diff --git "a/detection/mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.log" "b/detection/mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.log" new file mode 100644--- /dev/null +++ "b/detection/mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.log" @@ -0,0 +1,9749 @@ +2024-05-31 00:11:59,033 - mmdet - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.9.19 (main, May 6 2024, 19:43:03) [GCC 11.2.0] +CUDA available: True +GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB +CUDA_HOME: /mnt/petrelfs/share/cuda-11.7/ +NVCC: Cuda compilation tools, release 11.7, V11.7.99 +GCC: gcc (GCC) 7.3.0 +PyTorch: 1.12.0+cu113 +PyTorch compiling details: PyTorch built with: + - GCC 9.3 + - C++ Version: 201402 + - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-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 + - CuDNN 8.3.2 (built against CUDA 11.5) + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -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_VERSION=1.12.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.13.0+cu113 +OpenCV: 4.9.0 +MMCV: 1.7.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.7 +MMDetection: 2.25.3+c8d02d2 +------------------------------------------------------------ + +2024-05-31 00:12:00,476 - mmdet - INFO - Distributed training: True +2024-05-31 00:12:01,864 - mmdet - INFO - Config: +model = dict( + type='MaskRCNN', + backbone=dict( + type='PIIPThreeBranch', + n_points=4, + deform_num_heads=16, + cffn_ratio=0.25, + deform_ratio=0.5, + with_cffn=True, + interact_attn_type='deform', + interaction_drop_path_rate=0.4, + branch1=dict( + real_size=448, + pretrain_img_size=224, + patch_size=16, + pretrain_patch_size=16, + depth=12, + embed_dim=768, + num_heads=12, + mlp_ratio=4, + qkv_bias=True, + drop_path_rate=0.15, + init_scale=1.0, + with_fpn=False, + interaction_indexes=[[0, 0], [1, 1], [2, 2], [3, 3], [4, 4], + [5, 5], [6, 6], [7, 7], [8, 8], [9, 9], + [10, 10], [11, 11]], + pretrained='./pretrained/deit_3_base_224_21k.pth', + window_attn=[ + True, True, True, True, True, True, True, True, True, True, + True, True + ], + window_size=[14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14], + use_flash_attn=True, + img_norm_cfg=dict( + mean=[127.5, 127.5, 127.5], + std=[127.5, 127.5, 127.5], + to_rgb=True)), + branch2=dict( + real_size=896, + pretrain_img_size=224, + patch_size=16, + pretrain_patch_size=16, + depth=12, + embed_dim=384, + num_heads=6, + mlp_ratio=4, + qkv_bias=True, + drop_path_rate=0.05, + init_scale=1.0, + with_fpn=False, + interaction_indexes=[[0, 0], [1, 1], [2, 2], [3, 3], [4, 4], + [5, 5], [6, 6], [7, 7], [8, 8], [9, 9], + [10, 10], [11, 11]], + pretrained='./pretrained/deit_3_small_224_21k.pth', + window_attn=[ + True, True, True, True, True, True, True, True, True, True, + True, True + ], + window_size=[14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14], + use_flash_attn=True, + img_norm_cfg=dict( + mean=[127.5, 127.5, 127.5], + std=[127.5, 127.5, 127.5], + to_rgb=True)), + branch3=dict( + real_size=1568, + pretrain_img_size=224, + patch_size=16, + pretrain_patch_size=16, + depth=12, + embed_dim=192, + num_heads=3, + mlp_ratio=4, + qkv_bias=True, + drop_path_rate=0.05, + init_scale=1.0, + with_fpn=False, + interaction_indexes=[[0, 0], [1, 1], [2, 2], [3, 3], [4, 4], + [5, 5], [6, 6], [7, 7], [8, 8], [9, 9], + [10, 10], [11, 11]], + pretrained='./pretrained/deit_tiny_patch16_224-a1311bcf.pth', + window_attn=[ + True, True, True, True, True, True, True, True, True, True, + True, True + ], + window_size=[14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14], + use_flash_attn=True, + img_norm_cfg=dict( + mean=[127.5, 127.5, 127.5], + std=[127.5, 127.5, 127.5], + to_rgb=True))), + neck=dict( + type='FPN', + in_channels=[768, 768, 768, 768], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0.0, 0.0, 0.0, 0.0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0.0, 0.0, 0.0, 0.0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5))) +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1568, 941), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='Normalize', + mean=[127.5, 127.5, 127.5], + std=[127.5, 127.5, 127.5], + to_rgb=True), + dict(type='Pad', size_divisor=224), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1568, 941), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict( + type='Normalize', + mean=[127.5, 127.5, 127.5], + std=[127.5, 127.5, 127.5], + to_rgb=True), + dict(type='Pad', size_divisor=224), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='CocoDataset', + ann_file='data/coco/annotations/instances_train2017.json', + img_prefix='data/coco/train2017/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1568, 941), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='Normalize', + mean=[127.5, 127.5, 127.5], + std=[127.5, 127.5, 127.5], + to_rgb=True), + dict(type='Pad', size_divisor=224), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']) + ]), + val=dict( + type='CocoDataset', + ann_file='data/coco/annotations/instances_val2017.json', + img_prefix='data/coco/val2017/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1568, 941), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict( + type='Normalize', + mean=[127.5, 127.5, 127.5], + std=[127.5, 127.5, 127.5], + to_rgb=True), + dict(type='Pad', size_divisor=224), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) + ]), + test=dict( + type='CocoDataset', + ann_file='data/coco/annotations/instances_val2017.json', + img_prefix='data/coco/val2017/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1568, 941), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict( + type='Normalize', + mean=[127.5, 127.5, 127.5], + std=[127.5, 127.5, 127.5], + to_rgb=True), + dict(type='Pad', size_divisor=224), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) + ])) +evaluation = dict(metric=['bbox', 'segm'], interval=1, save_best=None) +optimizer = dict( + type='AdamW', + lr=0.0001, + betas=(0.9, 0.999), + weight_decay=0.05, + constructor='CustomLayerDecayOptimizerConstructorMMDet', + paramwise_cfg=dict( + num_layers=12, layer_decay_rate=0.85, skip_stride=[1, 1])) +optimizer_config = dict(grad_clip=None) +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[8, 11]) +runner = dict(type='EpochBasedRunner', max_epochs=12) +checkpoint_config = dict(interval=1, deepspeed=True, max_keep_ckpts=1) +log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) +custom_hooks = [dict(type='ToBFloat16HookMMDet', priority=49)] +dist_params = dict(backend='nccl') +log_level = 'INFO' +load_from = None +resume_from = None +workflow = [('train', 1)] +opencv_num_threads = 0 +mp_start_method = 'fork' +auto_scale_lr = dict(enable=False, base_batch_size=16) +deepspeed = True +deepspeed_config = 'zero_configs/adam_zero1_bf16.json' +custom_imports = dict( + imports=['mmdet.mmcv_custom'], allow_failed_imports=False) +work_dir = './work_dirs/mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16' +auto_resume = True +gpu_ids = range(0, 8) + +2024-05-31 00:12:06,920 - mmdet - INFO - Set random seed to 2052056755, deterministic: False +2024-05-31 00:12:09,722 - mmdet - INFO - _IncompatibleKeys(missing_keys=[], unexpected_keys=['cls_token', 'norm.weight', 'norm.bias', 'head.weight', 'head.bias']) +2024-05-31 00:12:11,765 - mmdet - INFO - _IncompatibleKeys(missing_keys=[], unexpected_keys=['cls_token', 'norm.weight', 'norm.bias', 'head.weight', 'head.bias']) +2024-05-31 00:12:13,510 - mmdet - INFO - _IncompatibleKeys(missing_keys=['blocks.0.gamma_1', 'blocks.0.gamma_2', 'blocks.1.gamma_1', 'blocks.1.gamma_2', 'blocks.2.gamma_1', 'blocks.2.gamma_2', 'blocks.3.gamma_1', 'blocks.3.gamma_2', 'blocks.4.gamma_1', 'blocks.4.gamma_2', 'blocks.5.gamma_1', 'blocks.5.gamma_2', 'blocks.6.gamma_1', 'blocks.6.gamma_2', 'blocks.7.gamma_1', 'blocks.7.gamma_2', 'blocks.8.gamma_1', 'blocks.8.gamma_2', 'blocks.9.gamma_1', 'blocks.9.gamma_2', 'blocks.10.gamma_1', 'blocks.10.gamma_2', 'blocks.11.gamma_1', 'blocks.11.gamma_2'], unexpected_keys=['cls_token', 'norm.weight', 'norm.bias', 'head.weight', 'head.bias']) +2024-05-31 00:13:10,218 - mmdet - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'} +2024-05-31 00:13:10,586 - mmdet - INFO - initialize RPNHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01} +2024-05-31 00:13:10,634 - mmdet - INFO - initialize Shared2FCBBoxHead with init_cfg [{'type': 'Normal', 'std': 0.01, 'override': {'name': 'fc_cls'}}, {'type': 'Normal', 'std': 0.001, 'override': {'name': 'fc_reg'}}, {'type': 'Xavier', 'distribution': 'uniform', 'override': [{'name': 'shared_fcs'}, {'name': 'cls_fcs'}, {'name': 'reg_fcs'}]}] +Name of parameter - Initialization information + +backbone.w1 - torch.Size([]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.w2 - torch.Size([]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.w3 - torch.Size([]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.pos_embed - torch.Size([1, 196, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.patch_embed.proj.weight - torch.Size([768, 3, 16, 16]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.patch_embed.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.pos_embed - torch.Size([1, 196, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.patch_embed.proj.weight - torch.Size([384, 3, 16, 16]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.patch_embed.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.pos_embed - torch.Size([1, 196, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.patch_embed.proj.weight - torch.Size([192, 3, 16, 16]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.patch_embed.proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.gamma_1 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.gamma_2 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.norm1.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.norm1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.attn.qkv.weight - torch.Size([576, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.attn.qkv.bias - torch.Size([576]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.attn.proj.weight - torch.Size([192, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.attn.proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.norm2.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.norm2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.mlp.fc1.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.mlp.fc1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.mlp.fc2.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.mlp.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.gamma_1 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.gamma_2 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.norm1.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.norm1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.attn.qkv.weight - torch.Size([576, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.attn.qkv.bias - torch.Size([576]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.attn.proj.weight - torch.Size([192, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.attn.proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.norm2.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.norm2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.mlp.fc1.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.mlp.fc1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.mlp.fc2.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.mlp.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.gamma_1 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.gamma_2 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.norm1.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.norm1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.attn.qkv.weight - torch.Size([576, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.attn.qkv.bias - torch.Size([576]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.attn.proj.weight - torch.Size([192, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.attn.proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.norm2.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.norm2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.mlp.fc1.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.mlp.fc1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.mlp.fc2.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.mlp.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.gamma_1 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.gamma_2 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.norm1.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.norm1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.attn.qkv.weight - torch.Size([576, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.attn.qkv.bias - torch.Size([576]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.attn.proj.weight - torch.Size([192, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.attn.proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.norm2.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.norm2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.mlp.fc1.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.mlp.fc1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.mlp.fc2.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.mlp.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.gamma_1 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.gamma_2 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.norm1.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.norm1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.attn.qkv.weight - torch.Size([576, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.attn.qkv.bias - torch.Size([576]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.attn.proj.weight - torch.Size([192, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.attn.proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.norm2.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.norm2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.mlp.fc1.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.mlp.fc1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.mlp.fc2.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.mlp.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.gamma_1 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.gamma_2 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.norm1.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.norm1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.attn.qkv.weight - torch.Size([576, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.attn.qkv.bias - torch.Size([576]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.attn.proj.weight - torch.Size([192, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.attn.proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.norm2.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.norm2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.mlp.fc1.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.mlp.fc1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.mlp.fc2.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.mlp.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.gamma_1 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.gamma_2 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.norm1.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.norm1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.attn.qkv.weight - torch.Size([576, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.attn.qkv.bias - torch.Size([576]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.attn.proj.weight - torch.Size([192, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.attn.proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.norm2.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.norm2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.mlp.fc1.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.mlp.fc1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.mlp.fc2.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.mlp.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.gamma_1 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.gamma_2 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.norm1.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.norm1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.attn.qkv.weight - torch.Size([576, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.attn.qkv.bias - torch.Size([576]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.attn.proj.weight - torch.Size([192, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.attn.proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.norm2.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.norm2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.mlp.fc1.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.mlp.fc1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.mlp.fc2.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.mlp.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.gamma_1 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.gamma_2 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.norm1.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.norm1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.attn.qkv.weight - torch.Size([576, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.attn.qkv.bias - torch.Size([576]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.attn.proj.weight - torch.Size([192, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.attn.proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.norm2.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.norm2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.mlp.fc1.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.mlp.fc1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.mlp.fc2.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.mlp.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.gamma_1 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.gamma_2 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.norm1.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.norm1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.attn.qkv.weight - torch.Size([576, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.attn.qkv.bias - torch.Size([576]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.attn.proj.weight - torch.Size([192, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.attn.proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.norm2.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.norm2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.mlp.fc1.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.mlp.fc1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.mlp.fc2.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.mlp.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.gamma_1 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.gamma_2 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.norm1.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.norm1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.attn.qkv.weight - torch.Size([576, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.attn.qkv.bias - torch.Size([576]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.attn.proj.weight - torch.Size([192, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.attn.proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.norm2.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.norm2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.mlp.fc1.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.mlp.fc1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.mlp.fc2.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.mlp.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.gamma_1 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.gamma_2 - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.norm1.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.norm1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.attn.qkv.weight - torch.Size([576, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.attn.qkv.bias - torch.Size([576]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.attn.proj.weight - torch.Size([192, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.attn.proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.norm2.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.norm2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.mlp.fc1.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.mlp.fc1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.mlp.fc2.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.mlp.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.query_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.query_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.feat_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.feat_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([192, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.ffn_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_injector.ffn_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch2to1_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.ca_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.cffn_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.query_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.query_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.feat_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.feat_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.attn.value_proj.weight - torch.Size([96, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.attn.value_proj.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.attn.output_proj.weight - torch.Size([192, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.attn.output_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.ffn.fc1.weight - torch.Size([48, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.ffn.fc1.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([48, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.ffn.fc2.weight - torch.Size([192, 48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.ffn.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.ffn_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.branch1to2_injector.ffn_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.query_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.query_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.feat_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.feat_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([192, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.ffn_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_injector.ffn_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch2to1_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.ca_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.cffn_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.query_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.query_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.feat_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.feat_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.attn.value_proj.weight - torch.Size([96, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.attn.value_proj.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.attn.output_proj.weight - torch.Size([192, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.attn.output_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.ffn.fc1.weight - torch.Size([48, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.ffn.fc1.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([48, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.ffn.fc2.weight - torch.Size([192, 48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.ffn.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.ffn_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.branch1to2_injector.ffn_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.query_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.query_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.feat_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.feat_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([192, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.ffn_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_injector.ffn_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch2to1_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.ca_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.cffn_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.query_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.query_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.feat_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.feat_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.attn.value_proj.weight - torch.Size([96, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.attn.value_proj.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.attn.output_proj.weight - torch.Size([192, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.attn.output_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.ffn.fc1.weight - torch.Size([48, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.ffn.fc1.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([48, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.ffn.fc2.weight - torch.Size([192, 48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.ffn.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.ffn_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.branch1to2_injector.ffn_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.query_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.query_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.feat_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.feat_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([192, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.ffn_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_injector.ffn_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch2to1_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.ca_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.cffn_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.query_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.query_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.feat_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.feat_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.attn.value_proj.weight - torch.Size([96, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.attn.value_proj.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.attn.output_proj.weight - torch.Size([192, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.attn.output_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.ffn.fc1.weight - torch.Size([48, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.ffn.fc1.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([48, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.ffn.fc2.weight - torch.Size([192, 48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.ffn.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.ffn_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.branch1to2_injector.ffn_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.query_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.query_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.feat_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.feat_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([192, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.ffn_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_injector.ffn_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch2to1_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.ca_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.cffn_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.query_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.query_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.feat_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.feat_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.attn.value_proj.weight - torch.Size([96, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.attn.value_proj.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.attn.output_proj.weight - torch.Size([192, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.attn.output_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.ffn.fc1.weight - torch.Size([48, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.ffn.fc1.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([48, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.ffn.fc2.weight - torch.Size([192, 48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.ffn.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.ffn_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.branch1to2_injector.ffn_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.query_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.query_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.feat_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.feat_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([192, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.ffn_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_injector.ffn_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch2to1_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.ca_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.cffn_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.query_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.query_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.feat_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.feat_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.attn.value_proj.weight - torch.Size([96, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.attn.value_proj.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.attn.output_proj.weight - torch.Size([192, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.attn.output_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.ffn.fc1.weight - torch.Size([48, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.ffn.fc1.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([48, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.ffn.fc2.weight - torch.Size([192, 48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.ffn.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.ffn_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.branch1to2_injector.ffn_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.query_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.query_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.feat_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.feat_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([192, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.ffn_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_injector.ffn_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch2to1_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.ca_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.cffn_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.query_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.query_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.feat_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.feat_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.attn.value_proj.weight - torch.Size([96, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.attn.value_proj.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.attn.output_proj.weight - torch.Size([192, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.attn.output_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.ffn.fc1.weight - torch.Size([48, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.ffn.fc1.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([48, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.ffn.fc2.weight - torch.Size([192, 48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.ffn.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.ffn_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.branch1to2_injector.ffn_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.query_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.query_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.feat_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.feat_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([192, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.ffn_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_injector.ffn_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch2to1_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.ca_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.cffn_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.query_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.query_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.feat_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.feat_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.attn.value_proj.weight - torch.Size([96, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.attn.value_proj.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.attn.output_proj.weight - torch.Size([192, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.attn.output_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.ffn.fc1.weight - torch.Size([48, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.ffn.fc1.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([48, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.ffn.fc2.weight - torch.Size([192, 48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.ffn.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.ffn_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.branch1to2_injector.ffn_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.query_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.query_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.feat_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.feat_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([192, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.ffn_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_injector.ffn_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch2to1_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.ca_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.cffn_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.query_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.query_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.feat_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.feat_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.attn.value_proj.weight - torch.Size([96, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.attn.value_proj.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.attn.output_proj.weight - torch.Size([192, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.attn.output_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.ffn.fc1.weight - torch.Size([48, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.ffn.fc1.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([48, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.ffn.fc2.weight - torch.Size([192, 48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.ffn.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.ffn_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.branch1to2_injector.ffn_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.query_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.query_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.feat_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.feat_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([192, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.ffn_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_injector.ffn_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch2to1_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.ca_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.cffn_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.query_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.query_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.feat_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.feat_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.attn.value_proj.weight - torch.Size([96, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.attn.value_proj.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.attn.output_proj.weight - torch.Size([192, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.attn.output_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.ffn.fc1.weight - torch.Size([48, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.ffn.fc1.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([48, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.ffn.fc2.weight - torch.Size([192, 48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.ffn.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.ffn_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.branch1to2_injector.ffn_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.query_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.query_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.feat_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.feat_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([192, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.ffn_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_injector.ffn_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch2to1_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.ca_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.cffn_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.query_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.query_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.feat_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.feat_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.attn.value_proj.weight - torch.Size([96, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.attn.value_proj.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.attn.output_proj.weight - torch.Size([192, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.attn.output_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.ffn.fc1.weight - torch.Size([48, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.ffn.fc1.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([48, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.ffn.fc2.weight - torch.Size([192, 48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.ffn.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.ffn_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.branch1to2_injector.ffn_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.query_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.query_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.feat_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.feat_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([768, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([192, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([192, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([768, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.ffn_norm.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_injector.ffn_norm.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_proj.weight - torch.Size([384, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.ca_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.cffn_gamma - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.query_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.query_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.feat_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.feat_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.attn.value_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.attn.value_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.attn.output_proj.weight - torch.Size([384, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.attn.output_proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.ffn.fc1.weight - torch.Size([96, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.ffn.fc1.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([96, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.ffn.fc2.weight - torch.Size([384, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.ffn.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.ffn_norm.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch2to1_injector.ffn_norm.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_proj.weight - torch.Size([192, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.ca_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.cffn_gamma - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.query_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.query_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.feat_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.feat_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.attn.value_proj.weight - torch.Size([96, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.attn.value_proj.bias - torch.Size([96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.attn.output_proj.weight - torch.Size([192, 96]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.attn.output_proj.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.ffn.fc1.weight - torch.Size([48, 192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.ffn.fc1.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([48, 1, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.ffn.fc2.weight - torch.Size([192, 48]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.ffn.fc2.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.ffn_norm.weight - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.branch1to2_injector.ffn_norm.bias - torch.Size([192]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch1.0.weight - torch.Size([768, 768, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch1.1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch1.1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch1.3.weight - torch.Size([768, 768, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch1.4.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch1.4.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch2.0.weight - torch.Size([768, 384, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch2.1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch2.1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch2.3.weight - torch.Size([768, 768, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch2.4.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch2.4.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch3.0.weight - torch.Size([768, 192, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch3.1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch3.1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch3.3.weight - torch.Size([768, 768, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch3.4.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch3.4.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.fpn1.0.weight - torch.Size([768, 768, 2, 2]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.fpn1.0.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.fpn1.1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.fpn1.1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.fpn1.3.weight - torch.Size([768, 768, 2, 2]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.fpn1.3.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.fpn2.0.weight - torch.Size([768, 768, 2, 2]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.fpn2.0.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +neck.lateral_convs.0.conv.weight - torch.Size([256, 768, 1, 1]): +XavierInit: gain=1, distribution=uniform, bias=0 + +neck.lateral_convs.0.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of MaskRCNN + +neck.lateral_convs.1.conv.weight - torch.Size([256, 768, 1, 1]): +XavierInit: gain=1, distribution=uniform, bias=0 + +neck.lateral_convs.1.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of MaskRCNN + +neck.lateral_convs.2.conv.weight - torch.Size([256, 768, 1, 1]): +XavierInit: gain=1, distribution=uniform, bias=0 + +neck.lateral_convs.2.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of MaskRCNN + +neck.lateral_convs.3.conv.weight - torch.Size([256, 768, 1, 1]): +XavierInit: gain=1, distribution=uniform, bias=0 + +neck.lateral_convs.3.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of MaskRCNN + +neck.fpn_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): +XavierInit: gain=1, distribution=uniform, bias=0 + +neck.fpn_convs.0.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of MaskRCNN + +neck.fpn_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): +XavierInit: gain=1, distribution=uniform, bias=0 + +neck.fpn_convs.1.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of MaskRCNN + +neck.fpn_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): +XavierInit: gain=1, distribution=uniform, bias=0 + +neck.fpn_convs.2.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of MaskRCNN + +neck.fpn_convs.3.conv.weight - torch.Size([256, 256, 3, 3]): +XavierInit: gain=1, distribution=uniform, bias=0 + +neck.fpn_convs.3.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of MaskRCNN + +rpn_head.rpn_conv.weight - torch.Size([256, 256, 3, 3]): +NormalInit: mean=0, std=0.01, bias=0 + +rpn_head.rpn_conv.bias - torch.Size([256]): +NormalInit: mean=0, std=0.01, bias=0 + +rpn_head.rpn_cls.weight - torch.Size([3, 256, 1, 1]): +NormalInit: mean=0, std=0.01, bias=0 + +rpn_head.rpn_cls.bias - torch.Size([3]): +NormalInit: mean=0, std=0.01, bias=0 + +rpn_head.rpn_reg.weight - torch.Size([12, 256, 1, 1]): +NormalInit: mean=0, std=0.01, bias=0 + +rpn_head.rpn_reg.bias - torch.Size([12]): +NormalInit: mean=0, std=0.01, bias=0 + +roi_head.bbox_head.fc_cls.weight - torch.Size([81, 1024]): +NormalInit: mean=0, std=0.01, bias=0 + +roi_head.bbox_head.fc_cls.bias - torch.Size([81]): +NormalInit: mean=0, std=0.01, bias=0 + +roi_head.bbox_head.fc_reg.weight - torch.Size([320, 1024]): +NormalInit: mean=0, std=0.001, bias=0 + +roi_head.bbox_head.fc_reg.bias - torch.Size([320]): +NormalInit: mean=0, std=0.001, bias=0 + +roi_head.bbox_head.shared_fcs.0.weight - torch.Size([1024, 12544]): +XavierInit: gain=1, distribution=uniform, bias=0 + +roi_head.bbox_head.shared_fcs.0.bias - torch.Size([1024]): +XavierInit: gain=1, distribution=uniform, bias=0 + +roi_head.bbox_head.shared_fcs.1.weight - torch.Size([1024, 1024]): +XavierInit: gain=1, distribution=uniform, bias=0 + +roi_head.bbox_head.shared_fcs.1.bias - torch.Size([1024]): +XavierInit: gain=1, distribution=uniform, bias=0 + +roi_head.mask_head.convs.0.conv.weight - torch.Size([256, 256, 3, 3]): +Initialized by user-defined `init_weights` in ConvModule + +roi_head.mask_head.convs.0.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of MaskRCNN + +roi_head.mask_head.convs.1.conv.weight - torch.Size([256, 256, 3, 3]): +Initialized by user-defined `init_weights` in ConvModule + +roi_head.mask_head.convs.1.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of MaskRCNN + +roi_head.mask_head.convs.2.conv.weight - torch.Size([256, 256, 3, 3]): +Initialized by user-defined `init_weights` in ConvModule + +roi_head.mask_head.convs.2.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of MaskRCNN + +roi_head.mask_head.convs.3.conv.weight - torch.Size([256, 256, 3, 3]): +Initialized by user-defined `init_weights` in ConvModule + +roi_head.mask_head.convs.3.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of MaskRCNN + +roi_head.mask_head.upsample.weight - torch.Size([256, 256, 2, 2]): +Initialized by user-defined `init_weights` in FCNMaskHead + +roi_head.mask_head.upsample.bias - torch.Size([256]): +Initialized by user-defined `init_weights` in FCNMaskHead + +roi_head.mask_head.conv_logits.weight - torch.Size([80, 256, 1, 1]): +Initialized by user-defined `init_weights` in FCNMaskHead + +roi_head.mask_head.conv_logits.bias - torch.Size([80]): +Initialized by user-defined `init_weights` in FCNMaskHead +2024-05-31 00:13:27,450 - mmdet - INFO - {'num_layers': 12, 'layer_decay_rate': 0.85, 'skip_stride': [1, 1]} +2024-05-31 00:13:27,451 - mmdet - INFO - Build LayerDecayOptimizerConstructor 0.850000 - 14 +2024-05-31 00:13:27,465 - mmdet - INFO - Param groups = { + "layer_13_decay": { + "param_names": [ + "backbone.w1", + "backbone.w2", + "backbone.w3", + 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"roi_head.mask_head.convs.2.conv.bias", + "roi_head.mask_head.convs.3.conv.bias", + "roi_head.mask_head.upsample.bias", + "roi_head.mask_head.conv_logits.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +2024-05-31 00:13:52,715 - mmdet - INFO - Automatic scaling of learning rate (LR) has been disabled. +2024-05-31 00:13:53,123 - mmdet - INFO - Start running, work_dir: /mnt/petrelfs/PIIP/mmdetection/work_dirs/mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16 +2024-05-31 00:13:53,124 - mmdet - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) StepLrUpdaterHook +(49 ) ToBFloat16HookMMDet +(NORMAL ) DeepspeedCheckpointHook +(LOW ) DeepspeedDistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) StepLrUpdaterHook +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(LOW ) DeepspeedDistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) StepLrUpdaterHook +(LOW ) IterTimerHook +(LOW ) DeepspeedDistEvalHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) DeepspeedCheckpointHook +(LOW ) IterTimerHook +(LOW ) DeepspeedDistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) DeepspeedCheckpointHook +(LOW ) DeepspeedDistEvalHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_epoch: +(NORMAL ) DistSamplerSeedHook +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook + -------------------- +2024-05-31 00:13:53,124 - mmdet - INFO - workflow: [('train', 1)], max: 12 epochs +2024-05-31 00:13:53,136 - mmdet - INFO - Checkpoints will be saved to /mnt/petrelfs/PIIP/mmdetection/work_dirs/mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16 by HardDiskBackend. +2024-05-31 00:14:31,486 - mmdet - INFO - Epoch [1][50/7330] lr: 9.890e-06, eta: 18:43:27, time: 0.767, data_time: 0.144, memory: 8895, loss_rpn_cls: 0.6830, loss_rpn_bbox: 0.1369, loss_cls: 2.1357, acc: 61.1882, loss_bbox: 0.0099, loss_mask: 1.1060, loss: 4.0714 +2024-05-31 00:15:01,775 - mmdet - INFO - Epoch [1][100/7330] lr: 1.988e-05, eta: 16:44:56, time: 0.606, data_time: 0.056, memory: 9324, loss_rpn_cls: 0.3457, loss_rpn_bbox: 0.1074, loss_cls: 0.4058, acc: 95.5569, loss_bbox: 0.1390, loss_mask: 0.7588, loss: 1.7567 +2024-05-31 00:15:31,034 - mmdet - INFO - Epoch [1][150/7330] lr: 2.987e-05, eta: 15:55:03, time: 0.585, data_time: 0.066, memory: 9324, loss_rpn_cls: 0.2298, loss_rpn_bbox: 0.1010, loss_cls: 0.3329, acc: 95.2529, loss_bbox: 0.1533, loss_mask: 0.6978, loss: 1.5148 +2024-05-31 00:16:01,072 - mmdet - INFO - Epoch [1][200/7330] lr: 3.986e-05, eta: 15:35:32, time: 0.601, data_time: 0.058, memory: 9470, loss_rpn_cls: 0.2146, loss_rpn_bbox: 0.1019, loss_cls: 0.3008, acc: 95.4341, loss_bbox: 0.1455, loss_mask: 0.6841, loss: 1.4469 +2024-05-31 00:16:31,185 - mmdet - INFO - Epoch [1][250/7330] lr: 4.985e-05, eta: 15:24:05, time: 0.602, data_time: 0.065, memory: 9470, loss_rpn_cls: 0.1954, loss_rpn_bbox: 0.1030, loss_cls: 0.3357, acc: 94.9312, loss_bbox: 0.1688, loss_mask: 0.6698, loss: 1.4727 +2024-05-31 00:17:00,907 - mmdet - INFO - Epoch [1][300/7330] lr: 5.984e-05, eta: 15:14:22, time: 0.594, data_time: 0.053, memory: 9470, loss_rpn_cls: 0.1578, loss_rpn_bbox: 0.0988, loss_cls: 0.4196, acc: 93.6304, loss_bbox: 0.2235, loss_mask: 0.6520, loss: 1.5518 +2024-05-31 00:17:31,392 - mmdet - INFO - Epoch [1][350/7330] lr: 6.983e-05, eta: 15:10:29, time: 0.610, data_time: 0.068, memory: 9508, loss_rpn_cls: 0.1355, loss_rpn_bbox: 0.0993, loss_cls: 0.4330, acc: 93.1040, loss_bbox: 0.2445, loss_mask: 0.6250, loss: 1.5374 +2024-05-31 00:18:00,958 - mmdet - INFO - Epoch [1][400/7330] lr: 7.982e-05, eta: 15:04:05, time: 0.591, data_time: 0.061, memory: 9508, loss_rpn_cls: 0.1163, loss_rpn_bbox: 0.0953, loss_cls: 0.4422, acc: 92.7566, loss_bbox: 0.2618, loss_mask: 0.5957, loss: 1.5113 +2024-05-31 00:18:30,890 - mmdet - INFO - Epoch [1][450/7330] lr: 8.981e-05, eta: 15:00:11, time: 0.599, data_time: 0.063, memory: 9508, loss_rpn_cls: 0.1141, loss_rpn_bbox: 0.0937, loss_cls: 0.4474, acc: 92.2542, loss_bbox: 0.2717, loss_mask: 0.5633, loss: 1.4903 +2024-05-31 00:19:00,566 - mmdet - INFO - Epoch [1][500/7330] lr: 9.980e-05, eta: 14:56:13, time: 0.594, data_time: 0.048, memory: 9649, loss_rpn_cls: 0.1056, loss_rpn_bbox: 0.0971, loss_cls: 0.4672, acc: 91.6892, loss_bbox: 0.2957, loss_mask: 0.5439, loss: 1.5094 +2024-05-31 00:19:30,765 - mmdet - INFO - Epoch [1][550/7330] lr: 1.000e-04, eta: 14:54:16, time: 0.604, data_time: 0.070, memory: 9649, loss_rpn_cls: 0.1016, loss_rpn_bbox: 0.0895, loss_cls: 0.4476, acc: 91.5349, loss_bbox: 0.2992, loss_mask: 0.5234, loss: 1.4613 +2024-05-31 00:20:15,177 - mmdet - INFO - Epoch [1][600/7330] lr: 1.000e-04, eta: 15:22:10, time: 0.848, data_time: 0.056, memory: 9649, loss_rpn_cls: 0.0933, loss_rpn_bbox: 0.0926, loss_cls: 0.4401, acc: 91.3206, loss_bbox: 0.3029, loss_mask: 0.5071, loss: 1.4360 +2024-05-31 00:20:46,539 - mmdet - INFO - Epoch [1][650/7330] lr: 1.000e-04, eta: 15:25:26, time: 0.667, data_time: 0.107, memory: 9653, loss_rpn_cls: 0.0931, loss_rpn_bbox: 0.0966, loss_cls: 0.4350, acc: 90.8689, loss_bbox: 0.3228, loss_mask: 0.4840, loss: 1.4314 +2024-05-31 00:21:17,425 - mmdet - INFO - Epoch [1][700/7330] lr: 1.000e-04, eta: 15:23:01, time: 0.618, data_time: 0.067, memory: 9653, loss_rpn_cls: 0.0886, loss_rpn_bbox: 0.0943, loss_cls: 0.4429, acc: 90.4128, loss_bbox: 0.3359, loss_mask: 0.4738, loss: 1.4354 +2024-05-31 00:21:48,627 - mmdet - INFO - Epoch [1][750/7330] lr: 1.000e-04, eta: 15:21:28, time: 0.624, data_time: 0.051, memory: 9653, loss_rpn_cls: 0.0862, loss_rpn_bbox: 0.0900, loss_cls: 0.4446, acc: 90.3782, loss_bbox: 0.3367, loss_mask: 0.4713, loss: 1.4288 +2024-05-31 00:22:18,829 - mmdet - INFO - Epoch [1][800/7330] lr: 1.000e-04, eta: 15:18:13, time: 0.604, data_time: 0.045, memory: 9653, loss_rpn_cls: 0.0804, loss_rpn_bbox: 0.0891, loss_cls: 0.4106, acc: 90.7634, loss_bbox: 0.3258, loss_mask: 0.4613, loss: 1.3673 +2024-05-31 00:22:48,961 - mmdet - INFO - Epoch [1][850/7330] lr: 1.000e-04, eta: 15:15:09, time: 0.602, data_time: 0.056, memory: 9653, loss_rpn_cls: 0.0773, loss_rpn_bbox: 0.0895, loss_cls: 0.4165, acc: 90.3545, loss_bbox: 0.3385, loss_mask: 0.4529, loss: 1.3746 +2024-05-31 00:23:18,603 - mmdet - INFO - Epoch [1][900/7330] lr: 1.000e-04, eta: 15:11:36, time: 0.593, data_time: 0.060, memory: 9653, loss_rpn_cls: 0.0769, loss_rpn_bbox: 0.0896, loss_cls: 0.4045, acc: 90.2568, loss_bbox: 0.3418, loss_mask: 0.4527, loss: 1.3655 +2024-05-31 00:23:49,445 - mmdet - INFO - Epoch [1][950/7330] lr: 1.000e-04, eta: 15:10:12, time: 0.617, data_time: 0.062, memory: 9653, loss_rpn_cls: 0.0742, loss_rpn_bbox: 0.0904, loss_cls: 0.3896, acc: 90.3452, loss_bbox: 0.3370, loss_mask: 0.4438, loss: 1.3350 +2024-05-31 00:24:19,699 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 00:24:19,699 - mmdet - INFO - Epoch [1][1000/7330] lr: 1.000e-04, eta: 15:08:04, time: 0.605, data_time: 0.068, memory: 9653, loss_rpn_cls: 0.0652, loss_rpn_bbox: 0.0807, loss_cls: 0.3842, acc: 90.4116, loss_bbox: 0.3362, loss_mask: 0.4394, loss: 1.3056 +2024-05-31 00:24:49,574 - mmdet - INFO - Epoch [1][1050/7330] lr: 1.000e-04, eta: 15:05:32, time: 0.597, data_time: 0.045, memory: 9654, loss_rpn_cls: 0.0712, loss_rpn_bbox: 0.0885, loss_cls: 0.3763, acc: 89.9604, loss_bbox: 0.3532, loss_mask: 0.4236, loss: 1.3127 +2024-05-31 00:25:20,582 - mmdet - INFO - Epoch [1][1100/7330] lr: 1.000e-04, eta: 15:04:41, time: 0.620, data_time: 0.056, memory: 9654, loss_rpn_cls: 0.0735, loss_rpn_bbox: 0.0899, loss_cls: 0.3849, acc: 89.8391, loss_bbox: 0.3609, loss_mask: 0.4283, loss: 1.3376 +2024-05-31 00:25:50,907 - mmdet - INFO - Epoch [1][1150/7330] lr: 1.000e-04, eta: 15:03:00, time: 0.607, data_time: 0.071, memory: 9654, loss_rpn_cls: 0.0673, loss_rpn_bbox: 0.0806, loss_cls: 0.3671, acc: 90.2593, loss_bbox: 0.3391, loss_mask: 0.4203, loss: 1.2744 +2024-05-31 00:26:21,206 - mmdet - INFO - Epoch [1][1200/7330] lr: 1.000e-04, eta: 15:01:24, time: 0.606, data_time: 0.058, memory: 9654, loss_rpn_cls: 0.0636, loss_rpn_bbox: 0.0813, loss_cls: 0.3551, acc: 90.4370, loss_bbox: 0.3438, loss_mask: 0.4096, loss: 1.2533 +2024-05-31 00:26:50,828 - mmdet - INFO - Epoch [1][1250/7330] lr: 1.000e-04, eta: 14:59:05, time: 0.592, data_time: 0.049, memory: 9654, loss_rpn_cls: 0.0614, loss_rpn_bbox: 0.0779, loss_cls: 0.3352, acc: 90.9346, loss_bbox: 0.3208, loss_mask: 0.4057, loss: 1.2011 +2024-05-31 00:27:21,044 - mmdet - INFO - Epoch [1][1300/7330] lr: 1.000e-04, eta: 14:57:34, time: 0.604, data_time: 0.063, memory: 9654, loss_rpn_cls: 0.0612, loss_rpn_bbox: 0.0836, loss_cls: 0.3593, acc: 90.1318, loss_bbox: 0.3472, loss_mask: 0.4061, loss: 1.2574 +2024-05-31 00:27:54,080 - mmdet - INFO - Epoch [1][1350/7330] lr: 1.000e-04, eta: 14:59:09, time: 0.661, data_time: 0.054, memory: 9654, loss_rpn_cls: 0.0689, loss_rpn_bbox: 0.0847, loss_cls: 0.3706, acc: 89.5474, loss_bbox: 0.3655, loss_mask: 0.4067, loss: 1.2964 +2024-05-31 00:28:39,870 - mmdet - INFO - Epoch [1][1400/7330] lr: 1.000e-04, eta: 15:13:42, time: 0.915, data_time: 0.082, memory: 9654, loss_rpn_cls: 0.0633, loss_rpn_bbox: 0.0833, loss_cls: 0.3802, acc: 89.4248, loss_bbox: 0.3744, loss_mask: 0.4039, loss: 1.3051 +2024-05-31 00:29:10,538 - mmdet - INFO - Epoch [1][1450/7330] lr: 1.000e-04, eta: 15:12:12, time: 0.614, data_time: 0.058, memory: 9654, loss_rpn_cls: 0.0606, loss_rpn_bbox: 0.0798, loss_cls: 0.3629, acc: 90.0120, loss_bbox: 0.3436, loss_mask: 0.4000, loss: 1.2468 +2024-05-31 00:29:41,325 - mmdet - INFO - Epoch [1][1500/7330] lr: 1.000e-04, eta: 15:10:52, time: 0.616, data_time: 0.067, memory: 9654, loss_rpn_cls: 0.0657, loss_rpn_bbox: 0.0851, loss_cls: 0.3577, acc: 89.8459, loss_bbox: 0.3588, loss_mask: 0.3956, loss: 1.2627 +2024-05-31 00:30:11,691 - mmdet - INFO - Epoch [1][1550/7330] lr: 1.000e-04, eta: 15:09:11, time: 0.607, data_time: 0.049, memory: 9654, loss_rpn_cls: 0.0650, loss_rpn_bbox: 0.0852, loss_cls: 0.3448, acc: 90.0886, loss_bbox: 0.3492, loss_mask: 0.3907, loss: 1.2350 +2024-05-31 00:30:41,392 - mmdet - INFO - Epoch [1][1600/7330] lr: 1.000e-04, eta: 15:06:59, time: 0.594, data_time: 0.042, memory: 9654, loss_rpn_cls: 0.0553, loss_rpn_bbox: 0.0754, loss_cls: 0.3428, acc: 90.1609, loss_bbox: 0.3418, loss_mask: 0.3852, loss: 1.2005 +2024-05-31 00:31:11,914 - mmdet - INFO - Epoch [1][1650/7330] lr: 1.000e-04, eta: 15:05:35, time: 0.610, data_time: 0.048, memory: 9654, loss_rpn_cls: 0.0603, loss_rpn_bbox: 0.0771, loss_cls: 0.3533, acc: 89.8457, loss_bbox: 0.3509, loss_mask: 0.3861, loss: 1.2277 +2024-05-31 00:31:42,092 - mmdet - INFO - Epoch [1][1700/7330] lr: 1.000e-04, eta: 15:03:58, time: 0.604, data_time: 0.061, memory: 9654, loss_rpn_cls: 0.0580, loss_rpn_bbox: 0.0763, loss_cls: 0.3446, acc: 90.3879, loss_bbox: 0.3359, loss_mask: 0.3792, loss: 1.1939 +2024-05-31 00:32:12,665 - mmdet - INFO - Epoch [1][1750/7330] lr: 1.000e-04, eta: 15:02:44, time: 0.611, data_time: 0.058, memory: 9654, loss_rpn_cls: 0.0579, loss_rpn_bbox: 0.0825, loss_cls: 0.3467, acc: 89.6655, loss_bbox: 0.3602, loss_mask: 0.3824, loss: 1.2297 +2024-05-31 00:32:43,272 - mmdet - INFO - Epoch [1][1800/7330] lr: 1.000e-04, eta: 15:01:34, time: 0.612, data_time: 0.069, memory: 9654, loss_rpn_cls: 0.0584, loss_rpn_bbox: 0.0810, loss_cls: 0.3419, acc: 89.8152, loss_bbox: 0.3534, loss_mask: 0.3811, loss: 1.2158 +2024-05-31 00:33:12,894 - mmdet - INFO - Epoch [1][1850/7330] lr: 1.000e-04, eta: 14:59:40, time: 0.592, data_time: 0.043, memory: 9654, loss_rpn_cls: 0.0573, loss_rpn_bbox: 0.0733, loss_cls: 0.3245, acc: 90.4294, loss_bbox: 0.3312, loss_mask: 0.3739, loss: 1.1603 +2024-05-31 00:33:43,525 - mmdet - INFO - Epoch [1][1900/7330] lr: 1.000e-04, eta: 14:58:37, time: 0.613, data_time: 0.064, memory: 9654, loss_rpn_cls: 0.0621, loss_rpn_bbox: 0.0798, loss_cls: 0.3463, acc: 89.8005, loss_bbox: 0.3557, loss_mask: 0.3807, loss: 1.2247 +2024-05-31 00:34:13,676 - mmdet - INFO - Epoch [1][1950/7330] lr: 1.000e-04, eta: 14:57:13, time: 0.603, data_time: 0.065, memory: 9654, loss_rpn_cls: 0.0571, loss_rpn_bbox: 0.0755, loss_cls: 0.3324, acc: 90.1841, loss_bbox: 0.3424, loss_mask: 0.3742, loss: 1.1817 +2024-05-31 00:34:44,242 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 00:34:44,242 - mmdet - INFO - Epoch [1][2000/7330] lr: 1.000e-04, eta: 14:56:11, time: 0.611, data_time: 0.052, memory: 9654, loss_rpn_cls: 0.0549, loss_rpn_bbox: 0.0768, loss_cls: 0.3481, acc: 89.7717, loss_bbox: 0.3549, loss_mask: 0.3753, loss: 1.2101 +2024-05-31 00:35:14,883 - mmdet - INFO - Epoch [1][2050/7330] lr: 1.000e-04, eta: 14:55:13, time: 0.613, data_time: 0.067, memory: 9654, loss_rpn_cls: 0.0564, loss_rpn_bbox: 0.0778, loss_cls: 0.3259, acc: 90.3789, loss_bbox: 0.3345, loss_mask: 0.3684, loss: 1.1630 +2024-05-31 00:35:47,663 - mmdet - INFO - Epoch [1][2100/7330] lr: 1.000e-04, eta: 14:55:44, time: 0.656, data_time: 0.068, memory: 9654, loss_rpn_cls: 0.0566, loss_rpn_bbox: 0.0791, loss_cls: 0.3474, acc: 89.7461, loss_bbox: 0.3587, loss_mask: 0.3806, loss: 1.2224 +2024-05-31 00:36:29,014 - mmdet - INFO - Epoch [1][2150/7330] lr: 1.000e-04, eta: 15:01:54, time: 0.827, data_time: 0.048, memory: 9654, loss_rpn_cls: 0.0560, loss_rpn_bbox: 0.0768, loss_cls: 0.3481, acc: 89.5208, loss_bbox: 0.3644, loss_mask: 0.3668, loss: 1.2120 +2024-05-31 00:36:59,611 - mmdet - INFO - Epoch [1][2200/7330] lr: 1.000e-04, eta: 15:00:45, time: 0.612, data_time: 0.062, memory: 9654, loss_rpn_cls: 0.0554, loss_rpn_bbox: 0.0751, loss_cls: 0.3301, acc: 89.9277, loss_bbox: 0.3473, loss_mask: 0.3715, loss: 1.1795 +2024-05-31 00:37:30,239 - mmdet - INFO - Epoch [1][2250/7330] lr: 1.000e-04, eta: 14:59:41, time: 0.613, data_time: 0.047, memory: 9654, loss_rpn_cls: 0.0492, loss_rpn_bbox: 0.0734, loss_cls: 0.3341, acc: 90.1301, loss_bbox: 0.3363, loss_mask: 0.3607, loss: 1.1538 +2024-05-31 00:38:00,790 - mmdet - INFO - Epoch [1][2300/7330] lr: 1.000e-04, eta: 14:58:34, time: 0.611, data_time: 0.068, memory: 9654, loss_rpn_cls: 0.0541, loss_rpn_bbox: 0.0751, loss_cls: 0.3279, acc: 90.0708, loss_bbox: 0.3540, loss_mask: 0.3608, loss: 1.1719 +2024-05-31 00:38:31,111 - mmdet - INFO - Epoch [1][2350/7330] lr: 1.000e-04, eta: 14:57:21, time: 0.606, data_time: 0.069, memory: 9654, loss_rpn_cls: 0.0519, loss_rpn_bbox: 0.0757, loss_cls: 0.3173, acc: 90.3779, loss_bbox: 0.3335, loss_mask: 0.3511, loss: 1.1296 +2024-05-31 00:39:01,230 - mmdet - INFO - Epoch [1][2400/7330] lr: 1.000e-04, eta: 14:56:02, time: 0.602, data_time: 0.054, memory: 9654, loss_rpn_cls: 0.0534, loss_rpn_bbox: 0.0749, loss_cls: 0.3124, acc: 90.5959, loss_bbox: 0.3320, loss_mask: 0.3554, loss: 1.1281 +2024-05-31 00:39:31,541 - mmdet - INFO - Epoch [1][2450/7330] lr: 1.000e-04, eta: 14:54:52, time: 0.606, data_time: 0.060, memory: 9654, loss_rpn_cls: 0.0524, loss_rpn_bbox: 0.0754, loss_cls: 0.3238, acc: 90.0588, loss_bbox: 0.3383, loss_mask: 0.3540, loss: 1.1439 +2024-05-31 00:40:02,150 - mmdet - INFO - Epoch [1][2500/7330] lr: 1.000e-04, eta: 14:53:54, time: 0.612, data_time: 0.069, memory: 9654, loss_rpn_cls: 0.0533, loss_rpn_bbox: 0.0719, loss_cls: 0.3346, acc: 89.9744, loss_bbox: 0.3429, loss_mask: 0.3525, loss: 1.1552 +2024-05-31 00:40:32,646 - mmdet - INFO - Epoch [1][2550/7330] lr: 1.000e-04, eta: 14:52:53, time: 0.610, data_time: 0.063, memory: 9654, loss_rpn_cls: 0.0515, loss_rpn_bbox: 0.0738, loss_cls: 0.3326, acc: 90.0422, loss_bbox: 0.3499, loss_mask: 0.3513, loss: 1.1592 +2024-05-31 00:41:03,607 - mmdet - INFO - Epoch [1][2600/7330] lr: 