 loss: 0.4597 loss_cls: 0.2148 loss_bbox: 0.2449 +2024/10/27 02:58:13 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 02:58:13 - mmengine - INFO - Epoch(train) [11][1700/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:52:48 time: 0.2808 data_time: 0.0099 memory: 5134 grad_norm: 2.0983 loss: 0.4192 loss_cls: 0.1903 loss_bbox: 0.2288 +2024/10/27 02:58:27 - mmengine - INFO - Epoch(train) [11][1750/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:52:20 time: 0.2694 data_time: 0.0100 memory: 5133 grad_norm: 2.2081 loss: 0.4204 loss_cls: 0.1980 loss_bbox: 0.2224 +2024/10/27 02:58:40 - mmengine - INFO - Epoch(train) [11][1800/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:51:52 time: 0.2699 data_time: 0.0095 memory: 5133 grad_norm: 2.0362 loss: 0.4254 loss_cls: 0.1890 loss_bbox: 0.2364 +2024/10/27 02:58:54 - mmengine - INFO - Epoch(train) [11][1850/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:51:23 time: 0.2690 data_time: 0.0110 memory: 5136 grad_norm: 2.0499 loss: 0.3894 loss_cls: 0.1728 loss_bbox: 0.2167 +2024/10/27 02:59:07 - mmengine - INFO - Epoch(train) [11][1900/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:50:55 time: 0.2730 data_time: 0.0086 memory: 5134 grad_norm: 2.1392 loss: 0.4212 loss_cls: 0.1920 loss_bbox: 0.2292 +2024/10/27 02:59:21 - mmengine - INFO - Epoch(train) [11][1950/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:50:27 time: 0.2717 data_time: 0.0090 memory: 5134 grad_norm: 1.9320 loss: 0.4002 loss_cls: 0.1786 loss_bbox: 0.2216 +2024/10/27 02:59:34 - mmengine - INFO - Epoch(train) [11][2000/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:49:59 time: 0.2662 data_time: 0.0092 memory: 5136 grad_norm: 2.1720 loss: 0.4132 loss_cls: 0.1850 loss_bbox: 0.2282 +2024/10/27 02:59:48 - mmengine - INFO - Epoch(train) [11][2050/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:49:31 time: 0.2693 data_time: 0.0084 memory: 5133 grad_norm: 1.9551 loss: 0.4127 loss_cls: 0.1893 loss_bbox: 0.2234 +2024/10/27 03:00:01 - mmengine - INFO - Epoch(train) [11][2100/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:49:02 time: 0.2637 data_time: 0.0087 memory: 5134 grad_norm: 2.1274 loss: 0.4146 loss_cls: 0.1890 loss_bbox: 0.2257 +2024/10/27 03:00:14 - mmengine - INFO - Epoch(train) [11][2150/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:48:34 time: 0.2736 data_time: 0.0128 memory: 5136 grad_norm: 2.2746 loss: 0.4348 loss_cls: 0.1991 loss_bbox: 0.2357 +2024/10/27 03:00:28 - mmengine - INFO - Epoch(train) [11][2200/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:48:06 time: 0.2716 data_time: 0.0119 memory: 5134 grad_norm: 2.0732 loss: 0.4094 loss_cls: 0.1833 loss_bbox: 0.2261 +2024/10/27 03:00:42 - mmengine - INFO - Epoch(train) [11][2250/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:47:38 time: 0.2812 data_time: 0.0093 memory: 5135 grad_norm: 2.1967 loss: 0.4103 loss_cls: 0.1804 loss_bbox: 0.2299 +2024/10/27 03:00:56 - mmengine - INFO - Epoch(train) [11][2300/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:47:10 time: 0.2710 data_time: 0.0092 memory: 5133 grad_norm: 2.0865 loss: 0.4087 loss_cls: 0.1840 loss_bbox: 0.2247 +2024/10/27 03:01:10 - mmengine - INFO - Epoch(train) [11][2350/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:46:42 time: 0.2823 data_time: 0.0168 memory: 5132 grad_norm: 2.1435 loss: 0.4096 loss_cls: 0.1907 loss_bbox: 0.2189 +2024/10/27 03:01:23 - mmengine - INFO - Epoch(train) [11][2400/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:46:14 time: 0.2689 data_time: 0.0086 memory: 5135 grad_norm: 2.1002 loss: 0.4410 loss_cls: 0.2058 loss_bbox: 0.2353 +2024/10/27 03:01:37 - mmengine - INFO - Epoch(train) [11][2450/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:45:46 time: 0.2697 data_time: 0.0091 memory: 5131 grad_norm: 2.1931 loss: 0.4310 loss_cls: 0.1946 loss_bbox: 0.2364 +2024/10/27 03:01:51 - mmengine - INFO - Epoch(train) [11][2500/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:45:18 time: 0.2791 data_time: 0.0130 memory: 5138 grad_norm: 2.2655 loss: 0.4388 loss_cls: 0.2060 loss_bbox: 0.2328 +2024/10/27 03:02:04 - mmengine - INFO - Epoch(train) [11][2550/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:44:50 time: 0.2706 data_time: 0.0086 memory: 5135 grad_norm: 2.1782 loss: 0.4155 loss_cls: 0.1832 loss_bbox: 0.2323 +2024/10/27 03:02:18 - mmengine - INFO - Epoch(train) [11][2600/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:44:22 time: 0.2825 data_time: 0.0231 memory: 5139 grad_norm: 2.1414 loss: 0.3985 loss_cls: 0.1750 loss_bbox: 0.2236 +2024/10/27 03:02:32 - mmengine - INFO - Epoch(train) [11][2650/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:43:55 time: 0.2717 data_time: 0.0102 memory: 5135 grad_norm: 2.0321 loss: 0.4099 loss_cls: 0.1840 loss_bbox: 0.2259 +2024/10/27 03:02:45 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 03:02:45 - mmengine - INFO - Epoch(train) [11][2700/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:43:27 time: 0.2710 data_time: 0.0099 memory: 5134 grad_norm: 2.1446 loss: 0.3922 loss_cls: 0.1656 loss_bbox: 0.2266 +2024/10/27 03:02:59 - mmengine - INFO - Epoch(train) [11][2750/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:42:59 time: 0.2710 data_time: 0.0089 memory: 5134 grad_norm: 2.0450 loss: 0.3888 loss_cls: 0.1778 loss_bbox: 0.2110 +2024/10/27 03:03:13 - mmengine - INFO - Epoch(train) [11][2800/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:42:31 time: 0.2735 data_time: 0.0095 memory: 5133 grad_norm: 2.2636 loss: 0.3996 loss_cls: 0.1838 loss_bbox: 0.2159 +2024/10/27 03:03:26 - mmengine - INFO - Epoch(train) [11][2850/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:42:03 time: 0.2709 data_time: 0.0087 memory: 5136 grad_norm: 2.0560 loss: 0.3860 loss_cls: 0.1710 loss_bbox: 0.2150 +2024/10/27 03:03:40 - mmengine - INFO - Epoch(train) [11][2900/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:41:35 time: 0.2724 data_time: 0.0113 memory: 5135 grad_norm: 2.2539 loss: 0.4222 loss_cls: 0.1831 loss_bbox: 0.2391 +2024/10/27 03:03:53 - mmengine - INFO - Epoch(train) [11][2950/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:41:07 time: 0.2720 data_time: 0.0087 memory: 5134 grad_norm: 2.1071 loss: 0.3904 loss_cls: 0.1693 loss_bbox: 0.2211 +2024/10/27 03:04:07 - mmengine - INFO - Epoch(train) [11][3000/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:40:40 time: 0.2767 data_time: 0.0086 memory: 5135 grad_norm: 2.2671 loss: 0.4201 loss_cls: 0.1922 loss_bbox: 0.2279 +2024/10/27 03:04:21 - mmengine - INFO - Epoch(train) [11][3050/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:40:12 time: 0.2742 data_time: 0.0090 memory: 5133 grad_norm: 2.1948 loss: 0.3889 loss_cls: 0.1735 loss_bbox: 0.2154 +2024/10/27 03:04:35 - mmengine - INFO - Epoch(train) [11][3100/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:39:44 time: 0.2700 data_time: 0.0093 memory: 5135 grad_norm: 2.1813 loss: 0.4402 loss_cls: 0.1942 loss_bbox: 0.2460 +2024/10/27 03:04:48 - mmengine - INFO - Epoch(train) [11][3150/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:39:16 time: 0.2744 data_time: 0.0143 memory: 5135 grad_norm: 2.1456 loss: 0.4239 loss_cls: 0.1951 loss_bbox: 0.2288 +2024/10/27 03:05:02 - mmengine - INFO - Epoch(train) [11][3200/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:38:49 time: 0.2820 data_time: 0.0084 memory: 5137 grad_norm: 2.1499 loss: 0.4013 loss_cls: 0.1842 loss_bbox: 0.2171 +2024/10/27 03:05:19 - mmengine - INFO - Epoch(train) [11][3250/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:38:22 time: 0.3315 data_time: 0.0688 memory: 5135 grad_norm: 2.1541 loss: 0.3981 loss_cls: 0.1772 loss_bbox: 0.2210 +2024/10/27 03:05:33 - mmengine - INFO - Epoch(train) [11][3300/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:37:54 time: 0.2721 data_time: 0.0089 memory: 5133 grad_norm: 2.2213 loss: 0.4160 loss_cls: 0.1893 loss_bbox: 0.2267 +2024/10/27 03:05:46 - mmengine - INFO - Epoch(train) [11][3350/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:37:26 time: 0.2733 data_time: 0.0088 memory: 5133 grad_norm: 2.2146 loss: 0.3969 loss_cls: 0.1785 loss_bbox: 0.2184 +2024/10/27 03:06:00 - mmengine - INFO - Epoch(train) [11][3400/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:36:58 time: 0.2672 data_time: 0.0088 memory: 5134 grad_norm: 2.1531 loss: 0.4084 loss_cls: 0.1844 loss_bbox: 0.2240 +2024/10/27 03:06:14 - mmengine - INFO - Epoch(train) [11][3450/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:36:31 time: 0.2838 data_time: 0.0087 memory: 5135 grad_norm: 2.0862 loss: 0.3900 loss_cls: 0.1751 loss_bbox: 0.2149 +2024/10/27 03:06:27 - mmengine - INFO - Epoch(train) [11][3500/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:36:03 time: 0.2732 data_time: 0.0095 memory: 5135 grad_norm: 2.2512 loss: 0.4367 loss_cls: 0.1966 loss_bbox: 0.2401 +2024/10/27 03:06:41 - mmengine - INFO - Epoch(train) [11][3550/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:35:36 time: 0.2687 data_time: 0.0094 memory: 5136 grad_norm: 2.1797 loss: 0.4299 loss_cls: 0.1936 loss_bbox: 0.2363 +2024/10/27 03:06:54 - mmengine - INFO - Epoch(train) [11][3600/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:35:08 time: 0.2704 data_time: 0.0089 memory: 5136 grad_norm: 2.1391 loss: 0.4040 loss_cls: 0.1842 loss_bbox: 0.2199 +2024/10/27 03:07:08 - mmengine - INFO - Epoch(train) [11][3650/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:34:41 time: 0.2745 data_time: 0.0088 memory: 5136 grad_norm: 2.2263 loss: 0.3835 loss_cls: 0.1724 loss_bbox: 0.2110 +2024/10/27 03:07:22 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 03:07:22 - mmengine - INFO - Epoch(train) [11][3700/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:34:13 time: 0.2755 data_time: 0.0086 memory: 5132 grad_norm: 2.1390 loss: 0.4201 loss_cls: 0.1887 loss_bbox: 0.2314 +2024/10/27 03:07:36 - mmengine - INFO - Epoch(train) [11][3750/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:33:46 time: 0.2727 data_time: 0.0087 memory: 5136 grad_norm: 2.2052 loss: 0.3913 loss_cls: 0.1703 loss_bbox: 0.2210 +2024/10/27 03:07:49 - mmengine - INFO - Epoch(train) [11][3800/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:33:18 time: 0.2718 data_time: 0.0087 memory: 5134 grad_norm: 2.2230 loss: 0.4063 loss_cls: 0.1899 loss_bbox: 0.2164 +2024/10/27 03:08:03 - mmengine - INFO - Epoch(train) [11][3850/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:32:51 time: 0.2736 data_time: 0.0113 memory: 5135 grad_norm: 2.2967 loss: 0.4136 loss_cls: 0.1922 loss_bbox: 0.2214 +2024/10/27 03:08:18 - mmengine - INFO - Epoch(train) [11][3900/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:32:23 time: 0.3110 data_time: 0.0511 memory: 5134 grad_norm: 2.1584 loss: 0.4115 loss_cls: 0.1820 loss_bbox: 0.2296 +2024/10/27 03:08:32 - mmengine - INFO - Epoch(train) [11][3950/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:31:56 time: 0.2707 data_time: 0.0087 memory: 5138 grad_norm: 2.1757 loss: 0.4014 loss_cls: 0.1781 loss_bbox: 0.2233 +2024/10/27 03:08:46 - mmengine - INFO - Epoch(train) [11][4000/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:31:29 time: 0.2849 data_time: 0.0083 memory: 5134 grad_norm: 2.1855 loss: 0.3992 loss_cls: 0.1822 loss_bbox: 0.2170 +2024/10/27 03:09:00 - mmengine - INFO - Epoch(train) [11][4050/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:31:01 time: 0.2688 data_time: 0.0084 memory: 5135 grad_norm: 2.0991 loss: 0.4118 loss_cls: 0.1820 loss_bbox: 0.2298 +2024/10/27 03:09:13 - mmengine - INFO - Epoch(train) [11][4100/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:30:34 time: 0.2754 data_time: 0.0092 memory: 5135 grad_norm: 2.1004 loss: 0.4301 loss_cls: 0.1910 loss_bbox: 0.2391 +2024/10/27 03:09:27 - mmengine - INFO - Epoch(train) [11][4150/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:30:06 time: 0.2763 data_time: 0.0089 memory: 5136 grad_norm: 2.1298 loss: 0.4164 loss_cls: 0.1801 loss_bbox: 0.2363 +2024/10/27 03:09:41 - mmengine - INFO - Epoch(train) [11][4200/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:29:39 time: 0.2736 data_time: 0.0094 memory: 5134 grad_norm: 2.2766 loss: 0.3814 loss_cls: 0.1648 loss_bbox: 0.2166 +2024/10/27 03:09:55 - mmengine - INFO - Epoch(train) [11][4250/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:29:12 time: 0.2862 data_time: 0.0099 memory: 5134 grad_norm: 2.3362 loss: 0.4156 loss_cls: 0.1888 loss_bbox: 0.2268 +2024/10/27 03:10:09 - mmengine - INFO - Epoch(train) [11][4300/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:28:45 time: 0.2817 data_time: 0.0164 memory: 5136 grad_norm: 2.0408 loss: 0.4475 loss_cls: 0.2040 loss_bbox: 0.2435 +2024/10/27 03:10:23 - mmengine - INFO - Epoch(train) [11][4350/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:28:17 time: 0.2797 data_time: 0.0189 memory: 5134 grad_norm: 2.1757 loss: 0.4315 loss_cls: 0.1982 loss_bbox: 0.2333 +2024/10/27 03:10:37 - mmengine - INFO - Epoch(train) [11][4400/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:27:50 time: 0.2689 data_time: 0.0097 memory: 5132 grad_norm: 2.2819 loss: 0.4386 loss_cls: 0.2021 loss_bbox: 0.2365 +2024/10/27 03:10:50 - mmengine - INFO - Epoch(train) [11][4450/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:27:23 time: 0.2672 data_time: 0.0097 memory: 5134 grad_norm: 2.1754 loss: 0.3968 loss_cls: 0.1741 loss_bbox: 0.2227 +2024/10/27 03:11:04 - mmengine - INFO - Epoch(train) [11][4500/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:26:55 time: 0.2692 data_time: 0.0107 memory: 5134 grad_norm: 2.2223 loss: 0.3981 loss_cls: 0.1817 loss_bbox: 0.2164 +2024/10/27 03:11:19 - mmengine - INFO - Epoch(train) [11][4550/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:26:28 time: 0.3053 data_time: 0.0455 memory: 5134 grad_norm: 2.1368 loss: 0.4550 loss_cls: 0.2079 loss_bbox: 0.2472 +2024/10/27 03:11:32 - mmengine - INFO - Epoch(train) [11][4600/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:26:01 time: 0.2711 data_time: 0.0099 memory: 5134 grad_norm: 2.1560 loss: 0.3803 loss_cls: 0.1704 loss_bbox: 0.2099 +2024/10/27 03:11:46 - mmengine - INFO - Epoch(train) [11][4650/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:25:34 time: 0.2786 data_time: 0.0098 memory: 5137 grad_norm: 2.3573 loss: 0.4079 loss_cls: 0.1899 loss_bbox: 0.2180 +2024/10/27 03:12:00 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 03:12:00 - mmengine - INFO - Epoch(train) [11][4700/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:25:07 time: 0.2762 data_time: 0.0102 memory: 5134 grad_norm: 2.1052 loss: 0.4026 loss_cls: 0.1780 loss_bbox: 0.2247 +2024/10/27 03:12:14 - mmengine - INFO - Epoch(train) [11][4750/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:24:40 time: 0.2751 data_time: 0.0102 memory: 5134 grad_norm: 2.4978 loss: 0.3972 loss_cls: 0.1804 loss_bbox: 0.2168 +2024/10/27 03:12:28 - mmengine - INFO - Epoch(train) [11][4800/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:24:13 time: 0.2751 data_time: 0.0122 memory: 5133 grad_norm: 2.0432 loss: 0.4423 loss_cls: 0.2016 loss_bbox: 0.2407 +2024/10/27 03:12:42 - mmengine - INFO - Epoch(train) [11][4850/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:23:46 time: 0.2845 data_time: 0.0098 memory: 5135 grad_norm: 2.1431 loss: 0.4195 loss_cls: 0.1942 loss_bbox: 0.2253 +2024/10/27 03:12:56 - mmengine - INFO - Epoch(train) [11][4900/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:23:18 time: 0.2728 data_time: 0.0104 memory: 5135 grad_norm: 2.0739 loss: 0.4266 loss_cls: 0.1914 loss_bbox: 0.2352 +2024/10/27 03:13:10 - mmengine - INFO - Epoch(train) [11][4950/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:22:51 time: 0.2864 data_time: 0.0127 memory: 5134 grad_norm: 2.2869 loss: 0.4196 loss_cls: 0.1897 loss_bbox: 0.2299 +2024/10/27 03:13:23 - mmengine - INFO - Epoch(train) [11][5000/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:22:24 time: 0.2703 data_time: 0.0095 memory: 5135 grad_norm: 2.2014 loss: 0.4393 loss_cls: 0.1988 loss_bbox: 0.2405 +2024/10/27 03:13:37 - mmengine - INFO - Epoch(train) [11][5050/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:21:57 time: 0.2697 data_time: 0.0094 memory: 5134 grad_norm: 2.2649 loss: 0.4080 loss_cls: 0.1788 loss_bbox: 0.2292 +2024/10/27 03:13:50 - mmengine - INFO - Epoch(train) [11][5100/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:21:30 time: 0.2700 data_time: 0.0096 memory: 5137 grad_norm: 2.1604 loss: 0.4234 loss_cls: 0.1946 loss_bbox: 0.2288 +2024/10/27 03:14:05 - mmengine - INFO - Epoch(train) [11][5150/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:21:03 time: 0.2841 data_time: 0.0194 memory: 5135 grad_norm: 2.1366 loss: 0.4019 loss_cls: 0.1808 loss_bbox: 0.2212 +2024/10/27 03:14:19 - mmengine - INFO - Epoch(train) [11][5200/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:20:36 time: 0.2837 data_time: 0.0190 memory: 5136 grad_norm: 2.3341 loss: 0.4097 loss_cls: 0.1872 loss_bbox: 0.2225 +2024/10/27 03:14:32 - mmengine - INFO - Epoch(train) [11][5250/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:20:09 time: 0.2694 data_time: 0.0103 memory: 5135 grad_norm: 2.1299 loss: 0.4064 loss_cls: 0.1801 loss_bbox: 0.2263 +2024/10/27 03:14:46 - mmengine - INFO - Epoch(train) [11][5300/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:19:42 time: 0.2754 data_time: 0.0101 memory: 5135 grad_norm: 2.1752 loss: 0.3914 loss_cls: 0.1693 loss_bbox: 0.2221 +2024/10/27 03:15:00 - mmengine - INFO - Epoch(train) [11][5350/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:19:15 time: 0.2802 data_time: 0.0133 memory: 5135 grad_norm: 2.2411 loss: 0.4154 loss_cls: 0.1853 loss_bbox: 0.2301 +2024/10/27 03:15:14 - mmengine - INFO - Epoch(train) [11][5400/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:18:49 time: 0.2761 data_time: 0.0101 memory: 5132 grad_norm: 2.2286 loss: 0.4161 loss_cls: 0.1852 loss_bbox: 0.2309 +2024/10/27 03:15:28 - mmengine - INFO - Epoch(train) [11][5450/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:18:22 time: 0.2777 data_time: 0.0143 memory: 5134 grad_norm: 2.1135 loss: 0.4210 loss_cls: 0.1978 loss_bbox: 0.2233 +2024/10/27 03:15:41 - mmengine - INFO - Epoch(train) [11][5500/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:17:55 time: 0.2721 data_time: 0.0109 memory: 5132 grad_norm: 2.2237 loss: 0.3976 loss_cls: 0.1820 loss_bbox: 0.2157 +2024/10/27 03:15:55 - mmengine - INFO - Epoch(train) [11][5550/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:17:28 time: 0.2720 data_time: 0.0099 memory: 5133 grad_norm: 2.2371 loss: 0.4038 loss_cls: 0.1798 loss_bbox: 0.2240 +2024/10/27 03:16:09 - mmengine - INFO - Epoch(train) [11][5600/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:17:01 time: 0.2720 data_time: 0.0095 memory: 5135 grad_norm: 2.1587 loss: 0.4083 loss_cls: 0.1841 loss_bbox: 0.2241 +2024/10/27 03:16:22 - mmengine - INFO - Epoch(train) [11][5650/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:16:34 time: 0.2672 data_time: 0.0095 memory: 5134 grad_norm: 2.0782 loss: 0.4103 loss_cls: 0.1847 loss_bbox: 0.2257 +2024/10/27 03:16:36 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 03:16:36 - mmengine - INFO - Epoch(train) [11][5700/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:16:07 time: 0.2704 data_time: 0.0097 memory: 5135 grad_norm: 2.0900 loss: 0.4137 loss_cls: 0.1844 loss_bbox: 0.2293 +2024/10/27 03:16:49 - mmengine - INFO - Epoch(train) [11][5750/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:15:40 time: 0.2725 data_time: 0.0100 memory: 5136 grad_norm: 2.1227 loss: 0.4189 loss_cls: 0.1889 loss_bbox: 0.2301 +2024/10/27 03:17:04 - mmengine - INFO - Epoch(train) [11][5800/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:15:14 time: 0.2884 data_time: 0.0093 memory: 5136 grad_norm: 2.3403 loss: 0.4217 loss_cls: 0.1832 loss_bbox: 0.2385 +2024/10/27 03:17:19 - mmengine - INFO - Epoch(train) [11][5850/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:14:47 time: 0.3053 data_time: 0.0406 memory: 5136 grad_norm: 2.1870 loss: 0.4181 loss_cls: 0.1883 loss_bbox: 0.2298 +2024/10/27 03:17:33 - mmengine - INFO - Epoch(train) [11][5900/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:14:20 time: 0.2867 data_time: 0.0253 memory: 5133 grad_norm: 2.2580 loss: 0.4211 loss_cls: 0.1863 loss_bbox: 0.2348 +2024/10/27 03:17:47 - mmengine - INFO - Epoch(train) [11][5950/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:13:54 time: 0.2744 data_time: 0.0099 memory: 5134 grad_norm: 2.1101 loss: 0.4171 loss_cls: 0.1924 loss_bbox: 0.2246 +2024/10/27 03:18:01 - mmengine - INFO - Epoch(train) [11][6000/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:13:27 time: 0.2777 data_time: 0.0108 memory: 5135 grad_norm: 2.2640 loss: 0.4184 loss_cls: 0.1875 loss_bbox: 0.2309 +2024/10/27 03:18:15 - mmengine - INFO - Epoch(train) [11][6050/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:13:00 time: 0.2815 data_time: 0.0169 memory: 5139 grad_norm: 2.3853 loss: 0.4018 loss_cls: 0.1821 loss_bbox: 0.2197 +2024/10/27 03:18:29 - mmengine - INFO - Epoch(train) [11][6100/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:12:33 time: 0.2719 data_time: 0.0100 memory: 5133 grad_norm: 2.2075 loss: 0.3918 loss_cls: 0.1719 loss_bbox: 0.2199 +2024/10/27 03:18:42 - mmengine - INFO - Epoch(train) [11][6150/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:12:07 time: 0.2725 data_time: 0.0092 memory: 5134 grad_norm: 2.1260 loss: 0.4018 loss_cls: 0.1823 loss_bbox: 0.2195 +2024/10/27 03:18:56 - mmengine - INFO - Epoch(train) [11][6200/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:11:40 time: 0.2689 data_time: 0.0085 memory: 5134 grad_norm: 2.1398 loss: 0.4109 loss_cls: 0.1813 loss_bbox: 0.2296 +2024/10/27 03:19:09 - mmengine - INFO - Epoch(train) [11][6250/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:11:13 time: 0.2733 data_time: 0.0094 memory: 5136 grad_norm: 2.2193 loss: 0.4009 loss_cls: 0.1834 loss_bbox: 0.2175 +2024/10/27 03:19:23 - mmengine - INFO - Epoch(train) [11][6300/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:10:47 time: 0.2712 data_time: 0.0089 memory: 5135 grad_norm: 2.2602 loss: 0.4229 loss_cls: 0.1955 loss_bbox: 0.2274 +2024/10/27 03:19:37 - mmengine - INFO - Epoch(train) [11][6350/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:10:20 time: 0.2728 data_time: 0.0122 memory: 5133 grad_norm: 2.3059 loss: 0.4117 loss_cls: 0.1869 loss_bbox: 0.2248 +2024/10/27 03:19:50 - mmengine - INFO - Epoch(train) [11][6400/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:09:53 time: 0.2666 data_time: 0.0084 memory: 5134 grad_norm: 2.1299 loss: 0.4258 loss_cls: 0.1902 loss_bbox: 0.2356 +2024/10/27 03:20:03 - mmengine - INFO - Epoch(train) [11][6450/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:09:27 time: 0.2687 data_time: 0.0105 memory: 5136 grad_norm: 2.1104 loss: 0.4525 loss_cls: 0.2080 loss_bbox: 0.2445 +2024/10/27 03:20:19 - mmengine - INFO - Epoch(train) [11][6500/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:09:00 time: 0.3082 data_time: 0.0456 memory: 5136 grad_norm: 2.2540 loss: 0.4401 loss_cls: 0.2052 loss_bbox: 0.2349 +2024/10/27 03:20:32 - mmengine - INFO - Epoch(train) [11][6550/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:08:34 time: 0.2654 data_time: 0.0088 memory: 5133 grad_norm: 2.2174 loss: 0.4082 loss_cls: 0.1827 loss_bbox: 0.2255 +2024/10/27 03:20:45 - mmengine - INFO - Epoch(train) [11][6600/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:08:07 time: 0.2691 data_time: 0.0086 memory: 5133 grad_norm: 2.1484 loss: 0.4014 loss_cls: 0.1829 loss_bbox: 0.2184 +2024/10/27 03:20:59 - mmengine - INFO - Epoch(train) [11][6650/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:07:41 time: 0.2685 data_time: 0.0089 memory: 5133 grad_norm: 2.2553 loss: 0.3930 loss_cls: 0.1776 loss_bbox: 0.2154 +2024/10/27 03:21:13 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 03:21:13 - mmengine - INFO - Epoch(train) [11][6700/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:07:14 time: 0.2842 data_time: 0.0087 memory: 5134 grad_norm: 2.1705 loss: 0.3962 loss_cls: 0.1731 loss_bbox: 0.2231 +2024/10/27 03:21:26 - mmengine - INFO - Epoch(train) [11][6750/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:06:48 time: 0.2672 data_time: 0.0088 memory: 5135 grad_norm: 2.2433 loss: 0.4492 loss_cls: 0.2060 loss_bbox: 0.2432 +2024/10/27 03:21:40 - mmengine - INFO - Epoch(train) [11][6800/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:06:21 time: 0.2641 data_time: 0.0091 memory: 5134 grad_norm: 2.1433 loss: 0.4096 loss_cls: 0.1855 loss_bbox: 0.2241 +2024/10/27 03:21:53 - mmengine - INFO - Epoch(train) [11][6850/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:05:55 time: 0.2654 data_time: 0.0086 memory: 5135 grad_norm: 2.2438 loss: 0.4101 loss_cls: 0.1847 loss_bbox: 0.2254 +2024/10/27 03:22:06 - mmengine - INFO - Epoch(train) [11][6900/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:05:28 time: 0.2682 data_time: 0.0085 memory: 5134 grad_norm: 2.3009 loss: 0.4043 loss_cls: 0.1863 loss_bbox: 0.2180 +2024/10/27 03:22:20 - mmengine - INFO - Epoch(train) [11][6950/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:05:02 time: 0.2729 data_time: 0.0086 memory: 5134 grad_norm: 2.1990 loss: 0.4366 loss_cls: 0.2010 loss_bbox: 0.2356 +2024/10/27 03:22:34 - mmengine - INFO - Epoch(train) [11][7000/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:04:35 time: 0.2717 data_time: 0.0126 memory: 5134 grad_norm: 2.2064 loss: 0.4275 loss_cls: 0.1971 loss_bbox: 0.2304 +2024/10/27 03:22:47 - mmengine - INFO - Epoch(train) [11][7050/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:04:09 time: 0.2731 data_time: 0.0119 memory: 5135 grad_norm: 2.2734 loss: 0.3771 loss_cls: 0.1659 loss_bbox: 0.2112 +2024/10/27 03:23:01 - mmengine - INFO - Epoch(train) [11][7100/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:03:43 