1.000e-04, eta: 14:52:08, time: 0.619, data_time: 0.058, memory: 9654, loss_rpn_cls: 0.0563, loss_rpn_bbox: 0.0769, loss_cls: 0.3382, acc: 89.6594, loss_bbox: 0.3550, loss_mask: 0.3605, loss: 1.1870 +2024-05-31 00:41:33,604 - mmdet - INFO - Epoch [1][2650/7330] lr: 1.000e-04, eta: 14:50:53, time: 0.600, data_time: 0.043, memory: 9654, loss_rpn_cls: 0.0479, loss_rpn_bbox: 0.0697, loss_cls: 0.3189, acc: 90.2532, loss_bbox: 0.3368, loss_mask: 0.3507, loss: 1.1240 +2024-05-31 00:42:04,152 - mmdet - INFO - Epoch [1][2700/7330] lr: 1.000e-04, eta: 14:49:57, time: 0.611, data_time: 0.057, memory: 9654, loss_rpn_cls: 0.0529, loss_rpn_bbox: 0.0757, loss_cls: 0.3239, acc: 89.9609, loss_bbox: 0.3455, loss_mask: 0.3526, loss: 1.1507 +2024-05-31 00:42:34,883 - mmdet - INFO - Epoch [1][2750/7330] lr: 1.000e-04, eta: 14:49:08, time: 0.615, data_time: 0.062, memory: 9654, loss_rpn_cls: 0.0495, loss_rpn_bbox: 0.0719, loss_cls: 0.3156, acc: 90.4250, loss_bbox: 0.3348, loss_mask: 0.3479, loss: 1.1197 +2024-05-31 00:43:05,900 - mmdet - INFO - Epoch [1][2800/7330] lr: 1.000e-04, eta: 14:48:28, time: 0.620, data_time: 0.059, memory: 9654, loss_rpn_cls: 0.0562, loss_rpn_bbox: 0.0728, loss_cls: 0.3375, acc: 89.9136, loss_bbox: 0.3463, loss_mask: 0.3526, loss: 1.1654 +2024-05-31 00:43:39,384 - mmdet - INFO - Epoch [1][2850/7330] lr: 1.000e-04, eta: 14:49:02, time: 0.670, data_time: 0.060, memory: 9654, loss_rpn_cls: 0.0504, loss_rpn_bbox: 0.0702, loss_cls: 0.3214, acc: 90.2695, loss_bbox: 0.3441, loss_mask: 0.3491, loss: 1.1352 +2024-05-31 00:44:23,368 - mmdet - INFO - Epoch [1][2900/7330] lr: 1.000e-04, eta: 14:54:41, time: 0.880, data_time: 0.049, memory: 9654, loss_rpn_cls: 0.0489, loss_rpn_bbox: 0.0716, loss_cls: 0.3199, acc: 90.1184, loss_bbox: 0.3468, loss_mask: 0.3421, loss: 1.1292 +2024-05-31 00:44:53,223 - mmdet - INFO - Epoch [1][2950/7330] lr: 1.000e-04, eta: 14:53:20, time: 0.597, data_time: 0.051, memory: 9654, loss_rpn_cls: 0.0476, loss_rpn_bbox: 0.0691, loss_cls: 0.3035, acc: 90.6672, loss_bbox: 0.3250, loss_mask: 0.3424, loss: 1.0876 +2024-05-31 00:45:23,899 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 00:45:23,899 - mmdet - INFO - Epoch [1][3000/7330] lr: 1.000e-04, eta: 14:52:25, time: 0.613, data_time: 0.061, memory: 9654, loss_rpn_cls: 0.0512, loss_rpn_bbox: 0.0746, loss_cls: 0.3200, acc: 90.2192, loss_bbox: 0.3362, loss_mask: 0.3450, loss: 1.1269 +2024-05-31 00:45:54,085 - mmdet - INFO - Epoch [1][3050/7330] lr: 1.000e-04, eta: 14:51:16, time: 0.604, data_time: 0.052, memory: 9654, loss_rpn_cls: 0.0476, loss_rpn_bbox: 0.0719, loss_cls: 0.3138, acc: 90.2917, loss_bbox: 0.3381, loss_mask: 0.3333, loss: 1.1047 +2024-05-31 00:46:24,352 - mmdet - INFO - Epoch [1][3100/7330] lr: 1.000e-04, eta: 14:50:11, time: 0.605, data_time: 0.054, memory: 9654, loss_rpn_cls: 0.0472, loss_rpn_bbox: 0.0681, loss_cls: 0.3233, acc: 90.1858, loss_bbox: 0.3406, loss_mask: 0.3431, loss: 1.1223 +2024-05-31 00:46:54,273 - mmdet - INFO - Epoch [1][3150/7330] lr: 1.000e-04, eta: 14:48:58, time: 0.598, data_time: 0.059, memory: 9654, loss_rpn_cls: 0.0513, loss_rpn_bbox: 0.0706, loss_cls: 0.3141, acc: 90.4758, loss_bbox: 0.3303, loss_mask: 0.3520, loss: 1.1183 +2024-05-31 00:47:23,946 - mmdet - INFO - Epoch [1][3200/7330] lr: 1.000e-04, eta: 14:47:40, time: 0.593, data_time: 0.050, memory: 9654, loss_rpn_cls: 0.0474, loss_rpn_bbox: 0.0685, loss_cls: 0.3072, acc: 90.4897, loss_bbox: 0.3338, loss_mask: 0.3430, loss: 1.0999 +2024-05-31 00:47:54,501 - mmdet - INFO - Epoch [1][3250/7330] lr: 1.000e-04, eta: 14:46:46, time: 0.611, data_time: 0.057, memory: 9654, loss_rpn_cls: 0.0508, loss_rpn_bbox: 0.0699, loss_cls: 0.3018, acc: 90.7036, loss_bbox: 0.3233, loss_mask: 0.3338, loss: 1.0796 +2024-05-31 00:48:25,246 - mmdet - INFO - Epoch [1][3300/7330] lr: 1.000e-04, eta: 14:45:58, time: 0.615, data_time: 0.062, memory: 9654, loss_rpn_cls: 0.0472, loss_rpn_bbox: 0.0676, loss_cls: 0.3033, acc: 90.6274, loss_bbox: 0.3282, loss_mask: 0.3382, loss: 1.0845 +2024-05-31 00:48:55,171 - mmdet - INFO - Epoch [1][3350/7330] lr: 1.000e-04, eta: 14:44:49, time: 0.598, data_time: 0.057, memory: 9654, loss_rpn_cls: 0.0477, loss_rpn_bbox: 0.0680, loss_cls: 0.2988, acc: 90.5881, loss_bbox: 0.3291, loss_mask: 0.3409, loss: 1.0845 +2024-05-31 00:49:25,376 - mmdet - INFO - Epoch [1][3400/7330] lr: 1.000e-04, eta: 14:43:49, time: 0.604, data_time: 0.054, memory: 9654, loss_rpn_cls: 0.0515, loss_rpn_bbox: 0.0700, loss_cls: 0.3098, acc: 90.5420, loss_bbox: 0.3271, loss_mask: 0.3441, loss: 1.1026 +2024-05-31 00:49:55,523 - mmdet - INFO - Epoch [1][3450/7330] lr: 1.000e-04, eta: 14:42:48, time: 0.603, data_time: 0.053, memory: 9654, loss_rpn_cls: 0.0444, loss_rpn_bbox: 0.0674, loss_cls: 0.2974, acc: 90.7527, loss_bbox: 0.3214, loss_mask: 0.3394, loss: 1.0700 +2024-05-31 00:50:25,817 - mmdet - INFO - Epoch [1][3500/7330] lr: 1.000e-04, eta: 14:41:51, time: 0.606, data_time: 0.064, memory: 9654, loss_rpn_cls: 0.0486, loss_rpn_bbox: 0.0684, loss_cls: 0.2992, acc: 90.6819, loss_bbox: 0.3191, loss_mask: 0.3406, loss: 1.0759 +2024-05-31 00:50:56,203 - mmdet - INFO - Epoch [1][3550/7330] lr: 1.000e-04, eta: 14:40:57, time: 0.608, data_time: 0.067, memory: 9654, loss_rpn_cls: 0.0473, loss_rpn_bbox: 0.0692, loss_cls: 0.3116, acc: 90.3882, loss_bbox: 0.3295, loss_mask: 0.3432, loss: 1.1007 +2024-05-31 00:51:29,217 - mmdet - INFO - Epoch [1][3600/7330] lr: 1.000e-04, eta: 14:41:06, time: 0.660, data_time: 0.050, memory: 9654, loss_rpn_cls: 0.0475, loss_rpn_bbox: 0.0697, loss_cls: 0.3086, acc: 90.4441, loss_bbox: 0.3342, loss_mask: 0.3354, loss: 1.0953 +2024-05-31 00:52:12,759 - mmdet - INFO - Epoch [1][3650/7330] lr: 1.000e-04, eta: 14:45:17, time: 0.871, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0466, loss_rpn_bbox: 0.0706, loss_cls: 0.3141, acc: 90.1970, loss_bbox: 0.3396, loss_mask: 0.3381, loss: 1.1090 +2024-05-31 00:52:43,389 - mmdet - INFO - Epoch [1][3700/7330] lr: 1.000e-04, eta: 14:44:25, time: 0.613, data_time: 0.072, memory: 9655, loss_rpn_cls: 0.0473, loss_rpn_bbox: 0.0651, loss_cls: 0.2992, acc: 90.7395, loss_bbox: 0.3223, loss_mask: 0.3321, loss: 1.0660 +2024-05-31 00:53:13,212 - mmdet - INFO - Epoch [1][3750/7330] lr: 1.000e-04, eta: 14:43:16, time: 0.597, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0421, loss_rpn_bbox: 0.0681, loss_cls: 0.3032, acc: 90.6033, loss_bbox: 0.3246, loss_mask: 0.3337, loss: 1.0716 +2024-05-31 00:53:43,892 - mmdet - INFO - Epoch [1][3800/7330] lr: 1.000e-04, eta: 14:42:28, time: 0.614, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0504, loss_rpn_bbox: 0.0706, loss_cls: 0.3104, acc: 90.3374, loss_bbox: 0.3376, loss_mask: 0.3311, loss: 1.1001 +2024-05-31 00:54:14,333 - mmdet - INFO - Epoch [1][3850/7330] lr: 1.000e-04, eta: 14:41:34, time: 0.608, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0439, loss_rpn_bbox: 0.0692, loss_cls: 0.2967, acc: 90.5012, loss_bbox: 0.3297, loss_mask: 0.3322, loss: 1.0717 +2024-05-31 00:54:44,278 - mmdet - INFO - Epoch [1][3900/7330] lr: 1.000e-04, eta: 14:40:30, time: 0.599, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0466, loss_rpn_bbox: 0.0663, loss_cls: 0.2977, acc: 90.7886, loss_bbox: 0.3198, loss_mask: 0.3282, loss: 1.0587 +2024-05-31 00:55:14,394 - mmdet - INFO - Epoch [1][3950/7330] lr: 1.000e-04, eta: 14:39:30, time: 0.602, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0490, loss_rpn_bbox: 0.0679, loss_cls: 0.3128, acc: 90.3987, loss_bbox: 0.3330, loss_mask: 0.3315, loss: 1.0943 +2024-05-31 00:55:45,145 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 00:55:45,145 - mmdet - INFO - Epoch [1][4000/7330] lr: 1.000e-04, eta: 14:38:46, time: 0.615, data_time: 0.072, memory: 9655, loss_rpn_cls: 0.0480, loss_rpn_bbox: 0.0689, loss_cls: 0.3044, acc: 90.3328, loss_bbox: 0.3350, loss_mask: 0.3331, loss: 1.0895 +2024-05-31 00:56:15,074 - mmdet - INFO - Epoch [1][4050/7330] lr: 1.000e-04, eta: 14:37:44, time: 0.599, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0444, loss_rpn_bbox: 0.0713, loss_cls: 0.3029, acc: 90.5632, loss_bbox: 0.3329, loss_mask: 0.3359, loss: 1.0874 +2024-05-31 00:56:45,628 - mmdet - INFO - Epoch [1][4100/7330] lr: 1.000e-04, eta: 14:36:56, time: 0.611, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0447, loss_rpn_bbox: 0.0716, loss_cls: 0.3067, acc: 90.3110, loss_bbox: 0.3339, loss_mask: 0.3368, loss: 1.0937 +2024-05-31 00:57:16,276 - mmdet - INFO - Epoch [1][4150/7330] lr: 1.000e-04, eta: 14:36:09, time: 0.613, data_time: 0.066, memory: 9655, loss_rpn_cls: 0.0464, loss_rpn_bbox: 0.0684, loss_cls: 0.2917, acc: 90.9490, loss_bbox: 0.3133, loss_mask: 0.3253, loss: 1.0451 +2024-05-31 00:57:46,113 - mmdet - INFO - Epoch [1][4200/7330] lr: 1.000e-04, eta: 14:35:08, time: 0.597, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0427, loss_rpn_bbox: 0.0706, loss_cls: 0.2986, acc: 90.6135, loss_bbox: 0.3229, loss_mask: 0.3212, loss: 1.0560 +2024-05-31 00:58:16,052 - mmdet - INFO - Epoch [1][4250/7330] lr: 1.000e-04, eta: 14:34:09, time: 0.599, data_time: 0.067, memory: 9655, loss_rpn_cls: 0.0432, loss_rpn_bbox: 0.0651, loss_cls: 0.2862, acc: 90.9043, loss_bbox: 0.3149, loss_mask: 0.3184, loss: 1.0278 +2024-05-31 00:58:46,189 - mmdet - INFO - Epoch [1][4300/7330] lr: 1.000e-04, eta: 14:33:14, time: 0.603, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0445, loss_rpn_bbox: 0.0655, loss_cls: 0.2893, acc: 90.9324, loss_bbox: 0.3143, loss_mask: 0.3316, loss: 1.0453 +2024-05-31 00:59:16,467 - mmdet - INFO - Epoch [1][4350/7330] lr: 1.000e-04, eta: 14:32:23, time: 0.606, data_time: 0.065, memory: 9655, loss_rpn_cls: 0.0443, loss_rpn_bbox: 0.0662, loss_cls: 0.2941, acc: 90.9185, loss_bbox: 0.3161, loss_mask: 0.3317, loss: 1.0524 +2024-05-31 00:59:49,371 - mmdet - INFO - Epoch [1][4400/7330] lr: 1.000e-04, eta: 14:32:22, time: 0.658, data_time: 0.063, memory: 9655, loss_rpn_cls: 0.0435, loss_rpn_bbox: 0.0646, loss_cls: 0.2976, acc: 90.5581, loss_bbox: 0.3262, loss_mask: 0.3351, loss: 1.0670 +2024-05-31 01:00:30,355 - mmdet - INFO - Epoch [1][4450/7330] lr: 1.000e-04, eta: 14:34:52, time: 0.820, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0443, loss_rpn_bbox: 0.0651, loss_cls: 0.2765, acc: 91.2141, loss_bbox: 0.3049, loss_mask: 0.3142, loss: 1.0050 +2024-05-31 01:01:00,943 - mmdet - INFO - Epoch [1][4500/7330] lr: 1.000e-04, eta: 14:34:05, time: 0.612, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0440, loss_rpn_bbox: 0.0674, loss_cls: 0.2880, acc: 90.7688, loss_bbox: 0.3171, loss_mask: 0.3261, loss: 1.0426 +2024-05-31 01:01:31,491 - mmdet - INFO - Epoch [1][4550/7330] lr: 1.000e-04, eta: 14:33:18, time: 0.611, data_time: 0.066, memory: 9655, loss_rpn_cls: 0.0451, loss_rpn_bbox: 0.0664, loss_cls: 0.2945, acc: 90.7502, loss_bbox: 0.3240, loss_mask: 0.3259, loss: 1.0558 +2024-05-31 01:02:01,922 - mmdet - INFO - Epoch [1][4600/7330] lr: 1.000e-04, eta: 14:32:29, time: 0.609, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0462, loss_rpn_bbox: 0.0685, loss_cls: 0.3028, acc: 90.5825, loss_bbox: 0.3208, loss_mask: 0.3237, loss: 1.0619 +2024-05-31 01:02:32,652 - mmdet - INFO - Epoch [1][4650/7330] lr: 1.000e-04, eta: 14:31:45, time: 0.615, data_time: 0.063, memory: 9655, loss_rpn_cls: 0.0501, loss_rpn_bbox: 0.0709, loss_cls: 0.3008, acc: 90.3506, loss_bbox: 0.3329, loss_mask: 0.3293, loss: 1.0841 +2024-05-31 01:03:02,939 - mmdet - INFO - Epoch [1][4700/7330] lr: 1.000e-04, eta: 14:30:54, time: 0.606, data_time: 0.063, memory: 9655, loss_rpn_cls: 0.0435, loss_rpn_bbox: 0.0656, loss_cls: 0.2982, acc: 90.6184, loss_bbox: 0.3262, loss_mask: 0.3287, loss: 1.0622 +2024-05-31 01:03:33,194 - mmdet - INFO - Epoch [1][4750/7330] lr: 1.000e-04, eta: 14:30:03, time: 0.605, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0461, loss_rpn_bbox: 0.0720, loss_cls: 0.2980, acc: 90.4592, loss_bbox: 0.3314, loss_mask: 0.3241, loss: 1.0715 +2024-05-31 01:04:03,399 - mmdet - INFO - Epoch [1][4800/7330] lr: 1.000e-04, eta: 14:29:12, time: 0.604, data_time: 0.065, memory: 9655, loss_rpn_cls: 0.0444, loss_rpn_bbox: 0.0709, loss_cls: 0.2960, acc: 90.7151, loss_bbox: 0.3170, loss_mask: 0.3234, loss: 1.0516 +2024-05-31 01:04:33,509 - mmdet - INFO - Epoch [1][4850/7330] lr: 1.000e-04, eta: 14:28:19, time: 0.602, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0434, loss_rpn_bbox: 0.0676, loss_cls: 0.2920, acc: 90.6411, loss_bbox: 0.3195, loss_mask: 0.3250, loss: 1.0476 +2024-05-31 01:05:03,880 - mmdet - INFO - Epoch [1][4900/7330] lr: 1.000e-04, eta: 14:27:31, time: 0.607, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0450, loss_rpn_bbox: 0.0645, loss_cls: 0.2895, acc: 90.7695, loss_bbox: 0.3176, loss_mask: 0.3167, loss: 1.0333 +2024-05-31 01:05:33,888 - mmdet - INFO - Epoch [1][4950/7330] lr: 1.000e-04, eta: 14:26:37, time: 0.600, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0387, loss_rpn_bbox: 0.0626, loss_cls: 0.2789, acc: 90.9617, loss_bbox: 0.3151, loss_mask: 0.3104, loss: 1.0057 +2024-05-31 01:06:04,077 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 01:06:04,078 - mmdet - INFO - Epoch [1][5000/7330] lr: 1.000e-04, eta: 14:25:47, time: 0.604, data_time: 0.092, memory: 9655, loss_rpn_cls: 0.0486, loss_rpn_bbox: 0.0678, loss_cls: 0.2786, acc: 91.0574, loss_bbox: 0.3135, loss_mask: 0.3171, loss: 1.0256 +2024-05-31 01:06:33,803 - mmdet - INFO - Epoch [1][5050/7330] lr: 1.000e-04, eta: 14:24:50, time: 0.595, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0418, loss_rpn_bbox: 0.0644, loss_cls: 0.2717, acc: 91.1436, loss_bbox: 0.3078, loss_mask: 0.3115, loss: 0.9973 +2024-05-31 01:07:04,282 - mmdet - INFO - Epoch [1][5100/7330] lr: 1.000e-04, eta: 14:24:05, time: 0.609, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0431, loss_rpn_bbox: 0.0650, loss_cls: 0.2941, acc: 90.5823, loss_bbox: 0.3285, loss_mask: 0.3234, loss: 1.0541 +2024-05-31 01:07:37,301 - mmdet - INFO - Epoch [1][5150/7330] lr: 1.000e-04, eta: 14:24:02, time: 0.661, data_time: 0.069, memory: 9655, loss_rpn_cls: 0.0416, loss_rpn_bbox: 0.0645, loss_cls: 0.2922, acc: 90.5549, loss_bbox: 0.3276, loss_mask: 0.3199, loss: 1.0458 +2024-05-31 01:08:20,403 - mmdet - INFO - Epoch [1][5200/7330] lr: 1.000e-04, eta: 14:26:39, time: 0.862, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0441, loss_rpn_bbox: 0.0689, loss_cls: 0.2888, acc: 90.8359, loss_bbox: 0.3191, loss_mask: 0.3173, loss: 1.0383 +2024-05-31 01:08:51,226 - mmdet - INFO - Epoch [1][5250/7330] lr: 1.000e-04, eta: 14:25:58, time: 0.616, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0433, loss_rpn_bbox: 0.0676, loss_cls: 0.2886, acc: 90.7200, loss_bbox: 0.3216, loss_mask: 0.3193, loss: 1.0404 +2024-05-31 01:09:21,757 - mmdet - INFO - Epoch [1][5300/7330] lr: 1.000e-04, eta: 14:25:13, time: 0.611, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0408, loss_rpn_bbox: 0.0609, loss_cls: 0.2834, acc: 90.8550, loss_bbox: 0.3131, loss_mask: 0.3163, loss: 1.0146 +2024-05-31 01:09:52,148 - mmdet - INFO - Epoch [1][5350/7330] lr: 1.000e-04, eta: 14:24:25, time: 0.607, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0415, loss_rpn_bbox: 0.0636, loss_cls: 0.2845, acc: 90.9675, loss_bbox: 0.3107, loss_mask: 0.3128, loss: 1.0131 +2024-05-31 01:10:21,717 - mmdet - INFO - Epoch [1][5400/7330] lr: 1.000e-04, eta: 14:23:26, time: 0.592, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0424, loss_rpn_bbox: 0.0638, loss_cls: 0.2712, acc: 91.3884, loss_bbox: 0.2999, loss_mask: 0.3145, loss: 0.9918 +2024-05-31 01:10:52,142 - mmdet - INFO - Epoch [1][5450/7330] lr: 1.000e-04, eta: 14:22:41, time: 0.609, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0399, loss_rpn_bbox: 0.0641, loss_cls: 0.2849, acc: 90.8621, loss_bbox: 0.3183, loss_mask: 0.3213, loss: 1.0285 +2024-05-31 01:11:22,748 - mmdet - INFO - Epoch [1][5500/7330] lr: 1.000e-04, eta: 14:21:58, time: 0.612, data_time: 0.067, memory: 9655, loss_rpn_cls: 0.0410, loss_rpn_bbox: 0.0674, loss_cls: 0.2803, acc: 91.0010, loss_bbox: 0.3148, loss_mask: 0.3238, loss: 1.0272 +2024-05-31 01:11:53,323 - mmdet - INFO - Epoch [1][5550/7330] lr: 1.000e-04, eta: 14:21:15, time: 0.611, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0432, loss_rpn_bbox: 0.0656, loss_cls: 0.2800, acc: 91.0630, loss_bbox: 0.3086, loss_mask: 0.3196, loss: 1.0170 +2024-05-31 01:12:23,823 - mmdet - INFO - Epoch [1][5600/7330] lr: 1.000e-04, eta: 14:20:31, time: 0.610, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0423, loss_rpn_bbox: 0.0657, loss_cls: 0.2996, acc: 90.4910, loss_bbox: 0.3284, loss_mask: 0.3139, loss: 1.0500 +2024-05-31 01:12:53,866 - mmdet - INFO - Epoch [1][5650/7330] lr: 1.000e-04, eta: 14:19:41, time: 0.601, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0410, loss_rpn_bbox: 0.0611, loss_cls: 0.2740, acc: 91.0559, loss_bbox: 0.3179, loss_mask: 0.3194, loss: 1.0135 +2024-05-31 01:13:24,048 - mmdet - INFO - Epoch [1][5700/7330] lr: 1.000e-04, eta: 14:18:53, time: 0.604, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0422, loss_rpn_bbox: 0.0641, loss_cls: 0.2858, acc: 90.9236, loss_bbox: 0.3153, loss_mask: 0.3121, loss: 1.0195 +2024-05-31 01:13:54,632 - mmdet - INFO - Epoch [1][5750/7330] lr: 1.000e-04, eta: 14:18:11, time: 0.612, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0424, loss_rpn_bbox: 0.0665, loss_cls: 0.2931, acc: 90.5796, loss_bbox: 0.3272, loss_mask: 0.3113, loss: 1.0405 +2024-05-31 01:14:25,265 - mmdet - INFO - Epoch [1][5800/7330] lr: 1.000e-04, eta: 14:17:30, time: 0.613, data_time: 0.064, memory: 9655, loss_rpn_cls: 0.0452, loss_rpn_bbox: 0.0666, loss_cls: 0.2924, acc: 90.7485, loss_bbox: 0.3218, loss_mask: 0.3134, loss: 1.0394 +2024-05-31 01:14:55,476 - mmdet - INFO - Epoch [1][5850/7330] lr: 1.000e-04, eta: 14:16:43, time: 0.604, data_time: 0.064, memory: 9655, loss_rpn_cls: 0.0403, loss_rpn_bbox: 0.0627, loss_cls: 0.2753, acc: 91.1277, loss_bbox: 0.3075, loss_mask: 0.3085, loss: 0.9944 +2024-05-31 01:15:29,341 - mmdet - INFO - Epoch [1][5900/7330] lr: 1.000e-04, eta: 14:16:47, time: 0.677, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0427, loss_rpn_bbox: 0.0682, loss_cls: 0.2871, acc: 90.8135, loss_bbox: 0.3196, loss_mask: 0.3198, loss: 1.0374 +2024-05-31 01:16:12,043 - mmdet - INFO - Epoch [1][5950/7330] lr: 1.000e-04, eta: 14:18:53, time: 0.854, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0434, loss_rpn_bbox: 0.0641, loss_cls: 0.2892, acc: 90.6575, loss_bbox: 0.3213, loss_mask: 0.3186, loss: 1.0365 +2024-05-31 01:16:42,328 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 01:16:42,328 - mmdet - INFO - Epoch [1][6000/7330] lr: 1.000e-04, eta: 14:18:06, time: 0.606, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0402, loss_rpn_bbox: 0.0619, loss_cls: 0.2759, acc: 91.1465, loss_bbox: 0.3073, loss_mask: 0.3109, loss: 0.9962 +2024-05-31 01:17:12,893 - mmdet - INFO - Epoch [1][6050/7330] lr: 1.000e-04, eta: 14:17:23, time: 0.611, data_time: 0.075, memory: 9655, loss_rpn_cls: 0.0429, loss_rpn_bbox: 0.0667, loss_cls: 0.2806, acc: 90.9019, loss_bbox: 0.3170, loss_mask: 0.3070, loss: 1.0141 +2024-05-31 01:17:42,573 - mmdet - INFO - Epoch [1][6100/7330] lr: 1.000e-04, eta: 14:16:29, time: 0.594, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0412, loss_rpn_bbox: 0.0640, loss_cls: 0.2757, acc: 91.0186, loss_bbox: 0.3154, loss_mask: 0.3109, loss: 1.0072 +2024-05-31 01:18:13,334 - mmdet - INFO - Epoch [1][6150/7330] lr: 1.000e-04, eta: 14:15:49, time: 0.615, data_time: 0.085, memory: 9655, loss_rpn_cls: 0.0388, loss_rpn_bbox: 0.0630, loss_cls: 0.2893, acc: 90.9375, loss_bbox: 0.3141, loss_mask: 0.3092, loss: 1.0144 +2024-05-31 01:18:43,340 - mmdet - INFO - Epoch [1][6200/7330] lr: 1.000e-04, eta: 14:14:59, time: 0.600, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0397, loss_rpn_bbox: 0.0620, loss_cls: 0.2723, acc: 91.2939, loss_bbox: 0.3055, loss_mask: 0.3111, loss: 0.9905 +2024-05-31 01:19:13,149 - mmdet - INFO - Epoch [1][6250/7330] lr: 1.000e-04, eta: 14:14:07, time: 0.596, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0384, loss_rpn_bbox: 0.0613, loss_cls: 0.2734, acc: 91.0884, loss_bbox: 0.3095, loss_mask: 0.3116, loss: 0.9942 +2024-05-31 01:19:43,340 - mmdet - INFO - Epoch [1][6300/7330] lr: 1.000e-04, eta: 14:13:21, time: 0.604, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0390, loss_rpn_bbox: 0.0624, loss_cls: 0.2773, acc: 91.1094, loss_bbox: 0.3059, loss_mask: 0.3059, loss: 0.9904 +2024-05-31 01:20:13,809 - mmdet - INFO - Epoch [1][6350/7330] lr: 1.000e-04, eta: 14:12:38, time: 0.610, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0400, loss_rpn_bbox: 0.0606, loss_cls: 0.2795, acc: 91.0164, loss_bbox: 0.3126, loss_mask: 0.3068, loss: 0.9995 +2024-05-31 01:20:44,015 - mmdet - INFO - Epoch [1][6400/7330] lr: 1.000e-04, eta: 14:11:52, time: 0.604, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0426, loss_rpn_bbox: 0.0664, loss_cls: 0.2779, acc: 91.0723, loss_bbox: 0.3114, loss_mask: 0.3122, loss: 1.0105 +2024-05-31 01:21:14,799 - mmdet - INFO - Epoch [1][6450/7330] lr: 1.000e-04, eta: 14:11:14, time: 0.616, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0451, loss_rpn_bbox: 0.0693, loss_cls: 0.2840, acc: 90.6758, loss_bbox: 0.3237, loss_mask: 0.3161, loss: 1.0382 +2024-05-31 01:21:45,207 - mmdet - INFO - Epoch [1][6500/7330] lr: 1.000e-04, eta: 14:10:31, time: 0.608, data_time: 0.071, memory: 9655, loss_rpn_cls: 0.0417, loss_rpn_bbox: 0.0656, loss_cls: 0.2882, acc: 90.6584, loss_bbox: 0.3281, loss_mask: 0.3078, loss: 1.0314 +2024-05-31 01:22:15,690 - mmdet - INFO - Epoch [1][6550/7330] lr: 1.000e-04, eta: 14:09:49, time: 0.610, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0421, loss_rpn_bbox: 0.0640, loss_cls: 0.2792, acc: 90.9841, loss_bbox: 0.3142, loss_mask: 0.3112, loss: 1.0107 +2024-05-31 01:22:46,242 - mmdet - INFO - Epoch [1][6600/7330] lr: 1.000e-04, eta: 14:09:09, time: 0.611, data_time: 0.065, memory: 9655, loss_rpn_cls: 0.0422, loss_rpn_bbox: 0.0620, loss_cls: 0.2777, acc: 91.0796, loss_bbox: 0.3042, loss_mask: 0.3140, loss: 1.0001 +2024-05-31 01:23:18,466 - mmdet - INFO - Epoch [1][6650/7330] lr: 1.000e-04, eta: 14:08:49, time: 0.644, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0403, loss_rpn_bbox: 0.0654, loss_cls: 0.2759, acc: 91.0347, loss_bbox: 0.3083, loss_mask: 0.3103, loss: 1.0002 +2024-05-31 01:24:01,602 - mmdet - INFO - Epoch [1][6700/7330] lr: 1.000e-04, eta: 14:10:41, time: 0.863, data_time: 0.065, memory: 9655, loss_rpn_cls: 0.0411, loss_rpn_bbox: 0.0653, loss_cls: 0.2836, acc: 90.9031, loss_bbox: 0.3177, loss_mask: 0.3140, loss: 1.0217 +2024-05-31 01:24:31,168 - mmdet - INFO - Epoch [1][6750/7330] lr: 1.000e-04, eta: 14:09:47, time: 0.591, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0380, loss_rpn_bbox: 0.0609, loss_cls: 0.2630, acc: 91.5798, loss_bbox: 0.2965, loss_mask: 0.3006, loss: 0.9590 +2024-05-31 01:25:01,368 - mmdet - INFO - Epoch [1][6800/7330] lr: 1.000e-04, eta: 14:09:01, time: 0.604, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0402, loss_rpn_bbox: 0.0641, loss_cls: 0.2709, acc: 91.1653, loss_bbox: 0.3096, loss_mask: 0.3060, loss: 0.9909 +2024-05-31 01:25:31,698 - mmdet - INFO - Epoch [1][6850/7330] lr: 1.000e-04, eta: 14:08:18, time: 0.607, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0397, loss_rpn_bbox: 0.0639, loss_cls: 0.2782, acc: 90.8469, loss_bbox: 0.3157, loss_mask: 0.3102, loss: 1.0076 +2024-05-31 01:26:01,808 - mmdet - INFO - Epoch [1][6900/7330] lr: 1.000e-04, eta: 14:07:31, time: 0.602, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0414, loss_rpn_bbox: 0.0639, loss_cls: 0.2757, acc: 91.0999, loss_bbox: 0.3123, loss_mask: 0.3110, loss: 1.0043 +2024-05-31 01:26:32,181 - mmdet - INFO - Epoch [1][6950/7330] lr: 1.000e-04, eta: 14:06:48, time: 0.607, data_time: 0.067, memory: 9655, loss_rpn_cls: 0.0415, loss_rpn_bbox: 0.0646, loss_cls: 0.2742, acc: 91.1855, loss_bbox: 0.3035, loss_mask: 0.3099, loss: 0.9936 +2024-05-31 01:27:03,046 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 01:27:03,046 - mmdet - INFO - Epoch [1][7000/7330] lr: 1.000e-04, eta: 14:06:11, time: 0.617, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0408, loss_rpn_bbox: 0.0674, loss_cls: 0.3014, acc: 90.2073, loss_bbox: 0.3310, loss_mask: 0.3162, loss: 1.0568 +2024-05-31 01:27:33,204 - mmdet - INFO - Epoch [1][7050/7330] lr: 1.000e-04, eta: 14:05:26, time: 0.603, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0378, loss_rpn_bbox: 0.0595, loss_cls: 0.2696, acc: 91.3469, loss_bbox: 0.2985, loss_mask: 0.3004, loss: 0.9658 +2024-05-31 01:28:03,223 - mmdet - INFO - Epoch [1][7100/7330] lr: 1.000e-04, eta: 14:04:40, time: 0.600, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0390, loss_rpn_bbox: 0.0589, loss_cls: 0.2651, acc: 91.4365, loss_bbox: 0.2997, loss_mask: 0.3030, loss: 0.9657 +2024-05-31 01:28:33,393 - mmdet - INFO - Epoch [1][7150/7330] lr: 1.000e-04, eta: 14:03:55, time: 0.603, data_time: 0.064, memory: 9655, loss_rpn_cls: 0.0396, loss_rpn_bbox: 0.0619, loss_cls: 0.2770, acc: 91.2634, loss_bbox: 0.3001, loss_mask: 0.3046, loss: 0.9833 +2024-05-31 01:29:04,006 - mmdet - INFO - Epoch [1][7200/7330] lr: 1.000e-04, eta: 14:03:16, time: 0.612, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0449, loss_rpn_bbox: 0.0659, loss_cls: 0.2723, acc: 91.0112, loss_bbox: 0.3127, loss_mask: 0.3066, loss: 1.0024 +2024-05-31 01:29:34,575 - mmdet - INFO - Epoch [1][7250/7330] lr: 1.000e-04, eta: 14:02:36, time: 0.611, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0385, loss_rpn_bbox: 0.0647, loss_cls: 0.2791, acc: 91.0090, loss_bbox: 0.3083, loss_mask: 0.3098, loss: 1.0003 +2024-05-31 01:30:04,862 - mmdet - INFO - Epoch [1][7300/7330] lr: 1.000e-04, eta: 14:01:53, time: 0.606, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0381, loss_rpn_bbox: 0.0599, loss_cls: 0.2731, acc: 91.0840, loss_bbox: 0.3062, loss_mask: 0.3096, loss: 0.9868 +2024-05-31 01:30:24,039 - mmdet - INFO - Saving checkpoint at 1 epochs +2024-05-31 01:32:09,100 - mmdet - INFO - Evaluating bbox... +2024-05-31 01:32:39,785 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.274 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.507 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.270 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.146 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.309 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.394 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.419 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.419 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.419 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.234 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.463 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.574 + +2024-05-31 01:32:39,785 - mmdet - INFO - Evaluating segm... +2024-05-31 01:33:15,527 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.271 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.474 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.276 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.099 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.290 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.454 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.402 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.402 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.402 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.198 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.449 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.585 + +2024-05-31 01:33:16,117 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 01:33:16,118 - mmdet - INFO - Epoch(val) [1][625] bbox_mAP: 0.2740, bbox_mAP_50: 0.5070, bbox_mAP_75: 0.2700, bbox_mAP_s: 0.1460, bbox_mAP_m: 0.3090, bbox_mAP_l: 0.3940, bbox_mAP_copypaste: 0.274 0.507 0.270 0.146 0.309 0.394, segm_mAP: 0.2710, segm_mAP_50: 0.4740, segm_mAP_75: 0.2760, segm_mAP_s: 0.0990, segm_mAP_m: 0.2900, segm_mAP_l: 0.4540, segm_mAP_copypaste: 0.271 0.474 0.276 0.099 0.290 0.454 +2024-05-31 01:33:55,792 - mmdet - INFO - Epoch [2][50/7330] lr: 1.000e-04, eta: 13:59:09, time: 0.793, data_time: 0.119, memory: 9655, loss_rpn_cls: 0.0374, loss_rpn_bbox: 0.0609, loss_cls: 0.2644, acc: 91.3438, loss_bbox: 0.2977, loss_mask: 0.2969, loss: 0.9573 +2024-05-31 01:34:28,651 - mmdet - INFO - Epoch [2][100/7330] lr: 1.000e-04, eta: 13:58:55, time: 0.657, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0414, loss_rpn_bbox: 0.0660, loss_cls: 0.2774, acc: 90.7686, loss_bbox: 0.3202, loss_mask: 0.3011, loss: 1.0061 +2024-05-31 01:34:58,739 - mmdet - INFO - Epoch [2][150/7330] lr: 1.000e-04, eta: 13:58:12, time: 0.602, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0359, loss_rpn_bbox: 0.0606, loss_cls: 0.2650, acc: 91.3516, loss_bbox: 0.3008, loss_mask: 0.2973, loss: 0.9596 +2024-05-31 01:35:28,706 - mmdet - INFO - Epoch [2][200/7330] lr: 1.000e-04, eta: 13:57:27, time: 0.599, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0376, loss_rpn_bbox: 0.0593, loss_cls: 0.2665, acc: 91.2300, loss_bbox: 0.3044, loss_mask: 0.2993, loss: 0.9670 +2024-05-31 01:35:59,006 - mmdet - INFO - Epoch [2][250/7330] lr: 1.000e-04, eta: 13:56:45, time: 0.606, data_time: 0.036, memory: 9655, loss_rpn_cls: 0.0363, loss_rpn_bbox: 0.0586, loss_cls: 0.2551, acc: 91.5767, loss_bbox: 0.2935, loss_mask: 0.2905, loss: 0.9339 +2024-05-31 01:36:28,887 - mmdet - INFO - Epoch [2][300/7330] lr: 1.000e-04, eta: 13:56:00, time: 0.598, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0342, loss_rpn_bbox: 0.0603, loss_cls: 0.2654, acc: 91.3198, loss_bbox: 0.3077, loss_mask: 0.3042, loss: 0.9717 +2024-05-31 01:36:59,150 - mmdet - INFO - Epoch [2][350/7330] lr: 1.000e-04, eta: 13:55:19, time: 0.605, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0382, loss_rpn_bbox: 0.0613, loss_cls: 0.2617, acc: 91.2493, loss_bbox: 0.3064, loss_mask: 0.2973, loss: 0.9649 +2024-05-31 01:37:29,686 - mmdet - INFO - Epoch [2][400/7330] lr: 1.000e-04, eta: 13:54:41, time: 0.611, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0405, loss_rpn_bbox: 0.0633, loss_cls: 0.2627, acc: 91.3918, loss_bbox: 0.2999, loss_mask: 0.3031, loss: 0.9695 +2024-05-31 01:38:00,325 - mmdet - INFO - Epoch [2][450/7330] lr: 1.000e-04, eta: 13:54:03, time: 0.613, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0345, loss_rpn_bbox: 0.0616, loss_cls: 0.2653, acc: 91.2456, loss_bbox: 0.3041, loss_mask: 0.2952, loss: 0.9606 +2024-05-31 01:38:31,469 - mmdet - INFO - Epoch [2][500/7330] lr: 1.000e-04, eta: 13:53:32, time: 0.623, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0388, loss_rpn_bbox: 0.0649, loss_cls: 0.2741, acc: 90.8240, loss_bbox: 0.3182, loss_mask: 0.3030, loss: 0.9990 +2024-05-31 01:39:02,112 - mmdet - INFO - Epoch [2][550/7330] lr: 1.000e-04, eta: 13:52:55, time: 0.613, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0337, loss_rpn_bbox: 0.0547, loss_cls: 0.2736, acc: 91.0056, loss_bbox: 0.3049, loss_mask: 0.2875, loss: 0.9544 +2024-05-31 01:39:32,648 - mmdet - INFO - Epoch [2][600/7330] lr: 1.000e-04, eta: 13:52:17, time: 0.611, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0415, loss_rpn_bbox: 0.0665, loss_cls: 0.2742, acc: 90.9709, loss_bbox: 0.3152, loss_mask: 0.3017, loss: 0.9991 +2024-05-31 01:40:03,299 - mmdet - INFO - Epoch [2][650/7330] lr: 1.000e-04, eta: 13:51:40, time: 0.613, data_time: 0.071, memory: 9655, loss_rpn_cls: 0.0402, loss_rpn_bbox: 0.0635, loss_cls: 0.2620, acc: 91.1528, loss_bbox: 0.3082, loss_mask: 0.2987, loss: 0.9726 +2024-05-31 01:40:33,310 - mmdet - INFO - Epoch [2][700/7330] lr: 1.000e-04, eta: 13:50:57, time: 0.600, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0392, loss_rpn_bbox: 0.0621, loss_cls: 0.2732, acc: 90.9392, loss_bbox: 0.3119, loss_mask: 0.3000, loss: 0.9863 +2024-05-31 01:41:04,114 - mmdet - INFO - Epoch [2][750/7330] lr: 1.000e-04, eta: 13:50:22, time: 0.616, data_time: 0.085, memory: 9655, loss_rpn_cls: 0.0365, loss_rpn_bbox: 0.0608, loss_cls: 0.2648, acc: 91.2539, loss_bbox: 0.3046, loss_mask: 0.3020, loss: 0.9687 +2024-05-31 01:41:34,766 - mmdet - INFO - Epoch [2][800/7330] lr: 1.000e-04, eta: 13:49:46, time: 0.613, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0351, loss_rpn_bbox: 0.0633, loss_cls: 0.2651, acc: 91.1450, loss_bbox: 0.3046, loss_mask: 0.2978, loss: 0.9659 +2024-05-31 01:42:06,929 - mmdet - INFO - Epoch [2][850/7330] lr: 1.000e-04, eta: 13:49:24, time: 0.643, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0374, loss_rpn_bbox: 0.0666, loss_cls: 0.2667, acc: 91.1084, loss_bbox: 0.3103, loss_mask: 0.2963, loss: 0.9773 +2024-05-31 01:42:37,140 - mmdet - INFO - Epoch [2][900/7330] lr: 1.000e-04, eta: 13:48:43, time: 0.604, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0365, loss_rpn_bbox: 0.0595, loss_cls: 0.2558, acc: 91.5020, loss_bbox: 0.2983, loss_mask: 0.2958, loss: 0.9459 +2024-05-31 01:43:07,552 - mmdet - INFO - Epoch [2][950/7330] lr: 1.000e-04, eta: 13:48:05, time: 0.608, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0371, loss_rpn_bbox: 0.0602, loss_cls: 0.2569, acc: 91.5632, loss_bbox: 0.2968, loss_mask: 0.2993, loss: 0.9504 +2024-05-31 01:43:51,095 - mmdet - INFO - Epoch [2][1000/7330] lr: 1.000e-04, eta: 13:49:32, time: 0.871, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0366, loss_rpn_bbox: 0.0593, loss_cls: 0.2570, acc: 91.4441, loss_bbox: 0.2970, loss_mask: 0.2970, loss: 0.9470 +2024-05-31 01:44:24,388 - mmdet - INFO - Epoch [2][1050/7330] lr: 1.000e-04, eta: 13:49:20, time: 0.666, data_time: 0.073, memory: 9655, loss_rpn_cls: 0.0374, loss_rpn_bbox: 0.0583, loss_cls: 0.2568, acc: 91.4873, loss_bbox: 0.2991, loss_mask: 0.2928, loss: 0.9444 +2024-05-31 01:44:54,470 - mmdet - INFO - Epoch [2][1100/7330] lr: 1.000e-04, eta: 13:48:37, time: 0.602, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0346, loss_rpn_bbox: 0.0592, loss_cls: 0.2513, acc: 91.4846, loss_bbox: 0.2934, loss_mask: 0.2894, loss: 0.9278 +2024-05-31 01:45:24,464 - mmdet - INFO - Epoch [2][1150/7330] lr: 1.000e-04, eta: 13:47:54, time: 0.600, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0379, loss_rpn_bbox: 0.0627, loss_cls: 0.2611, acc: 91.2703, loss_bbox: 0.3038, loss_mask: 0.3009, loss: 0.9665 +2024-05-31 01:45:54,153 - mmdet - INFO - Epoch [2][1200/7330] lr: 1.000e-04, eta: 13:47:09, time: 0.594, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0357, loss_rpn_bbox: 0.0588, loss_cls: 0.2680, acc: 91.1162, loss_bbox: 0.3064, loss_mask: 0.2984, loss: 0.9672 +2024-05-31 01:46:24,783 - mmdet - INFO - Epoch [2][1250/7330] lr: 1.000e-04, eta: 13:46:32, time: 0.613, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0375, loss_rpn_bbox: 0.0635, loss_cls: 0.2602, acc: 91.2881, loss_bbox: 0.2962, loss_mask: 0.2947, loss: 0.9521 +2024-05-31 01:46:54,643 - mmdet - INFO - Epoch [2][1300/7330] lr: 1.000e-04, eta: 13:45:48, time: 0.597, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0399, loss_rpn_bbox: 0.0597, loss_cls: 0.2650, acc: 91.2664, loss_bbox: 0.2991, loss_mask: 0.3007, loss: 0.9644 +2024-05-31 01:47:24,176 - mmdet - INFO - Epoch [2][1350/7330] lr: 1.000e-04, eta: 13:45:01, time: 0.591, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0345, loss_rpn_bbox: 0.0556, loss_cls: 0.2539, acc: 91.7732, loss_bbox: 0.2873, loss_mask: 0.2876, loss: 0.9189 +2024-05-31 01:47:54,228 - mmdet - INFO - Epoch [2][1400/7330] lr: 1.000e-04, eta: 13:44:19, time: 0.601, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0377, loss_rpn_bbox: 0.0597, loss_cls: 0.2632, acc: 91.3279, loss_bbox: 0.3007, loss_mask: 0.2962, loss: 0.9576 +2024-05-31 01:48:24,459 - mmdet - INFO - Epoch [2][1450/7330] lr: 1.000e-04, eta: 13:43:39, time: 0.605, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0364, loss_rpn_bbox: 0.0587, loss_cls: 0.2528, acc: 91.7390, loss_bbox: 0.2834, loss_mask: 0.2929, loss: 0.9241 +2024-05-31 01:48:55,050 - mmdet - INFO - Epoch [2][1500/7330] lr: 1.000e-04, eta: 13:43:02, time: 0.612, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0395, loss_rpn_bbox: 0.0614, loss_cls: 0.2524, acc: 91.6692, loss_bbox: 0.2931, loss_mask: 0.2943, loss: 0.9408 +2024-05-31 01:49:25,346 - mmdet - INFO - Epoch [2][1550/7330] lr: 1.000e-04, eta: 13:42:23, time: 0.606, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0348, loss_rpn_bbox: 0.0588, loss_cls: 0.2552, acc: 91.5430, loss_bbox: 0.2890, loss_mask: 0.2947, loss: 0.9325 +2024-05-31 01:49:55,796 - mmdet - INFO - Epoch [2][1600/7330] lr: 1.000e-04, eta: 13:41:45, time: 0.609, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0343, loss_rpn_bbox: 0.0589, loss_cls: 0.2551, acc: 91.4573, loss_bbox: 0.2962, loss_mask: 0.2947, loss: 0.9391 +2024-05-31 01:50:26,363 - mmdet - INFO - Epoch [2][1650/7330] lr: 1.000e-04, eta: 13:41:09, time: 0.611, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0388, loss_rpn_bbox: 0.0608, loss_cls: 0.2611, acc: 91.2068, loss_bbox: 0.3013, loss_mask: 0.2945, loss: 0.9565 +2024-05-31 01:50:55,918 - mmdet - INFO - Epoch [2][1700/7330] lr: 1.000e-04, eta: 13:40:23, time: 0.591, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0330, loss_rpn_bbox: 0.0565, loss_cls: 0.2498, acc: 91.7749, loss_bbox: 0.2929, loss_mask: 0.2926, loss: 0.9247 +2024-05-31 01:51:26,065 - mmdet - INFO - Epoch [2][1750/7330] lr: 1.000e-04, eta: 13:39:43, time: 0.603, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0339, loss_rpn_bbox: 0.0601, loss_cls: 0.2669, acc: 91.3015, loss_bbox: 0.2987, loss_mask: 0.2956, loss: 0.9553 +2024-05-31 01:51:55,862 - mmdet - INFO - Epoch [2][1800/7330] lr: 1.000e-04, eta: 13:39:00, time: 0.596, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0340, loss_rpn_bbox: 0.0573, loss_cls: 0.2517, acc: 91.5066, loss_bbox: 0.2987, loss_mask: 0.2912, loss: 0.9329 +2024-05-31 01:52:25,915 - mmdet - INFO - Epoch [2][1850/7330] lr: 1.000e-04, eta: 13:38:19, time: 0.601, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0370, loss_rpn_bbox: 0.0609, loss_cls: 0.2575, acc: 91.3218, loss_bbox: 0.2987, loss_mask: 0.2930, loss: 0.9471 +2024-05-31 01:52:56,235 - mmdet - INFO - Epoch [2][1900/7330] lr: 1.000e-04, eta: 13:37:41, time: 0.606, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0344, loss_rpn_bbox: 0.0598, loss_cls: 0.2524, acc: 91.6704, loss_bbox: 0.2889, loss_mask: 0.2941, loss: 0.9297 +2024-05-31 01:53:47,010 - mmdet - INFO - Epoch [2][1950/7330] lr: 1.000e-04, eta: 13:39:56, time: 1.015, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0328, loss_rpn_bbox: 0.0605, loss_cls: 0.2493, acc: 91.7668, loss_bbox: 0.2852, loss_mask: 0.2823, loss: 0.9103 +2024-05-31 01:54:17,249 - mmdet - INFO - Epoch [2][2000/7330] lr: 1.000e-04, eta: 13:39:16, time: 0.605, data_time: 0.066, memory: 9655, loss_rpn_cls: 0.0374, loss_rpn_bbox: 0.0612, loss_cls: 0.2617, acc: 91.2532, loss_bbox: 0.2983, loss_mask: 0.2930, loss: 0.9516 +2024-05-31 01:54:47,452 - mmdet - INFO - Epoch [2][2050/7330] lr: 1.000e-04, eta: 13:38:36, time: 0.604, data_time: 0.066, memory: 9655, loss_rpn_cls: 0.0373, loss_rpn_bbox: 0.0634, loss_cls: 0.2583, acc: 91.4011, loss_bbox: 0.2965, loss_mask: 0.2946, loss: 0.9501 +2024-05-31 01:55:17,433 - mmdet - INFO - Epoch [2][2100/7330] lr: 1.000e-04, eta: 13:37:54, time: 0.600, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0350, loss_rpn_bbox: 0.0607, loss_cls: 0.2670, acc: 91.1470, loss_bbox: 0.3038, loss_mask: 0.3036, loss: 0.9702 +2024-05-31 01:55:47,181 - mmdet - INFO - Epoch [2][2150/7330] lr: 1.000e-04, eta: 13:37:10, time: 0.595, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0354, loss_rpn_bbox: 0.0612, loss_cls: 0.2513, acc: 91.6599, loss_bbox: 0.2870, loss_mask: 0.2925, loss: 0.9274 +2024-05-31 01:56:17,385 - mmdet - INFO - Epoch [2][2200/7330] lr: 1.000e-04, eta: 13:36:31, time: 0.604, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0373, loss_rpn_bbox: 0.0622, loss_cls: 0.2598, acc: 91.2881, loss_bbox: 0.3004, loss_mask: 0.3009, loss: 0.9607 +2024-05-31 01:56:47,643 - mmdet - INFO - Epoch [2][2250/7330] lr: 1.000e-04, eta: 13:35:51, time: 0.605, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0387, loss_rpn_bbox: 0.0626, loss_cls: 0.2638, acc: 91.2605, loss_bbox: 0.3028, loss_mask: 0.2965, loss: 0.9645 +2024-05-31 01:57:18,506 - mmdet - INFO - Epoch [2][2300/7330] lr: 1.000e-04, eta: 13:35:17, time: 0.617, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0346, loss_rpn_bbox: 0.0600, loss_cls: 0.2564, acc: 91.4297, loss_bbox: 0.2972, loss_mask: 0.2920, loss: 0.9403 +2024-05-31 01:57:48,784 - mmdet - INFO - Epoch [2][2350/7330] lr: 1.000e-04, eta: 13:34:38, time: 0.606, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0340, loss_rpn_bbox: 0.0618, loss_cls: 0.2638, acc: 91.1782, loss_bbox: 0.3057, loss_mask: 0.2964, loss: 0.9617 +2024-05-31 01:58:18,771 - mmdet - INFO - Epoch [2][2400/7330] lr: 1.000e-04, eta: 13:33:57, time: 0.600, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0339, loss_rpn_bbox: 0.0600, loss_cls: 0.2599, acc: 91.4146, loss_bbox: 0.2974, loss_mask: 0.2888, loss: 0.9401 +2024-05-31 01:58:48,675 - mmdet - INFO - Epoch [2][2450/7330] lr: 1.000e-04, eta: 13:33:15, time: 0.598, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0356, loss_rpn_bbox: 0.0564, loss_cls: 0.2532, acc: 91.4971, loss_bbox: 0.2934, loss_mask: 0.2935, loss: 0.9320 +2024-05-31 01:59:18,068 - mmdet - INFO - Epoch [2][2500/7330] lr: 1.000e-04, eta: 13:32:30, time: 0.588, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0319, loss_rpn_bbox: 0.0601, loss_cls: 0.2596, acc: 91.4841, loss_bbox: 0.2920, loss_mask: 0.2852, loss: 0.9288 +2024-05-31 01:59:47,661 - mmdet - INFO - Epoch [2][2550/7330] lr: 1.000e-04, eta: 13:31:46, time: 0.592, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0346, loss_rpn_bbox: 0.0582, loss_cls: 0.2505, acc: 91.7639, loss_bbox: 0.2864, loss_mask: 0.2896, loss: 0.9194 +2024-05-31 02:00:17,892 - mmdet - INFO - Epoch [2][2600/7330] lr: 1.000e-04, eta: 13:31:07, time: 0.605, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0350, loss_rpn_bbox: 0.0596, loss_cls: 0.2625, acc: 91.3362, loss_bbox: 0.2966, loss_mask: 0.2895, loss: 0.9432 +2024-05-31 02:00:48,211 - mmdet - INFO - Epoch [2][2650/7330] lr: 1.000e-04, eta: 13:30:29, time: 0.606, data_time: 0.064, memory: 9655, loss_rpn_cls: 0.0356, loss_rpn_bbox: 0.0627, loss_cls: 0.2592, acc: 91.3618, loss_bbox: 0.2965, loss_mask: 0.2937, loss: 0.9477 +2024-05-31 02:01:18,332 - mmdet - INFO - Epoch [2][2700/7330] lr: 1.000e-04, eta: 13:29:50, time: 0.602, data_time: 0.065, memory: 9655, loss_rpn_cls: 0.0341, loss_rpn_bbox: 0.0562, loss_cls: 0.2562, acc: 91.6580, loss_bbox: 0.2829, loss_mask: 0.2810, loss: 0.9104 +2024-05-31 02:01:48,250 - mmdet - INFO - Epoch [2][2750/7330] lr: 1.000e-04, eta: 13:29:09, time: 0.598, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0335, loss_rpn_bbox: 0.0572, loss_cls: 0.2470, acc: 91.6980, loss_bbox: 0.2842, loss_mask: 0.2803, loss: 0.9022 +2024-05-31 02:02:18,243 - mmdet - INFO - Epoch [2][2800/7330] lr: 1.000e-04, eta: 13:28:29, time: 0.600, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0318, loss_rpn_bbox: 0.0577, loss_cls: 0.2497, acc: 91.7676, loss_bbox: 0.2850, loss_mask: 0.2892, loss: 0.9134 +2024-05-31 02:03:05,567 - mmdet - INFO - Epoch [2][2850/7330] lr: 1.000e-04, eta: 13:30:01, time: 0.947, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0363, loss_rpn_bbox: 0.0594, loss_cls: 0.2541, acc: 91.5588, loss_bbox: 0.2914, loss_mask: 0.2868, loss: 0.9280 +2024-05-31 02:03:38,404 - mmdet - INFO - Epoch [2][2900/7330] lr: 1.000e-04, eta: 13:29:42, time: 0.657, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0371, loss_rpn_bbox: 0.0631, loss_cls: 0.2666, acc: 91.2236, loss_bbox: 0.3029, loss_mask: 0.3023, loss: 0.9719 +2024-05-31 02:04:08,530 - mmdet - INFO - Epoch [2][2950/7330] lr: 1.000e-04, eta: 13:29:02, time: 0.603, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0358, loss_rpn_bbox: 0.0588, loss_cls: 0.2589, acc: 91.4761, loss_bbox: 0.2913, loss_mask: 0.2907, loss: 0.9355 +2024-05-31 02:04:39,242 - mmdet - INFO - Epoch [2][3000/7330] lr: 1.000e-04, eta: 13:28:27, time: 0.614, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0357, loss_rpn_bbox: 0.0622, loss_cls: 0.2540, acc: 91.5789, loss_bbox: 0.2862, loss_mask: 0.2905, loss: 0.9287 +2024-05-31 02:05:09,844 - mmdet - INFO - Epoch [2][3050/7330] lr: 1.000e-04, eta: 13:27:51, time: 0.612, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0360, loss_rpn_bbox: 0.0640, loss_cls: 0.2668, acc: 90.9749, loss_bbox: 0.3111, loss_mask: 0.2978, loss: 0.9757 +2024-05-31 02:05:40,509 - mmdet - INFO - Epoch [2][3100/7330] lr: 1.000e-04, eta: 13:27:15, time: 0.613, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0367, loss_rpn_bbox: 0.0609, loss_cls: 0.2658, acc: 90.9529, loss_bbox: 0.3079, loss_mask: 0.2896, loss: 0.9609 +2024-05-31 02:06:11,028 - mmdet - INFO - Epoch [2][3150/7330] lr: 1.000e-04, eta: 13:26:39, time: 0.610, data_time: 0.063, memory: 9655, loss_rpn_cls: 0.0355, loss_rpn_bbox: 0.0600, loss_cls: 0.2505, acc: 91.6245, loss_bbox: 0.2914, loss_mask: 0.2949, loss: 0.9323 +2024-05-31 02:06:41,263 - mmdet - INFO - Epoch [2][3200/7330] lr: 1.000e-04, eta: 13:26:00, time: 0.605, data_time: 0.067, memory: 9655, loss_rpn_cls: 0.0319, loss_rpn_bbox: 0.0572, loss_cls: 0.2480, acc: 91.7195, loss_bbox: 0.2890, loss_mask: 0.2890, loss: 0.9151 +2024-05-31 02:07:11,607 - mmdet - INFO - Epoch [2][3250/7330] lr: 1.000e-04, eta: 13:25:23, time: 0.607, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0337, loss_rpn_bbox: 0.0579, loss_cls: 0.2644, acc: 91.2441, loss_bbox: 0.3010, loss_mask: 0.2887, loss: 0.9457 +2024-05-31 02:07:42,788 - mmdet - INFO - Epoch [2][3300/7330] lr: 1.000e-04, eta: 13:24:51, time: 0.624, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0352, loss_rpn_bbox: 0.0646, loss_cls: 0.2631, acc: 91.0833, loss_bbox: 0.3134, loss_mask: 0.2932, loss: 0.9695 +2024-05-31 02:08:13,039 - mmdet - INFO - Epoch [2][3350/7330] lr: 1.000e-04, eta: 13:24:13, time: 0.605, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0351, loss_rpn_bbox: 0.0589, loss_cls: 0.2520, acc: 91.5127, loss_bbox: 0.2918, loss_mask: 0.2918, loss: 0.9296 +2024-05-31 02:08:43,442 - mmdet - INFO - Epoch [2][3400/7330] lr: 1.000e-04, eta: 13:23:36, time: 0.608, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0337, loss_rpn_bbox: 0.0579, loss_cls: 0.2475, acc: 91.6826, loss_bbox: 0.2870, loss_mask: 0.2835, loss: 0.9095 +2024-05-31 02:09:13,638 - mmdet - INFO - Epoch [2][3450/7330] lr: 1.000e-04, eta: 13:22:57, time: 0.604, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0335, loss_rpn_bbox: 0.0551, loss_cls: 0.2358, acc: 92.1963, loss_bbox: 0.2710, loss_mask: 0.2807, loss: 0.8761 +2024-05-31 02:09:44,041 - mmdet - INFO - Epoch [2][3500/7330] lr: 1.000e-04, eta: 13:22:20, time: 0.608, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0363, loss_rpn_bbox: 0.0594, loss_cls: 0.2554, acc: 91.4658, loss_bbox: 0.2933, loss_mask: 0.2939, loss: 0.9384 +2024-05-31 02:10:14,027 - mmdet - INFO - Epoch [2][3550/7330] lr: 1.000e-04, eta: 13:21:40, time: 0.599, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0354, loss_rpn_bbox: 0.0629, loss_cls: 0.2607, acc: 91.5034, loss_bbox: 0.2926, loss_mask: 0.2878, loss: 0.9394 +2024-05-31 02:10:43,834 - mmdet - INFO - Epoch [2][3600/7330] lr: 1.000e-04, eta: 13:20:59, time: 0.597, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0327, loss_rpn_bbox: 0.0548, loss_cls: 0.2441, acc: 91.8938, loss_bbox: 0.2797, loss_mask: 0.2818, loss: 0.8932 +2024-05-31 02:11:14,235 - mmdet - INFO - Epoch [2][3650/7330] lr: 1.000e-04, eta: 13:20:22, time: 0.608, data_time: 0.068, memory: 9655, loss_rpn_cls: 0.0338, loss_rpn_bbox: 0.0582, loss_cls: 0.2462, acc: 91.7671, loss_bbox: 0.2875, loss_mask: 0.2894, loss: 0.9151 +2024-05-31 02:11:44,673 - mmdet - INFO - Epoch [2][3700/7330] lr: 1.000e-04, eta: 13:19:46, time: 0.609, data_time: 0.067, memory: 9655, loss_rpn_cls: 0.0356, loss_rpn_bbox: 0.0617, loss_cls: 0.2603, acc: 91.1462, loss_bbox: 0.3072, loss_mask: 0.2907, loss: 0.9554 +2024-05-31 02:12:17,354 - mmdet - INFO - Epoch [2][3750/7330] lr: 1.000e-04, eta: 13:19:25, time: 0.654, data_time: 0.067, memory: 9655, loss_rpn_cls: 0.0355, loss_rpn_bbox: 0.0581, loss_cls: 0.2542, acc: 91.3418, loss_bbox: 0.2999, loss_mask: 0.2923, loss: 0.9400 +2024-05-31 02:13:03,942 - mmdet - INFO - Epoch [2][3800/7330] lr: 1.000e-04, eta: 13:20:40, time: 0.932, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0352, loss_rpn_bbox: 0.0608, loss_cls: 0.2626, acc: 91.1082, loss_bbox: 0.3032, loss_mask: 0.2938, loss: 0.9556 +2024-05-31 02:13:34,122 - mmdet - INFO - Epoch [2][3850/7330] lr: 1.000e-04, eta: 13:20:02, time: 0.604, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0336, loss_rpn_bbox: 0.0602, loss_cls: 0.2611, acc: 91.3228, loss_bbox: 0.2970, loss_mask: 0.2841, loss: 0.9360 +2024-05-31 02:14:04,044 - mmdet - INFO - Epoch [2][3900/7330] lr: 1.000e-04, eta: 13:19:21, time: 0.598, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0342, loss_rpn_bbox: 0.0558, loss_cls: 0.2516, acc: 91.6064, loss_bbox: 0.2909, loss_mask: 0.2825, loss: 0.9149 +2024-05-31 02:14:34,130 - mmdet - INFO - Epoch [2][3950/7330] lr: 1.000e-04, eta: 13:18:42, time: 0.602, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0327, loss_rpn_bbox: 0.0610, loss_cls: 0.2468, acc: 91.8044, loss_bbox: 0.2867, loss_mask: 0.2911, loss: 0.9183 +2024-05-31 02:15:04,539 - mmdet - INFO - Epoch [2][4000/7330] lr: 1.000e-04, eta: 13:18:05, time: 0.608, data_time: 0.065, memory: 9655, loss_rpn_cls: 0.0358, loss_rpn_bbox: 0.0602, loss_cls: 0.2470, acc: 91.5833, loss_bbox: 0.2923, loss_mask: 0.2864, loss: 0.9217 +2024-05-31 02:15:34,759 - mmdet - INFO - Epoch [2][4050/7330] lr: 1.000e-04, eta: 13:17:27, time: 0.604, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0330, loss_rpn_bbox: 0.0588, loss_cls: 0.2432, acc: 91.7871, loss_bbox: 0.2843, loss_mask: 0.2740, loss: 0.8934 +2024-05-31 02:16:04,365 - mmdet - INFO - Epoch [2][4100/7330] lr: 1.000e-04, eta: 13:16:45, time: 0.592, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0315, loss_rpn_bbox: 0.0546, loss_cls: 0.2537, acc: 91.7568, loss_bbox: 0.2848, loss_mask: 0.2799, loss: 0.9045 +2024-05-31 02:16:34,368 - mmdet - INFO - Epoch [2][4150/7330] lr: 1.000e-04, eta: 13:16:05, time: 0.600, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0317, loss_rpn_bbox: 0.0546, loss_cls: 0.2418, acc: 91.9626, loss_bbox: 0.2804, loss_mask: 0.2859, loss: 0.8944 +2024-05-31 02:17:04,027 - mmdet - INFO - Epoch [2][4200/7330] lr: 1.000e-04, eta: 13:15:24, time: 0.593, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0341, loss_rpn_bbox: 0.0582, loss_cls: 0.2501, acc: 91.6414, loss_bbox: 0.2836, loss_mask: 0.2853, loss: 0.9114 +2024-05-31 02:17:34,145 - mmdet - INFO - Epoch [2][4250/7330] lr: 1.000e-04, eta: 13:14:45, time: 0.602, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0331, loss_rpn_bbox: 0.0553, loss_cls: 0.2417, acc: 91.9348, loss_bbox: 0.2809, loss_mask: 0.2841, loss: 0.8951 +2024-05-31 02:18:04,121 - mmdet - INFO - Epoch [2][4300/7330] lr: 1.000e-04, eta: 13:14:06, time: 0.600, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0330, loss_rpn_bbox: 0.0580, loss_cls: 0.2459, acc: 91.8167, loss_bbox: 0.2828, loss_mask: 0.2827, loss: 0.9024 +2024-05-31 02:18:34,462 - mmdet - INFO - Epoch [2][4350/7330] lr: 1.000e-04, eta: 13:13:29, time: 0.606, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0336, loss_rpn_bbox: 0.0563, loss_cls: 0.2485, acc: 91.6248, loss_bbox: 0.2870, loss_mask: 0.2844, loss: 0.9097 +2024-05-31 02:19:05,062 - mmdet - INFO - Epoch [2][4400/7330] lr: 1.000e-04, eta: 13:12:54, time: 0.612, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0336, loss_rpn_bbox: 0.0603, loss_cls: 0.2478, acc: 91.6470, loss_bbox: 0.2863, loss_mask: 0.2830, loss: 0.9111 +2024-05-31 02:19:35,809 - mmdet - INFO - Epoch [2][4450/7330] lr: 1.000e-04, eta: 13:12:20, time: 0.615, data_time: 0.064, memory: 9655, loss_rpn_cls: 0.0376, loss_rpn_bbox: 0.0601, loss_cls: 0.2563, acc: 91.4424, loss_bbox: 0.2958, loss_mask: 0.2882, loss: 0.9380 +2024-05-31 02:20:05,919 - mmdet - INFO - Epoch [2][4500/7330] lr: 1.000e-04, eta: 13:11:41, time: 0.602, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0313, loss_rpn_bbox: 0.0556, loss_cls: 0.2437, acc: 91.8879, loss_bbox: 0.2799, loss_mask: 0.2800, loss: 0.8906 +2024-05-31 02:20:36,137 - mmdet - INFO - Epoch [2][4550/7330] lr: 1.000e-04, eta: 13:11:04, time: 0.604, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0311, loss_rpn_bbox: 0.0544, loss_cls: 0.2499, acc: 91.5398, loss_bbox: 0.2879, loss_mask: 0.2822, loss: 0.9055 +2024-05-31 02:21:06,232 - mmdet - INFO - Epoch [2][4600/7330] lr: 1.000e-04, eta: 13:10:26, time: 0.602, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0344, loss_rpn_bbox: 0.0588, loss_cls: 0.2545, acc: 91.3223, loss_bbox: 0.2922, loss_mask: 0.2894, loss: 0.9292 +2024-05-31 02:21:36,423 - mmdet - INFO - Epoch [2][4650/7330] lr: 1.000e-04, eta: 13:09:48, time: 0.604, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0341, loss_rpn_bbox: 0.0587, loss_cls: 0.2435, acc: 91.9065, loss_bbox: 0.2772, loss_mask: 0.2813, loss: 0.8948 +2024-05-31 02:22:22,187 - mmdet - INFO - Epoch [2][4700/7330] lr: 1.000e-04, eta: 13:10:49, time: 0.915, data_time: 0.068, memory: 9655, loss_rpn_cls: 0.0323, loss_rpn_bbox: 0.0595, loss_cls: 0.2510, acc: 91.5605, loss_bbox: 0.2951, loss_mask: 0.2921, loss: 0.9301 +2024-05-31 02:22:54,606 - mmdet - INFO - Epoch [2][4750/7330] lr: 1.000e-04, eta: 13:10:25, time: 0.648, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0317, loss_rpn_bbox: 0.0559, loss_cls: 0.2463, acc: 91.9968, loss_bbox: 0.2760, loss_mask: 0.2853, loss: 0.8951 +2024-05-31 02:23:25,006 - mmdet - INFO - Epoch [2][4800/7330] lr: 1.000e-04, eta: 13:09:49, time: 0.608, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0383, loss_rpn_bbox: 0.0607, loss_cls: 0.2511, acc: 91.5581, loss_bbox: 0.2896, loss_mask: 0.2900, loss: 0.9297 +2024-05-31 02:23:55,198 - mmdet - INFO - Epoch [2][4850/7330] lr: 1.000e-04, eta: 13:09:11, time: 0.604, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0328, loss_rpn_bbox: 0.0563, loss_cls: 0.2473, acc: 91.7908, loss_bbox: 0.2779, loss_mask: 0.2776, loss: 0.8919 +2024-05-31 02:24:25,241 - mmdet - INFO - Epoch [2][4900/7330] lr: 1.000e-04, eta: 13:08:32, time: 0.601, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0343, loss_rpn_bbox: 0.0613, loss_cls: 0.2518, acc: 91.6587, loss_bbox: 0.2880, loss_mask: 0.2894, loss: 0.9248 +2024-05-31 02:24:55,066 - mmdet - INFO - Epoch [2][4950/7330] lr: 1.000e-04, eta: 13:07:52, time: 0.596, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0329, loss_rpn_bbox: 0.0596, loss_cls: 0.2472, acc: 91.6023, loss_bbox: 0.2889, loss_mask: 0.2892, loss: 0.9178 +2024-05-31 02:25:24,774 - mmdet - INFO - Epoch [2][5000/7330] lr: 1.000e-04, eta: 13:07:12, time: 0.594, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0296, loss_rpn_bbox: 0.0557, loss_cls: 0.2367, acc: 91.9465, loss_bbox: 0.2771, loss_mask: 0.2856, loss: 0.8848 +2024-05-31 02:25:54,972 - mmdet - INFO - Epoch [2][5050/7330] lr: 1.000e-04, eta: 13:06:34, time: 0.604, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0326, loss_rpn_bbox: 0.0567, loss_cls: 0.2417, acc: 91.8267, loss_bbox: 0.2808, loss_mask: 0.2848, loss: 0.8966 +2024-05-31 02:26:25,234 - mmdet - INFO - Epoch [2][5100/7330] lr: 1.000e-04, eta: 13:05:57, time: 0.605, data_time: 0.064, memory: 9655, loss_rpn_cls: 0.0345, loss_rpn_bbox: 0.0608, loss_cls: 0.2522, acc: 91.5513, loss_bbox: 0.2903, loss_mask: 0.2902, loss: 0.9279 +2024-05-31 02:26:55,006 - mmdet - INFO - Epoch [2][5150/7330] lr: 1.000e-04, eta: 13:05:17, time: 0.595, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0326, loss_rpn_bbox: 0.0574, loss_cls: 0.2494, acc: 91.6438, loss_bbox: 0.2860, loss_mask: 0.2875, loss: 0.9128 +2024-05-31 02:27:25,092 - mmdet - INFO - Epoch [2][5200/7330] lr: 1.000e-04, eta: 13:04:39, time: 0.602, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0296, loss_rpn_bbox: 0.0560, loss_cls: 0.2402, acc: 91.9641, loss_bbox: 0.2774, loss_mask: 0.2811, loss: 0.8843 +2024-05-31 02:27:54,929 - mmdet - INFO - Epoch [2][5250/7330] lr: 1.000e-04, eta: 13:04:00, time: 0.597, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0300, loss_rpn_bbox: 0.0533, loss_cls: 0.2501, acc: 91.7676, loss_bbox: 0.2783, loss_mask: 0.2694, loss: 0.8811 +2024-05-31 02:28:25,289 - mmdet - INFO - Epoch [2][5300/7330] lr: 1.000e-04, eta: 13:03:23, time: 0.607, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0327, loss_rpn_bbox: 0.0566, loss_cls: 0.2550, acc: 91.5005, loss_bbox: 0.2882, loss_mask: 0.2870, loss: 0.9194 +2024-05-31 02:28:55,581 - mmdet - INFO - Epoch [2][5350/7330] lr: 1.000e-04, eta: 13:02:47, time: 0.606, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0320, loss_rpn_bbox: 0.0552, loss_cls: 0.2491, acc: 91.6169, loss_bbox: 0.2885, loss_mask: 0.2768, loss: 0.9017 +2024-05-31 02:29:25,787 - mmdet - INFO - Epoch [2][5400/7330] lr: 1.000e-04, eta: 13:02:10, time: 0.604, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0321, loss_rpn_bbox: 0.0552, loss_cls: 0.2460, acc: 91.6145, loss_bbox: 0.2831, loss_mask: 0.2823, loss: 0.8987 +2024-05-31 02:29:56,262 - mmdet - INFO - Epoch [2][5450/7330] lr: 1.000e-04, eta: 13:01:34, time: 0.610, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0332, loss_rpn_bbox: 0.0590, loss_cls: 0.2470, acc: 91.7715, loss_bbox: 0.2816, loss_mask: 0.2851, loss: 0.9059 +2024-05-31 02:30:25,785 - mmdet - INFO - Epoch [2][5500/7330] lr: 1.000e-04, eta: 13:00:53, time: 0.590, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0314, loss_rpn_bbox: 0.0564, loss_cls: 0.2462, acc: 91.6274, loss_bbox: 0.2883, loss_mask: 0.2788, loss: 0.9012 +2024-05-31 02:30:56,124 - mmdet - INFO - Epoch [2][5550/7330] lr: 1.000e-04, eta: 13:00:17, time: 0.606, data_time: 0.068, memory: 9655, loss_rpn_cls: 0.0313, loss_rpn_bbox: 0.0583, loss_cls: 0.2551, acc: 91.3022, loss_bbox: 0.3033, loss_mask: 0.2833, loss: 0.9312 +2024-05-31 02:31:33,610 - mmdet - INFO - Epoch [2][5600/7330] lr: 1.000e-04, eta: 13:00:23, time: 0.750, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0309, loss_rpn_bbox: 0.0555, loss_cls: 0.2487, acc: 91.5945, loss_bbox: 0.2845, loss_mask: 0.2778, loss: 0.8973 +2024-05-31 02:32:13,070 - mmdet - INFO - Epoch [2][5650/7330] lr: 1.000e-04, eta: 13:00:39, time: 0.789, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0319, loss_rpn_bbox: 0.0561, loss_cls: 0.2539, acc: 91.5493, loss_bbox: 0.2908, loss_mask: 0.2793, loss: 0.9120 +2024-05-31 02:32:43,256 - mmdet - INFO - Epoch [2][5700/7330] lr: 1.000e-04, eta: 13:00:02, time: 0.604, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0327, loss_rpn_bbox: 0.0565, loss_cls: 0.2505, acc: 91.4495, loss_bbox: 0.2907, loss_mask: 0.2798, loss: 0.9102 +2024-05-31 02:33:13,593 - mmdet - INFO - Epoch [2][5750/7330] lr: 1.000e-04, eta: 12:59:25, time: 0.606, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0312, loss_rpn_bbox: 0.0571, loss_cls: 0.2511, acc: 91.6338, loss_bbox: 0.2836, loss_mask: 0.2783, loss: 0.9012 +2024-05-31 02:33:43,415 - mmdet - INFO - Epoch [2][5800/7330] lr: 1.000e-04, eta: 12:58:46, time: 0.596, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0311, loss_rpn_bbox: 0.0542, loss_cls: 0.2466, acc: 91.7832, loss_bbox: 0.2812, loss_mask: 0.2712, loss: 0.8843 +2024-05-31 02:34:13,491 - mmdet - INFO - Epoch [2][5850/7330] lr: 1.000e-04, eta: 12:58:09, time: 0.602, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0310, loss_rpn_bbox: 0.0549, loss_cls: 0.2369, acc: 91.9658, loss_bbox: 0.2786, loss_mask: 0.2767, loss: 0.8781 +2024-05-31 02:34:43,844 - mmdet - INFO - Epoch [2][5900/7330] lr: 1.000e-04, eta: 12:57:32, time: 0.607, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0349, loss_rpn_bbox: 0.0607, loss_cls: 0.2518, acc: 91.5332, loss_bbox: 0.2901, loss_mask: 0.2871, loss: 0.9245 +2024-05-31 02:35:14,349 - mmdet - INFO - Epoch [2][5950/7330] lr: 1.000e-04, eta: 12:56:57, time: 0.610, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0330, loss_rpn_bbox: 0.0579, loss_cls: 0.2508, acc: 91.6694, loss_bbox: 0.2866, loss_mask: 0.2831, loss: 0.9114 +2024-05-31 02:35:44,086 - mmdet - INFO - Epoch [2][6000/7330] lr: 1.000e-04, eta: 12:56:18, time: 0.595, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0307, loss_rpn_bbox: 0.0554, loss_cls: 0.2400, acc: 91.8569, loss_bbox: 0.2818, loss_mask: 0.2776, loss: 0.8855 +2024-05-31 02:36:13,962 - mmdet - INFO - Epoch [2][6050/7330] lr: 1.000e-04, eta: 12:55:39, time: 0.598, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0302, loss_rpn_bbox: 0.0525, loss_cls: 0.2400, acc: 91.8926, loss_bbox: 0.2779, loss_mask: 0.2743, loss: 0.8749 +2024-05-31 02:36:43,203 - mmdet - INFO - Epoch [2][6100/7330] lr: 1.000e-04, eta: 12:54:57, time: 0.585, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0309, loss_rpn_bbox: 0.0545, loss_cls: 0.2412, acc: 91.9749, loss_bbox: 0.2727, loss_mask: 0.2732, loss: 0.8725 +2024-05-31 02:37:13,933 - mmdet - INFO - Epoch [2][6150/7330] lr: 1.000e-04, eta: 12:54:23, time: 0.615, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0321, loss_rpn_bbox: 0.0555, loss_cls: 0.2423, acc: 91.8521, loss_bbox: 0.2778, loss_mask: 0.2758, loss: 0.8835 +2024-05-31 02:37:44,332 - mmdet - INFO - Epoch [2][6200/7330] lr: 1.000e-04, eta: 12:53:48, time: 0.608, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0305, loss_rpn_bbox: 0.0563, loss_cls: 0.2433, acc: 91.6357, loss_bbox: 0.2865, loss_mask: 0.2836, loss: 0.9003 +2024-05-31 02:38:14,486 - mmdet - INFO - Epoch [2][6250/7330] lr: 1.000e-04, eta: 12:53:11, time: 0.603, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0326, loss_rpn_bbox: 0.0589, loss_cls: 0.2470, acc: 91.6921, loss_bbox: 0.2852, loss_mask: 0.2807, loss: 0.9043 +2024-05-31 02:38:44,281 - mmdet - INFO - Epoch [2][6300/7330] lr: 1.000e-04, eta: 12:52:32, time: 0.596, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0298, loss_rpn_bbox: 0.0555, loss_cls: 0.2390, acc: 91.8989, loss_bbox: 0.2808, loss_mask: 0.2803, loss: 0.8854 +2024-05-31 02:39:14,261 - mmdet - INFO - Epoch [2][6350/7330] lr: 1.000e-04, eta: 12:51:54, time: 0.599, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0302, loss_rpn_bbox: 0.0553, loss_cls: 0.2405, acc: 91.6770, loss_bbox: 0.2869, loss_mask: 0.2767, loss: 0.8897 +2024-05-31 02:39:44,125 - mmdet - INFO - Epoch [2][6400/7330] lr: 1.000e-04, eta: 12:51:16, time: 0.598, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0319, loss_rpn_bbox: 0.0561, loss_cls: 0.2585, acc: 91.2393, loss_bbox: 0.2978, loss_mask: 0.2875, loss: 0.9317 +2024-05-31 02:40:14,320 - mmdet - INFO - Epoch [2][6450/7330] lr: 1.000e-04, eta: 12:50:40, time: 0.604, data_time: 0.065, memory: 9655, loss_rpn_cls: 0.0314, loss_rpn_bbox: 0.0572, loss_cls: 0.2411, acc: 91.8740, loss_bbox: 0.2764, loss_mask: 0.2674, loss: 0.8735 +2024-05-31 02:40:45,116 - mmdet - INFO - Epoch [2][6500/7330] lr: 1.000e-04, eta: 12:50:07, time: 0.616, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0351, loss_rpn_bbox: 0.0592, loss_cls: 0.2500, acc: 91.5334, loss_bbox: 0.2939, loss_mask: 0.2830, loss: 0.9213 +2024-05-31 02:41:31,860 - mmdet - INFO - Epoch [2][6550/7330] lr: 1.000e-04, eta: 12:50:59, time: 0.935, data_time: 0.066, memory: 9655, loss_rpn_cls: 0.0348, loss_rpn_bbox: 0.0594, loss_cls: 0.2554, acc: 91.4253, loss_bbox: 0.2941, loss_mask: 0.2828, loss: 0.9265 +2024-05-31 02:42:02,666 - mmdet - INFO - Epoch [2][6600/7330] lr: 1.000e-04, eta: 12:50:25, time: 0.616, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0325, loss_rpn_bbox: 0.0592, loss_cls: 0.2507, acc: 91.4846, loss_bbox: 0.2906, loss_mask: 0.2892, loss: 0.9223 +2024-05-31 02:42:33,150 - mmdet - INFO - Epoch [2][6650/7330] lr: 1.000e-04, eta: 12:49:50, time: 0.610, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0325, loss_rpn_bbox: 0.0556, loss_cls: 0.2452, acc: 91.8127, loss_bbox: 0.2769, loss_mask: 0.2755, loss: 0.8856 +2024-05-31 02:43:03,719 - mmdet - INFO - Epoch [2][6700/7330] lr: 1.000e-04, eta: 12:49:15, time: 0.611, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0318, loss_rpn_bbox: 0.0572, loss_cls: 0.2454, acc: 91.6970, loss_bbox: 0.2843, loss_mask: 0.2840, loss: 0.9027 +2024-05-31 02:43:33,500 - mmdet - INFO - Epoch [2][6750/7330] lr: 1.000e-04, eta: 12:48:37, time: 0.596, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0298, loss_rpn_bbox: 0.0539, loss_cls: 0.2398, acc: 91.7881, loss_bbox: 0.2809, loss_mask: 0.2785, loss: 0.8829 +2024-05-31 02:44:03,653 - mmdet - INFO - Epoch [2][6800/7330] lr: 1.000e-04, eta: 12:48:00, time: 0.603, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0315, loss_rpn_bbox: 0.0561, loss_cls: 0.2415, acc: 91.9353, loss_bbox: 0.2759, loss_mask: 0.2737, loss: 0.8786 +2024-05-31 02:44:34,000 - mmdet - INFO - Epoch [2][6850/7330] lr: 1.000e-04, eta: 12:47:24, time: 0.607, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0337, loss_rpn_bbox: 0.0605, loss_cls: 0.2434, acc: 91.9536, loss_bbox: 0.2753, loss_mask: 0.2764, loss: 0.8893 +2024-05-31 02:45:04,022 - mmdet - INFO - Epoch [2][6900/7330] lr: 1.000e-04, eta: 12:46:47, time: 0.600, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0317, loss_rpn_bbox: 0.0596, loss_cls: 0.2570, acc: 91.3162, loss_bbox: 0.2960, loss_mask: 0.2828, loss: 0.9272 +2024-05-31 02:45:34,345 - mmdet - INFO - Epoch [2][6950/7330] lr: 1.000e-04, eta: 12:46:11, time: 0.606, data_time: 0.070, memory: 9655, loss_rpn_cls: 0.0339, loss_rpn_bbox: 0.0579, loss_cls: 0.2554, acc: 91.3882, loss_bbox: 0.2930, loss_mask: 0.2747, loss: 0.9150 +2024-05-31 02:46:04,080 - mmdet - INFO - Epoch [2][7000/7330] lr: 1.000e-04, eta: 12:45:32, time: 0.595, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0301, loss_rpn_bbox: 0.0556, loss_cls: 0.2487, acc: 91.6179, loss_bbox: 0.2810, loss_mask: 0.2804, loss: 0.8958 +2024-05-31 02:46:34,206 - mmdet - INFO - Epoch [2][7050/7330] lr: 1.000e-04, eta: 12:44:56, time: 0.602, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0300, loss_rpn_bbox: 0.0538, loss_cls: 0.2434, acc: 91.9104, loss_bbox: 0.2783, loss_mask: 0.2771, loss: 0.8825 +2024-05-31 02:47:04,387 - mmdet - INFO - Epoch [2][7100/7330] lr: 1.000e-04, eta: 12:44:19, time: 0.604, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0325, loss_rpn_bbox: 0.0560, loss_cls: 0.2431, acc: 91.7195, loss_bbox: 0.2844, loss_mask: 0.2766, loss: 0.8926 +2024-05-31 02:47:34,613 - mmdet - INFO - Epoch [2][7150/7330] lr: 1.000e-04, eta: 12:43:43, time: 0.605, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0310, loss_rpn_bbox: 0.0549, loss_cls: 0.2490, acc: 91.6936, loss_bbox: 0.2817, loss_mask: 0.2764, loss: 0.8930 +2024-05-31 02:48:04,476 - mmdet - INFO - Epoch [2][7200/7330] lr: 1.000e-04, eta: 12:43:06, time: 0.597, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0296, loss_rpn_bbox: 0.0517, loss_cls: 0.2326, acc: 92.2517, loss_bbox: 0.2639, loss_mask: 0.2739, loss: 0.8517 +2024-05-31 02:48:35,226 - mmdet - INFO - Epoch [2][7250/7330] lr: 1.000e-04, eta: 12:42:32, time: 0.615, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0318, loss_rpn_bbox: 0.0575, loss_cls: 0.2578, acc: 91.4907, loss_bbox: 0.2904, loss_mask: 0.2869, loss: 0.9244 +2024-05-31 02:49:04,956 - mmdet - INFO - Epoch [2][7300/7330] lr: 1.000e-04, eta: 12:41:54, time: 0.595, data_time: 0.069, memory: 9655, loss_rpn_cls: 0.0312, loss_rpn_bbox: 0.0542, loss_cls: 0.2473, acc: 91.7493, loss_bbox: 0.2799, loss_mask: 0.2779, loss: 0.8906 +2024-05-31 02:49:23,573 - mmdet - INFO - Saving checkpoint at 2 epochs +2024-05-31 02:51:05,945 - mmdet - INFO - Evaluating bbox... +2024-05-31 02:51:31,656 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.344 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.576 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.366 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.191 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.380 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.481 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.479 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.479 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.479 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.279 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.525 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.643 + +2024-05-31 02:51:31,656 - mmdet - INFO - Evaluating segm... +2024-05-31 02:52:04,027 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.325 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.543 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.338 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.134 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.349 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.519 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.446 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.446 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.446 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.233 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.495 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.634 + +2024-05-31 02:52:04,411 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 02:52:04,414 - mmdet - INFO - Epoch(val) [2][625] bbox_mAP: 0.3440, bbox_mAP_50: 0.5760, bbox_mAP_75: 0.3660, bbox_mAP_s: 0.1910, bbox_mAP_m: 0.3800, bbox_mAP_l: 0.4810, bbox_mAP_copypaste: 0.344 0.576 0.366 0.191 0.380 0.481, segm_mAP: 0.3250, segm_mAP_50: 0.5430, segm_mAP_75: 0.3380, segm_mAP_s: 0.1340, segm_mAP_m: 0.3490, segm_mAP_l: 0.5190, segm_mAP_copypaste: 0.325 0.543 0.338 0.134 0.349 0.519 +2024-05-31 02:52:43,219 - mmdet - INFO - Epoch [3][50/7330] lr: 1.000e-04, eta: 12:40:09, time: 0.776, data_time: 0.115, memory: 9655, loss_rpn_cls: 0.0305, loss_rpn_bbox: 0.0547, loss_cls: 0.2340, acc: 91.8953, loss_bbox: 0.2784, loss_mask: 0.2740, loss: 0.8716 +2024-05-31 02:53:14,063 - mmdet - INFO - Epoch [3][100/7330] lr: 1.000e-04, eta: 12:39:36, time: 0.617, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0301, loss_rpn_bbox: 0.0571, loss_cls: 0.2380, acc: 91.7131, loss_bbox: 0.2890, loss_mask: 0.2735, loss: 0.8877 +2024-05-31 02:53:44,588 - mmdet - INFO - Epoch [3][150/7330] lr: 1.000e-04, eta: 12:39:02, time: 0.610, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0295, loss_rpn_bbox: 0.0566, loss_cls: 0.2283, acc: 92.1208, loss_bbox: 0.2683, loss_mask: 0.2694, loss: 0.8521 +2024-05-31 02:54:14,275 - mmdet - INFO - Epoch [3][200/7330] lr: 1.000e-04, eta: 12:38:24, time: 0.594, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0277, loss_rpn_bbox: 0.0528, loss_cls: 0.2240, acc: 92.1975, loss_bbox: 0.2721, loss_mask: 0.2693, loss: 0.8459 +2024-05-31 02:54:45,411 - mmdet - INFO - Epoch [3][250/7330] lr: 1.000e-04, eta: 12:37:53, time: 0.623, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0320, loss_rpn_bbox: 0.0597, loss_cls: 0.2343, acc: 91.7791, loss_bbox: 0.2847, loss_mask: 0.2730, loss: 0.8837 +2024-05-31 02:55:15,827 - mmdet - INFO - Epoch [3][300/7330] lr: 1.000e-04, eta: 12:37:18, time: 0.608, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0321, loss_rpn_bbox: 0.0589, loss_cls: 0.2431, acc: 91.6174, loss_bbox: 0.2876, loss_mask: 0.2764, loss: 0.8982 +2024-05-31 02:55:45,993 - mmdet - INFO - Epoch [3][350/7330] lr: 1.000e-04, eta: 12:36:42, time: 0.603, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0288, loss_rpn_bbox: 0.0587, loss_cls: 0.2314, acc: 92.0322, loss_bbox: 0.2736, loss_mask: 0.2755, loss: 0.8681 +2024-05-31 02:56:16,220 - mmdet - INFO - Epoch [3][400/7330] lr: 1.000e-04, eta: 12:36:07, time: 0.604, data_time: 0.064, memory: 9655, loss_rpn_cls: 0.0258, loss_rpn_bbox: 0.0548, loss_cls: 0.2297, acc: 92.0549, loss_bbox: 0.2784, loss_mask: 0.2657, loss: 0.8545 +2024-05-31 02:56:46,408 - mmdet - INFO - Epoch [3][450/7330] lr: 1.000e-04, eta: 12:35:31, time: 0.604, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0285, loss_rpn_bbox: 0.0525, loss_cls: 0.2307, acc: 92.0071, loss_bbox: 0.2730, loss_mask: 0.2725, loss: 0.8572 +2024-05-31 02:57:17,082 - mmdet - INFO - Epoch [3][500/7330] lr: 1.000e-04, eta: 12:34:58, time: 0.613, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0301, loss_rpn_bbox: 0.0566, loss_cls: 0.2322, acc: 92.1243, loss_bbox: 0.2768, loss_mask: 0.2738, loss: 0.8695 +2024-05-31 02:57:47,454 - mmdet - INFO - Epoch [3][550/7330] lr: 1.000e-04, eta: 12:34:24, time: 0.607, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0289, loss_rpn_bbox: 0.0526, loss_cls: 0.2328, acc: 92.1106, loss_bbox: 0.2736, loss_mask: 0.2678, loss: 0.8557 +2024-05-31 02:58:17,020 - mmdet - INFO - Epoch [3][600/7330] lr: 1.000e-04, eta: 12:33:45, time: 0.591, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0267, loss_rpn_bbox: 0.0515, loss_cls: 0.2236, acc: 92.4534, loss_bbox: 0.2604, loss_mask: 0.2644, loss: 0.8266 +2024-05-31 02:58:47,398 - mmdet - INFO - Epoch [3][650/7330] lr: 1.000e-04, eta: 12:33:10, time: 0.608, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0278, loss_rpn_bbox: 0.0547, loss_cls: 0.2282, acc: 92.0168, loss_bbox: 0.2773, loss_mask: 0.2736, loss: 0.8616 +2024-05-31 02:59:17,236 - mmdet - INFO - Epoch [3][700/7330] lr: 1.000e-04, eta: 12:32:33, time: 0.597, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0292, loss_rpn_bbox: 0.0522, loss_cls: 0.2228, acc: 92.2759, loss_bbox: 0.2715, loss_mask: 0.2720, loss: 0.8477 +2024-05-31 02:59:47,812 - mmdet - INFO - Epoch [3][750/7330] lr: 1.000e-04, eta: 12:32:00, time: 0.611, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0308, loss_rpn_bbox: 0.0550, loss_cls: 0.2351, acc: 91.8896, loss_bbox: 0.2793, loss_mask: 0.2730, loss: 0.8733 +2024-05-31 03:00:17,320 - mmdet - INFO - Epoch [3][800/7330] lr: 1.000e-04, eta: 12:31:21, time: 0.590, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0276, loss_rpn_bbox: 0.0527, loss_cls: 0.2294, acc: 92.1692, loss_bbox: 0.2711, loss_mask: 0.2720, loss: 0.8529 +2024-05-31 03:00:47,459 - mmdet - INFO - Epoch [3][850/7330] lr: 1.000e-04, eta: 12:30:46, time: 0.603, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0288, loss_rpn_bbox: 0.0518, loss_cls: 0.2332, acc: 91.9832, loss_bbox: 0.2769, loss_mask: 0.2696, loss: 0.8603 +2024-05-31 03:01:17,279 - mmdet - INFO - Epoch [3][900/7330] lr: 1.000e-04, eta: 12:30:09, time: 0.596, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0297, loss_rpn_bbox: 0.0526, loss_cls: 0.2388, acc: 91.8428, loss_bbox: 0.2782, loss_mask: 0.2704, loss: 0.8697 +2024-05-31 03:01:47,975 - mmdet - INFO - Epoch [3][950/7330] lr: 1.000e-04, eta: 12:29:36, time: 0.614, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0293, loss_rpn_bbox: 0.0559, loss_cls: 0.2444, acc: 91.5139, loss_bbox: 0.2929, loss_mask: 0.2704, loss: 0.8928 +2024-05-31 03:02:24,518 - mmdet - INFO - Epoch [3][1000/7330] lr: 1.000e-04, eta: 12:29:30, time: 0.731, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0262, loss_rpn_bbox: 0.0511, loss_cls: 0.2285, acc: 92.1936, loss_bbox: 0.2687, loss_mask: 0.2689, loss: 0.8435 +2024-05-31 03:03:04,988 - mmdet - INFO - Epoch [3][1050/7330] lr: 1.000e-04, eta: 12:29:42, time: 0.809, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0301, loss_rpn_bbox: 0.0539, loss_cls: 0.2332, acc: 92.1218, loss_bbox: 0.2704, loss_mask: 0.2644, loss: 0.8519 +2024-05-31 03:03:38,315 - mmdet - INFO - Epoch [3][1100/7330] lr: 1.000e-04, eta: 12:29:21, time: 0.667, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0308, loss_rpn_bbox: 0.0558, loss_cls: 0.2317, acc: 91.9575, loss_bbox: 0.2780, loss_mask: 0.2719, loss: 0.8682 +2024-05-31 03:04:09,083 - mmdet - INFO - Epoch [3][1150/7330] lr: 1.000e-04, eta: 12:28:48, time: 0.615, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0305, loss_rpn_bbox: 0.0581, loss_cls: 0.2434, acc: 91.6633, loss_bbox: 0.2917, loss_mask: 0.2814, loss: 0.9051 +2024-05-31 03:04:39,520 - mmdet - INFO - Epoch [3][1200/7330] lr: 1.000e-04, eta: 12:28:14, time: 0.609, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0288, loss_rpn_bbox: 0.0510, loss_cls: 0.2289, acc: 92.1826, loss_bbox: 0.2661, loss_mask: 0.2671, loss: 0.8419 +2024-05-31 03:05:10,075 - mmdet - INFO - Epoch [3][1250/7330] lr: 1.000e-04, eta: 12:27:40, time: 0.611, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0292, loss_rpn_bbox: 0.0570, loss_cls: 0.2435, acc: 91.5989, loss_bbox: 0.2864, loss_mask: 0.2760, loss: 0.8921 +2024-05-31 03:05:40,311 - mmdet - INFO - Epoch [3][1300/7330] lr: 1.000e-04, eta: 12:27:05, time: 0.605, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0273, loss_rpn_bbox: 0.0525, loss_cls: 0.2303, acc: 92.0234, loss_bbox: 0.2765, loss_mask: 0.2673, loss: 0.8540 +2024-05-31 03:06:10,896 - mmdet - INFO - Epoch [3][1350/7330] lr: 1.000e-04, eta: 12:26:31, time: 0.612, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0282, loss_rpn_bbox: 0.0556, loss_cls: 0.2298, acc: 92.1040, loss_bbox: 0.2757, loss_mask: 0.2707, loss: 0.8600 +2024-05-31 03:06:41,086 - mmdet - INFO - Epoch [3][1400/7330] lr: 1.000e-04, eta: 12:25:56, time: 0.604, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0290, loss_rpn_bbox: 0.0527, loss_cls: 0.2398, acc: 91.8584, loss_bbox: 0.2828, loss_mask: 0.2750, loss: 0.8792 +2024-05-31 03:07:11,456 - mmdet - INFO - Epoch [3][1450/7330] lr: 1.000e-04, eta: 12:25:21, time: 0.607, data_time: 0.036, memory: 9655, loss_rpn_cls: 0.0295, loss_rpn_bbox: 0.0593, loss_cls: 0.2349, acc: 91.8518, loss_bbox: 0.2782, loss_mask: 0.2767, loss: 0.8786 +2024-05-31 03:07:41,639 - mmdet - INFO - Epoch [3][1500/7330] lr: 1.000e-04, eta: 12:24:46, time: 0.604, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0292, loss_rpn_bbox: 0.0577, loss_cls: 0.2382, acc: 91.8257, loss_bbox: 0.2803, loss_mask: 0.2714, loss: 0.8768 +2024-05-31 03:08:11,853 - mmdet - INFO - Epoch [3][1550/7330] lr: 1.000e-04, eta: 12:24:11, time: 0.604, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0279, loss_rpn_bbox: 0.0530, loss_cls: 0.2213, acc: 92.3486, loss_bbox: 0.2633, loss_mask: 0.2679, loss: 0.8335 +2024-05-31 03:08:42,179 - mmdet - INFO - Epoch [3][1600/7330] lr: 1.000e-04, eta: 12:23:36, time: 0.606, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0292, loss_rpn_bbox: 0.0553, loss_cls: 0.2227, acc: 92.3010, loss_bbox: 0.2658, loss_mask: 0.2629, loss: 0.8360 +2024-05-31 03:09:12,494 - mmdet - INFO - Epoch [3][1650/7330] lr: 1.000e-04, eta: 12:23:02, time: 0.606, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0288, loss_rpn_bbox: 0.0540, loss_cls: 0.2373, acc: 91.7517, loss_bbox: 0.2809, loss_mask: 0.2684, loss: 0.8693 +2024-05-31 03:09:42,154 - mmdet - INFO - Epoch [3][1700/7330] lr: 1.000e-04, eta: 12:22:24, time: 0.593, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0275, loss_rpn_bbox: 0.0525, loss_cls: 0.2331, acc: 92.1738, loss_bbox: 0.2630, loss_mask: 0.2610, loss: 0.8371 +2024-05-31 03:10:12,356 - mmdet - INFO - Epoch [3][1750/7330] lr: 1.000e-04, eta: 12:21:49, time: 0.604, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0280, loss_rpn_bbox: 0.0554, loss_cls: 0.2248, acc: 92.3538, loss_bbox: 0.2614, loss_mask: 0.2629, loss: 0.8325 +2024-05-31 03:10:42,014 - mmdet - INFO - Epoch [3][1800/7330] lr: 1.000e-04, eta: 12:21:12, time: 0.593, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0267, loss_rpn_bbox: 0.0522, loss_cls: 0.2310, acc: 91.9714, loss_bbox: 0.2722, loss_mask: 0.2653, loss: 0.8475 +2024-05-31 03:11:11,822 - mmdet - INFO - Epoch [3][1850/7330] lr: 1.000e-04, eta: 12:20:35, time: 0.596, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0293, loss_rpn_bbox: 0.0545, loss_cls: 0.2286, acc: 92.0867, loss_bbox: 0.2711, loss_mask: 0.2714, loss: 0.8549 +2024-05-31 03:11:49,513 - mmdet - INFO - Epoch [3][1900/7330] lr: 1.000e-04, eta: 12:20:33, time: 0.754, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0311, loss_rpn_bbox: 0.0558, loss_cls: 0.2393, acc: 91.7412, loss_bbox: 0.2824, loss_mask: 0.2753, loss: 0.8839 +2024-05-31 03:12:29,010 - mmdet - INFO - Epoch [3][1950/7330] lr: 1.000e-04, eta: 12:20:37, time: 0.790, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0278, loss_rpn_bbox: 0.0543, loss_cls: 0.2264, acc: 92.2258, loss_bbox: 0.2693, loss_mask: 0.2665, loss: 0.8443 +2024-05-31 03:13:01,413 - mmdet - INFO - Epoch [3][2000/7330] lr: 1.000e-04, eta: 12:20:12, time: 0.648, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0301, loss_rpn_bbox: 0.0566, loss_cls: 0.2345, acc: 91.9963, loss_bbox: 0.2742, loss_mask: 0.2700, loss: 0.8655 +2024-05-31 03:13:31,320 - mmdet - INFO - Epoch [3][2050/7330] lr: 1.000e-04, eta: 12:19:35, time: 0.598, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0288, loss_rpn_bbox: 0.0558, loss_cls: 0.2279, acc: 92.1152, loss_bbox: 0.2722, loss_mask: 0.2643, loss: 0.8490 +2024-05-31 03:14:01,945 - mmdet - INFO - Epoch [3][2100/7330] lr: 1.000e-04, eta: 12:19:02, time: 0.612, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0303, loss_rpn_bbox: 0.0543, loss_cls: 0.2313, acc: 91.9700, loss_bbox: 0.2731, loss_mask: 0.2660, loss: 0.8549 +2024-05-31 03:14:31,902 - mmdet - INFO - Epoch [3][2150/7330] lr: 1.000e-04, eta: 12:18:26, time: 0.599, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0282, loss_rpn_bbox: 0.0502, loss_cls: 0.2239, acc: 92.2334, loss_bbox: 0.2667, loss_mask: 0.2657, loss: 0.8346 +2024-05-31 03:15:02,182 - mmdet - INFO - Epoch [3][2200/7330] lr: 1.000e-04, eta: 12:17:51, time: 0.606, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0272, loss_rpn_bbox: 0.0536, loss_cls: 0.2296, acc: 91.9258, loss_bbox: 0.2781, loss_mask: 0.2712, loss: 0.8597 +2024-05-31 03:15:32,655 - mmdet - INFO - Epoch [3][2250/7330] lr: 1.000e-04, eta: 12:17:17, time: 0.609, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0277, loss_rpn_bbox: 0.0507, loss_cls: 0.2252, acc: 92.2114, loss_bbox: 0.2696, loss_mask: 0.2644, loss: 0.8376 +2024-05-31 03:16:03,173 - mmdet - INFO - Epoch [3][2300/7330] lr: 1.000e-04, eta: 12:16:43, time: 0.611, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0288, loss_rpn_bbox: 0.0555, loss_cls: 0.2298, acc: 92.0249, loss_bbox: 0.2774, loss_mask: 0.2730, loss: 0.8645 +2024-05-31 03:16:34,118 - mmdet - INFO - Epoch [3][2350/7330] lr: 1.000e-04, eta: 12:16:12, time: 0.619, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0296, loss_rpn_bbox: 0.0570, loss_cls: 0.2318, acc: 91.9778, loss_bbox: 0.2762, loss_mask: 0.2708, loss: 0.8654 +2024-05-31 03:17:04,255 - mmdet - INFO - Epoch [3][2400/7330] lr: 1.000e-04, eta: 12:15:36, time: 0.603, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0277, loss_rpn_bbox: 0.0526, loss_cls: 0.2230, acc: 92.3052, loss_bbox: 0.2684, loss_mask: 0.2649, loss: 0.8366 +2024-05-31 03:17:34,344 - mmdet - INFO - Epoch [3][2450/7330] lr: 1.000e-04, eta: 12:15:01, time: 0.602, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0284, loss_rpn_bbox: 0.0527, loss_cls: 0.2233, acc: 92.2104, loss_bbox: 0.2700, loss_mask: 0.2650, loss: 0.8395 +2024-05-31 03:18:04,242 - mmdet - INFO - Epoch [3][2500/7330] lr: 1.000e-04, eta: 12:14:25, time: 0.598, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0285, loss_rpn_bbox: 0.0561, loss_cls: 0.2399, acc: 91.6208, loss_bbox: 0.2892, loss_mask: 0.2789, loss: 0.8925 +2024-05-31 03:18:34,448 - mmdet - INFO - Epoch [3][2550/7330] lr: 1.000e-04, eta: 12:13:50, time: 0.604, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0273, loss_rpn_bbox: 0.0529, loss_cls: 0.2205, acc: 92.1733, loss_bbox: 0.2706, loss_mask: 0.2678, loss: 0.8391 +2024-05-31 03:19:04,339 - mmdet - INFO - Epoch [3][2600/7330] lr: 1.000e-04, eta: 12:13:14, time: 0.598, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0290, loss_rpn_bbox: 0.0506, loss_cls: 0.2208, acc: 92.4631, loss_bbox: 0.2564, loss_mask: 0.2635, loss: 0.8203 +2024-05-31 03:19:34,127 - mmdet - INFO - Epoch [3][2650/7330] lr: 1.000e-04, eta: 12:12:37, time: 0.596, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0283, loss_rpn_bbox: 0.0520, loss_cls: 0.2314, acc: 92.1926, loss_bbox: 0.2683, loss_mask: 0.2728, loss: 0.8528 +2024-05-31 03:20:05,029 - mmdet - INFO - Epoch [3][2700/7330] lr: 1.000e-04, eta: 12:12:05, time: 0.618, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0284, loss_rpn_bbox: 0.0552, loss_cls: 0.2326, acc: 91.9570, loss_bbox: 0.2755, loss_mask: 0.2746, loss: 0.8663 +2024-05-31 03:20:35,317 - mmdet - INFO - Epoch [3][2750/7330] lr: 1.000e-04, eta: 12:11:31, time: 0.606, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0290, loss_rpn_bbox: 0.0553, loss_cls: 0.2311, acc: 92.0549, loss_bbox: 0.2716, loss_mask: 0.2671, loss: 0.8541 +2024-05-31 03:21:10,476 - mmdet - INFO - Epoch [3][2800/7330] lr: 1.000e-04, eta: 12:11:16, time: 0.703, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0297, loss_rpn_bbox: 0.0559, loss_cls: 0.2412, acc: 91.6409, loss_bbox: 0.2865, loss_mask: 0.2722, loss: 0.8855 +2024-05-31 03:21:53,709 - mmdet - INFO - Epoch [3][2850/7330] lr: 1.000e-04, eta: 12:11:34, time: 0.864, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0280, loss_rpn_bbox: 0.0530, loss_cls: 0.2332, acc: 91.8896, loss_bbox: 0.2704, loss_mask: 0.2664, loss: 0.8511 +2024-05-31 03:22:25,689 - mmdet - INFO - Epoch [3][2900/7330] lr: 1.000e-04, eta: 12:11:06, time: 0.640, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0262, loss_rpn_bbox: 0.0536, loss_cls: 0.2252, acc: 92.2395, loss_bbox: 0.2719, loss_mask: 0.2717, loss: 0.8486 +2024-05-31 03:22:56,061 - mmdet - INFO - Epoch [3][2950/7330] lr: 1.000e-04, eta: 12:10:32, time: 0.607, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0280, loss_rpn_bbox: 0.0532, loss_cls: 0.2326, acc: 92.1052, loss_bbox: 0.2698, loss_mask: 0.2657, loss: 0.8494 +2024-05-31 03:23:26,353 - mmdet - INFO - Epoch [3][3000/7330] lr: 1.000e-04, eta: 12:09:57, time: 0.606, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0277, loss_rpn_bbox: 0.0563, loss_cls: 0.2371, acc: 91.9602, loss_bbox: 0.2762, loss_mask: 0.2725, loss: 0.8697 +2024-05-31 03:23:56,818 - mmdet - INFO - Epoch [3][3050/7330] lr: 1.000e-04, eta: 12:09:23, time: 0.609, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0288, loss_rpn_bbox: 0.0551, loss_cls: 0.2346, acc: 91.8982, loss_bbox: 0.2779, loss_mask: 0.2721, loss: 0.8685 +2024-05-31 03:24:26,984 - mmdet - INFO - Epoch [3][3100/7330] lr: 1.000e-04, eta: 12:08:48, time: 0.603, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0301, loss_rpn_bbox: 0.0540, loss_cls: 0.2230, acc: 92.2588, loss_bbox: 0.2660, loss_mask: 0.2696, loss: 0.8428 +2024-05-31 03:24:56,982 - mmdet - INFO - Epoch [3][3150/7330] lr: 1.000e-04, eta: 12:08:12, time: 0.600, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0271, loss_rpn_bbox: 0.0517, loss_cls: 0.2251, acc: 92.2097, loss_bbox: 0.2716, loss_mask: 0.2691, loss: 0.8446 +2024-05-31 03:25:27,413 - mmdet - INFO - Epoch [3][3200/7330] lr: 1.000e-04, eta: 12:07:38, time: 0.609, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0272, loss_rpn_bbox: 0.0523, loss_cls: 0.2283, acc: 92.0178, loss_bbox: 0.2689, loss_mask: 0.2686, loss: 0.8453 +2024-05-31 03:25:57,617 - mmdet - INFO - Epoch [3][3250/7330] lr: 1.000e-04, eta: 12:07:04, time: 0.604, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0276, loss_rpn_bbox: 0.0538, loss_cls: 0.2301, acc: 92.0635, loss_bbox: 0.2741, loss_mask: 0.2679, loss: 0.8535 +2024-05-31 03:26:27,686 - mmdet - INFO - Epoch [3][3300/7330] lr: 1.000e-04, eta: 12:06:28, time: 0.601, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0254, loss_rpn_bbox: 0.0507, loss_cls: 0.2302, acc: 92.0750, loss_bbox: 0.2706, loss_mask: 0.2697, loss: 0.8466 +2024-05-31 03:26:57,912 - mmdet - INFO - Epoch [3][3350/7330] lr: 1.000e-04, eta: 12:05:54, time: 0.605, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0291, loss_rpn_bbox: 0.0571, loss_cls: 0.2279, acc: 92.2009, loss_bbox: 0.2663, loss_mask: 0.2667, loss: 0.8472 +2024-05-31 03:27:28,304 - mmdet - INFO - Epoch [3][3400/7330] lr: 1.000e-04, eta: 12:05:20, time: 0.607, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0274, loss_rpn_bbox: 0.0556, loss_cls: 0.2351, acc: 91.8120, loss_bbox: 0.2802, loss_mask: 0.2664, loss: 0.8646 +2024-05-31 03:27:58,701 - mmdet - INFO - Epoch [3][3450/7330] lr: 1.000e-04, eta: 12:04:46, time: 0.608, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0271, loss_rpn_bbox: 0.0553, loss_cls: 0.2356, acc: 91.8518, loss_bbox: 0.2762, loss_mask: 0.2704, loss: 0.8646 +2024-05-31 03:28:29,444 - mmdet - INFO - Epoch [3][3500/7330] lr: 1.000e-04, eta: 12:04:13, time: 0.615, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0281, loss_rpn_bbox: 0.0550, loss_cls: 0.2338, acc: 91.8464, loss_bbox: 0.2819, loss_mask: 0.2683, loss: 0.8671 +2024-05-31 03:29:00,013 - mmdet - INFO - Epoch [3][3550/7330] lr: 1.000e-04, eta: 12:03:40, time: 0.611, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0289, loss_rpn_bbox: 0.0555, loss_cls: 0.2369, acc: 91.8445, loss_bbox: 0.2775, loss_mask: 0.2723, loss: 0.8711 +2024-05-31 03:29:30,373 - mmdet - INFO - Epoch [3][3600/7330] lr: 1.000e-04, eta: 12:03:06, time: 0.607, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0305, loss_rpn_bbox: 0.0568, loss_cls: 0.2396, acc: 91.7771, loss_bbox: 0.2823, loss_mask: 0.2810, loss: 0.8902 +2024-05-31 03:30:00,988 - mmdet - INFO - Epoch [3][3650/7330] lr: 1.000e-04, eta: 12:02:33, time: 0.612, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0294, loss_rpn_bbox: 0.0562, loss_cls: 0.2369, acc: 91.9233, loss_bbox: 0.2771, loss_mask: 0.2670, loss: 0.8665 +2024-05-31 03:30:36,508 - mmdet - INFO - Epoch [3][3700/7330] lr: 1.000e-04, eta: 12:02:18, time: 0.710, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0276, loss_rpn_bbox: 0.0514, loss_cls: 0.2264, acc: 92.0725, loss_bbox: 0.2764, loss_mask: 0.2717, loss: 0.8535 +2024-05-31 03:31:17,110 - mmdet - INFO - Epoch [3][3750/7330] lr: 1.000e-04, eta: 12:02:23, time: 0.812, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0273, loss_rpn_bbox: 0.0538, loss_cls: 0.2294, acc: 92.1086, loss_bbox: 0.2717, loss_mask: 0.2706, loss: 0.8528 +2024-05-31 03:31:49,869 - mmdet - INFO - Epoch [3][3800/7330] lr: 1.000e-04, eta: 12:01:58, time: 0.655, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0263, loss_rpn_bbox: 0.0537, loss_cls: 0.2255, acc: 92.2351, loss_bbox: 0.2680, loss_mask: 0.2691, loss: 0.8427 +2024-05-31 03:32:20,218 - mmdet - INFO - Epoch [3][3850/7330] lr: 1.000e-04, eta: 12:01:24, time: 0.607, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0274, loss_rpn_bbox: 0.0534, loss_cls: 0.2274, acc: 92.2822, loss_bbox: 0.2690, loss_mask: 0.2603, loss: 0.8374 +2024-05-31 03:32:49,946 - mmdet - INFO - Epoch [3][3900/7330] lr: 1.000e-04, eta: 12:00:47, time: 0.595, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0264, loss_rpn_bbox: 0.0532, loss_cls: 0.2259, acc: 92.2615, loss_bbox: 0.2669, loss_mask: 0.2727, loss: 0.8451 +2024-05-31 03:33:19,642 - mmdet - INFO - Epoch [3][3950/7330] lr: 1.000e-04, eta: 12:00:10, time: 0.594, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0280, loss_rpn_bbox: 0.0514, loss_cls: 0.2372, acc: 92.0044, loss_bbox: 0.2694, loss_mask: 0.2674, loss: 0.8535 +2024-05-31 03:33:49,960 - mmdet - INFO - Epoch [3][4000/7330] lr: 1.000e-04, eta: 11:59:36, time: 0.607, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0276, loss_rpn_bbox: 0.0516, loss_cls: 0.2239, acc: 92.3198, loss_bbox: 0.2671, loss_mask: 0.2661, loss: 0.8363 +2024-05-31 03:34:19,389 - mmdet - INFO - Epoch [3][4050/7330] lr: 1.000e-04, eta: 11:58:59, time: 0.589, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0258, loss_rpn_bbox: 0.0475, loss_cls: 0.2165, acc: 92.5315, loss_bbox: 0.2514, loss_mask: 0.2593, loss: 0.8006 +2024-05-31 03:34:49,440 - mmdet - INFO - Epoch [3][4100/7330] lr: 1.000e-04, eta: 11:58:23, time: 0.601, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0280, loss_rpn_bbox: 0.0536, loss_cls: 0.2321, acc: 91.9282, loss_bbox: 0.2695, loss_mask: 0.2661, loss: 0.8494 +2024-05-31 03:35:19,581 - mmdet - INFO - Epoch [3][4150/7330] lr: 1.000e-04, eta: 11:57:49, time: 0.603, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0306, loss_rpn_bbox: 0.0558, loss_cls: 0.2354, acc: 91.8875, loss_bbox: 0.2812, loss_mask: 0.2739, loss: 0.8768 +2024-05-31 03:35:49,924 - mmdet - INFO - Epoch [3][4200/7330] lr: 1.000e-04, eta: 11:57:14, time: 0.607, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0298, loss_rpn_bbox: 0.0514, loss_cls: 0.2260, acc: 92.4607, loss_bbox: 0.2582, loss_mask: 0.2650, loss: 0.8303 +2024-05-31 03:36:19,519 - mmdet - INFO - Epoch [3][4250/7330] lr: 1.000e-04, eta: 11:56:38, time: 0.592, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0247, loss_rpn_bbox: 0.0486, loss_cls: 0.2225, acc: 92.3928, loss_bbox: 0.2571, loss_mask: 0.2640, loss: 0.8168 +2024-05-31 03:36:49,456 - mmdet - INFO - Epoch [3][4300/7330] lr: 1.000e-04, eta: 11:56:02, time: 0.598, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0269, loss_rpn_bbox: 0.0535, loss_cls: 0.2234, acc: 92.0996, loss_bbox: 0.2691, loss_mask: 0.2678, loss: 0.8407 +2024-05-31 03:37:19,553 - mmdet - INFO - Epoch [3][4350/7330] lr: 1.000e-04, eta: 11:55:27, time: 0.602, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0271, loss_rpn_bbox: 0.0544, loss_cls: 0.2313, acc: 92.0112, loss_bbox: 0.2745, loss_mask: 0.2721, loss: 0.8595 +2024-05-31 03:37:49,684 - mmdet - INFO - Epoch [3][4400/7330] lr: 1.000e-04, eta: 11:54:53, time: 0.603, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0261, loss_rpn_bbox: 0.0530, loss_cls: 0.2199, acc: 92.3350, loss_bbox: 0.2656, loss_mask: 0.2582, loss: 0.8227 +2024-05-31 03:38:20,047 - mmdet - INFO - Epoch [3][4450/7330] lr: 1.000e-04, eta: 11:54:19, time: 0.607, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0299, loss_rpn_bbox: 0.0522, loss_cls: 0.2180, acc: 92.5415, loss_bbox: 0.2585, loss_mask: 0.2664, loss: 0.8249 +2024-05-31 03:38:50,010 - mmdet - INFO - Epoch [3][4500/7330] lr: 1.000e-04, eta: 11:53:43, time: 0.599, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0298, loss_rpn_bbox: 0.0555, loss_cls: 0.2284, acc: 92.2146, loss_bbox: 0.2651, loss_mask: 0.2640, loss: 0.8428 +2024-05-31 03:39:20,392 - mmdet - INFO - Epoch [3][4550/7330] lr: 1.000e-04, eta: 11:53:10, time: 0.608, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0312, loss_rpn_bbox: 0.0573, loss_cls: 0.2376, acc: 91.7993, loss_bbox: 0.2791, loss_mask: 0.2698, loss: 0.8750 +2024-05-31 03:39:56,588 - mmdet - INFO - Epoch [3][4600/7330] lr: 1.000e-04, eta: 11:52:49, time: 0.678, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0281, loss_rpn_bbox: 0.0551, loss_cls: 0.2326, acc: 92.0039, loss_bbox: 0.2712, loss_mask: 0.2657, loss: 0.8528 +2024-05-31 03:40:35,052 - mmdet - INFO - Epoch [3][4650/7330] lr: 1.000e-04, eta: 11:52:52, time: 0.815, data_time: 0.096, memory: 9655, loss_rpn_cls: 0.0291, loss_rpn_bbox: 0.0544, loss_cls: 0.2395, acc: 91.7173, loss_bbox: 0.2798, loss_mask: 0.2681, loss: 0.8708 +2024-05-31 03:41:12,301 - mmdet - INFO - Epoch [3][4700/7330] lr: 1.000e-04, eta: 11:52:42, time: 0.745, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0288, loss_rpn_bbox: 0.0541, loss_cls: 0.2387, acc: 91.8296, loss_bbox: 0.2817, loss_mask: 0.2745, loss: 0.8777 +2024-05-31 03:41:42,762 - mmdet - INFO - Epoch [3][4750/7330] lr: 1.000e-04, eta: 11:52:08, time: 0.609, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0270, loss_rpn_bbox: 0.0523, loss_cls: 0.2288, acc: 92.0837, loss_bbox: 0.2722, loss_mask: 0.2640, loss: 0.8442 +2024-05-31 03:42:13,459 - mmdet - INFO - Epoch [3][4800/7330] lr: 1.000e-04, eta: 11:51:35, time: 0.614, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0261, loss_rpn_bbox: 0.0513, loss_cls: 0.2284, acc: 92.3440, loss_bbox: 0.2651, loss_mask: 0.2683, loss: 0.8392 +2024-05-31 03:42:43,968 - mmdet - INFO - Epoch [3][4850/7330] lr: 1.000e-04, eta: 11:51:02, time: 0.611, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0285, loss_rpn_bbox: 0.0552, loss_cls: 0.2402, acc: 91.6721, loss_bbox: 0.2843, loss_mask: 0.2709, loss: 0.8791 +2024-05-31 03:43:13,883 - mmdet - INFO - Epoch [3][4900/7330] lr: 1.000e-04, eta: 11:50:27, time: 0.598, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0279, loss_rpn_bbox: 0.0541, loss_cls: 0.2292, acc: 91.9424, loss_bbox: 0.2726, loss_mask: 0.2715, loss: 0.8552 +2024-05-31 03:43:44,042 - mmdet - INFO - Epoch [3][4950/7330] lr: 1.000e-04, eta: 11:49:52, time: 0.603, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0269, loss_rpn_bbox: 0.0506, loss_cls: 0.2159, acc: 92.5378, loss_bbox: 0.2595, loss_mask: 0.2623, loss: 0.8153 +2024-05-31 03:44:14,713 - mmdet - INFO - Epoch [3][5000/7330] lr: 1.000e-04, eta: 11:49:19, time: 0.613, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0277, loss_rpn_bbox: 0.0539, loss_cls: 0.2318, acc: 91.9602, loss_bbox: 0.2763, loss_mask: 0.2688, loss: 0.8586 +2024-05-31 03:44:45,022 - mmdet - INFO - Epoch [3][5050/7330] lr: 1.000e-04, eta: 11:48:45, time: 0.606, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0287, loss_rpn_bbox: 0.0553, loss_cls: 0.2352, acc: 91.8960, loss_bbox: 0.2769, loss_mask: 0.2646, loss: 0.8607 +2024-05-31 03:45:15,155 - mmdet - INFO - Epoch [3][5100/7330] lr: 1.000e-04, eta: 11:48:10, time: 0.603, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0273, loss_rpn_bbox: 0.0521, loss_cls: 0.2360, acc: 91.8228, loss_bbox: 0.2750, loss_mask: 0.2676, loss: 0.8581 +2024-05-31 03:45:45,226 - mmdet - INFO - Epoch [3][5150/7330] lr: 1.000e-04, eta: 11:47:35, time: 0.601, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0291, loss_rpn_bbox: 0.0532, loss_cls: 0.2333, acc: 92.1218, loss_bbox: 0.2656, loss_mask: 0.2663, loss: 0.8475 +2024-05-31 03:46:15,406 - mmdet - INFO - Epoch [3][5200/7330] lr: 1.000e-04, eta: 11:47:01, time: 0.604, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0284, loss_rpn_bbox: 0.0530, loss_cls: 0.2360, acc: 91.8789, loss_bbox: 0.2739, loss_mask: 0.2687, loss: 0.8599 +2024-05-31 03:46:44,659 - mmdet - INFO - Epoch [3][5250/7330] lr: 1.000e-04, eta: 11:46:23, time: 0.585, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0242, loss_rpn_bbox: 0.0502, loss_cls: 0.2242, acc: 92.2812, loss_bbox: 0.2689, loss_mask: 0.2642, loss: 0.8317 +2024-05-31 03:47:14,859 - mmdet - INFO - Epoch [3][5300/7330] lr: 1.000e-04, eta: 11:45:49, time: 0.604, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0289, loss_rpn_bbox: 0.0511, loss_cls: 0.2251, acc: 92.3018, loss_bbox: 0.2629, loss_mask: 0.2637, loss: 0.8317 +2024-05-31 03:47:44,415 - mmdet - INFO - Epoch [3][5350/7330] lr: 1.000e-04, eta: 11:45:12, time: 0.591, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0264, loss_rpn_bbox: 0.0513, loss_cls: 0.2262, acc: 92.1558, loss_bbox: 0.2647, loss_mask: 0.2627, loss: 0.8312 +2024-05-31 03:48:14,362 - mmdet - INFO - Epoch [3][5400/7330] lr: 1.000e-04, eta: 11:44:37, time: 0.599, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0268, loss_rpn_bbox: 0.0534, loss_cls: 0.2280, acc: 92.1865, loss_bbox: 0.2643, loss_mask: 0.2618, loss: 0.8342 +2024-05-31 03:48:44,513 - mmdet - INFO - Epoch [3][5450/7330] lr: 1.000e-04, eta: 11:44:03, time: 0.603, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0269, loss_rpn_bbox: 0.0535, loss_cls: 0.2241, acc: 92.2134, loss_bbox: 0.2630, loss_mask: 0.2677, loss: 0.8351 +2024-05-31 03:49:17,613 - mmdet - INFO - Epoch [3][5500/7330] lr: 1.000e-04, eta: 11:43:38, time: 0.662, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0275, loss_rpn_bbox: 0.0530, loss_cls: 0.2271, acc: 92.2158, loss_bbox: 0.2686, loss_mask: 0.2693, loss: 0.8455 +2024-05-31 03:49:56,693 - mmdet - INFO - Epoch [3][5550/7330] lr: 1.000e-04, eta: 11:43:34, time: 0.782, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0307, loss_rpn_bbox: 0.0542, loss_cls: 0.2358, acc: 92.0283, loss_bbox: 0.2735, loss_mask: 0.2666, loss: 0.8609 +2024-05-31 03:50:35,581 - mmdet - INFO - Epoch [3][5600/7330] lr: 1.000e-04, eta: 11:43:29, time: 0.778, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0269, loss_rpn_bbox: 0.0524, loss_cls: 0.2264, acc: 92.2883, loss_bbox: 0.2632, loss_mask: 0.2626, loss: 0.8316 +2024-05-31 03:51:05,234 - mmdet - INFO - Epoch [3][5650/7330] lr: 1.000e-04, eta: 11:42:52, time: 0.593, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0277, loss_rpn_bbox: 0.0515, loss_cls: 0.2233, acc: 92.2881, loss_bbox: 0.2702, loss_mask: 0.2685, loss: 0.8412 +2024-05-31 03:51:34,983 - mmdet - INFO - Epoch [3][5700/7330] lr: 1.000e-04, eta: 11:42:16, time: 0.595, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0264, loss_rpn_bbox: 0.0506, loss_cls: 0.2141, acc: 92.6057, loss_bbox: 0.2583, loss_mask: 0.2621, loss: 0.8114 +2024-05-31 03:52:04,794 - mmdet - INFO - Epoch [3][5750/7330] lr: 1.000e-04, eta: 11:41:41, time: 0.597, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0279, loss_rpn_bbox: 0.0566, loss_cls: 0.2356, acc: 91.7537, loss_bbox: 0.2840, loss_mask: 0.2771, loss: 0.8811 +2024-05-31 03:52:35,483 - mmdet - INFO - Epoch [3][5800/7330] lr: 1.000e-04, eta: 11:41:08, time: 0.614, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0288, loss_rpn_bbox: 0.0512, loss_cls: 0.2282, acc: 92.0994, loss_bbox: 0.2680, loss_mask: 0.2666, loss: 0.8428 +2024-05-31 03:53:05,661 - mmdet - INFO - Epoch [3][5850/7330] lr: 1.000e-04, eta: 11:40:34, time: 0.604, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0250, loss_rpn_bbox: 0.0523, loss_cls: 0.2276, acc: 92.0989, loss_bbox: 0.2660, loss_mask: 0.2610, loss: 0.8320 +2024-05-31 03:53:35,429 - mmdet - INFO - Epoch [3][5900/7330] lr: 1.000e-04, eta: 11:39:58, time: 0.595, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0239, loss_rpn_bbox: 0.0492, loss_cls: 0.2181, acc: 92.4717, loss_bbox: 0.2578, loss_mask: 0.2630, loss: 0.8119 +2024-05-31 03:54:05,315 - mmdet - INFO - Epoch [3][5950/7330] lr: 1.000e-04, eta: 11:39:23, time: 0.598, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0254, loss_rpn_bbox: 0.0505, loss_cls: 0.2125, acc: 92.5845, loss_bbox: 0.2551, loss_mask: 0.2536, loss: 0.7971 +2024-05-31 03:54:35,960 - mmdet - INFO - Epoch [3][6000/7330] lr: 1.000e-04, eta: 11:38:50, time: 0.613, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0285, loss_rpn_bbox: 0.0532, loss_cls: 0.2283, acc: 92.1362, loss_bbox: 0.2714, loss_mask: 0.2592, loss: 0.8406 +2024-05-31 03:55:06,015 - mmdet - INFO - Epoch [3][6050/7330] lr: 1.000e-04, eta: 11:38:15, time: 0.601, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0297, loss_rpn_bbox: 0.0558, loss_cls: 0.2308, acc: 91.9993, loss_bbox: 0.2738, loss_mask: 0.2651, loss: 0.8552 +2024-05-31 03:55:36,950 - mmdet - INFO - Epoch [3][6100/7330] lr: 1.000e-04, eta: 11:37:43, time: 0.619, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0310, loss_rpn_bbox: 0.0550, loss_cls: 0.2367, acc: 91.8376, loss_bbox: 0.2780, loss_mask: 0.2631, loss: 0.8638 +2024-05-31 03:56:07,013 - mmdet - INFO - Epoch [3][6150/7330] lr: 1.000e-04, eta: 11:37:09, time: 0.601, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0258, loss_rpn_bbox: 0.0523, loss_cls: 0.2268, acc: 92.1843, loss_bbox: 0.2641, loss_mask: 0.2641, loss: 0.8330 +2024-05-31 03:56:36,665 - mmdet - INFO - Epoch [3][6200/7330] lr: 1.000e-04, eta: 11:36:33, time: 0.593, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0259, loss_rpn_bbox: 0.0495, loss_cls: 0.2229, acc: 92.3145, loss_bbox: 0.2604, loss_mask: 0.2641, loss: 0.8229 +2024-05-31 03:57:06,240 - mmdet - INFO - Epoch [3][6250/7330] lr: 1.000e-04, eta: 11:35:56, time: 0.592, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0271, loss_rpn_bbox: 0.0493, loss_cls: 0.2202, acc: 92.4651, loss_bbox: 0.2579, loss_mask: 0.2612, loss: 0.8158 +2024-05-31 03:57:36,718 - mmdet - INFO - Epoch [3][6300/7330] lr: 1.000e-04, eta: 11:35:23, time: 0.610, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0261, loss_rpn_bbox: 0.0527, loss_cls: 0.2152, acc: 92.4978, loss_bbox: 0.2586, loss_mask: 0.2662, loss: 0.8188 +2024-05-31 03:58:06,873 - mmdet - INFO - Epoch [3][6350/7330] lr: 1.000e-04, eta: 11:34:49, time: 0.603, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0293, loss_rpn_bbox: 0.0561, loss_cls: 0.2271, acc: 92.2524, loss_bbox: 0.2702, loss_mask: 0.2705, loss: 0.8532 +2024-05-31 03:58:39,924 - mmdet - INFO - Epoch [3][6400/7330] lr: 1.000e-04, eta: 11:34:24, time: 0.661, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0288, loss_rpn_bbox: 0.0535, loss_cls: 0.2238, acc: 92.2690, loss_bbox: 0.2622, loss_mask: 0.2612, loss: 0.8294 +2024-05-31 03:59:17,400 - mmdet - INFO - Epoch [3][6450/7330] lr: 1.000e-04, eta: 11:34:13, time: 0.750, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0294, loss_rpn_bbox: 0.0530, loss_cls: 0.2299, acc: 92.1106, loss_bbox: 0.2701, loss_mask: 0.2645, loss: 0.8468 +2024-05-31 03:59:56,376 - mmdet - INFO - Epoch [3][6500/7330] lr: 1.000e-04, eta: 11:34:06, time: 0.780, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0267, loss_rpn_bbox: 0.0522, loss_cls: 0.2204, acc: 92.3623, loss_bbox: 0.2646, loss_mask: 0.2592, loss: 0.8231 +2024-05-31 04:00:26,566 - mmdet - INFO - Epoch [3][6550/7330] lr: 1.000e-04, eta: 11:33:32, time: 0.604, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0301, loss_rpn_bbox: 0.0546, loss_cls: 0.2270, acc: 92.2642, loss_bbox: 0.2647, loss_mask: 0.2703, loss: 0.8467 +2024-05-31 04:00:57,140 - mmdet - INFO - Epoch [3][6600/7330] lr: 1.000e-04, eta: 11:32:59, time: 0.611, data_time: 0.063, memory: 9655, loss_rpn_cls: 0.0310, loss_rpn_bbox: 0.0552, loss_cls: 0.2268, acc: 92.0427, loss_bbox: 0.2685, loss_mask: 0.2699, loss: 0.8513 +2024-05-31 04:01:27,405 - mmdet - INFO - Epoch [3][6650/7330] lr: 1.000e-04, eta: 11:32:25, time: 0.605, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0285, loss_rpn_bbox: 0.0537, loss_cls: 0.2346, acc: 91.8411, loss_bbox: 0.2772, loss_mask: 0.2672, loss: 0.8613 +2024-05-31 04:01:57,655 - mmdet - INFO - Epoch [3][6700/7330] lr: 1.000e-04, eta: 11:31:51, time: 0.605, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0283, loss_rpn_bbox: 0.0545, loss_cls: 0.2330, acc: 92.0859, loss_bbox: 0.2709, loss_mask: 0.2664, loss: 0.8531 +2024-05-31 04:02:27,717 - mmdet - INFO - Epoch [3][6750/7330] lr: 1.000e-04, eta: 11:31:16, time: 0.601, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0255, loss_rpn_bbox: 0.0508, loss_cls: 0.2200, acc: 92.4944, loss_bbox: 0.2569, loss_mask: 0.2631, loss: 0.8162 +2024-05-31 04:02:58,461 - mmdet - INFO - Epoch [3][6800/7330] lr: 1.000e-04, eta: 11:30:44, time: 0.615, data_time: 0.063, memory: 9655, loss_rpn_cls: 0.0272, loss_rpn_bbox: 0.0554, loss_cls: 0.2349, acc: 91.7856, loss_bbox: 0.2733, loss_mask: 0.2690, loss: 0.8600 +2024-05-31 04:03:28,562 - mmdet - INFO - Epoch [3][6850/7330] lr: 1.000e-04, eta: 11:30:10, time: 0.602, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0270, loss_rpn_bbox: 0.0498, loss_cls: 0.2233, acc: 92.3267, loss_bbox: 0.2571, loss_mask: 0.2623, loss: 0.8195 +2024-05-31 04:03:59,254 - mmdet - INFO - Epoch [3][6900/7330] lr: 1.000e-04, eta: 11:29:37, time: 0.614, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0264, loss_rpn_bbox: 0.0532, loss_cls: 0.2275, acc: 92.1509, loss_bbox: 0.2718, loss_mask: 0.2689, loss: 0.8478 +2024-05-31 04:04:28,972 - mmdet - INFO - Epoch [3][6950/7330] lr: 1.000e-04, eta: 11:29:01, time: 0.595, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0265, loss_rpn_bbox: 0.0506, loss_cls: 0.2122, acc: 92.6602, loss_bbox: 0.2578, loss_mask: 0.2568, loss: 0.8039 +2024-05-31 04:04:58,921 - mmdet - INFO - Epoch [3][7000/7330] lr: 1.000e-04, eta: 11:28:26, time: 0.599, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0260, loss_rpn_bbox: 0.0511, loss_cls: 0.2181, acc: 92.4631, loss_bbox: 0.2609, loss_mask: 0.2567, loss: 0.8128 +2024-05-31 04:05:28,922 - mmdet - INFO - Epoch [3][7050/7330] lr: 1.000e-04, eta: 11:27:52, time: 0.600, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0299, loss_rpn_bbox: 0.0543, loss_cls: 0.2252, acc: 92.2405, loss_bbox: 0.2641, loss_mask: 0.2590, loss: 0.8325 +2024-05-31 04:05:59,079 - mmdet - INFO - Epoch [3][7100/7330] lr: 1.000e-04, eta: 11:27:18, time: 0.603, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0292, loss_rpn_bbox: 0.0531, loss_cls: 0.2269, acc: 92.2749, loss_bbox: 0.2683, loss_mask: 0.2660, loss: 0.8436 +2024-05-31 04:06:29,736 - mmdet - INFO - Epoch [3][7150/7330] lr: 1.000e-04, eta: 11:26:45, time: 0.613, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0266, loss_rpn_bbox: 0.0499, loss_cls: 0.2223, acc: 92.3252, loss_bbox: 0.2537, loss_mask: 0.2606, loss: 0.8132 +2024-05-31 04:06:59,999 - mmdet - INFO - Epoch [3][7200/7330] lr: 1.000e-04, eta: 11:26:11, time: 0.606, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0255, loss_rpn_bbox: 0.0537, loss_cls: 0.2290, acc: 92.1240, loss_bbox: 0.2683, loss_mask: 0.2636, loss: 0.8401 +2024-05-31 04:07:29,706 - mmdet - INFO - Epoch [3][7250/7330] lr: 1.000e-04, eta: 11:25:36, time: 0.594, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0267, loss_rpn_bbox: 0.0508, loss_cls: 0.2202, acc: 92.3782, loss_bbox: 0.2591, loss_mask: 0.2596, loss: 0.8163 +2024-05-31 04:08:02,334 - mmdet - INFO - Epoch [3][7300/7330] lr: 1.000e-04, eta: 11:25:09, time: 0.653, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0286, loss_rpn_bbox: 0.0547, loss_cls: 0.2257, acc: 92.1218, loss_bbox: 0.2694, loss_mask: 0.2648, loss: 0.8432 +2024-05-31 04:08:25,289 - mmdet - INFO - Saving checkpoint at 3 epochs +2024-05-31 04:10:03,744 - mmdet - INFO - Evaluating bbox... +2024-05-31 04:10:27,286 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.375 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.606 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.406 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.210 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.409 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.526 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.503 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.503 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.503 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.304 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.546 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.668 + +2024-05-31 04:10:27,287 - mmdet - INFO - Evaluating segm... +2024-05-31 04:10:53,758 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.348 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.574 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.365 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.150 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.375 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.554 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.463 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.463 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.463 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.249 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.508 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.657 + +2024-05-31 04:10:54,203 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 04:10:54,204 - mmdet - INFO - Epoch(val) [3][625] bbox_mAP: 0.3750, bbox_mAP_50: 0.6060, bbox_mAP_75: 0.4060, bbox_mAP_s: 0.2100, bbox_mAP_m: 0.4090, bbox_mAP_l: 0.5260, bbox_mAP_copypaste: 0.375 0.606 0.406 0.210 0.409 0.526, segm_mAP: 0.3480, segm_mAP_50: 0.5740, segm_mAP_75: 0.3650, segm_mAP_s: 0.1500, segm_mAP_m: 0.3750, segm_mAP_l: 0.5540, segm_mAP_copypaste: 0.348 0.574 0.365 0.150 0.375 0.554 +2024-05-31 04:11:35,373 - mmdet - INFO - Epoch [4][50/7330] lr: 1.000e-04, eta: 11:23:53, time: 0.823, data_time: 0.115, memory: 9655, loss_rpn_cls: 0.0238, loss_rpn_bbox: 0.0495, loss_cls: 0.2147, acc: 92.4702, loss_bbox: 0.2609, loss_mask: 0.2548, loss: 0.8037 +2024-05-31 04:12:12,192 - mmdet - INFO - Epoch [4][100/7330] lr: 1.000e-04, eta: 11:23:39, time: 0.736, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0249, loss_rpn_bbox: 0.0512, loss_cls: 0.2220, acc: 92.1599, loss_bbox: 0.2695, loss_mask: 0.2604, loss: 0.8280 +2024-05-31 04:12:42,141 - mmdet - INFO - Epoch [4][150/7330] lr: 1.000e-04, eta: 11:23:04, time: 0.599, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0242, loss_rpn_bbox: 0.0510, loss_cls: 0.2211, acc: 92.1882, loss_bbox: 0.2660, loss_mask: 0.2538, loss: 0.8161 +2024-05-31 04:13:11,784 - mmdet - INFO - Epoch [4][200/7330] lr: 1.000e-04, eta: 11:22:29, time: 0.593, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0228, loss_rpn_bbox: 0.0494, loss_cls: 0.2138, acc: 92.4377, loss_bbox: 0.2651, loss_mask: 0.2580, loss: 0.8091 +2024-05-31 04:13:42,159 - mmdet - INFO - Epoch [4][250/7330] lr: 1.000e-04, eta: 11:21:56, time: 0.607, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0222, loss_rpn_bbox: 0.0495, loss_cls: 0.2188, acc: 92.2651, loss_bbox: 0.2649, loss_mask: 0.2531, loss: 0.8085 +2024-05-31 04:14:12,067 - mmdet - INFO - Epoch [4][300/7330] lr: 1.000e-04, eta: 11:21:21, time: 0.598, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0248, loss_rpn_bbox: 0.0513, loss_cls: 0.2064, acc: 92.6538, loss_bbox: 0.2518, loss_mask: 0.2583, loss: 0.7926 +2024-05-31 04:14:41,763 - mmdet - INFO - Epoch [4][350/7330] lr: 1.000e-04, eta: 11:20:45, time: 0.594, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0247, loss_rpn_bbox: 0.0505, loss_cls: 0.2197, acc: 92.1804, loss_bbox: 0.2647, loss_mask: 0.2545, loss: 0.8142 +2024-05-31 04:15:12,102 - mmdet - INFO - Epoch [4][400/7330] lr: 1.000e-04, eta: 11:20:12, time: 0.606, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0261, loss_rpn_bbox: 0.0528, loss_cls: 0.2223, acc: 92.1545, loss_bbox: 0.2692, loss_mask: 0.2617, loss: 0.8321 +2024-05-31 04:15:42,233 - mmdet - INFO - Epoch [4][450/7330] lr: 1.000e-04, eta: 11:19:38, time: 0.603, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0250, loss_rpn_bbox: 0.0512, loss_cls: 0.2216, acc: 92.1094, loss_bbox: 0.2702, loss_mask: 0.2604, loss: 0.8285 +2024-05-31 04:16:12,602 - mmdet - INFO - Epoch [4][500/7330] lr: 1.000e-04, eta: 11:19:05, time: 0.607, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0240, loss_rpn_bbox: 0.0511, loss_cls: 0.2125, acc: 92.6021, loss_bbox: 0.2565, loss_mask: 0.2518, loss: 0.7958 +2024-05-31 04:16:43,590 - mmdet - INFO - Epoch [4][550/7330] lr: 1.000e-04, eta: 11:18:33, time: 0.620, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0269, loss_rpn_bbox: 0.0523, loss_cls: 0.2195, acc: 92.2688, loss_bbox: 0.2623, loss_mask: 0.2504, loss: 0.8114 +2024-05-31 04:17:13,795 - mmdet - INFO - Epoch [4][600/7330] lr: 1.000e-04, eta: 11:17:59, time: 0.604, data_time: 0.063, memory: 9655, loss_rpn_cls: 0.0244, loss_rpn_bbox: 0.0504, loss_cls: 0.2135, acc: 92.4700, loss_bbox: 0.2605, loss_mask: 0.2553, loss: 0.8041 +2024-05-31 04:17:43,837 - mmdet - INFO - Epoch [4][650/7330] lr: 1.000e-04, eta: 11:17:25, time: 0.601, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0210, loss_rpn_bbox: 0.0488, loss_cls: 0.2162, acc: 92.2827, loss_bbox: 0.2632, loss_mask: 0.2587, loss: 0.8079 +2024-05-31 04:18:13,559 - mmdet - INFO - Epoch [4][700/7330] lr: 1.000e-04, eta: 11:16:50, time: 0.594, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0239, loss_rpn_bbox: 0.0511, loss_cls: 0.2157, acc: 92.4128, loss_bbox: 0.2622, loss_mask: 0.2586, loss: 0.8115 +2024-05-31 04:18:43,799 - mmdet - INFO - Epoch [4][750/7330] lr: 1.000e-04, eta: 11:16:17, time: 0.605, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0239, loss_rpn_bbox: 0.0519, loss_cls: 0.2156, acc: 92.4028, loss_bbox: 0.2652, loss_mask: 0.2577, loss: 0.8143 +2024-05-31 04:19:13,323 - mmdet - INFO - Epoch [4][800/7330] lr: 1.000e-04, eta: 11:15:41, time: 0.590, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0242, loss_rpn_bbox: 0.0510, loss_cls: 0.2119, acc: 92.6147, loss_bbox: 0.2584, loss_mask: 0.2670, loss: 0.8125 +2024-05-31 04:19:43,247 - mmdet - INFO - Epoch [4][850/7330] lr: 1.000e-04, eta: 11:15:06, time: 0.598, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0238, loss_rpn_bbox: 0.0513, loss_cls: 0.2086, acc: 92.6741, loss_bbox: 0.2561, loss_mask: 0.2587, loss: 0.7985 +2024-05-31 04:20:15,655 - mmdet - INFO - Epoch [4][900/7330] lr: 1.000e-04, eta: 11:14:39, time: 0.648, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0254, loss_rpn_bbox: 0.0519, loss_cls: 0.2163, acc: 92.3081, loss_bbox: 0.2629, loss_mask: 0.2629, loss: 0.8194 +2024-05-31 04:20:48,007 - mmdet - INFO - Epoch [4][950/7330] lr: 1.000e-04, eta: 11:14:11, time: 0.647, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0246, loss_rpn_bbox: 0.0507, loss_cls: 0.2245, acc: 92.2454, loss_bbox: 0.2691, loss_mask: 0.2588, loss: 0.8277 +2024-05-31 04:21:25,055 - mmdet - INFO - Epoch [4][1000/7330] lr: 1.000e-04, eta: 11:13:57, time: 0.741, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0231, loss_rpn_bbox: 0.0505, loss_cls: 0.2150, acc: 92.4729, loss_bbox: 0.2595, loss_mask: 0.2595, loss: 0.8076 +2024-05-31 04:21:55,091 - mmdet - INFO - Epoch [4][1050/7330] lr: 1.000e-04, eta: 11:13:23, time: 0.601, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0221, loss_rpn_bbox: 0.0516, loss_cls: 0.2074, acc: 92.7163, loss_bbox: 0.2595, loss_mask: 0.2622, loss: 0.8029 +2024-05-31 04:22:25,379 - mmdet - INFO - Epoch [4][1100/7330] lr: 1.000e-04, eta: 11:12:50, time: 0.606, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0248, loss_rpn_bbox: 0.0501, loss_cls: 0.2058, acc: 92.7559, loss_bbox: 0.2488, loss_mask: 0.2484, loss: 0.7780 +2024-05-31 04:22:57,943 - mmdet - INFO - Epoch [4][1150/7330] lr: 1.000e-04, eta: 11:12:22, time: 0.651, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0251, loss_rpn_bbox: 0.0516, loss_cls: 0.2092, acc: 92.6533, loss_bbox: 0.2527, loss_mask: 0.2564, loss: 0.7949 +2024-05-31 04:23:31,439 - mmdet - INFO - Epoch [4][1200/7330] lr: 1.000e-04, eta: 11:11:58, time: 0.670, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0280, loss_rpn_bbox: 0.0531, loss_cls: 0.2150, acc: 92.3848, loss_bbox: 0.2630, loss_mask: 0.2550, loss: 0.8142 +2024-05-31 04:24:04,023 - mmdet - INFO - Epoch [4][1250/7330] lr: 1.000e-04, eta: 11:11:31, time: 0.652, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0516, loss_cls: 0.2136, acc: 92.4092, loss_bbox: 0.2617, loss_mask: 0.2593, loss: 0.8098 +2024-05-31 04:24:34,160 - mmdet - INFO - Epoch [4][1300/7330] lr: 1.000e-04, eta: 11:10:57, time: 0.603, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0239, loss_rpn_bbox: 0.0510, loss_cls: 0.2208, acc: 92.4170, loss_bbox: 0.2613, loss_mask: 0.2569, loss: 0.8139 +2024-05-31 04:25:04,422 - mmdet - INFO - Epoch [4][1350/7330] lr: 1.000e-04, eta: 11:10:24, time: 0.605, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0263, loss_rpn_bbox: 0.0529, loss_cls: 0.2217, acc: 92.2920, loss_bbox: 0.2626, loss_mask: 0.2566, loss: 0.8200 +2024-05-31 04:25:34,990 - mmdet - INFO - Epoch [4][1400/7330] lr: 1.000e-04, eta: 11:09:51, time: 0.611, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0258, loss_rpn_bbox: 0.0534, loss_cls: 0.2196, acc: 92.2678, loss_bbox: 0.2650, loss_mask: 0.2584, loss: 0.8221 +2024-05-31 04:26:05,248 - mmdet - INFO - Epoch [4][1450/7330] lr: 1.000e-04, eta: 11:09:17, time: 0.605, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0240, loss_rpn_bbox: 0.0528, loss_cls: 0.2208, acc: 92.3806, loss_bbox: 0.2640, loss_mask: 0.2614, loss: 0.8229 +2024-05-31 04:26:35,248 - mmdet - INFO - Epoch [4][1500/7330] lr: 1.000e-04, eta: 11:08:43, time: 0.600, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0251, loss_rpn_bbox: 0.0504, loss_cls: 0.2208, acc: 92.2676, loss_bbox: 0.2618, loss_mask: 0.2578, loss: 0.8159 +2024-05-31 04:27:06,141 - mmdet - INFO - Epoch [4][1550/7330] lr: 1.000e-04, eta: 11:08:11, time: 0.617, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0274, loss_rpn_bbox: 0.0551, loss_cls: 0.2256, acc: 92.0691, loss_bbox: 0.2676, loss_mask: 0.2569, loss: 0.8325 +2024-05-31 04:27:35,624 - mmdet - INFO - Epoch [4][1600/7330] lr: 1.000e-04, eta: 11:07:36, time: 0.590, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0234, loss_rpn_bbox: 0.0525, loss_cls: 0.2157, acc: 92.3708, loss_bbox: 0.2597, loss_mask: 0.2566, loss: 0.8080 +2024-05-31 04:28:05,959 - mmdet - INFO - Epoch [4][1650/7330] lr: 1.000e-04, eta: 11:07:03, time: 0.607, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0250, loss_rpn_bbox: 0.0519, loss_cls: 0.2192, acc: 92.2532, loss_bbox: 0.2653, loss_mask: 0.2646, loss: 0.8260 +2024-05-31 04:28:36,515 - mmdet - INFO - Epoch [4][1700/7330] lr: 1.000e-04, eta: 11:06:30, time: 0.611, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0245, loss_rpn_bbox: 0.0509, loss_cls: 0.2213, acc: 92.2361, loss_bbox: 0.2668, loss_mask: 0.2624, loss: 0.8259 +2024-05-31 04:29:07,114 - mmdet - INFO - Epoch [4][1750/7330] lr: 1.000e-04, eta: 11:05:57, time: 0.612, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0261, loss_rpn_bbox: 0.0535, loss_cls: 0.2178, acc: 92.2620, loss_bbox: 0.2673, loss_mask: 0.2554, loss: 0.8201 +2024-05-31 04:29:38,643 - mmdet - INFO - Epoch [4][1800/7330] lr: 1.000e-04, eta: 11:05:28, time: 0.631, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0218, loss_rpn_bbox: 0.0456, loss_cls: 0.1960, acc: 93.1094, loss_bbox: 0.2416, loss_mask: 0.2520, loss: 0.7570 +2024-05-31 04:30:17,033 - mmdet - INFO - Epoch [4][1850/7330] lr: 1.000e-04, eta: 11:05:16, time: 0.768, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0258, loss_rpn_bbox: 0.0528, loss_cls: 0.2176, acc: 92.3240, loss_bbox: 0.2630, loss_mask: 0.2580, loss: 0.8172 +2024-05-31 04:30:49,696 - mmdet - INFO - Epoch [4][1900/7330] lr: 1.000e-04, eta: 11:04:49, time: 0.653, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0260, loss_rpn_bbox: 0.0489, loss_cls: 0.2177, acc: 92.3484, loss_bbox: 0.2549, loss_mask: 0.2587, loss: 0.8063 +2024-05-31 04:31:19,589 - mmdet - INFO - Epoch [4][1950/7330] lr: 1.000e-04, eta: 11:04:15, time: 0.598, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0247, loss_rpn_bbox: 0.0517, loss_cls: 0.2231, acc: 92.1648, loss_bbox: 0.2688, loss_mask: 0.2596, loss: 0.8279 +2024-05-31 04:31:49,736 - mmdet - INFO - Epoch [4][2000/7330] lr: 1.000e-04, eta: 11:03:41, time: 0.603, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0252, loss_rpn_bbox: 0.0514, loss_cls: 0.2147, acc: 92.3459, loss_bbox: 0.2600, loss_mask: 0.2631, loss: 0.8144 +2024-05-31 04:32:22,401 - mmdet - INFO - Epoch [4][2050/7330] lr: 1.000e-04, eta: 11:03:14, time: 0.653, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0259, loss_rpn_bbox: 0.0523, loss_cls: 0.2178, acc: 92.2891, loss_bbox: 0.2610, loss_mask: 0.2606, loss: 0.8176 +2024-05-31 04:32:54,796 - mmdet - INFO - Epoch [4][2100/7330] lr: 1.000e-04, eta: 11:02:46, time: 0.648, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0243, loss_rpn_bbox: 0.0527, loss_cls: 0.2171, acc: 92.4055, loss_bbox: 0.2627, loss_mask: 0.2505, loss: 0.8073 +2024-05-31 04:33:27,569 - mmdet - INFO - Epoch [4][2150/7330] lr: 1.000e-04, eta: 11:02:19, time: 0.655, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0256, loss_rpn_bbox: 0.0520, loss_cls: 0.2171, acc: 92.3577, loss_bbox: 0.2646, loss_mask: 0.2566, loss: 0.8158 +2024-05-31 04:33:57,825 - mmdet - INFO - Epoch [4][2200/7330] lr: 1.000e-04, eta: 11:01:46, time: 0.605, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0249, loss_rpn_bbox: 0.0503, loss_cls: 0.2108, acc: 92.7065, loss_bbox: 0.2496, loss_mask: 0.2516, loss: 0.7873 +2024-05-31 04:34:27,996 - mmdet - INFO - Epoch [4][2250/7330] lr: 1.000e-04, eta: 11:01:12, time: 0.603, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0255, loss_rpn_bbox: 0.0501, loss_cls: 0.2144, acc: 92.4343, loss_bbox: 0.2627, loss_mask: 0.2549, loss: 0.8075 +2024-05-31 04:34:57,808 - mmdet - INFO - Epoch [4][2300/7330] lr: 1.000e-04, eta: 11:00:37, time: 0.596, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0231, loss_rpn_bbox: 0.0477, loss_cls: 0.2117, acc: 92.5742, loss_bbox: 0.2597, loss_mask: 0.2610, loss: 0.8032 +2024-05-31 04:35:28,385 - mmdet - INFO - Epoch [4][2350/7330] lr: 1.000e-04, eta: 11:00:05, time: 0.612, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0247, loss_rpn_bbox: 0.0512, loss_cls: 0.2204, acc: 92.2231, loss_bbox: 0.2629, loss_mask: 0.2565, loss: 0.8157 +2024-05-31 04:35:58,316 - mmdet - INFO - Epoch [4][2400/7330] lr: 1.000e-04, eta: 10:59:31, time: 0.599, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0243, loss_rpn_bbox: 0.0521, loss_cls: 0.2171, acc: 92.4739, loss_bbox: 0.2619, loss_mask: 0.2589, loss: 0.8144 +2024-05-31 04:36:28,810 - mmdet - INFO - Epoch [4][2450/7330] lr: 1.000e-04, eta: 10:58:58, time: 0.609, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0243, loss_rpn_bbox: 0.0487, loss_cls: 0.2116, acc: 92.5020, loss_bbox: 0.2529, loss_mask: 0.2540, loss: 0.7915 +2024-05-31 04:36:58,681 - mmdet - INFO - Epoch [4][2500/7330] lr: 1.000e-04, eta: 10:58:24, time: 0.598, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0240, loss_rpn_bbox: 0.0501, loss_cls: 0.2070, acc: 92.6477, loss_bbox: 0.2541, loss_mask: 0.2528, loss: 0.7880 +2024-05-31 04:37:28,446 - mmdet - INFO - Epoch [4][2550/7330] lr: 1.000e-04, eta: 10:57:49, time: 0.595, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0218, loss_rpn_bbox: 0.0457, loss_cls: 0.2007, acc: 92.9307, loss_bbox: 0.2436, loss_mask: 0.2507, loss: 0.7626 +2024-05-31 04:37:58,077 - mmdet - INFO - Epoch [4][2600/7330] lr: 1.000e-04, eta: 10:57:14, time: 0.593, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0265, loss_rpn_bbox: 0.0533, loss_cls: 0.2203, acc: 92.3301, loss_bbox: 0.2663, loss_mask: 0.2637, loss: 0.8301 +2024-05-31 04:38:27,798 - mmdet - INFO - Epoch [4][2650/7330] lr: 1.000e-04, eta: 10:56:39, time: 0.594, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0250, loss_rpn_bbox: 0.0487, loss_cls: 0.2109, acc: 92.4443, loss_bbox: 0.2569, loss_mask: 0.2555, loss: 0.7969 +2024-05-31 04:39:00,183 - mmdet - INFO - Epoch [4][2700/7330] lr: 1.000e-04, eta: 10:56:11, time: 0.648, data_time: 0.063, memory: 9655, loss_rpn_cls: 0.0255, loss_rpn_bbox: 0.0529, loss_cls: 0.2180, acc: 92.2534, loss_bbox: 0.2676, loss_mask: 0.2600, loss: 0.8240 +2024-05-31 04:39:40,823 - mmdet - INFO - Epoch [4][2750/7330] lr: 1.000e-04, eta: 10:56:05, time: 0.813, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0228, loss_rpn_bbox: 0.0483, loss_cls: 0.2165, acc: 92.3801, loss_bbox: 0.2599, loss_mask: 0.2545, loss: 0.8020 +2024-05-31 04:40:10,260 - mmdet - INFO - Epoch [4][2800/7330] lr: 1.000e-04, eta: 10:55:29, time: 0.589, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0246, loss_rpn_bbox: 0.0495, loss_cls: 0.2055, acc: 92.7549, loss_bbox: 0.2496, loss_mask: 0.2519, loss: 0.7812 +2024-05-31 04:40:40,473 - mmdet - INFO - Epoch [4][2850/7330] lr: 1.000e-04, eta: 10:54:56, time: 0.604, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0476, loss_cls: 0.2153, acc: 92.4966, loss_bbox: 0.2551, loss_mask: 0.2558, loss: 0.7973 +2024-05-31 04:41:10,580 - mmdet - INFO - Epoch [4][2900/7330] lr: 1.000e-04, eta: 10:54:22, time: 0.602, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0251, loss_rpn_bbox: 0.0541, loss_cls: 0.2178, acc: 92.3203, loss_bbox: 0.2612, loss_mask: 0.2645, loss: 0.8228 +2024-05-31 04:41:45,162 - mmdet - INFO - Epoch [4][2950/7330] lr: 1.000e-04, eta: 10:54:00, time: 0.692, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0250, loss_rpn_bbox: 0.0504, loss_cls: 0.2164, acc: 92.4087, loss_bbox: 0.2628, loss_mask: 0.2621, loss: 0.8166 +2024-05-31 04:42:19,710 - mmdet - INFO - Epoch [4][3000/7330] lr: 1.000e-04, eta: 10:53:37, time: 0.691, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0239, loss_rpn_bbox: 0.0499, loss_cls: 0.2172, acc: 92.4746, loss_bbox: 0.2575, loss_mask: 0.2634, loss: 0.8121 +2024-05-31 04:42:50,078 - mmdet - INFO - Epoch [4][3050/7330] lr: 1.000e-04, eta: 10:53:04, time: 0.608, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0251, loss_rpn_bbox: 0.0515, loss_cls: 0.2207, acc: 92.2007, loss_bbox: 0.2658, loss_mask: 0.2594, loss: 0.8225 +2024-05-31 04:43:20,157 - mmdet - INFO - Epoch [4][3100/7330] lr: 1.000e-04, eta: 10:52:30, time: 0.602, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0229, loss_rpn_bbox: 0.0481, loss_cls: 0.2138, acc: 92.6699, loss_bbox: 0.2515, loss_mask: 0.2588, loss: 0.7952 +2024-05-31 04:43:50,712 - mmdet - INFO - Epoch [4][3150/7330] lr: 1.000e-04, eta: 10:51:58, time: 0.611, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0240, loss_rpn_bbox: 0.0478, loss_cls: 0.2060, acc: 92.8694, loss_bbox: 0.2485, loss_mask: 0.2575, loss: 0.7839 +2024-05-31 04:44:20,842 - mmdet - INFO - Epoch [4][3200/7330] lr: 1.000e-04, eta: 10:51:24, time: 0.603, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0252, loss_rpn_bbox: 0.0506, loss_cls: 0.2183, acc: 92.2542, loss_bbox: 0.2690, loss_mask: 0.2605, loss: 0.8236 +2024-05-31 04:44:51,648 - mmdet - INFO - Epoch [4][3250/7330] lr: 1.000e-04, eta: 10:50:52, time: 0.616, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0247, loss_rpn_bbox: 0.0524, loss_cls: 0.2204, acc: 92.2231, loss_bbox: 0.2665, loss_mask: 0.2621, loss: 0.8260 +2024-05-31 04:45:21,622 - mmdet - INFO - Epoch [4][3300/7330] lr: 1.000e-04, eta: 10:50:18, time: 0.599, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0241, loss_rpn_bbox: 