time: 0.2708 data_time: 0.0091 memory: 5135 grad_norm: 2.4554 loss: 0.4035 loss_cls: 0.1856 loss_bbox: 0.2179 +2024/10/27 03:23:14 - mmengine - INFO - Epoch(train) [11][7150/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:03:16 time: 0.2707 data_time: 0.0098 memory: 5134 grad_norm: 2.2991 loss: 0.4055 loss_cls: 0.1840 loss_bbox: 0.2215 +2024/10/27 03:23:28 - mmengine - INFO - Epoch(train) [11][7200/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:02:50 time: 0.2717 data_time: 0.0090 memory: 5134 grad_norm: 2.3497 loss: 0.4196 loss_cls: 0.1912 loss_bbox: 0.2284 +2024/10/27 03:23:41 - mmengine - INFO - Epoch(train) [11][7250/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:02:23 time: 0.2695 data_time: 0.0086 memory: 5133 grad_norm: 2.1325 loss: 0.3955 loss_cls: 0.1802 loss_bbox: 0.2153 +2024/10/27 03:23:55 - mmengine - INFO - Epoch(train) [11][7300/7330] base_lr: 5.0000e-05 lr: 5.0000e-05 eta: 1:01:57 time: 0.2703 data_time: 0.0089 memory: 5134 grad_norm: 2.1797 loss: 0.4388 loss_cls: 0.1964 loss_bbox: 0.2423 +2024/10/27 03:24:09 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 03:24:09 - mmengine - INFO - Saving checkpoint at 11 epochs +2024/10/27 03:24:18 - mmengine - INFO - Epoch(val) [11][ 50/1250] eta: 0:01:23 time: 0.0694 data_time: 0.0021 memory: 6187 +2024/10/27 03:24:21 - mmengine - INFO - Epoch(val) [11][ 100/1250] eta: 0:01:18 time: 0.0673 data_time: 0.0017 memory: 630 +2024/10/27 03:24:24 - mmengine - INFO - Epoch(val) [11][ 150/1250] eta: 0:01:14 time: 0.0671 data_time: 0.0018 memory: 636 +2024/10/27 03:24:28 - mmengine - INFO - Epoch(val) [11][ 200/1250] eta: 0:01:10 time: 0.0646 data_time: 0.0018 memory: 636 +2024/10/27 03:24:31 - mmengine - INFO - Epoch(val) [11][ 250/1250] eta: 0:01:06 time: 0.0663 data_time: 0.0019 memory: 626 +2024/10/27 03:24:34 - mmengine - INFO - Epoch(val) [11][ 300/1250] eta: 0:01:03 time: 0.0664 data_time: 0.0019 memory: 626 +2024/10/27 03:24:38 - mmengine - INFO - Epoch(val) [11][ 350/1250] eta: 0:01:00 time: 0.0656 data_time: 0.0019 memory: 626 +2024/10/27 03:24:41 - mmengine - INFO - Epoch(val) [11][ 400/1250] eta: 0:00:56 time: 0.0642 data_time: 0.0017 memory: 615 +2024/10/27 03:24:44 - mmengine - INFO - Epoch(val) [11][ 450/1250] eta: 0:00:53 time: 0.0659 data_time: 0.0018 memory: 636 +2024/10/27 03:24:47 - mmengine - INFO - Epoch(val) [11][ 500/1250] eta: 0:00:49 time: 0.0668 data_time: 0.0017 memory: 636 +2024/10/27 03:24:51 - mmengine - INFO - Epoch(val) [11][ 550/1250] eta: 0:00:46 time: 0.0668 data_time: 0.0019 memory: 615 +2024/10/27 03:24:54 - mmengine - INFO - Epoch(val) [11][ 600/1250] eta: 0:00:43 time: 0.0644 data_time: 0.0018 memory: 626 +2024/10/27 03:24:57 - mmengine - INFO - Epoch(val) [11][ 650/1250] eta: 0:00:39 time: 0.0676 data_time: 0.0018 memory: 626 +2024/10/27 03:25:01 - mmengine - INFO - Epoch(val) [11][ 700/1250] eta: 0:00:36 time: 0.0675 data_time: 0.0017 memory: 630 +2024/10/27 03:25:04 - mmengine - INFO - Epoch(val) [11][ 750/1250] eta: 0:00:33 time: 0.0663 data_time: 0.0018 memory: 630 +2024/10/27 03:25:07 - mmengine - INFO - Epoch(val) [11][ 800/1250] eta: 0:00:29 time: 0.0683 data_time: 0.0019 memory: 636 +2024/10/27 03:25:11 - mmengine - INFO - Epoch(val) [11][ 850/1250] eta: 0:00:26 time: 0.0664 data_time: 0.0017 memory: 636 +2024/10/27 03:25:14 - mmengine - INFO - Epoch(val) [11][ 900/1250] eta: 0:00:23 time: 0.0674 data_time: 0.0018 memory: 636 +2024/10/27 03:25:17 - mmengine - INFO - Epoch(val) [11][ 950/1250] eta: 0:00:19 time: 0.0664 data_time: 0.0017 memory: 626 +2024/10/27 03:25:21 - mmengine - INFO - Epoch(val) [11][1000/1250] eta: 0:00:16 time: 0.0668 data_time: 0.0017 memory: 626 +2024/10/27 03:25:24 - mmengine - INFO - Epoch(val) [11][1050/1250] eta: 0:00:13 time: 0.0663 data_time: 0.0018 memory: 630 +2024/10/27 03:25:27 - mmengine - INFO - Epoch(val) [11][1100/1250] eta: 0:00:09 time: 0.0652 data_time: 0.0017 memory: 636 +2024/10/27 03:25:31 - mmengine - INFO - Epoch(val) [11][1150/1250] eta: 0:00:06 time: 0.0670 data_time: 0.0016 memory: 630 +2024/10/27 03:25:34 - mmengine - INFO - Epoch(val) [11][1200/1250] eta: 0:00:03 time: 0.0677 data_time: 0.0016 memory: 630 +2024/10/27 03:25:38 - mmengine - INFO - Epoch(val) [11][1250/1250] eta: 0:00:00 time: 0.0684 data_time: 0.0017 memory: 636 +2024/10/27 03:25:46 - mmengine - INFO - Evaluating bbox... +2024/10/27 03:26:41 - mmengine - INFO - bbox_mAP_copypaste: 0.387 0.589 0.412 0.210 0.424 0.527 +2024/10/27 03:26:42 - mmengine - INFO - Epoch(val) [11][1250/1250] coco/bbox_mAP: 0.3870 coco/bbox_mAP_50: 0.5890 coco/bbox_mAP_75: 0.4120 coco/bbox_mAP_s: 0.2100 coco/bbox_mAP_m: 0.4240 coco/bbox_mAP_l: 0.5270 data_time: 0.0018 time: 0.0666 +2024/10/27 03:26:56 - mmengine - INFO - Epoch(train) [12][ 50/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 1:01:16 time: 0.2767 data_time: 0.0095 memory: 5136 grad_norm: 1.9609 loss: 0.4002 loss_cls: 0.1716 loss_bbox: 0.2286 +2024/10/27 03:27:25 - mmengine - INFO - Epoch(train) [12][ 100/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 1:00:51 time: 0.5764 data_time: 0.0103 memory: 5133 grad_norm: 2.1258 loss: 0.3940 loss_cls: 0.1781 loss_bbox: 0.2159 +2024/10/27 03:27:57 - mmengine - INFO - Epoch(train) [12][ 150/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 1:00:26 time: 0.6475 data_time: 0.0120 memory: 5133 grad_norm: 1.9733 loss: 0.3704 loss_cls: 0.1634 loss_bbox: 0.2070 +2024/10/27 03:28:30 - mmengine - INFO - Epoch(train) [12][ 200/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 1:00:01 time: 0.6469 data_time: 0.0109 memory: 5137 grad_norm: 2.0421 loss: 0.3922 loss_cls: 0.1753 loss_bbox: 0.2169 +2024/10/27 03:29:03 - mmengine - INFO - Epoch(train) [12][ 250/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:59:37 time: 0.6652 data_time: 0.0100 memory: 5135 grad_norm: 1.8997 loss: 0.3812 loss_cls: 0.1693 loss_bbox: 0.2119 +2024/10/27 03:29:34 - mmengine - INFO - Epoch(train) [12][ 300/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:59:12 time: 0.6159 data_time: 0.0624 memory: 5132 grad_norm: 2.0670 loss: 0.3791 loss_cls: 0.1657 loss_bbox: 0.2134 +2024/10/27 03:30:05 - mmengine - INFO - Epoch(train) [12][ 350/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:58:47 time: 0.6260 data_time: 0.0154 memory: 5133 grad_norm: 2.0831 loss: 0.3928 loss_cls: 0.1719 loss_bbox: 0.2209 +2024/10/27 03:30:16 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 03:30:36 - mmengine - INFO - Epoch(train) [12][ 400/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:58:23 time: 0.6279 data_time: 0.0118 memory: 5134 grad_norm: 1.9824 loss: 0.3710 loss_cls: 0.1640 loss_bbox: 0.2070 +2024/10/27 03:31:09 - mmengine - INFO - Epoch(train) [12][ 450/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:57:58 time: 0.6445 data_time: 0.0108 memory: 5133 grad_norm: 1.9907 loss: 0.3958 loss_cls: 0.1781 loss_bbox: 0.2177 +2024/10/27 03:31:38 - mmengine - INFO - Epoch(train) [12][ 500/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:57:33 time: 0.5874 data_time: 0.0099 memory: 5134 grad_norm: 2.0954 loss: 0.3696 loss_cls: 0.1672 loss_bbox: 0.2025 +2024/10/27 03:32:10 - mmengine - INFO - Epoch(train) [12][ 550/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:57:08 time: 0.6366 data_time: 0.0097 memory: 5135 grad_norm: 1.9945 loss: 0.3702 loss_cls: 0.1587 loss_bbox: 0.2115 +2024/10/27 03:32:41 - mmengine - INFO - Epoch(train) [12][ 600/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:56:43 time: 0.6287 data_time: 0.0118 memory: 5134 grad_norm: 2.0755 loss: 0.3205 loss_cls: 0.1391 loss_bbox: 0.1814 +2024/10/27 03:33:12 - mmengine - INFO - Epoch(train) [12][ 650/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:56:19 time: 0.6112 data_time: 0.0102 memory: 5133 grad_norm: 2.0020 loss: 0.4017 loss_cls: 0.1788 loss_bbox: 0.2229 +2024/10/27 03:33:43 - mmengine - INFO - Epoch(train) [12][ 700/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:55:54 time: 0.6231 data_time: 0.0105 memory: 5136 grad_norm: 2.0150 loss: 0.3900 loss_cls: 0.1698 loss_bbox: 0.2202 +2024/10/27 03:34:14 - mmengine - INFO - Epoch(train) [12][ 750/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:55:29 time: 0.6137 data_time: 0.0105 memory: 5136 grad_norm: 1.9049 loss: 0.3845 loss_cls: 0.1656 loss_bbox: 0.2189 +2024/10/27 03:34:44 - mmengine - INFO - Epoch(train) [12][ 800/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:55:04 time: 0.6054 data_time: 0.0098 memory: 5136 grad_norm: 1.9985 loss: 0.3880 loss_cls: 0.1739 loss_bbox: 0.2140 +2024/10/27 03:35:15 - mmengine - INFO - Epoch(train) [12][ 