0.0485, loss_cls: 0.2107, acc: 92.6260, loss_bbox: 0.2526, loss_mask: 0.2540, loss: 0.7898 +2024-05-31 04:45:51,735 - mmdet - INFO - Epoch [4][3350/7330] lr: 1.000e-04, eta: 10:49:44, time: 0.602, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0237, loss_rpn_bbox: 0.0502, loss_cls: 0.2112, acc: 92.5520, loss_bbox: 0.2611, loss_mask: 0.2575, loss: 0.8037 +2024-05-31 04:46:22,087 - mmdet - INFO - Epoch [4][3400/7330] lr: 1.000e-04, eta: 10:49:11, time: 0.607, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0258, loss_rpn_bbox: 0.0533, loss_cls: 0.2145, acc: 92.3726, loss_bbox: 0.2622, loss_mask: 0.2543, loss: 0.8100 +2024-05-31 04:46:52,039 - mmdet - INFO - Epoch [4][3450/7330] lr: 1.000e-04, eta: 10:48:37, time: 0.599, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0232, loss_rpn_bbox: 0.0472, loss_cls: 0.2106, acc: 92.5525, loss_bbox: 0.2560, loss_mask: 0.2597, loss: 0.7966 +2024-05-31 04:47:22,195 - mmdet - INFO - Epoch [4][3500/7330] lr: 1.000e-04, eta: 10:48:04, time: 0.603, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0251, loss_rpn_bbox: 0.0515, loss_cls: 0.2237, acc: 92.1423, loss_bbox: 0.2712, loss_mask: 0.2639, loss: 0.8355 +2024-05-31 04:47:52,277 - mmdet - INFO - Epoch [4][3550/7330] lr: 1.000e-04, eta: 10:47:30, time: 0.602, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0266, loss_rpn_bbox: 0.0520, loss_cls: 0.2178, acc: 92.3518, loss_bbox: 0.2567, loss_mask: 0.2540, loss: 0.8071 +2024-05-31 04:48:24,928 - mmdet - INFO - Epoch [4][3600/7330] lr: 1.000e-04, eta: 10:47:03, time: 0.653, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0243, loss_rpn_bbox: 0.0511, loss_cls: 0.2129, acc: 92.4792, loss_bbox: 0.2589, loss_mask: 0.2526, loss: 0.7999 +2024-05-31 04:49:04,620 - mmdet - INFO - Epoch [4][3650/7330] lr: 1.000e-04, eta: 10:46:53, time: 0.794, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0245, loss_rpn_bbox: 0.0497, loss_cls: 0.2138, acc: 92.3984, loss_bbox: 0.2616, loss_mask: 0.2571, loss: 0.8067 +2024-05-31 04:49:35,148 - mmdet - INFO - Epoch [4][3700/7330] lr: 1.000e-04, eta: 10:46:20, time: 0.611, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0493, loss_cls: 0.2099, acc: 92.6289, loss_bbox: 0.2526, loss_mask: 0.2495, loss: 0.7848 +2024-05-31 04:50:05,840 - mmdet - INFO - Epoch [4][3750/7330] lr: 1.000e-04, eta: 10:45:48, time: 0.614, data_time: 0.065, memory: 9655, loss_rpn_cls: 0.0239, loss_rpn_bbox: 0.0504, loss_cls: 0.2155, acc: 92.4104, loss_bbox: 0.2612, loss_mask: 0.2548, loss: 0.8059 +2024-05-31 04:50:36,665 - mmdet - INFO - Epoch [4][3800/7330] lr: 1.000e-04, eta: 10:45:16, time: 0.617, data_time: 0.063, memory: 9655, loss_rpn_cls: 0.0249, loss_rpn_bbox: 0.0514, loss_cls: 0.2131, acc: 92.6089, loss_bbox: 0.2558, loss_mask: 0.2607, loss: 0.8059 +2024-05-31 04:51:10,250 - mmdet - INFO - Epoch [4][3850/7330] lr: 1.000e-04, eta: 10:44:51, time: 0.672, data_time: 0.066, memory: 9655, loss_rpn_cls: 0.0256, loss_rpn_bbox: 0.0505, loss_cls: 0.2101, acc: 92.6206, loss_bbox: 0.2516, loss_mask: 0.2583, loss: 0.7961 +2024-05-31 04:51:44,627 - mmdet - INFO - Epoch [4][3900/7330] lr: 1.000e-04, eta: 10:44:27, time: 0.688, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0242, loss_rpn_bbox: 0.0519, loss_cls: 0.2170, acc: 92.5527, loss_bbox: 0.2590, loss_mask: 0.2549, loss: 0.8070 +2024-05-31 04:52:14,463 - mmdet - INFO - Epoch [4][3950/7330] lr: 1.000e-04, eta: 10:43:53, time: 0.597, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0226, loss_rpn_bbox: 0.0490, loss_cls: 0.2067, acc: 92.7400, loss_bbox: 0.2516, loss_mask: 0.2510, loss: 0.7808 +2024-05-31 04:52:44,816 - mmdet - INFO - Epoch [4][4000/7330] lr: 1.000e-04, eta: 10:43:20, time: 0.607, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0264, loss_rpn_bbox: 0.0573, loss_cls: 0.2205, acc: 92.1492, loss_bbox: 0.2685, loss_mask: 0.2673, loss: 0.8400 +2024-05-31 04:53:15,318 - mmdet - INFO - Epoch [4][4050/7330] lr: 1.000e-04, eta: 10:42:47, time: 0.610, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0243, loss_rpn_bbox: 0.0527, loss_cls: 0.2128, acc: 92.4504, loss_bbox: 0.2622, loss_mask: 0.2596, loss: 0.8116 +2024-05-31 04:53:45,455 - mmdet - INFO - Epoch [4][4100/7330] lr: 1.000e-04, eta: 10:42:14, time: 0.603, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0246, loss_rpn_bbox: 0.0509, loss_cls: 0.2040, acc: 92.8716, loss_bbox: 0.2476, loss_mask: 0.2519, loss: 0.7790 +2024-05-31 04:54:15,594 - mmdet - INFO - Epoch [4][4150/7330] lr: 1.000e-04, eta: 10:41:40, time: 0.603, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0237, loss_rpn_bbox: 0.0481, loss_cls: 0.2114, acc: 92.5906, loss_bbox: 0.2563, loss_mask: 0.2529, loss: 0.7924 +2024-05-31 04:54:45,948 - mmdet - INFO - Epoch [4][4200/7330] lr: 1.000e-04, eta: 10:41:07, time: 0.607, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0241, loss_rpn_bbox: 0.0511, loss_cls: 0.2180, acc: 92.3862, loss_bbox: 0.2583, loss_mask: 0.2535, loss: 0.8051 +2024-05-31 04:55:15,502 - mmdet - INFO - Epoch [4][4250/7330] lr: 1.000e-04, eta: 10:40:32, time: 0.591, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0227, loss_rpn_bbox: 0.0472, loss_cls: 0.2033, acc: 92.9497, loss_bbox: 0.2452, loss_mask: 0.2535, loss: 0.7719 +2024-05-31 04:55:45,749 - mmdet - INFO - Epoch [4][4300/7330] lr: 1.000e-04, eta: 10:39:59, time: 0.605, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0234, loss_rpn_bbox: 0.0506, loss_cls: 0.2170, acc: 92.3735, loss_bbox: 0.2612, loss_mask: 0.2594, loss: 0.8117 +2024-05-31 04:56:15,823 - mmdet - INFO - Epoch [4][4350/7330] lr: 1.000e-04, eta: 10:39:25, time: 0.601, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0240, loss_rpn_bbox: 0.0475, loss_cls: 0.2165, acc: 92.4121, loss_bbox: 0.2570, loss_mask: 0.2554, loss: 0.8005 +2024-05-31 04:56:45,976 - mmdet - INFO - Epoch [4][4400/7330] lr: 1.000e-04, eta: 10:38:52, time: 0.603, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0274, loss_rpn_bbox: 0.0510, loss_cls: 0.2187, acc: 92.4197, loss_bbox: 0.2534, loss_mask: 0.2573, loss: 0.8078 +2024-05-31 04:57:18,828 - mmdet - INFO - Epoch [4][4450/7330] lr: 1.000e-04, eta: 10:38:25, time: 0.657, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0240, loss_rpn_bbox: 0.0490, loss_cls: 0.2106, acc: 92.5723, loss_bbox: 0.2567, loss_mask: 0.2522, loss: 0.7925 +2024-05-31 04:57:50,875 - mmdet - INFO - Epoch [4][4500/7330] lr: 1.000e-04, eta: 10:37:56, time: 0.641, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0269, loss_rpn_bbox: 0.0511, loss_cls: 0.2167, acc: 92.4131, loss_bbox: 0.2600, loss_mask: 0.2518, loss: 0.8064 +2024-05-31 04:58:29,411 - mmdet - INFO - Epoch [4][4550/7330] lr: 1.000e-04, eta: 10:37:42, time: 0.771, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0239, loss_rpn_bbox: 0.0480, loss_cls: 0.2090, acc: 92.7573, loss_bbox: 0.2475, loss_mask: 0.2548, loss: 0.7832 +2024-05-31 04:59:00,767 - mmdet - INFO - Epoch [4][4600/7330] lr: 1.000e-04, eta: 10:37:11, time: 0.627, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0258, loss_rpn_bbox: 0.0542, loss_cls: 0.2188, acc: 92.2822, loss_bbox: 0.2638, loss_mask: 0.2584, loss: 0.8209 +2024-05-31 04:59:30,877 - mmdet - INFO - Epoch [4][4650/7330] lr: 1.000e-04, eta: 10:36:38, time: 0.602, data_time: 0.038, memory: 9655, loss_rpn_cls: 0.0240, loss_rpn_bbox: 0.0504, loss_cls: 0.2098, acc: 92.5691, loss_bbox: 0.2510, loss_mask: 0.2560, loss: 0.7912 +2024-05-31 05:00:00,923 - mmdet - INFO - Epoch [4][4700/7330] lr: 1.000e-04, eta: 10:36:04, time: 0.601, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0251, loss_rpn_bbox: 0.0495, loss_cls: 0.2074, acc: 92.7178, loss_bbox: 0.2534, loss_mask: 0.2566, loss: 0.7921 +2024-05-31 05:00:32,979 - mmdet - INFO - Epoch [4][4750/7330] lr: 1.000e-04, eta: 10:35:35, time: 0.641, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0275, loss_rpn_bbox: 0.0547, loss_cls: 0.2238, acc: 92.2561, loss_bbox: 0.2678, loss_mask: 0.2619, loss: 0.8357 +2024-05-31 05:01:07,166 - mmdet - INFO - Epoch [4][4800/7330] lr: 1.000e-04, eta: 10:35:11, time: 0.684, data_time: 0.070, memory: 9655, loss_rpn_cls: 0.0256, loss_rpn_bbox: 0.0529, loss_cls: 0.2216, acc: 92.2505, loss_bbox: 0.2671, loss_mask: 0.2577, loss: 0.8249 +2024-05-31 05:01:37,299 - mmdet - INFO - Epoch [4][4850/7330] lr: 1.000e-04, eta: 10:34:37, time: 0.603, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0245, loss_rpn_bbox: 0.0509, loss_cls: 0.2208, acc: 92.1960, loss_bbox: 0.2699, loss_mask: 0.2588, loss: 0.8249 +2024-05-31 05:02:07,633 - mmdet - INFO - Epoch [4][4900/7330] lr: 1.000e-04, eta: 10:34:04, time: 0.607, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0240, loss_rpn_bbox: 0.0500, loss_cls: 0.2130, acc: 92.4890, loss_bbox: 0.2546, loss_mask: 0.2568, loss: 0.7984 +2024-05-31 05:02:37,702 - mmdet - INFO - Epoch [4][4950/7330] lr: 1.000e-04, eta: 10:33:31, time: 0.601, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0243, loss_rpn_bbox: 0.0486, loss_cls: 0.2154, acc: 92.4375, loss_bbox: 0.2548, loss_mask: 0.2579, loss: 0.8010 +2024-05-31 05:03:07,650 - mmdet - INFO - Epoch [4][5000/7330] lr: 1.000e-04, eta: 10:32:57, time: 0.599, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0242, loss_rpn_bbox: 0.0521, loss_cls: 0.2126, acc: 92.4084, loss_bbox: 0.2572, loss_mask: 0.2572, loss: 0.8034 +2024-05-31 05:03:37,685 - mmdet - INFO - Epoch [4][5050/7330] lr: 1.000e-04, eta: 10:32:23, time: 0.601, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0245, loss_rpn_bbox: 0.0519, loss_cls: 0.2155, acc: 92.3555, loss_bbox: 0.2638, loss_mask: 0.2609, loss: 0.8166 +2024-05-31 05:04:07,702 - mmdet - INFO - Epoch [4][5100/7330] lr: 1.000e-04, eta: 10:31:49, time: 0.600, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0259, loss_rpn_bbox: 0.0504, loss_cls: 0.2174, acc: 92.3289, loss_bbox: 0.2593, loss_mask: 0.2630, loss: 0.8161 +2024-05-31 05:04:37,546 - mmdet - INFO - Epoch [4][5150/7330] lr: 1.000e-04, eta: 10:31:15, time: 0.597, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0253, loss_rpn_bbox: 0.0468, loss_cls: 0.2148, acc: 92.5483, loss_bbox: 0.2550, loss_mask: 0.2513, loss: 0.7932 +2024-05-31 05:05:07,640 - mmdet - INFO - Epoch [4][5200/7330] lr: 1.000e-04, eta: 10:30:42, time: 0.602, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0504, loss_cls: 0.2109, acc: 92.6287, loss_bbox: 0.2564, loss_mask: 0.2537, loss: 0.7949 +2024-05-31 05:05:37,724 - mmdet - INFO - Epoch [4][5250/7330] lr: 1.000e-04, eta: 10:30:08, time: 0.602, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0240, loss_rpn_bbox: 0.0494, loss_cls: 0.2086, acc: 92.7400, loss_bbox: 0.2479, loss_mask: 0.2519, loss: 0.7819 +2024-05-31 05:06:07,382 - mmdet - INFO - Epoch [4][5300/7330] lr: 1.000e-04, eta: 10:29:34, time: 0.593, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0245, loss_rpn_bbox: 0.0509, loss_cls: 0.2093, acc: 92.6763, loss_bbox: 0.2560, loss_mask: 0.2545, loss: 0.7951 +2024-05-31 05:06:39,527 - mmdet - INFO - Epoch [4][5350/7330] lr: 1.000e-04, eta: 10:29:05, time: 0.643, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0270, loss_rpn_bbox: 0.0539, loss_cls: 0.2210, acc: 92.1594, loss_bbox: 0.2738, loss_mask: 0.2623, loss: 0.8380 +2024-05-31 05:07:14,656 - mmdet - INFO - Epoch [4][5400/7330] lr: 1.000e-04, eta: 10:28:43, time: 0.703, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0258, loss_rpn_bbox: 0.0507, loss_cls: 0.2221, acc: 92.3103, loss_bbox: 0.2570, loss_mask: 0.2513, loss: 0.8069 +2024-05-31 05:07:49,332 - mmdet - INFO - Epoch [4][5450/7330] lr: 1.000e-04, eta: 10:28:19, time: 0.693, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0253, loss_rpn_bbox: 0.0507, loss_cls: 0.2183, acc: 92.4275, loss_bbox: 0.2601, loss_mask: 0.2589, loss: 0.8132 +2024-05-31 05:08:19,285 - mmdet - INFO - Epoch [4][5500/7330] lr: 1.000e-04, eta: 10:27:46, time: 0.599, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0236, loss_rpn_bbox: 0.0499, loss_cls: 0.2099, acc: 92.6208, loss_bbox: 0.2530, loss_mask: 0.2568, loss: 0.7933 +2024-05-31 05:08:49,061 - mmdet - INFO - Epoch [4][5550/7330] lr: 1.000e-04, eta: 10:27:12, time: 0.596, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0245, loss_rpn_bbox: 0.0484, loss_cls: 0.2132, acc: 92.5378, loss_bbox: 0.2589, loss_mask: 0.2539, loss: 0.7988 +2024-05-31 05:09:22,902 - mmdet - INFO - Epoch [4][5600/7330] lr: 1.000e-04, eta: 10:26:46, time: 0.677, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0234, loss_rpn_bbox: 0.0477, loss_cls: 0.2102, acc: 92.5466, loss_bbox: 0.2566, loss_mask: 0.2524, loss: 0.7903 +2024-05-31 05:09:55,970 - mmdet - INFO - Epoch [4][5650/7330] lr: 1.000e-04, eta: 10:26:19, time: 0.661, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0238, loss_rpn_bbox: 0.0531, loss_cls: 0.2189, acc: 92.3323, loss_bbox: 0.2585, loss_mask: 0.2534, loss: 0.8076 +2024-05-31 05:10:27,974 - mmdet - INFO - Epoch [4][5700/7330] lr: 1.000e-04, eta: 10:25:50, time: 0.640, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0232, loss_rpn_bbox: 0.0457, loss_cls: 0.2037, acc: 92.8020, loss_bbox: 0.2459, loss_mask: 0.2525, loss: 0.7709 +2024-05-31 05:10:58,427 - mmdet - INFO - Epoch [4][5750/7330] lr: 1.000e-04, eta: 10:25:17, time: 0.609, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0270, loss_rpn_bbox: 0.0525, loss_cls: 0.2237, acc: 92.1414, loss_bbox: 0.2636, loss_mask: 0.2591, loss: 0.8260 +2024-05-31 05:11:28,267 - mmdet - INFO - Epoch [4][5800/7330] lr: 1.000e-04, eta: 10:24:43, time: 0.597, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0225, loss_rpn_bbox: 0.0490, loss_cls: 0.2070, acc: 92.6592, loss_bbox: 0.2528, loss_mask: 0.2530, loss: 0.7843 +2024-05-31 05:11:58,957 - mmdet - INFO - Epoch [4][5850/7330] lr: 1.000e-04, eta: 10:24:11, time: 0.614, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0251, loss_rpn_bbox: 0.0538, loss_cls: 0.2183, acc: 92.3530, loss_bbox: 0.2596, loss_mask: 0.2564, loss: 0.8131 +2024-05-31 05:12:28,412 - mmdet - INFO - Epoch [4][5900/7330] lr: 1.000e-04, eta: 10:23:37, time: 0.589, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0249, loss_rpn_bbox: 0.0475, loss_cls: 0.2099, acc: 92.7034, loss_bbox: 0.2552, loss_mask: 0.2611, loss: 0.7987 +2024-05-31 05:12:58,102 - mmdet - INFO - Epoch [4][5950/7330] lr: 1.000e-04, eta: 10:23:02, time: 0.593, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0247, loss_rpn_bbox: 0.0498, loss_cls: 0.2121, acc: 92.6501, loss_bbox: 0.2550, loss_mask: 0.2553, loss: 0.7969 +2024-05-31 05:13:28,387 - mmdet - INFO - Epoch [4][6000/7330] lr: 1.000e-04, eta: 10:22:29, time: 0.606, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0256, loss_rpn_bbox: 0.0518, loss_cls: 0.2204, acc: 92.3184, loss_bbox: 0.2634, loss_mask: 0.2528, loss: 0.8141 +2024-05-31 05:13:58,450 - mmdet - INFO - Epoch [4][6050/7330] lr: 1.000e-04, eta: 10:21:56, time: 0.601, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0250, loss_rpn_bbox: 0.0501, loss_cls: 0.2242, acc: 92.2043, loss_bbox: 0.2584, loss_mask: 0.2572, loss: 0.8149 +2024-05-31 05:14:28,616 - mmdet - INFO - Epoch [4][6100/7330] lr: 1.000e-04, eta: 10:21:23, time: 0.603, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0236, loss_rpn_bbox: 0.0497, loss_cls: 0.2153, acc: 92.5803, loss_bbox: 0.2535, loss_mask: 0.2517, loss: 0.7938 +2024-05-31 05:14:59,160 - mmdet - INFO - Epoch [4][6150/7330] lr: 1.000e-04, eta: 10:20:50, time: 0.611, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0255, loss_rpn_bbox: 0.0489, loss_cls: 0.2216, acc: 92.2266, loss_bbox: 0.2639, loss_mask: 0.2567, loss: 0.8168 +2024-05-31 05:15:29,588 - mmdet - INFO - Epoch [4][6200/7330] lr: 1.000e-04, eta: 10:20:18, time: 0.609, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0278, loss_rpn_bbox: 0.0535, loss_cls: 0.2304, acc: 92.0115, loss_bbox: 0.2698, loss_mask: 0.2582, loss: 0.8397 +2024-05-31 05:16:01,861 - mmdet - INFO - Epoch [4][6250/7330] lr: 1.000e-04, eta: 10:19:49, time: 0.645, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0256, loss_rpn_bbox: 0.0469, loss_cls: 0.2148, acc: 92.4673, loss_bbox: 0.2548, loss_mask: 0.2546, loss: 0.7968 +2024-05-31 05:16:39,204 - mmdet - INFO - Epoch [4][6300/7330] lr: 1.000e-04, eta: 10:19:31, time: 0.747, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0215, loss_rpn_bbox: 0.0499, loss_cls: 0.2113, acc: 92.6582, loss_bbox: 0.2528, loss_mask: 0.2494, loss: 0.7849 +2024-05-31 05:17:10,825 - mmdet - INFO - Epoch [4][6350/7330] lr: 1.000e-04, eta: 10:19:01, time: 0.632, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0228, loss_rpn_bbox: 0.0492, loss_cls: 0.2050, acc: 92.7732, loss_bbox: 0.2470, loss_mask: 0.2555, loss: 0.7793 +2024-05-31 05:17:40,333 - mmdet - INFO - Epoch [4][6400/7330] lr: 1.000e-04, eta: 10:18:26, time: 0.590, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0265, loss_rpn_bbox: 0.0500, loss_cls: 0.2207, acc: 92.2141, loss_bbox: 0.2655, loss_mask: 0.2636, loss: 0.8263 +2024-05-31 05:18:10,005 - mmdet - INFO - Epoch [4][6450/7330] lr: 1.000e-04, eta: 10:17:52, time: 0.593, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0231, loss_rpn_bbox: 0.0495, loss_cls: 0.2097, acc: 92.7246, loss_bbox: 0.2481, loss_mask: 0.2475, loss: 0.7779 +2024-05-31 05:18:42,159 - mmdet - INFO - Epoch [4][6500/7330] lr: 1.000e-04, eta: 10:17:23, time: 0.643, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0247, loss_rpn_bbox: 0.0497, loss_cls: 0.2142, acc: 92.4880, loss_bbox: 0.2575, loss_mask: 0.2570, loss: 0.8032 +2024-05-31 05:19:16,656 - mmdet - INFO - Epoch [4][6550/7330] lr: 1.000e-04, eta: 10:16:59, time: 0.690, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0255, loss_rpn_bbox: 0.0508, loss_cls: 0.2101, acc: 92.4229, loss_bbox: 0.2560, loss_mask: 0.2481, loss: 0.7904 +2024-05-31 05:19:46,787 - mmdet - INFO - Epoch [4][6600/7330] lr: 1.000e-04, eta: 10:16:25, time: 0.603, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0252, loss_rpn_bbox: 0.0511, loss_cls: 0.2164, acc: 92.4399, loss_bbox: 0.2571, loss_mask: 0.2575, loss: 0.8073 +2024-05-31 05:20:16,488 - mmdet - INFO - Epoch [4][6650/7330] lr: 1.000e-04, eta: 10:15:51, time: 0.594, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0232, loss_rpn_bbox: 0.0461, loss_cls: 0.2041, acc: 92.8254, loss_bbox: 0.2482, loss_mask: 0.2505, loss: 0.7721 +2024-05-31 05:20:46,763 - mmdet - INFO - Epoch [4][6700/7330] lr: 1.000e-04, eta: 10:15:18, time: 0.605, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0242, loss_rpn_bbox: 0.0505, loss_cls: 0.2166, acc: 92.3828, loss_bbox: 0.2618, loss_mask: 0.2593, loss: 0.8123 +2024-05-31 05:21:16,253 - mmdet - INFO - Epoch [4][6750/7330] lr: 1.000e-04, eta: 10:14:44, time: 0.590, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0446, loss_cls: 0.1991, acc: 93.0435, loss_bbox: 0.2394, loss_mask: 0.2465, loss: 0.7504 +2024-05-31 05:21:46,126 - mmdet - INFO - Epoch [4][6800/7330] lr: 1.000e-04, eta: 10:14:10, time: 0.597, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0277, loss_rpn_bbox: 0.0496, loss_cls: 0.2162, acc: 92.5757, loss_bbox: 0.2494, loss_mask: 0.2635, loss: 0.8064 +2024-05-31 05:22:16,802 - mmdet - INFO - Epoch [4][6850/7330] lr: 1.000e-04, eta: 10:13:38, time: 0.614, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0245, loss_rpn_bbox: 0.0496, loss_cls: 0.2144, acc: 92.6653, loss_bbox: 0.2539, loss_mask: 0.2560, loss: 0.7984 +2024-05-31 05:22:46,941 - mmdet - INFO - Epoch [4][6900/7330] lr: 1.000e-04, eta: 10:13:05, time: 0.603, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0247, loss_rpn_bbox: 0.0521, loss_cls: 0.2141, acc: 92.6262, loss_bbox: 0.2518, loss_mask: 0.2534, loss: 0.7961 +2024-05-31 05:23:16,591 - mmdet - INFO - Epoch [4][6950/7330] lr: 1.000e-04, eta: 10:12:31, time: 0.593, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0252, loss_rpn_bbox: 0.0499, loss_cls: 0.2095, acc: 92.7644, loss_bbox: 0.2430, loss_mask: 0.2526, loss: 0.7803 +2024-05-31 05:23:46,760 - mmdet - INFO - Epoch [4][7000/7330] lr: 1.000e-04, eta: 10:11:57, time: 0.603, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0227, loss_rpn_bbox: 0.0472, loss_cls: 0.2109, acc: 92.5586, loss_bbox: 0.2543, loss_mask: 0.2470, loss: 0.7821 +2024-05-31 05:24:16,970 - mmdet - INFO - Epoch [4][7050/7330] lr: 1.000e-04, eta: 10:11:24, time: 0.604, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0261, loss_rpn_bbox: 0.0495, loss_cls: 0.2179, acc: 92.4177, loss_bbox: 0.2570, loss_mask: 0.2525, loss: 0.8030 +2024-05-31 05:24:50,002 - mmdet - INFO - Epoch [4][7100/7330] lr: 1.000e-04, eta: 10:10:57, time: 0.661, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0239, loss_rpn_bbox: 0.0517, loss_cls: 0.2126, acc: 92.4587, loss_bbox: 0.2615, loss_mask: 0.2515, loss: 0.8011 +2024-05-31 05:25:20,039 - mmdet - INFO - Epoch [4][7150/7330] lr: 1.000e-04, eta: 10:10:24, time: 0.601, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0219, loss_rpn_bbox: 0.0470, loss_cls: 0.2036, acc: 92.7639, loss_bbox: 0.2460, loss_mask: 0.2440, loss: 0.7626 +2024-05-31 05:25:58,765 - mmdet - INFO - Epoch [4][7200/7330] lr: 1.000e-04, eta: 10:10:08, time: 0.775, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0243, loss_rpn_bbox: 0.0467, loss_cls: 0.2144, acc: 92.5076, loss_bbox: 0.2576, loss_mask: 0.2530, loss: 0.7960 +2024-05-31 05:26:28,464 - mmdet - INFO - Epoch [4][7250/7330] lr: 1.000e-04, eta: 10:09:34, time: 0.594, data_time: 0.065, memory: 9655, loss_rpn_cls: 0.0263, loss_rpn_bbox: 0.0531, loss_cls: 0.2063, acc: 92.7549, loss_bbox: 0.2491, loss_mask: 0.2583, loss: 0.7932 +2024-05-31 05:26:58,422 - mmdet - INFO - Epoch [4][7300/7330] lr: 1.000e-04, eta: 10:09:00, time: 0.599, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0229, loss_rpn_bbox: 0.0489, loss_cls: 0.2054, acc: 92.8062, loss_bbox: 0.2489, loss_mask: 0.2546, loss: 0.7808 +2024-05-31 05:27:17,014 - mmdet - INFO - Saving checkpoint at 4 epochs +2024-05-31 05:28:58,670 - mmdet - INFO - Evaluating bbox... +2024-05-31 05:29:25,991 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.392 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.620 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.426 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.221 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.428 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.545 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.522 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.522 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.522 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.325 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.566 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.683 + +2024-05-31 05:29:25,992 - mmdet - INFO - Evaluating segm... +2024-05-31 05:29:53,776 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.358 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.584 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.373 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.154 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.386 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.565 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.474 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.474 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.474 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.266 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.519 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.662 + +2024-05-31 05:29:54,221 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 05:29:54,222 - mmdet - INFO - Epoch(val) [4][625] bbox_mAP: 0.3920, bbox_mAP_50: 0.6200, bbox_mAP_75: 0.4260, bbox_mAP_s: 0.2210, bbox_mAP_m: 0.4280, bbox_mAP_l: 0.5450, bbox_mAP_copypaste: 0.392 0.620 0.426 0.221 0.428 0.545, segm_mAP: 0.3580, segm_mAP_50: 0.5840, segm_mAP_75: 0.3730, segm_mAP_s: 0.1540, segm_mAP_m: 0.3860, segm_mAP_l: 0.5650, segm_mAP_copypaste: 0.358 0.584 0.373 0.154 0.386 0.565 +2024-05-31 05:30:32,139 - mmdet - INFO - Epoch [5][50/7330] lr: 1.000e-04, eta: 10:07:47, time: 0.758, data_time: 0.123, memory: 9655, loss_rpn_cls: 0.0224, loss_rpn_bbox: 0.0500, loss_cls: 0.1964, acc: 92.9324, loss_bbox: 0.2424, loss_mask: 0.2481, loss: 0.7593 +2024-05-31 05:31:02,030 - mmdet - INFO - Epoch [5][100/7330] lr: 1.000e-04, eta: 10:07:13, time: 0.598, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0459, loss_cls: 0.1930, acc: 92.9678, loss_bbox: 0.2460, loss_mask: 0.2495, loss: 0.7541 +2024-05-31 05:31:32,577 - mmdet - INFO - Epoch [5][150/7330] lr: 1.000e-04, eta: 10:06:41, time: 0.611, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0204, loss_rpn_bbox: 0.0500, loss_cls: 0.2003, acc: 92.9248, loss_bbox: 0.2462, loss_mask: 0.2486, loss: 0.7655 +2024-05-31 05:32:02,418 - mmdet - INFO - Epoch [5][200/7330] lr: 1.000e-04, eta: 10:06:07, time: 0.597, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0196, loss_rpn_bbox: 0.0475, loss_cls: 0.1939, acc: 93.0569, loss_bbox: 0.2458, loss_mask: 0.2423, loss: 0.7491 +2024-05-31 05:32:32,407 - mmdet - INFO - Epoch [5][250/7330] lr: 1.000e-04, eta: 10:05:34, time: 0.600, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0207, loss_rpn_bbox: 0.0463, loss_cls: 0.1956, acc: 92.9507, loss_bbox: 0.2427, loss_mask: 0.2435, loss: 0.7487 +2024-05-31 05:33:02,743 - mmdet - INFO - Epoch [5][300/7330] lr: 1.000e-04, eta: 10:05:01, time: 0.607, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0215, loss_rpn_bbox: 0.0474, loss_cls: 0.1956, acc: 93.0437, loss_bbox: 0.2417, loss_mask: 0.2382, loss: 0.7445 +2024-05-31 05:33:35,525 - mmdet - INFO - Epoch [5][350/7330] lr: 1.000e-04, eta: 10:04:33, time: 0.656, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0221, loss_rpn_bbox: 0.0510, loss_cls: 0.2066, acc: 92.6521, loss_bbox: 0.2576, loss_mask: 0.2559, loss: 0.7931 +2024-05-31 05:34:05,445 - mmdet - INFO - Epoch [5][400/7330] lr: 1.000e-04, eta: 10:04:00, time: 0.598, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0216, loss_rpn_bbox: 0.0484, loss_cls: 0.2071, acc: 92.5974, loss_bbox: 0.2544, loss_mask: 0.2531, loss: 0.7846 +2024-05-31 05:34:35,512 - mmdet - INFO - Epoch [5][450/7330] lr: 1.000e-04, eta: 10:03:27, time: 0.601, data_time: 0.038, memory: 9655, loss_rpn_cls: 0.0231, loss_rpn_bbox: 0.0484, loss_cls: 0.2050, acc: 92.6443, loss_bbox: 0.2517, loss_mask: 0.2491, loss: 0.7773 +2024-05-31 05:35:08,919 - mmdet - INFO - Epoch [5][500/7330] lr: 1.000e-04, eta: 10:03:00, time: 0.668, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0236, loss_rpn_bbox: 0.0527, loss_cls: 0.2156, acc: 92.2166, loss_bbox: 0.2664, loss_mask: 0.2532, loss: 0.8115 +2024-05-31 05:35:44,202 - mmdet - INFO - Epoch [5][550/7330] lr: 1.000e-04, eta: 10:02:37, time: 0.706, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0216, loss_rpn_bbox: 0.0479, loss_cls: 0.2026, acc: 92.8359, loss_bbox: 0.2481, loss_mask: 0.2475, loss: 0.7679 +2024-05-31 05:36:17,141 - mmdet - INFO - Epoch [5][600/7330] lr: 1.000e-04, eta: 10:02:10, time: 0.659, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0227, loss_rpn_bbox: 0.0499, loss_cls: 0.1990, acc: 92.8699, loss_bbox: 0.2465, loss_mask: 0.2486, loss: 0.7666 +2024-05-31 05:36:47,131 - mmdet - INFO - Epoch [5][650/7330] lr: 1.000e-04, eta: 10:01:36, time: 0.600, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0211, loss_rpn_bbox: 0.0501, loss_cls: 0.1993, acc: 92.8669, loss_bbox: 0.2479, loss_mask: 0.2459, loss: 0.7643 +2024-05-31 05:37:17,462 - mmdet - INFO - Epoch [5][700/7330] lr: 1.000e-04, eta: 10:01:04, time: 0.607, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0221, loss_rpn_bbox: 0.0481, loss_cls: 0.2044, acc: 92.7681, loss_bbox: 0.2514, loss_mask: 0.2570, loss: 0.7830 +2024-05-31 05:37:47,506 - mmdet - INFO - Epoch [5][750/7330] lr: 1.000e-04, eta: 10:00:30, time: 0.601, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0221, loss_rpn_bbox: 0.0479, loss_cls: 0.2028, acc: 92.8042, loss_bbox: 0.2462, loss_mask: 0.2466, loss: 0.7656 +2024-05-31 05:38:17,634 - mmdet - INFO - Epoch [5][800/7330] lr: 1.000e-04, eta: 9:59:57, time: 0.603, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0236, loss_rpn_bbox: 0.0503, loss_cls: 0.2120, acc: 92.4966, loss_bbox: 0.2586, loss_mask: 0.2578, loss: 0.8023 +2024-05-31 05:38:47,158 - mmdet - INFO - Epoch [5][850/7330] lr: 1.000e-04, eta: 9:59:23, time: 0.590, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0222, loss_rpn_bbox: 0.0497, loss_cls: 0.1930, acc: 93.0933, loss_bbox: 0.2433, loss_mask: 0.2459, loss: 0.7541 +2024-05-31 05:39:17,247 - mmdet - INFO - Epoch [5][900/7330] lr: 1.000e-04, eta: 9:58:50, time: 0.602, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0226, loss_rpn_bbox: 0.0477, loss_cls: 0.2049, acc: 92.7615, loss_bbox: 0.2482, loss_mask: 0.2459, loss: 0.7693 +2024-05-31 05:39:47,197 - mmdet - INFO - Epoch [5][950/7330] lr: 1.000e-04, eta: 9:58:17, time: 0.599, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0252, loss_rpn_bbox: 0.0483, loss_cls: 0.1972, acc: 92.9812, loss_bbox: 0.2424, loss_mask: 0.2480, loss: 0.7610 +2024-05-31 05:40:17,850 - mmdet - INFO - Epoch [5][1000/7330] lr: 1.000e-04, eta: 9:57:45, time: 0.613, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0199, loss_rpn_bbox: 0.0468, loss_cls: 0.1939, acc: 93.0752, loss_bbox: 0.2388, loss_mask: 0.2410, loss: 0.7404 +2024-05-31 05:40:47,560 - mmdet - INFO - Epoch [5][1050/7330] lr: 1.000e-04, eta: 9:57:11, time: 0.594, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0243, loss_rpn_bbox: 0.0490, loss_cls: 0.2147, acc: 92.3372, loss_bbox: 0.2605, loss_mask: 0.2570, loss: 0.8055 +2024-05-31 05:41:19,967 - mmdet - INFO - Epoch [5][1100/7330] lr: 1.000e-04, eta: 9:56:42, time: 0.648, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0226, loss_rpn_bbox: 0.0486, loss_cls: 0.2096, acc: 92.5042, loss_bbox: 0.2570, loss_mask: 0.2536, loss: 0.7915 +2024-05-31 05:41:49,843 - mmdet - INFO - Epoch [5][1150/7330] lr: 1.000e-04, eta: 9:56:09, time: 0.597, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0203, loss_rpn_bbox: 0.0460, loss_cls: 0.2015, acc: 92.7693, loss_bbox: 0.2458, loss_mask: 0.2512, loss: 0.7648 +2024-05-31 05:42:22,352 - mmdet - INFO - Epoch [5][1200/7330] lr: 1.000e-04, eta: 9:55:40, time: 0.650, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0485, loss_cls: 0.2128, acc: 92.4331, loss_bbox: 0.2552, loss_mask: 0.2499, loss: 0.7899 +2024-05-31 05:42:57,294 - mmdet - INFO - Epoch [5][1250/7330] lr: 1.000e-04, eta: 9:55:17, time: 0.699, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0218, loss_rpn_bbox: 0.0493, loss_cls: 0.2041, acc: 92.7874, loss_bbox: 0.2487, loss_mask: 0.2449, loss: 0.7688 +2024-05-31 05:43:27,411 - mmdet - INFO - Epoch [5][1300/7330] lr: 1.000e-04, eta: 9:54:44, time: 0.602, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0234, loss_rpn_bbox: 0.0488, loss_cls: 0.1990, acc: 92.7734, loss_bbox: 0.2475, loss_mask: 0.2452, loss: 0.7638 +2024-05-31 05:43:57,116 - mmdet - INFO - Epoch [5][1350/7330] lr: 1.000e-04, eta: 9:54:10, time: 0.594, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0214, loss_rpn_bbox: 0.0475, loss_cls: 0.1956, acc: 92.8289, loss_bbox: 0.2430, loss_mask: 0.2506, loss: 0.7580 +2024-05-31 05:44:28,977 - mmdet - INFO - Epoch [5][1400/7330] lr: 1.000e-04, eta: 9:53:40, time: 0.637, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0224, loss_rpn_bbox: 0.0466, loss_cls: 0.2068, acc: 92.7666, loss_bbox: 0.2466, loss_mask: 0.2455, loss: 0.7679 +2024-05-31 05:45:05,260 - mmdet - INFO - Epoch [5][1450/7330] lr: 1.000e-04, eta: 9:53:19, time: 0.726, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0236, loss_rpn_bbox: 0.0512, loss_cls: 0.2123, acc: 92.4033, loss_bbox: 0.2560, loss_mask: 0.2539, loss: 0.7969 +2024-05-31 05:45:37,963 - mmdet - INFO - Epoch [5][1500/7330] lr: 1.000e-04, eta: 9:52:50, time: 0.654, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0227, loss_rpn_bbox: 0.0496, loss_cls: 0.2098, acc: 92.5723, loss_bbox: 0.2541, loss_mask: 0.2506, loss: 0.7868 +2024-05-31 05:46:07,849 - mmdet - INFO - Epoch [5][1550/7330] lr: 1.000e-04, eta: 9:52:17, time: 0.597, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0205, loss_rpn_bbox: 0.0472, loss_cls: 0.1990, acc: 92.9543, loss_bbox: 0.2461, loss_mask: 0.2460, loss: 0.7588 +2024-05-31 05:46:38,143 - mmdet - INFO - Epoch [5][1600/7330] lr: 1.000e-04, eta: 9:51:44, time: 0.606, data_time: 0.064, memory: 9655, loss_rpn_cls: 0.0227, loss_rpn_bbox: 0.0490, loss_cls: 0.2054, acc: 92.6553, loss_bbox: 0.2547, loss_mask: 0.2496, loss: 0.7814 +2024-05-31 05:47:07,971 - mmdet - INFO - Epoch [5][1650/7330] lr: 1.000e-04, eta: 9:51:11, time: 0.596, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0236, loss_rpn_bbox: 0.0514, loss_cls: 0.2065, acc: 92.5535, loss_bbox: 0.2570, loss_mask: 0.2500, loss: 0.7885 +2024-05-31 05:47:37,469 - mmdet - INFO - Epoch [5][1700/7330] lr: 1.000e-04, eta: 9:50:37, time: 0.590, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0225, loss_rpn_bbox: 0.0465, loss_cls: 0.1947, acc: 93.0615, loss_bbox: 0.2398, loss_mask: 0.2491, loss: 0.7525 +2024-05-31 05:48:07,293 - mmdet - INFO - Epoch [5][1750/7330] lr: 1.000e-04, eta: 9:50:03, time: 0.596, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0238, loss_rpn_bbox: 0.0499, loss_cls: 0.2053, acc: 92.7026, loss_bbox: 0.2492, loss_mask: 0.2478, loss: 0.7760 +2024-05-31 05:48:36,870 - mmdet - INFO - Epoch [5][1800/7330] lr: 1.000e-04, eta: 9:49:29, time: 0.592, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0205, loss_rpn_bbox: 0.0454, loss_cls: 0.1927, acc: 93.0730, loss_bbox: 0.2376, loss_mask: 0.2381, loss: 0.7343 +2024-05-31 05:49:06,997 - mmdet - INFO - Epoch [5][1850/7330] lr: 1.000e-04, eta: 9:48:56, time: 0.603, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0238, loss_rpn_bbox: 0.0522, loss_cls: 0.2050, acc: 92.6406, loss_bbox: 0.2506, loss_mask: 0.2458, loss: 0.7774 +2024-05-31 05:49:36,793 - mmdet - INFO - Epoch [5][1900/7330] lr: 1.000e-04, eta: 9:48:23, time: 0.596, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0226, loss_rpn_bbox: 0.0488, loss_cls: 0.2016, acc: 92.8784, loss_bbox: 0.2422, loss_mask: 0.2462, loss: 0.7616 +2024-05-31 05:50:06,359 - mmdet - INFO - Epoch [5][1950/7330] lr: 1.000e-04, eta: 9:47:49, time: 0.591, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0211, loss_rpn_bbox: 0.0475, loss_cls: 0.2069, acc: 92.7542, loss_bbox: 0.2540, loss_mask: 0.2523, loss: 0.7818 +2024-05-31 05:50:38,110 - mmdet - INFO - Epoch [5][2000/7330] lr: 1.000e-04, eta: 9:47:19, time: 0.635, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0215, loss_rpn_bbox: 0.0489, loss_cls: 0.2036, acc: 92.7524, loss_bbox: 0.2515, loss_mask: 0.2472, loss: 0.7727 +2024-05-31 05:51:07,958 - mmdet - INFO - Epoch [5][2050/7330] lr: 1.000e-04, eta: 9:46:46, time: 0.597, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0209, loss_rpn_bbox: 0.0469, loss_cls: 0.2006, acc: 92.9670, loss_bbox: 0.2425, loss_mask: 0.2578, loss: 0.7687 +2024-05-31 05:51:40,657 - mmdet - INFO - Epoch [5][2100/7330] lr: 1.000e-04, eta: 9:46:17, time: 0.654, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0229, loss_rpn_bbox: 0.0485, loss_cls: 0.2002, acc: 92.7197, loss_bbox: 0.2527, loss_mask: 0.2486, loss: 0.7730 +2024-05-31 05:52:15,601 - mmdet - INFO - Epoch [5][2150/7330] lr: 1.000e-04, eta: 9:45:53, time: 0.699, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0217, loss_rpn_bbox: 0.0483, loss_cls: 0.2039, acc: 92.6311, loss_bbox: 0.2536, loss_mask: 0.2462, loss: 0.7736 +2024-05-31 05:52:45,876 - mmdet - INFO - Epoch [5][2200/7330] lr: 1.000e-04, eta: 9:45:21, time: 0.606, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0224, loss_rpn_bbox: 0.0506, loss_cls: 0.2028, acc: 92.6653, loss_bbox: 0.2554, loss_mask: 0.2486, loss: 0.7798 +2024-05-31 05:53:15,826 - mmdet - INFO - Epoch [5][2250/7330] lr: 1.000e-04, eta: 9:44:47, time: 0.599, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0502, loss_cls: 0.2020, acc: 92.8193, loss_bbox: 0.2516, loss_mask: 0.2437, loss: 0.7688 +2024-05-31 05:53:49,872 - mmdet - INFO - Epoch [5][2300/7330] lr: 1.000e-04, eta: 9:44:21, time: 0.681, data_time: 0.065, memory: 9655, loss_rpn_cls: 0.0224, loss_rpn_bbox: 0.0501, loss_cls: 0.2092, acc: 92.5286, loss_bbox: 0.2581, loss_mask: 0.2527, loss: 0.7925 +2024-05-31 05:54:27,918 - mmdet - INFO - Epoch [5][2350/7330] lr: 1.000e-04, eta: 9:44:03, time: 0.761, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0222, loss_rpn_bbox: 0.0473, loss_cls: 0.2052, acc: 92.6467, loss_bbox: 0.2541, loss_mask: 0.2469, loss: 0.7758 +2024-05-31 05:54:58,147 - mmdet - INFO - Epoch [5][2400/7330] lr: 1.000e-04, eta: 9:43:30, time: 0.605, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0231, loss_rpn_bbox: 0.0490, loss_cls: 0.2089, acc: 92.6238, loss_bbox: 0.2562, loss_mask: 0.2487, loss: 0.7859 +2024-05-31 05:55:27,905 - mmdet - INFO - Epoch [5][2450/7330] lr: 1.000e-04, eta: 9:42:56, time: 0.595, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0218, loss_rpn_bbox: 0.0477, loss_cls: 0.2037, acc: 92.9177, loss_bbox: 0.2467, loss_mask: 0.2533, loss: 0.7733 +2024-05-31 05:55:57,798 - mmdet - INFO - Epoch [5][2500/7330] lr: 1.000e-04, eta: 9:42:23, time: 0.598, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0230, loss_rpn_bbox: 0.0483, loss_cls: 0.2046, acc: 92.6614, loss_bbox: 0.2509, loss_mask: 0.2494, loss: 0.7762 +2024-05-31 05:56:27,637 - mmdet - INFO - Epoch [5][2550/7330] lr: 1.000e-04, eta: 9:41:50, time: 0.597, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0238, loss_rpn_bbox: 0.0499, loss_cls: 0.2090, acc: 92.6616, loss_bbox: 0.2508, loss_mask: 0.2460, loss: 0.7795 +2024-05-31 05:56:58,220 - mmdet - INFO - Epoch [5][2600/7330] lr: 1.000e-04, eta: 9:41:18, time: 0.612, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0222, loss_rpn_bbox: 0.0502, loss_cls: 0.2030, acc: 92.7749, loss_bbox: 0.2496, loss_mask: 0.2528, loss: 0.7778 +2024-05-31 05:57:28,837 - mmdet - INFO - Epoch [5][2650/7330] lr: 1.000e-04, eta: 9:40:46, time: 0.612, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0247, loss_rpn_bbox: 0.0524, loss_cls: 0.2136, acc: 92.3533, loss_bbox: 0.2628, loss_mask: 0.2555, loss: 0.8090 +2024-05-31 05:57:58,555 - mmdet - INFO - Epoch [5][2700/7330] lr: 1.000e-04, eta: 9:40:12, time: 0.594, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0203, loss_rpn_bbox: 0.0468, loss_cls: 0.1956, acc: 92.9475, loss_bbox: 0.2418, loss_mask: 0.2530, loss: 0.7575 +2024-05-31 05:58:28,340 - mmdet - INFO - Epoch [5][2750/7330] lr: 1.000e-04, eta: 9:39:39, time: 0.596, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0236, loss_rpn_bbox: 0.0440, loss_cls: 0.1991, acc: 93.0154, loss_bbox: 0.2449, loss_mask: 0.2451, loss: 0.7565 +2024-05-31 05:58:58,329 - mmdet - INFO - Epoch [5][2800/7330] lr: 1.000e-04, eta: 9:39:06, time: 0.600, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0227, loss_rpn_bbox: 0.0494, loss_cls: 0.2014, acc: 92.9050, loss_bbox: 0.2452, loss_mask: 0.2430, loss: 0.7617 +2024-05-31 05:59:28,943 - mmdet - INFO - Epoch [5][2850/7330] lr: 1.000e-04, eta: 9:38:34, time: 0.612, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0250, loss_rpn_bbox: 0.0503, loss_cls: 0.2043, acc: 92.8525, loss_bbox: 0.2519, loss_mask: 0.2551, loss: 0.7867 +2024-05-31 06:00:00,881 - mmdet - INFO - Epoch [5][2900/7330] lr: 1.000e-04, eta: 9:38:04, time: 0.639, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0217, loss_rpn_bbox: 0.0467, loss_cls: 0.2018, acc: 92.9558, loss_bbox: 0.2464, loss_mask: 0.2484, loss: 0.7651 +2024-05-31 06:00:31,047 - mmdet - INFO - Epoch [5][2950/7330] lr: 1.000e-04, eta: 9:37:31, time: 0.603, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0476, loss_cls: 0.2051, acc: 92.7339, loss_bbox: 0.2527, loss_mask: 0.2545, loss: 0.7834 +2024-05-31 06:01:03,179 - mmdet - INFO - Epoch [5][3000/7330] lr: 1.000e-04, eta: 9:37:02, time: 0.643, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0207, loss_rpn_bbox: 0.0495, loss_cls: 0.2020, acc: 92.8013, loss_bbox: 0.2466, loss_mask: 0.2499, loss: 0.7687 +2024-05-31 06:01:36,849 - mmdet - INFO - Epoch [5][3050/7330] lr: 1.000e-04, eta: 9:36:35, time: 0.673, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0205, loss_rpn_bbox: 0.0497, loss_cls: 0.2057, acc: 92.7058, loss_bbox: 0.2524, loss_mask: 0.2505, loss: 0.7787 +2024-05-31 06:02:07,217 - mmdet - INFO - Epoch [5][3100/7330] lr: 1.000e-04, eta: 9:36:03, time: 0.607, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0231, loss_rpn_bbox: 0.0476, loss_cls: 0.2060, acc: 92.6272, loss_bbox: 0.2499, loss_mask: 0.2508, loss: 0.7775 +2024-05-31 06:02:37,155 - mmdet - INFO - Epoch [5][3150/7330] lr: 1.000e-04, eta: 9:35:30, time: 0.599, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0222, loss_rpn_bbox: 0.0460, loss_cls: 0.1948, acc: 93.0515, loss_bbox: 0.2360, loss_mask: 0.2493, loss: 0.7482 +2024-05-31 06:03:09,619 - mmdet - INFO - Epoch [5][3200/7330] lr: 1.000e-04, eta: 9:35:01, time: 0.649, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0237, loss_rpn_bbox: 0.0503, loss_cls: 0.2155, acc: 92.3818, loss_bbox: 0.2580, loss_mask: 0.2509, loss: 0.7984 +2024-05-31 06:03:48,027 - mmdet - INFO - Epoch [5][3250/7330] lr: 1.000e-04, eta: 9:34:42, time: 0.768, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0233, loss_rpn_bbox: 0.0479, loss_cls: 0.2011, acc: 92.8054, loss_bbox: 0.2451, loss_mask: 0.2485, loss: 0.7659 +2024-05-31 06:04:18,422 - mmdet - INFO - Epoch [5][3300/7330] lr: 1.000e-04, eta: 9:34:10, time: 0.608, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0206, loss_rpn_bbox: 0.0464, loss_cls: 0.1943, acc: 92.9773, loss_bbox: 0.2394, loss_mask: 0.2491, loss: 0.7499 +2024-05-31 06:04:48,911 - mmdet - INFO - Epoch [5][3350/7330] lr: 1.000e-04, eta: 9:33:37, time: 0.610, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0213, loss_rpn_bbox: 0.0486, loss_cls: 0.2031, acc: 92.7773, loss_bbox: 0.2493, loss_mask: 0.2466, loss: 0.7689 +2024-05-31 06:05:18,323 - mmdet - INFO - Epoch [5][3400/7330] lr: 1.000e-04, eta: 9:33:03, time: 0.588, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0192, loss_rpn_bbox: 0.0411, loss_cls: 0.1887, acc: 93.2700, loss_bbox: 0.2323, loss_mask: 0.2439, loss: 0.7253 +2024-05-31 06:05:48,702 - mmdet - INFO - Epoch [5][3450/7330] lr: 1.000e-04, eta: 9:32:31, time: 0.608, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0210, loss_rpn_bbox: 0.0483, loss_cls: 0.1970, acc: 92.9929, loss_bbox: 0.2405, loss_mask: 0.2456, loss: 0.7524 +2024-05-31 06:06:18,633 - mmdet - INFO - Epoch [5][3500/7330] lr: 1.000e-04, eta: 9:31:58, time: 0.599, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0211, loss_rpn_bbox: 0.0467, loss_cls: 0.2006, acc: 92.8003, loss_bbox: 0.2486, loss_mask: 0.2469, loss: 0.7640 +2024-05-31 06:06:48,761 - mmdet - INFO - Epoch [5][3550/7330] lr: 1.000e-04, eta: 9:31:25, time: 0.603, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0219, loss_rpn_bbox: 0.0490, loss_cls: 0.2031, acc: 92.7817, loss_bbox: 0.2479, loss_mask: 0.2413, loss: 0.7631 +2024-05-31 06:07:18,072 - mmdet - INFO - Epoch [5][3600/7330] lr: 1.000e-04, eta: 9:30:51, time: 0.586, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0471, loss_cls: 0.1995, acc: 92.9712, loss_bbox: 0.2414, loss_mask: 0.2398, loss: 0.7475 +2024-05-31 06:07:48,596 - mmdet - INFO - Epoch [5][3650/7330] lr: 1.000e-04, eta: 9:30:19, time: 0.610, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0236, loss_rpn_bbox: 0.0508, loss_cls: 0.1979, acc: 92.8835, loss_bbox: 0.2442, loss_mask: 0.2442, loss: 0.7606 +2024-05-31 06:08:18,639 - mmdet - INFO - Epoch [5][3700/7330] lr: 1.000e-04, eta: 9:29:46, time: 0.601, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0234, loss_rpn_bbox: 0.0509, loss_cls: 0.2104, acc: 92.5967, loss_bbox: 0.2570, loss_mask: 0.2560, loss: 0.7978 +2024-05-31 06:08:49,403 - mmdet - INFO - Epoch [5][3750/7330] lr: 1.000e-04, eta: 9:29:14, time: 0.615, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0227, loss_rpn_bbox: 0.0480, loss_cls: 0.1954, acc: 93.0745, loss_bbox: 0.2391, loss_mask: 0.2445, loss: 0.7497 +2024-05-31 06:09:21,598 - mmdet - INFO - Epoch [5][3800/7330] lr: 1.000e-04, eta: 9:28:45, time: 0.644, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0211, loss_rpn_bbox: 0.0478, loss_cls: 0.2009, acc: 92.8735, loss_bbox: 0.2468, loss_mask: 0.2495, loss: 0.7662 +2024-05-31 06:09:52,295 - mmdet - INFO - Epoch [5][3850/7330] lr: 1.000e-04, eta: 9:28:13, time: 0.614, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0222, loss_rpn_bbox: 0.0508, loss_cls: 0.2066, acc: 92.7024, loss_bbox: 0.2509, loss_mask: 0.2489, loss: 0.7794 +2024-05-31 06:10:24,842 - mmdet - INFO - Epoch [5][3900/7330] lr: 1.000e-04, eta: 9:27:44, time: 0.651, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0230, loss_rpn_bbox: 0.0497, loss_cls: 0.2016, acc: 92.7952, loss_bbox: 0.2475, loss_mask: 0.2490, loss: 0.7709 +2024-05-31 06:10:59,035 - mmdet - INFO - Epoch [5][3950/7330] lr: 1.000e-04, eta: 9:27:18, time: 0.684, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0230, loss_rpn_bbox: 0.0473, loss_cls: 0.2032, acc: 92.7686, loss_bbox: 0.2464, loss_mask: 0.2470, loss: 0.7669 +2024-05-31 06:11:29,302 - mmdet - INFO - Epoch [5][4000/7330] lr: 1.000e-04, eta: 9:26:46, time: 0.605, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0475, loss_cls: 0.2059, acc: 92.7388, loss_bbox: 0.2455, loss_mask: 0.2486, loss: 0.7711 +2024-05-31 06:11:59,080 - mmdet - INFO - Epoch [5][4050/7330] lr: 1.000e-04, eta: 9:26:13, time: 0.596, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0223, loss_rpn_bbox: 0.0453, loss_cls: 0.2012, acc: 92.8870, loss_bbox: 0.2408, loss_mask: 0.2500, loss: 0.7595 +2024-05-31 06:12:32,209 - mmdet - INFO - Epoch [5][4100/7330] lr: 1.000e-04, eta: 9:25:45, time: 0.663, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0220, loss_rpn_bbox: 0.0477, loss_cls: 0.2007, acc: 92.8657, loss_bbox: 0.2457, loss_mask: 0.2467, loss: 0.7627 +2024-05-31 06:13:09,869 - mmdet - INFO - Epoch [5][4150/7330] lr: 1.000e-04, eta: 9:25:24, time: 0.753, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0210, loss_rpn_bbox: 0.0468, loss_cls: 0.2102, acc: 92.5918, loss_bbox: 0.2527, loss_mask: 0.2480, loss: 0.7787 +2024-05-31 06:13:39,906 - mmdet - INFO - Epoch [5][4200/7330] lr: 1.000e-04, eta: 9:24:51, time: 0.601, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0482, loss_cls: 0.2110, acc: 92.4773, loss_bbox: 0.2610, loss_mask: 0.2475, loss: 0.7885 +2024-05-31 06:14:10,228 - mmdet - INFO - Epoch [5][4250/7330] lr: 1.000e-04, eta: 9:24:19, time: 0.606, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0231, loss_rpn_bbox: 0.0486, loss_cls: 0.2029, acc: 92.8147, loss_bbox: 0.2449, loss_mask: 0.2473, loss: 0.7668 +2024-05-31 06:14:40,563 - mmdet - INFO - Epoch [5][4300/7330] lr: 1.000e-04, eta: 9:23:47, time: 0.607, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0223, loss_rpn_bbox: 0.0467, loss_cls: 0.2004, acc: 92.8616, loss_bbox: 0.2438, loss_mask: 0.2417, loss: 0.7550 +2024-05-31 06:15:11,313 - mmdet - INFO - Epoch [5][4350/7330] lr: 1.000e-04, eta: 9:23:15, time: 0.615, data_time: 0.069, memory: 9655, loss_rpn_cls: 0.0229, loss_rpn_bbox: 0.0470, loss_cls: 0.2022, acc: 92.9253, loss_bbox: 0.2431, loss_mask: 0.2461, loss: 0.7614 +2024-05-31 06:15:41,770 - mmdet - INFO - Epoch [5][4400/7330] lr: 1.000e-04, eta: 9:22:43, time: 0.609, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0207, loss_rpn_bbox: 0.0464, loss_cls: 0.2025, acc: 92.8059, loss_bbox: 0.2428, loss_mask: 0.2461, loss: 0.7585 +2024-05-31 06:16:11,736 - mmdet - INFO - Epoch [5][4450/7330] lr: 1.000e-04, eta: 9:22:10, time: 0.599, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0229, loss_rpn_bbox: 0.0470, loss_cls: 0.2075, acc: 92.5754, loss_bbox: 0.2526, loss_mask: 0.2500, loss: 0.7800 +2024-05-31 06:16:42,464 - mmdet - INFO - Epoch [5][4500/7330] lr: 1.000e-04, eta: 9:21:38, time: 0.615, data_time: 0.065, memory: 9655, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0511, loss_cls: 0.2147, acc: 92.3225, loss_bbox: 0.2646, loss_mask: 0.2540, loss: 0.8078 +2024-05-31 06:17:12,930 - mmdet - INFO - Epoch [5][4550/7330] lr: 1.000e-04, eta: 9:21:06, time: 0.609, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0219, loss_rpn_bbox: 0.0503, loss_cls: 0.2151, acc: 92.2339, loss_bbox: 0.2629, loss_mask: 0.2561, loss: 0.8062 +2024-05-31 06:17:42,814 - mmdet - INFO - Epoch [5][4600/7330] lr: 1.000e-04, eta: 9:20:33, time: 0.598, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0207, loss_rpn_bbox: 0.0489, loss_cls: 0.2032, acc: 92.7419, loss_bbox: 0.2495, loss_mask: 0.2509, loss: 0.7732 +2024-05-31 06:18:12,791 - mmdet - INFO - Epoch [5][4650/7330] lr: 1.000e-04, eta: 9:20:00, time: 0.600, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0223, loss_rpn_bbox: 0.0475, loss_cls: 0.2044, acc: 92.7085, loss_bbox: 0.2530, loss_mask: 0.2521, loss: 0.7793 +2024-05-31 06:18:44,419 - mmdet - INFO - Epoch [5][4700/7330] lr: 1.000e-04, eta: 9:19:29, time: 0.633, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0213, loss_rpn_bbox: 0.0447, loss_cls: 0.2003, acc: 92.9236, loss_bbox: 0.2409, loss_mask: 0.2401, loss: 0.7474 +2024-05-31 06:19:14,415 - mmdet - INFO - Epoch [5][4750/7330] lr: 1.000e-04, eta: 9:18:56, time: 0.600, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0213, loss_rpn_bbox: 0.0487, loss_cls: 0.2021, acc: 92.7793, loss_bbox: 0.2472, loss_mask: 0.2428, loss: 0.7622 +2024-05-31 06:19:46,470 - mmdet - INFO - Epoch [5][4800/7330] lr: 1.000e-04, eta: 9:18:27, time: 0.641, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0233, loss_rpn_bbox: 0.0487, loss_cls: 0.2103, acc: 92.5334, loss_bbox: 0.2557, loss_mask: 0.2601, loss: 0.7982 +2024-05-31 06:20:20,672 - mmdet - INFO - Epoch [5][4850/7330] lr: 1.000e-04, eta: 9:18:01, time: 0.684, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0230, loss_rpn_bbox: 0.0494, loss_cls: 0.2107, acc: 92.4485, loss_bbox: 0.2567, loss_mask: 0.2518, loss: 0.7916 +2024-05-31 06:20:50,739 - mmdet - INFO - Epoch [5][4900/7330] lr: 1.000e-04, eta: 9:17:28, time: 0.601, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0213, loss_rpn_bbox: 0.0456, loss_cls: 0.1994, acc: 92.8323, loss_bbox: 0.2516, loss_mask: 0.2514, loss: 0.7694 +2024-05-31 06:21:20,943 - mmdet - INFO - Epoch [5][4950/7330] lr: 1.000e-04, eta: 9:16:55, time: 0.604, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0244, loss_rpn_bbox: 0.0480, loss_cls: 0.2034, acc: 92.8467, loss_bbox: 0.2415, loss_mask: 0.2447, loss: 0.7621 +2024-05-31 06:21:53,371 - mmdet - INFO - Epoch [5][5000/7330] lr: 1.000e-04, eta: 9:16:26, time: 0.649, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0222, loss_rpn_bbox: 0.0492, loss_cls: 0.2060, acc: 92.7388, loss_bbox: 0.2531, loss_mask: 0.2539, loss: 0.7844 +2024-05-31 06:22:31,775 - mmdet - INFO - Epoch [5][5050/7330] lr: 1.000e-04, eta: 9:16:06, time: 0.768, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0490, loss_cls: 0.2018, acc: 92.8975, loss_bbox: 0.2461, loss_mask: 0.2482, loss: 0.7663 +2024-05-31 06:23:02,581 - mmdet - INFO - Epoch [5][5100/7330] lr: 1.000e-04, eta: 9:15:35, time: 0.616, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0230, loss_rpn_bbox: 0.0496, loss_cls: 0.2005, acc: 92.8240, loss_bbox: 0.2499, loss_mask: 0.2488, loss: 0.7717 +2024-05-31 06:23:32,486 - mmdet - INFO - Epoch [5][5150/7330] lr: 1.000e-04, eta: 9:15:02, time: 0.598, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0239, loss_rpn_bbox: 0.0485, loss_cls: 0.2028, acc: 92.7454, loss_bbox: 0.2466, loss_mask: 0.2425, loss: 0.7642 +2024-05-31 06:24:02,672 - mmdet - INFO - Epoch [5][5200/7330] lr: 1.000e-04, eta: 9:14:29, time: 0.604, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0203, loss_rpn_bbox: 0.0470, loss_cls: 0.1998, acc: 92.9365, loss_bbox: 0.2461, loss_mask: 0.2436, loss: 0.7567 +2024-05-31 06:24:33,378 - mmdet - INFO - Epoch [5][5250/7330] lr: 1.000e-04, eta: 9:13:57, time: 0.614, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0225, loss_rpn_bbox: 0.0474, loss_cls: 0.1983, acc: 92.8450, loss_bbox: 0.2464, loss_mask: 0.2439, loss: 0.7586 +2024-05-31 06:25:02,986 - mmdet - INFO - Epoch [5][5300/7330] lr: 1.000e-04, eta: 9:13:24, time: 0.592, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0225, loss_rpn_bbox: 0.0462, loss_cls: 0.2076, acc: 92.6621, loss_bbox: 0.2491, loss_mask: 0.2464, loss: 0.7719 +2024-05-31 06:25:33,166 - mmdet - INFO - Epoch [5][5350/7330] lr: 1.000e-04, eta: 9:12:51, time: 0.604, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0238, loss_rpn_bbox: 0.0515, loss_cls: 0.2124, acc: 92.5859, loss_bbox: 0.2523, loss_mask: 0.2481, loss: 0.7882 +2024-05-31 06:26:03,544 - mmdet - INFO - Epoch [5][5400/7330] lr: 1.000e-04, eta: 9:12:19, time: 0.608, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0244, loss_rpn_bbox: 0.0501, loss_cls: 0.2074, acc: 92.6975, loss_bbox: 0.2472, loss_mask: 0.2470, loss: 0.7761 +2024-05-31 06:26:33,119 - mmdet - INFO - Epoch [5][5450/7330] lr: 1.000e-04, eta: 9:11:46, time: 0.591, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0191, loss_rpn_bbox: 0.0461, loss_cls: 0.2000, acc: 92.8870, loss_bbox: 0.2431, loss_mask: 0.2474, loss: 0.7558 +2024-05-31 06:27:03,321 - mmdet - INFO - Epoch [5][5500/7330] lr: 1.000e-04, eta: 9:11:13, time: 0.604, data_time: 0.066, memory: 9655, loss_rpn_cls: 0.0236, loss_rpn_bbox: 0.0471, loss_cls: 0.2012, acc: 92.8130, loss_bbox: 0.2509, loss_mask: 0.2434, loss: 0.7661 +2024-05-31 06:27:35,593 - mmdet - INFO - Epoch [5][5550/7330] lr: 1.000e-04, eta: 9:10:44, time: 0.645, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0233, loss_rpn_bbox: 0.0493, loss_cls: 0.2044, acc: 92.6794, loss_bbox: 0.2513, loss_mask: 0.2507, loss: 0.7790 +2024-05-31 06:28:05,745 - mmdet - INFO - Epoch [5][5600/7330] lr: 1.000e-04, eta: 9:10:11, time: 0.603, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0224, loss_rpn_bbox: 0.0475, loss_cls: 0.1977, acc: 92.8560, loss_bbox: 0.2439, loss_mask: 0.2464, loss: 0.7580 +2024-05-31 06:28:35,656 - mmdet - INFO - Epoch [5][5650/7330] lr: 1.000e-04, eta: 9:09:38, time: 0.598, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0442, loss_cls: 0.1902, acc: 93.2039, loss_bbox: 0.2369, loss_mask: 0.2434, loss: 0.7345 +2024-05-31 06:29:08,089 - mmdet - INFO - Epoch [5][5700/7330] lr: 1.000e-04, eta: 9:09:09, time: 0.649, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0251, loss_rpn_bbox: 0.0508, loss_cls: 0.2144, acc: 92.4370, loss_bbox: 0.2545, loss_mask: 0.2533, loss: 0.7981 +2024-05-31 06:29:43,360 - mmdet - INFO - Epoch [5][5750/7330] lr: 1.000e-04, eta: 9:08:44, time: 0.705, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0205, loss_rpn_bbox: 0.0438, loss_cls: 0.1955, acc: 93.0688, loss_bbox: 0.2319, loss_mask: 0.2406, loss: 0.7324 +2024-05-31 06:30:14,189 - mmdet - INFO - Epoch [5][5800/7330] lr: 1.000e-04, eta: 9:08:13, time: 0.617, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0243, loss_rpn_bbox: 0.0515, loss_cls: 0.2146, acc: 92.4890, loss_bbox: 0.2562, loss_mask: 0.2564, loss: 0.8031 +2024-05-31 06:30:44,266 - mmdet - INFO - Epoch [5][5850/7330] lr: 1.000e-04, eta: 9:07:40, time: 0.602, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0209, loss_rpn_bbox: 0.0463, loss_cls: 0.2041, acc: 92.6526, loss_bbox: 0.2446, loss_mask: 0.2520, loss: 0.7678 +2024-05-31 06:31:17,535 - mmdet - INFO - Epoch [5][5900/7330] lr: 1.000e-04, eta: 9:07:12, time: 0.665, data_time: 0.064, memory: 9655, loss_rpn_cls: 0.0240, loss_rpn_bbox: 0.0521, loss_cls: 0.2075, acc: 92.5361, loss_bbox: 0.2533, loss_mask: 0.2518, loss: 0.7887 +2024-05-31 06:31:54,634 - mmdet - INFO - Epoch [5][5950/7330] lr: 1.000e-04, eta: 9:06:50, time: 0.742, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0223, loss_rpn_bbox: 0.0496, loss_cls: 0.2110, acc: 92.6245, loss_bbox: 0.2553, loss_mask: 0.2513, loss: 0.7896 +2024-05-31 06:32:24,642 - mmdet - INFO - Epoch [5][6000/7330] lr: 1.000e-04, eta: 9:06:17, time: 0.600, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0211, loss_rpn_bbox: 0.0445, loss_cls: 0.2032, acc: 92.8511, loss_bbox: 0.2455, loss_mask: 0.2521, loss: 0.7665 +2024-05-31 06:32:54,465 - mmdet - INFO - Epoch [5][6050/7330] lr: 1.000e-04, eta: 9:05:44, time: 0.596, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0194, loss_rpn_bbox: 0.0458, loss_cls: 0.2035, acc: 92.8022, loss_bbox: 0.2472, loss_mask: 0.2518, loss: 0.7677 +2024-05-31 06:33:24,216 - mmdet - INFO - Epoch [5][6100/7330] lr: 1.000e-04, eta: 9:05:11, time: 0.595, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0224, loss_rpn_bbox: 0.0504, loss_cls: 0.1997, acc: 92.9204, loss_bbox: 0.2441, loss_mask: 0.2461, loss: 0.7627 +2024-05-31 06:33:54,394 - mmdet - INFO - Epoch [5][6150/7330] lr: 1.000e-04, eta: 9:04:38, time: 0.604, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0472, loss_cls: 0.1989, acc: 92.9102, loss_bbox: 0.2459, loss_mask: 0.2445, loss: 0.7572 +2024-05-31 06:34:24,780 - mmdet - INFO - Epoch [5][6200/7330] lr: 1.000e-04, eta: 9:04:06, time: 0.608, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0229, loss_rpn_bbox: 0.0479, loss_cls: 0.2032, acc: 92.6897, loss_bbox: 0.2516, loss_mask: 0.2439, loss: 0.7695 +2024-05-31 06:34:55,035 - mmdet - INFO - Epoch [5][6250/7330] lr: 1.000e-04, eta: 9:03:33, time: 0.605, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0209, loss_rpn_bbox: 0.0484, loss_cls: 0.2030, acc: 92.7708, loss_bbox: 0.2506, loss_mask: 0.2508, loss: 0.7738 +2024-05-31 06:35:25,324 - mmdet - INFO - Epoch [5][6300/7330] lr: 1.000e-04, eta: 9:03:01, time: 0.606, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0239, loss_rpn_bbox: 0.0504, loss_cls: 0.2096, acc: 92.5681, loss_bbox: 0.2587, loss_mask: 0.2574, loss: 0.8000 +2024-05-31 06:35:55,880 - mmdet - INFO - Epoch [5][6350/7330] lr: 1.000e-04, eta: 9:02:29, time: 0.611, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0239, loss_rpn_bbox: 0.0486, loss_cls: 0.2055, acc: 92.7627, loss_bbox: 0.2453, loss_mask: 0.2523, loss: 0.7756 +2024-05-31 06:36:26,799 - mmdet - INFO - Epoch [5][6400/7330] lr: 1.000e-04, eta: 9:01:58, time: 0.618, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0227, loss_rpn_bbox: 0.0485, loss_cls: 0.2022, acc: 92.6929, loss_bbox: 0.2471, loss_mask: 0.2426, loss: 0.7630 +2024-05-31 06:36:59,121 - mmdet - INFO - Epoch [5][6450/7330] lr: 1.000e-04, eta: 9:01:28, time: 0.646, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0218, loss_rpn_bbox: 0.0482, loss_cls: 0.2139, acc: 92.6907, loss_bbox: 0.2465, loss_mask: 0.2433, loss: 0.7736 +2024-05-31 06:37:29,888 - mmdet - INFO - Epoch [5][6500/7330] lr: 1.000e-04, eta: 9:00:57, time: 0.615, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0509, loss_cls: 0.2101, acc: 92.4175, loss_bbox: 0.2590, loss_mask: 0.2487, loss: 0.7923 +2024-05-31 06:37:59,888 - mmdet - INFO - Epoch [5][6550/7330] lr: 1.000e-04, eta: 9:00:24, time: 0.600, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0223, loss_rpn_bbox: 0.0476, loss_cls: 0.1956, acc: 92.9700, loss_bbox: 0.2405, loss_mask: 0.2478, loss: 0.7539 +2024-05-31 06:38:33,084 - mmdet - INFO - Epoch [5][6600/7330] lr: 1.000e-04, eta: 8:59:56, time: 0.664, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0227, loss_rpn_bbox: 0.0507, loss_cls: 0.2081, acc: 92.4861, loss_bbox: 0.2571, loss_mask: 0.2500, loss: 0.7887 +2024-05-31 06:39:07,331 - mmdet - INFO - Epoch [5][6650/7330] lr: 1.000e-04, eta: 8:59:29, time: 0.685, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0222, loss_rpn_bbox: 0.0461, loss_cls: 0.1960, acc: 93.1045, loss_bbox: 0.2389, loss_mask: 0.2455, loss: 0.7487 +2024-05-31 06:39:37,248 - mmdet - INFO - Epoch [5][6700/7330] lr: 1.000e-04, eta: 8:58:56, time: 0.598, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0256, loss_rpn_bbox: 0.0489, loss_cls: 0.1982, acc: 92.9954, loss_bbox: 0.2413, loss_mask: 0.2508, loss: 0.7649 +2024-05-31 06:40:07,378 - mmdet - INFO - Epoch [5][6750/7330] lr: 1.000e-04, eta: 8:58:24, time: 0.603, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0211, loss_rpn_bbox: 0.0454, loss_cls: 0.1939, acc: 93.0308, loss_bbox: 0.2400, loss_mask: 0.2489, loss: 0.7494 +2024-05-31 06:40:40,228 - mmdet - INFO - Epoch [5][6800/7330] lr: 1.000e-04, eta: 8:57:55, time: 0.657, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0214, loss_rpn_bbox: 0.0467, loss_cls: 0.2067, acc: 92.5977, loss_bbox: 0.2513, loss_mask: 0.2488, loss: 0.7749 +2024-05-31 06:41:18,113 - mmdet - INFO - Epoch [5][6850/7330] lr: 1.000e-04, eta: 8:57:34, time: 0.758, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0221, loss_rpn_bbox: 0.0484, loss_cls: 0.2028, acc: 92.9241, loss_bbox: 0.2462, loss_mask: 0.2497, loss: 0.7692 +2024-05-31 06:41:48,471 - mmdet - INFO - Epoch [5][6900/7330] lr: 1.000e-04, eta: 8:57:01, time: 0.607, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0224, loss_rpn_bbox: 0.0490, loss_cls: 0.2052, acc: 92.7063, loss_bbox: 0.2453, loss_mask: 0.2492, loss: 0.7712 +2024-05-31 06:42:18,533 - mmdet - INFO - Epoch [5][6950/7330] lr: 1.000e-04, eta: 8:56:29, time: 0.601, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0232, loss_rpn_bbox: 0.0474, loss_cls: 0.2063, acc: 92.6455, loss_bbox: 0.2466, loss_mask: 0.2445, loss: 0.7681 +2024-05-31 06:42:48,595 - mmdet - INFO - Epoch [5][7000/7330] lr: 1.000e-04, eta: 8:55:56, time: 0.601, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0225, loss_rpn_bbox: 0.0488, loss_cls: 0.2049, acc: 92.8057, loss_bbox: 0.2469, loss_mask: 0.2448, loss: 0.7679 +2024-05-31 06:43:19,544 - mmdet - INFO - Epoch [5][7050/7330] lr: 1.000e-04, eta: 8:55:25, time: 0.619, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0240, loss_rpn_bbox: 0.0495, loss_cls: 0.2073, acc: 92.5825, loss_bbox: 0.2495, loss_mask: 0.2470, loss: 0.7772 +2024-05-31 06:43:49,501 - mmdet - INFO - Epoch [5][7100/7330] lr: 1.000e-04, eta: 8:54:52, time: 0.599, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0455, loss_cls: 0.1984, acc: 92.9795, loss_bbox: 0.2390, loss_mask: 0.2432, loss: 0.7470 +2024-05-31 06:44:19,790 - mmdet - INFO - Epoch [5][7150/7330] lr: 1.000e-04, eta: 8:54:20, time: 0.606, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0468, loss_cls: 0.2034, acc: 92.8206, loss_bbox: 0.2462, loss_mask: 0.2470, loss: 0.7669 +2024-05-31 06:44:50,044 - mmdet - INFO - Epoch [5][7200/7330] lr: 1.000e-04, eta: 8:53:47, time: 0.605, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0200, loss_rpn_bbox: 0.0476, loss_cls: 0.1976, acc: 92.9038, loss_bbox: 0.2389, loss_mask: 0.2420, loss: 0.7462 +2024-05-31 06:45:19,862 - mmdet - INFO - Epoch [5][7250/7330] lr: 1.000e-04, eta: 8:53:14, time: 0.596, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0226, loss_rpn_bbox: 0.0502, loss_cls: 0.2057, acc: 92.5303, loss_bbox: 0.2534, loss_mask: 0.2495, loss: 0.7813 +2024-05-31 06:45:50,327 - mmdet - INFO - Epoch [5][7300/7330] lr: 1.000e-04, eta: 8:52:42, time: 0.609, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0224, loss_rpn_bbox: 0.0460, loss_cls: 0.2058, acc: 92.7065, loss_bbox: 0.2493, loss_mask: 0.2478, loss: 0.7713 +2024-05-31 06:46:08,973 - mmdet - INFO - Saving checkpoint at 5 epochs +2024-05-31 06:47:47,513 - mmdet - INFO - Evaluating bbox... +2024-05-31 06:48:16,320 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.405 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.632 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.441 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.234 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.441 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.560 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.532 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.532 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.532 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.338 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.578 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.699 + +2024-05-31 06:48:16,320 - mmdet - INFO - Evaluating segm... +2024-05-31 06:48:43,049 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.371 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.598 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.391 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.169 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.402 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.581 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.489 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.489 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.489 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.277 