850/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:54:39 time: 0.6241 data_time: 0.0100 memory: 5134 grad_norm: 1.9360 loss: 0.4049 loss_cls: 0.1810 loss_bbox: 0.2239 +2024/10/27 03:35:48 - mmengine - INFO - Epoch(train) [12][ 900/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:54:15 time: 0.6584 data_time: 0.0105 memory: 5133 grad_norm: 2.0251 loss: 0.3686 loss_cls: 0.1645 loss_bbox: 0.2041 +2024/10/27 03:36:17 - mmengine - INFO - Epoch(train) [12][ 950/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:53:50 time: 0.5838 data_time: 0.0098 memory: 5133 grad_norm: 1.9955 loss: 0.3876 loss_cls: 0.1669 loss_bbox: 0.2207 +2024/10/27 03:36:51 - mmengine - INFO - Epoch(train) [12][1000/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:53:25 time: 0.6685 data_time: 0.0109 memory: 5134 grad_norm: 2.1593 loss: 0.3815 loss_cls: 0.1726 loss_bbox: 0.2089 +2024/10/27 03:37:20 - mmengine - INFO - Epoch(train) [12][1050/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:53:00 time: 0.5916 data_time: 0.0115 memory: 5133 grad_norm: 2.0397 loss: 0.3614 loss_cls: 0.1592 loss_bbox: 0.2023 +2024/10/27 03:37:52 - mmengine - INFO - Epoch(train) [12][1100/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:52:35 time: 0.6415 data_time: 0.0098 memory: 5137 grad_norm: 1.9981 loss: 0.3940 loss_cls: 0.1760 loss_bbox: 0.2181 +2024/10/27 03:38:24 - mmengine - INFO - Epoch(train) [12][1150/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:52:10 time: 0.6256 data_time: 0.0101 memory: 5134 grad_norm: 2.0576 loss: 0.3938 loss_cls: 0.1672 loss_bbox: 0.2265 +2024/10/27 03:38:55 - mmengine - INFO - Epoch(train) [12][1200/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:51:45 time: 0.6277 data_time: 0.0104 memory: 5133 grad_norm: 2.0728 loss: 0.3639 loss_cls: 0.1594 loss_bbox: 0.2045 +2024/10/27 03:39:25 - mmengine - INFO - Epoch(train) [12][1250/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:51:20 time: 0.6073 data_time: 0.0098 memory: 5134 grad_norm: 1.9651 loss: 0.3567 loss_cls: 0.1534 loss_bbox: 0.2033 +2024/10/27 03:39:58 - mmengine - INFO - Epoch(train) [12][1300/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:50:56 time: 0.6502 data_time: 0.0100 memory: 5133 grad_norm: 1.9705 loss: 0.3783 loss_cls: 0.1649 loss_bbox: 0.2133 +2024/10/27 03:40:27 - mmengine - INFO - Epoch(train) [12][1350/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:50:30 time: 0.5865 data_time: 0.0098 memory: 5137 grad_norm: 2.0313 loss: 0.3778 loss_cls: 0.1613 loss_bbox: 0.2165 +2024/10/27 03:40:41 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 03:41:01 - mmengine - INFO - Epoch(train) [12][1400/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:50:06 time: 0.6692 data_time: 0.0099 memory: 5135 grad_norm: 2.0884 loss: 0.3717 loss_cls: 0.1581 loss_bbox: 0.2137 +2024/10/27 03:41:29 - mmengine - INFO - Epoch(train) [12][1450/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:49:41 time: 0.5657 data_time: 0.0098 memory: 5136 grad_norm: 2.0219 loss: 0.3908 loss_cls: 0.1731 loss_bbox: 0.2177 +2024/10/27 03:42:01 - mmengine - INFO - Epoch(train) [12][1500/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:49:16 time: 0.6442 data_time: 0.0097 memory: 5133 grad_norm: 1.9940 loss: 0.3846 loss_cls: 0.1705 loss_bbox: 0.2141 +2024/10/27 03:42:33 - mmengine - INFO - Epoch(train) [12][1550/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:48:51 time: 0.6279 data_time: 0.0777 memory: 5134 grad_norm: 1.9982 loss: 0.3707 loss_cls: 0.1595 loss_bbox: 0.2112 +2024/10/27 03:43:04 - mmengine - INFO - Epoch(train) [12][1600/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:48:26 time: 0.6170 data_time: 0.0105 memory: 5135 grad_norm: 2.0005 loss: 0.3733 loss_cls: 0.1613 loss_bbox: 0.2119 +2024/10/27 03:43:37 - mmengine - INFO - Epoch(train) [12][1650/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:48:01 time: 0.6674 data_time: 0.0453 memory: 5135 grad_norm: 1.9658 loss: 0.3694 loss_cls: 0.1608 loss_bbox: 0.2086 +2024/10/27 03:44:09 - mmengine - INFO - Epoch(train) [12][1700/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:47:36 time: 0.6433 data_time: 0.0095 memory: 5134 grad_norm: 1.9806 loss: 0.3687 loss_cls: 0.1592 loss_bbox: 0.2095 +2024/10/27 03:44:40 - mmengine - INFO - Epoch(train) [12][1750/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:47:11 time: 0.6109 data_time: 0.0100 memory: 5134 grad_norm: 2.0264 loss: 0.4065 loss_cls: 0.1802 loss_bbox: 0.2263 +2024/10/27 03:45:10 - mmengine - INFO - Epoch(train) [12][1800/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:46:46 time: 0.6126 data_time: 0.0101 memory: 5135 grad_norm: 2.1830 loss: 0.3795 loss_cls: 0.1651 loss_bbox: 0.2145 +2024/10/27 03:45:39 - mmengine - INFO - Epoch(train) [12][1850/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:46:21 time: 0.5771 data_time: 0.0097 memory: 5133 grad_norm: 2.0732 loss: 0.3646 loss_cls: 0.1574 loss_bbox: 0.2072 +2024/10/27 03:46:11 - mmengine - INFO - Epoch(train) [12][1900/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:45:56 time: 0.6313 data_time: 0.0138 memory: 5135 grad_norm: 2.0608 loss: 0.3528 loss_cls: 0.1518 loss_bbox: 0.2010 +2024/10/27 03:46:42 - mmengine - INFO - Epoch(train) [12][1950/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:45:31 time: 0.6258 data_time: 0.0137 memory: 5135 grad_norm: 2.1323 loss: 0.3847 loss_cls: 0.1655 loss_bbox: 0.2192 +2024/10/27 03:47:13 - mmengine - INFO - Epoch(train) [12][2000/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:45:06 time: 0.6176 data_time: 0.0096 memory: 5135 grad_norm: 2.1049 loss: 0.3783 loss_cls: 0.1674 loss_bbox: 0.2108 +2024/10/27 03:47:44 - mmengine - INFO - Epoch(train) [12][2050/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:44:41 time: 0.6304 data_time: 0.0095 memory: 5135 grad_norm: 2.0859 loss: 0.3795 loss_cls: 0.1709 loss_bbox: 0.2086 +2024/10/27 03:48:14 - mmengine - INFO - Epoch(train) [12][2100/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:44:16 time: 0.5821 data_time: 0.0098 memory: 5132 grad_norm: 1.9695 loss: 0.3915 loss_cls: 0.1700 loss_bbox: 0.2215 +2024/10/27 03:48:45 - mmengine - INFO - Epoch(train) [12][2150/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:43:51 time: 0.6241 data_time: 0.0090 memory: 5136 grad_norm: 1.9930 loss: 0.3656 loss_cls: 0.1634 loss_bbox: 0.2022 +2024/10/27 03:49:15 - mmengine - INFO - Epoch(train) [12][2200/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:43:26 time: 0.6008 data_time: 0.0092 memory: 5138 grad_norm: 2.0164 loss: 0.3804 loss_cls: 0.1737 loss_bbox: 0.2067 +2024/10/27 03:49:49 - mmengine - INFO - Epoch(train) [12][2250/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:43:01 time: 0.6761 data_time: 0.0095 memory: 5136 grad_norm: 2.1174 loss: 0.4035 loss_cls: 0.1842 loss_bbox: 0.2193 +2024/10/27 03:50:18 - mmengine - INFO - Epoch(train) [12][2300/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:42:36 time: 0.5857 data_time: 0.0095 memory: 5134 grad_norm: 2.0824 loss: 0.3309 loss_cls: 0.1414 loss_bbox: 0.1895 +2024/10/27 03:50:52 - mmengine - INFO - Epoch(train) [12][2350/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:42:11 time: 0.6748 data_time: 0.0097 memory: 5135 grad_norm: 2.0267 loss: 0.3670 loss_cls: 0.1633 loss_bbox: 0.2037 +2024/10/27 03:51:05 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 03:51:23 - mmengine - INFO - Epoch(train) [12][2400/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:41:46 time: 0.6168 data_time: 0.0100 memory: 5133 grad_norm: 2.0017 loss: 0.3767 loss_cls: 0.1673 loss_bbox: 0.2094 +2024/10/27 03:51:54 - mmengine - INFO - Epoch(train) [12][2450/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:41:21 time: 0.6324 data_time: 0.0094 memory: 5135 grad_norm: 1.9768 loss: 0.3806 loss_cls: 0.1708 loss_bbox: 0.2097 +2024/10/27 03:52:25 - mmengine - INFO - Epoch(train) [12][2500/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:40:55 time: 0.6247 data_time: 0.0102 memory: 5136 grad_norm: 1.9576 loss: 0.3442 loss_cls: 0.1498 loss_bbox: 0.1943 +2024/10/27 03:52:56 - mmengine - INFO - Epoch(train) [12][2550/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:40:30 time: 0.6183 data_time: 0.0099 memory: 5135 grad_norm: 2.0207 loss: 0.3819 loss_cls: 0.1604 loss_bbox: 0.2215 +2024/10/27 03:53:27 - mmengine - INFO - Epoch(train) [12][2600/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:40:05 time: 0.6083 data_time: 0.0095 memory: 5136 grad_norm: 2.0387 loss: 0.3521 loss_cls: 0.1560 loss_bbox: 0.1960 +2024/10/27 03:54:00 - mmengine - INFO - Epoch(train) [12][2650/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:39:40 time: 