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.535 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.683 + +2024-05-31 06:48:43,479 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 06:48:43,481 - mmdet - INFO - Epoch(val) [5][625] bbox_mAP: 0.4050, bbox_mAP_50: 0.6320, bbox_mAP_75: 0.4410, bbox_mAP_s: 0.2340, bbox_mAP_m: 0.4410, bbox_mAP_l: 0.5600, bbox_mAP_copypaste: 0.405 0.632 0.441 0.234 0.441 0.560, segm_mAP: 0.3710, segm_mAP_50: 0.5980, segm_mAP_75: 0.3910, segm_mAP_s: 0.1690, segm_mAP_m: 0.4020, segm_mAP_l: 0.5810, segm_mAP_copypaste: 0.371 0.598 0.391 0.169 0.402 0.581 +2024-05-31 06:49:20,935 - mmdet - INFO - Epoch [6][50/7330] lr: 1.000e-04, eta: 8:51:35, time: 0.749, data_time: 0.127, memory: 9655, loss_rpn_cls: 0.0215, loss_rpn_bbox: 0.0487, loss_cls: 0.1992, acc: 92.7236, loss_bbox: 0.2518, loss_mask: 0.2447, loss: 0.7660 +2024-05-31 06:49:51,897 - mmdet - INFO - Epoch [6][100/7330] lr: 1.000e-04, eta: 8:51:04, time: 0.619, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0199, loss_rpn_bbox: 0.0494, loss_cls: 0.2017, acc: 92.7227, loss_bbox: 0.2523, loss_mask: 0.2444, loss: 0.7676 +2024-05-31 06:50:24,820 - mmdet - INFO - Epoch [6][150/7330] lr: 1.000e-04, eta: 8:50:35, time: 0.658, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0471, loss_cls: 0.1954, acc: 92.8831, loss_bbox: 0.2429, loss_mask: 0.2387, loss: 0.7431 +2024-05-31 06:50:57,227 - mmdet - INFO - Epoch [6][200/7330] lr: 1.000e-04, eta: 8:50:06, time: 0.648, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0181, loss_rpn_bbox: 0.0431, loss_cls: 0.1931, acc: 93.0879, loss_bbox: 0.2417, loss_mask: 0.2417, loss: 0.7377 +2024-05-31 06:51:30,232 - mmdet - INFO - Epoch [6][250/7330] lr: 1.000e-04, eta: 8:49:37, time: 0.660, data_time: 0.064, memory: 9655, loss_rpn_cls: 0.0213, loss_rpn_bbox: 0.0476, loss_cls: 0.2018, acc: 92.6404, loss_bbox: 0.2516, loss_mask: 0.2452, loss: 0.7675 +2024-05-31 06:52:05,196 - mmdet - INFO - Epoch [6][300/7330] lr: 1.000e-04, eta: 8:49:11, time: 0.699, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0190, loss_rpn_bbox: 0.0476, loss_cls: 0.1915, acc: 93.0522, loss_bbox: 0.2470, loss_mask: 0.2433, loss: 0.7484 +2024-05-31 06:52:34,667 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 06:52:34,667 - mmdet - INFO - Epoch [6][350/7330] lr: 1.000e-04, eta: 8:48:38, time: 0.589, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0439, loss_cls: 0.1879, acc: 93.2332, loss_bbox: 0.2343, loss_mask: 0.2366, loss: 0.7226 +2024-05-31 06:53:04,626 - mmdet - INFO - Epoch [6][400/7330] lr: 1.000e-04, eta: 8:48:05, time: 0.599, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0188, loss_rpn_bbox: 0.0457, loss_cls: 0.1924, acc: 93.0483, loss_bbox: 0.2406, loss_mask: 0.2440, loss: 0.7416 +2024-05-31 06:53:34,995 - mmdet - INFO - Epoch [6][450/7330] lr: 1.000e-04, eta: 8:47:33, time: 0.607, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0207, loss_rpn_bbox: 0.0464, loss_cls: 0.1907, acc: 93.0149, loss_bbox: 0.2401, loss_mask: 0.2382, loss: 0.7361 +2024-05-31 06:54:04,978 - mmdet - INFO - Epoch [6][500/7330] lr: 1.000e-04, eta: 8:47:01, time: 0.600, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0205, loss_rpn_bbox: 0.0471, loss_cls: 0.1895, acc: 93.1008, loss_bbox: 0.2396, loss_mask: 0.2384, loss: 0.7350 +2024-05-31 06:54:35,229 - mmdet - INFO - Epoch [6][550/7330] lr: 1.000e-04, eta: 8:46:28, time: 0.605, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0473, loss_cls: 0.1987, acc: 92.9211, loss_bbox: 0.2426, loss_mask: 0.2440, loss: 0.7537 +2024-05-31 06:55:05,105 - mmdet - INFO - Epoch [6][600/7330] lr: 1.000e-04, eta: 8:45:55, time: 0.597, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0216, loss_rpn_bbox: 0.0486, loss_cls: 0.1963, acc: 92.9338, loss_bbox: 0.2443, loss_mask: 0.2436, loss: 0.7545 +2024-05-31 06:55:34,740 - mmdet - INFO - Epoch [6][650/7330] lr: 1.000e-04, eta: 8:45:22, time: 0.593, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0470, loss_cls: 0.1952, acc: 92.8755, loss_bbox: 0.2456, loss_mask: 0.2413, loss: 0.7480 +2024-05-31 06:56:05,057 - mmdet - INFO - Epoch [6][700/7330] lr: 1.000e-04, eta: 8:44:50, time: 0.606, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0192, loss_rpn_bbox: 0.0458, loss_cls: 0.1864, acc: 93.3896, loss_bbox: 0.2329, loss_mask: 0.2412, loss: 0.7255 +2024-05-31 06:56:35,204 - mmdet - INFO - Epoch [6][750/7330] lr: 1.000e-04, eta: 8:44:18, time: 0.603, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0197, loss_rpn_bbox: 0.0463, loss_cls: 0.1923, acc: 93.0791, loss_bbox: 0.2399, loss_mask: 0.2427, loss: 0.7407 +2024-05-31 06:57:05,751 - mmdet - INFO - Epoch [6][800/7330] lr: 1.000e-04, eta: 8:43:46, time: 0.611, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0475, loss_cls: 0.1937, acc: 93.0974, loss_bbox: 0.2401, loss_mask: 0.2405, loss: 0.7407 +2024-05-31 06:57:35,861 - mmdet - INFO - Epoch [6][850/7330] lr: 1.000e-04, eta: 8:43:13, time: 0.602, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0466, loss_cls: 0.1891, acc: 93.1458, loss_bbox: 0.2393, loss_mask: 0.2410, loss: 0.7369 +2024-05-31 06:58:09,079 - mmdet - INFO - Epoch [6][900/7330] lr: 1.000e-04, eta: 8:42:45, time: 0.664, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0209, loss_rpn_bbox: 0.0488, loss_cls: 0.2049, acc: 92.5291, loss_bbox: 0.2553, loss_mask: 0.2508, loss: 0.7808 +2024-05-31 06:58:39,573 - mmdet - INFO - Epoch [6][950/7330] lr: 1.000e-04, eta: 8:42:13, time: 0.609, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0217, loss_rpn_bbox: 0.0483, loss_cls: 0.1940, acc: 93.0977, loss_bbox: 0.2423, loss_mask: 0.2419, loss: 0.7481 +2024-05-31 06:59:11,818 - mmdet - INFO - Epoch [6][1000/7330] lr: 1.000e-04, eta: 8:41:44, time: 0.645, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0193, loss_rpn_bbox: 0.0454, loss_cls: 0.1969, acc: 92.9292, loss_bbox: 0.2436, loss_mask: 0.2449, loss: 0.7502 +2024-05-31 06:59:47,859 - mmdet - INFO - Epoch [6][1050/7330] lr: 1.000e-04, eta: 8:41:19, time: 0.721, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0207, loss_rpn_bbox: 0.0512, loss_cls: 0.2003, acc: 92.7852, loss_bbox: 0.2499, loss_mask: 0.2416, loss: 0.7638 +2024-05-31 07:00:21,259 - mmdet - INFO - Epoch [6][1100/7330] lr: 1.000e-04, eta: 8:40:51, time: 0.668, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0202, loss_rpn_bbox: 0.0475, loss_cls: 0.1915, acc: 93.0034, loss_bbox: 0.2483, loss_mask: 0.2534, loss: 0.7608 +2024-05-31 07:00:54,034 - mmdet - INFO - Epoch [6][1150/7330] lr: 1.000e-04, eta: 8:40:22, time: 0.655, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0193, loss_rpn_bbox: 0.0447, loss_cls: 0.1855, acc: 93.2778, loss_bbox: 0.2356, loss_mask: 0.2397, loss: 0.7248 +2024-05-31 07:01:27,986 - mmdet - INFO - Epoch [6][1200/7330] lr: 1.000e-04, eta: 8:39:55, time: 0.679, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0217, loss_rpn_bbox: 0.0452, loss_cls: 0.1835, acc: 93.3909, loss_bbox: 0.2343, loss_mask: 0.2381, loss: 0.7227 +2024-05-31 07:01:58,114 - mmdet - INFO - Epoch [6][1250/7330] lr: 1.000e-04, eta: 8:39:22, time: 0.602, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0190, loss_rpn_bbox: 0.0454, loss_cls: 0.1986, acc: 92.8687, loss_bbox: 0.2414, loss_mask: 0.2392, loss: 0.7435 +2024-05-31 07:02:28,166 - mmdet - INFO - Epoch [6][1300/7330] lr: 1.000e-04, eta: 8:38:50, time: 0.601, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0217, loss_rpn_bbox: 0.0468, loss_cls: 0.1943, acc: 92.9817, loss_bbox: 0.2417, loss_mask: 0.2451, loss: 0.7497 +2024-05-31 07:02:57,659 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 07:02:57,659 - mmdet - INFO - Epoch [6][1350/7330] lr: 1.000e-04, eta: 8:38:16, time: 0.590, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0197, loss_rpn_bbox: 0.0444, loss_cls: 0.1842, acc: 93.2959, loss_bbox: 0.2340, loss_mask: 0.2370, loss: 0.7193 +2024-05-31 07:03:28,434 - mmdet - INFO - Epoch [6][1400/7330] lr: 1.000e-04, eta: 8:37:45, time: 0.615, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0467, loss_cls: 0.1875, acc: 93.1394, loss_bbox: 0.2369, loss_mask: 0.2370, loss: 0.7289 +2024-05-31 07:03:58,897 - mmdet - INFO - Epoch [6][1450/7330] lr: 1.000e-04, eta: 8:37:13, time: 0.610, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0216, loss_rpn_bbox: 0.0476, loss_cls: 0.1878, acc: 93.2590, loss_bbox: 0.2323, loss_mask: 0.2428, loss: 0.7322 +2024-05-31 07:04:28,716 - mmdet - INFO - Epoch [6][1500/7330] lr: 1.000e-04, eta: 8:36:40, time: 0.596, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0224, loss_rpn_bbox: 0.0483, loss_cls: 0.1986, acc: 92.8342, loss_bbox: 0.2438, loss_mask: 0.2461, loss: 0.7592 +2024-05-31 07:04:58,298 - mmdet - INFO - Epoch [6][1550/7330] lr: 1.000e-04, eta: 8:36:07, time: 0.592, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0203, loss_rpn_bbox: 0.0453, loss_cls: 0.1915, acc: 93.0618, loss_bbox: 0.2389, loss_mask: 0.2408, loss: 0.7368 +2024-05-31 07:05:28,464 - mmdet - INFO - Epoch [6][1600/7330] lr: 1.000e-04, eta: 8:35:35, time: 0.603, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0169, loss_rpn_bbox: 0.0454, loss_cls: 0.1884, acc: 93.2227, loss_bbox: 0.2325, loss_mask: 0.2403, loss: 0.7235 +2024-05-31 07:05:58,827 - mmdet - INFO - Epoch [6][1650/7330] lr: 1.000e-04, eta: 8:35:02, time: 0.607, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0229, loss_rpn_bbox: 0.0486, loss_cls: 0.2018, acc: 92.8052, loss_bbox: 0.2449, loss_mask: 0.2439, loss: 0.7621 +2024-05-31 07:06:29,032 - mmdet - INFO - Epoch [6][1700/7330] lr: 1.000e-04, eta: 8:34:30, time: 0.604, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0214, loss_rpn_bbox: 0.0470, loss_cls: 0.1895, acc: 93.1741, loss_bbox: 0.2364, loss_mask: 0.2433, loss: 0.7376 +2024-05-31 07:06:59,285 - mmdet - INFO - Epoch [6][1750/7330] lr: 1.000e-04, eta: 8:33:58, time: 0.605, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0207, loss_rpn_bbox: 0.0476, loss_cls: 0.1950, acc: 92.8567, loss_bbox: 0.2468, loss_mask: 0.2435, loss: 0.7536 +2024-05-31 07:07:31,996 - mmdet - INFO - Epoch [6][1800/7330] lr: 1.000e-04, eta: 8:33:29, time: 0.654, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0204, loss_rpn_bbox: 0.0469, loss_cls: 0.1970, acc: 92.8899, loss_bbox: 0.2430, loss_mask: 0.2423, loss: 0.7496 +2024-05-31 07:08:01,989 - mmdet - INFO - Epoch [6][1850/7330] lr: 1.000e-04, eta: 8:32:56, time: 0.600, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0195, loss_rpn_bbox: 0.0461, loss_cls: 0.1953, acc: 92.9333, loss_bbox: 0.2415, loss_mask: 0.2465, loss: 0.7490 +2024-05-31 07:08:33,932 - mmdet - INFO - Epoch [6][1900/7330] lr: 1.000e-04, eta: 8:32:26, time: 0.639, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0185, loss_rpn_bbox: 0.0432, loss_cls: 0.1902, acc: 93.1729, loss_bbox: 0.2403, loss_mask: 0.2405, loss: 0.7328 +2024-05-31 07:09:09,262 - mmdet - INFO - Epoch [6][1950/7330] lr: 1.000e-04, eta: 8:32:01, time: 0.707, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0222, loss_rpn_bbox: 0.0497, loss_cls: 0.1947, acc: 92.9592, loss_bbox: 0.2482, loss_mask: 0.2530, loss: 0.7678 +2024-05-31 07:09:43,303 - mmdet - INFO - Epoch [6][2000/7330] lr: 1.000e-04, eta: 8:31:33, time: 0.681, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0222, loss_rpn_bbox: 0.0472, loss_cls: 0.2035, acc: 92.6025, loss_bbox: 0.2535, loss_mask: 0.2448, loss: 0.7711 +2024-05-31 07:10:15,567 - mmdet - INFO - Epoch [6][2050/7330] lr: 1.000e-04, eta: 8:31:03, time: 0.645, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0187, loss_rpn_bbox: 0.0448, loss_cls: 0.1890, acc: 93.1953, loss_bbox: 0.2329, loss_mask: 0.2356, loss: 0.7209 +2024-05-31 07:10:49,070 - mmdet - INFO - Epoch [6][2100/7330] lr: 1.000e-04, eta: 8:30:35, time: 0.670, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0186, loss_rpn_bbox: 0.0426, loss_cls: 0.1859, acc: 93.3401, loss_bbox: 0.2320, loss_mask: 0.2407, loss: 0.7198 +2024-05-31 07:11:18,799 - mmdet - INFO - Epoch [6][2150/7330] lr: 1.000e-04, eta: 8:30:02, time: 0.595, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0417, loss_cls: 0.1880, acc: 93.2263, loss_bbox: 0.2378, loss_mask: 0.2417, loss: 0.7281 +2024-05-31 07:11:48,657 - mmdet - INFO - Epoch [6][2200/7330] lr: 1.000e-04, eta: 8:29:30, time: 0.597, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0192, loss_rpn_bbox: 0.0452, loss_cls: 0.1931, acc: 93.0193, loss_bbox: 0.2401, loss_mask: 0.2389, loss: 0.7365 +2024-05-31 07:12:18,719 - mmdet - INFO - Epoch [6][2250/7330] lr: 1.000e-04, eta: 8:28:57, time: 0.601, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0209, loss_rpn_bbox: 0.0484, loss_cls: 0.2004, acc: 92.7815, loss_bbox: 0.2453, loss_mask: 0.2473, loss: 0.7623 +2024-05-31 07:12:49,038 - mmdet - INFO - Epoch [6][2300/7330] lr: 1.000e-04, eta: 8:28:25, time: 0.606, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0229, loss_rpn_bbox: 0.0487, loss_cls: 0.2029, acc: 92.6465, loss_bbox: 0.2522, loss_mask: 0.2492, loss: 0.7759 +2024-05-31 07:13:19,618 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 07:13:19,618 - mmdet - INFO - Epoch [6][2350/7330] lr: 1.000e-04, eta: 8:27:53, time: 0.612, data_time: 0.063, memory: 9655, loss_rpn_cls: 0.0207, loss_rpn_bbox: 0.0490, loss_cls: 0.1991, acc: 92.8176, loss_bbox: 0.2487, loss_mask: 0.2467, loss: 0.7641 +2024-05-31 07:13:49,879 - mmdet - INFO - Epoch [6][2400/7330] lr: 1.000e-04, eta: 8:27:21, time: 0.605, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0217, loss_rpn_bbox: 0.0465, loss_cls: 0.1904, acc: 93.0925, loss_bbox: 0.2389, loss_mask: 0.2409, loss: 0.7383 +2024-05-31 07:14:19,948 - mmdet - INFO - Epoch [6][2450/7330] lr: 1.000e-04, eta: 8:26:49, time: 0.601, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0211, loss_rpn_bbox: 0.0466, loss_cls: 0.1936, acc: 93.0317, loss_bbox: 0.2422, loss_mask: 0.2458, loss: 0.7493 +2024-05-31 07:14:49,710 - mmdet - INFO - Epoch [6][2500/7330] lr: 1.000e-04, eta: 8:26:16, time: 0.595, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0214, loss_rpn_bbox: 0.0481, loss_cls: 0.1946, acc: 93.0073, loss_bbox: 0.2431, loss_mask: 0.2432, loss: 0.7503 +2024-05-31 07:15:19,847 - mmdet - INFO - Epoch [6][2550/7330] lr: 1.000e-04, eta: 8:25:44, time: 0.603, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0476, loss_cls: 0.1910, acc: 93.1851, loss_bbox: 0.2386, loss_mask: 0.2435, loss: 0.7419 +2024-05-31 07:15:49,605 - mmdet - INFO - Epoch [6][2600/7330] lr: 1.000e-04, eta: 8:25:11, time: 0.595, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0191, loss_rpn_bbox: 0.0454, loss_cls: 0.1935, acc: 93.0168, loss_bbox: 0.2368, loss_mask: 0.2440, loss: 0.7388 +2024-05-31 07:16:19,562 - mmdet - INFO - Epoch [6][2650/7330] lr: 1.000e-04, eta: 8:24:38, time: 0.599, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0210, loss_rpn_bbox: 0.0465, loss_cls: 0.1888, acc: 93.1599, loss_bbox: 0.2398, loss_mask: 0.2421, loss: 0.7383 +2024-05-31 07:16:51,403 - mmdet - INFO - Epoch [6][2700/7330] lr: 1.000e-04, eta: 8:24:08, time: 0.637, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0185, loss_rpn_bbox: 0.0439, loss_cls: 0.1857, acc: 93.3162, loss_bbox: 0.2274, loss_mask: 0.2402, loss: 0.7156 +2024-05-31 07:17:21,153 - mmdet - INFO - Epoch [6][2750/7330] lr: 1.000e-04, eta: 8:23:35, time: 0.595, data_time: 0.038, memory: 9655, loss_rpn_cls: 0.0226, loss_rpn_bbox: 0.0469, loss_cls: 0.1930, acc: 93.0557, loss_bbox: 0.2417, loss_mask: 0.2394, loss: 0.7436 +2024-05-31 07:17:55,537 - mmdet - INFO - Epoch [6][2800/7330] lr: 1.000e-04, eta: 8:23:08, time: 0.688, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0203, loss_rpn_bbox: 0.0452, loss_cls: 0.1926, acc: 93.0979, loss_bbox: 0.2383, loss_mask: 0.2391, loss: 0.7354 +2024-05-31 07:18:30,053 - mmdet - INFO - Epoch [6][2850/7330] lr: 1.000e-04, eta: 8:22:41, time: 0.690, data_time: 0.038, memory: 9655, loss_rpn_cls: 0.0196, loss_rpn_bbox: 0.0461, loss_cls: 0.1854, acc: 93.3110, loss_bbox: 0.2340, loss_mask: 0.2375, loss: 0.7226 +2024-05-31 07:18:59,718 - mmdet - INFO - Epoch [6][2900/7330] lr: 1.000e-04, eta: 8:22:08, time: 0.593, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0190, loss_rpn_bbox: 0.0472, loss_cls: 0.1924, acc: 93.0833, loss_bbox: 0.2391, loss_mask: 0.2402, loss: 0.7377 +2024-05-31 07:19:32,180 - mmdet - INFO - Epoch [6][2950/7330] lr: 1.000e-04, eta: 8:21:39, time: 0.649, data_time: 0.067, memory: 9655, loss_rpn_cls: 0.0218, loss_rpn_bbox: 0.0474, loss_cls: 0.1937, acc: 93.1272, loss_bbox: 0.2374, loss_mask: 0.2406, loss: 0.7410 +2024-05-31 07:20:06,073 - mmdet - INFO - Epoch [6][3000/7330] lr: 1.000e-04, eta: 8:21:11, time: 0.678, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0191, loss_rpn_bbox: 0.0448, loss_cls: 0.1876, acc: 93.2397, loss_bbox: 0.2371, loss_mask: 0.2385, loss: 0.7272 +2024-05-31 07:20:36,112 - mmdet - INFO - Epoch [6][3050/7330] lr: 1.000e-04, eta: 8:20:39, time: 0.601, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0193, loss_rpn_bbox: 0.0442, loss_cls: 0.1907, acc: 93.1379, loss_bbox: 0.2380, loss_mask: 0.2390, loss: 0.7312 +2024-05-31 07:21:05,805 - mmdet - INFO - Epoch [6][3100/7330] lr: 1.000e-04, eta: 8:20:06, time: 0.594, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0205, loss_rpn_bbox: 0.0481, loss_cls: 0.1967, acc: 92.9065, loss_bbox: 0.2366, loss_mask: 0.2514, loss: 0.7534 +2024-05-31 07:21:35,444 - mmdet - INFO - Epoch [6][3150/7330] lr: 1.000e-04, eta: 8:19:33, time: 0.593, data_time: 0.038, memory: 9655, loss_rpn_cls: 0.0206, loss_rpn_bbox: 0.0454, loss_cls: 0.1913, acc: 93.2114, loss_bbox: 0.2350, loss_mask: 0.2421, loss: 0.7343 +2024-05-31 07:22:05,500 - mmdet - INFO - Epoch [6][3200/7330] lr: 1.000e-04, eta: 8:19:00, time: 0.601, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0181, loss_rpn_bbox: 0.0462, loss_cls: 0.1896, acc: 93.0835, loss_bbox: 0.2385, loss_mask: 0.2397, loss: 0.7321 +2024-05-31 07:22:35,293 - mmdet - INFO - Epoch [6][3250/7330] lr: 1.000e-04, eta: 8:18:28, time: 0.596, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0195, loss_rpn_bbox: 0.0456, loss_cls: 0.1953, acc: 92.9731, loss_bbox: 0.2397, loss_mask: 0.2430, loss: 0.7430 +2024-05-31 07:23:05,392 - mmdet - INFO - Epoch [6][3300/7330] lr: 1.000e-04, eta: 8:17:55, time: 0.602, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0205, loss_rpn_bbox: 0.0481, loss_cls: 0.1974, acc: 93.0415, loss_bbox: 0.2366, loss_mask: 0.2426, loss: 0.7453 +2024-05-31 07:23:35,158 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 07:23:35,158 - mmdet - INFO - Epoch [6][3350/7330] lr: 1.000e-04, eta: 8:17:23, time: 0.595, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0217, loss_rpn_bbox: 0.0453, loss_cls: 0.2008, acc: 92.8809, loss_bbox: 0.2411, loss_mask: 0.2436, loss: 0.7525 +2024-05-31 07:24:05,197 - mmdet - INFO - Epoch [6][3400/7330] lr: 1.000e-04, eta: 8:16:50, time: 0.601, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0213, loss_rpn_bbox: 0.0474, loss_cls: 0.1962, acc: 93.0012, loss_bbox: 0.2435, loss_mask: 0.2426, loss: 0.7509 +2024-05-31 07:24:35,097 - mmdet - INFO - Epoch [6][3450/7330] lr: 1.000e-04, eta: 8:16:18, time: 0.598, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0205, loss_rpn_bbox: 0.0439, loss_cls: 0.1930, acc: 93.0066, loss_bbox: 0.2360, loss_mask: 0.2410, loss: 0.7344 +2024-05-31 07:25:05,494 - mmdet - INFO - Epoch [6][3500/7330] lr: 1.000e-04, eta: 8:15:46, time: 0.608, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0209, loss_rpn_bbox: 0.0481, loss_cls: 0.2010, acc: 92.6931, loss_bbox: 0.2494, loss_mask: 0.2425, loss: 0.7618 +2024-05-31 07:25:35,983 - mmdet - INFO - Epoch [6][3550/7330] lr: 1.000e-04, eta: 8:15:14, time: 0.610, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0197, loss_rpn_bbox: 0.0473, loss_cls: 0.2002, acc: 92.8828, loss_bbox: 0.2420, loss_mask: 0.2415, loss: 0.7507 +2024-05-31 07:26:07,983 - mmdet - INFO - Epoch [6][3600/7330] lr: 1.000e-04, eta: 8:14:44, time: 0.640, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0176, loss_rpn_bbox: 0.0436, loss_cls: 0.1844, acc: 93.3528, loss_bbox: 0.2334, loss_mask: 0.2376, loss: 0.7166 +2024-05-31 07:26:38,032 - mmdet - INFO - Epoch [6][3650/7330] lr: 1.000e-04, eta: 8:14:11, time: 0.601, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0181, loss_rpn_bbox: 0.0454, loss_cls: 0.1981, acc: 92.9099, loss_bbox: 0.2441, loss_mask: 0.2408, loss: 0.7465 +2024-05-31 07:27:12,466 - mmdet - INFO - Epoch [6][3700/7330] lr: 1.000e-04, eta: 8:13:44, time: 0.689, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0215, loss_rpn_bbox: 0.0476, loss_cls: 0.1963, acc: 92.9697, loss_bbox: 0.2414, loss_mask: 0.2417, loss: 0.7484 +2024-05-31 07:27:46,741 - mmdet - INFO - Epoch [6][3750/7330] lr: 1.000e-04, eta: 8:13:17, time: 0.686, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0202, loss_rpn_bbox: 0.0444, loss_cls: 0.1944, acc: 93.0503, loss_bbox: 0.2332, loss_mask: 0.2416, loss: 0.7338 +2024-05-31 07:28:19,593 - mmdet - INFO - Epoch [6][3800/7330] lr: 1.000e-04, eta: 8:12:48, time: 0.657, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0455, loss_cls: 0.1950, acc: 92.9026, loss_bbox: 0.2379, loss_mask: 0.2376, loss: 0.7368 +2024-05-31 07:28:50,123 - mmdet - INFO - Epoch [6][3850/7330] lr: 1.000e-04, eta: 8:12:16, time: 0.611, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0206, loss_rpn_bbox: 0.0478, loss_cls: 0.2000, acc: 92.6521, loss_bbox: 0.2518, loss_mask: 0.2476, loss: 0.7677 +2024-05-31 07:29:24,801 - mmdet - INFO - Epoch [6][3900/7330] lr: 1.000e-04, eta: 8:11:49, time: 0.694, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0199, loss_rpn_bbox: 0.0442, loss_cls: 0.1876, acc: 93.4023, loss_bbox: 0.2294, loss_mask: 0.2412, loss: 0.7224 +2024-05-31 07:29:55,199 - mmdet - INFO - Epoch [6][3950/7330] lr: 1.000e-04, eta: 8:11:17, time: 0.608, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0225, loss_rpn_bbox: 0.0492, loss_cls: 0.1992, acc: 92.7881, loss_bbox: 0.2462, loss_mask: 0.2441, loss: 0.7612 +2024-05-31 07:30:24,928 - mmdet - INFO - Epoch [6][4000/7330] lr: 1.000e-04, eta: 8:10:44, time: 0.595, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0187, loss_rpn_bbox: 0.0451, loss_cls: 0.1933, acc: 92.9985, loss_bbox: 0.2442, loss_mask: 0.2397, loss: 0.7409 +2024-05-31 07:30:54,753 - mmdet - INFO - Epoch [6][4050/7330] lr: 1.000e-04, eta: 8:10:12, time: 0.596, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0455, loss_cls: 0.1945, acc: 92.9646, loss_bbox: 0.2445, loss_mask: 0.2427, loss: 0.7484 +2024-05-31 07:31:24,325 - mmdet - INFO - Epoch [6][4100/7330] lr: 1.000e-04, eta: 8:09:39, time: 0.591, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0207, loss_rpn_bbox: 0.0459, loss_cls: 0.1985, acc: 92.8469, loss_bbox: 0.2457, loss_mask: 0.2433, loss: 0.7542 +2024-05-31 07:31:54,226 - mmdet - INFO - Epoch [6][4150/7330] lr: 1.000e-04, eta: 8:09:06, time: 0.598, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0220, loss_rpn_bbox: 0.0423, loss_cls: 0.1923, acc: 93.0259, loss_bbox: 0.2399, loss_mask: 0.2429, loss: 0.7394 +2024-05-31 07:32:24,275 - mmdet - INFO - Epoch [6][4200/7330] lr: 1.000e-04, eta: 8:08:34, time: 0.601, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0434, loss_cls: 0.1885, acc: 93.1665, loss_bbox: 0.2372, loss_mask: 0.2400, loss: 0.7303 +2024-05-31 07:32:54,128 - mmdet - INFO - Epoch [6][4250/7330] lr: 1.000e-04, eta: 8:08:01, time: 0.597, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0200, loss_rpn_bbox: 0.0433, loss_cls: 0.1939, acc: 93.0469, loss_bbox: 0.2402, loss_mask: 0.2444, loss: 0.7418 +2024-05-31 07:33:24,080 - mmdet - INFO - Epoch [6][4300/7330] lr: 1.000e-04, eta: 8:07:29, time: 0.599, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0204, loss_rpn_bbox: 0.0477, loss_cls: 0.1989, acc: 92.8601, loss_bbox: 0.2460, loss_mask: 0.2415, loss: 0.7545 +2024-05-31 07:33:55,159 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 07:33:55,159 - mmdet - INFO - Epoch [6][4350/7330] lr: 1.000e-04, eta: 8:06:58, time: 0.622, data_time: 0.064, memory: 9655, loss_rpn_cls: 0.0240, loss_rpn_bbox: 0.0497, loss_cls: 0.2032, acc: 92.6833, loss_bbox: 0.2469, loss_mask: 0.2470, loss: 0.7708 +2024-05-31 07:34:25,213 - mmdet - INFO - Epoch [6][4400/7330] lr: 1.000e-04, eta: 8:06:26, time: 0.601, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0224, loss_rpn_bbox: 0.0485, loss_cls: 0.2076, acc: 92.5447, loss_bbox: 0.2549, loss_mask: 0.2497, loss: 0.7831 +2024-05-31 07:34:55,434 - mmdet - INFO - Epoch [6][4450/7330] lr: 1.000e-04, eta: 8:05:53, time: 0.604, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0217, loss_rpn_bbox: 0.0459, loss_cls: 0.1933, acc: 93.0615, loss_bbox: 0.2427, loss_mask: 0.2449, loss: 0.7485 +2024-05-31 07:35:28,525 - mmdet - INFO - Epoch [6][4500/7330] lr: 1.000e-04, eta: 8:05:25, time: 0.662, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0211, loss_rpn_bbox: 0.0443, loss_cls: 0.1865, acc: 93.2996, loss_bbox: 0.2310, loss_mask: 0.2373, loss: 0.7201 +2024-05-31 07:35:58,572 - mmdet - INFO - Epoch [6][4550/7330] lr: 1.000e-04, eta: 8:04:52, time: 0.601, data_time: 0.038, memory: 9655, loss_rpn_cls: 0.0210, loss_rpn_bbox: 0.0491, loss_cls: 0.1967, acc: 93.0068, loss_bbox: 0.2437, loss_mask: 0.2435, loss: 0.7540 +2024-05-31 07:36:38,131 - mmdet - INFO - Epoch [6][4600/7330] lr: 1.000e-04, eta: 8:04:31, time: 0.791, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0192, loss_rpn_bbox: 0.0465, loss_cls: 0.2022, acc: 92.7827, loss_bbox: 0.2423, loss_mask: 0.2360, loss: 0.7463 +2024-05-31 07:37:10,447 - mmdet - INFO - Epoch [6][4650/7330] lr: 1.000e-04, eta: 8:04:01, time: 0.646, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0197, loss_rpn_bbox: 0.0450, loss_cls: 0.1950, acc: 92.9436, loss_bbox: 0.2411, loss_mask: 0.2434, loss: 0.7442 +2024-05-31 07:37:42,965 - mmdet - INFO - Epoch [6][4700/7330] lr: 1.000e-04, eta: 8:03:31, time: 0.650, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0201, loss_rpn_bbox: 0.0457, loss_cls: 0.1989, acc: 92.9885, loss_bbox: 0.2411, loss_mask: 0.2379, loss: 0.7436 +2024-05-31 07:38:19,055 - mmdet - INFO - Epoch [6][4750/7330] lr: 1.000e-04, eta: 8:03:06, time: 0.721, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0223, loss_rpn_bbox: 0.0485, loss_cls: 0.1982, acc: 92.9290, loss_bbox: 0.2434, loss_mask: 0.2471, loss: 0.7594 +2024-05-31 07:38:48,696 - mmdet - INFO - Epoch [6][4800/7330] lr: 1.000e-04, eta: 8:02:33, time: 0.593, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0206, loss_rpn_bbox: 0.0466, loss_cls: 0.1993, acc: 92.8230, loss_bbox: 0.2424, loss_mask: 0.2438, loss: 0.7527 +2024-05-31 07:39:18,276 - mmdet - INFO - Epoch [6][4850/7330] lr: 1.000e-04, eta: 8:02:00, time: 0.592, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0207, loss_rpn_bbox: 0.0460, loss_cls: 0.1931, acc: 93.0811, loss_bbox: 0.2370, loss_mask: 0.2391, loss: 0.7359 +2024-05-31 07:39:48,288 - mmdet - INFO - Epoch [6][4900/7330] lr: 1.000e-04, eta: 8:01:28, time: 0.600, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0186, loss_rpn_bbox: 0.0445, loss_cls: 0.1862, acc: 93.2710, loss_bbox: 0.2318, loss_mask: 0.2361, loss: 0.7170 +2024-05-31 07:40:18,251 - mmdet - INFO - Epoch [6][4950/7330] lr: 1.000e-04, eta: 8:00:55, time: 0.599, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0201, loss_rpn_bbox: 0.0451, loss_cls: 0.1908, acc: 93.0337, loss_bbox: 0.2382, loss_mask: 0.2415, loss: 0.7356 +2024-05-31 07:40:48,144 - mmdet - INFO - Epoch [6][5000/7330] lr: 1.000e-04, eta: 8:00:23, time: 0.598, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0237, loss_rpn_bbox: 0.0456, loss_cls: 0.1893, acc: 93.1450, loss_bbox: 0.2361, loss_mask: 0.2398, loss: 0.7345 +2024-05-31 07:41:18,486 - mmdet - INFO - Epoch [6][5050/7330] lr: 1.000e-04, eta: 7:59:51, time: 0.607, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0491, loss_cls: 0.2009, acc: 92.8040, loss_bbox: 0.2497, loss_mask: 0.2447, loss: 0.7655 +2024-05-31 07:41:48,126 - mmdet - INFO - Epoch [6][5100/7330] lr: 1.000e-04, eta: 7:59:18, time: 0.593, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0203, loss_rpn_bbox: 0.0469, loss_cls: 0.1959, acc: 92.9387, loss_bbox: 0.2401, loss_mask: 0.2440, loss: 0.7472 +2024-05-31 07:42:17,992 - mmdet - INFO - Epoch [6][5150/7330] lr: 1.000e-04, eta: 7:58:46, time: 0.597, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0213, loss_rpn_bbox: 0.0459, loss_cls: 0.1974, acc: 93.0146, loss_bbox: 0.2378, loss_mask: 0.2436, loss: 0.7459 +2024-05-31 07:42:47,445 - mmdet - INFO - Epoch [6][5200/7330] lr: 1.000e-04, eta: 7:58:13, time: 0.589, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0471, loss_cls: 0.1938, acc: 92.9265, loss_bbox: 0.2415, loss_mask: 0.2436, loss: 0.7473 +2024-05-31 07:43:17,349 - mmdet - INFO - Epoch [6][5250/7330] lr: 1.000e-04, eta: 7:57:40, time: 0.598, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0193, loss_rpn_bbox: 0.0454, loss_cls: 0.1899, acc: 93.1382, loss_bbox: 0.2357, loss_mask: 0.2437, loss: 0.7341 +2024-05-31 07:43:46,912 - mmdet - INFO - Epoch [6][5300/7330] lr: 1.000e-04, eta: 7:57:07, time: 0.591, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0213, loss_rpn_bbox: 0.0466, loss_cls: 0.1914, acc: 93.0664, loss_bbox: 0.2372, loss_mask: 0.2459, loss: 0.7426 +2024-05-31 07:44:19,318 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 07:44:19,319 - mmdet - INFO - Epoch [6][5350/7330] lr: 1.000e-04, eta: 7:56:38, time: 0.648, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0199, loss_rpn_bbox: 0.0465, loss_cls: 0.1930, acc: 93.0171, loss_bbox: 0.2434, loss_mask: 0.2412, loss: 0.7441 +2024-05-31 07:44:49,335 - mmdet - INFO - Epoch [6][5400/7330] lr: 1.000e-04, eta: 7:56:05, time: 0.600, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0227, loss_rpn_bbox: 0.0480, loss_cls: 0.1966, acc: 92.9561, loss_bbox: 0.2489, loss_mask: 0.2507, loss: 0.7669 +2024-05-31 07:45:19,644 - mmdet - INFO - Epoch [6][5450/7330] lr: 1.000e-04, eta: 7:55:33, time: 0.606, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0215, loss_rpn_bbox: 0.0471, loss_cls: 0.1896, acc: 93.1128, loss_bbox: 0.2374, loss_mask: 0.2423, loss: 0.7380 +2024-05-31 07:45:58,276 - mmdet - INFO - Epoch [6][5500/7330] lr: 1.000e-04, eta: 7:55:11, time: 0.773, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0210, loss_rpn_bbox: 0.0475, loss_cls: 0.1988, acc: 92.8811, loss_bbox: 0.2428, loss_mask: 0.2421, loss: 0.7522 +2024-05-31 07:46:30,375 - mmdet - INFO - Epoch [6][5550/7330] lr: 1.000e-04, eta: 7:54:40, time: 0.642, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0222, loss_rpn_bbox: 0.0495, loss_cls: 0.2017, acc: 92.8027, loss_bbox: 0.2456, loss_mask: 0.2454, loss: 0.7643 +2024-05-31 07:47:02,996 - mmdet - INFO - Epoch [6][5600/7330] lr: 1.000e-04, eta: 7:54:11, time: 0.652, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0225, loss_rpn_bbox: 0.0470, loss_cls: 0.2022, acc: 92.7104, loss_bbox: 0.2495, loss_mask: 0.2404, loss: 0.7617 +2024-05-31 07:47:37,333 - mmdet - INFO - Epoch [6][5650/7330] lr: 1.000e-04, eta: 7:53:43, time: 0.687, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0188, loss_rpn_bbox: 0.0440, loss_cls: 0.2022, acc: 92.8218, loss_bbox: 0.2444, loss_mask: 0.2431, loss: 0.7524 +2024-05-31 07:48:07,017 - mmdet - INFO - Epoch [6][5700/7330] lr: 1.000e-04, eta: 7:53:11, time: 0.594, data_time: 0.037, memory: 9655, loss_rpn_cls: 0.0221, loss_rpn_bbox: 0.0484, loss_cls: 0.2013, acc: 92.6985, loss_bbox: 0.2496, loss_mask: 0.2482, loss: 0.7695 +2024-05-31 07:48:36,823 - mmdet - INFO - Epoch [6][5750/7330] lr: 1.000e-04, eta: 7:52:38, time: 0.596, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0209, loss_rpn_bbox: 0.0484, loss_cls: 0.1963, acc: 92.9126, loss_bbox: 0.2462, loss_mask: 0.2465, loss: 0.7583 +2024-05-31 07:49:07,482 - mmdet - INFO - Epoch [6][5800/7330] lr: 1.000e-04, eta: 7:52:07, time: 0.613, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0200, loss_rpn_bbox: 0.0486, loss_cls: 0.1997, acc: 92.8486, loss_bbox: 0.2449, loss_mask: 0.2465, loss: 0.7596 +2024-05-31 07:49:37,750 - mmdet - INFO - Epoch [6][5850/7330] lr: 1.000e-04, eta: 7:51:34, time: 0.605, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0471, loss_cls: 0.1990, acc: 92.8394, loss_bbox: 0.2435, loss_mask: 0.2395, loss: 0.7500 +2024-05-31 07:50:08,672 - mmdet - INFO - Epoch [6][5900/7330] lr: 1.000e-04, eta: 7:51:03, time: 0.619, data_time: 0.065, memory: 9655, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0483, loss_cls: 0.1983, acc: 92.7886, loss_bbox: 0.2476, loss_mask: 0.2480, loss: 0.7630 +2024-05-31 07:50:38,795 - mmdet - INFO - Epoch [6][5950/7330] lr: 1.000e-04, eta: 7:50:31, time: 0.602, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0216, loss_rpn_bbox: 0.0481, loss_cls: 0.1993, acc: 92.8101, loss_bbox: 0.2481, loss_mask: 0.2420, loss: 0.7590 +2024-05-31 07:51:09,071 - mmdet - INFO - Epoch [6][6000/7330] lr: 1.000e-04, eta: 7:49:59, time: 0.606, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0226, loss_rpn_bbox: 0.0481, loss_cls: 0.2009, acc: 92.8865, loss_bbox: 0.2483, loss_mask: 0.2483, loss: 0.7682 +2024-05-31 07:51:38,690 - mmdet - INFO - Epoch [6][6050/7330] lr: 1.000e-04, eta: 7:49:26, time: 0.592, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0193, loss_rpn_bbox: 0.0456, loss_cls: 0.1956, acc: 92.9402, loss_bbox: 0.2418, loss_mask: 0.2441, loss: 0.7463 +2024-05-31 07:52:08,537 - mmdet - INFO - Epoch [6][6100/7330] lr: 1.000e-04, eta: 7:48:54, time: 0.597, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0191, loss_rpn_bbox: 0.0462, loss_cls: 0.1951, acc: 93.0610, loss_bbox: 0.2381, loss_mask: 0.2427, loss: 0.7413 +2024-05-31 07:52:38,817 - mmdet - INFO - Epoch [6][6150/7330] lr: 1.000e-04, eta: 7:48:22, time: 0.606, data_time: 0.037, memory: 9655, loss_rpn_cls: 0.0213, loss_rpn_bbox: 0.0506, loss_cls: 0.2025, acc: 92.8345, loss_bbox: 0.2447, loss_mask: 0.2458, loss: 0.7649 +2024-05-31 07:53:09,098 - mmdet - INFO - Epoch [6][6200/7330] lr: 1.000e-04, eta: 7:47:50, time: 0.606, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0217, loss_rpn_bbox: 0.0461, loss_cls: 0.2011, acc: 92.8867, loss_bbox: 0.2417, loss_mask: 0.2428, loss: 0.7535 +2024-05-31 07:53:41,262 - mmdet - INFO - Epoch [6][6250/7330] lr: 1.000e-04, eta: 7:47:20, time: 0.643, data_time: 0.036, memory: 9655, loss_rpn_cls: 0.0190, loss_rpn_bbox: 0.0433, loss_cls: 0.1846, acc: 93.2979, loss_bbox: 0.2297, loss_mask: 0.2362, loss: 0.7127 +2024-05-31 07:54:11,533 - mmdet - INFO - Epoch [6][6300/7330] lr: 1.000e-04, eta: 7:46:48, time: 0.605, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0203, loss_rpn_bbox: 0.0457, loss_cls: 0.1908, acc: 93.2002, loss_bbox: 0.2332, loss_mask: 0.2333, loss: 0.7232 +2024-05-31 07:54:44,495 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 07:54:44,495 - mmdet - INFO - Epoch [6][6350/7330] lr: 1.000e-04, eta: 7:46:19, time: 0.659, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0210, loss_rpn_bbox: 0.0460, loss_cls: 0.1988, acc: 92.8970, loss_bbox: 0.2422, loss_mask: 0.2447, loss: 0.7526 +2024-05-31 07:55:19,998 - mmdet - INFO - Epoch [6][6400/7330] lr: 1.000e-04, eta: 7:45:52, time: 0.710, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0202, loss_rpn_bbox: 0.0466, loss_cls: 0.1996, acc: 92.9221, loss_bbox: 0.2359, loss_mask: 0.2439, loss: 0.7462 +2024-05-31 07:55:51,937 - mmdet - INFO - Epoch [6][6450/7330] lr: 1.000e-04, eta: 7:45:22, time: 0.639, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0202, loss_rpn_bbox: 0.0449, loss_cls: 0.1901, acc: 93.1736, loss_bbox: 0.2369, loss_mask: 0.2360, loss: 0.7282 +2024-05-31 07:56:24,611 - mmdet - INFO - Epoch [6][6500/7330] lr: 1.000e-04, eta: 7:44:52, time: 0.654, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0220, loss_rpn_bbox: 0.0496, loss_cls: 0.2037, acc: 92.7019, loss_bbox: 0.2416, loss_mask: 0.2432, loss: 0.7601 +2024-05-31 07:56:59,164 - mmdet - INFO - Epoch [6][6550/7330] lr: 1.000e-04, eta: 7:44:25, time: 0.691, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0213, loss_rpn_bbox: 0.0469, loss_cls: 0.1974, acc: 93.0251, loss_bbox: 0.2390, loss_mask: 0.2410, loss: 0.7456 +2024-05-31 07:57:29,088 - mmdet - INFO - Epoch [6][6600/7330] lr: 1.000e-04, eta: 7:43:52, time: 0.598, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0219, loss_rpn_bbox: 0.0454, loss_cls: 0.1944, acc: 92.9224, loss_bbox: 0.2431, loss_mask: 0.2449, loss: 0.7497 +2024-05-31 07:57:59,088 - mmdet - INFO - Epoch [6][6650/7330] lr: 1.000e-04, eta: 7:43:20, time: 0.600, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0197, loss_rpn_bbox: 0.0437, loss_cls: 0.1798, acc: 93.4546, loss_bbox: 0.2288, loss_mask: 0.2334, loss: 0.7053 +2024-05-31 07:58:29,426 - mmdet - INFO - Epoch [6][6700/7330] lr: 1.000e-04, eta: 7:42:48, time: 0.607, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0209, loss_rpn_bbox: 0.0475, loss_cls: 0.1933, acc: 93.1248, loss_bbox: 0.2382, loss_mask: 0.2439, loss: 0.7438 +2024-05-31 07:58:59,079 - mmdet - INFO - Epoch [6][6750/7330] lr: 1.000e-04, eta: 7:42:15, time: 0.593, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0211, loss_rpn_bbox: 0.0459, loss_cls: 0.1949, acc: 93.0291, loss_bbox: 0.2419, loss_mask: 0.2430, loss: 0.7468 +2024-05-31 07:59:29,316 - mmdet - INFO - Epoch [6][6800/7330] lr: 1.000e-04, eta: 7:41:43, time: 0.605, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0200, loss_rpn_bbox: 0.0476, loss_cls: 0.1922, acc: 93.0750, loss_bbox: 0.2402, loss_mask: 0.2417, loss: 0.7416 +2024-05-31 07:59:59,336 - mmdet - INFO - Epoch [6][6850/7330] lr: 1.000e-04, eta: 7:41:11, time: 0.600, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0193, loss_rpn_bbox: 0.0438, loss_cls: 0.1902, acc: 93.1892, loss_bbox: 0.2344, loss_mask: 0.2382, loss: 0.7258 +2024-05-31 08:00:29,222 - mmdet - INFO - Epoch [6][6900/7330] lr: 1.000e-04, eta: 7:40:39, time: 0.598, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0214, loss_rpn_bbox: 0.0479, loss_cls: 0.2038, acc: 92.6748, loss_bbox: 0.2517, loss_mask: 0.2458, loss: 0.7705 +2024-05-31 08:00:59,442 - mmdet - INFO - Epoch [6][6950/7330] lr: 1.000e-04, eta: 7:40:07, time: 0.604, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0202, loss_rpn_bbox: 0.0456, loss_cls: 0.1924, acc: 93.0374, loss_bbox: 0.2411, loss_mask: 0.2362, loss: 0.7356 +2024-05-31 08:01:29,159 - mmdet - INFO - Epoch [6][7000/7330] lr: 1.000e-04, eta: 7:39:34, time: 0.594, data_time: 0.063, memory: 9655, loss_rpn_cls: 0.0202, loss_rpn_bbox: 0.0435, loss_cls: 0.1939, acc: 93.0117, loss_bbox: 0.2353, loss_mask: 0.2409, loss: 0.7338 +2024-05-31 08:01:59,060 - mmdet - INFO - Epoch [6][7050/7330] lr: 1.000e-04, eta: 7:39:02, time: 0.598, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0215, loss_rpn_bbox: 0.0452, loss_cls: 0.1966, acc: 92.9390, loss_bbox: 0.2422, loss_mask: 0.2427, loss: 0.7482 +2024-05-31 08:02:29,120 - mmdet - INFO - Epoch [6][7100/7330] lr: 1.000e-04, eta: 7:38:30, time: 0.601, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0204, loss_rpn_bbox: 0.0451, loss_cls: 0.1970, acc: 92.9258, loss_bbox: 0.2379, loss_mask: 0.2456, loss: 0.7460 +2024-05-31 08:03:02,281 - mmdet - INFO - Epoch [6][7150/7330] lr: 1.000e-04, eta: 7:38:01, time: 0.663, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0196, loss_rpn_bbox: 0.0465, loss_cls: 0.1910, acc: 93.0789, loss_bbox: 0.2359, loss_mask: 0.2438, loss: 0.7368 +2024-05-31 08:03:32,529 - mmdet - INFO - Epoch [6][7200/7330] lr: 1.000e-04, eta: 7:37:29, time: 0.605, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0202, loss_rpn_bbox: 0.0444, loss_cls: 0.1929, acc: 93.0735, loss_bbox: 0.2350, loss_mask: 0.2431, loss: 0.7356 +2024-05-31 08:04:04,539 - mmdet - INFO - Epoch [6][7250/7330] lr: 1.000e-04, eta: 7:36:58, time: 0.640, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0214, loss_rpn_bbox: 0.0476, loss_cls: 0.1894, acc: 93.2498, loss_bbox: 0.2322, loss_mask: 0.2358, loss: 0.7264 +2024-05-31 08:04:39,244 - mmdet - INFO - Epoch [6][7300/7330] lr: 1.000e-04, eta: 7:36:31, time: 0.694, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0199, loss_rpn_bbox: 0.0475, loss_cls: 0.1989, acc: 92.7212, loss_bbox: 0.2464, loss_mask: 0.2381, loss: 0.7507 +2024-05-31 08:05:00,461 - mmdet - INFO - Saving checkpoint at 6 epochs +2024-05-31 08:06:35,343 - mmdet - INFO - Evaluating bbox... +2024-05-31 08:06:57,896 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.412 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.637 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.445 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.240 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.445 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.572 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.536 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.536 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.536 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.343 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.574 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.705 + +2024-05-31 08:06:57,896 - mmdet - INFO - Evaluating segm... +2024-05-31 08:07:23,854 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.375 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.605 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.397 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.173 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.402 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.583 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.489 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.489 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.489 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.291 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.531 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.676 + +2024-05-31 08:07:24,206 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 08:07:24,208 - mmdet - INFO - Epoch(val) [6][625] bbox_mAP: 0.4120, bbox_mAP_50: 0.6370, bbox_mAP_75: 0.4450, bbox_mAP_s: 0.2400, bbox_mAP_m: 0.4450, bbox_mAP_l: 0.5720, bbox_mAP_copypaste: 0.412 0.637 0.445 0.240 0.445 0.572, segm_mAP: 0.3750, segm_mAP_50: 0.6050, segm_mAP_75: 0.3970, segm_mAP_s: 0.1730, segm_mAP_m: 0.4020, segm_mAP_l: 0.5830, segm_mAP_copypaste: 0.375 0.605 0.397 0.173 0.402 0.583 +2024-05-31 08:08:08,674 - mmdet - INFO - Epoch [7][50/7330] lr: 1.000e-04, eta: 7:35:36, time: 0.889, data_time: 0.124, memory: 9655, loss_rpn_cls: 0.0196, loss_rpn_bbox: 0.0455, loss_cls: 0.1855, acc: 93.1985, loss_bbox: 0.2328, loss_mask: 0.2399, loss: 0.7233 +2024-05-31 08:08:39,382 - mmdet - INFO - Epoch [7][100/7330] lr: 1.000e-04, eta: 7:35:04, time: 0.614, data_time: 0.038, memory: 9655, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0463, loss_cls: 0.1820, acc: 93.3601, loss_bbox: 0.2336, loss_mask: 0.2439, loss: 0.7270 +2024-05-31 08:09:09,677 - mmdet - INFO - Epoch [7][150/7330] lr: 1.000e-04, eta: 7:34:32, time: 0.606, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0183, loss_rpn_bbox: 0.0451, loss_cls: 0.1927, acc: 92.9426, loss_bbox: 0.2475, loss_mask: 0.2379, loss: 0.7415 +2024-05-31 08:09:39,183 - mmdet - INFO - Epoch [7][200/7330] lr: 1.000e-04, eta: 7:34:00, time: 0.590, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0163, loss_rpn_bbox: 0.0435, loss_cls: 0.1825, acc: 93.4495, loss_bbox: 0.2246, loss_mask: 0.2290, loss: 0.6959 +2024-05-31 08:10:09,899 - mmdet - INFO - Epoch [7][250/7330] lr: 1.000e-04, eta: 7:33:28, time: 0.614, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0177, loss_rpn_bbox: 0.0439, loss_cls: 0.1782, acc: 93.4309, loss_bbox: 0.2330, loss_mask: 0.2351, loss: 0.7079 +2024-05-31 08:10:40,576 - mmdet - INFO - Epoch [7][300/7330] lr: 1.000e-04, eta: 7:32:57, time: 0.613, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0194, loss_rpn_bbox: 0.0463, loss_cls: 0.1876, acc: 93.1731, loss_bbox: 0.2356, loss_mask: 0.2404, loss: 0.7293 +2024-05-31 08:11:10,620 - mmdet - INFO - Epoch [7][350/7330] lr: 1.000e-04, eta: 7:32:24, time: 0.601, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0183, loss_rpn_bbox: 0.0441, loss_cls: 0.1809, acc: 93.3713, loss_bbox: 0.2327, loss_mask: 0.2384, loss: 0.7143 +2024-05-31 08:11:40,738 - mmdet - INFO - Epoch [7][400/7330] lr: 1.000e-04, eta: 7:31:52, time: 0.602, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0169, loss_rpn_bbox: 0.0419, loss_cls: 0.1811, acc: 93.2258, loss_bbox: 0.2320, loss_mask: 0.2374, loss: 0.7093 +2024-05-31 08:12:11,420 - mmdet - INFO - Epoch [7][450/7330] lr: 1.000e-04, eta: 7:31:21, time: 0.614, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0474, loss_cls: 0.1841, acc: 93.3694, loss_bbox: 0.2322, loss_mask: 0.2355, loss: 0.7180 +2024-05-31 08:12:41,722 - mmdet - INFO - Epoch [7][500/7330] lr: 1.000e-04, eta: 7:30:49, time: 0.606, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0177, loss_rpn_bbox: 0.0461, loss_cls: 0.1854, acc: 93.1594, loss_bbox: 0.2321, loss_mask: 0.2354, loss: 0.7167 +2024-05-31 08:13:11,466 - mmdet - INFO - Epoch [7][550/7330] lr: 1.000e-04, eta: 7:30:17, time: 0.595, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0179, loss_rpn_bbox: 0.0422, loss_cls: 0.1717, acc: 93.7480, loss_bbox: 0.2200, loss_mask: 0.2281, loss: 0.6799 +2024-05-31 08:13:41,592 - mmdet - INFO - Epoch [7][600/7330] lr: 1.000e-04, eta: 7:29:44, time: 0.602, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0465, loss_cls: 0.1905, acc: 93.0669, loss_bbox: 0.2420, loss_mask: 0.2379, loss: 0.7366 +2024-05-31 08:14:12,318 - mmdet - INFO - Epoch [7][650/7330] lr: 1.000e-04, eta: 7:29:13, time: 0.614, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0204, loss_rpn_bbox: 0.0499, loss_cls: 0.1925, acc: 93.0876, loss_bbox: 0.2424, loss_mask: 0.2386, loss: 0.7439 +2024-05-31 08:14:42,795 - mmdet - INFO - Epoch [7][700/7330] lr: 1.000e-04, eta: 7:28:41, time: 0.610, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0205, loss_rpn_bbox: 0.0496, loss_cls: 0.1934, acc: 92.8994, loss_bbox: 0.2425, loss_mask: 0.2380, loss: 0.7440 +2024-05-31 08:15:13,699 - mmdet - INFO - Epoch [7][750/7330] lr: 1.000e-04, eta: 7:28:10, time: 0.618, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0187, loss_rpn_bbox: 0.0465, loss_cls: 0.1863, acc: 93.1479, loss_bbox: 0.2338, loss_mask: 0.2362, loss: 0.7215 +2024-05-31 08:15:44,753 - mmdet - INFO - Epoch [7][800/7330] lr: 1.000e-04, eta: 7:27:39, time: 0.621, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0177, loss_rpn_bbox: 0.0476, loss_cls: 0.1886, acc: 93.1501, loss_bbox: 0.2378, loss_mask: 0.2360, loss: 0.7278 +2024-05-31 08:16:14,815 - mmdet - INFO - Epoch [7][850/7330] lr: 1.000e-04, eta: 7:27:07, time: 0.601, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0433, loss_cls: 0.1802, acc: 93.4241, loss_bbox: 0.2333, loss_mask: 0.2304, loss: 0.7033 +2024-05-31 08:16:52,632 - mmdet - INFO - Epoch [7][900/7330] lr: 1.000e-04, eta: 7:26:42, time: 0.756, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0197, loss_rpn_bbox: 0.0428, loss_cls: 0.1893, acc: 93.2261, loss_bbox: 0.2336, loss_mask: 0.2364, loss: 0.7218 +2024-05-31 08:17:24,746 - mmdet - INFO - Epoch [7][950/7330] lr: 1.000e-04, eta: 7:26:12, time: 0.642, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0181, loss_rpn_bbox: 0.0436, loss_cls: 0.1778, acc: 93.4905, loss_bbox: 0.2292, loss_mask: 0.2343, loss: 0.7030 +2024-05-31 08:17:59,633 - mmdet - INFO - Epoch [7][1000/7330] lr: 1.000e-04, eta: 7:25:44, time: 0.698, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0181, loss_rpn_bbox: 0.0422, loss_cls: 0.1708, acc: 93.8562, loss_bbox: 0.2135, loss_mask: 0.2264, loss: 0.6710 +2024-05-31 08:18:29,478 - mmdet - INFO - Epoch [7][1050/7330] lr: 1.000e-04, eta: 7:25:12, time: 0.597, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0184, loss_rpn_bbox: 0.0436, loss_cls: 0.1843, acc: 93.3486, loss_bbox: 0.2317, loss_mask: 0.2384, loss: 0.7165 +2024-05-31 08:18:59,371 - mmdet - INFO - Epoch [7][1100/7330] lr: 1.000e-04, eta: 7:24:40, time: 0.598, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0433, loss_cls: 0.1850, acc: 93.2017, loss_bbox: 0.2273, loss_mask: 0.2395, loss: 0.7149 +2024-05-31 08:19:29,299 - mmdet - INFO - Epoch [7][1150/7330] lr: 1.000e-04, eta: 7:24:08, time: 0.599, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0194, loss_rpn_bbox: 0.0427, loss_cls: 0.1850, acc: 93.2671, loss_bbox: 0.2328, loss_mask: 0.2363, loss: 0.7161 +2024-05-31 08:20:01,255 - mmdet - INFO - Epoch [7][1200/7330] lr: 1.000e-04, eta: 7:23:37, time: 0.639, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0456, loss_cls: 0.1950, acc: 92.7957, loss_bbox: 0.2426, loss_mask: 0.2418, loss: 0.7432 +2024-05-31 08:20:35,526 - mmdet - INFO - Epoch [7][1250/7330] lr: 1.000e-04, eta: 7:23:09, time: 0.685, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0450, loss_cls: 0.1886, acc: 93.1060, loss_bbox: 0.2378, loss_mask: 0.2392, loss: 0.7294 +2024-05-31 08:21:06,444 - mmdet - INFO - Epoch [7][1300/7330] lr: 1.000e-04, eta: 7:22:38, time: 0.618, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0200, loss_rpn_bbox: 0.0454, loss_cls: 0.1911, acc: 93.0044, loss_bbox: 0.2407, loss_mask: 0.2402, loss: 0.7374 +2024-05-31 08:21:36,782 - mmdet - INFO - Epoch [7][1350/7330] lr: 1.000e-04, eta: 7:22:06, time: 0.607, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0193, loss_rpn_bbox: 0.0481, loss_cls: 0.1927, acc: 92.9417, loss_bbox: 0.2391, loss_mask: 0.2373, loss: 0.7365 +2024-05-31 08:22:06,946 - mmdet - INFO - Epoch [7][1400/7330] lr: 1.000e-04, eta: 7:21:34, time: 0.603, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0193, loss_rpn_bbox: 0.0446, loss_cls: 0.1874, acc: 93.1602, loss_bbox: 0.2377, loss_mask: 0.2363, loss: 0.7253 +2024-05-31 08:22:36,895 - mmdet - INFO - Epoch [7][1450/7330] lr: 1.000e-04, eta: 7:21:02, time: 0.599, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0185, loss_rpn_bbox: 0.0417, loss_cls: 0.1763, acc: 93.6418, loss_bbox: 0.2233, loss_mask: 0.2344, loss: 0.6942 +2024-05-31 08:23:07,815 - mmdet - INFO - Epoch [7][1500/7330] lr: 1.000e-04, eta: 7:20:31, time: 0.618, data_time: 0.064, memory: 9655, loss_rpn_cls: 0.0200, loss_rpn_bbox: 0.0490, loss_cls: 0.1893, acc: 93.1216, loss_bbox: 0.2423, loss_mask: 0.2425, loss: 0.7431 +2024-05-31 08:23:37,507 - mmdet - INFO - Epoch [7][1550/7330] lr: 1.000e-04, eta: 7:19:58, time: 0.594, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0184, loss_rpn_bbox: 0.0462, loss_cls: 0.1868, acc: 93.2197, loss_bbox: 0.2394, loss_mask: 0.2377, loss: 0.7284 +2024-05-31 08:24:07,481 - mmdet - INFO - Epoch [7][1600/7330] lr: 1.000e-04, eta: 7:19:26, time: 0.599, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0183, loss_rpn_bbox: 0.0448, loss_cls: 0.1852, acc: 93.2556, loss_bbox: 0.2340, loss_mask: 0.2350, loss: 0.7174 +2024-05-31 08:24:37,083 - mmdet - INFO - Epoch [7][1650/7330] lr: 1.000e-04, eta: 7:18:53, time: 0.592, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0179, loss_rpn_bbox: 0.0452, loss_cls: 0.1805, acc: 93.3823, loss_bbox: 0.2323, loss_mask: 0.2364, loss: 0.7123 +2024-05-31 08:25:07,186 - mmdet - INFO - Epoch [7][1700/7330] lr: 1.000e-04, eta: 7:18:21, time: 0.602, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0183, loss_rpn_bbox: 0.0441, loss_cls: 0.1877, acc: 93.2029, loss_bbox: 0.2333, loss_mask: 0.2373, loss: 0.7208 +2024-05-31 08:25:40,129 - mmdet - INFO - Epoch [7][1750/7330] lr: 1.000e-04, eta: 7:17:52, time: 0.659, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0187, loss_rpn_bbox: 0.0438, loss_cls: 0.1884, acc: 93.1245, loss_bbox: 0.2358, loss_mask: 0.2375, loss: 0.7242 +2024-05-31 08:26:15,423 - mmdet - INFO - Epoch [7][1800/7330] lr: 1.000e-04, eta: 7:17:25, time: 0.706, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0187, loss_rpn_bbox: 0.0442, loss_cls: 0.1842, acc: 93.3005, loss_bbox: 0.2279, loss_mask: 0.2421, loss: 0.7171 +2024-05-31 08:26:51,685 - mmdet - INFO - Epoch [7][1850/7330] lr: 1.000e-04, eta: 7:16:58, time: 0.725, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0168, loss_rpn_bbox: 0.0459, loss_cls: 0.1885, acc: 93.1399, loss_bbox: 0.2370, loss_mask: 0.2371, loss: 0.7253 +2024-05-31 08:27:21,381 - mmdet - INFO - Epoch [7][1900/7330] lr: 1.000e-04, eta: 7:16:26, time: 0.594, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0156, loss_rpn_bbox: 0.0423, loss_cls: 0.1811, acc: 93.4604, loss_bbox: 0.2226, loss_mask: 0.2290, loss: 0.6906 +2024-05-31 08:27:51,556 - mmdet - INFO - Epoch [7][1950/7330] lr: 1.000e-04, eta: 7:15:54, time: 0.604, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0484, loss_cls: 0.1996, acc: 92.7732, loss_bbox: 0.2506, loss_mask: 0.2417, loss: 0.7610 +2024-05-31 08:28:21,463 - mmdet - INFO - Epoch [7][2000/7330] lr: 1.000e-04, eta: 7:15:22, time: 0.598, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0191, loss_rpn_bbox: 0.0442, loss_cls: 0.1807, acc: 93.3931, loss_bbox: 0.2295, loss_mask: 0.2355, loss: 0.7090 +2024-05-31 08:28:54,063 - mmdet - INFO - Epoch [7][2050/7330] lr: 1.000e-04, eta: 7:14:52, time: 0.652, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0192, loss_rpn_bbox: 0.0459, loss_cls: 0.1910, acc: 92.9846, loss_bbox: 0.2426, loss_mask: 0.2394, loss: 0.7380 +2024-05-31 08:29:23,836 - mmdet - INFO - Epoch [7][2100/7330] lr: 1.000e-04, eta: 7:14:20, time: 0.595, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0177, loss_rpn_bbox: 0.0432, loss_cls: 0.1836, acc: 93.2852, loss_bbox: 0.2302, loss_mask: 0.2354, loss: 0.7102 +2024-05-31 08:29:57,377 - mmdet - INFO - Epoch [7][2150/7330] lr: 1.000e-04, eta: 7:13:51, time: 0.671, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0192, loss_rpn_bbox: 0.0439, loss_cls: 0.1848, acc: 93.3794, loss_bbox: 0.2315, loss_mask: 0.2377, loss: 0.7171 +2024-05-31 08:30:27,754 - mmdet - INFO - Epoch [7][2200/7330] lr: 1.000e-04, eta: 7:13:19, time: 0.608, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0474, loss_cls: 0.1887, acc: 93.1213, loss_bbox: 0.2334, loss_mask: 0.2376, loss: 0.7269 +2024-05-31 08:30:57,167 - mmdet - INFO - Epoch [7][2250/7330] lr: 1.000e-04, eta: 7:12:46, time: 0.588, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0184, loss_rpn_bbox: 0.0449, loss_cls: 0.1804, acc: 93.3757, loss_bbox: 0.2328, loss_mask: 0.2358, loss: 0.7123 +2024-05-31 08:31:27,092 - mmdet - INFO - Epoch [7][2300/7330] lr: 1.000e-04, eta: 7:12:14, time: 0.598, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0199, loss_rpn_bbox: 0.0435, loss_cls: 0.1864, acc: 93.2344, loss_bbox: 0.2340, loss_mask: 0.2394, loss: 0.7232 +2024-05-31 08:31:56,831 - mmdet - INFO - Epoch [7][2350/7330] lr: 1.000e-04, eta: 7:11:42, time: 0.595, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0422, loss_cls: 0.1756, acc: 93.5115, loss_bbox: 0.2263, loss_mask: 0.2270, loss: 0.6900 +2024-05-31 08:32:26,611 - mmdet - INFO - Epoch [7][2400/7330] lr: 1.000e-04, eta: 7:11:10, time: 0.596, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0188, loss_rpn_bbox: 0.0446, loss_cls: 0.1801, acc: 93.4229, loss_bbox: 0.2280, loss_mask: 0.2356, loss: 0.7072 +2024-05-31 08:32:56,983 - mmdet - INFO - Epoch [7][2450/7330] lr: 1.000e-04, eta: 7:10:38, time: 0.607, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0196, loss_rpn_bbox: 0.0451, loss_cls: 0.1766, acc: 93.5432, loss_bbox: 0.2253, loss_mask: 0.2341, loss: 0.7007 +2024-05-31 08:33:27,472 - mmdet - INFO - Epoch [7][2500/7330] lr: 1.000e-04, eta: 7:10:06, time: 0.610, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0195, loss_rpn_bbox: 0.0453, loss_cls: 0.1918, acc: 92.9937, loss_bbox: 0.2417, loss_mask: 0.2412, loss: 0.7394 +2024-05-31 08:33:58,285 - mmdet - INFO - Epoch [7][2550/7330] lr: 1.000e-04, eta: 7:09:35, time: 0.616, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0443, loss_cls: 0.1874, acc: 93.1121, loss_bbox: 0.2401, loss_mask: 0.2381, loss: 0.7297 +2024-05-31 08:34:28,034 - mmdet - INFO - Epoch [7][2600/7330] lr: 1.000e-04, eta: 7:09:02, time: 0.595, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0433, loss_cls: 0.1948, acc: 92.8921, loss_bbox: 0.2395, loss_mask: 0.2342, loss: 0.7315 +2024-05-31 08:35:01,166 - mmdet - INFO - Epoch [7][2650/7330] lr: 1.000e-04, eta: 7:08:33, time: 0.663, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0207, loss_rpn_bbox: 0.0477, loss_cls: 0.1877, acc: 93.2141, loss_bbox: 0.2320, loss_mask: 0.2333, loss: 0.7214 +2024-05-31 08:35:35,882 - mmdet - INFO - Epoch [7][2700/7330] lr: 1.000e-04, eta: 7:08:05, time: 0.694, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0183, loss_rpn_bbox: 0.0429, loss_cls: 0.1706, acc: 93.8340, loss_bbox: 0.2230, loss_mask: 0.2345, loss: 0.6893 +2024-05-31 08:36:13,114 - mmdet - INFO - Epoch [7][2750/7330] lr: 1.000e-04, eta: 7:07:39, time: 0.745, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0430, loss_cls: 0.1774, acc: 93.5906, loss_bbox: 0.2254, loss_mask: 0.2361, loss: 0.7016 +2024-05-31 08:36:42,986 - mmdet - INFO - Epoch [7][2800/7330] lr: 1.000e-04, eta: 7:07:07, time: 0.597, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0437, loss_cls: 0.1847, acc: 93.3545, loss_bbox: 0.2300, loss_mask: 0.2372, loss: 0.7145 +2024-05-31 08:37:13,505 - mmdet - INFO - Epoch [7][2850/7330] lr: 1.000e-04, eta: 7:06:36, time: 0.610, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0203, loss_rpn_bbox: 0.0466, loss_cls: 0.1958, acc: 92.8918, loss_bbox: 0.2395, loss_mask: 0.2364, loss: 0.7386 +2024-05-31 08:37:43,373 - mmdet - INFO - Epoch [7][2900/7330] lr: 1.000e-04, eta: 7:06:03, time: 0.597, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0190, loss_rpn_bbox: 0.0469, loss_cls: 0.1889, acc: 93.1836, loss_bbox: 0.2373, loss_mask: 0.2375, loss: 0.7296 +2024-05-31 08:38:15,700 - mmdet - INFO - Epoch [7][2950/7330] lr: 1.000e-04, eta: 7:05:33, time: 0.647, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0181, loss_rpn_bbox: 0.0444, loss_cls: 0.1725, acc: 93.6951, loss_bbox: 0.2209, loss_mask: 0.2350, loss: 0.6909 +2024-05-31 08:38:48,061 - mmdet - INFO - Epoch [7][3000/7330] lr: 1.000e-04, eta: 7:05:03, time: 0.647, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0186, loss_rpn_bbox: 0.0465, loss_cls: 0.1936, acc: 92.9265, loss_bbox: 0.2413, loss_mask: 0.2439, loss: 0.7439 +2024-05-31 08:39:18,070 - mmdet - INFO - Epoch [7][3050/7330] lr: 1.000e-04, eta: 7:04:31, time: 0.600, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0179, loss_rpn_bbox: 0.0430, loss_cls: 0.1806, acc: 93.5273, loss_bbox: 0.2223, loss_mask: 0.2275, loss: 0.6913 +2024-05-31 08:39:48,449 - mmdet - INFO - Epoch [7][3100/7330] lr: 1.000e-04, eta: 7:03:59, time: 0.608, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0184, loss_rpn_bbox: 0.0450, loss_cls: 0.1949, acc: 92.9885, loss_bbox: 0.2396, loss_mask: 0.2358, loss: 0.7336 +2024-05-31 08:40:18,710 - mmdet - INFO - Epoch [7][3150/7330] lr: 1.000e-04, eta: 7:03:28, time: 0.605, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0188, loss_rpn_bbox: 0.0494, loss_cls: 0.1858, acc: 93.3152, loss_bbox: 0.2287, loss_mask: 0.2365, loss: 0.7191 +2024-05-31 08:40:49,030 - mmdet - INFO - Epoch [7][3200/7330] lr: 1.000e-04, eta: 7:02:56, time: 0.606, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0181, loss_rpn_bbox: 0.0449, loss_cls: 0.1916, acc: 93.0095, loss_bbox: 0.2412, loss_mask: 0.2380, loss: 0.7338 +2024-05-31 08:41:18,587 - mmdet - INFO - Epoch [7][3250/7330] lr: 1.000e-04, eta: 7:02:23, time: 0.591, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0426, loss_cls: 0.1790, acc: 93.4609, loss_bbox: 0.2221, loss_mask: 0.2286, loss: 0.6912 +2024-05-31 08:41:48,266 - mmdet - INFO - Epoch [7][3300/7330] lr: 1.000e-04, eta: 7:01:51, time: 0.594, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0175, loss_rpn_bbox: 0.0447, loss_cls: 0.1898, acc: 93.1296, loss_bbox: 0.2343, loss_mask: 0.2345, loss: 0.7208 +2024-05-31 08:42:18,170 - mmdet - INFO - Epoch [7][3350/7330] lr: 1.000e-04, eta: 7:01:19, time: 0.598, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0187, loss_rpn_bbox: 0.0431, loss_cls: 0.1839, acc: 93.3081, loss_bbox: 0.2301, loss_mask: 0.2380, loss: 0.7138 +2024-05-31 08:42:48,834 - mmdet - INFO - Epoch [7][3400/7330] lr: 1.000e-04, eta: 7:00:47, time: 0.613, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0192, loss_rpn_bbox: 0.0463, loss_cls: 0.1909, acc: 93.0547, loss_bbox: 0.2371, loss_mask: 0.2363, loss: 0.7298 +2024-05-31 08:43:19,269 - mmdet - INFO - Epoch [7][3450/7330] lr: 1.000e-04, eta: 7:00:16, time: 0.609, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0218, loss_rpn_bbox: 0.0477, loss_cls: 0.1975, acc: 92.8315, loss_bbox: 0.2437, loss_mask: 0.2408, loss: 0.7515 +2024-05-31 08:43:49,256 - mmdet - INFO - Epoch [7][3500/7330] lr: 1.000e-04, eta: 6:59:44, time: 0.600, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0187, loss_rpn_bbox: 0.0450, loss_cls: 0.1893, acc: 93.0532, loss_bbox: 0.2404, loss_mask: 0.2420, loss: 0.7354 +2024-05-31 08:44:25,835 - mmdet - INFO - Epoch [7][3550/7330] lr: 1.000e-04, eta: 6:59:17, time: 0.732, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0184, loss_rpn_bbox: 0.0451, loss_cls: 0.1852, acc: 93.2905, loss_bbox: 0.2316, loss_mask: 0.2350, loss: 0.7152 +2024-05-31 08:44:58,115 - mmdet - INFO - Epoch [7][3600/7330] lr: 1.000e-04, eta: 6:58:47, time: 0.646, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0178, loss_rpn_bbox: 0.0444, loss_cls: 0.1956, acc: 93.0168, loss_bbox: 0.2375, loss_mask: 0.2363, loss: 0.7316 +2024-05-31 08:45:33,303 - mmdet - INFO - Epoch [7][3650/7330] lr: 1.000e-04, eta: 6:58:19, time: 0.704, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0484, loss_cls: 0.1921, acc: 93.0496, loss_bbox: 0.2418, loss_mask: 0.2365, loss: 0.7401 +2024-05-31 08:46:03,588 - mmdet - INFO - Epoch [7][3700/7330] lr: 1.000e-04, eta: 6:57:48, time: 0.606, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0187, loss_rpn_bbox: 0.0455, loss_cls: 0.1871, acc: 93.1514, loss_bbox: 0.2336, loss_mask: 0.2401, loss: 0.7249 +2024-05-31 08:46:33,467 - mmdet - INFO - Epoch [7][3750/7330] lr: 1.000e-04, eta: 6:57:15, time: 0.598, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0188, loss_rpn_bbox: 0.0434, loss_cls: 0.1900, acc: 93.0042, loss_bbox: 0.2406, loss_mask: 0.2393, loss: 0.7322 +2024-05-31 08:47:03,436 - mmdet - INFO - Epoch [7][3800/7330] lr: 1.000e-04, eta: 6:56:43, time: 0.599, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0177, loss_rpn_bbox: 0.0465, loss_cls: 0.1875, acc: 93.0637, loss_bbox: 0.2404, loss_mask: 0.2425, loss: 0.7346 +2024-05-31 08:47:35,976 - mmdet - INFO - Epoch [7][3850/7330] lr: 1.000e-04, eta: 6:56:13, time: 0.651, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0197, loss_rpn_bbox: 0.0455, loss_cls: 0.1903, acc: 93.0505, loss_bbox: 0.2428, loss_mask: 0.2414, loss: 0.7398 +2024-05-31 08:48:08,589 - mmdet - INFO - Epoch [7][3900/7330] lr: 1.000e-04, eta: 6:55:44, time: 0.652, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0441, loss_cls: 0.1899, acc: 93.0757, loss_bbox: 0.2428, loss_mask: 0.2377, loss: 0.7333 +2024-05-31 08:48:38,646 - mmdet - INFO - Epoch [7][3950/7330] lr: 1.000e-04, eta: 6:55:12, time: 0.601, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0178, loss_rpn_bbox: 0.0460, loss_cls: 0.1926, acc: 92.9355, loss_bbox: 0.2362, loss_mask: 0.2390, loss: 0.7317 +2024-05-31 08:49:09,139 - mmdet - INFO - Epoch [7][4000/7330] lr: 1.000e-04, eta: 6:54:40, time: 0.610, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0179, loss_rpn_bbox: 0.0451, loss_cls: 0.1883, acc: 93.1277, loss_bbox: 0.2329, loss_mask: 0.2364, loss: 0.7206 +2024-05-31 08:49:39,582 - mmdet - INFO - Epoch [7][4050/7330] lr: 1.000e-04, eta: 6:54:08, time: 0.609, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0194, loss_rpn_bbox: 0.0472, loss_cls: 0.1901, acc: 93.0864, loss_bbox: 0.2428, loss_mask: 0.2404, loss: 0.7399 +2024-05-31 08:50:09,302 - mmdet - INFO - Epoch [7][4100/7330] lr: 1.000e-04, eta: 6:53:36, time: 0.594, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0181, loss_rpn_bbox: 0.0454, loss_cls: 0.1866, acc: 93.2366, loss_bbox: 0.2373, loss_mask: 0.2410, loss: 0.7284 +2024-05-31 08:50:39,215 - mmdet - INFO - Epoch [7][4150/7330] lr: 1.000e-04, eta: 6:53:04, time: 0.598, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0179, loss_rpn_bbox: 0.0440, loss_cls: 0.1855, acc: 93.3384, loss_bbox: 0.2226, loss_mask: 0.2320, loss: 0.7020 +2024-05-31 08:51:09,294 - mmdet - INFO - Epoch [7][4200/7330] lr: 1.000e-04, eta: 6:52:32, time: 0.602, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0186, loss_rpn_bbox: 0.0415, loss_cls: 0.1804, acc: 93.4443, loss_bbox: 