0.6598 data_time: 0.0110 memory: 5135 grad_norm: 2.0845 loss: 0.3668 loss_cls: 0.1610 loss_bbox: 0.2058 +2024/10/27 03:54:31 - mmengine - INFO - Epoch(train) [12][2700/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:39:15 time: 0.6167 data_time: 0.0096 memory: 5134 grad_norm: 2.0258 loss: 0.3893 loss_cls: 0.1742 loss_bbox: 0.2151 +2024/10/27 03:55:03 - mmengine - INFO - Epoch(train) [12][2750/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:38:50 time: 0.6449 data_time: 0.0130 memory: 5135 grad_norm: 2.0583 loss: 0.3628 loss_cls: 0.1589 loss_bbox: 0.2039 +2024/10/27 03:55:39 - mmengine - INFO - Epoch(train) [12][2800/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:38:25 time: 0.7223 data_time: 0.0697 memory: 5134 grad_norm: 2.1180 loss: 0.3786 loss_cls: 0.1641 loss_bbox: 0.2145 +2024/10/27 03:56:11 - mmengine - INFO - Epoch(train) [12][2850/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:38:00 time: 0.6455 data_time: 0.0098 memory: 5132 grad_norm: 2.0947 loss: 0.3620 loss_cls: 0.1533 loss_bbox: 0.2087 +2024/10/27 03:56:41 - mmengine - INFO - Epoch(train) [12][2900/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:37:35 time: 0.6016 data_time: 0.0136 memory: 5135 grad_norm: 2.0860 loss: 0.3918 loss_cls: 0.1689 loss_bbox: 0.2229 +2024/10/27 03:57:10 - mmengine - INFO - Epoch(train) [12][2950/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:37:10 time: 0.5782 data_time: 0.0102 memory: 5137 grad_norm: 2.0876 loss: 0.3777 loss_cls: 0.1644 loss_bbox: 0.2133 +2024/10/27 03:57:42 - mmengine - INFO - Epoch(train) [12][3000/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:36:44 time: 0.6295 data_time: 0.0088 memory: 5134 grad_norm: 2.0105 loss: 0.3892 loss_cls: 0.1648 loss_bbox: 0.2243 +2024/10/27 03:58:12 - mmengine - INFO - Epoch(train) [12][3050/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:36:19 time: 0.5947 data_time: 0.0090 memory: 5133 grad_norm: 2.0104 loss: 0.3493 loss_cls: 0.1481 loss_bbox: 0.2013 +2024/10/27 03:58:43 - mmengine - INFO - Epoch(train) [12][3100/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:35:54 time: 0.6297 data_time: 0.0112 memory: 5133 grad_norm: 2.1603 loss: 0.3924 loss_cls: 0.1715 loss_bbox: 0.2209 +2024/10/27 03:59:15 - mmengine - INFO - Epoch(train) [12][3150/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:35:29 time: 0.6322 data_time: 0.0094 memory: 5132 grad_norm: 2.0965 loss: 0.3891 loss_cls: 0.1691 loss_bbox: 0.2199 +2024/10/27 03:59:47 - mmengine - INFO - Epoch(train) [12][3200/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:35:04 time: 0.6422 data_time: 0.0089 memory: 5134 grad_norm: 2.0284 loss: 0.3752 loss_cls: 0.1606 loss_bbox: 0.2147 +2024/10/27 04:00:15 - mmengine - INFO - Epoch(train) [12][3250/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:34:38 time: 0.5722 data_time: 0.0087 memory: 5133 grad_norm: 2.1080 loss: 0.3832 loss_cls: 0.1742 loss_bbox: 0.2090 +2024/10/27 04:00:48 - mmengine - INFO - Epoch(train) [12][3300/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:34:13 time: 0.6428 data_time: 0.0132 memory: 5135 grad_norm: 2.0385 loss: 0.3650 loss_cls: 0.1609 loss_bbox: 0.2041 +2024/10/27 04:01:16 - mmengine - INFO - Epoch(train) [12][3350/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:33:48 time: 0.5703 data_time: 0.0085 memory: 5136 grad_norm: 2.0980 loss: 0.3722 loss_cls: 0.1609 loss_bbox: 0.2113 +2024/10/27 04:01:29 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 04:01:48 - mmengine - INFO - Epoch(train) [12][3400/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:33:23 time: 0.6420 data_time: 0.0150 memory: 5135 grad_norm: 2.1597 loss: 0.3802 loss_cls: 0.1669 loss_bbox: 0.2133 +2024/10/27 04:02:19 - mmengine - INFO - Epoch(train) [12][3450/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:32:57 time: 0.6213 data_time: 0.0093 memory: 5134 grad_norm: 1.9857 loss: 0.4142 loss_cls: 0.1817 loss_bbox: 0.2325 +2024/10/27 04:02:50 - mmengine - INFO - Epoch(train) [12][3500/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:32:32 time: 0.6241 data_time: 0.0099 memory: 5133 grad_norm: 2.0171 loss: 0.3878 loss_cls: 0.1713 loss_bbox: 0.2164 +2024/10/27 04:03:20 - mmengine - INFO - Epoch(train) [12][3550/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:32:07 time: 0.5888 data_time: 0.0098 memory: 5134 grad_norm: 2.1469 loss: 0.4009 loss_cls: 0.1781 loss_bbox: 0.2228 +2024/10/27 04:03:52 - mmengine - INFO - Epoch(train) [12][3600/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:31:42 time: 0.6505 data_time: 0.0100 memory: 5133 grad_norm: 2.0628 loss: 0.3923 loss_cls: 0.1697 loss_bbox: 0.2226 +2024/10/27 04:04:21 - mmengine - INFO - Epoch(train) [12][3650/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:31:16 time: 0.5690 data_time: 0.0108 memory: 5134 grad_norm: 2.1855 loss: 0.3838 loss_cls: 0.1729 loss_bbox: 0.2108 +2024/10/27 04:04:54 - mmengine - INFO - Epoch(train) [12][3700/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:30:51 time: 0.6556 data_time: 0.0097 memory: 5135 grad_norm: 2.0429 loss: 0.3528 loss_cls: 0.1517 loss_bbox: 0.2012 +2024/10/27 04:05:21 - mmengine - INFO - Epoch(train) [12][3750/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:30:26 time: 0.5522 data_time: 0.0098 memory: 5134 grad_norm: 2.0589 loss: 0.3755 loss_cls: 0.1681 loss_bbox: 0.2074 +2024/10/27 04:05:53 - mmengine - INFO - Epoch(train) [12][3800/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:30:01 time: 0.6390 data_time: 0.0113 memory: 5136 grad_norm: 2.1063 loss: 0.3552 loss_cls: 0.1534 loss_bbox: 0.2018 +2024/10/27 04:06:24 - mmengine - INFO - Epoch(train) [12][3850/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:29:35 time: 0.6067 data_time: 0.0095 memory: 5133 grad_norm: 1.9873 loss: 0.3831 loss_cls: 0.1770 loss_bbox: 0.2061 +2024/10/27 04:06:57 - mmengine - INFO - Epoch(train) [12][3900/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:29:10 time: 0.6733 data_time: 0.0148 memory: 5134 grad_norm: 2.0565 loss: 0.4009 loss_cls: 0.1784 loss_bbox: 0.2225 +2024/10/27 04:07:28 - mmengine - INFO - Epoch(train) [12][3950/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:28:45 time: 0.6221 data_time: 0.0096 memory: 5134 grad_norm: 2.1365 loss: 0.3957 loss_cls: 0.1748 loss_bbox: 0.2209 +2024/10/27 04:08:01 - mmengine - INFO - Epoch(train) [12][4000/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:28:20 time: 0.6512 data_time: 0.0140 memory: 5134 grad_norm: 2.0252 loss: 0.3816 loss_cls: 0.1661 loss_bbox: 0.2155 +2024/10/27 04:08:32 - mmengine - INFO - Epoch(train) [12][4050/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:27:54 time: 0.6141 data_time: 0.0104 memory: 5136 grad_norm: 2.0769 loss: 0.3741 loss_cls: 0.1670 loss_bbox: 0.2070 +2024/10/27 04:09:04 - mmengine - INFO - Epoch(train) [12][4100/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:27:29 time: 0.6449 data_time: 0.0100 memory: 5134 grad_norm: 2.1120 loss: 0.3947 loss_cls: 0.1794 loss_bbox: 0.2153 +2024/10/27 04:09:38 - mmengine - INFO - Epoch(train) [12][4150/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:27:04 time: 0.6872 data_time: 0.0469 memory: 5134 grad_norm: 2.0794 loss: 0.3875 loss_cls: 0.1762 loss_bbox: 0.2113 +2024/10/27 04:10:10 - mmengine - INFO - Epoch(train) [12][4200/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:26:38 time: 0.6347 data_time: 0.0105 memory: 5133 grad_norm: 1.9841 loss: 0.3673 loss_cls: 0.1567 loss_bbox: 0.2106 +2024/10/27 04:10:42 - mmengine - INFO - Epoch(train) [12][4250/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:26:13 time: 0.6355 data_time: 0.0105 memory: 5134 grad_norm: 2.1780 loss: 0.3687 loss_cls: 0.1652 loss_bbox: 0.2035 +2024/10/27 04:11:13 - mmengine - INFO - Epoch(train) [12][4300/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:25:48 time: 0.6202 data_time: 0.0102 memory: 5134 grad_norm: 1.9095 loss: 0.3780 loss_cls: 0.1642 loss_bbox: 0.2138 +2024/10/27 04:11:45 - mmengine - INFO - Epoch(train) [12][4350/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:25:22 time: 0.6375 data_time: 0.0105 memory: 5133 grad_norm: 2.1017 loss: 0.4013 loss_cls: 0.1828 loss_bbox: 0.2185 +2024/10/27 04:11:58 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 04:12:16 - mmengine - INFO - Epoch(train) [12][4400/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:24:57 time: 0.6276 data_time: 0.0095 memory: 5134 grad_norm: 2.1423 loss: 0.3986 loss_cls: 0.1775 loss_bbox: 0.2211 +2024/10/27 04:12:48 - mmengine - INFO - Epoch(train) [12][4450/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:24:32 time: 0.6307 data_time: 0.0137 memory: 5136 grad_norm: 2.1664 loss: 0.3602 loss_cls: 0.1573 loss_bbox: 0.2029 +2024/10/27 04:13:16 - mmengine - INFO - Epoch(train) [12][4500/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:24:06 time: 0.5678 data_time: 0.0098 memory: 5136 grad_norm: 2.1290 loss: 0.3800 loss_cls: 0.1725 loss_bbox: 0.2075 +2024/10/27 04:13:50 - mmengine - INFO - Epoch(train) [12][4550/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:23:41 time: 0.6715 data_time: 0.0143 memory: 5133 grad_norm: 2.0438 loss: 0.3893 loss_cls: 0.1708 loss_bbox: 0.2185 +2024/10/27 04:14:20 - mmengine - INFO - Epoch(train) [12][4600/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:23:16 time: 0.6065 data_time: 0.0119 memory: 5134 grad_norm: 2.0473 loss: 0.3705 loss_cls: 0.1607 loss_bbox: 0.2098 +2024/10/27 04:14:52 - mmengine - INFO - Epoch(train) [12][4650/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:22:50 time: 0.6413 data_time: 0.0099 memory: 5132 grad_norm: 2.1636 loss: 0.3910 loss_cls: 0.1687 loss_bbox: 0.2223 +2024/10/27 04:15:21 - mmengine - INFO - Epoch(train) [12][4700/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:22:25 time: 0.5764 data_time: 0.0090 memory: 5134 grad_norm: 2.0838 loss: 0.3880 loss_cls: 0.1697 loss_bbox: 0.2183 +2024/10/27 04:15:54 - mmengine - INFO - Epoch(train) [12][4750/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:21:59 time: 0.6604 data_time: 0.0093 memory: 5134 grad_norm: 2.1366 loss: 0.3893 loss_cls: 0.1755 loss_bbox: 0.2138 +2024/10/27 04:16:24 - mmengine - INFO - Epoch(train) [12][4800/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:21:34 time: 0.6111 data_time: 0.0136 memory: 5133 grad_norm: 2.1284 loss: 0.3714 loss_cls: 0.1608 loss_bbox: 0.2106 +2024/10/27 04:16:58 - mmengine - INFO - Epoch(train) [12][4850/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:21:09 time: 0.6692 data_time: 0.0143 memory: 5133 grad_norm: 2.0545 loss: 0.3650 loss_cls: 0.1556 loss_bbox: 0.2094 +2024/10/27 04:17:28 - mmengine - INFO - Epoch(train) [12][4900/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:20:43 time: 0.6020 data_time: 0.0118 memory: 5134 grad_norm: 1.9939 loss: 0.3721 loss_cls: 0.1629 loss_bbox: 0.2093 +2024/10/27 04:18:02 - mmengine - INFO - Epoch(train) [12][4950/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:20:18 time: 0.6709 data_time: 0.0090 memory: 5135 grad_norm: 2.0759 loss: 0.3539 loss_cls: 0.1600 loss_bbox: 0.1939 +2024/10/27 04:18:32 - mmengine - INFO - Epoch(train) [12][5000/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:19:52 time: 0.6134 data_time: 0.0122 memory: 5132 grad_norm: 2.0387 loss: 0.3931 loss_cls: 0.1799 loss_bbox: 0.2132 +2024/10/27 04:19:04 - mmengine - INFO - Epoch(train) [12][5050/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:19:27 time: 0.6365 data_time: 0.0089 memory: 5135 grad_norm: 2.1755 loss: 0.3919 loss_cls: 0.1685 loss_bbox: 0.2234 +2024/10/27 04:19:36 - mmengine - INFO - Epoch(train) [12][5100/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:19:01 time: 0.6307 data_time: 0.0349 memory: 5133 grad_norm: 2.1165 loss: 0.3732 loss_cls: 0.1642 loss_bbox: 0.2089 +2024/10/27 04:20:08 - mmengine - INFO - Epoch(train) [12][5150/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:18:36 time: 0.6459 data_time: 0.0085 memory: 5136 grad_norm: 2.0174 loss: 0.3713 loss_cls: 0.1613 loss_bbox: 0.2100 +2024/10/27 04:20:40 - mmengine - INFO - Epoch(train) [12][5200/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:18:11 time: 0.6483 data_time: 0.0086 memory: 5138 grad_norm: 1.9442 loss: 0.4005 loss_cls: 0.1743 loss_bbox: 0.2262 +2024/10/27 04:21:12 - mmengine - INFO - Epoch(train) [12][5250/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:17:45 time: 0.6296 data_time: 0.0085 memory: 5136 grad_norm: 2.0374 loss: 0.3796 loss_cls: 0.1632 loss_bbox: 0.2163 +2024/10/27 04:21:39 - mmengine - INFO - Epoch(train) [12][5300/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:17:19 time: 0.5388 data_time: 0.0083 memory: 5133 grad_norm: 2.0138 loss: 0.3788 loss_cls: 0.1647 loss_bbox: 0.2141 +2024/10/27 04:22:08 - mmengine - INFO - Epoch(train) [12][5350/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:16:54 time: 0.5813 data_time: 0.0084 memory: 5135 grad_norm: 1.9919 loss: 0.3703 loss_cls: 0.1606 loss_bbox: 0.2097 +2024/10/27 04:22:18 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 04:22:38 - mmengine - INFO - Epoch(train) [12][5400/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:16:28 time: 0.5991 data_time: 0.0083 memory: 5134 grad_norm: 2.1193 loss: 0.3690 loss_cls: 0.1582 loss_bbox: 0.2108 +2024/10/27 04:23:09 - mmengine - INFO - Epoch(train) [12][5450/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:16:03 time: 0.6162 data_time: 0.0085 memory: 5133 grad_norm: 2.0790 loss: 0.3632 loss_cls: 0.1571 loss_bbox: 0.2061 +2024/10/27 04:23:38 - mmengine - INFO - Epoch(train) [12][5500/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:15:37 time: 0.5907 data_time: 0.0085 memory: 5135 grad_norm: 2.1050 loss: 0.3635 loss_cls: 0.1547 loss_bbox: 0.2088 +2024/10/27 04:24:09 - mmengine - INFO - Epoch(train) [12][5550/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:15:12 time: 0.6109 data_time: 0.0130 memory: 5136 grad_norm: 2.0202 loss: 0.3526 loss_cls: 0.1535 loss_bbox: 0.1991 +2024/10/27 04:24:42 - mmengine - INFO - Epoch(train) [12][5600/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:14:46 time: 0.6686 data_time: 0.0085 memory: 5134 grad_norm: 2.0040 loss: 0.3725 loss_cls: 0.1614 loss_bbox: 0.2111 +2024/10/27 04:25:12 - mmengine - INFO - Epoch(train) [12][5650/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:14:21 time: 0.5894 data_time: 0.0088 memory: 5133 grad_norm: 2.1190 loss: 0.3823 loss_cls: 0.1657 loss_bbox: 0.2166 +2024/10/27 04:25:45 - mmengine - INFO - Epoch(train) [12][5700/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:13:55 time: 0.6634 data_time: 0.0119 memory: 5133 grad_norm: 2.0223 loss: 0.3619 loss_cls: 0.1539 loss_bbox: 0.2080 +2024/10/27 04:26:16 - mmengine - INFO - Epoch(train) [12][5750/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:13:30 time: 0.6295 data_time: 0.0086 memory: 5132 grad_norm: 2.1156 loss: 0.3429 loss_cls: 0.1495 loss_bbox: 0.1934 +2024/10/27 04:26:49 - mmengine - INFO - Epoch(train) [12][5800/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:13:04 time: 0.6609 data_time: 0.0111 memory: 5137 grad_norm: 2.0725 loss: 0.3827 loss_cls: 0.1671 loss_bbox: 0.2156 +2024/10/27 04:27:18 - mmengine - INFO - Epoch(train) [12][5850/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:12:39 time: 0.5701 data_time: 0.0098 memory: 5135 grad_norm: 2.0982 loss: 0.3661 loss_cls: 0.1562 loss_bbox: 0.2099 +2024/10/27 04:27:50 - mmengine - INFO - Epoch(train) [12][5900/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:12:13 time: 0.6509 data_time: 0.0093 memory: 5136 grad_norm: 2.1039 loss: 0.3782 loss_cls: 0.1707 loss_bbox: 0.2075 +2024/10/27 04:28:18 - mmengine - INFO - Epoch(train) [12][5950/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:11:48 time: 0.5448 data_time: 0.0091 memory: 5133 grad_norm: 2.0588 loss: 0.3985 loss_cls: 0.1761 loss_bbox: 0.2224 +2024/10/27 04:28:51 - mmengine - INFO - Epoch(train) [12][6000/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:11:22 time: 0.6665 data_time: 0.0087 memory: 5135 grad_norm: 2.1295 loss: 0.4048 loss_cls: 0.1770 loss_bbox: 0.2278 +2024/10/27 04:29:22 - mmengine - INFO - Epoch(train) [12][6050/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:10:56 time: 0.6125 data_time: 0.0092 memory: 5134 grad_norm: 2.1155 loss: 0.3454 loss_cls: 0.1515 loss_bbox: 0.1939 +2024/10/27 04:29:54 - mmengine - INFO - Epoch(train) [12][6100/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:10:31 time: 0.6550 data_time: 0.0099 memory: 5134 grad_norm: 2.0965 loss: 0.4037 loss_cls: 0.1782 loss_bbox: 0.2254 +2024/10/27 04:30:23 - mmengine - INFO - Epoch(train) [12][6150/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:10:05 time: 0.5691 data_time: 0.0136 memory: 5133 grad_norm: 2.0322 loss: 0.3628 loss_cls: 0.1567 loss_bbox: 0.2060 +2024/10/27 04:30:56 - mmengine - INFO - Epoch(train) [12][6200/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:09:40 time: 0.6594 data_time: 0.0087 memory: 5133 grad_norm: 2.0754 loss: 0.3700 loss_cls: 0.1584 loss_bbox: 0.2116 +2024/10/27 04:31:23 - mmengine - INFO - Epoch(train) [12][6250/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:09:14 time: 0.5355 data_time: 0.0101 memory: 5133 grad_norm: 2.0921 loss: 0.3559 loss_cls: 0.1555 loss_bbox: 0.2004 +2024/10/27 04:31:56 - mmengine - INFO - Epoch(train) [12][6300/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:08:48 time: 0.6614 data_time: 0.0096 memory: 5133 grad_norm: 1.9319 loss: 0.3704 loss_cls: 0.1589 loss_bbox: 0.2115 +2024/10/27 04:32:26 - mmengine - INFO - Epoch(train) [12][6350/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:08:23 time: 0.6054 data_time: 0.0092 memory: 5134 grad_norm: 2.0858 loss: 0.3835 loss_cls: 0.1726 loss_bbox: 0.2109 +2024/10/27 04:32:39 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 04:32:57 - mmengine - INFO - Epoch(train) [12][6400/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:07:57 time: 0.6107 data_time: 0.0160 memory: 5134 grad_norm: 2.1055 loss: 0.3634 loss_cls: 0.1597 loss_bbox: 0.2036 +2024/10/27 04:33:26 - mmengine - INFO - Epoch(train) [12][6450/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:07:32 time: 0.5943 data_time: 0.0099 memory: 5136 grad_norm: 2.0254 loss: 0.3839 loss_cls: 0.1664 loss_bbox: 0.2174 +2024/10/27 04:34:00 - mmengine - INFO - Epoch(train) [12][6500/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:07:06 time: 0.6672 data_time: 0.0094 memory: 5135 grad_norm: 2.0820 loss: 0.3568 loss_cls: 0.1488 loss_bbox: 0.2080 +2024/10/27 04:34:30 - mmengine - INFO - Epoch(train) [12][6550/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:06:40 time: 0.6120 data_time: 0.0089 memory: 5133 grad_norm: 2.1236 loss: 0.3886 loss_cls: 0.1769 loss_bbox: 0.2117 +2024/10/27 04:35:03 - mmengine - INFO - Epoch(train) [12][6600/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:06:15 time: 0.6498 data_time: 0.0119 memory: 5134 grad_norm: 2.0093 loss: 0.3728 loss_cls: 0.1611 loss_bbox: 0.2118 +2024/10/27 04:35:36 - mmengine - INFO - Epoch(train) [12][6650/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:05:49 time: 0.6687 data_time: 0.0583 memory: 5133 grad_norm: 2.0885 loss: 0.3827 loss_cls: 0.1701 loss_bbox: 0.2127 +2024/10/27 04:36:08 - mmengine - INFO - Epoch(train) [12][6700/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:05:23 time: 0.6427 data_time: 0.0115 memory: 5134 grad_norm: 1.9175 loss: 0.3819 loss_cls: 0.1656 loss_bbox: 0.2164 +2024/10/27 04:36:40 - mmengine - INFO - Epoch(train) [12][6750/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:04:58 time: 0.6415 data_time: 0.0102 memory: 5134 grad_norm: 2.0054 loss: 0.3752 loss_cls: 0.1647 loss_bbox: 0.2105 +2024/10/27 04:37:11 - mmengine - INFO - Epoch(train) [12][6800/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:04:32 time: 0.6098 data_time: 0.0136 memory: 5135 grad_norm: 2.0964 loss: 0.3597 loss_cls: 0.1591 loss_bbox: 0.2006 +2024/10/27 04:37:43 - mmengine - INFO - Epoch(train) [12][6850/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:04:06 time: 0.6418 data_time: 0.0114 memory: 5134 grad_norm: 2.0648 loss: 0.3429 loss_cls: 0.1453 loss_bbox: 0.1976 +2024/10/27 04:38:14 - mmengine - INFO - Epoch(train) [12][6900/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:03:41 time: 0.6196 data_time: 0.0092 memory: 5135 grad_norm: 2.2165 loss: 0.3892 loss_cls: 0.1724 loss_bbox: 0.2168 +2024/10/27 04:38:45 - mmengine - INFO - Epoch(train) [12][6950/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:03:15 time: 0.6300 data_time: 0.0146 memory: 5132 grad_norm: 2.0307 loss: 0.4012 loss_cls: 0.1812 loss_bbox: 0.2200 +2024/10/27 04:39:16 - mmengine - INFO - Epoch(train) [12][7000/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:02:49 time: 0.6095 data_time: 0.0089 memory: 5134 grad_norm: 2.1173 loss: 0.3906 loss_cls: 0.1740 loss_bbox: 0.2165 +2024/10/27 04:39:49 - mmengine - INFO - Epoch(train) [12][7050/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:02:24 time: 0.6630 data_time: 0.0089 memory: 5134 grad_norm: 2.1737 loss: 0.3835 loss_cls: 0.1704 loss_bbox: 0.2130 +2024/10/27 04:40:16 - mmengine - INFO - Epoch(train) [12][7100/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:01:58 time: 0.5419 data_time: 0.0088 memory: 5134 grad_norm: 2.1834 loss: 0.3509 loss_cls: 0.1537 loss_bbox: 0.1971 +2024/10/27 04:40:48 - mmengine - INFO - Epoch(train) [12][7150/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:01:32 time: 0.6390 data_time: 0.0088 memory: 5133 grad_norm: 2.1584 loss: 0.3556 loss_cls: 0.1526 loss_bbox: 0.2030 +2024/10/27 04:41:19 - mmengine - INFO - Epoch(train) [12][7200/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:01:06 time: 0.6146 data_time: 0.0108 memory: 5134 grad_norm: 2.0048 loss: 0.3590 loss_cls: 0.1523 loss_bbox: 0.2066 +2024/10/27 04:41:52 - mmengine - INFO - Epoch(train) [12][7250/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:00:41 time: 0.6648 data_time: 0.0096 memory: 5134 grad_norm: 1.9997 loss: 0.3737 loss_cls: 0.1631 loss_bbox: 0.2106 +2024/10/27 04:42:22 - mmengine - INFO - Epoch(train) [12][7300/7330] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 0:00:15 time: 0.5992 data_time: 0.0090 memory: 5135 grad_norm: 2.0155 loss: 0.3972 loss_cls: 0.1752 loss_bbox: 0.2221 +2024/10/27 04:42:48 - mmengine - INFO - Exp name: retinanet_mobilemamba_b1_fpn_1x_coco_20241026_153140 +2024/10/27 04:42:48 - mmengine - INFO - Saving checkpoint at 12 epochs +2024/10/27 04:42:59 - mmengine - INFO - Epoch(val) [12][ 50/1250] eta: 0:02:09 time: 0.1080 data_time: 0.0019 memory: 7050 +2024/10/27 04:43:04 - mmengine - INFO - Epoch(val) [12][ 100/1250] eta: 0:02:04 time: 0.1077 data_time: 0.0016 memory: 629 +2024/10/27 04:43:09 - mmengine - INFO - Epoch(val) [12][ 150/1250] eta: 0:01:58 time: 0.1078 data_time: 0.0016 memory: 634 +2024/10/27 04:43:14 - mmengine - INFO - Epoch(val) [12][ 200/1250] eta: 0:01:50 time: 0.0977 data_time: 0.0016 memory: 634 +2024/10/27 04:43:20 - mmengine - INFO - Epoch(val) [12][ 250/1250] eta: 0:01:47 time: 0.1183 data_time: 0.0016 memory: 624 +2024/10/27 04:43:26 - mmengine - INFO - Epoch(val) [12][ 300/1250] eta: 0:01:42 time: 0.1079 data_time: 0.0017 memory: 624 +2024/10/27 04:43:31 - mmengine - INFO - Epoch(val) [12][ 350/1250] eta: 0:01:37 time: 0.1102 data_time: 0.0016 memory: 624 +2024/10/27 04:43:37 - mmengine - INFO - Epoch(val) [12][ 400/1250] eta: 0:01:32 time: 0.1128 data_time: 0.0016 memory: 613 +2024/10/27 04:43:42 - mmengine - INFO - Epoch(val) [12][ 450/1250] eta: 0:01:27 time: 0.1119 data_time: 0.0016 memory: 634 +2024/10/27 04:43:48 - mmengine - INFO - Epoch(val) [12][ 500/1250] eta: 0:01:21 time: 0.1068 data_time: 0.0016 memory: 634 +2024/10/27 04:43:53 - mmengine - INFO - Epoch(val) [12][ 550/1250] eta: 0:01:16 time: 0.1119 data_time: 0.0016 memory: 614 +2024/10/27 04:43:59 - mmengine - INFO - Epoch(val) [12][ 600/1250] eta: 0:01:10 time: 0.1087 data_time: 0.0017 memory: 624 +2024/10/27 04:44:04 - mmengine - INFO - Epoch(val) [12][ 650/1250] eta: 0:01:05 time: 0.1126 data_time: 0.0017 memory: 624 +2024/10/27 04:44:10 - mmengine - INFO - Epoch(val) [12][ 700/1250] eta: 0:00:59 time: 0.1045 data_time: 0.0017 memory: 629 +2024/10/27 04:44:16 - mmengine - INFO - Epoch(val) [12][ 750/1250] eta: 0:00:55 time: 0.1240 data_time: 0.0015 memory: 629 +2024/10/27 04:44:22 - mmengine - INFO - Epoch(val) [12][ 800/1250] eta: 0:00:49 time: 0.1215 data_time: 0.0015 memory: 634 +2024/10/27 04:44:27 - mmengine - INFO - Epoch(val) [12][ 850/1250] eta: 0:00:44 time: 0.1060 data_time: 0.0014 memory: 634 +2024/10/27 04:44:32 - mmengine - INFO - Epoch(val) [12][ 900/1250] eta: 0:00:38 time: 0.1057 data_time: 0.0015 memory: 634 +2024/10/27 04:44:38 - mmengine - INFO - Epoch(val) [12][ 950/1250] eta: 0:00:33 time: 0.1099 data_time: 0.0015 memory: 624 +2024/10/27 04:44:44 - mmengine - INFO - Epoch(val) [12][1000/1250] eta: 0:00:27 time: 0.1133 data_time: 0.0015 memory: 625 +2024/10/27 04:44:49 - mmengine - INFO - Epoch(val) [12][1050/1250] eta: 0:00:22 time: 0.1126 data_time: 0.0015 memory: 629 +2024/10/27 04:44:55 - mmengine - INFO - Epoch(val) [12][1100/1250] eta: 0:00:16 time: 0.1096 data_time: 0.0015 memory: 634 +2024/10/27 04:45:00 - mmengine - INFO - Epoch(val) [12][1150/1250] eta: 0:00:11 time: 0.1068 data_time: 0.0014 memory: 629 +2024/10/27 04:45:05 - mmengine - INFO - Epoch(val) [12][1200/1250] eta: 0:00:05 time: 0.1065 data_time: 0.0016 memory: 629 +2024/10/27 04:45:11 - mmengine - INFO - Epoch(val) [12][1250/1250] eta: 0:00:00 time: 0.1086 data_time: 0.0015 memory: 634 +2024/10/27 04:45:17 - mmengine - INFO - Evaluating bbox... +2024/10/27 04:46:01 - mmengine - INFO - bbox_mAP_copypaste: 0.396 0.598 0.424 0.215 0.431 0.539 +2024/10/27 04:46:02 - mmengine - INFO - Epoch(val) [12][1250/1250] coco/bbox_mAP: 0.3960 coco/bbox_mAP_50: 0.5980 coco/bbox_mAP_75: 0.4240 coco/bbox_mAP_s: 0.2150 coco/bbox_mAP_m: 0.4310 coco/bbox_mAP_l: 0.5390 data_time: 0.0016 time: 0.1100