0.2229, loss_mask: 0.2372, loss: 0.7006 +2024-05-31 08:51:39,050 - mmdet - INFO - Epoch [7][4250/7330] lr: 1.000e-04, eta: 6:52:00, time: 0.595, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0194, loss_rpn_bbox: 0.0442, loss_cls: 0.1800, acc: 93.3948, loss_bbox: 0.2324, loss_mask: 0.2385, loss: 0.7145 +2024-05-31 08:52:08,983 - mmdet - INFO - Epoch [7][4300/7330] lr: 1.000e-04, eta: 6:51:28, time: 0.599, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0188, loss_rpn_bbox: 0.0448, loss_cls: 0.1930, acc: 92.9517, loss_bbox: 0.2392, loss_mask: 0.2398, loss: 0.7356 +2024-05-31 08:52:39,162 - mmdet - INFO - Epoch [7][4350/7330] lr: 1.000e-04, eta: 6:50:56, time: 0.603, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0200, loss_rpn_bbox: 0.0455, loss_cls: 0.1927, acc: 92.9800, loss_bbox: 0.2390, loss_mask: 0.2384, loss: 0.7356 +2024-05-31 08:53:11,574 - mmdet - INFO - Epoch [7][4400/7330] lr: 1.000e-04, eta: 6:50:26, time: 0.648, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0194, loss_rpn_bbox: 0.0465, loss_cls: 0.1893, acc: 93.2014, loss_bbox: 0.2400, loss_mask: 0.2432, loss: 0.7385 +2024-05-31 08:53:45,427 - mmdet - INFO - Epoch [7][4450/7330] lr: 1.000e-04, eta: 6:49:57, time: 0.677, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0185, loss_rpn_bbox: 0.0446, loss_cls: 0.1862, acc: 93.2859, loss_bbox: 0.2336, loss_mask: 0.2317, loss: 0.7145 +2024-05-31 08:54:23,094 - mmdet - INFO - Epoch [7][4500/7330] lr: 1.000e-04, eta: 6:49:31, time: 0.753, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0201, loss_rpn_bbox: 0.0466, loss_cls: 0.1888, acc: 93.1575, loss_bbox: 0.2360, loss_mask: 0.2411, loss: 0.7326 +2024-05-31 08:54:52,885 - mmdet - INFO - Epoch [7][4550/7330] lr: 1.000e-04, eta: 6:48:59, time: 0.596, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0410, loss_cls: 0.1767, acc: 93.6150, loss_bbox: 0.2152, loss_mask: 0.2281, loss: 0.6791 +2024-05-31 08:55:23,321 - mmdet - INFO - Epoch [7][4600/7330] lr: 1.000e-04, eta: 6:48:27, time: 0.609, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0185, loss_rpn_bbox: 0.0464, loss_cls: 0.1901, acc: 93.1768, loss_bbox: 0.2405, loss_mask: 0.2439, loss: 0.7394 +2024-05-31 08:55:52,708 - mmdet - INFO - Epoch [7][4650/7330] lr: 1.000e-04, eta: 6:47:55, time: 0.588, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0184, loss_rpn_bbox: 0.0422, loss_cls: 0.1809, acc: 93.5193, loss_bbox: 0.2242, loss_mask: 0.2343, loss: 0.7000 +2024-05-31 08:56:22,430 - mmdet - INFO - Epoch [7][4700/7330] lr: 1.000e-04, eta: 6:47:22, time: 0.594, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0196, loss_rpn_bbox: 0.0473, loss_cls: 0.1875, acc: 93.1663, loss_bbox: 0.2316, loss_mask: 0.2409, loss: 0.7270 +2024-05-31 08:56:54,292 - mmdet - INFO - Epoch [7][4750/7330] lr: 1.000e-04, eta: 6:46:52, time: 0.637, data_time: 0.034, memory: 9655, loss_rpn_cls: 0.0203, loss_rpn_bbox: 0.0416, loss_cls: 0.1786, acc: 93.5986, loss_bbox: 0.2231, loss_mask: 0.2355, loss: 0.6992 +2024-05-31 08:57:26,964 - mmdet - INFO - Epoch [7][4800/7330] lr: 1.000e-04, eta: 6:46:22, time: 0.653, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0197, loss_rpn_bbox: 0.0443, loss_cls: 0.1871, acc: 93.2507, loss_bbox: 0.2335, loss_mask: 0.2383, loss: 0.7229 +2024-05-31 08:57:57,484 - mmdet - INFO - Epoch [7][4850/7330] lr: 1.000e-04, eta: 6:45:50, time: 0.610, data_time: 0.066, memory: 9655, loss_rpn_cls: 0.0192, loss_rpn_bbox: 0.0458, loss_cls: 0.1857, acc: 93.2571, loss_bbox: 0.2296, loss_mask: 0.2372, loss: 0.7175 +2024-05-31 08:58:27,647 - mmdet - INFO - Epoch [7][4900/7330] lr: 1.000e-04, eta: 6:45:19, time: 0.603, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0180, loss_rpn_bbox: 0.0480, loss_cls: 0.1860, acc: 93.2310, loss_bbox: 0.2300, loss_mask: 0.2337, loss: 0.7158 +2024-05-31 08:58:57,809 - mmdet - INFO - Epoch [7][4950/7330] lr: 1.000e-04, eta: 6:44:47, time: 0.603, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0188, loss_rpn_bbox: 0.0462, loss_cls: 0.1827, acc: 93.3623, loss_bbox: 0.2312, loss_mask: 0.2396, loss: 0.7185 +2024-05-31 08:59:27,708 - mmdet - INFO - Epoch [7][5000/7330] lr: 1.000e-04, eta: 6:44:15, time: 0.598, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0192, loss_rpn_bbox: 0.0445, loss_cls: 0.1879, acc: 93.1621, loss_bbox: 0.2355, loss_mask: 0.2403, loss: 0.7274 +2024-05-31 08:59:57,499 - mmdet - INFO - Epoch [7][5050/7330] lr: 1.000e-04, eta: 6:43:42, time: 0.596, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0183, loss_rpn_bbox: 0.0435, loss_cls: 0.1872, acc: 93.1484, loss_bbox: 0.2370, loss_mask: 0.2359, loss: 0.7219 +2024-05-31 09:00:27,676 - mmdet - INFO - Epoch [7][5100/7330] lr: 1.000e-04, eta: 6:43:11, time: 0.603, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0184, loss_rpn_bbox: 0.0440, loss_cls: 0.1832, acc: 93.4358, loss_bbox: 0.2296, loss_mask: 0.2334, loss: 0.7086 +2024-05-31 09:00:58,154 - mmdet - INFO - Epoch [7][5150/7330] lr: 1.000e-04, eta: 6:42:39, time: 0.610, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0187, loss_rpn_bbox: 0.0447, loss_cls: 0.1892, acc: 93.0925, loss_bbox: 0.2342, loss_mask: 0.2357, loss: 0.7225 +2024-05-31 09:01:28,031 - mmdet - INFO - Epoch [7][5200/7330] lr: 1.000e-04, eta: 6:42:07, time: 0.598, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0193, loss_rpn_bbox: 0.0438, loss_cls: 0.1810, acc: 93.3003, loss_bbox: 0.2292, loss_mask: 0.2331, loss: 0.7064 +2024-05-31 09:01:58,236 - mmdet - INFO - Epoch [7][5250/7330] lr: 1.000e-04, eta: 6:41:35, time: 0.604, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0197, loss_rpn_bbox: 0.0446, loss_cls: 0.1904, acc: 93.0029, loss_bbox: 0.2409, loss_mask: 0.2394, loss: 0.7350 +2024-05-31 09:02:30,518 - mmdet - INFO - Epoch [7][5300/7330] lr: 1.000e-04, eta: 6:41:05, time: 0.646, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0202, loss_rpn_bbox: 0.0468, loss_cls: 0.1936, acc: 92.9485, loss_bbox: 0.2382, loss_mask: 0.2370, loss: 0.7359 +2024-05-31 09:03:04,984 - mmdet - INFO - Epoch [7][5350/7330] lr: 1.000e-04, eta: 6:40:36, time: 0.689, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0187, loss_rpn_bbox: 0.0421, loss_cls: 0.1816, acc: 93.4758, loss_bbox: 0.2244, loss_mask: 0.2342, loss: 0.7010 +2024-05-31 09:03:42,521 - mmdet - INFO - Epoch [7][5400/7330] lr: 1.000e-04, eta: 6:40:10, time: 0.751, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0193, loss_rpn_bbox: 0.0469, loss_cls: 0.1954, acc: 92.8955, loss_bbox: 0.2436, loss_mask: 0.2419, loss: 0.7471 +2024-05-31 09:04:13,239 - mmdet - INFO - Epoch [7][5450/7330] lr: 1.000e-04, eta: 6:39:39, time: 0.614, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0193, loss_rpn_bbox: 0.0453, loss_cls: 0.1898, acc: 93.1697, loss_bbox: 0.2348, loss_mask: 0.2348, loss: 0.7239 +2024-05-31 09:04:43,324 - mmdet - INFO - Epoch [7][5500/7330] lr: 1.000e-04, eta: 6:39:07, time: 0.602, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0178, loss_rpn_bbox: 0.0433, loss_cls: 0.1938, acc: 93.0884, loss_bbox: 0.2347, loss_mask: 0.2406, loss: 0.7301 +2024-05-31 09:05:13,743 - mmdet - INFO - Epoch [7][5550/7330] lr: 1.000e-04, eta: 6:38:35, time: 0.608, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0181, loss_rpn_bbox: 0.0477, loss_cls: 0.1865, acc: 93.1616, loss_bbox: 0.2372, loss_mask: 0.2380, loss: 0.7275 +2024-05-31 09:05:46,286 - mmdet - INFO - Epoch [7][5600/7330] lr: 1.000e-04, eta: 6:38:05, time: 0.651, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0186, loss_rpn_bbox: 0.0468, loss_cls: 0.1945, acc: 92.9490, loss_bbox: 0.2392, loss_mask: 0.2399, loss: 0.7390 +2024-05-31 09:06:15,730 - mmdet - INFO - Epoch [7][5650/7330] lr: 1.000e-04, eta: 6:37:33, time: 0.589, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0168, loss_rpn_bbox: 0.0426, loss_cls: 0.1852, acc: 93.3848, loss_bbox: 0.2261, loss_mask: 0.2362, loss: 0.7070 +2024-05-31 09:06:48,274 - mmdet - INFO - Epoch [7][5700/7330] lr: 1.000e-04, eta: 6:37:03, time: 0.651, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0192, loss_rpn_bbox: 0.0466, loss_cls: 0.1919, acc: 92.9827, loss_bbox: 0.2388, loss_mask: 0.2353, loss: 0.7319 +2024-05-31 09:07:18,138 - mmdet - INFO - Epoch [7][5750/7330] lr: 1.000e-04, eta: 6:36:31, time: 0.597, data_time: 0.038, memory: 9655, loss_rpn_cls: 0.0200, loss_rpn_bbox: 0.0474, loss_cls: 0.1915, acc: 92.8733, loss_bbox: 0.2442, loss_mask: 0.2430, loss: 0.7461 +2024-05-31 09:07:48,317 - mmdet - INFO - Epoch [7][5800/7330] lr: 1.000e-04, eta: 6:35:59, time: 0.603, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0195, loss_rpn_bbox: 0.0450, loss_cls: 0.1831, acc: 93.2842, loss_bbox: 0.2339, loss_mask: 0.2426, loss: 0.7241 +2024-05-31 09:08:18,623 - mmdet - INFO - Epoch [7][5850/7330] lr: 1.000e-04, eta: 6:35:27, time: 0.606, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0177, loss_rpn_bbox: 0.0449, loss_cls: 0.1832, acc: 93.3552, loss_bbox: 0.2311, loss_mask: 0.2351, loss: 0.7120 +2024-05-31 09:08:48,755 - mmdet - INFO - Epoch [7][5900/7330] lr: 1.000e-04, eta: 6:34:55, time: 0.603, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0190, loss_rpn_bbox: 0.0456, loss_cls: 0.1912, acc: 93.1174, loss_bbox: 0.2372, loss_mask: 0.2360, loss: 0.7289 +2024-05-31 09:09:18,923 - mmdet - INFO - Epoch [7][5950/7330] lr: 1.000e-04, eta: 6:34:24, time: 0.603, data_time: 0.038, memory: 9655, loss_rpn_cls: 0.0193, loss_rpn_bbox: 0.0463, loss_cls: 0.1917, acc: 93.1340, loss_bbox: 0.2366, loss_mask: 0.2388, loss: 0.7328 +2024-05-31 09:09:49,495 - mmdet - INFO - Epoch [7][6000/7330] lr: 1.000e-04, eta: 6:33:52, time: 0.611, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0217, loss_rpn_bbox: 0.0478, loss_cls: 0.1963, acc: 92.9409, loss_bbox: 0.2467, loss_mask: 0.2422, loss: 0.7547 +2024-05-31 09:10:20,310 - mmdet - INFO - Epoch [7][6050/7330] lr: 1.000e-04, eta: 6:33:21, time: 0.616, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0194, loss_rpn_bbox: 0.0470, loss_cls: 0.1928, acc: 93.0042, loss_bbox: 0.2422, loss_mask: 0.2376, loss: 0.7390 +2024-05-31 09:10:50,269 - mmdet - INFO - Epoch [7][6100/7330] lr: 1.000e-04, eta: 6:32:49, time: 0.599, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0179, loss_rpn_bbox: 0.0425, loss_cls: 0.1825, acc: 93.4583, loss_bbox: 0.2282, loss_mask: 0.2349, loss: 0.7060 +2024-05-31 09:11:20,950 - mmdet - INFO - Epoch [7][6150/7330] lr: 1.000e-04, eta: 6:32:17, time: 0.614, data_time: 0.066, memory: 9655, loss_rpn_cls: 0.0195, loss_rpn_bbox: 0.0458, loss_cls: 0.1857, acc: 93.2092, loss_bbox: 0.2335, loss_mask: 0.2415, loss: 0.7260 +2024-05-31 09:11:55,998 - mmdet - INFO - Epoch [7][6200/7330] lr: 1.000e-04, eta: 6:31:49, time: 0.701, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0207, loss_rpn_bbox: 0.0482, loss_cls: 0.1965, acc: 92.8145, loss_bbox: 0.2467, loss_mask: 0.2361, loss: 0.7482 +2024-05-31 09:12:30,519 - mmdet - INFO - Epoch [7][6250/7330] lr: 1.000e-04, eta: 6:31:21, time: 0.690, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0197, loss_rpn_bbox: 0.0484, loss_cls: 0.1971, acc: 92.9172, loss_bbox: 0.2443, loss_mask: 0.2355, loss: 0.7449 +2024-05-31 09:13:05,184 - mmdet - INFO - Epoch [7][6300/7330] lr: 1.000e-04, eta: 6:30:52, time: 0.693, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0200, loss_rpn_bbox: 0.0444, loss_cls: 0.1890, acc: 93.1782, loss_bbox: 0.2321, loss_mask: 0.2363, loss: 0.7219 +2024-05-31 09:13:34,812 - mmdet - INFO - Epoch [7][6350/7330] lr: 1.000e-04, eta: 6:30:20, time: 0.593, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0194, loss_rpn_bbox: 0.0451, loss_cls: 0.1814, acc: 93.3975, loss_bbox: 0.2318, loss_mask: 0.2295, loss: 0.7073 +2024-05-31 09:14:04,912 - mmdet - INFO - Epoch [7][6400/7330] lr: 1.000e-04, eta: 6:29:48, time: 0.602, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0213, loss_rpn_bbox: 0.0455, loss_cls: 0.1895, acc: 93.1233, loss_bbox: 0.2376, loss_mask: 0.2379, loss: 0.7318 +2024-05-31 09:14:35,572 - mmdet - INFO - Epoch [7][6450/7330] lr: 1.000e-04, eta: 6:29:17, time: 0.613, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0205, loss_rpn_bbox: 0.0463, loss_cls: 0.1851, acc: 93.2793, loss_bbox: 0.2279, loss_mask: 0.2307, loss: 0.7106 +2024-05-31 09:15:07,237 - mmdet - INFO - Epoch [7][6500/7330] lr: 1.000e-04, eta: 6:28:46, time: 0.633, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0438, loss_cls: 0.1855, acc: 93.2078, loss_bbox: 0.2344, loss_mask: 0.2414, loss: 0.7249 +2024-05-31 09:15:40,210 - mmdet - INFO - Epoch [7][6550/7330] lr: 1.000e-04, eta: 6:28:16, time: 0.659, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0178, loss_rpn_bbox: 0.0439, loss_cls: 0.1846, acc: 93.2627, loss_bbox: 0.2325, loss_mask: 0.2409, loss: 0.7197 +2024-05-31 09:16:10,157 - mmdet - INFO - Epoch [7][6600/7330] lr: 1.000e-04, eta: 6:27:44, time: 0.599, data_time: 0.037, memory: 9655, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0439, loss_cls: 0.1860, acc: 93.1760, loss_bbox: 0.2361, loss_mask: 0.2381, loss: 0.7239 +2024-05-31 09:16:40,282 - mmdet - INFO - Epoch [7][6650/7330] lr: 1.000e-04, eta: 6:27:12, time: 0.602, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0186, loss_rpn_bbox: 0.0445, loss_cls: 0.1878, acc: 93.1685, loss_bbox: 0.2386, loss_mask: 0.2398, loss: 0.7293 +2024-05-31 09:17:10,246 - mmdet - INFO - Epoch [7][6700/7330] lr: 1.000e-04, eta: 6:26:40, time: 0.599, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0439, loss_cls: 0.1889, acc: 93.1865, loss_bbox: 0.2302, loss_mask: 0.2368, loss: 0.7188 +2024-05-31 09:17:40,025 - mmdet - INFO - Epoch [7][6750/7330] lr: 1.000e-04, eta: 6:26:08, time: 0.596, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0197, loss_rpn_bbox: 0.0433, loss_cls: 0.1910, acc: 93.0981, loss_bbox: 0.2359, loss_mask: 0.2427, loss: 0.7326 +2024-05-31 09:18:10,130 - mmdet - INFO - Epoch [7][6800/7330] lr: 1.000e-04, eta: 6:25:36, time: 0.602, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0202, loss_rpn_bbox: 0.0451, loss_cls: 0.1865, acc: 93.2043, loss_bbox: 0.2352, loss_mask: 0.2432, loss: 0.7302 +2024-05-31 09:18:40,039 - mmdet - INFO - Epoch [7][6850/7330] lr: 1.000e-04, eta: 6:25:04, time: 0.598, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0195, loss_rpn_bbox: 0.0438, loss_cls: 0.1815, acc: 93.3704, loss_bbox: 0.2355, loss_mask: 0.2380, loss: 0.7185 +2024-05-31 09:19:10,474 - mmdet - INFO - Epoch [7][6900/7330] lr: 1.000e-04, eta: 6:24:33, time: 0.609, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0466, loss_cls: 0.1900, acc: 93.0244, loss_bbox: 0.2421, loss_mask: 0.2435, loss: 0.7404 +2024-05-31 09:19:40,535 - mmdet - INFO - Epoch [7][6950/7330] lr: 1.000e-04, eta: 6:24:01, time: 0.601, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0199, loss_rpn_bbox: 0.0440, loss_cls: 0.1818, acc: 93.4546, loss_bbox: 0.2260, loss_mask: 0.2325, loss: 0.7042 +2024-05-31 09:20:10,800 - mmdet - INFO - Epoch [7][7000/7330] lr: 1.000e-04, eta: 6:23:29, time: 0.605, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0194, loss_rpn_bbox: 0.0481, loss_cls: 0.1998, acc: 92.7979, loss_bbox: 0.2476, loss_mask: 0.2406, loss: 0.7555 +2024-05-31 09:20:41,285 - mmdet - INFO - Epoch [7][7050/7330] lr: 1.000e-04, eta: 6:22:58, time: 0.610, data_time: 0.065, memory: 9655, loss_rpn_cls: 0.0180, loss_rpn_bbox: 0.0441, loss_cls: 0.1836, acc: 93.3557, loss_bbox: 0.2302, loss_mask: 0.2361, loss: 0.7120 +2024-05-31 09:21:18,483 - mmdet - INFO - Epoch [7][7100/7330] lr: 1.000e-04, eta: 6:22:31, time: 0.744, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0425, loss_cls: 0.1832, acc: 93.4656, loss_bbox: 0.2192, loss_mask: 0.2313, loss: 0.6943 +2024-05-31 09:21:50,459 - mmdet - INFO - Epoch [7][7150/7330] lr: 1.000e-04, eta: 6:22:00, time: 0.640, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0178, loss_rpn_bbox: 0.0423, loss_cls: 0.1859, acc: 93.2537, loss_bbox: 0.2296, loss_mask: 0.2348, loss: 0.7104 +2024-05-31 09:22:25,538 - mmdet - INFO - Epoch [7][7200/7330] lr: 1.000e-04, eta: 6:21:32, time: 0.702, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0197, loss_rpn_bbox: 0.0454, loss_cls: 0.1920, acc: 92.9321, loss_bbox: 0.2380, loss_mask: 0.2404, loss: 0.7354 +2024-05-31 09:22:55,495 - mmdet - INFO - Epoch [7][7250/7330] lr: 1.000e-04, eta: 6:21:00, time: 0.599, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0204, loss_rpn_bbox: 0.0432, loss_cls: 0.1900, acc: 93.1748, loss_bbox: 0.2317, loss_mask: 0.2372, loss: 0.7225 +2024-05-31 09:23:26,299 - mmdet - INFO - Epoch [7][7300/7330] lr: 1.000e-04, eta: 6:20:29, time: 0.616, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0196, loss_rpn_bbox: 0.0440, loss_cls: 0.1900, acc: 93.0544, loss_bbox: 0.2411, loss_mask: 0.2385, loss: 0.7333 +2024-05-31 09:23:45,179 - mmdet - INFO - Saving checkpoint at 7 epochs +2024-05-31 09:25:21,564 - mmdet - INFO - Evaluating bbox... +2024-05-31 09:25:42,335 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.415 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.640 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.450 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.237 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.449 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.576 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.539 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.539 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.539 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.332 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.578 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.706 + +2024-05-31 09:25:42,335 - mmdet - INFO - Evaluating segm... +2024-05-31 09:26:08,670 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.378 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.608 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.400 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.173 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.404 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.592 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.491 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.491 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.491 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.276 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.532 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.682 + +2024-05-31 09:26:09,066 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 09:26:09,068 - mmdet - INFO - Epoch(val) [7][625] bbox_mAP: 0.4150, bbox_mAP_50: 0.6400, bbox_mAP_75: 0.4500, bbox_mAP_s: 0.2370, bbox_mAP_m: 0.4490, bbox_mAP_l: 0.5760, bbox_mAP_copypaste: 0.415 0.640 0.450 0.237 0.449 0.576, segm_mAP: 0.3780, segm_mAP_50: 0.6080, segm_mAP_75: 0.4000, segm_mAP_s: 0.1730, segm_mAP_m: 0.4040, segm_mAP_l: 0.5920, segm_mAP_copypaste: 0.378 0.608 0.400 0.173 0.404 0.592 +2024-05-31 09:26:50,683 - mmdet - INFO - Epoch [8][50/7330] lr: 1.000e-04, eta: 6:19:33, time: 0.832, data_time: 0.108, memory: 9655, loss_rpn_cls: 0.0181, loss_rpn_bbox: 0.0427, loss_cls: 0.1845, acc: 93.2224, loss_bbox: 0.2317, loss_mask: 0.2278, loss: 0.7048 +2024-05-31 09:27:20,245 - mmdet - INFO - Epoch [8][100/7330] lr: 1.000e-04, eta: 6:19:01, time: 0.591, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0169, loss_rpn_bbox: 0.0433, loss_cls: 0.1813, acc: 93.4304, loss_bbox: 0.2301, loss_mask: 0.2302, loss: 0.7018 +2024-05-31 09:27:50,423 - mmdet - INFO - Epoch [8][150/7330] lr: 1.000e-04, eta: 6:18:29, time: 0.604, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0176, loss_rpn_bbox: 0.0441, loss_cls: 0.1796, acc: 93.3972, loss_bbox: 0.2271, loss_mask: 0.2346, loss: 0.7030 +2024-05-31 09:28:20,677 - mmdet - INFO - Epoch [8][200/7330] lr: 1.000e-04, eta: 6:17:57, time: 0.605, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0180, loss_rpn_bbox: 0.0454, loss_cls: 0.1779, acc: 93.5134, loss_bbox: 0.2204, loss_mask: 0.2327, loss: 0.6944 +2024-05-31 09:28:50,263 - mmdet - INFO - Epoch [8][250/7330] lr: 1.000e-04, eta: 6:17:25, time: 0.592, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0179, loss_rpn_bbox: 0.0428, loss_cls: 0.1797, acc: 93.3813, loss_bbox: 0.2350, loss_mask: 0.2306, loss: 0.7060 +2024-05-31 09:29:20,624 - mmdet - INFO - Epoch [8][300/7330] lr: 1.000e-04, eta: 6:16:54, time: 0.607, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0456, loss_cls: 0.1836, acc: 93.2942, loss_bbox: 0.2321, loss_mask: 0.2354, loss: 0.7156 +2024-05-31 09:29:50,254 - mmdet - INFO - Epoch [8][350/7330] lr: 1.000e-04, eta: 6:16:21, time: 0.593, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0172, loss_rpn_bbox: 0.0450, loss_cls: 0.1728, acc: 93.6653, loss_bbox: 0.2229, loss_mask: 0.2379, loss: 0.6958 +2024-05-31 09:30:20,445 - mmdet - INFO - Epoch [8][400/7330] lr: 1.000e-04, eta: 6:15:50, time: 0.604, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0443, loss_cls: 0.1844, acc: 93.2957, loss_bbox: 0.2378, loss_mask: 0.2334, loss: 0.7181 +2024-05-31 09:30:50,265 - mmdet - INFO - Epoch [8][450/7330] lr: 1.000e-04, eta: 6:15:18, time: 0.596, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0173, loss_rpn_bbox: 0.0435, loss_cls: 0.1786, acc: 93.4436, loss_bbox: 0.2280, loss_mask: 0.2360, loss: 0.7034 +2024-05-31 09:31:19,638 - mmdet - INFO - Epoch [8][500/7330] lr: 1.000e-04, eta: 6:14:45, time: 0.587, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0157, loss_rpn_bbox: 0.0424, loss_cls: 0.1739, acc: 93.5771, loss_bbox: 0.2238, loss_mask: 0.2282, loss: 0.6840 +2024-05-31 09:31:49,484 - mmdet - INFO - Epoch [8][550/7330] lr: 1.000e-04, eta: 6:14:13, time: 0.597, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0184, loss_rpn_bbox: 0.0439, loss_cls: 0.1764, acc: 93.5986, loss_bbox: 0.2183, loss_mask: 0.2322, loss: 0.6892 +2024-05-31 09:32:20,086 - mmdet - INFO - Epoch [8][600/7330] lr: 1.000e-04, eta: 6:13:42, time: 0.612, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0164, loss_rpn_bbox: 0.0450, loss_cls: 0.1779, acc: 93.4473, loss_bbox: 0.2309, loss_mask: 0.2287, loss: 0.6989 +2024-05-31 09:32:50,499 - mmdet - INFO - Epoch [8][650/7330] lr: 1.000e-04, eta: 6:13:10, time: 0.608, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0171, loss_rpn_bbox: 0.0422, loss_cls: 0.1720, acc: 93.6741, loss_bbox: 0.2253, loss_mask: 0.2345, loss: 0.6911 +2024-05-31 09:33:20,439 - mmdet - INFO - Epoch [8][700/7330] lr: 1.000e-04, eta: 6:12:39, time: 0.599, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0423, loss_cls: 0.1751, acc: 93.5803, loss_bbox: 0.2216, loss_mask: 0.2278, loss: 0.6827 +2024-05-31 09:33:50,300 - mmdet - INFO - Epoch [8][750/7330] lr: 1.000e-04, eta: 6:12:07, time: 0.597, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0171, loss_rpn_bbox: 0.0422, loss_cls: 0.1695, acc: 93.7510, loss_bbox: 0.2194, loss_mask: 0.2291, loss: 0.6773 +2024-05-31 09:34:21,389 - mmdet - INFO - Epoch [8][800/7330] lr: 1.000e-04, eta: 6:11:35, time: 0.622, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0174, loss_rpn_bbox: 0.0412, loss_cls: 0.1707, acc: 93.7358, loss_bbox: 0.2200, loss_mask: 0.2293, loss: 0.6786 +2024-05-31 09:34:56,242 - mmdet - INFO - Epoch [8][850/7330] lr: 1.000e-04, eta: 6:11:07, time: 0.697, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0168, loss_rpn_bbox: 0.0435, loss_cls: 0.1689, acc: 93.7178, loss_bbox: 0.2212, loss_mask: 0.2337, loss: 0.6840 +2024-05-31 09:35:28,241 - mmdet - INFO - Epoch [8][900/7330] lr: 1.000e-04, eta: 6:10:36, time: 0.640, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0180, loss_rpn_bbox: 0.0463, loss_cls: 0.1813, acc: 93.2439, loss_bbox: 0.2389, loss_mask: 0.2387, loss: 0.7232 +2024-05-31 09:36:00,968 - mmdet - INFO - Epoch [8][950/7330] lr: 1.000e-04, eta: 6:10:06, time: 0.654, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0169, loss_rpn_bbox: 0.0437, loss_cls: 0.1786, acc: 93.3433, loss_bbox: 0.2313, loss_mask: 0.2337, loss: 0.7043 +2024-05-31 09:36:34,403 - mmdet - INFO - Epoch [8][1000/7330] lr: 1.000e-04, eta: 6:09:37, time: 0.669, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0190, loss_rpn_bbox: 0.0440, loss_cls: 0.1816, acc: 93.2659, loss_bbox: 0.2299, loss_mask: 0.2364, loss: 0.7109 +2024-05-31 09:37:08,273 - mmdet - INFO - Epoch [8][1050/7330] lr: 1.000e-04, eta: 6:09:08, time: 0.677, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0173, loss_rpn_bbox: 0.0414, loss_cls: 0.1692, acc: 93.6987, loss_bbox: 0.2205, loss_mask: 0.2304, loss: 0.6788 +2024-05-31 09:37:41,529 - mmdet - INFO - Epoch [8][1100/7330] lr: 1.000e-04, eta: 6:08:38, time: 0.665, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0191, loss_rpn_bbox: 0.0484, loss_cls: 0.1758, acc: 93.5259, loss_bbox: 0.2287, loss_mask: 0.2368, loss: 0.7089 +2024-05-31 09:38:11,577 - mmdet - INFO - Epoch [8][1150/7330] lr: 1.000e-04, eta: 6:08:06, time: 0.601, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0179, loss_rpn_bbox: 0.0432, loss_cls: 0.1728, acc: 93.6819, loss_bbox: 0.2197, loss_mask: 0.2278, loss: 0.6814 +2024-05-31 09:38:41,840 - mmdet - INFO - Epoch [8][1200/7330] lr: 1.000e-04, eta: 6:07:35, time: 0.605, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0437, loss_cls: 0.1801, acc: 93.4163, loss_bbox: 0.2300, loss_mask: 0.2340, loss: 0.7060 +2024-05-31 09:39:10,939 - mmdet - INFO - Epoch [8][1250/7330] lr: 1.000e-04, eta: 6:07:02, time: 0.582, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0427, loss_cls: 0.1747, acc: 93.6021, loss_bbox: 0.2201, loss_mask: 0.2289, loss: 0.6818 +2024-05-31 09:39:41,172 - mmdet - INFO - Epoch [8][1300/7330] lr: 1.000e-04, eta: 6:06:30, time: 0.605, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0163, loss_rpn_bbox: 0.0414, loss_cls: 0.1749, acc: 93.5002, loss_bbox: 0.2214, loss_mask: 0.2311, loss: 0.6852 +2024-05-31 09:40:10,841 - mmdet - INFO - Epoch [8][1350/7330] lr: 1.000e-04, eta: 6:05:58, time: 0.593, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0178, loss_rpn_bbox: 0.0448, loss_cls: 0.1834, acc: 93.3049, loss_bbox: 0.2340, loss_mask: 0.2308, loss: 0.7108 +2024-05-31 09:40:41,049 - mmdet - INFO - Epoch [8][1400/7330] lr: 1.000e-04, eta: 6:05:27, time: 0.604, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0181, loss_rpn_bbox: 0.0444, loss_cls: 0.1799, acc: 93.4663, loss_bbox: 0.2276, loss_mask: 0.2278, loss: 0.6978 +2024-05-31 09:41:11,203 - mmdet - INFO - Epoch [8][1450/7330] lr: 1.000e-04, eta: 6:04:55, time: 0.603, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0172, loss_rpn_bbox: 0.0414, loss_cls: 0.1764, acc: 93.6104, loss_bbox: 0.2240, loss_mask: 0.2335, loss: 0.6926 +2024-05-31 09:41:41,537 - mmdet - INFO - Epoch [8][1500/7330] lr: 1.000e-04, eta: 6:04:23, time: 0.607, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0191, loss_rpn_bbox: 0.0457, loss_cls: 0.1833, acc: 93.1934, loss_bbox: 0.2369, loss_mask: 0.2377, loss: 0.7226 +2024-05-31 09:42:11,379 - mmdet - INFO - Epoch [8][1550/7330] lr: 1.000e-04, eta: 6:03:51, time: 0.597, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0194, loss_rpn_bbox: 0.0475, loss_cls: 0.1863, acc: 93.1196, loss_bbox: 0.2346, loss_mask: 0.2377, loss: 0.7256 +2024-05-31 09:42:41,450 - mmdet - INFO - Epoch [8][1600/7330] lr: 1.000e-04, eta: 6:03:20, time: 0.601, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0431, loss_cls: 0.1730, acc: 93.6580, loss_bbox: 0.2208, loss_mask: 0.2286, loss: 0.6808 +2024-05-31 09:43:10,509 - mmdet - INFO - Epoch [8][1650/7330] lr: 1.000e-04, eta: 6:02:47, time: 0.581, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0171, loss_rpn_bbox: 0.0414, loss_cls: 0.1694, acc: 93.7544, loss_bbox: 0.2211, loss_mask: 0.2293, loss: 0.6784 +2024-05-31 09:43:40,240 - mmdet - INFO - Epoch [8][1700/7330] lr: 1.000e-04, eta: 6:02:15, time: 0.595, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0421, loss_cls: 0.1691, acc: 93.8489, loss_bbox: 0.2173, loss_mask: 0.2226, loss: 0.6665 +2024-05-31 09:44:14,480 - mmdet - INFO - Epoch [8][1750/7330] lr: 1.000e-04, eta: 6:01:46, time: 0.685, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0177, loss_rpn_bbox: 0.0443, loss_cls: 0.1786, acc: 93.4485, loss_bbox: 0.2311, loss_mask: 0.2327, loss: 0.7045 +2024-05-31 09:44:49,338 - mmdet - INFO - Epoch [8][1800/7330] lr: 1.000e-04, eta: 6:01:17, time: 0.697, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0173, loss_rpn_bbox: 0.0448, loss_cls: 0.1805, acc: 93.3267, loss_bbox: 0.2323, loss_mask: 0.2306, loss: 0.7055 +2024-05-31 09:45:19,126 - mmdet - INFO - Epoch [8][1850/7330] lr: 1.000e-04, eta: 6:00:46, time: 0.596, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0174, loss_rpn_bbox: 0.0438, loss_cls: 0.1792, acc: 93.4026, loss_bbox: 0.2286, loss_mask: 0.2325, loss: 0.7015 +2024-05-31 09:45:52,311 - mmdet - INFO - Epoch [8][1900/7330] lr: 1.000e-04, eta: 6:00:16, time: 0.664, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0186, loss_rpn_bbox: 0.0442, loss_cls: 0.1817, acc: 93.4038, loss_bbox: 0.2306, loss_mask: 0.2335, loss: 0.7086 +2024-05-31 09:46:31,338 - mmdet - INFO - Epoch [8][1950/7330] lr: 1.000e-04, eta: 5:59:50, time: 0.781, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0459, loss_cls: 0.1845, acc: 93.1521, loss_bbox: 0.2363, loss_mask: 0.2349, loss: 0.7205 +2024-05-31 09:47:01,686 - mmdet - INFO - Epoch [8][2000/7330] lr: 1.000e-04, eta: 5:59:18, time: 0.607, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0442, loss_cls: 0.1839, acc: 93.2424, loss_bbox: 0.2326, loss_mask: 0.2317, loss: 0.7106 +2024-05-31 09:47:31,472 - mmdet - INFO - Epoch [8][2050/7330] lr: 1.000e-04, eta: 5:58:46, time: 0.596, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0190, loss_rpn_bbox: 0.0443, loss_cls: 0.1789, acc: 93.3152, loss_bbox: 0.2304, loss_mask: 0.2323, loss: 0.7049 +2024-05-31 09:48:01,380 - mmdet - INFO - Epoch [8][2100/7330] lr: 1.000e-04, eta: 5:58:14, time: 0.598, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0180, loss_rpn_bbox: 0.0439, loss_cls: 0.1784, acc: 93.4365, loss_bbox: 0.2267, loss_mask: 0.2326, loss: 0.6996 +2024-05-31 09:48:30,527 - mmdet - INFO - Epoch [8][2150/7330] lr: 1.000e-04, eta: 5:57:42, time: 0.583, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0167, loss_rpn_bbox: 0.0412, loss_cls: 0.1729, acc: 93.6382, loss_bbox: 0.2247, loss_mask: 0.2330, loss: 0.6885 +2024-05-31 09:49:01,685 - mmdet - INFO - Epoch [8][2200/7330] lr: 1.000e-04, eta: 5:57:11, time: 0.623, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0185, loss_rpn_bbox: 0.0431, loss_cls: 0.1784, acc: 93.4084, loss_bbox: 0.2266, loss_mask: 0.2266, loss: 0.6933 +2024-05-31 09:49:31,888 - mmdet - INFO - Epoch [8][2250/7330] lr: 1.000e-04, eta: 5:56:39, time: 0.604, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0173, loss_rpn_bbox: 0.0434, loss_cls: 0.1743, acc: 93.6799, loss_bbox: 0.2223, loss_mask: 0.2313, loss: 0.6886 +2024-05-31 09:50:02,006 - mmdet - INFO - Epoch [8][2300/7330] lr: 1.000e-04, eta: 5:56:08, time: 0.602, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0186, loss_rpn_bbox: 0.0441, loss_cls: 0.1794, acc: 93.3530, loss_bbox: 0.2319, loss_mask: 0.2381, loss: 0.7120 +2024-05-31 09:50:32,131 - mmdet - INFO - Epoch [8][2350/7330] lr: 1.000e-04, eta: 5:55:36, time: 0.603, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0180, loss_rpn_bbox: 0.0436, loss_cls: 0.1846, acc: 93.2988, loss_bbox: 0.2316, loss_mask: 0.2368, loss: 0.7146 +2024-05-31 09:51:02,648 - mmdet - INFO - Epoch [8][2400/7330] lr: 1.000e-04, eta: 5:55:04, time: 0.610, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0168, loss_rpn_bbox: 0.0436, loss_cls: 0.1832, acc: 93.2524, loss_bbox: 0.2305, loss_mask: 0.2254, loss: 0.6995 +2024-05-31 09:51:31,986 - mmdet - INFO - Epoch [8][2450/7330] lr: 1.000e-04, eta: 5:54:32, time: 0.587, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0422, loss_cls: 0.1765, acc: 93.4407, loss_bbox: 0.2266, loss_mask: 0.2293, loss: 0.6901 +2024-05-31 09:52:02,072 - mmdet - INFO - Epoch [8][2500/7330] lr: 1.000e-04, eta: 5:54:00, time: 0.602, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0180, loss_rpn_bbox: 0.0438, loss_cls: 0.1827, acc: 93.4065, loss_bbox: 0.2334, loss_mask: 0.2319, loss: 0.7097 +2024-05-31 09:52:31,396 - mmdet - INFO - Epoch [8][2550/7330] lr: 1.000e-04, eta: 5:53:28, time: 0.586, data_time: 0.037, memory: 9655, loss_rpn_cls: 0.0180, loss_rpn_bbox: 0.0417, loss_cls: 0.1719, acc: 93.8635, loss_bbox: 0.2125, loss_mask: 0.2267, loss: 0.6707 +2024-05-31 09:53:03,985 - mmdet - INFO - Epoch [8][2600/7330] lr: 1.000e-04, eta: 5:52:58, time: 0.652, data_time: 0.037, memory: 9655, loss_rpn_cls: 0.0169, loss_rpn_bbox: 0.0388, loss_cls: 0.1795, acc: 93.5237, loss_bbox: 0.2201, loss_mask: 0.2289, loss: 0.6842 +2024-05-31 09:53:36,156 - mmdet - INFO - Epoch [8][2650/7330] lr: 1.000e-04, eta: 5:52:28, time: 0.643, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0441, loss_cls: 0.1874, acc: 93.2056, loss_bbox: 0.2321, loss_mask: 0.2348, loss: 0.7149 +2024-05-31 09:54:10,595 - mmdet - INFO - Epoch [8][2700/7330] lr: 1.000e-04, eta: 5:51:59, time: 0.689, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0168, loss_rpn_bbox: 0.0412, loss_cls: 0.1839, acc: 93.2371, loss_bbox: 0.2376, loss_mask: 0.2314, loss: 0.7109 +2024-05-31 09:54:40,822 - mmdet - INFO - Epoch [8][2750/7330] lr: 1.000e-04, eta: 5:51:27, time: 0.604, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0197, loss_rpn_bbox: 0.0431, loss_cls: 0.1793, acc: 93.4368, loss_bbox: 0.2321, loss_mask: 0.2320, loss: 0.7063 +2024-05-31 09:55:16,605 - mmdet - INFO - Epoch [8][2800/7330] lr: 1.000e-04, eta: 5:50:59, time: 0.716, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0457, loss_cls: 0.1910, acc: 92.9250, loss_bbox: 0.2423, loss_mask: 0.2361, loss: 0.7341 +2024-05-31 09:55:50,810 - mmdet - INFO - Epoch [8][2850/7330] lr: 1.000e-04, eta: 5:50:30, time: 0.684, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0180, loss_rpn_bbox: 0.0439, loss_cls: 0.1826, acc: 93.2720, loss_bbox: 0.2319, loss_mask: 0.2331, loss: 0.7094 +2024-05-31 09:56:20,381 - mmdet - INFO - Epoch [8][2900/7330] lr: 1.000e-04, eta: 5:49:57, time: 0.591, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0174, loss_rpn_bbox: 0.0430, loss_cls: 0.1812, acc: 93.2546, loss_bbox: 0.2344, loss_mask: 0.2320, loss: 0.7080 +2024-05-31 09:56:50,423 - mmdet - INFO - Epoch [8][2950/7330] lr: 1.000e-04, eta: 5:49:26, time: 0.601, data_time: 0.064, memory: 9655, loss_rpn_cls: 0.0173, loss_rpn_bbox: 0.0404, loss_cls: 0.1684, acc: 93.8677, loss_bbox: 0.2105, loss_mask: 0.2255, loss: 0.6620 +2024-05-31 09:57:19,799 - mmdet - INFO - Epoch [8][3000/7330] lr: 1.000e-04, eta: 5:48:54, time: 0.587, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0181, loss_rpn_bbox: 0.0435, loss_cls: 0.1773, acc: 93.5220, loss_bbox: 0.2255, loss_mask: 0.2371, loss: 0.7016 +2024-05-31 09:57:50,234 - mmdet - INFO - Epoch [8][3050/7330] lr: 1.000e-04, eta: 5:48:22, time: 0.609, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0181, loss_rpn_bbox: 0.0450, loss_cls: 0.1777, acc: 93.4692, loss_bbox: 0.2225, loss_mask: 0.2285, loss: 0.6918 +2024-05-31 09:58:19,956 - mmdet - INFO - Epoch [8][3100/7330] lr: 1.000e-04, eta: 5:47:50, time: 0.594, data_time: 0.033, memory: 9655, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0417, loss_cls: 0.1734, acc: 93.6504, loss_bbox: 0.2197, loss_mask: 0.2276, loss: 0.6779 +2024-05-31 09:58:50,298 - mmdet - INFO - Epoch [8][3150/7330] lr: 1.000e-04, eta: 5:47:18, time: 0.607, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0191, loss_rpn_bbox: 0.0452, loss_cls: 0.1830, acc: 93.2888, loss_bbox: 0.2341, loss_mask: 0.2350, loss: 0.7163 +2024-05-31 09:59:20,922 - mmdet - INFO - Epoch [8][3200/7330] lr: 1.000e-04, eta: 5:46:47, time: 0.613, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0194, loss_rpn_bbox: 0.0450, loss_cls: 0.1830, acc: 93.3132, loss_bbox: 0.2338, loss_mask: 0.2391, loss: 0.7203 +2024-05-31 09:59:51,899 - mmdet - INFO - Epoch [8][3250/7330] lr: 1.000e-04, eta: 5:46:16, time: 0.619, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0191, loss_rpn_bbox: 0.0455, loss_cls: 0.1813, acc: 93.4031, loss_bbox: 0.2323, loss_mask: 0.2325, loss: 0.7107 +2024-05-31 10:00:21,749 - mmdet - INFO - Epoch [8][3300/7330] lr: 1.000e-04, eta: 5:45:44, time: 0.597, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0447, loss_cls: 0.1784, acc: 93.4561, loss_bbox: 0.2249, loss_mask: 0.2350, loss: 0.6988 +2024-05-31 10:00:51,724 - mmdet - INFO - Epoch [8][3350/7330] lr: 1.000e-04, eta: 5:45:12, time: 0.599, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0188, loss_rpn_bbox: 0.0429, loss_cls: 0.1785, acc: 93.4929, loss_bbox: 0.2277, loss_mask: 0.2348, loss: 0.7027 +2024-05-31 10:01:21,946 - mmdet - INFO - Epoch [8][3400/7330] lr: 1.000e-04, eta: 5:44:41, time: 0.605, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0207, loss_rpn_bbox: 0.0469, loss_cls: 0.1874, acc: 93.2200, loss_bbox: 0.2382, loss_mask: 0.2353, loss: 0.7285 +2024-05-31 10:01:51,794 - mmdet - INFO - Epoch [8][3450/7330] lr: 1.000e-04, eta: 5:44:09, time: 0.597, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0425, loss_cls: 0.1769, acc: 93.4690, loss_bbox: 0.2252, loss_mask: 0.2253, loss: 0.6880 +2024-05-31 10:02:26,526 - mmdet - INFO - Epoch [8][3500/7330] lr: 1.000e-04, eta: 5:43:40, time: 0.695, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0184, loss_rpn_bbox: 0.0441, loss_cls: 0.1782, acc: 93.4456, loss_bbox: 0.2283, loss_mask: 0.2321, loss: 0.7011 +2024-05-31 10:02:58,083 - mmdet - INFO - Epoch [8][3550/7330] lr: 1.000e-04, eta: 5:43:09, time: 0.631, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0412, loss_cls: 0.1753, acc: 93.5654, loss_bbox: 0.2244, loss_mask: 0.2341, loss: 0.6905 +2024-05-31 10:03:30,270 - mmdet - INFO - Epoch [8][3600/7330] lr: 1.000e-04, eta: 5:42:39, time: 0.644, data_time: 0.037, memory: 9655, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0424, loss_cls: 0.1792, acc: 93.4199, loss_bbox: 0.2268, loss_mask: 0.2332, loss: 0.6978 +2024-05-31 10:04:02,658 - mmdet - INFO - Epoch [8][3650/7330] lr: 1.000e-04, eta: 5:42:08, time: 0.648, data_time: 0.036, memory: 9655, loss_rpn_cls: 0.0171, loss_rpn_bbox: 0.0411, loss_cls: 0.1804, acc: 93.4724, loss_bbox: 0.2276, loss_mask: 0.2323, loss: 0.6985 +2024-05-31 10:04:34,443 - mmdet - INFO - Epoch [8][3700/7330] lr: 1.000e-04, eta: 5:41:38, time: 0.636, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0442, loss_cls: 0.1821, acc: 93.2188, loss_bbox: 0.2336, loss_mask: 0.2402, loss: 0.7182 +2024-05-31 10:05:09,043 - mmdet - INFO - Epoch [8][3750/7330] lr: 1.000e-04, eta: 5:41:09, time: 0.692, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0174, loss_rpn_bbox: 0.0459, loss_cls: 0.1837, acc: 93.2029, loss_bbox: 0.2329, loss_mask: 0.2316, loss: 0.7114 +2024-05-31 10:05:38,691 - mmdet - INFO - Epoch [8][3800/7330] lr: 1.000e-04, eta: 5:40:37, time: 0.593, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0419, loss_cls: 0.1825, acc: 93.3596, loss_bbox: 0.2280, loss_mask: 0.2366, loss: 0.7057 +2024-05-31 10:06:09,112 - mmdet - INFO - Epoch [8][3850/7330] lr: 1.000e-04, eta: 5:40:05, time: 0.608, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0190, loss_rpn_bbox: 0.0446, loss_cls: 0.1819, acc: 93.2573, loss_bbox: 0.2338, loss_mask: 0.2373, loss: 0.7167 +2024-05-31 10:06:38,847 - mmdet - INFO - Epoch [8][3900/7330] lr: 1.000e-04, eta: 5:39:33, time: 0.595, data_time: 0.035, memory: 9655, loss_rpn_cls: 0.0176, loss_rpn_bbox: 0.0409, loss_cls: 0.1765, acc: 93.5447, loss_bbox: 0.2244, loss_mask: 0.2346, loss: 0.6939 +2024-05-31 10:07:09,030 - mmdet - INFO - Epoch [8][3950/7330] lr: 1.000e-04, eta: 5:39:01, time: 0.604, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0465, loss_cls: 0.1901, acc: 92.9993, loss_bbox: 0.2391, loss_mask: 0.2334, loss: 0.7274 +2024-05-31 10:07:38,005 - mmdet - INFO - Epoch [8][4000/7330] lr: 1.000e-04, eta: 5:38:29, time: 0.579, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0163, loss_rpn_bbox: 0.0427, loss_cls: 0.1710, acc: 93.7800, loss_bbox: 0.2198, loss_mask: 0.2287, loss: 0.6785 +2024-05-31 10:08:08,262 - mmdet - INFO - Epoch [8][4050/7330] lr: 1.000e-04, eta: 5:37:58, time: 0.605, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0175, loss_rpn_bbox: 0.0468, loss_cls: 0.1874, acc: 93.1038, loss_bbox: 0.2432, loss_mask: 0.2381, loss: 0.7331 +2024-05-31 10:08:38,121 - mmdet - INFO - Epoch [8][4100/7330] lr: 1.000e-04, eta: 5:37:26, time: 0.597, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0192, loss_rpn_bbox: 0.0416, loss_cls: 0.1834, acc: 93.5347, loss_bbox: 0.2277, loss_mask: 0.2285, loss: 0.7004 +2024-05-31 10:09:08,054 - mmdet - INFO - Epoch [8][4150/7330] lr: 1.000e-04, eta: 5:36:54, time: 0.599, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0427, loss_cls: 0.1801, acc: 93.4221, loss_bbox: 0.2306, loss_mask: 0.2320, loss: 0.7020 +2024-05-31 10:09:38,286 - mmdet - INFO - Epoch [8][4200/7330] lr: 1.000e-04, eta: 5:36:22, time: 0.605, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0190, loss_rpn_bbox: 0.0459, loss_cls: 0.1875, acc: 93.0806, loss_bbox: 0.2371, loss_mask: 0.2411, loss: 0.7305 +2024-05-31 10:10:08,083 - mmdet - INFO - Epoch [8][4250/7330] lr: 1.000e-04, eta: 5:35:50, time: 0.596, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0173, loss_rpn_bbox: 0.0436, loss_cls: 0.1765, acc: 93.5017, loss_bbox: 0.2253, loss_mask: 0.2276, loss: 0.6903 +2024-05-31 10:10:37,834 - mmdet - INFO - Epoch [8][4300/7330] lr: 1.000e-04, eta: 5:35:19, time: 0.595, data_time: 0.038, memory: 9655, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0446, loss_cls: 0.1772, acc: 93.5278, loss_bbox: 0.2249, loss_mask: 0.2308, loss: 0.6956 +2024-05-31 10:11:07,531 - mmdet - INFO - Epoch [8][4350/7330] lr: 1.000e-04, eta: 5:34:47, time: 0.594, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0174, loss_rpn_bbox: 0.0435, loss_cls: 0.1785, acc: 93.5181, loss_bbox: 0.2274, loss_mask: 0.2333, loss: 0.7001 +2024-05-31 10:11:41,472 - mmdet - INFO - Epoch [8][4400/7330] lr: 1.000e-04, eta: 5:34:17, time: 0.679, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0176, loss_rpn_bbox: 0.0412, loss_cls: 0.1766, acc: 93.6104, loss_bbox: 0.2222, loss_mask: 0.2282, loss: 0.6858 +2024-05-31 10:12:15,228 - mmdet - INFO - Epoch [8][4450/7330] lr: 1.000e-04, eta: 5:33:48, time: 0.675, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0181, loss_rpn_bbox: 0.0444, loss_cls: 0.1833, acc: 93.2427, loss_bbox: 0.2364, loss_mask: 0.2330, loss: 0.7153 +2024-05-31 10:12:44,882 - mmdet - INFO - Epoch [8][4500/7330] lr: 1.000e-04, eta: 5:33:16, time: 0.593, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0185, loss_rpn_bbox: 0.0434, loss_cls: 0.1817, acc: 93.4473, loss_bbox: 0.2280, loss_mask: 0.2321, loss: 0.7037 +2024-05-31 10:13:17,614 - mmdet - INFO - Epoch [8][4550/7330] lr: 1.000e-04, eta: 5:32:46, time: 0.655, data_time: 0.069, memory: 9655, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0426, loss_cls: 0.1821, acc: 93.2822, loss_bbox: 0.2308, loss_mask: 0.2332, loss: 0.7069 +2024-05-31 10:13:54,142 - mmdet - INFO - Epoch [8][4600/7330] lr: 1.000e-04, eta: 5:32:18, time: 0.731, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0186, loss_rpn_bbox: 0.0456, loss_cls: 0.1835, acc: 93.2920, loss_bbox: 0.2309, loss_mask: 0.2333, loss: 0.7118 +2024-05-31 10:14:24,319 - mmdet - INFO - Epoch [8][4650/7330] lr: 1.000e-04, eta: 5:31:46, time: 0.603, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0177, loss_rpn_bbox: 0.0418, loss_cls: 0.1787, acc: 93.4875, loss_bbox: 0.2220, loss_mask: 0.2324, loss: 0.6925 +2024-05-31 10:14:54,040 - mmdet - INFO - Epoch [8][4700/7330] lr: 1.000e-04, eta: 5:31:14, time: 0.594, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0174, loss_rpn_bbox: 0.0437, loss_cls: 0.1796, acc: 93.4124, loss_bbox: 0.2265, loss_mask: 0.2314, loss: 0.6987 +2024-05-31 10:15:23,846 - mmdet - INFO - Epoch [8][4750/7330] lr: 1.000e-04, eta: 5:30:42, time: 0.596, data_time: 0.038, memory: 9655, loss_rpn_cls: 0.0179, loss_rpn_bbox: 0.0430, loss_cls: 0.1771, acc: 93.4900, loss_bbox: 0.2261, loss_mask: 0.2295, loss: 0.6935 +2024-05-31 10:15:54,147 - mmdet - INFO - Epoch [8][4800/7330] lr: 1.000e-04, eta: 5:30:11, time: 0.606, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0202, loss_rpn_bbox: 0.0452, loss_cls: 0.1878, acc: 93.2444, loss_bbox: 0.2324, loss_mask: 0.2394, loss: 0.7250 +2024-05-31 10:16:24,126 - mmdet - INFO - Epoch [8][4850/7330] lr: 1.000e-04, eta: 5:29:39, time: 0.599, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0450, loss_cls: 0.1822, acc: 93.3223, loss_bbox: 0.2314, loss_mask: 0.2336, loss: 0.7112 +2024-05-31 10:16:54,113 - mmdet - INFO - Epoch [8][4900/7330] lr: 1.000e-04, eta: 5:29:07, time: 0.600, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0437, loss_cls: 0.1813, acc: 93.4084, loss_bbox: 0.2279, loss_mask: 0.2305, loss: 0.7000 +2024-05-31 10:17:24,236 - mmdet - INFO - Epoch [8][4950/7330] lr: 1.000e-04, eta: 5:28:36, time: 0.602, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0174, loss_rpn_bbox: 0.0456, loss_cls: 0.1825, acc: 93.2683, loss_bbox: 0.2325, loss_mask: 0.2342, loss: 0.7123 +2024-05-31 10:17:54,247 - mmdet - INFO - Epoch [8][5000/7330] lr: 1.000e-04, eta: 5:28:04, time: 0.600, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0192, loss_rpn_bbox: 0.0440, loss_cls: 0.1812, acc: 93.4365, loss_bbox: 0.2262, loss_mask: 0.2334, loss: 0.7040 +2024-05-31 10:18:24,090 - mmdet - INFO - Epoch [8][5050/7330] lr: 1.000e-04, eta: 5:27:32, time: 0.597, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0195, loss_rpn_bbox: 0.0454, loss_cls: 0.1856, acc: 93.2197, loss_bbox: 0.2335, loss_mask: 0.2369, loss: 0.7209 +2024-05-31 10:18:53,733 - mmdet - INFO - Epoch [8][5100/7330] lr: 1.000e-04, eta: 5:27:00, time: 0.593, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0175, loss_rpn_bbox: 0.0444, loss_cls: 0.1775, acc: 93.5371, loss_bbox: 0.2219, loss_mask: 0.2318, loss: 0.6931 +2024-05-31 10:19:24,233 - mmdet - INFO - Epoch [8][5150/7330] lr: 1.000e-04, eta: 5:26:29, time: 0.610, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0183, loss_rpn_bbox: 0.0446, loss_cls: 0.1847, acc: 93.2185, loss_bbox: 0.2325, loss_mask: 0.2361, loss: 0.7161 +2024-05-31 10:19:54,605 - mmdet - INFO - Epoch [8][5200/7330] lr: 1.000e-04, eta: 5:25:57, time: 0.607, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0469, loss_cls: 0.1896, acc: 93.0378, loss_bbox: 0.2385, loss_mask: 0.2368, loss: 0.7300 +2024-05-31 10:20:27,884 - mmdet - INFO - Epoch [8][5250/7330] lr: 1.000e-04, eta: 5:25:27, time: 0.666, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0184, loss_rpn_bbox: 0.0451, loss_cls: 0.1917, acc: 92.9963, loss_bbox: 0.2397, loss_mask: 0.2359, loss: 0.7308 +2024-05-31 10:20:59,186 - mmdet - INFO - Epoch [8][5300/7330] lr: 1.000e-04, eta: 5:24:56, time: 0.626, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0180, loss_rpn_bbox: 0.0437, loss_cls: 0.1790, acc: 93.4424, loss_bbox: 0.2215, loss_mask: 0.2301, loss: 0.6922 +2024-05-31 10:21:34,374 - mmdet - INFO - Epoch [8][5350/7330] lr: 1.000e-04, eta: 5:24:27, time: 0.704, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0176, loss_rpn_bbox: 0.0444, loss_cls: 0.1849, acc: 93.2480, loss_bbox: 0.2320, loss_mask: 0.2359, loss: 0.7149 +2024-05-31 10:22:04,026 - mmdet - INFO - Epoch [8][5400/7330] lr: 1.000e-04, eta: 5:23:56, time: 0.593, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0204, loss_rpn_bbox: 0.0463, loss_cls: 0.1823, acc: 93.3899, loss_bbox: 0.2270, loss_mask: 0.2341, loss: 0.7101 +2024-05-31 10:22:38,940 - mmdet - INFO - Epoch [8][5450/7330] lr: 1.000e-04, eta: 5:23:27, time: 0.698, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0180, loss_rpn_bbox: 0.0441, loss_cls: 0.1808, acc: 93.4709, loss_bbox: 0.2255, loss_mask: 0.2408, loss: 0.7092 +2024-05-31 10:23:13,017 - mmdet - INFO - Epoch [8][5500/7330] lr: 1.000e-04, eta: 5:22:57, time: 0.682, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0194, loss_rpn_bbox: 0.0458, loss_cls: 0.1793, acc: 93.4146, loss_bbox: 0.2312, loss_mask: 0.2385, loss: 0.7143 +2024-05-31 10:23:43,171 - mmdet - INFO - Epoch [8][5550/7330] lr: 1.000e-04, eta: 5:22:25, time: 0.603, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0164, loss_rpn_bbox: 0.0426, loss_cls: 0.1751, acc: 93.6016, loss_bbox: 0.2219, loss_mask: 0.2306, loss: 0.6866 +2024-05-31 10:24:13,437 - mmdet - INFO - Epoch [8][5600/7330] lr: 1.000e-04, eta: 5:21:54, time: 0.605, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0405, loss_cls: 0.1785, acc: 93.5508, loss_bbox: 0.2215, loss_mask: 0.2267, loss: 0.6861 +2024-05-31 10:24:43,214 - mmdet - INFO - Epoch [8][5650/7330] lr: 1.000e-04, eta: 5:21:22, time: 0.595, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0178, loss_rpn_bbox: 0.0430, loss_cls: 0.1862, acc: 93.2690, loss_bbox: 0.2374, loss_mask: 0.2360, loss: 0.7204 +2024-05-31 10:25:13,287 - mmdet - INFO - Epoch [8][5700/7330] lr: 1.000e-04, eta: 5:20:50, time: 0.602, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0175, loss_rpn_bbox: 0.0437, loss_cls: 0.1784, acc: 93.4287, loss_bbox: 0.2263, loss_mask: 0.2371, loss: 0.7030 +2024-05-31 10:25:43,143 - mmdet - INFO - Epoch [8][5750/7330] lr: 1.000e-04, eta: 5:20:19, time: 0.597, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0433, loss_cls: 0.1824, acc: 93.3142, loss_bbox: 0.2343, loss_mask: 0.2369, loss: 0.7134 +2024-05-31 10:26:12,961 - mmdet - INFO - Epoch [8][5800/7330] lr: 1.000e-04, eta: 5:19:47, time: 0.596, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0181, loss_rpn_bbox: 0.0440, loss_cls: 0.1842, acc: 93.3645, loss_bbox: 0.2279, loss_mask: 0.2356, loss: 0.7098 +2024-05-31 10:26:42,743 - mmdet - INFO - Epoch [8][5850/7330] lr: 1.000e-04, eta: 5:19:15, time: 0.596, data_time: 0.038, memory: 9655, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0421, loss_cls: 0.1741, acc: 93.6245, loss_bbox: 0.2208, loss_mask: 0.2384, loss: 0.6906 +2024-05-31 10:27:12,580 - mmdet - INFO - Epoch [8][5900/7330] lr: 1.000e-04, eta: 5:18:43, time: 0.597, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0175, loss_rpn_bbox: 0.0428, loss_cls: 0.1833, acc: 93.3728, loss_bbox: 0.2235, loss_mask: 0.2301, loss: 0.6973 +2024-05-31 10:27:43,452 - mmdet - INFO - Epoch [8][5950/7330] lr: 1.000e-04, eta: 5:18:12, time: 0.617, data_time: 0.038, memory: 9655, loss_rpn_cls: 0.0197, loss_rpn_bbox: 0.0455, loss_cls: 0.1892, acc: 93.0889, loss_bbox: 0.2350, loss_mask: 0.2339, loss: 0.7232 +2024-05-31 10:28:13,077 - mmdet - INFO - Epoch [8][6000/7330] lr: 1.000e-04, eta: 5:17:40, time: 0.592, data_time: 0.036, memory: 9655, loss_rpn_cls: 0.0175, loss_rpn_bbox: 0.0425, loss_cls: 0.1770, acc: 93.4702, loss_bbox: 0.2219, loss_mask: 0.2316, loss: 0.6904 +2024-05-31 10:28:42,608 - mmdet - INFO - Epoch [8][6050/7330] lr: 1.000e-04, eta: 5:17:08, time: 0.591, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0184, loss_rpn_bbox: 0.0439, loss_cls: 0.1863, acc: 93.1392, loss_bbox: 0.2344, loss_mask: 0.2312, loss: 0.7144 +2024-05-31 10:29:12,919 - mmdet - INFO - Epoch [8][6100/7330] lr: 1.000e-04, eta: 5:16:37, time: 0.606, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0194, loss_rpn_bbox: 0.0454, loss_cls: 0.1871, acc: 93.1179, loss_bbox: 0.2304, loss_mask: 0.2381, loss: 0.7205 +2024-05-31 10:29:46,762 - mmdet - INFO - Epoch [8][6150/7330] lr: 1.000e-04, eta: 5:16:07, time: 0.677, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0181, loss_rpn_bbox: 0.0429, loss_cls: 0.1824, acc: 93.3191, loss_bbox: 0.2265, loss_mask: 0.2324, loss: 0.7024 +2024-05-31 10:30:18,919 - mmdet - INFO - Epoch [8][6200/7330] lr: 1.000e-04, eta: 5:15:37, time: 0.643, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0420, loss_cls: 0.1879, acc: 93.2405, loss_bbox: 0.2306, loss_mask: 0.2332, loss: 0.7127 +2024-05-31 10:30:51,494 - mmdet - INFO - Epoch [8][6250/7330] lr: 1.000e-04, eta: 5:15:06, time: 0.652, data_time: 0.036, memory: 9655, loss_rpn_cls: 0.0173, loss_rpn_bbox: 0.0419, loss_cls: 0.1794, acc: 93.4587, loss_bbox: 0.2293, loss_mask: 0.2368, loss: 0.7046 +2024-05-31 10:31:23,818 - mmdet - INFO - Epoch [8][6300/7330] lr: 1.000e-04, eta: 5:14:36, time: 0.646, data_time: 0.037, memory: 9655, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0429, loss_cls: 0.1787, acc: 93.4478, loss_bbox: 0.2245, loss_mask: 0.2276, loss: 0.6920 +2024-05-31 10:31:56,693 - mmdet - INFO - Epoch [8][6350/7330] lr: 1.000e-04, eta: 5:14:06, time: 0.658, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0207, loss_rpn_bbox: 0.0447, loss_cls: 0.1906, acc: 93.0928, loss_bbox: 0.2394, loss_mask: 0.2426, loss: 0.7381 +2024-05-31 10:32:32,360 - mmdet - INFO - Epoch [8][6400/7330] lr: 1.000e-04, eta: 5:13:37, time: 0.713, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0450, loss_cls: 0.1868, acc: 93.0808, loss_bbox: 0.2355, loss_mask: 0.2356, loss: 0.7218 +2024-05-31 10:33:02,392 - mmdet - INFO - Epoch [8][6450/7330] lr: 1.000e-04, eta: 5:13:05, time: 0.601, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0192, loss_rpn_bbox: 0.0426, loss_cls: 0.1915, acc: 93.0093, loss_bbox: 0.2431, loss_mask: 0.2409, loss: 0.7373 +2024-05-31 10:33:32,494 - mmdet - INFO - Epoch [8][6500/7330] lr: 1.000e-04, eta: 5:12:34, time: 0.602, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0177, loss_rpn_bbox: 0.0420, loss_cls: 0.1766, acc: 93.4250, loss_bbox: 0.2275, loss_mask: 0.2288, loss: 0.6925 +2024-05-31 10:34:03,055 - mmdet - INFO - Epoch [8][6550/7330] lr: 1.000e-04, eta: 5:12:02, time: 0.611, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0190, loss_rpn_bbox: 0.0442, loss_cls: 0.1795, acc: 93.4736, loss_bbox: 0.2249, loss_mask: 0.2290, loss: 0.6967 +2024-05-31 10:34:33,298 - mmdet - INFO - Epoch [8][6600/7330] lr: 1.000e-04, eta: 5:11:31, time: 0.605, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0172, loss_rpn_bbox: 0.0434, loss_cls: 0.1780, acc: 93.5076, loss_bbox: 0.2216, loss_mask: 0.2262, loss: 0.6864 +2024-05-31 10:35:03,175 - mmdet - INFO - Epoch [8][6650/7330] lr: 1.000e-04, eta: 5:10:59, time: 0.597, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0416, loss_cls: 0.1809, acc: 93.3599, loss_bbox: 0.2272, loss_mask: 0.2259, loss: 0.6911 +2024-05-31 10:35:33,191 - mmdet - INFO - Epoch [8][6700/7330] lr: 1.000e-04, eta: 5:10:27, time: 0.600, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0179, loss_rpn_bbox: 0.0442, loss_cls: 0.1842, acc: 93.2444, loss_bbox: 0.2288, loss_mask: 0.2302, loss: 0.7053 +2024-05-31 10:36:03,371 - mmdet - INFO - Epoch [8][6750/7330] lr: 1.000e-04, eta: 5:09:56, time: 0.604, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0179, loss_rpn_bbox: 0.0468, loss_cls: 0.1887, acc: 93.1062, loss_bbox: 0.2322, loss_mask: 0.2340, loss: 0.7197 +2024-05-31 10:36:33,590 - mmdet - INFO - Epoch [8][6800/7330] lr: 1.000e-04, eta: 5:09:24, time: 0.604, data_time: 0.034, memory: 9655, loss_rpn_cls: 0.0179, loss_rpn_bbox: 0.0435, loss_cls: 0.1779, acc: 93.4741, loss_bbox: 0.2275, loss_mask: 0.2287, loss: 0.6954 +2024-05-31 10:37:03,169 - mmdet - INFO - Epoch [8][6850/7330] lr: 1.000e-04, eta: 5:08:52, time: 0.591, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0447, loss_cls: 0.1827, acc: 93.3564, loss_bbox: 0.2264, loss_mask: 0.2317, loss: 0.7037 +2024-05-31 10:37:33,445 - mmdet - INFO - Epoch [8][6900/7330] lr: 1.000e-04, eta: 5:08:21, time: 0.606, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0453, loss_cls: 0.1886, acc: 93.1543, loss_bbox: 0.2333, loss_mask: 0.2309, loss: 0.7188 +2024-05-31 10:38:03,494 - mmdet - INFO - Epoch [8][6950/7330] lr: 1.000e-04, eta: 5:07:49, time: 0.601, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0412, loss_cls: 0.1731, acc: 93.6953, loss_bbox: 0.2205, loss_mask: 0.2310, loss: 0.6816 +2024-05-31 10:38:33,495 - mmdet - INFO - Epoch [8][7000/7330] lr: 1.000e-04, eta: 5:07:18, time: 0.600, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0193, loss_rpn_bbox: 0.0463, loss_cls: 0.1851, acc: 93.1836, loss_bbox: 0.2343, loss_mask: 0.2386, loss: 0.7237 +2024-05-31 10:39:07,799 - mmdet - INFO - Epoch [8][7050/7330] lr: 1.000e-04, eta: 5:06:48, time: 0.686, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0187, loss_rpn_bbox: 0.0426, loss_cls: 0.1862, acc: 93.2258, loss_bbox: 0.2358, loss_mask: 0.2398, loss: 0.7230 +2024-05-31 10:39:42,784 - mmdet - INFO - Epoch [8][7100/7330] lr: 1.000e-04, eta: 5:06:19, time: 0.700, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0188, loss_rpn_bbox: 0.0451, loss_cls: 0.1855, acc: 93.1953, loss_bbox: 0.2348, loss_mask: 0.2348, loss: 0.7190 +2024-05-31 10:40:13,434 - mmdet - INFO - Epoch [8][7150/7330] lr: 1.000e-04, eta: 5:05:48, time: 0.613, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0188, loss_rpn_bbox: 0.0447, loss_cls: 0.1931, acc: 93.0137, loss_bbox: 0.2350, loss_mask: 0.2398, loss: 0.7314 +2024-05-31 10:40:46,284 - mmdet - INFO - Epoch [8][7200/7330] lr: 1.000e-04, eta: 5:05:17, time: 0.657, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0188, loss_rpn_bbox: 0.0436, loss_cls: 0.1929, acc: 93.0984, loss_bbox: 0.2342, loss_mask: 0.2360, loss: 0.7256 +2024-05-31 10:41:21,419 - mmdet - INFO - Epoch [8][7250/7330] lr: 1.000e-04, eta: 5:04:48, time: 0.703, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0175, loss_rpn_bbox: 0.0446, loss_cls: 0.1850, acc: 93.2561, loss_bbox: 0.2345, loss_mask: 0.2335, loss: 0.7150 +2024-05-31 10:41:53,209 - mmdet - INFO - Epoch [8][7300/7330] lr: 1.000e-04, eta: 5:04:18, time: 0.636, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0183, loss_rpn_bbox: 0.0440, loss_cls: 0.1867, acc: 93.1067, loss_bbox: 0.2313, loss_mask: 0.2384, loss: 0.7186 +2024-05-31 10:42:12,119 - mmdet - INFO - Saving checkpoint at 8 epochs +2024-05-31 10:43:45,658 - mmdet - INFO - Evaluating bbox... +2024-05-31 10:44:06,736 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.422 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.647 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.462 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.245 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.457 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.586 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.547 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.547 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.547 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.346 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.589 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.714 + +2024-05-31 10:44:06,736 - mmdet - INFO - Evaluating segm... +2024-05-31 10:44:33,097 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.383 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.613 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.404 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.172 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.411 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.592 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.496 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.496 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.496 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.283 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.541 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.681 + +2024-05-31 10:44:33,510 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 10:44:33,512 - mmdet - INFO - Epoch(val) [8][625] bbox_mAP: 0.4220, bbox_mAP_50: 0.6470, bbox_mAP_75: 0.4620, bbox_mAP_s: 0.2450, bbox_mAP_m: 0.4570, bbox_mAP_l: 0.5860, bbox_mAP_copypaste: 0.422 0.647 0.462 0.245 0.457 0.586, segm_mAP: 0.3830, segm_mAP_50: 0.6130, segm_mAP_75: 0.4040, segm_mAP_s: 0.1720, segm_mAP_m: 0.4110, segm_mAP_l: 0.5920, segm_mAP_copypaste: 0.383 0.613 0.404 0.172 0.411 0.592 +2024-05-31 10:45:18,336 - mmdet - INFO - Epoch [9][50/7330] lr: 1.000e-05, eta: 5:03:25, time: 0.896, data_time: 0.112, memory: 9655, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0413, loss_cls: 0.1703, acc: 93.6313, loss_bbox: 0.2214, loss_mask: 0.2237, loss: 0.6725 +2024-05-31 10:45:48,638 - mmdet - INFO - Epoch [9][100/7330] lr: 1.000e-05, eta: 5:02:54, time: 0.606, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0394, loss_cls: 0.1680, acc: 93.7712, loss_bbox: 0.2188, loss_mask: 0.2271, loss: 0.6676 +2024-05-31 10:46:18,939 - mmdet - INFO - Epoch [9][150/7330] lr: 1.000e-05, eta: 5:02:22, time: 0.606, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0410, loss_cls: 0.1719, acc: 93.5552, loss_bbox: 0.2237, loss_mask: 0.2211, loss: 0.6735 +2024-05-31 10:46:48,900 - mmdet - INFO - Epoch [9][200/7330] lr: 1.000e-05, eta: 5:01:51, time: 0.599, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0406, loss_cls: 0.1644, acc: 93.9126, loss_bbox: 0.2132, loss_mask: 0.2216, loss: 0.6563 +2024-05-31 10:47:18,708 - mmdet - INFO - Epoch [9][250/7330] lr: 1.000e-05, eta: 5:01:19, time: 0.596, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0416, loss_cls: 0.1713, acc: 93.6577, loss_bbox: 0.2201, loss_mask: 0.2299, loss: 0.6782 +2024-05-31 10:47:48,814 - mmdet - INFO - Epoch [9][300/7330] lr: 1.000e-05, eta: 5:00:47, time: 0.602, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0406, loss_cls: 0.1645, acc: 93.9016, loss_bbox: 0.2131, loss_mask: 0.2209, loss: 0.6557 +2024-05-31 10:48:18,641 - mmdet - INFO - Epoch [9][350/7330] lr: 1.000e-05, eta: 5:00:16, time: 0.597, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0396, loss_cls: 0.1688, acc: 93.7031, loss_bbox: 0.2215, loss_mask: 0.2252, loss: 0.6691 +2024-05-31 10:48:47,952 - mmdet - INFO - Epoch [9][400/7330] lr: 1.000e-05, eta: 4:59:44, time: 0.586, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0412, loss_cls: 0.1638, acc: 93.8882, loss_bbox: 0.2143, loss_mask: 0.2219, loss: 0.6564 +2024-05-31 10:49:17,690 - mmdet - INFO - Epoch [9][450/7330] lr: 1.000e-05, eta: 4:59:12, time: 0.595, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0404, loss_cls: 0.1631, acc: 93.8533, loss_bbox: 0.2179, loss_mask: 0.2244, loss: 0.6619 +2024-05-31 10:49:47,326 - mmdet - INFO - Epoch [9][500/7330] lr: 1.000e-05, eta: 4:58:40, time: 0.593, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0148, loss_rpn_bbox: 0.0374, loss_cls: 0.1589, acc: 93.9749, loss_bbox: 0.2116, loss_mask: 0.2191, loss: 0.6418 +2024-05-31 10:50:18,122 - mmdet - INFO - Epoch [9][550/7330] lr: 1.000e-05, eta: 4:58:09, time: 0.616, data_time: 0.066, memory: 9655, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0433, loss_cls: 0.1699, acc: 93.6826, loss_bbox: 0.2200, loss_mask: 0.2252, loss: 0.6735 +2024-05-31 10:50:48,299 - mmdet - INFO - Epoch [9][600/7330] lr: 1.000e-05, eta: 4:57:37, time: 0.603, data_time: 0.036, memory: 9655, loss_rpn_cls: 0.0156, loss_rpn_bbox: 0.0395, loss_cls: 0.1640, acc: 93.8630, loss_bbox: 0.2128, loss_mask: 0.2189, loss: 0.6508 +2024-05-31 10:51:18,691 - mmdet - INFO - Epoch [9][650/7330] lr: 1.000e-05, eta: 4:57:06, time: 0.608, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0156, loss_rpn_bbox: 0.0427, loss_cls: 0.1640, acc: 93.8171, loss_bbox: 0.2184, loss_mask: 0.2314, loss: 0.6722 +2024-05-31 10:51:48,809 - mmdet - INFO - Epoch [9][700/7330] lr: 1.000e-05, eta: 4:56:34, time: 0.602, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0146, loss_rpn_bbox: 0.0395, loss_cls: 0.1599, acc: 93.9919, loss_bbox: 0.2104, loss_mask: 0.2234, loss: 0.6477 +2024-05-31 10:52:18,506 - mmdet - INFO - Epoch [9][750/7330] lr: 1.000e-05, eta: 4:56:03, time: 0.594, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0417, loss_cls: 0.1624, acc: 93.9502, loss_bbox: 0.2135, loss_mask: 0.2222, loss: 0.6543 +2024-05-31 10:52:48,608 - mmdet - INFO - Epoch [9][800/7330] lr: 1.000e-05, eta: 4:55:31, time: 0.602, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0416, loss_cls: 0.1668, acc: 93.6772, loss_bbox: 0.2177, loss_mask: 0.2247, loss: 0.6660 +2024-05-31 10:53:22,917 - mmdet - INFO - Epoch [9][850/7330] lr: 1.000e-05, eta: 4:55:02, time: 0.686, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0404, loss_cls: 0.1649, acc: 93.9221, loss_bbox: 0.2083, loss_mask: 0.2157, loss: 0.6443 +2024-05-31 10:53:55,146 - mmdet - INFO - Epoch [9][900/7330] lr: 1.000e-05, eta: 4:54:31, time: 0.645, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0383, loss_cls: 0.1589, acc: 94.0479, loss_bbox: 0.2051, loss_mask: 0.2173, loss: 0.6342 +2024-05-31 10:54:27,168 - mmdet - INFO - Epoch [9][950/7330] lr: 1.000e-05, eta: 4:54:00, time: 0.640, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0412, loss_cls: 0.1643, acc: 93.8740, loss_bbox: 0.2177, loss_mask: 0.2285, loss: 0.6670 +2024-05-31 10:54:57,677 - mmdet - INFO - Epoch [9][1000/7330] lr: 1.000e-05, eta: 4:53:29, time: 0.610, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0438, loss_cls: 0.1693, acc: 93.6470, loss_bbox: 0.2156, loss_mask: 0.2180, loss: 0.6621 +2024-05-31 10:55:30,540 - mmdet - INFO - Epoch [9][1050/7330] lr: 1.000e-05, eta: 4:52:59, time: 0.657, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0157, loss_rpn_bbox: 0.0413, loss_cls: 0.1681, acc: 93.7085, loss_bbox: 0.2201, loss_mask: 0.2289, loss: 0.6742 +2024-05-31 10:56:00,656 - mmdet - INFO - Epoch [9][1100/7330] lr: 1.000e-05, eta: 4:52:27, time: 0.602, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0406, loss_cls: 0.1667, acc: 93.7441, loss_bbox: 0.2215, loss_mask: 0.2216, loss: 0.6658 +2024-05-31 10:56:32,842 - mmdet - INFO - Epoch [9][1150/7330] lr: 1.000e-05, eta: 4:51:57, time: 0.644, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0402, loss_cls: 0.1694, acc: 93.5903, loss_bbox: 0.2219, loss_mask: 0.2256, loss: 0.6733 +2024-05-31 10:57:02,789 - mmdet - INFO - Epoch [9][1200/7330] lr: 1.000e-05, eta: 4:51:25, time: 0.599, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0367, loss_cls: 0.1587, acc: 94.1091, loss_bbox: 0.2065, loss_mask: 0.2162, loss: 0.6321 +2024-05-31 10:57:38,093 - mmdet - INFO - Epoch [9][1250/7330] lr: 1.000e-05, eta: 4:50:56, time: 0.706, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0409, loss_cls: 0.1672, acc: 93.7954, loss_bbox: 0.2192, loss_mask: 0.2206, loss: 0.6631 +2024-05-31 10:58:08,719 - mmdet - INFO - Epoch [9][1300/7330] lr: 1.000e-05, eta: 4:50:25, time: 0.613, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0421, loss_cls: 0.1664, acc: 93.8098, loss_bbox: 0.2187, loss_mask: 0.2258, loss: 0.6689 +2024-05-31 10:58:38,954 - mmdet - INFO - Epoch [9][1350/7330] lr: 1.000e-05, eta: 4:49:53, time: 0.605, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0413, loss_cls: 0.1633, acc: 93.8499, loss_bbox: 0.2171, loss_mask: 0.2279, loss: 0.6655 +2024-05-31 10:59:08,805 - mmdet - INFO - Epoch [9][1400/7330] lr: 1.000e-05, eta: 4:49:21, time: 0.597, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0390, loss_cls: 0.1596, acc: 94.0005, loss_bbox: 0.2115, loss_mask: 0.2210, loss: 0.6453 +2024-05-31 10:59:39,103 - mmdet - INFO - Epoch [9][1450/7330] lr: 1.000e-05, eta: 4:48:50, time: 0.606, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0421, loss_cls: 0.1632, acc: 93.8855, loss_bbox: 0.2149, loss_mask: 0.2236, loss: 0.6591 +2024-05-31 11:00:09,511 - mmdet - INFO - Epoch [9][1500/7330] lr: 1.000e-05, eta: 4:48:18, time: 0.608, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0391, loss_cls: 0.1584, acc: 94.1250, loss_bbox: 0.2103, loss_mask: 0.2197, loss: 0.6428 +2024-05-31 11:00:39,370 - mmdet - INFO - Epoch [9][1550/7330] lr: 1.000e-05, eta: 4:47:47, time: 0.597, data_time: 0.038, memory: 9655, loss_rpn_cls: 0.0135, loss_rpn_bbox: 0.0401, loss_cls: 0.1561, acc: 94.1248, loss_bbox: 0.2104, loss_mask: 0.2191, loss: 0.6392 +2024-05-31 11:01:11,388 - mmdet - INFO - Epoch [9][1600/7330] lr: 1.000e-05, eta: 4:47:16, time: 0.640, data_time: 0.071, memory: 9655, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0450, loss_cls: 0.1729, acc: 93.5237, loss_bbox: 0.2301, loss_mask: 0.2255, loss: 0.6894 +2024-05-31 11:01:41,822 - mmdet - INFO - Epoch [9][1650/7330] lr: 1.000e-05, eta: 4:46:45, time: 0.609, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0406, loss_cls: 0.1629, acc: 93.8997, loss_bbox: 0.2139, loss_mask: 0.2239, loss: 0.6564 +2024-05-31 11:02:13,579 - mmdet - INFO - Epoch [9][1700/7330] lr: 1.000e-05, eta: 4:46:14, time: 0.635, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0146, loss_rpn_bbox: 0.0399, loss_cls: 0.1631, acc: 93.9351, loss_bbox: 0.2149, loss_mask: 0.2220, loss: 0.6543 +2024-05-31 11:02:46,484 - mmdet - INFO - Epoch [9][1750/7330] lr: 1.000e-05, eta: 4:45:44, time: 0.658, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0431, loss_cls: 0.1733, acc: 93.4431, loss_bbox: 0.2311, loss_mask: 0.2261, loss: 0.6888 +2024-05-31 11:03:19,522 - mmdet - INFO - Epoch [9][1800/7330] lr: 1.000e-05, eta: 4:45:14, time: 0.661, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0391, loss_cls: 0.1544, acc: 94.1338, loss_bbox: 0.2011, loss_mask: 0.2139, loss: 0.6224 +2024-05-31 11:03:52,212 - mmdet - INFO - Epoch [9][1850/7330] lr: 1.000e-05, eta: 4:44:43, time: 0.654, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0390, loss_cls: 0.1612, acc: 94.0449, loss_bbox: 0.2104, loss_mask: 0.2209, loss: 0.6451 +2024-05-31 11:04:22,216 - mmdet - INFO - Epoch [9][1900/7330] lr: 1.000e-05, eta: 4:44:12, time: 0.600, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0390, loss_cls: 0.1604, acc: 93.8831, loss_bbox: 0.2132, loss_mask: 0.2238, loss: 0.6507 +2024-05-31 11:04:54,522 - mmdet - INFO - Epoch [9][1950/7330] lr: 1.000e-05, eta: 4:43:41, time: 0.646, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0421, loss_cls: 0.1622, acc: 93.8828, loss_bbox: 0.2117, loss_mask: 0.2196, loss: 0.6508 +2024-05-31 11:05:26,850 - mmdet - INFO - Epoch [9][2000/7330] lr: 1.000e-05, eta: 4:43:10, time: 0.647, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0399, loss_cls: 0.1615, acc: 93.9377, loss_bbox: 0.2144, loss_mask: 0.2209, loss: 0.6510 +2024-05-31 11:05:56,609 - mmdet - INFO - Epoch [9][2050/7330] lr: 1.000e-05, eta: 4:42:39, time: 0.595, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0156, loss_rpn_bbox: 0.0400, loss_cls: 0.1612, acc: 93.9412, loss_bbox: 0.2152, loss_mask: 0.2254, loss: 0.6575 +2024-05-31 11:06:32,090 - mmdet - INFO - Epoch [9][2100/7330] lr: 1.000e-05, eta: 4:42:10, time: 0.710, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0385, loss_cls: 0.1620, acc: 93.9116, loss_bbox: 0.2150, loss_mask: 0.2251, loss: 0.6540 +2024-05-31 11:07:02,191 - mmdet - INFO - Epoch [9][2150/7330] lr: 1.000e-05, eta: 4:41:38, time: 0.602, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0395, loss_cls: 0.1578, acc: 94.1299, loss_bbox: 0.2080, loss_mask: 0.2196, loss: 0.6403 +2024-05-31 11:07:32,289 - mmdet - INFO - Epoch [9][2200/7330] lr: 1.000e-05, eta: 4:41:07, time: 0.602, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0420, loss_cls: 0.1606, acc: 93.9819, loss_bbox: 0.2106, loss_mask: 0.2217, loss: 0.6499 +2024-05-31 11:08:02,500 - mmdet - INFO - Epoch [9][2250/7330] lr: 1.000e-05, eta: 4:40:35, time: 0.604, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0388, loss_cls: 0.1595, acc: 93.9888, loss_bbox: 0.2133, loss_mask: 0.2181, loss: 0.6433 +2024-05-31 11:08:32,694 - mmdet - INFO - Epoch [9][2300/7330] lr: 1.000e-05, eta: 4:40:04, time: 0.604, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0396, loss_cls: 0.1625, acc: 94.0264, loss_bbox: 0.2089, loss_mask: 0.2164, loss: 0.6418 +2024-05-31 11:09:03,298 - mmdet - INFO - Epoch [9][2350/7330] lr: 1.000e-05, eta: 4:39:32, time: 0.612, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0401, loss_cls: 0.1604, acc: 93.9766, loss_bbox: 0.2121, loss_mask: 0.2198, loss: 0.6464 +2024-05-31 11:09:33,357 - mmdet - INFO - Epoch [9][2400/7330] lr: 1.000e-05, eta: 4:39:01, time: 0.601, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0412, loss_cls: 0.1634, acc: 93.8130, loss_bbox: 0.2174, loss_mask: 0.2263, loss: 0.6633 +2024-05-31 11:10:03,698 - mmdet - INFO - Epoch [9][2450/7330] lr: 1.000e-05, eta: 4:38:29, time: 0.607, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0415, loss_cls: 0.1640, acc: 93.8809, loss_bbox: 0.2186, loss_mask: 0.2238, loss: 0.6613 +2024-05-31 11:10:33,559 - mmdet - INFO - Epoch [9][2500/7330] lr: 1.000e-05, eta: 4:37:58, time: 0.597, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0388, loss_cls: 0.1545, acc: 94.1392, loss_bbox: 0.2044, loss_mask: 0.2188, loss: 0.6304 +2024-05-31 11:11:03,871 - mmdet - INFO - Epoch [9][2550/7330] lr: 1.000e-05, eta: 4:37:26, time: 0.606, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0383, loss_cls: 0.1535, acc: 94.2393, loss_bbox: 0.2053, loss_mask: 0.2177, loss: 0.6284 +2024-05-31 11:11:36,046 - mmdet - INFO - Epoch [9][2600/7330] lr: 1.000e-05, eta: 4:36:56, time: 0.643, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0377, loss_cls: 0.1597, acc: 94.0190, loss_bbox: 0.2059, loss_mask: 0.2211, loss: 0.6391 +2024-05-31 11:12:10,868 - mmdet - INFO - Epoch [9][2650/7330] lr: 1.000e-05, eta: 4:36:26, time: 0.696, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0382, loss_cls: 0.1535, acc: 94.1194, loss_bbox: 0.2059, loss_mask: 0.2164, loss: 0.6282 +2024-05-31 11:12:44,587 - mmdet - INFO - Epoch [9][2700/7330] lr: 1.000e-05, eta: 4:35:56, time: 0.674, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0424, loss_cls: 0.1660, acc: 93.7505, loss_bbox: 0.2184, loss_mask: 0.2234, loss: 0.6667 +2024-05-31 11:13:14,953 - mmdet - INFO - Epoch [9][2750/7330] lr: 1.000e-05, eta: 4:35:25, time: 0.607, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0390, loss_cls: 0.1565, acc: 94.1431, loss_bbox: 0.2055, loss_mask: 0.2142, loss: 0.6283 +2024-05-31 11:13:44,914 - mmdet - INFO - Epoch [9][2800/7330] lr: 1.000e-05, eta: 4:34:53, time: 0.599, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0408, loss_cls: 0.1650, acc: 93.7205, loss_bbox: 0.2198, loss_mask: 0.2229, loss: 0.6638 +2024-05-31 11:14:17,275 - mmdet - INFO - Epoch [9][2850/7330] lr: 1.000e-05, eta: 4:34:23, time: 0.647, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0399, loss_cls: 0.1616, acc: 93.9688, loss_bbox: 0.2126, loss_mask: 0.2181, loss: 0.6473 +2024-05-31 11:14:48,960 - mmdet - INFO - Epoch [9][2900/7330] lr: 1.000e-05, eta: 4:33:52, time: 0.634, data_time: 0.038, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0362, loss_cls: 0.1482, acc: 94.4143, loss_bbox: 0.2011, loss_mask: 0.2181, loss: 0.6166 +2024-05-31 11:15:19,882 - mmdet - INFO - Epoch [9][2950/7330] lr: 1.000e-05, eta: 4:33:21, time: 0.618, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0157, loss_rpn_bbox: 0.0414, loss_cls: 0.1696, acc: 93.6267, loss_bbox: 0.2243, loss_mask: 0.2241, loss: 0.6752 +2024-05-31 11:15:55,283 - mmdet - INFO - Epoch [9][3000/7330] lr: 1.000e-05, eta: 4:32:51, time: 0.708, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0167, loss_rpn_bbox: 0.0423, loss_cls: 0.1684, acc: 93.7168, loss_bbox: 0.2194, loss_mask: 0.2284, loss: 0.6754 +2024-05-31 11:16:26,092 - mmdet - INFO - Epoch [9][3050/7330] lr: 1.000e-05, eta: 4:32:20, time: 0.616, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0401, loss_cls: 0.1630, acc: 93.8774, loss_bbox: 0.2149, loss_mask: 0.2225, loss: 0.6545 +2024-05-31 11:16:55,986 - mmdet - INFO - Epoch [9][3100/7330] lr: 1.000e-05, eta: 4:31:48, time: 0.598, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0397, loss_cls: 0.1606, acc: 93.9727, loss_bbox: 0.2133, loss_mask: 0.2261, loss: 0.6542 +2024-05-31 11:17:26,061 - mmdet - INFO - Epoch [9][3150/7330] lr: 1.000e-05, eta: 4:31:17, time: 0.602, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0150, loss_rpn_bbox: 0.0428, loss_cls: 0.1683, acc: 93.7202, loss_bbox: 0.2236, loss_mask: 0.2280, loss: 0.6778 +2024-05-31 11:17:56,609 - mmdet - INFO - Epoch [9][3200/7330] lr: 1.000e-05, eta: 4:30:46, time: 0.611, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0398, loss_cls: 0.1581, acc: 94.0713, loss_bbox: 0.2116, loss_mask: 0.2215, loss: 0.6456 +2024-05-31 11:18:26,679 - mmdet - INFO - Epoch [9][3250/7330] lr: 1.000e-05, eta: 4:30:14, time: 0.601, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0411, loss_cls: 0.1579, acc: 94.0659, loss_bbox: 0.2056, loss_mask: 0.2156, loss: 0.6344 +2024-05-31 11:18:56,332 - mmdet - INFO - Epoch [9][3300/7330] lr: 1.000e-05, eta: 4:29:42, time: 0.593, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0140, loss_rpn_bbox: 0.0387, loss_cls: 0.1666, acc: 93.7773, loss_bbox: 0.2174, loss_mask: 0.2198, loss: 0.6565 +2024-05-31 11:19:26,310 - mmdet - INFO - Epoch [9][3350/7330] lr: 1.000e-05, eta: 4:29:11, time: 0.600, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0388, loss_cls: 0.1627, acc: 93.9148, loss_bbox: 0.2084, loss_mask: 0.2176, loss: 0.6410 +2024-05-31 11:19:56,433 - mmdet - INFO - Epoch [9][3400/7330] lr: 1.000e-05, eta: 4:28:39, time: 0.602, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0371, loss_cls: 0.1536, acc: 94.2239, loss_bbox: 0.2025, loss_mask: 0.2139, loss: 0.6206 +2024-05-31 11:20:26,461 - mmdet - INFO - Epoch [9][3450/7330] lr: 1.000e-05, eta: 4:28:08, time: 0.601, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0390, loss_cls: 0.1539, acc: 94.1877, loss_bbox: 0.2109, loss_mask: 0.2211, loss: 0.6384 +2024-05-31 11:20:59,030 - mmdet - INFO - Epoch [9][3500/7330] lr: 1.000e-05, eta: 4:27:37, time: 0.651, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0397, loss_cls: 0.1585, acc: 94.0730, loss_bbox: 0.2124, loss_mask: 0.2205, loss: 0.6457 +2024-05-31 11:21:34,210 - mmdet - INFO - Epoch [9][3550/7330] lr: 1.000e-05, eta: 4:27:08, time: 0.704, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0386, loss_cls: 0.1520, acc: 94.2500, loss_bbox: 0.2080, loss_mask: 0.2220, loss: 0.6345 +2024-05-31 11:22:08,641 - mmdet - INFO - Epoch [9][3600/7330] lr: 1.000e-05, eta: 4:26:38, time: 0.689, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0138, loss_rpn_bbox: 0.0394, loss_cls: 0.1586, acc: 94.0410, loss_bbox: 0.2082, loss_mask: 0.2209, loss: 0.6408 +2024-05-31 11:22:39,322 - mmdet - INFO - Epoch [9][3650/7330] lr: 1.000e-05, eta: 4:26:07, time: 0.614, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0419, loss_cls: 0.1625, acc: 93.9102, loss_bbox: 0.2156, loss_mask: 0.2189, loss: 0.6543 +2024-05-31 11:23:11,886 - mmdet - INFO - Epoch [9][3700/7330] lr: 1.000e-05, eta: 4:25:36, time: 0.651, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0428, loss_cls: 0.1650, acc: 93.8147, loss_bbox: 0.2155, loss_mask: 0.2209, loss: 0.6598 +2024-05-31 11:23:42,024 - mmdet - INFO - Epoch [9][3750/7330] lr: 1.000e-05, eta: 4:25:05, time: 0.603, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0349, loss_cls: 0.1492, acc: 94.4060, loss_bbox: 0.1984, loss_mask: 0.2093, loss: 0.6041 +2024-05-31 11:24:14,456 - mmdet - INFO - Epoch [9][3800/7330] lr: 1.000e-05, eta: 4:24:34, time: 0.649, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0135, loss_rpn_bbox: 0.0388, loss_cls: 0.1585, acc: 93.9849, loss_bbox: 0.2092, loss_mask: 0.2172, loss: 0.6372 +2024-05-31 11:24:44,785 - mmdet - INFO - Epoch [9][3850/7330] lr: 1.000e-05, eta: 4:24:03, time: 0.607, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0404, loss_cls: 0.1530, acc: 94.2720, loss_bbox: 0.2070, loss_mask: 0.2142, loss: 0.6288 +2024-05-31 11:25:19,654 - mmdet - INFO - Epoch [9][3900/7330] lr: 1.000e-05, eta: 4:23:33, time: 0.697, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0401, loss_cls: 0.1600, acc: 94.0579, loss_bbox: 0.2114, loss_mask: 0.2180, loss: 0.6431 +2024-05-31 11:25:50,017 - mmdet - INFO - Epoch [9][3950/7330] lr: 1.000e-05, eta: 4:23:02, time: 0.607, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0404, loss_cls: 0.1652, acc: 93.8384, loss_bbox: 0.2184, loss_mask: 0.2234, loss: 0.6615 +2024-05-31 11:26:19,960 - mmdet - INFO - Epoch [9][4000/7330] lr: 1.000e-05, eta: 4:22:30, time: 0.599, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0389, loss_cls: 0.1527, acc: 94.2900, loss_bbox: 0.2021, loss_mask: 0.2141, loss: 0.6208 +2024-05-31 11:26:50,237 - mmdet - INFO - Epoch [9][4050/7330] lr: 1.000e-05, eta: 4:21:59, time: 0.606, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0410, loss_cls: 0.1634, acc: 93.8716, loss_bbox: 0.2133, loss_mask: 0.2196, loss: 0.6524 +2024-05-31 11:27:20,174 - mmdet - INFO - Epoch [9][4100/7330] lr: 1.000e-05, eta: 4:21:27, time: 0.599, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0402, loss_cls: 0.1634, acc: 93.9375, loss_bbox: 0.2160, loss_mask: 0.2238, loss: 0.6590 +2024-05-31 11:27:50,878 - mmdet - INFO - Epoch [9][4150/7330] lr: 1.000e-05, eta: 4:20:56, time: 0.614, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0398, loss_cls: 0.1580, acc: 94.0671, loss_bbox: 0.2077, loss_mask: 0.2214, loss: 0.6423 +2024-05-31 11:28:21,595 - mmdet - INFO - Epoch [9][4200/7330] lr: 1.000e-05, eta: 4:20:25, time: 0.614, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0138, loss_rpn_bbox: 0.0401, loss_cls: 0.1594, acc: 94.0261, loss_bbox: 0.2104, loss_mask: 0.2172, loss: 0.6410 +2024-05-31 11:28:51,885 - mmdet - INFO - Epoch [9][4250/7330] lr: 1.000e-05, eta: 4:19:53, time: 0.606, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0391, loss_cls: 0.1579, acc: 94.0181, loss_bbox: 0.2074, loss_mask: 0.2158, loss: 0.6339 +2024-05-31 11:29:22,110 - mmdet - INFO - Epoch [9][4300/7330] lr: 1.000e-05, eta: 4:19:22, time: 0.604, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0383, loss_cls: 0.1564, acc: 94.1162, loss_bbox: 0.2084, loss_mask: 0.2204, loss: 0.6376 +2024-05-31 11:29:54,701 - mmdet - INFO - Epoch [9][4350/7330] lr: 1.000e-05, eta: 4:18:51, time: 0.652, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0122, loss_rpn_bbox: 0.0372, loss_cls: 0.1520, acc: 94.3176, loss_bbox: 0.2024, loss_mask: 0.2140, loss: 0.6178 +2024-05-31 11:30:27,386 - mmdet - INFO - Epoch [9][4400/7330] lr: 1.000e-05, eta: 4:18:21, time: 0.654, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0396, loss_cls: 0.1626, acc: 93.8499, loss_bbox: 0.2121, loss_mask: 0.2197, loss: 0.6475 +2024-05-31 11:31:00,222 - mmdet - INFO - Epoch [9][4450/7330] lr: 1.000e-05, eta: 4:17:51, time: 0.657, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0148, loss_rpn_bbox: 0.0405, loss_cls: 0.1617, acc: 93.9712, loss_bbox: 0.2135, loss_mask: 0.2177, loss: 0.6483 +2024-05-31 11:31:32,958 - mmdet - INFO - Epoch [9][4500/7330] lr: 1.000e-05, eta: 4:17:20, time: 0.655, data_time: 0.063, memory: 9655, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0380, loss_cls: 0.1499, acc: 94.3103, loss_bbox: 0.2022, loss_mask: 0.2189, loss: 0.6228 +2024-05-31 11:32:03,074 - mmdet - INFO - Epoch [9][4550/7330] lr: 1.000e-05, eta: 4:16:49, time: 0.602, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0384, loss_cls: 0.1525, acc: 94.2378, loss_bbox: 0.2012, loss_mask: 0.2172, loss: 0.6235 +2024-05-31 11:32:34,984 - mmdet - INFO - Epoch [9][4600/7330] lr: 1.000e-05, eta: 4:16:18, time: 0.638, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0376, loss_cls: 0.1510, acc: 94.3606, loss_bbox: 0.2016, loss_mask: 0.2216, loss: 0.6260 +2024-05-31 11:33:07,437 - mmdet - INFO - Epoch [9][4650/7330] lr: 1.000e-05, eta: 4:15:47, time: 0.649, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0392, loss_cls: 0.1539, acc: 94.1838, loss_bbox: 0.2084, loss_mask: 0.2240, loss: 0.6383 +2024-05-31 11:33:38,102 - mmdet - INFO - Epoch [9][4700/7330] lr: 1.000e-05, eta: 4:15:16, time: 0.613, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0406, loss_cls: 0.1556, acc: 94.0613, loss_bbox: 0.2092, loss_mask: 0.2213, loss: 0.6408 +2024-05-31 11:34:08,240 - mmdet - INFO - Epoch [9][4750/7330] lr: 1.000e-05, eta: 4:14:44, time: 0.603, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0414, loss_cls: 0.1620, acc: 93.9390, loss_bbox: 0.2180, loss_mask: 0.2257, loss: 0.6614 +2024-05-31 11:34:43,723 - mmdet - INFO - Epoch [9][4800/7330] lr: 1.000e-05, eta: 4:14:15, time: 0.710, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0126, loss_rpn_bbox: 0.0367, loss_cls: 0.1568, acc: 94.1824, loss_bbox: 0.2043, loss_mask: 0.2147, loss: 0.6250 +2024-05-31 11:35:13,855 - mmdet - INFO - Epoch [9][4850/7330] lr: 1.000e-05, eta: 4:13:44, time: 0.603, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0385, loss_cls: 0.1587, acc: 93.9531, loss_bbox: 0.2099, loss_mask: 0.2151, loss: 0.6359 +2024-05-31 11:35:43,893 - mmdet - INFO - Epoch [9][4900/7330] lr: 1.000e-05, eta: 4:13:12, time: 0.600, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0386, loss_cls: 0.1580, acc: 93.9924, loss_bbox: 0.2110, loss_mask: 0.2166, loss: 0.6381 +2024-05-31 11:36:14,232 - mmdet - INFO - Epoch [9][4950/7330] lr: 1.000e-05, eta: 4:12:41, time: 0.607, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0405, loss_cls: 0.1621, acc: 93.9438, loss_bbox: 0.2153, loss_mask: 0.2203, loss: 0.6525 +2024-05-31 11:36:44,484 - mmdet - INFO - Epoch [9][5000/7330] lr: 1.000e-05, eta: 4:12:09, time: 0.605, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0396, loss_cls: 0.1569, acc: 94.1196, loss_bbox: 0.2102, loss_mask: 0.2194, loss: 0.6391 +2024-05-31 11:37:14,719 - mmdet - INFO - Epoch [9][5050/7330] lr: 1.000e-05, eta: 4:11:38, time: 0.605, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0138, loss_rpn_bbox: 0.0386, loss_cls: 0.1574, acc: 94.0217, loss_bbox: 0.2134, loss_mask: 0.2191, loss: 0.6423 +2024-05-31 11:37:44,470 - mmdet - INFO - Epoch [9][5100/7330] lr: 1.000e-05, eta: 4:11:06, time: 0.595, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0140, loss_rpn_bbox: 0.0406, loss_cls: 0.1576, acc: 94.0630, loss_bbox: 0.2129, loss_mask: 0.2202, loss: 0.6454 +2024-05-31 11:38:13,651 - mmdet - INFO - Epoch [9][5150/7330] lr: 1.000e-05, eta: 4:10:34, time: 0.584, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0381, loss_cls: 0.1533, acc: 94.2144, loss_bbox: 0.2038, loss_mask: 0.2182, loss: 0.6276 +2024-05-31 11:38:44,029 - mmdet - INFO - Epoch [9][5200/7330] lr: 1.000e-05, eta: 4:10:03, time: 0.608, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0405, loss_cls: 0.1593, acc: 93.9719, loss_bbox: 0.2120, loss_mask: 0.2209, loss: 0.6480 +2024-05-31 11:39:16,230 - mmdet - INFO - Epoch [9][5250/7330] lr: 1.000e-05, eta: 4:09:32, time: 0.644, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0393, loss_cls: 0.1579, acc: 94.1003, loss_bbox: 0.2092, loss_mask: 0.2163, loss: 0.6369 +2024-05-31 11:39:51,995 - mmdet - INFO - Epoch [9][5300/7330] lr: 1.000e-05, eta: 4:09:03, time: 0.715, data_time: 0.063, memory: 9655, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0418, loss_cls: 0.1631, acc: 93.9041, loss_bbox: 0.2164, loss_mask: 0.2204, loss: 0.6564 +2024-05-31 11:40:22,099 - mmdet - INFO - Epoch [9][5350/7330] lr: 1.000e-05, eta: 4:08:31, time: 0.602, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0410, loss_cls: 0.1532, acc: 94.1716, loss_bbox: 0.2069, loss_mask: 0.2161, loss: 0.6317 +2024-05-31 11:40:54,804 - mmdet - INFO - Epoch [9][5400/7330] lr: 1.000e-05, eta: 4:08:01, time: 0.654, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0384, loss_cls: 0.1486, acc: 94.4146, loss_bbox: 0.1993, loss_mask: 0.2162, loss: 0.6159 +2024-05-31 11:41:24,665 - mmdet - INFO - Epoch [9][5450/7330] lr: 1.000e-05, eta: 4:07:29, time: 0.597, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0138, loss_rpn_bbox: 0.0380, loss_cls: 0.1552, acc: 94.2278, loss_bbox: 0.2060, loss_mask: 0.2167, loss: 0.6298 +2024-05-31 11:41:56,991 - mmdet - INFO - Epoch [9][5500/7330] lr: 1.000e-05, eta: 4:06:59, time: 0.647, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0386, loss_cls: 0.1547, acc: 94.1387, loss_bbox: 0.2073, loss_mask: 0.2169, loss: 0.6305 +2024-05-31 11:42:29,363 - mmdet - INFO - Epoch [9][5550/7330] lr: 1.000e-05, eta: 4:06:28, time: 0.647, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0374, loss_cls: 0.1491, acc: 94.4182, loss_bbox: 0.2007, loss_mask: 0.2139, loss: 0.6134 +2024-05-31 11:42:59,747 - mmdet - INFO - Epoch [9][5600/7330] lr: 1.000e-05, eta: 4:05:57, time: 0.608, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0391, loss_cls: 0.1488, acc: 94.3735, loss_bbox: 0.1998, loss_mask: 0.2138, loss: 0.6161 +2024-05-31 11:43:36,150 - mmdet - INFO - Epoch [9][5650/7330] lr: 1.000e-05, eta: 4:05:28, time: 0.728, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0140, loss_rpn_bbox: 0.0395, loss_cls: 0.1593, acc: 94.0359, loss_bbox: 0.2103, loss_mask: 0.2228, loss: 0.6458 +2024-05-31 11:44:06,037 - mmdet - INFO - Epoch [9][5700/7330] lr: 1.000e-05, eta: 4:04:56, time: 0.598, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0370, loss_cls: 0.1548, acc: 94.1357, loss_bbox: 0.2097, loss_mask: 0.2192, loss: 0.6340 +2024-05-31 11:44:37,111 - mmdet - INFO - Epoch [9][5750/7330] lr: 1.000e-05, eta: 4:04:25, time: 0.621, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0426, loss_cls: 0.1681, acc: 93.5896, loss_bbox: 0.2260, loss_mask: 0.2239, loss: 0.6765 +2024-05-31 11:45:07,392 - mmdet - INFO - Epoch [9][5800/7330] lr: 1.000e-05, eta: 4:03:53, time: 0.606, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0364, loss_cls: 0.1522, acc: 94.2932, loss_bbox: 0.2058, loss_mask: 0.2223, loss: 0.6301 +2024-05-31 11:45:37,421 - mmdet - INFO - Epoch [9][5850/7330] lr: 1.000e-05, eta: 4:03:22, time: 0.601, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0395, loss_cls: 0.1606, acc: 93.9138, loss_bbox: 0.2105, loss_mask: 0.2124, loss: 0.6381 +2024-05-31 11:46:07,125 - mmdet - INFO - Epoch [9][5900/7330] lr: 1.000e-05, eta: 4:02:50, time: 0.594, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0135, loss_rpn_bbox: 0.0394, loss_cls: 0.1600, acc: 94.0073, loss_bbox: 0.2105, loss_mask: 0.2172, loss: 0.6405 +2024-05-31 11:46:37,546 - mmdet - INFO - Epoch [9][5950/7330] lr: 1.000e-05, eta: 4:02:19, time: 0.608, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0140, loss_rpn_bbox: 0.0393, loss_cls: 0.1601, acc: 93.9712, loss_bbox: 0.2144, loss_mask: 0.2163, loss: 0.6441 +2024-05-31 11:47:07,571 - mmdet - INFO - Epoch [9][6000/7330] lr: 1.000e-05, eta: 4:01:47, time: 0.600, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0390, loss_cls: 0.1531, acc: 94.2756, loss_bbox: 0.2047, loss_mask: 0.2142, loss: 0.6242 +2024-05-31 11:47:38,115 - mmdet - INFO - Epoch [9][6050/7330] lr: 1.000e-05, eta: 4:01:16, time: 0.611, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0400, loss_cls: 0.1627, acc: 93.8523, loss_bbox: 0.2141, loss_mask: 0.2187, loss: 0.6492 +2024-05-31 11:48:08,001 - mmdet - INFO - Epoch [9][6100/7330] lr: 1.000e-05, eta: 4:00:45, time: 0.598, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0370, loss_cls: 0.1501, acc: 94.3318, loss_bbox: 0.1965, loss_mask: 0.2122, loss: 0.6087 +2024-05-31 11:48:40,741 - mmdet - INFO - Epoch [9][6150/7330] lr: 1.000e-05, eta: 4:00:14, time: 0.655, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0381, loss_cls: 0.1491, acc: 94.4551, loss_bbox: 0.1993, loss_mask: 0.2129, loss: 0.6124 +2024-05-31 11:49:16,002 - mmdet - INFO - Epoch [9][6200/7330] lr: 1.000e-05, eta: 3:59:44, time: 0.705, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0443, loss_cls: 0.1690, acc: 93.6167, loss_bbox: 0.2201, loss_mask: 0.2279, loss: 0.6773 +2024-05-31 11:49:49,131 - mmdet - INFO - Epoch [9][6250/7330] lr: 1.000e-05, eta: 3:59:14, time: 0.663, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0413, loss_cls: 0.1651, acc: 93.8076, loss_bbox: 0.2176, loss_mask: 0.2230, loss: 0.6626 +2024-05-31 11:50:19,366 - mmdet - INFO - Epoch [9][6300/7330] lr: 1.000e-05, eta: 3:58:43, time: 0.605, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0389, loss_cls: 0.1591, acc: 94.0630, loss_bbox: 0.2087, loss_mask: 0.2247, loss: 0.6458 +2024-05-31 11:50:52,148 - mmdet - INFO - Epoch [9][6350/7330] lr: 1.000e-05, eta: 3:58:12, time: 0.656, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0388, loss_cls: 0.1573, acc: 94.1648, loss_bbox: 0.2075, loss_mask: 0.2143, loss: 0.6317 +2024-05-31 11:51:22,756 - mmdet - INFO - Epoch [9][6400/7330] lr: 1.000e-05, eta: 3:57:41, time: 0.612, data_time: 0.063, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0391, loss_cls: 0.1559, acc: 94.0715, loss_bbox: 0.2095, loss_mask: 0.2156, loss: 0.6335 +2024-05-31 11:51:55,179 - mmdet - INFO - Epoch [9][6450/7330] lr: 1.000e-05, eta: 3:57:10, time: 0.648, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0380, loss_cls: 0.1516, acc: 94.2310, loss_bbox: 0.2085, loss_mask: 0.2168, loss: 0.6278 +2024-05-31 11:52:25,801 - mmdet - INFO - Epoch [9][6500/7330] lr: 1.000e-05, eta: 3:56:39, time: 0.612, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0417, loss_cls: 0.1713, acc: 93.5793, loss_bbox: 0.2226, loss_mask: 0.2305, loss: 0.6821 +2024-05-31 11:53:01,840 - mmdet - INFO - Epoch [9][6550/7330] lr: 1.000e-05, eta: 3:56:10, time: 0.721, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0393, loss_cls: 0.1519, acc: 94.2791, loss_bbox: 0.2045, loss_mask: 0.2130, loss: 0.6218 +2024-05-31 11:53:32,634 - mmdet - INFO - Epoch [9][6600/7330] lr: 1.000e-05, eta: 3:55:38, time: 0.616, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0400, loss_cls: 0.1547, acc: 94.2058, loss_bbox: 0.2043, loss_mask: 0.2184, loss: 0.6310 +2024-05-31 11:54:03,369 - mmdet - INFO - Epoch [9][6650/7330] lr: 1.000e-05, eta: 3:55:07, time: 0.615, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0415, loss_cls: 0.1550, acc: 94.1829, loss_bbox: 0.2101, loss_mask: 0.2177, loss: 0.6397 +2024-05-31 11:54:33,877 - mmdet - INFO - Epoch [9][6700/7330] lr: 1.000e-05, eta: 3:54:36, time: 0.610, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0395, loss_cls: 0.1590, acc: 94.0901, loss_bbox: 0.2155, loss_mask: 0.2218, loss: 0.6495 +2024-05-31 11:55:03,634 - mmdet - INFO - Epoch [9][6750/7330] lr: 1.000e-05, eta: 3:54:04, time: 0.595, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0365, loss_cls: 0.1482, acc: 94.4324, loss_bbox: 0.1994, loss_mask: 0.2155, loss: 0.6131 +2024-05-31 11:55:34,001 - mmdet - INFO - Epoch [9][6800/7330] lr: 1.000e-05, eta: 3:53:33, time: 0.607, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0415, loss_cls: 0.1620, acc: 93.8647, loss_bbox: 0.2169, loss_mask: 0.2205, loss: 0.6553 +2024-05-31 11:56:04,332 - mmdet - INFO - Epoch [9][6850/7330] lr: 1.000e-05, eta: 3:53:01, time: 0.607, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0140, loss_rpn_bbox: 0.0389, loss_cls: 0.1574, acc: 94.0710, loss_bbox: 0.2126, loss_mask: 0.2171, loss: 0.6401 +2024-05-31 11:56:34,854 - mmdet - INFO - Epoch [9][6900/7330] lr: 1.000e-05, eta: 3:52:30, time: 0.610, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0138, loss_rpn_bbox: 0.0387, loss_cls: 0.1601, acc: 93.8828, loss_bbox: 0.2166, loss_mask: 0.2257, loss: 0.6550 +2024-05-31 11:57:04,458 - mmdet - INFO - Epoch [9][6950/7330] lr: 1.000e-05, eta: 3:51:58, time: 0.592, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0365, loss_cls: 0.1531, acc: 94.2446, loss_bbox: 0.2029, loss_mask: 0.2162, loss: 0.6220 +2024-05-31 11:57:34,935 - mmdet - INFO - Epoch [9][7000/7330] lr: 1.000e-05, eta: 3:51:27, time: 0.610, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0408, loss_cls: 0.1575, acc: 93.9968, loss_bbox: 0.2140, loss_mask: 0.2183, loss: 0.6445 +2024-05-31 11:58:06,960 - mmdet - INFO - Epoch [9][7050/7330] lr: 1.000e-05, eta: 3:50:56, time: 0.640, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0405, loss_cls: 0.1611, acc: 93.9221, loss_bbox: 0.2197, loss_mask: 0.2222, loss: 0.6579 +2024-05-31 11:58:41,976 - mmdet - INFO - Epoch [9][7100/7330] lr: 1.000e-05, eta: 3:50:27, time: 0.700, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0374, loss_cls: 0.1523, acc: 94.2585, loss_bbox: 0.2061, loss_mask: 0.2153, loss: 0.6256 +2024-05-31 11:59:14,402 - mmdet - INFO - Epoch [9][7150/7330] lr: 1.000e-05, eta: 3:49:56, time: 0.649, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0426, loss_cls: 0.1552, acc: 94.0605, loss_bbox: 0.2143, loss_mask: 0.2225, loss: 0.6492 +2024-05-31 11:59:45,324 - mmdet - INFO - Epoch [9][7200/7330] lr: 1.000e-05, eta: 3:49:25, time: 0.618, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0140, loss_rpn_bbox: 0.0423, loss_cls: 0.1651, acc: 93.8567, loss_bbox: 0.2191, loss_mask: 0.2251, loss: 0.6657 +2024-05-31 12:00:17,216 - mmdet - INFO - Epoch [9][7250/7330] lr: 1.000e-05, eta: 3:48:54, time: 0.638, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0373, loss_cls: 0.1480, acc: 94.5591, loss_bbox: 0.1948, loss_mask: 0.2132, loss: 0.6061 +2024-05-31 12:00:47,454 - mmdet - INFO - Epoch [9][7300/7330] lr: 1.000e-05, eta: 3:48:22, time: 0.605, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0416, loss_cls: 0.1628, acc: 93.9060, loss_bbox: 0.2164, loss_mask: 0.2218, loss: 0.6571 +2024-05-31 12:01:08,456 - mmdet - INFO - Saving checkpoint at 9 epochs +2024-05-31 12:02:45,004 - mmdet - INFO - Evaluating bbox... +2024-05-31 12:03:07,124 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.447 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.669 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.483 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.267 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.482 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.614 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.566 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.566 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.566 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.375 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.606 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.728 + +2024-05-31 12:03:07,125 - mmdet - INFO - Evaluating segm... +2024-05-31 12:03:36,627 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.401 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.636 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.426 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.190 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.430 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.611 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.510 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.510 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.510 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.307 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.553 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.693 + +2024-05-31 12:03:36,986 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 12:03:36,988 - mmdet - INFO - Epoch(val) [9][625] bbox_mAP: 0.4470, bbox_mAP_50: 0.6690, bbox_mAP_75: 0.4830, bbox_mAP_s: 0.2670, bbox_mAP_m: 0.4820, bbox_mAP_l: 0.6140, bbox_mAP_copypaste: 0.447 0.669 0.483 0.267 0.482 0.614, segm_mAP: 0.4010, segm_mAP_50: 0.6360, segm_mAP_75: 0.4260, segm_mAP_s: 0.1900, segm_mAP_m: 0.4300, segm_mAP_l: 0.6110, segm_mAP_copypaste: 0.401 0.636 0.426 0.190 0.430 0.611 +2024-05-31 12:04:12,028 - mmdet - INFO - Epoch [10][50/7330] lr: 1.000e-05, eta: 3:47:28, time: 0.700, data_time: 0.118, memory: 9655, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0422, loss_cls: 0.1568, acc: 94.0564, loss_bbox: 0.2103, loss_mask: 0.2133, loss: 0.6370 +2024-05-31 12:04:44,624 - mmdet - INFO - Epoch [10][100/7330] lr: 1.000e-05, eta: 3:46:57, time: 0.652, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0396, loss_cls: 0.1520, acc: 94.2207, loss_bbox: 0.2070, loss_mask: 0.2173, loss: 0.6295 +2024-05-31 12:05:15,131 - mmdet - INFO - Epoch [10][150/7330] lr: 1.000e-05, eta: 3:46:26, time: 0.610, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0138, loss_rpn_bbox: 0.0419, loss_cls: 0.1520, acc: 94.2415, loss_bbox: 0.2099, loss_mask: 0.2197, loss: 0.6373 +2024-05-31 12:05:45,797 - mmdet - INFO - Epoch [10][200/7330] lr: 1.000e-05, eta: 3:45:55, time: 0.613, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0390, loss_cls: 0.1495, acc: 94.3601, loss_bbox: 0.2051, loss_mask: 0.2147, loss: 0.6215 +2024-05-31 12:06:16,313 - mmdet - INFO - Epoch [10][250/7330] lr: 1.000e-05, eta: 3:45:23, time: 0.610, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0122, loss_rpn_bbox: 0.0369, loss_cls: 0.1491, acc: 94.3052, loss_bbox: 0.2027, loss_mask: 0.2137, loss: 0.6147 +2024-05-31 12:06:47,121 - mmdet - INFO - Epoch [10][300/7330] lr: 1.000e-05, eta: 3:44:52, time: 0.616, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0393, loss_cls: 0.1573, acc: 94.0347, loss_bbox: 0.2106, loss_mask: 0.2178, loss: 0.6403 +2024-05-31 12:07:17,716 - mmdet - INFO - Epoch [10][350/7330] lr: 1.000e-05, eta: 3:44:21, time: 0.612, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0400, loss_cls: 0.1576, acc: 94.0823, loss_bbox: 0.2115, loss_mask: 0.2157, loss: 0.6379 +2024-05-31 12:07:47,781 - mmdet - INFO - Epoch [10][400/7330] lr: 1.000e-05, eta: 3:43:49, time: 0.601, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0135, loss_rpn_bbox: 0.0373, loss_cls: 0.1471, acc: 94.3438, loss_bbox: 0.2034, loss_mask: 0.2182, loss: 0.6196 +2024-05-31 12:08:18,561 - mmdet - INFO - Epoch [10][450/7330] lr: 1.000e-05, eta: 3:43:18, time: 0.616, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0427, loss_cls: 0.1609, acc: 93.9036, loss_bbox: 0.2185, loss_mask: 0.2222, loss: 0.6586 +2024-05-31 12:08:49,177 - mmdet - INFO - Epoch [10][500/7330] lr: 1.000e-05, eta: 3:42:47, time: 0.612, data_time: 0.037, memory: 9655, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0399, loss_cls: 0.1503, acc: 94.1831, loss_bbox: 0.2074, loss_mask: 0.2175, loss: 0.6276 +2024-05-31 12:09:20,176 - mmdet - INFO - Epoch [10][550/7330] lr: 1.000e-05, eta: 3:42:16, time: 0.620, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0396, loss_cls: 0.1569, acc: 94.0430, loss_bbox: 0.2124, loss_mask: 0.2174, loss: 0.6405 +2024-05-31 12:09:50,390 - mmdet - INFO - Epoch [10][600/7330] lr: 1.000e-05, eta: 3:41:44, time: 0.604, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0385, loss_cls: 0.1498, acc: 94.3772, loss_bbox: 0.1982, loss_mask: 0.2159, loss: 0.6144 +2024-05-31 12:10:20,553 - mmdet - INFO - Epoch [10][650/7330] lr: 1.000e-05, eta: 3:41:13, time: 0.603, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0391, loss_cls: 0.1546, acc: 94.1211, loss_bbox: 0.2084, loss_mask: 0.2194, loss: 0.6342 +2024-05-31 12:10:51,351 - mmdet - INFO - Epoch [10][700/7330] lr: 1.000e-05, eta: 3:40:42, time: 0.616, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0371, loss_cls: 0.1533, acc: 94.1604, loss_bbox: 0.2077, loss_mask: 0.2133, loss: 0.6251 +2024-05-31 12:11:22,283 - mmdet - INFO - Epoch [10][750/7330] lr: 1.000e-05, eta: 3:40:11, time: 0.618, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0400, loss_cls: 0.1556, acc: 94.0339, loss_bbox: 0.2124, loss_mask: 0.2182, loss: 0.6403 +2024-05-31 12:11:55,305 - mmdet - INFO - Epoch [10][800/7330] lr: 1.000e-05, eta: 3:39:40, time: 0.661, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0400, loss_cls: 0.1540, acc: 94.1326, loss_bbox: 0.2061, loss_mask: 0.2144, loss: 0.6280 +2024-05-31 12:12:25,177 - mmdet - INFO - Epoch [10][850/7330] lr: 1.000e-05, eta: 3:39:09, time: 0.597, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0383, loss_cls: 0.1501, acc: 94.3274, loss_bbox: 0.1986, loss_mask: 0.2104, loss: 0.6106 +2024-05-31 12:12:55,696 - mmdet - INFO - Epoch [10][900/7330] lr: 1.000e-05, eta: 3:38:37, time: 0.610, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0389, loss_cls: 0.1523, acc: 94.1943, loss_bbox: 0.2058, loss_mask: 0.2181, loss: 0.6295 +2024-05-31 12:13:30,929 - mmdet - INFO - Epoch [10][950/7330] lr: 1.000e-05, eta: 3:38:08, time: 0.705, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0398, loss_cls: 0.1508, acc: 94.2520, loss_bbox: 0.2095, loss_mask: 0.2165, loss: 0.6298 +2024-05-31 12:14:03,426 - mmdet - INFO - Epoch [10][1000/7330] lr: 1.000e-05, eta: 3:37:37, time: 0.650, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0393, loss_cls: 0.1543, acc: 94.2515, loss_bbox: 0.2060, loss_mask: 0.2184, loss: 0.6319 +2024-05-31 12:14:36,925 - mmdet - INFO - Epoch [10][1050/7330] lr: 1.000e-05, eta: 3:37:07, time: 0.670, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0380, loss_cls: 0.1465, acc: 94.4351, loss_bbox: 0.2003, loss_mask: 0.2140, loss: 0.6121 +2024-05-31 12:15:09,640 - mmdet - INFO - Epoch [10][1100/7330] lr: 1.000e-05, eta: 3:36:36, time: 0.654, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0357, loss_cls: 0.1461, acc: 94.5579, loss_bbox: 0.1937, loss_mask: 0.2070, loss: 0.5954 +2024-05-31 12:15:41,163 - mmdet - INFO - Epoch [10][1150/7330] lr: 1.000e-05, eta: 3:36:05, time: 0.630, data_time: 0.078, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0412, loss_cls: 0.1559, acc: 94.0388, loss_bbox: 0.2132, loss_mask: 0.2228, loss: 0.6466 +2024-05-31 12:16:17,429 - mmdet - INFO - Epoch [10][1200/7330] lr: 1.000e-05, eta: 3:35:35, time: 0.726, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0395, loss_cls: 0.1535, acc: 94.1396, loss_bbox: 0.2100, loss_mask: 0.2211, loss: 0.6381 +2024-05-31 12:16:48,232 - mmdet - INFO - Epoch [10][1250/7330] lr: 1.000e-05, eta: 3:35:04, time: 0.616, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0404, loss_cls: 0.1583, acc: 94.1108, loss_bbox: 0.2140, loss_mask: 0.2185, loss: 0.6454 +2024-05-31 12:17:20,000 - mmdet - INFO - Epoch [10][1300/7330] lr: 1.000e-05, eta: 3:34:33, time: 0.635, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0164, loss_rpn_bbox: 0.0419, loss_cls: 0.1643, acc: 93.7842, loss_bbox: 0.2187, loss_mask: 0.2228, loss: 0.6642 +2024-05-31 12:17:50,164 - mmdet - INFO - Epoch [10][1350/7330] lr: 1.000e-05, eta: 3:34:02, time: 0.603, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0389, loss_cls: 0.1537, acc: 94.2480, loss_bbox: 0.2029, loss_mask: 0.2126, loss: 0.6220 +2024-05-31 12:18:20,894 - mmdet - INFO - Epoch [10][1400/7330] lr: 1.000e-05, eta: 3:33:31, time: 0.615, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0119, loss_rpn_bbox: 0.0377, loss_cls: 0.1488, acc: 94.4585, loss_bbox: 0.2017, loss_mask: 0.2152, loss: 0.6153 +2024-05-31 12:18:51,148 - mmdet - INFO - Epoch [10][1450/7330] lr: 1.000e-05, eta: 3:32:59, time: 0.605, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0123, loss_rpn_bbox: 0.0360, loss_cls: 0.1432, acc: 94.6218, loss_bbox: 0.1900, loss_mask: 0.2114, loss: 0.5929 +2024-05-31 12:19:21,641 - mmdet - INFO - Epoch [10][1500/7330] lr: 1.000e-05, eta: 3:32:28, time: 0.610, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0374, loss_cls: 0.1501, acc: 94.3040, loss_bbox: 0.2040, loss_mask: 0.2124, loss: 0.6175 +2024-05-31 12:19:52,844 - mmdet - INFO - Epoch [10][1550/7330] lr: 1.000e-05, eta: 3:31:57, time: 0.624, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0120, loss_rpn_bbox: 0.0354, loss_cls: 0.1404, acc: 94.7168, loss_bbox: 0.1938, loss_mask: 0.2082, loss: 0.5897 +2024-05-31 12:20:22,992 - mmdet - INFO - Epoch [10][1600/7330] lr: 1.000e-05, eta: 3:31:26, time: 0.603, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0385, loss_cls: 0.1582, acc: 94.0522, loss_bbox: 0.2132, loss_mask: 0.2224, loss: 0.6466 +2024-05-31 12:20:53,150 - mmdet - INFO - Epoch [10][1650/7330] lr: 1.000e-05, eta: 3:30:54, time: 0.603, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0358, loss_cls: 0.1455, acc: 94.4429, loss_bbox: 0.1983, loss_mask: 0.2080, loss: 0.5997 +2024-05-31 12:21:25,164 - mmdet - INFO - Epoch [10][1700/7330] lr: 1.000e-05, eta: 3:30:23, time: 0.640, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0354, loss_cls: 0.1462, acc: 94.4475, loss_bbox: 0.1977, loss_mask: 0.2077, loss: 0.6001 +2024-05-31 12:21:55,526 - mmdet - INFO - Epoch [10][1750/7330] lr: 1.000e-05, eta: 3:29:52, time: 0.607, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0370, loss_cls: 0.1458, acc: 94.4473, loss_bbox: 0.2027, loss_mask: 0.2212, loss: 0.6192 +2024-05-31 12:22:25,902 - mmdet - INFO - Epoch [10][1800/7330] lr: 1.000e-05, eta: 3:29:21, time: 0.608, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0398, loss_cls: 0.1566, acc: 94.1448, loss_bbox: 0.2083, loss_mask: 0.2229, loss: 0.6417 +2024-05-31 12:23:03,241 - mmdet - INFO - Epoch [10][1850/7330] lr: 1.000e-05, eta: 3:28:51, time: 0.747, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0404, loss_cls: 0.1613, acc: 93.9966, loss_bbox: 0.2106, loss_mask: 0.2173, loss: 0.6443 +2024-05-31 12:23:36,357 - mmdet - INFO - Epoch [10][1900/7330] lr: 1.000e-05, eta: 3:28:21, time: 0.662, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0150, loss_rpn_bbox: 0.0397, loss_cls: 0.1574, acc: 94.0940, loss_bbox: 0.2112, loss_mask: 0.2163, loss: 0.6396 +2024-05-31 12:24:06,949 - mmdet - INFO - Epoch [10][1950/7330] lr: 1.000e-05, eta: 3:27:50, time: 0.612, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0403, loss_cls: 0.1591, acc: 94.0562, loss_bbox: 0.2079, loss_mask: 0.2148, loss: 0.6353 +2024-05-31 12:24:41,081 - mmdet - INFO - Epoch [10][2000/7330] lr: 1.000e-05, eta: 3:27:19, time: 0.683, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0135, loss_rpn_bbox: 0.0374, loss_cls: 0.1475, acc: 94.4695, loss_bbox: 0.2010, loss_mask: 0.2088, loss: 0.6082 +2024-05-31 12:25:14,871 - mmdet - INFO - Epoch [10][2050/7330] lr: 1.000e-05, eta: 3:26:49, time: 0.676, data_time: 0.069, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0385, loss_cls: 0.1545, acc: 94.1743, loss_bbox: 0.2089, loss_mask: 0.2184, loss: 0.6335 +2024-05-31 12:25:47,709 - mmdet - INFO - Epoch [10][2100/7330] lr: 1.000e-05, eta: 3:26:18, time: 0.657, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0413, loss_cls: 0.1551, acc: 94.1223, loss_bbox: 0.2126, loss_mask: 0.2177, loss: 0.6410 +2024-05-31 12:26:17,394 - mmdet - INFO - Epoch [10][2150/7330] lr: 1.000e-05, eta: 3:25:47, time: 0.594, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0375, loss_cls: 0.1478, acc: 94.3972, loss_bbox: 0.2009, loss_mask: 0.2107, loss: 0.6103 +2024-05-31 12:26:48,215 - mmdet - INFO - Epoch [10][2200/7330] lr: 1.000e-05, eta: 3:25:16, time: 0.616, data_time: 0.064, memory: 9655, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0410, loss_cls: 0.1580, acc: 93.9607, loss_bbox: 0.2152, loss_mask: 0.2194, loss: 0.6467 +2024-05-31 12:27:18,328 - mmdet - INFO - Epoch [10][2250/7330] lr: 1.000e-05, eta: 3:24:44, time: 0.602, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0403, loss_cls: 0.1543, acc: 94.1580, loss_bbox: 0.2098, loss_mask: 0.2211, loss: 0.6395 +2024-05-31 12:27:48,712 - mmdet - INFO - Epoch [10][2300/7330] lr: 1.000e-05, eta: 3:24:13, time: 0.608, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0363, loss_cls: 0.1523, acc: 94.2280, loss_bbox: 0.2047, loss_mask: 0.2189, loss: 0.6264 +2024-05-31 12:28:18,821 - mmdet - INFO - Epoch [10][2350/7330] lr: 1.000e-05, eta: 3:23:42, time: 0.602, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0407, loss_cls: 0.1555, acc: 94.1262, loss_bbox: 0.2095, loss_mask: 0.2200, loss: 0.6393 +2024-05-31 12:28:49,596 - mmdet - INFO - Epoch [10][2400/7330] lr: 1.000e-05, eta: 3:23:10, time: 0.616, data_time: 0.065, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0390, loss_cls: 0.1545, acc: 94.1626, loss_bbox: 0.2117, loss_mask: 0.2183, loss: 0.6364 +2024-05-31 12:29:19,795 - mmdet - INFO - Epoch [10][2450/7330] lr: 1.000e-05, eta: 3:22:39, time: 0.604, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0396, loss_cls: 0.1524, acc: 94.2620, loss_bbox: 0.2006, loss_mask: 0.2168, loss: 0.6221 +2024-05-31 12:29:50,718 - mmdet - INFO - Epoch [10][2500/7330] lr: 1.000e-05, eta: 3:22:08, time: 0.618, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0381, loss_cls: 0.1544, acc: 94.0864, loss_bbox: 0.2077, loss_mask: 0.2155, loss: 0.6290 +2024-05-31 12:30:21,945 - mmdet - INFO - Epoch [10][2550/7330] lr: 1.000e-05, eta: 3:21:37, time: 0.624, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0374, loss_cls: 0.1478, acc: 94.4468, loss_bbox: 0.1990, loss_mask: 0.2138, loss: 0.6117 +2024-05-31 12:30:54,866 - mmdet - INFO - Epoch [10][2600/7330] lr: 1.000e-05, eta: 3:21:06, time: 0.659, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0384, loss_cls: 0.1491, acc: 94.4050, loss_bbox: 0.1983, loss_mask: 0.2179, loss: 0.6171 +2024-05-31 12:31:25,611 - mmdet - INFO - Epoch [10][2650/7330] lr: 1.000e-05, eta: 3:20:35, time: 0.615, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0138, loss_rpn_bbox: 0.0379, loss_cls: 0.1466, acc: 94.3967, loss_bbox: 0.2041, loss_mask: 0.2166, loss: 0.6189 +2024-05-31 12:31:58,281 - mmdet - INFO - Epoch [10][2700/7330] lr: 1.000e-05, eta: 3:20:04, time: 0.653, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0391, loss_cls: 0.1549, acc: 94.1584, loss_bbox: 0.2086, loss_mask: 0.2182, loss: 0.6341 +2024-05-31 12:32:35,034 - mmdet - INFO - Epoch [10][2750/7330] lr: 1.000e-05, eta: 3:19:35, time: 0.735, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0113, loss_rpn_bbox: 0.0368, loss_cls: 0.1506, acc: 94.3147, loss_bbox: 0.2031, loss_mask: 0.2108, loss: 0.6127 +2024-05-31 12:33:07,746 - mmdet - INFO - Epoch [10][2800/7330] lr: 1.000e-05, eta: 3:19:04, time: 0.654, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0389, loss_cls: 0.1534, acc: 94.1865, loss_bbox: 0.2053, loss_mask: 0.2161, loss: 0.6274 +2024-05-31 12:33:41,294 - mmdet - INFO - Epoch [10][2850/7330] lr: 1.000e-05, eta: 3:18:34, time: 0.671, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0418, loss_cls: 0.1624, acc: 93.8250, loss_bbox: 0.2169, loss_mask: 0.2235, loss: 0.6589 +2024-05-31 12:34:11,301 - mmdet - INFO - Epoch [10][2900/7330] lr: 1.000e-05, eta: 3:18:02, time: 0.600, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0126, loss_rpn_bbox: 0.0383, loss_cls: 0.1536, acc: 94.1514, loss_bbox: 0.2066, loss_mask: 0.2173, loss: 0.6283 +2024-05-31 12:34:46,534 - mmdet - INFO - Epoch [10][2950/7330] lr: 1.000e-05, eta: 3:17:32, time: 0.705, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0411, loss_cls: 0.1659, acc: 93.7908, loss_bbox: 0.2178, loss_mask: 0.2250, loss: 0.6649 +2024-05-31 12:35:16,690 - mmdet - INFO - Epoch [10][3000/7330] lr: 1.000e-05, eta: 3:17:01, time: 0.603, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0140, loss_rpn_bbox: 0.0391, loss_cls: 0.1553, acc: 94.2217, loss_bbox: 0.2075, loss_mask: 0.2225, loss: 0.6384 +2024-05-31 12:35:46,839 - mmdet - INFO - Epoch [10][3050/7330] lr: 1.000e-05, eta: 3:16:29, time: 0.603, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0138, loss_rpn_bbox: 0.0383, loss_cls: 0.1557, acc: 94.1150, loss_bbox: 0.2098, loss_mask: 0.2208, loss: 0.6384 +2024-05-31 12:36:17,210 - mmdet - INFO - Epoch [10][3100/7330] lr: 1.000e-05, eta: 3:15:58, time: 0.607, data_time: 0.063, memory: 9655, loss_rpn_cls: 0.0117, loss_rpn_bbox: 0.0370, loss_cls: 0.1490, acc: 94.3809, loss_bbox: 0.2011, loss_mask: 0.2109, loss: 0.6096 +2024-05-31 12:36:47,389 - mmdet - INFO - Epoch [10][3150/7330] lr: 1.000e-05, eta: 3:15:27, time: 0.604, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0140, loss_rpn_bbox: 0.0395, loss_cls: 0.1582, acc: 93.9988, loss_bbox: 0.2089, loss_mask: 0.2138, loss: 0.6344 +2024-05-31 12:37:18,091 - mmdet - INFO - Epoch [10][3200/7330] lr: 1.000e-05, eta: 3:14:55, time: 0.614, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0135, loss_rpn_bbox: 0.0393, loss_cls: 0.1575, acc: 94.0195, loss_bbox: 0.2147, loss_mask: 0.2198, loss: 0.6448 +2024-05-31 12:37:48,946 - mmdet - INFO - Epoch [10][3250/7330] lr: 1.000e-05, eta: 3:14:24, time: 0.617, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0403, loss_cls: 0.1565, acc: 94.0491, loss_bbox: 0.2119, loss_mask: 0.2187, loss: 0.6421 +2024-05-31 12:38:19,458 - mmdet - INFO - Epoch [10][3300/7330] lr: 1.000e-05, eta: 3:13:53, time: 0.610, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0386, loss_cls: 0.1499, acc: 94.3567, loss_bbox: 0.2029, loss_mask: 0.2128, loss: 0.6172 +2024-05-31 12:38:50,004 - mmdet - INFO - Epoch [10][3350/7330] lr: 1.000e-05, eta: 3:13:22, time: 0.611, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0391, loss_cls: 0.1587, acc: 94.0420, loss_bbox: 0.2092, loss_mask: 0.2187, loss: 0.6404 +2024-05-31 12:39:20,251 - mmdet - INFO - Epoch [10][3400/7330] lr: 1.000e-05, eta: 3:12:50, time: 0.605, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0375, loss_cls: 0.1499, acc: 94.2488, loss_bbox: 0.2100, loss_mask: 0.2180, loss: 0.6283 +2024-05-31 12:39:52,353 - mmdet - INFO - Epoch [10][3450/7330] lr: 1.000e-05, eta: 3:12:19, time: 0.642, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0369, loss_cls: 0.1451, acc: 94.6218, loss_bbox: 0.1954, loss_mask: 0.2092, loss: 0.6000 +2024-05-31 12:40:22,687 - mmdet - INFO - Epoch [10][3500/7330] lr: 1.000e-05, eta: 3:11:48, time: 0.607, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0126, loss_rpn_bbox: 0.0366, loss_cls: 0.1499, acc: 94.3271, loss_bbox: 0.2024, loss_mask: 0.2146, loss: 0.6161 +2024-05-31 12:40:53,206 - mmdet - INFO - Epoch [10][3550/7330] lr: 1.000e-05, eta: 3:11:17, time: 0.610, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0384, loss_cls: 0.1529, acc: 94.2180, loss_bbox: 0.2087, loss_mask: 0.2185, loss: 0.6319 +2024-05-31 12:41:32,095 - mmdet - INFO - Epoch [10][3600/7330] lr: 1.000e-05, eta: 3:10:48, time: 0.778, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0416, loss_cls: 0.1602, acc: 93.8965, loss_bbox: 0.2149, loss_mask: 0.2200, loss: 0.6518 +2024-05-31 12:42:02,255 - mmdet - INFO - Epoch [10][3650/7330] lr: 1.000e-05, eta: 3:10:16, time: 0.603, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0392, loss_cls: 0.1474, acc: 94.4141, loss_bbox: 0.1971, loss_mask: 0.2120, loss: 0.6091 +2024-05-31 12:42:34,577 - mmdet - INFO - Epoch [10][3700/7330] lr: 1.000e-05, eta: 3:09:46, time: 0.646, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0374, loss_cls: 0.1496, acc: 94.3276, loss_bbox: 0.2018, loss_mask: 0.2161, loss: 0.6174 +2024-05-31 12:43:08,515 - mmdet - INFO - Epoch [10][3750/7330] lr: 1.000e-05, eta: 3:09:15, time: 0.679, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0409, loss_cls: 0.1593, acc: 94.0222, loss_bbox: 0.2139, loss_mask: 0.2222, loss: 0.6516 +2024-05-31 12:43:38,357 - mmdet - INFO - Epoch [10][3800/7330] lr: 1.000e-05, eta: 3:08:44, time: 0.597, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0366, loss_cls: 0.1441, acc: 94.5537, loss_bbox: 0.1975, loss_mask: 0.2090, loss: 0.6000 +2024-05-31 12:44:14,211 - mmdet - INFO - Epoch [10][3850/7330] lr: 1.000e-05, eta: 3:08:14, time: 0.717, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0384, loss_cls: 0.1547, acc: 94.1965, loss_bbox: 0.2041, loss_mask: 0.2194, loss: 0.6295 +2024-05-31 12:44:44,329 - mmdet - INFO - Epoch [10][3900/7330] lr: 1.000e-05, eta: 3:07:42, time: 0.602, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0407, loss_cls: 0.1587, acc: 94.0459, loss_bbox: 0.2135, loss_mask: 0.2290, loss: 0.6555 +2024-05-31 12:45:14,587 - mmdet - INFO - Epoch [10][3950/7330] lr: 1.000e-05, eta: 3:07:11, time: 0.605, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0405, loss_cls: 0.1563, acc: 94.0798, loss_bbox: 0.2107, loss_mask: 0.2161, loss: 0.6382 +2024-05-31 12:45:44,888 - mmdet - INFO - Epoch [10][4000/7330] lr: 1.000e-05, eta: 3:06:40, time: 0.606, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0391, loss_cls: 0.1478, acc: 94.4280, loss_bbox: 0.2024, loss_mask: 0.2188, loss: 0.6205 +2024-05-31 12:46:15,243 - mmdet - INFO - Epoch [10][4050/7330] lr: 1.000e-05, eta: 3:06:08, time: 0.607, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0400, loss_cls: 0.1574, acc: 93.9885, loss_bbox: 0.2081, loss_mask: 0.2240, loss: 0.6424 +2024-05-31 12:46:46,124 - mmdet - INFO - Epoch [10][4100/7330] lr: 1.000e-05, eta: 3:05:37, time: 0.618, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0403, loss_cls: 0.1650, acc: 93.7283, loss_bbox: 0.2216, loss_mask: 0.2198, loss: 0.6618 +2024-05-31 12:47:16,591 - mmdet - INFO - Epoch [10][4150/7330] lr: 1.000e-05, eta: 3:05:06, time: 0.609, data_time: 0.077, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0387, loss_cls: 0.1502, acc: 94.4419, loss_bbox: 0.1993, loss_mask: 0.2118, loss: 0.6126 +2024-05-31 12:47:46,180 - mmdet - INFO - Epoch [10][4200/7330] lr: 1.000e-05, eta: 3:04:34, time: 0.592, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0403, loss_cls: 0.1555, acc: 94.1133, loss_bbox: 0.2069, loss_mask: 0.2202, loss: 0.6357 +2024-05-31 12:48:16,454 - mmdet - INFO - Epoch [10][4250/7330] lr: 1.000e-05, eta: 3:04:03, time: 0.605, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0150, loss_rpn_bbox: 0.0394, loss_cls: 0.1575, acc: 94.0913, loss_bbox: 0.2094, loss_mask: 0.2145, loss: 0.6358 +2024-05-31 12:48:46,219 - mmdet - INFO - Epoch [10][4300/7330] lr: 1.000e-05, eta: 3:03:32, time: 0.596, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0378, loss_cls: 0.1530, acc: 94.1941, loss_bbox: 0.2015, loss_mask: 0.2122, loss: 0.6190 +2024-05-31 12:49:21,011 - mmdet - INFO - Epoch [10][4350/7330] lr: 1.000e-05, eta: 3:03:01, time: 0.696, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0387, loss_cls: 0.1608, acc: 93.9238, loss_bbox: 0.2178, loss_mask: 0.2203, loss: 0.6515 +2024-05-31 12:49:50,719 - mmdet - INFO - Epoch [10][4400/7330] lr: 1.000e-05, eta: 3:02:30, time: 0.594, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0385, loss_cls: 0.1504, acc: 94.2275, loss_bbox: 0.2058, loss_mask: 0.2189, loss: 0.6266 +2024-05-31 12:50:20,836 - mmdet - INFO - Epoch [10][4450/7330] lr: 1.000e-05, eta: 3:01:59, time: 0.602, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0398, loss_cls: 0.1527, acc: 94.2329, loss_bbox: 0.2038, loss_mask: 0.2177, loss: 0.6281 +2024-05-31 12:50:58,664 - mmdet - INFO - Epoch [10][4500/7330] lr: 1.000e-05, eta: 3:01:29, time: 0.757, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0400, loss_cls: 0.1520, acc: 94.2771, loss_bbox: 0.2011, loss_mask: 0.2074, loss: 0.6134 +2024-05-31 12:51:31,232 - mmdet - INFO - Epoch [10][4550/7330] lr: 1.000e-05, eta: 3:00:58, time: 0.651, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0138, loss_rpn_bbox: 0.0394, loss_cls: 0.1570, acc: 94.1077, loss_bbox: 0.2110, loss_mask: 0.2174, loss: 0.6385 +2024-05-31 12:52:00,769 - mmdet - INFO - Epoch [10][4600/7330] lr: 1.000e-05, eta: 3:00:27, time: 0.591, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0373, loss_cls: 0.1531, acc: 94.2439, loss_bbox: 0.2126, loss_mask: 0.2179, loss: 0.6337 +2024-05-31 12:52:34,157 - mmdet - INFO - Epoch [10][4650/7330] lr: 1.000e-05, eta: 2:59:56, time: 0.667, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0146, loss_rpn_bbox: 0.0393, loss_cls: 0.1646, acc: 93.7244, loss_bbox: 0.2169, loss_mask: 0.2212, loss: 0.6566 +2024-05-31 12:53:07,440 - mmdet - INFO - Epoch [10][4700/7330] lr: 1.000e-05, eta: 2:59:26, time: 0.666, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0404, loss_cls: 0.1621, acc: 93.8906, loss_bbox: 0.2138, loss_mask: 0.2160, loss: 0.6477 +2024-05-31 12:53:39,722 - mmdet - INFO - Epoch [10][4750/7330] lr: 1.000e-05, eta: 2:58:55, time: 0.646, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0413, loss_cls: 0.1556, acc: 94.0881, loss_bbox: 0.2084, loss_mask: 0.2183, loss: 0.6368 +2024-05-31 12:54:09,650 - mmdet - INFO - Epoch [10][4800/7330] lr: 1.000e-05, eta: 2:58:23, time: 0.598, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0122, loss_rpn_bbox: 0.0380, loss_cls: 0.1467, acc: 94.3892, loss_bbox: 0.2032, loss_mask: 0.2142, loss: 0.6142 +2024-05-31 12:54:39,791 - mmdet - INFO - Epoch [10][4850/7330] lr: 1.000e-05, eta: 2:57:52, time: 0.603, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0376, loss_cls: 0.1508, acc: 94.4053, loss_bbox: 0.2054, loss_mask: 0.2203, loss: 0.6278 +2024-05-31 12:55:09,897 - mmdet - INFO - Epoch [10][4900/7330] lr: 1.000e-05, eta: 2:57:21, time: 0.602, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0376, loss_cls: 0.1489, acc: 94.2725, loss_bbox: 0.2076, loss_mask: 0.2171, loss: 0.6239 +2024-05-31 12:55:40,273 - mmdet - INFO - Epoch [10][4950/7330] lr: 1.000e-05, eta: 2:56:49, time: 0.608, data_time: 0.067, memory: 9655, loss_rpn_cls: 0.0140, loss_rpn_bbox: 0.0430, loss_cls: 0.1606, acc: 93.8306, loss_bbox: 0.2182, loss_mask: 0.2200, loss: 0.6558 +2024-05-31 12:56:11,164 - mmdet - INFO - Epoch [10][5000/7330] lr: 1.000e-05, eta: 2:56:18, time: 0.617, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0421, loss_cls: 0.1628, acc: 93.7437, loss_bbox: 0.2212, loss_mask: 0.2268, loss: 0.6670 +2024-05-31 12:56:41,215 - mmdet - INFO - Epoch [10][5050/7330] lr: 1.000e-05, eta: 2:55:47, time: 0.601, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0378, loss_cls: 0.1520, acc: 94.2358, loss_bbox: 0.2069, loss_mask: 0.2169, loss: 0.6267 +2024-05-31 12:57:11,460 - mmdet - INFO - Epoch [10][5100/7330] lr: 1.000e-05, eta: 2:55:15, time: 0.605, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0382, loss_cls: 0.1514, acc: 94.3135, loss_bbox: 0.2047, loss_mask: 0.2119, loss: 0.6195 +2024-05-31 12:57:41,694 - mmdet - INFO - Epoch [10][5150/7330] lr: 1.000e-05, eta: 2:54:44, time: 0.604, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0381, loss_cls: 0.1568, acc: 94.1653, loss_bbox: 0.2089, loss_mask: 0.2192, loss: 0.6363 +2024-05-31 12:58:14,573 - mmdet - INFO - Epoch [10][5200/7330] lr: 1.000e-05, eta: 2:54:13, time: 0.658, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0378, loss_cls: 0.1562, acc: 94.1133, loss_bbox: 0.2096, loss_mask: 0.2191, loss: 0.6364 +2024-05-31 12:58:44,552 - mmdet - INFO - Epoch [10][5250/7330] lr: 1.000e-05, eta: 2:53:42, time: 0.600, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0370, loss_cls: 0.1507, acc: 94.2788, loss_bbox: 0.2072, loss_mask: 0.2189, loss: 0.6270 +2024-05-31 12:59:14,893 - mmdet - INFO - Epoch [10][5300/7330] lr: 1.000e-05, eta: 2:53:11, time: 0.607, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0138, loss_rpn_bbox: 0.0405, loss_cls: 0.1590, acc: 93.9917, loss_bbox: 0.2092, loss_mask: 0.2175, loss: 0.6400 +2024-05-31 12:59:47,355 - mmdet - INFO - Epoch [10][5350/7330] lr: 1.000e-05, eta: 2:52:40, time: 0.649, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0372, loss_cls: 0.1507, acc: 94.3655, loss_bbox: 0.2012, loss_mask: 0.2164, loss: 0.6194 +2024-05-31 13:00:20,562 - mmdet - INFO - Epoch [10][5400/7330] lr: 1.000e-05, eta: 2:52:09, time: 0.664, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0123, loss_rpn_bbox: 0.0388, loss_cls: 0.1499, acc: 94.3105, loss_bbox: 0.2018, loss_mask: 0.2096, loss: 0.6124 +2024-05-31 13:00:53,254 - mmdet - INFO - Epoch [10][5450/7330] lr: 1.000e-05, eta: 2:51:38, time: 0.654, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0135, loss_rpn_bbox: 0.0419, loss_cls: 0.1563, acc: 94.1047, loss_bbox: 0.2114, loss_mask: 0.2209, loss: 0.6441 +2024-05-31 13:01:25,995 - mmdet - INFO - Epoch [10][5500/7330] lr: 1.000e-05, eta: 2:51:08, time: 0.655, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0135, loss_rpn_bbox: 0.0373, loss_cls: 0.1488, acc: 94.3140, loss_bbox: 0.1992, loss_mask: 0.2118, loss: 0.6105 +2024-05-31 13:01:55,385 - mmdet - INFO - Epoch [10][5550/7330] lr: 1.000e-05, eta: 2:50:36, time: 0.588, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0382, loss_cls: 0.1532, acc: 94.2144, loss_bbox: 0.2033, loss_mask: 0.2181, loss: 0.6272 +2024-05-31 13:02:29,859 - mmdet - INFO - Epoch [10][5600/7330] lr: 1.000e-05, eta: 2:50:06, time: 0.689, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0396, loss_cls: 0.1526, acc: 94.2249, loss_bbox: 0.2064, loss_mask: 0.2191, loss: 0.6313 +2024-05-31 13:03:00,393 - mmdet - INFO - Epoch [10][5650/7330] lr: 1.000e-05, eta: 2:49:34, time: 0.611, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0376, loss_cls: 0.1527, acc: 94.3435, loss_bbox: 0.2011, loss_mask: 0.2126, loss: 0.6178 +2024-05-31 13:03:31,368 - mmdet - INFO - Epoch [10][5700/7330] lr: 1.000e-05, eta: 2:49:03, time: 0.619, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0146, loss_rpn_bbox: 0.0405, loss_cls: 0.1595, acc: 93.9846, loss_bbox: 0.2118, loss_mask: 0.2201, loss: 0.6465 +2024-05-31 13:04:01,723 - mmdet - INFO - Epoch [10][5750/7330] lr: 1.000e-05, eta: 2:48:32, time: 0.607, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0386, loss_cls: 0.1565, acc: 94.0498, loss_bbox: 0.2087, loss_mask: 0.2138, loss: 0.6317 +2024-05-31 13:04:31,737 - mmdet - INFO - Epoch [10][5800/7330] lr: 1.000e-05, eta: 2:48:00, time: 0.600, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0392, loss_cls: 0.1586, acc: 94.0110, loss_bbox: 0.2152, loss_mask: 0.2203, loss: 0.6471 +2024-05-31 13:05:02,240 - mmdet - INFO - Epoch [10][5850/7330] lr: 1.000e-05, eta: 2:47:29, time: 0.610, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0382, loss_cls: 0.1507, acc: 94.2427, loss_bbox: 0.2029, loss_mask: 0.2183, loss: 0.6234 +2024-05-31 13:05:32,532 - mmdet - INFO - Epoch [10][5900/7330] lr: 1.000e-05, eta: 2:46:58, time: 0.606, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0387, loss_cls: 0.1586, acc: 93.9653, loss_bbox: 0.2124, loss_mask: 0.2186, loss: 0.6424 +2024-05-31 13:06:03,053 - mmdet - INFO - Epoch [10][5950/7330] lr: 1.000e-05, eta: 2:46:27, time: 0.610, data_time: 0.069, memory: 9655, loss_rpn_cls: 0.0135, loss_rpn_bbox: 0.0382, loss_cls: 0.1568, acc: 94.1001, loss_bbox: 0.2099, loss_mask: 0.2158, loss: 0.6342 +2024-05-31 13:06:33,085 - mmdet - INFO - Epoch [10][6000/7330] lr: 1.000e-05, eta: 2:45:55, time: 0.601, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0381, loss_cls: 0.1515, acc: 94.2947, loss_bbox: 0.2071, loss_mask: 0.2207, loss: 0.6311 +2024-05-31 13:07:03,543 - mmdet - INFO - Epoch [10][6050/7330] lr: 1.000e-05, eta: 2:45:24, time: 0.609, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0405, loss_cls: 0.1593, acc: 93.9094, loss_bbox: 0.2106, loss_mask: 0.2236, loss: 0.6494 +2024-05-31 13:07:36,703 - mmdet - INFO - Epoch [10][6100/7330] lr: 1.000e-05, eta: 2:44:53, time: 0.663, data_time: 0.071, memory: 9655, loss_rpn_cls: 0.0150, loss_rpn_bbox: 0.0433, loss_cls: 0.1612, acc: 93.9058, loss_bbox: 0.2116, loss_mask: 0.2166, loss: 0.6476 +2024-05-31 13:08:07,212 - mmdet - INFO - Epoch [10][6150/7330] lr: 1.000e-05, eta: 2:44:22, time: 0.610, data_time: 0.063, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0362, loss_cls: 0.1510, acc: 94.2131, loss_bbox: 0.2043, loss_mask: 0.2164, loss: 0.6213 +2024-05-31 13:08:37,445 - mmdet - INFO - Epoch [10][6200/7330] lr: 1.000e-05, eta: 2:43:51, time: 0.605, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0388, loss_cls: 0.1494, acc: 94.3215, loss_bbox: 0.2076, loss_mask: 0.2197, loss: 0.6291 +2024-05-31 13:09:13,136 - mmdet - INFO - Epoch [10][6250/7330] lr: 1.000e-05, eta: 2:43:21, time: 0.714, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0387, loss_cls: 0.1549, acc: 94.1487, loss_bbox: 0.2087, loss_mask: 0.2151, loss: 0.6309 +2024-05-31 13:09:43,040 - mmdet - INFO - Epoch [10][6300/7330] lr: 1.000e-05, eta: 2:42:49, time: 0.598, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0135, loss_rpn_bbox: 0.0390, loss_cls: 0.1545, acc: 94.2031, loss_bbox: 0.2047, loss_mask: 0.2146, loss: 0.6263 +2024-05-31 13:10:15,499 - mmdet - INFO - Epoch [10][6350/7330] lr: 1.000e-05, eta: 2:42:18, time: 0.649, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0123, loss_rpn_bbox: 0.0368, loss_cls: 0.1455, acc: 94.5930, loss_bbox: 0.1989, loss_mask: 0.2136, loss: 0.6072 +2024-05-31 13:10:47,891 - mmdet - INFO - Epoch [10][6400/7330] lr: 1.000e-05, eta: 2:41:47, time: 0.647, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0359, loss_cls: 0.1502, acc: 94.3713, loss_bbox: 0.1995, loss_mask: 0.2142, loss: 0.6137 +2024-05-31 13:11:19,909 - mmdet - INFO - Epoch [10][6450/7330] lr: 1.000e-05, eta: 2:41:16, time: 0.641, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0361, loss_cls: 0.1482, acc: 94.3789, loss_bbox: 0.2040, loss_mask: 0.2156, loss: 0.6171 +2024-05-31 13:11:52,394 - mmdet - INFO - Epoch [10][6500/7330] lr: 1.000e-05, eta: 2:40:46, time: 0.650, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0372, loss_cls: 0.1538, acc: 94.1360, loss_bbox: 0.2115, loss_mask: 0.2184, loss: 0.6342 +2024-05-31 13:12:23,181 - mmdet - INFO - Epoch [10][6550/7330] lr: 1.000e-05, eta: 2:40:14, time: 0.616, data_time: 0.066, memory: 9655, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0392, loss_cls: 0.1565, acc: 94.0027, loss_bbox: 0.2124, loss_mask: 0.2218, loss: 0.6430 +2024-05-31 13:12:53,771 - mmdet - INFO - Epoch [10][6600/7330] lr: 1.000e-05, eta: 2:39:43, time: 0.612, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0404, loss_cls: 0.1479, acc: 94.3938, loss_bbox: 0.2047, loss_mask: 0.2175, loss: 0.6245 +2024-05-31 13:13:24,170 - mmdet - INFO - Epoch [10][6650/7330] lr: 1.000e-05, eta: 2:39:12, time: 0.608, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0388, loss_cls: 0.1509, acc: 94.2671, loss_bbox: 0.2095, loss_mask: 0.2155, loss: 0.6273 +2024-05-31 13:13:55,099 - mmdet - INFO - Epoch [10][6700/7330] lr: 1.000e-05, eta: 2:38:41, time: 0.618, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0140, loss_rpn_bbox: 0.0397, loss_cls: 0.1538, acc: 94.2417, loss_bbox: 0.2069, loss_mask: 0.2141, loss: 0.6286 +2024-05-31 13:14:24,991 - mmdet - INFO - Epoch [10][6750/7330] lr: 1.000e-05, eta: 2:38:09, time: 0.598, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0366, loss_cls: 0.1476, acc: 94.4285, loss_bbox: 0.1978, loss_mask: 0.2119, loss: 0.6067 +2024-05-31 13:14:55,311 - mmdet - INFO - Epoch [10][6800/7330] lr: 1.000e-05, eta: 2:37:38, time: 0.606, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0422, loss_cls: 0.1585, acc: 94.0161, loss_bbox: 0.2185, loss_mask: 0.2208, loss: 0.6529 +2024-05-31 13:15:25,727 - mmdet - INFO - Epoch [10][6850/7330] lr: 1.000e-05, eta: 2:37:07, time: 0.608, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0369, loss_cls: 0.1469, acc: 94.4429, loss_bbox: 0.1967, loss_mask: 0.2104, loss: 0.6039 +2024-05-31 13:15:55,907 - mmdet - INFO - Epoch [10][6900/7330] lr: 1.000e-05, eta: 2:36:35, time: 0.604, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0396, loss_cls: 0.1573, acc: 94.0913, loss_bbox: 0.2154, loss_mask: 0.2158, loss: 0.6406 +2024-05-31 13:16:26,826 - mmdet - INFO - Epoch [10][6950/7330] lr: 1.000e-05, eta: 2:36:04, time: 0.618, data_time: 0.072, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0389, loss_cls: 0.1515, acc: 94.2698, loss_bbox: 0.2050, loss_mask: 0.2182, loss: 0.6270 +2024-05-31 13:16:59,234 - mmdet - INFO - Epoch [10][7000/7330] lr: 1.000e-05, eta: 2:35:33, time: 0.648, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0123, loss_rpn_bbox: 0.0380, loss_cls: 0.1467, acc: 94.4817, loss_bbox: 0.1998, loss_mask: 0.2125, loss: 0.6092 +2024-05-31 13:17:29,644 - mmdet - INFO - Epoch [10][7050/7330] lr: 1.000e-05, eta: 2:35:02, time: 0.608, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0389, loss_cls: 0.1527, acc: 94.2095, loss_bbox: 0.2075, loss_mask: 0.2165, loss: 0.6287 +2024-05-31 13:17:59,562 - mmdet - INFO - Epoch [10][7100/7330] lr: 1.000e-05, eta: 2:34:31, time: 0.598, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0388, loss_cls: 0.1568, acc: 94.0686, loss_bbox: 0.2136, loss_mask: 0.2225, loss: 0.6453 +2024-05-31 13:18:36,916 - mmdet - INFO - Epoch [10][7150/7330] lr: 1.000e-05, eta: 2:34:01, time: 0.747, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0369, loss_cls: 0.1420, acc: 94.5906, loss_bbox: 0.1911, loss_mask: 0.2132, loss: 0.5965 +2024-05-31 13:19:09,727 - mmdet - INFO - Epoch [10][7200/7330] lr: 1.000e-05, eta: 2:33:30, time: 0.657, data_time: 0.066, memory: 9655, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0411, loss_cls: 0.1626, acc: 93.8337, loss_bbox: 0.2148, loss_mask: 0.2228, loss: 0.6557 +2024-05-31 13:19:43,084 - mmdet - INFO - Epoch [10][7250/7330] lr: 1.000e-05, eta: 2:32:59, time: 0.667, data_time: 0.066, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0398, loss_cls: 0.1553, acc: 94.1426, loss_bbox: 0.2111, loss_mask: 0.2233, loss: 0.6431 +2024-05-31 13:20:13,273 - mmdet - INFO - Epoch [10][7300/7330] lr: 1.000e-05, eta: 2:32:28, time: 0.604, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0376, loss_cls: 0.1448, acc: 94.4795, loss_bbox: 0.1966, loss_mask: 0.2144, loss: 0.6056 +2024-05-31 13:20:34,513 - mmdet - INFO - Saving checkpoint at 10 epochs +2024-05-31 13:22:10,869 - mmdet - INFO - Evaluating bbox... +2024-05-31 13:22:30,481 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.447 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.669 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.487 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.265 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.481 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.616 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.565 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.565 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.565 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.373 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.605 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.725 + +2024-05-31 13:22:30,481 - mmdet - INFO - Evaluating segm... +2024-05-31 13:22:54,811 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.401 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.635 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.426 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.191 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.429 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.611 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.508 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.508 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.508 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.305 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.551 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.691 + +2024-05-31 13:22:55,137 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 13:22:55,139 - mmdet - INFO - Epoch(val) [10][625] bbox_mAP: 0.4470, bbox_mAP_50: 0.6690, bbox_mAP_75: 0.4870, bbox_mAP_s: 0.2650, bbox_mAP_m: 0.4810, bbox_mAP_l: 0.6160, bbox_mAP_copypaste: 0.447 0.669 0.487 0.265 0.481 0.616, segm_mAP: 0.4010, segm_mAP_50: 0.6350, segm_mAP_75: 0.4260, segm_mAP_s: 0.1910, segm_mAP_m: 0.4290, segm_mAP_l: 0.6110, segm_mAP_copypaste: 0.401 0.635 0.426 0.191 0.429 0.611 +2024-05-31 13:23:38,917 - mmdet - INFO - Epoch [11][50/7330] lr: 1.000e-05, eta: 2:31:37, time: 0.873, data_time: 0.111, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0374, loss_cls: 0.1509, acc: 94.2505, loss_bbox: 0.2061, loss_mask: 0.2175, loss: 0.6243 +2024-05-31 13:24:09,183 - mmdet - INFO - Epoch [11][100/7330] lr: 1.000e-05, eta: 2:31:06, time: 0.605, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0369, loss_cls: 0.1453, acc: 94.4541, loss_bbox: 0.1989, loss_mask: 0.2102, loss: 0.6040 +2024-05-31 13:24:39,664 - mmdet - INFO - Epoch [11][150/7330] lr: 1.000e-05, eta: 2:30:34, time: 0.610, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0388, loss_cls: 0.1550, acc: 94.1592, loss_bbox: 0.2084, loss_mask: 0.2174, loss: 0.6323 +2024-05-31 13:25:09,452 - mmdet - INFO - Epoch [11][200/7330] lr: 1.000e-05, eta: 2:30:03, time: 0.596, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0368, loss_cls: 0.1488, acc: 94.4436, loss_bbox: 0.1994, loss_mask: 0.2089, loss: 0.6067 +2024-05-31 13:25:39,348 - mmdet - INFO - Epoch [11][250/7330] lr: 1.000e-05, eta: 2:29:32, time: 0.598, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0384, loss_cls: 0.1559, acc: 94.0693, loss_bbox: 0.2155, loss_mask: 0.2191, loss: 0.6411 +2024-05-31 13:26:09,797 - mmdet - INFO - Epoch [11][300/7330] lr: 1.000e-05, eta: 2:29:00, time: 0.609, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0408, loss_cls: 0.1555, acc: 94.0081, loss_bbox: 0.2129, loss_mask: 0.2179, loss: 0.6404 +2024-05-31 13:26:39,814 - mmdet - INFO - Epoch [11][350/7330] lr: 1.000e-05, eta: 2:28:29, time: 0.601, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0388, loss_cls: 0.1463, acc: 94.5000, loss_bbox: 0.2027, loss_mask: 0.2119, loss: 0.6123 +2024-05-31 13:27:10,732 - mmdet - INFO - Epoch [11][400/7330] lr: 1.000e-05, eta: 2:27:58, time: 0.618, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0379, loss_cls: 0.1549, acc: 94.0798, loss_bbox: 0.2073, loss_mask: 0.2165, loss: 0.6314 +2024-05-31 13:27:40,742 - mmdet - INFO - Epoch [11][450/7330] lr: 1.000e-05, eta: 2:27:26, time: 0.600, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0366, loss_cls: 0.1455, acc: 94.4211, loss_bbox: 0.1954, loss_mask: 0.2118, loss: 0.6018 +2024-05-31 13:28:11,370 - mmdet - INFO - Epoch [11][500/7330] lr: 1.000e-05, eta: 2:26:55, time: 0.613, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0377, loss_cls: 0.1502, acc: 94.3230, loss_bbox: 0.1987, loss_mask: 0.2093, loss: 0.6090 +2024-05-31 13:28:41,517 - mmdet - INFO - Epoch [11][550/7330] lr: 1.000e-05, eta: 2:26:24, time: 0.603, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0387, loss_cls: 0.1548, acc: 94.1321, loss_bbox: 0.2074, loss_mask: 0.2172, loss: 0.6315 +2024-05-31 13:29:12,350 - mmdet - INFO - Epoch [11][600/7330] lr: 1.000e-05, eta: 2:25:53, time: 0.617, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0390, loss_cls: 0.1556, acc: 94.0564, loss_bbox: 0.2113, loss_mask: 0.2194, loss: 0.6381 +2024-05-31 13:29:43,279 - mmdet - INFO - Epoch [11][650/7330] lr: 1.000e-05, eta: 2:25:22, time: 0.619, data_time: 0.072, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0388, loss_cls: 0.1519, acc: 94.3391, loss_bbox: 0.2030, loss_mask: 0.2133, loss: 0.6199 +2024-05-31 13:30:14,060 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 13:30:14,060 - mmdet - INFO - Epoch [11][700/7330] lr: 1.000e-05, eta: 2:24:50, time: 0.616, data_time: 0.038, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0392, loss_cls: 0.1511, acc: 94.2212, loss_bbox: 0.2048, loss_mask: 0.2121, loss: 0.6202 +2024-05-31 13:30:44,030 - mmdet - INFO - Epoch [11][750/7330] lr: 1.000e-05, eta: 2:24:19, time: 0.599, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0362, loss_cls: 0.1417, acc: 94.6995, loss_bbox: 0.1945, loss_mask: 0.2119, loss: 0.5964 +2024-05-31 13:31:14,999 - mmdet - INFO - Epoch [11][800/7330] lr: 1.000e-05, eta: 2:23:48, time: 0.619, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0400, loss_cls: 0.1533, acc: 94.0742, loss_bbox: 0.2101, loss_mask: 0.2173, loss: 0.6337 +2024-05-31 13:31:48,618 - mmdet - INFO - Epoch [11][850/7330] lr: 1.000e-05, eta: 2:23:17, time: 0.672, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0412, loss_cls: 0.1587, acc: 93.9490, loss_bbox: 0.2154, loss_mask: 0.2187, loss: 0.6481 +2024-05-31 13:32:26,136 - mmdet - INFO - Epoch [11][900/7330] lr: 1.000e-05, eta: 2:22:47, time: 0.750, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0110, loss_rpn_bbox: 0.0358, loss_cls: 0.1475, acc: 94.3496, loss_bbox: 0.2052, loss_mask: 0.2151, loss: 0.6145 +2024-05-31 13:32:59,006 - mmdet - INFO - Epoch [11][950/7330] lr: 1.000e-05, eta: 2:22:16, time: 0.657, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0393, loss_cls: 0.1503, acc: 94.3010, loss_bbox: 0.2027, loss_mask: 0.2103, loss: 0.6157 +2024-05-31 13:33:29,581 - mmdet - INFO - Epoch [11][1000/7330] lr: 1.000e-05, eta: 2:21:45, time: 0.611, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0400, loss_cls: 0.1548, acc: 94.1277, loss_bbox: 0.2103, loss_mask: 0.2168, loss: 0.6349 +2024-05-31 13:34:02,791 - mmdet - INFO - Epoch [11][1050/7330] lr: 1.000e-05, eta: 2:21:14, time: 0.664, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0392, loss_cls: 0.1516, acc: 94.2856, loss_bbox: 0.2045, loss_mask: 0.2145, loss: 0.6234 +2024-05-31 13:34:33,715 - mmdet - INFO - Epoch [11][1100/7330] lr: 1.000e-05, eta: 2:20:43, time: 0.618, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0421, loss_cls: 0.1580, acc: 93.9202, loss_bbox: 0.2162, loss_mask: 0.2207, loss: 0.6508 +2024-05-31 13:35:07,216 - mmdet - INFO - Epoch [11][1150/7330] lr: 1.000e-05, eta: 2:20:13, time: 0.670, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0388, loss_cls: 0.1472, acc: 94.4202, loss_bbox: 0.2037, loss_mask: 0.2126, loss: 0.6152 +2024-05-31 13:35:39,331 - mmdet - INFO - Epoch [11][1200/7330] lr: 1.000e-05, eta: 2:19:42, time: 0.642, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0390, loss_cls: 0.1550, acc: 93.9727, loss_bbox: 0.2122, loss_mask: 0.2188, loss: 0.6384 +2024-05-31 13:36:09,058 - mmdet - INFO - Epoch [11][1250/7330] lr: 1.000e-05, eta: 2:19:10, time: 0.595, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0122, loss_rpn_bbox: 0.0373, loss_cls: 0.1467, acc: 94.4827, loss_bbox: 0.2021, loss_mask: 0.2154, loss: 0.6137 +2024-05-31 13:36:39,012 - mmdet - INFO - Epoch [11][1300/7330] lr: 1.000e-05, eta: 2:18:39, time: 0.599, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0111, loss_rpn_bbox: 0.0360, loss_cls: 0.1392, acc: 94.6458, loss_bbox: 0.1925, loss_mask: 0.2119, loss: 0.5907 +2024-05-31 13:37:09,140 - mmdet - INFO - Epoch [11][1350/7330] lr: 1.000e-05, eta: 2:18:08, time: 0.603, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0113, loss_rpn_bbox: 0.0364, loss_cls: 0.1440, acc: 94.5925, loss_bbox: 0.1962, loss_mask: 0.2057, loss: 0.5936 +2024-05-31 13:37:39,106 - mmdet - INFO - Epoch [11][1400/7330] lr: 1.000e-05, eta: 2:17:36, time: 0.599, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0365, loss_cls: 0.1478, acc: 94.4353, loss_bbox: 0.1993, loss_mask: 0.2146, loss: 0.6109 +2024-05-31 13:38:09,455 - mmdet - INFO - Epoch [11][1450/7330] lr: 1.000e-05, eta: 2:17:05, time: 0.607, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0394, loss_cls: 0.1539, acc: 94.1174, loss_bbox: 0.2104, loss_mask: 0.2155, loss: 0.6328 +2024-05-31 13:38:39,882 - mmdet - INFO - Epoch [11][1500/7330] lr: 1.000e-05, eta: 2:16:34, time: 0.609, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0135, loss_rpn_bbox: 0.0390, loss_cls: 0.1541, acc: 94.1245, loss_bbox: 0.2140, loss_mask: 0.2171, loss: 0.6377 +2024-05-31 13:39:09,792 - mmdet - INFO - Epoch [11][1550/7330] lr: 1.000e-05, eta: 2:16:02, time: 0.598, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0399, loss_cls: 0.1517, acc: 94.2158, loss_bbox: 0.2107, loss_mask: 0.2155, loss: 0.6318 +2024-05-31 13:39:40,128 - mmdet - INFO - Epoch [11][1600/7330] lr: 1.000e-05, eta: 2:15:31, time: 0.607, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0395, loss_cls: 0.1521, acc: 94.1907, loss_bbox: 0.2077, loss_mask: 0.2135, loss: 0.6271 +2024-05-31 13:40:10,662 - mmdet - INFO - Epoch [11][1650/7330] lr: 1.000e-05, eta: 2:15:00, time: 0.611, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0393, loss_cls: 0.1540, acc: 94.1155, loss_bbox: 0.2075, loss_mask: 0.2201, loss: 0.6337 +2024-05-31 13:40:41,215 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 13:40:41,215 - mmdet - INFO - Epoch [11][1700/7330] lr: 1.000e-05, eta: 2:14:29, time: 0.611, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0390, loss_cls: 0.1528, acc: 94.1411, loss_bbox: 0.2086, loss_mask: 0.2139, loss: 0.6272 +2024-05-31 13:41:17,865 - mmdet - INFO - Epoch [11][1750/7330] lr: 1.000e-05, eta: 2:13:58, time: 0.733, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0394, loss_cls: 0.1508, acc: 94.2788, loss_bbox: 0.2090, loss_mask: 0.2158, loss: 0.6286 +2024-05-31 13:41:57,282 - mmdet - INFO - Epoch [11][1800/7330] lr: 1.000e-05, eta: 2:13:29, time: 0.789, data_time: 0.067, memory: 9655, loss_rpn_cls: 0.0122, loss_rpn_bbox: 0.0389, loss_cls: 0.1509, acc: 94.3762, loss_bbox: 0.2053, loss_mask: 0.2175, loss: 0.6248 +2024-05-31 13:42:27,914 - mmdet - INFO - Epoch [11][1850/7330] lr: 1.000e-05, eta: 2:12:57, time: 0.613, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0122, loss_rpn_bbox: 0.0376, loss_cls: 0.1433, acc: 94.6506, loss_bbox: 0.1948, loss_mask: 0.2134, loss: 0.6013 +2024-05-31 13:43:01,326 - mmdet - INFO - Epoch [11][1900/7330] lr: 1.000e-05, eta: 2:12:27, time: 0.668, data_time: 0.042, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0382, loss_cls: 0.1491, acc: 94.2954, loss_bbox: 0.2024, loss_mask: 0.2161, loss: 0.6188 +2024-05-31 13:43:31,882 - mmdet - INFO - Epoch [11][1950/7330] lr: 1.000e-05, eta: 2:11:55, time: 0.611, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0375, loss_cls: 0.1507, acc: 94.2612, loss_bbox: 0.1997, loss_mask: 0.2140, loss: 0.6152 +2024-05-31 13:44:06,071 - mmdet - INFO - Epoch [11][2000/7330] lr: 1.000e-05, eta: 2:11:25, time: 0.684, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0371, loss_cls: 0.1464, acc: 94.4368, loss_bbox: 0.1987, loss_mask: 0.2109, loss: 0.6056 +2024-05-31 13:44:36,384 - mmdet - INFO - Epoch [11][2050/7330] lr: 1.000e-05, eta: 2:10:54, time: 0.606, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0123, loss_rpn_bbox: 0.0365, loss_cls: 0.1437, acc: 94.5500, loss_bbox: 0.1955, loss_mask: 0.2110, loss: 0.5989 +2024-05-31 13:45:09,057 - mmdet - INFO - Epoch [11][2100/7330] lr: 1.000e-05, eta: 2:10:23, time: 0.653, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0380, loss_cls: 0.1479, acc: 94.4187, loss_bbox: 0.2003, loss_mask: 0.2179, loss: 0.6166 +2024-05-31 13:45:39,782 - mmdet - INFO - Epoch [11][2150/7330] lr: 1.000e-05, eta: 2:09:51, time: 0.614, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0394, loss_cls: 0.1519, acc: 94.2927, loss_bbox: 0.2097, loss_mask: 0.2153, loss: 0.6296 +2024-05-31 13:46:10,040 - mmdet - INFO - Epoch [11][2200/7330] lr: 1.000e-05, eta: 2:09:20, time: 0.605, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0358, loss_cls: 0.1475, acc: 94.4126, loss_bbox: 0.1989, loss_mask: 0.2117, loss: 0.6065 +2024-05-31 13:46:40,165 - mmdet - INFO - Epoch [11][2250/7330] lr: 1.000e-05, eta: 2:08:49, time: 0.603, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0114, loss_rpn_bbox: 0.0365, loss_cls: 0.1443, acc: 94.5378, loss_bbox: 0.1960, loss_mask: 0.2111, loss: 0.5993 +2024-05-31 13:47:10,874 - mmdet - INFO - Epoch [11][2300/7330] lr: 1.000e-05, eta: 2:08:18, time: 0.614, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0396, loss_cls: 0.1557, acc: 94.0957, loss_bbox: 0.2121, loss_mask: 0.2154, loss: 0.6368 +2024-05-31 13:47:40,764 - mmdet - INFO - Epoch [11][2350/7330] lr: 1.000e-05, eta: 2:07:46, time: 0.598, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0377, loss_cls: 0.1489, acc: 94.3625, loss_bbox: 0.1984, loss_mask: 0.2135, loss: 0.6117 +2024-05-31 13:48:11,098 - mmdet - INFO - Epoch [11][2400/7330] lr: 1.000e-05, eta: 2:07:15, time: 0.607, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0423, loss_cls: 0.1555, acc: 94.1184, loss_bbox: 0.2163, loss_mask: 0.2229, loss: 0.6502 +2024-05-31 13:48:41,097 - mmdet - INFO - Epoch [11][2450/7330] lr: 1.000e-05, eta: 2:06:44, time: 0.600, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0123, loss_rpn_bbox: 0.0387, loss_cls: 0.1428, acc: 94.5435, loss_bbox: 0.2037, loss_mask: 0.2168, loss: 0.6142 +2024-05-31 13:49:11,319 - mmdet - INFO - Epoch [11][2500/7330] lr: 1.000e-05, eta: 2:06:12, time: 0.604, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0376, loss_cls: 0.1463, acc: 94.4380, loss_bbox: 0.1986, loss_mask: 0.2113, loss: 0.6075 +2024-05-31 13:49:41,514 - mmdet - INFO - Epoch [11][2550/7330] lr: 1.000e-05, eta: 2:05:41, time: 0.604, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0382, loss_cls: 0.1495, acc: 94.3604, loss_bbox: 0.2065, loss_mask: 0.2173, loss: 0.6245 +2024-05-31 13:50:11,974 - mmdet - INFO - Epoch [11][2600/7330] lr: 1.000e-05, eta: 2:05:10, time: 0.609, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0117, loss_rpn_bbox: 0.0384, loss_cls: 0.1457, acc: 94.4353, loss_bbox: 0.1991, loss_mask: 0.2108, loss: 0.6056 +2024-05-31 13:50:52,395 - mmdet - INFO - Epoch [11][2650/7330] lr: 1.000e-05, eta: 2:04:40, time: 0.808, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0385, loss_cls: 0.1519, acc: 94.3325, loss_bbox: 0.2031, loss_mask: 0.2167, loss: 0.6233 +2024-05-31 13:51:24,717 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 13:51:24,718 - mmdet - INFO - Epoch [11][2700/7330] lr: 1.000e-05, eta: 2:04:09, time: 0.646, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0383, loss_cls: 0.1448, acc: 94.4412, loss_bbox: 0.1965, loss_mask: 0.2148, loss: 0.6075 +2024-05-31 13:51:54,800 - mmdet - INFO - Epoch [11][2750/7330] lr: 1.000e-05, eta: 2:03:38, time: 0.602, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0366, loss_cls: 0.1457, acc: 94.4561, loss_bbox: 0.2004, loss_mask: 0.2162, loss: 0.6122 +2024-05-31 13:52:27,617 - mmdet - INFO - Epoch [11][2800/7330] lr: 1.000e-05, eta: 2:03:07, time: 0.656, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0126, loss_rpn_bbox: 0.0362, loss_cls: 0.1459, acc: 94.5046, loss_bbox: 0.1965, loss_mask: 0.2094, loss: 0.6006 +2024-05-31 13:52:58,345 - mmdet - INFO - Epoch [11][2850/7330] lr: 1.000e-05, eta: 2:02:36, time: 0.615, data_time: 0.069, memory: 9655, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0428, loss_cls: 0.1518, acc: 94.2195, loss_bbox: 0.2080, loss_mask: 0.2203, loss: 0.6372 +2024-05-31 13:53:31,752 - mmdet - INFO - Epoch [11][2900/7330] lr: 1.000e-05, eta: 2:02:05, time: 0.668, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0395, loss_cls: 0.1577, acc: 94.0337, loss_bbox: 0.2138, loss_mask: 0.2176, loss: 0.6437 +2024-05-31 13:54:04,302 - mmdet - INFO - Epoch [11][2950/7330] lr: 1.000e-05, eta: 2:01:34, time: 0.651, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0146, loss_rpn_bbox: 0.0423, loss_cls: 0.1632, acc: 93.8835, loss_bbox: 0.2173, loss_mask: 0.2206, loss: 0.6580 +2024-05-31 13:54:34,793 - mmdet - INFO - Epoch [11][3000/7330] lr: 1.000e-05, eta: 2:01:03, time: 0.609, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0389, loss_cls: 0.1522, acc: 94.2134, loss_bbox: 0.2052, loss_mask: 0.2125, loss: 0.6223 +2024-05-31 13:55:06,048 - mmdet - INFO - Epoch [11][3050/7330] lr: 1.000e-05, eta: 2:00:32, time: 0.625, data_time: 0.064, memory: 9655, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0406, loss_cls: 0.1529, acc: 94.1814, loss_bbox: 0.2080, loss_mask: 0.2173, loss: 0.6334 +2024-05-31 13:55:36,595 - mmdet - INFO - Epoch [11][3100/7330] lr: 1.000e-05, eta: 2:00:01, time: 0.611, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0107, loss_rpn_bbox: 0.0362, loss_cls: 0.1458, acc: 94.5291, loss_bbox: 0.2011, loss_mask: 0.2127, loss: 0.6066 +2024-05-31 13:56:06,719 - mmdet - INFO - Epoch [11][3150/7330] lr: 1.000e-05, eta: 1:59:29, time: 0.602, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0371, loss_cls: 0.1514, acc: 94.2017, loss_bbox: 0.2068, loss_mask: 0.2172, loss: 0.6250 +2024-05-31 13:56:37,146 - mmdet - INFO - Epoch [11][3200/7330] lr: 1.000e-05, eta: 1:58:58, time: 0.609, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0413, loss_cls: 0.1553, acc: 94.1147, loss_bbox: 0.2110, loss_mask: 0.2208, loss: 0.6432 +2024-05-31 13:57:07,040 - mmdet - INFO - Epoch [11][3250/7330] lr: 1.000e-05, eta: 1:58:27, time: 0.598, data_time: 0.039, memory: 9655, loss_rpn_cls: 0.0117, loss_rpn_bbox: 0.0387, loss_cls: 0.1468, acc: 94.4487, loss_bbox: 0.2024, loss_mask: 0.2143, loss: 0.6139 +2024-05-31 13:57:36,911 - mmdet - INFO - Epoch [11][3300/7330] lr: 1.000e-05, eta: 1:57:55, time: 0.597, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0111, loss_rpn_bbox: 0.0375, loss_cls: 0.1442, acc: 94.5039, loss_bbox: 0.2020, loss_mask: 0.2161, loss: 0.6108 +2024-05-31 13:58:07,789 - mmdet - INFO - Epoch [11][3350/7330] lr: 1.000e-05, eta: 1:57:24, time: 0.618, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0398, loss_cls: 0.1628, acc: 93.8433, loss_bbox: 0.2146, loss_mask: 0.2217, loss: 0.6535 +2024-05-31 13:58:37,628 - mmdet - INFO - Epoch [11][3400/7330] lr: 1.000e-05, eta: 1:56:53, time: 0.597, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0382, loss_cls: 0.1541, acc: 94.1233, loss_bbox: 0.2099, loss_mask: 0.2188, loss: 0.6341 +2024-05-31 13:59:08,300 - mmdet - INFO - Epoch [11][3450/7330] lr: 1.000e-05, eta: 1:56:22, time: 0.613, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0384, loss_cls: 0.1532, acc: 94.1155, loss_bbox: 0.2085, loss_mask: 0.2192, loss: 0.6322 +2024-05-31 13:59:42,503 - mmdet - INFO - Epoch [11][3500/7330] lr: 1.000e-05, eta: 1:55:51, time: 0.684, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0368, loss_cls: 0.1461, acc: 94.5530, loss_bbox: 0.1992, loss_mask: 0.2105, loss: 0.6047 +2024-05-31 14:00:20,094 - mmdet - INFO - Epoch [11][3550/7330] lr: 1.000e-05, eta: 1:55:21, time: 0.752, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0357, loss_cls: 0.1461, acc: 94.4993, loss_bbox: 0.1997, loss_mask: 0.2125, loss: 0.6064 +2024-05-31 14:00:49,990 - mmdet - INFO - Epoch [11][3600/7330] lr: 1.000e-05, eta: 1:54:49, time: 0.598, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0379, loss_cls: 0.1554, acc: 94.1846, loss_bbox: 0.2044, loss_mask: 0.2154, loss: 0.6266 +2024-05-31 14:01:19,937 - mmdet - INFO - Epoch [11][3650/7330] lr: 1.000e-05, eta: 1:54:18, time: 0.599, data_time: 0.070, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0406, loss_cls: 0.1515, acc: 94.2017, loss_bbox: 0.2077, loss_mask: 0.2200, loss: 0.6334 +2024-05-31 14:01:53,133 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 14:01:53,133 - mmdet - INFO - Epoch [11][3700/7330] lr: 1.000e-05, eta: 1:53:47, time: 0.664, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0120, loss_rpn_bbox: 0.0366, loss_cls: 0.1476, acc: 94.4102, loss_bbox: 0.2020, loss_mask: 0.2120, loss: 0.6102 +2024-05-31 14:02:25,487 - mmdet - INFO - Epoch [11][3750/7330] lr: 1.000e-05, eta: 1:53:16, time: 0.647, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0122, loss_rpn_bbox: 0.0381, loss_cls: 0.1476, acc: 94.3293, loss_bbox: 0.2026, loss_mask: 0.2122, loss: 0.6127 +2024-05-31 14:02:55,149 - mmdet - INFO - Epoch [11][3800/7330] lr: 1.000e-05, eta: 1:52:45, time: 0.593, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0373, loss_cls: 0.1519, acc: 94.2021, loss_bbox: 0.2079, loss_mask: 0.2167, loss: 0.6262 +2024-05-31 14:03:27,398 - mmdet - INFO - Epoch [11][3850/7330] lr: 1.000e-05, eta: 1:52:14, time: 0.645, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0390, loss_cls: 0.1534, acc: 94.1372, loss_bbox: 0.2096, loss_mask: 0.2203, loss: 0.6347 +2024-05-31 14:03:57,881 - mmdet - INFO - Epoch [11][3900/7330] lr: 1.000e-05, eta: 1:51:43, time: 0.609, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0387, loss_cls: 0.1512, acc: 94.2556, loss_bbox: 0.2089, loss_mask: 0.2168, loss: 0.6287 +2024-05-31 14:04:28,097 - mmdet - INFO - Epoch [11][3950/7330] lr: 1.000e-05, eta: 1:51:11, time: 0.605, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0117, loss_rpn_bbox: 0.0366, loss_cls: 0.1452, acc: 94.4841, loss_bbox: 0.1983, loss_mask: 0.2107, loss: 0.6026 +2024-05-31 14:04:58,352 - mmdet - INFO - Epoch [11][4000/7330] lr: 1.000e-05, eta: 1:50:40, time: 0.605, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0392, loss_cls: 0.1533, acc: 94.2217, loss_bbox: 0.2076, loss_mask: 0.2160, loss: 0.6292 +2024-05-31 14:05:28,303 - mmdet - INFO - Epoch [11][4050/7330] lr: 1.000e-05, eta: 1:50:09, time: 0.599, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0346, loss_cls: 0.1463, acc: 94.4126, loss_bbox: 0.2008, loss_mask: 0.2127, loss: 0.6080 +2024-05-31 14:05:58,199 - mmdet - INFO - Epoch [11][4100/7330] lr: 1.000e-05, eta: 1:49:37, time: 0.598, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0122, loss_rpn_bbox: 0.0362, loss_cls: 0.1452, acc: 94.4705, loss_bbox: 0.1976, loss_mask: 0.2180, loss: 0.6092 +2024-05-31 14:06:28,002 - mmdet - INFO - Epoch [11][4150/7330] lr: 1.000e-05, eta: 1:49:06, time: 0.596, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0369, loss_cls: 0.1452, acc: 94.4924, loss_bbox: 0.2011, loss_mask: 0.2123, loss: 0.6085 +2024-05-31 14:06:58,253 - mmdet - INFO - Epoch [11][4200/7330] lr: 1.000e-05, eta: 1:48:35, time: 0.605, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0411, loss_cls: 0.1587, acc: 93.9482, loss_bbox: 0.2171, loss_mask: 0.2159, loss: 0.6462 +2024-05-31 14:07:28,273 - mmdet - INFO - Epoch [11][4250/7330] lr: 1.000e-05, eta: 1:48:04, time: 0.600, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0398, loss_cls: 0.1499, acc: 94.3960, loss_bbox: 0.2050, loss_mask: 0.2184, loss: 0.6267 +2024-05-31 14:07:58,574 - mmdet - INFO - Epoch [11][4300/7330] lr: 1.000e-05, eta: 1:47:32, time: 0.606, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0394, loss_cls: 0.1587, acc: 93.9854, loss_bbox: 0.2143, loss_mask: 0.2214, loss: 0.6473 +2024-05-31 14:08:29,253 - mmdet - INFO - Epoch [11][4350/7330] lr: 1.000e-05, eta: 1:47:01, time: 0.614, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0381, loss_cls: 0.1475, acc: 94.4739, loss_bbox: 0.2038, loss_mask: 0.2161, loss: 0.6187 +2024-05-31 14:09:08,789 - mmdet - INFO - Epoch [11][4400/7330] lr: 1.000e-05, eta: 1:46:31, time: 0.791, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0398, loss_cls: 0.1546, acc: 94.1125, loss_bbox: 0.2096, loss_mask: 0.2182, loss: 0.6355 +2024-05-31 14:09:41,713 - mmdet - INFO - Epoch [11][4450/7330] lr: 1.000e-05, eta: 1:46:00, time: 0.658, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0391, loss_cls: 0.1567, acc: 94.0120, loss_bbox: 0.2148, loss_mask: 0.2173, loss: 0.6421 +2024-05-31 14:10:11,848 - mmdet - INFO - Epoch [11][4500/7330] lr: 1.000e-05, eta: 1:45:29, time: 0.603, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0378, loss_cls: 0.1497, acc: 94.2456, loss_bbox: 0.2044, loss_mask: 0.2099, loss: 0.6146 +2024-05-31 14:10:44,281 - mmdet - INFO - Epoch [11][4550/7330] lr: 1.000e-05, eta: 1:44:58, time: 0.649, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0381, loss_cls: 0.1539, acc: 94.1753, loss_bbox: 0.2085, loss_mask: 0.2182, loss: 0.6316 +2024-05-31 14:11:13,784 - mmdet - INFO - Epoch [11][4600/7330] lr: 1.000e-05, eta: 1:44:26, time: 0.590, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0356, loss_cls: 0.1539, acc: 94.1821, loss_bbox: 0.2087, loss_mask: 0.2141, loss: 0.6257 +2024-05-31 14:11:46,939 - mmdet - INFO - Epoch [11][4650/7330] lr: 1.000e-05, eta: 1:43:56, time: 0.663, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0406, loss_cls: 0.1456, acc: 94.4775, loss_bbox: 0.2011, loss_mask: 0.2136, loss: 0.6150 +2024-05-31 14:12:19,465 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 14:12:19,466 - mmdet - INFO - Epoch [11][4700/7330] lr: 1.000e-05, eta: 1:43:25, time: 0.651, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0123, loss_rpn_bbox: 0.0401, loss_cls: 0.1516, acc: 94.1719, loss_bbox: 0.2100, loss_mask: 0.2195, loss: 0.6336 +2024-05-31 14:12:49,602 - mmdet - INFO - Epoch [11][4750/7330] lr: 1.000e-05, eta: 1:42:53, time: 0.603, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0392, loss_cls: 0.1467, acc: 94.4275, loss_bbox: 0.2004, loss_mask: 0.2136, loss: 0.6133 +2024-05-31 14:13:19,873 - mmdet - INFO - Epoch [11][4800/7330] lr: 1.000e-05, eta: 1:42:22, time: 0.605, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0421, loss_cls: 0.1541, acc: 94.0693, loss_bbox: 0.2088, loss_mask: 0.2150, loss: 0.6344 +2024-05-31 14:13:49,756 - mmdet - INFO - Epoch [11][4850/7330] lr: 1.000e-05, eta: 1:41:51, time: 0.598, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0116, loss_rpn_bbox: 0.0348, loss_cls: 0.1442, acc: 94.5120, loss_bbox: 0.1938, loss_mask: 0.2095, loss: 0.5939 +2024-05-31 14:14:21,008 - mmdet - INFO - Epoch [11][4900/7330] lr: 1.000e-05, eta: 1:41:20, time: 0.625, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0135, loss_rpn_bbox: 0.0394, loss_cls: 0.1558, acc: 93.9939, loss_bbox: 0.2101, loss_mask: 0.2181, loss: 0.6370 +2024-05-31 14:14:50,838 - mmdet - INFO - Epoch [11][4950/7330] lr: 1.000e-05, eta: 1:40:48, time: 0.597, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0374, loss_cls: 0.1456, acc: 94.4521, loss_bbox: 0.2044, loss_mask: 0.2109, loss: 0.6107 +2024-05-31 14:15:20,602 - mmdet - INFO - Epoch [11][5000/7330] lr: 1.000e-05, eta: 1:40:17, time: 0.595, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0405, loss_cls: 0.1550, acc: 94.1653, loss_bbox: 0.2056, loss_mask: 0.2160, loss: 0.6314 +2024-05-31 14:15:51,107 - mmdet - INFO - Epoch [11][5050/7330] lr: 1.000e-05, eta: 1:39:46, time: 0.610, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0390, loss_cls: 0.1474, acc: 94.4207, loss_bbox: 0.2009, loss_mask: 0.2192, loss: 0.6197 +2024-05-31 14:16:21,773 - mmdet - INFO - Epoch [11][5100/7330] lr: 1.000e-05, eta: 1:39:15, time: 0.613, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0392, loss_cls: 0.1550, acc: 94.1204, loss_bbox: 0.2073, loss_mask: 0.2156, loss: 0.6303 +2024-05-31 14:16:51,576 - mmdet - INFO - Epoch [11][5150/7330] lr: 1.000e-05, eta: 1:38:43, time: 0.596, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0397, loss_cls: 0.1465, acc: 94.4629, loss_bbox: 0.1994, loss_mask: 0.2114, loss: 0.6097 +2024-05-31 14:17:21,384 - mmdet - INFO - Epoch [11][5200/7330] lr: 1.000e-05, eta: 1:38:12, time: 0.596, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0126, loss_rpn_bbox: 0.0390, loss_cls: 0.1492, acc: 94.2825, loss_bbox: 0.2012, loss_mask: 0.2156, loss: 0.6176 +2024-05-31 14:17:54,481 - mmdet - INFO - Epoch [11][5250/7330] lr: 1.000e-05, eta: 1:37:41, time: 0.662, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0138, loss_rpn_bbox: 0.0413, loss_cls: 0.1586, acc: 94.0371, loss_bbox: 0.2097, loss_mask: 0.2168, loss: 0.6402 +2024-05-31 14:18:31,352 - mmdet - INFO - Epoch [11][5300/7330] lr: 1.000e-05, eta: 1:37:11, time: 0.737, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0372, loss_cls: 0.1469, acc: 94.4199, loss_bbox: 0.1972, loss_mask: 0.2108, loss: 0.6043 +2024-05-31 14:19:03,440 - mmdet - INFO - Epoch [11][5350/7330] lr: 1.000e-05, eta: 1:36:40, time: 0.642, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0135, loss_rpn_bbox: 0.0408, loss_cls: 0.1539, acc: 94.1628, loss_bbox: 0.2057, loss_mask: 0.2137, loss: 0.6276 +2024-05-31 14:19:34,027 - mmdet - INFO - Epoch [11][5400/7330] lr: 1.000e-05, eta: 1:36:08, time: 0.612, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0384, loss_cls: 0.1459, acc: 94.4460, loss_bbox: 0.1984, loss_mask: 0.2100, loss: 0.6061 +2024-05-31 14:20:07,913 - mmdet - INFO - Epoch [11][5450/7330] lr: 1.000e-05, eta: 1:35:38, time: 0.677, data_time: 0.065, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0388, loss_cls: 0.1549, acc: 94.1780, loss_bbox: 0.2055, loss_mask: 0.2146, loss: 0.6268 +2024-05-31 14:20:38,693 - mmdet - INFO - Epoch [11][5500/7330] lr: 1.000e-05, eta: 1:35:06, time: 0.616, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0414, loss_cls: 0.1551, acc: 94.0796, loss_bbox: 0.2141, loss_mask: 0.2213, loss: 0.6457 +2024-05-31 14:21:11,387 - mmdet - INFO - Epoch [11][5550/7330] lr: 1.000e-05, eta: 1:34:35, time: 0.654, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0392, loss_cls: 0.1498, acc: 94.3406, loss_bbox: 0.2006, loss_mask: 0.2140, loss: 0.6168 +2024-05-31 14:21:43,922 - mmdet - INFO - Epoch [11][5600/7330] lr: 1.000e-05, eta: 1:34:04, time: 0.651, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0387, loss_cls: 0.1573, acc: 93.9636, loss_bbox: 0.2143, loss_mask: 0.2166, loss: 0.6400 +2024-05-31 14:22:14,128 - mmdet - INFO - Epoch [11][5650/7330] lr: 1.000e-05, eta: 1:33:33, time: 0.604, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0389, loss_cls: 0.1477, acc: 94.4172, loss_bbox: 0.2007, loss_mask: 0.2155, loss: 0.6159 +2024-05-31 14:22:43,628 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 14:22:43,628 - mmdet - INFO - Epoch [11][5700/7330] lr: 1.000e-05, eta: 1:33:02, time: 0.590, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0373, loss_cls: 0.1458, acc: 94.4812, loss_bbox: 0.1923, loss_mask: 0.2109, loss: 0.5988 +2024-05-31 14:23:13,822 - mmdet - INFO - Epoch [11][5750/7330] lr: 1.000e-05, eta: 1:32:31, time: 0.604, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0397, loss_cls: 0.1498, acc: 94.3853, loss_bbox: 0.2064, loss_mask: 0.2175, loss: 0.6264 +2024-05-31 14:23:43,980 - mmdet - INFO - Epoch [11][5800/7330] lr: 1.000e-05, eta: 1:31:59, time: 0.603, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0120, loss_rpn_bbox: 0.0380, loss_cls: 0.1513, acc: 94.1292, loss_bbox: 0.2096, loss_mask: 0.2186, loss: 0.6295 +2024-05-31 14:24:14,013 - mmdet - INFO - Epoch [11][5850/7330] lr: 1.000e-05, eta: 1:31:28, time: 0.600, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0112, loss_rpn_bbox: 0.0356, loss_cls: 0.1410, acc: 94.5830, loss_bbox: 0.1883, loss_mask: 0.2071, loss: 0.5832 +2024-05-31 14:24:44,102 - mmdet - INFO - Epoch [11][5900/7330] lr: 1.000e-05, eta: 1:30:57, time: 0.602, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0117, loss_rpn_bbox: 0.0362, loss_cls: 0.1459, acc: 94.4792, loss_bbox: 0.2035, loss_mask: 0.2124, loss: 0.6097 +2024-05-31 14:25:14,855 - mmdet - INFO - Epoch [11][5950/7330] lr: 1.000e-05, eta: 1:30:26, time: 0.615, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0366, loss_cls: 0.1459, acc: 94.4338, loss_bbox: 0.1984, loss_mask: 0.2102, loss: 0.6044 +2024-05-31 14:25:45,558 - mmdet - INFO - Epoch [11][6000/7330] lr: 1.000e-05, eta: 1:29:54, time: 0.614, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0399, loss_cls: 0.1559, acc: 94.0669, loss_bbox: 0.2117, loss_mask: 0.2187, loss: 0.6396 +2024-05-31 14:26:15,299 - mmdet - INFO - Epoch [11][6050/7330] lr: 1.000e-05, eta: 1:29:23, time: 0.595, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0351, loss_cls: 0.1420, acc: 94.6477, loss_bbox: 0.1962, loss_mask: 0.2137, loss: 0.5993 +2024-05-31 14:26:45,867 - mmdet - INFO - Epoch [11][6100/7330] lr: 1.000e-05, eta: 1:28:52, time: 0.611, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0400, loss_cls: 0.1531, acc: 94.1699, loss_bbox: 0.2088, loss_mask: 0.2180, loss: 0.6331 +2024-05-31 14:27:20,435 - mmdet - INFO - Epoch [11][6150/7330] lr: 1.000e-05, eta: 1:28:21, time: 0.691, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0384, loss_cls: 0.1499, acc: 94.2346, loss_bbox: 0.2090, loss_mask: 0.2161, loss: 0.6258 +2024-05-31 14:27:57,085 - mmdet - INFO - Epoch [11][6200/7330] lr: 1.000e-05, eta: 1:27:50, time: 0.733, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0116, loss_rpn_bbox: 0.0358, loss_cls: 0.1426, acc: 94.5315, loss_bbox: 0.1976, loss_mask: 0.2112, loss: 0.5988 +2024-05-31 14:28:27,284 - mmdet - INFO - Epoch [11][6250/7330] lr: 1.000e-05, eta: 1:27:19, time: 0.604, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0395, loss_cls: 0.1525, acc: 94.1875, loss_bbox: 0.2104, loss_mask: 0.2195, loss: 0.6350 +2024-05-31 14:28:57,170 - mmdet - INFO - Epoch [11][6300/7330] lr: 1.000e-05, eta: 1:26:48, time: 0.598, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0363, loss_cls: 0.1390, acc: 94.7288, loss_bbox: 0.1894, loss_mask: 0.2083, loss: 0.5854 +2024-05-31 14:29:29,645 - mmdet - INFO - Epoch [11][6350/7330] lr: 1.000e-05, eta: 1:26:17, time: 0.649, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0372, loss_cls: 0.1514, acc: 94.2656, loss_bbox: 0.2030, loss_mask: 0.2114, loss: 0.6154 +2024-05-31 14:30:01,999 - mmdet - INFO - Epoch [11][6400/7330] lr: 1.000e-05, eta: 1:25:46, time: 0.647, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0372, loss_cls: 0.1465, acc: 94.3633, loss_bbox: 0.2012, loss_mask: 0.2112, loss: 0.6087 +2024-05-31 14:30:31,711 - mmdet - INFO - Epoch [11][6450/7330] lr: 1.000e-05, eta: 1:25:15, time: 0.594, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0108, loss_rpn_bbox: 0.0352, loss_cls: 0.1400, acc: 94.7009, loss_bbox: 0.1900, loss_mask: 0.2058, loss: 0.5817 +2024-05-31 14:31:04,096 - mmdet - INFO - Epoch [11][6500/7330] lr: 1.000e-05, eta: 1:24:44, time: 0.648, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0370, loss_cls: 0.1512, acc: 94.2607, loss_bbox: 0.2034, loss_mask: 0.2161, loss: 0.6207 +2024-05-31 14:31:33,760 - mmdet - INFO - Epoch [11][6550/7330] lr: 1.000e-05, eta: 1:24:12, time: 0.593, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0380, loss_cls: 0.1490, acc: 94.3406, loss_bbox: 0.2010, loss_mask: 0.2138, loss: 0.6147 +2024-05-31 14:32:04,541 - mmdet - INFO - Epoch [11][6600/7330] lr: 1.000e-05, eta: 1:23:41, time: 0.616, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0138, loss_rpn_bbox: 0.0403, loss_cls: 0.1535, acc: 94.1606, loss_bbox: 0.2107, loss_mask: 0.2146, loss: 0.6329 +2024-05-31 14:32:34,587 - mmdet - INFO - Epoch [11][6650/7330] lr: 1.000e-05, eta: 1:23:10, time: 0.601, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0376, loss_cls: 0.1485, acc: 94.3701, loss_bbox: 0.2027, loss_mask: 0.2153, loss: 0.6171 +2024-05-31 14:33:05,111 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 14:33:05,111 - mmdet - INFO - Epoch [11][6700/7330] lr: 1.000e-05, eta: 1:22:39, time: 0.610, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0364, loss_cls: 0.1495, acc: 94.2771, loss_bbox: 0.2048, loss_mask: 0.2167, loss: 0.6212 +2024-05-31 14:33:35,218 - mmdet - INFO - Epoch [11][6750/7330] lr: 1.000e-05, eta: 1:22:07, time: 0.602, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0382, loss_cls: 0.1492, acc: 94.3987, loss_bbox: 0.2011, loss_mask: 0.2139, loss: 0.6157 +2024-05-31 14:34:05,640 - mmdet - INFO - Epoch [11][6800/7330] lr: 1.000e-05, eta: 1:21:36, time: 0.608, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0398, loss_cls: 0.1500, acc: 94.2742, loss_bbox: 0.2059, loss_mask: 0.2173, loss: 0.6253 +2024-05-31 14:34:35,491 - mmdet - INFO - Epoch [11][6850/7330] lr: 1.000e-05, eta: 1:21:05, time: 0.597, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0126, loss_rpn_bbox: 0.0375, loss_cls: 0.1514, acc: 94.2883, loss_bbox: 0.2054, loss_mask: 0.2172, loss: 0.6242 +2024-05-31 14:35:05,639 - mmdet - INFO - Epoch [11][6900/7330] lr: 1.000e-05, eta: 1:20:34, time: 0.603, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0403, loss_cls: 0.1578, acc: 93.9265, loss_bbox: 0.2168, loss_mask: 0.2171, loss: 0.6450 +2024-05-31 14:35:36,474 - mmdet - INFO - Epoch [11][6950/7330] lr: 1.000e-05, eta: 1:20:02, time: 0.617, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0398, loss_cls: 0.1541, acc: 94.1624, loss_bbox: 0.2098, loss_mask: 0.2156, loss: 0.6335 +2024-05-31 14:36:06,510 - mmdet - INFO - Epoch [11][7000/7330] lr: 1.000e-05, eta: 1:19:31, time: 0.601, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0148, loss_rpn_bbox: 0.0393, loss_cls: 0.1502, acc: 94.2952, loss_bbox: 0.2068, loss_mask: 0.2173, loss: 0.6284 +2024-05-31 14:36:45,185 - mmdet - INFO - Epoch [11][7050/7330] lr: 1.000e-05, eta: 1:19:01, time: 0.773, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0119, loss_rpn_bbox: 0.0375, loss_cls: 0.1487, acc: 94.3669, loss_bbox: 0.2000, loss_mask: 0.2132, loss: 0.6112 +2024-05-31 14:37:17,711 - mmdet - INFO - Epoch [11][7100/7330] lr: 1.000e-05, eta: 1:18:30, time: 0.650, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0115, loss_rpn_bbox: 0.0343, loss_cls: 0.1418, acc: 94.6309, loss_bbox: 0.1893, loss_mask: 0.2067, loss: 0.5836 +2024-05-31 14:37:48,078 - mmdet - INFO - Epoch [11][7150/7330] lr: 1.000e-05, eta: 1:17:58, time: 0.607, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0385, loss_cls: 0.1435, acc: 94.5369, loss_bbox: 0.1943, loss_mask: 0.2151, loss: 0.6044 +2024-05-31 14:38:20,817 - mmdet - INFO - Epoch [11][7200/7330] lr: 1.000e-05, eta: 1:17:27, time: 0.655, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0403, loss_cls: 0.1525, acc: 94.2656, loss_bbox: 0.2058, loss_mask: 0.2166, loss: 0.6275 +2024-05-31 14:38:51,244 - mmdet - INFO - Epoch [11][7250/7330] lr: 1.000e-05, eta: 1:16:56, time: 0.609, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0379, loss_cls: 0.1552, acc: 94.1592, loss_bbox: 0.2041, loss_mask: 0.2189, loss: 0.6297 +2024-05-31 14:39:23,731 - mmdet - INFO - Epoch [11][7300/7330] lr: 1.000e-05, eta: 1:16:25, time: 0.650, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0389, loss_cls: 0.1506, acc: 94.2859, loss_bbox: 0.2045, loss_mask: 0.2126, loss: 0.6191 +2024-05-31 14:39:42,344 - mmdet - INFO - Saving checkpoint at 11 epochs +2024-05-31 14:41:16,922 - mmdet - INFO - Evaluating bbox... +2024-05-31 14:41:39,438 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.450 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.670 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.488 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.271 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.481 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.619 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.567 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.567 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.567 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.376 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.604 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.730 + +2024-05-31 14:41:39,439 - mmdet - INFO - Evaluating segm... +2024-05-31 14:42:03,895 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.403 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.637 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.431 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.193 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.430 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.613 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.511 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.511 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.511 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.309 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.553 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.693 + +2024-05-31 14:42:04,275 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 14:42:04,277 - mmdet - INFO - Epoch(val) [11][625] bbox_mAP: 0.4500, bbox_mAP_50: 0.6700, bbox_mAP_75: 0.4880, bbox_mAP_s: 0.2710, bbox_mAP_m: 0.4810, bbox_mAP_l: 0.6190, bbox_mAP_copypaste: 0.450 0.670 0.488 0.271 0.481 0.619, segm_mAP: 0.4030, segm_mAP_50: 0.6370, segm_mAP_75: 0.4310, segm_mAP_s: 0.1930, segm_mAP_m: 0.4300, segm_mAP_l: 0.6130, segm_mAP_copypaste: 0.403 0.637 0.431 0.193 0.430 0.613 +2024-05-31 14:42:44,576 - mmdet - INFO - Epoch [12][50/7330] lr: 1.000e-06, eta: 1:15:35, time: 0.803, data_time: 0.124, memory: 9655, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0370, loss_cls: 0.1419, acc: 94.5854, loss_bbox: 0.1983, loss_mask: 0.2131, loss: 0.6024 +2024-05-31 14:43:18,492 - mmdet - INFO - Epoch [12][100/7330] lr: 1.000e-06, eta: 1:15:04, time: 0.678, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0393, loss_cls: 0.1471, acc: 94.4382, loss_bbox: 0.2020, loss_mask: 0.2157, loss: 0.6175 +2024-05-31 14:43:48,804 - mmdet - INFO - Epoch [12][150/7330] lr: 1.000e-06, eta: 1:14:32, time: 0.606, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0382, loss_cls: 0.1470, acc: 94.4451, loss_bbox: 0.2028, loss_mask: 0.2167, loss: 0.6174 +2024-05-31 14:44:19,730 - mmdet - INFO - Epoch [12][200/7330] lr: 1.000e-06, eta: 1:14:01, time: 0.619, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0389, loss_cls: 0.1498, acc: 94.3284, loss_bbox: 0.2073, loss_mask: 0.2113, loss: 0.6204 +2024-05-31 14:44:50,643 - mmdet - INFO - Epoch [12][250/7330] lr: 1.000e-06, eta: 1:13:30, time: 0.618, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0388, loss_cls: 0.1477, acc: 94.3162, loss_bbox: 0.2047, loss_mask: 0.2190, loss: 0.6236 +2024-05-31 14:45:20,891 - mmdet - INFO - Epoch [12][300/7330] lr: 1.000e-06, eta: 1:12:59, time: 0.605, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0135, loss_rpn_bbox: 0.0364, loss_cls: 0.1444, acc: 94.6079, loss_bbox: 0.1971, loss_mask: 0.2102, loss: 0.6016 +2024-05-31 14:45:51,230 - mmdet - INFO - Epoch [12][350/7330] lr: 1.000e-06, eta: 1:12:28, time: 0.607, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0123, loss_rpn_bbox: 0.0351, loss_cls: 0.1435, acc: 94.5916, loss_bbox: 0.1954, loss_mask: 0.2122, loss: 0.5985 +2024-05-31 14:46:21,466 - mmdet - INFO - Epoch [12][400/7330] lr: 1.000e-06, eta: 1:11:56, time: 0.605, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0117, loss_rpn_bbox: 0.0387, loss_cls: 0.1494, acc: 94.3948, loss_bbox: 0.2036, loss_mask: 0.2139, loss: 0.6173 +2024-05-31 14:46:51,821 - mmdet - INFO - Epoch [12][450/7330] lr: 1.000e-06, eta: 1:11:25, time: 0.607, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0377, loss_cls: 0.1499, acc: 94.3574, loss_bbox: 0.2029, loss_mask: 0.2168, loss: 0.6198 +2024-05-31 14:47:21,957 - mmdet - INFO - Epoch [12][500/7330] lr: 1.000e-06, eta: 1:10:54, time: 0.603, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0126, loss_rpn_bbox: 0.0379, loss_cls: 0.1485, acc: 94.2668, loss_bbox: 0.2037, loss_mask: 0.2196, loss: 0.6223 +2024-05-31 14:47:52,176 - mmdet - INFO - Epoch [12][550/7330] lr: 1.000e-06, eta: 1:10:23, time: 0.604, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0117, loss_rpn_bbox: 0.0359, loss_cls: 0.1399, acc: 94.6460, loss_bbox: 0.1942, loss_mask: 0.2108, loss: 0.5925 +2024-05-31 14:48:22,579 - mmdet - INFO - Epoch [12][600/7330] lr: 1.000e-06, eta: 1:09:52, time: 0.608, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0123, loss_rpn_bbox: 0.0390, loss_cls: 0.1481, acc: 94.3364, loss_bbox: 0.2071, loss_mask: 0.2135, loss: 0.6200 +2024-05-31 14:48:52,811 - mmdet - INFO - Epoch [12][650/7330] lr: 1.000e-06, eta: 1:09:20, time: 0.605, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0364, loss_cls: 0.1453, acc: 94.4778, loss_bbox: 0.1967, loss_mask: 0.2101, loss: 0.6015 +2024-05-31 14:49:23,622 - mmdet - INFO - Epoch [12][700/7330] lr: 1.000e-06, eta: 1:08:49, time: 0.616, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0396, loss_cls: 0.1485, acc: 94.2878, loss_bbox: 0.2043, loss_mask: 0.2189, loss: 0.6234 +2024-05-31 14:49:54,480 - mmdet - INFO - Epoch [12][750/7330] lr: 1.000e-06, eta: 1:08:18, time: 0.617, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0386, loss_cls: 0.1462, acc: 94.3884, loss_bbox: 0.2070, loss_mask: 0.2121, loss: 0.6171 +2024-05-31 14:50:24,622 - mmdet - INFO - Epoch [12][800/7330] lr: 1.000e-06, eta: 1:07:47, time: 0.603, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0109, loss_rpn_bbox: 0.0359, loss_cls: 0.1394, acc: 94.6450, loss_bbox: 0.1948, loss_mask: 0.2133, loss: 0.5943 +2024-05-31 14:50:54,661 - mmdet - INFO - Epoch [12][850/7330] lr: 1.000e-06, eta: 1:07:15, time: 0.601, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0118, loss_rpn_bbox: 0.0375, loss_cls: 0.1434, acc: 94.5957, loss_bbox: 0.1968, loss_mask: 0.2095, loss: 0.5990 +2024-05-31 14:51:24,692 - mmdet - INFO - Epoch [12][900/7330] lr: 1.000e-06, eta: 1:06:44, time: 0.601, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0377, loss_cls: 0.1516, acc: 94.2246, loss_bbox: 0.2025, loss_mask: 0.2123, loss: 0.6165 +2024-05-31 14:52:00,193 - mmdet - INFO - Epoch [12][950/7330] lr: 1.000e-06, eta: 1:06:13, time: 0.710, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0126, loss_rpn_bbox: 0.0395, loss_cls: 0.1482, acc: 94.2898, loss_bbox: 0.2076, loss_mask: 0.2155, loss: 0.6233 +2024-05-31 14:52:32,821 - mmdet - INFO - Epoch [12][1000/7330] lr: 1.000e-06, eta: 1:05:42, time: 0.653, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0108, loss_rpn_bbox: 0.0371, loss_cls: 0.1392, acc: 94.6704, loss_bbox: 0.1960, loss_mask: 0.2100, loss: 0.5930 +2024-05-31 14:53:09,427 - mmdet - INFO - Epoch [12][1050/7330] lr: 1.000e-06, eta: 1:05:12, time: 0.732, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0363, loss_cls: 0.1451, acc: 94.5007, loss_bbox: 0.2032, loss_mask: 0.2149, loss: 0.6123 +2024-05-31 14:53:45,008 - mmdet - INFO - Epoch [12][1100/7330] lr: 1.000e-06, eta: 1:04:41, time: 0.712, data_time: 0.063, memory: 9655, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0398, loss_cls: 0.1509, acc: 94.2336, loss_bbox: 0.2064, loss_mask: 0.2160, loss: 0.6263 +2024-05-31 14:54:15,391 - mmdet - INFO - Epoch [12][1150/7330] lr: 1.000e-06, eta: 1:04:10, time: 0.608, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0383, loss_cls: 0.1508, acc: 94.2488, loss_bbox: 0.2070, loss_mask: 0.2142, loss: 0.6231 +2024-05-31 14:54:45,516 - mmdet - INFO - Epoch [12][1200/7330] lr: 1.000e-06, eta: 1:03:38, time: 0.602, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0120, loss_rpn_bbox: 0.0351, loss_cls: 0.1402, acc: 94.6189, loss_bbox: 0.1945, loss_mask: 0.2048, loss: 0.5865 +2024-05-31 14:55:15,836 - mmdet - INFO - Epoch [12][1250/7330] lr: 1.000e-06, eta: 1:03:07, time: 0.607, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0122, loss_rpn_bbox: 0.0377, loss_cls: 0.1489, acc: 94.4241, loss_bbox: 0.2015, loss_mask: 0.2142, loss: 0.6145 +2024-05-31 14:55:46,049 - mmdet - INFO - Epoch [12][1300/7330] lr: 1.000e-06, eta: 1:02:36, time: 0.604, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0354, loss_cls: 0.1435, acc: 94.4868, loss_bbox: 0.1961, loss_mask: 0.2105, loss: 0.5976 +2024-05-31 14:56:16,650 - mmdet - INFO - Epoch [12][1350/7330] lr: 1.000e-06, eta: 1:02:05, time: 0.612, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0386, loss_cls: 0.1447, acc: 94.5027, loss_bbox: 0.2017, loss_mask: 0.2114, loss: 0.6092 +2024-05-31 14:56:46,956 - mmdet - INFO - Epoch [12][1400/7330] lr: 1.000e-06, eta: 1:01:34, time: 0.606, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0126, loss_rpn_bbox: 0.0408, loss_cls: 0.1481, acc: 94.3262, loss_bbox: 0.2053, loss_mask: 0.2143, loss: 0.6210 +2024-05-31 14:57:17,033 - mmdet - INFO - Epoch [12][1450/7330] lr: 1.000e-06, eta: 1:01:02, time: 0.602, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0120, loss_rpn_bbox: 0.0366, loss_cls: 0.1430, acc: 94.5283, loss_bbox: 0.2002, loss_mask: 0.2122, loss: 0.6039 +2024-05-31 14:57:47,654 - mmdet - INFO - Epoch [12][1500/7330] lr: 1.000e-06, eta: 1:00:31, time: 0.612, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0123, loss_rpn_bbox: 0.0398, loss_cls: 0.1509, acc: 94.2451, loss_bbox: 0.2118, loss_mask: 0.2194, loss: 0.6343 +2024-05-31 14:58:17,817 - mmdet - INFO - Epoch [12][1550/7330] lr: 1.000e-06, eta: 1:00:00, time: 0.603, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0112, loss_rpn_bbox: 0.0357, loss_cls: 0.1447, acc: 94.5117, loss_bbox: 0.1940, loss_mask: 0.2071, loss: 0.5926 +2024-05-31 14:58:48,026 - mmdet - INFO - Epoch [12][1600/7330] lr: 1.000e-06, eta: 0:59:29, time: 0.604, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0382, loss_cls: 0.1465, acc: 94.4497, loss_bbox: 0.2004, loss_mask: 0.2128, loss: 0.6108 +2024-05-31 14:59:18,483 - mmdet - INFO - Epoch [12][1650/7330] lr: 1.000e-06, eta: 0:58:58, time: 0.609, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0138, loss_rpn_bbox: 0.0415, loss_cls: 0.1496, acc: 94.2961, loss_bbox: 0.2039, loss_mask: 0.2123, loss: 0.6212 +2024-05-31 14:59:48,915 - mmdet - INFO - Epoch [12][1700/7330] lr: 1.000e-06, eta: 0:58:26, time: 0.609, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0373, loss_cls: 0.1514, acc: 94.1672, loss_bbox: 0.2056, loss_mask: 0.2181, loss: 0.6261 +2024-05-31 15:00:19,196 - mmdet - INFO - Epoch [12][1750/7330] lr: 1.000e-06, eta: 0:57:55, time: 0.606, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0114, loss_rpn_bbox: 0.0355, loss_cls: 0.1416, acc: 94.6086, loss_bbox: 0.1944, loss_mask: 0.2069, loss: 0.5898 +2024-05-31 15:00:52,747 - mmdet - INFO - Epoch [12][1800/7330] lr: 1.000e-06, eta: 0:57:24, time: 0.671, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0399, loss_cls: 0.1511, acc: 94.2205, loss_bbox: 0.2078, loss_mask: 0.2120, loss: 0.6240 +2024-05-31 15:01:27,882 - mmdet - INFO - Epoch [12][1850/7330] lr: 1.000e-06, eta: 0:56:53, time: 0.703, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0392, loss_cls: 0.1465, acc: 94.3865, loss_bbox: 0.2004, loss_mask: 0.2127, loss: 0.6117 +2024-05-31 15:02:04,273 - mmdet - INFO - Epoch [12][1900/7330] lr: 1.000e-06, eta: 0:56:23, time: 0.728, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0126, loss_rpn_bbox: 0.0372, loss_cls: 0.1463, acc: 94.4797, loss_bbox: 0.1997, loss_mask: 0.2143, loss: 0.6101 +2024-05-31 15:02:37,602 - mmdet - INFO - Epoch [12][1950/7330] lr: 1.000e-06, eta: 0:55:52, time: 0.667, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0120, loss_rpn_bbox: 0.0366, loss_cls: 0.1447, acc: 94.4524, loss_bbox: 0.1949, loss_mask: 0.2105, loss: 0.5987 +2024-05-31 15:03:14,140 - mmdet - INFO - Epoch [12][2000/7330] lr: 1.000e-06, eta: 0:55:21, time: 0.731, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0385, loss_cls: 0.1462, acc: 94.4814, loss_bbox: 0.1973, loss_mask: 0.2086, loss: 0.6039 +2024-05-31 15:03:44,218 - mmdet - INFO - Epoch [12][2050/7330] lr: 1.000e-06, eta: 0:54:50, time: 0.602, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0380, loss_cls: 0.1503, acc: 94.2585, loss_bbox: 0.2024, loss_mask: 0.2160, loss: 0.6200 +2024-05-31 15:04:14,630 - mmdet - INFO - Epoch [12][2100/7330] lr: 1.000e-06, eta: 0:54:18, time: 0.608, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0126, loss_rpn_bbox: 0.0378, loss_cls: 0.1512, acc: 94.3040, loss_bbox: 0.2068, loss_mask: 0.2126, loss: 0.6210 +2024-05-31 15:04:45,446 - mmdet - INFO - Epoch [12][2150/7330] lr: 1.000e-06, eta: 0:53:47, time: 0.616, data_time: 0.066, memory: 9655, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0380, loss_cls: 0.1462, acc: 94.4451, loss_bbox: 0.1988, loss_mask: 0.2140, loss: 0.6099 +2024-05-31 15:05:15,189 - mmdet - INFO - Epoch [12][2200/7330] lr: 1.000e-06, eta: 0:53:16, time: 0.595, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0365, loss_cls: 0.1526, acc: 94.2124, loss_bbox: 0.2064, loss_mask: 0.2194, loss: 0.6278 +2024-05-31 15:05:45,123 - mmdet - INFO - Epoch [12][2250/7330] lr: 1.000e-06, eta: 0:52:45, time: 0.599, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0379, loss_cls: 0.1471, acc: 94.3984, loss_bbox: 0.2027, loss_mask: 0.2174, loss: 0.6174 +2024-05-31 15:06:15,216 - mmdet - INFO - Epoch [12][2300/7330] lr: 1.000e-06, eta: 0:52:13, time: 0.602, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0116, loss_rpn_bbox: 0.0363, loss_cls: 0.1475, acc: 94.4141, loss_bbox: 0.2017, loss_mask: 0.2150, loss: 0.6121 +2024-05-31 15:06:45,546 - mmdet - INFO - Epoch [12][2350/7330] lr: 1.000e-06, eta: 0:51:42, time: 0.607, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0374, loss_cls: 0.1476, acc: 94.3706, loss_bbox: 0.2005, loss_mask: 0.2116, loss: 0.6104 +2024-05-31 15:07:15,335 - mmdet - INFO - Epoch [12][2400/7330] lr: 1.000e-06, eta: 0:51:11, time: 0.596, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0120, loss_rpn_bbox: 0.0372, loss_cls: 0.1457, acc: 94.5220, loss_bbox: 0.1993, loss_mask: 0.2160, loss: 0.6102 +2024-05-31 15:07:46,091 - mmdet - INFO - Epoch [12][2450/7330] lr: 1.000e-06, eta: 0:50:40, time: 0.615, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0119, loss_rpn_bbox: 0.0375, loss_cls: 0.1450, acc: 94.4539, loss_bbox: 0.1953, loss_mask: 0.2083, loss: 0.5979 +2024-05-31 15:08:16,739 - mmdet - INFO - Epoch [12][2500/7330] lr: 1.000e-06, eta: 0:50:09, time: 0.613, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0391, loss_cls: 0.1500, acc: 94.2383, loss_bbox: 0.2059, loss_mask: 0.2139, loss: 0.6219 +2024-05-31 15:08:47,085 - mmdet - INFO - Epoch [12][2550/7330] lr: 1.000e-06, eta: 0:49:37, time: 0.607, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0111, loss_rpn_bbox: 0.0370, loss_cls: 0.1495, acc: 94.3589, loss_bbox: 0.2052, loss_mask: 0.2092, loss: 0.6121 +2024-05-31 15:09:17,281 - mmdet - INFO - Epoch [12][2600/7330] lr: 1.000e-06, eta: 0:49:06, time: 0.604, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0382, loss_cls: 0.1504, acc: 94.3223, loss_bbox: 0.2051, loss_mask: 0.2186, loss: 0.6248 +2024-05-31 15:09:47,990 - mmdet - INFO - Epoch [12][2650/7330] lr: 1.000e-06, eta: 0:48:35, time: 0.614, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0123, loss_rpn_bbox: 0.0387, loss_cls: 0.1550, acc: 94.0464, loss_bbox: 0.2140, loss_mask: 0.2158, loss: 0.6358 +2024-05-31 15:10:22,025 - mmdet - INFO - Epoch [12][2700/7330] lr: 1.000e-06, eta: 0:48:04, time: 0.681, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0381, loss_cls: 0.1449, acc: 94.4827, loss_bbox: 0.2011, loss_mask: 0.2109, loss: 0.6078 +2024-05-31 15:10:54,867 - mmdet - INFO - Epoch [12][2750/7330] lr: 1.000e-06, eta: 0:47:33, time: 0.657, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0379, loss_cls: 0.1433, acc: 94.5264, loss_bbox: 0.1986, loss_mask: 0.2141, loss: 0.6064 +2024-05-31 15:11:31,643 - mmdet - INFO - Epoch [12][2800/7330] lr: 1.000e-06, eta: 0:47:02, time: 0.735, data_time: 0.063, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0380, loss_cls: 0.1516, acc: 94.1267, loss_bbox: 0.2083, loss_mask: 0.2122, loss: 0.6225 +2024-05-31 15:12:11,465 - mmdet - INFO - Epoch [12][2850/7330] lr: 1.000e-06, eta: 0:46:31, time: 0.796, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0389, loss_cls: 0.1470, acc: 94.3625, loss_bbox: 0.2025, loss_mask: 0.2133, loss: 0.6141 +2024-05-31 15:12:41,726 - mmdet - INFO - Epoch [12][2900/7330] lr: 1.000e-06, eta: 0:46:00, time: 0.605, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0388, loss_cls: 0.1439, acc: 94.4722, loss_bbox: 0.1986, loss_mask: 0.2122, loss: 0.6072 +2024-05-31 15:13:12,223 - mmdet - INFO - Epoch [12][2950/7330] lr: 1.000e-06, eta: 0:45:29, time: 0.610, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0123, loss_rpn_bbox: 0.0364, loss_cls: 0.1459, acc: 94.4800, loss_bbox: 0.1996, loss_mask: 0.2150, loss: 0.6092 +2024-05-31 15:13:43,033 - mmdet - INFO - Epoch [12][3000/7330] lr: 1.000e-06, eta: 0:44:58, time: 0.616, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0122, loss_rpn_bbox: 0.0380, loss_cls: 0.1455, acc: 94.4646, loss_bbox: 0.1978, loss_mask: 0.2107, loss: 0.6042 +2024-05-31 15:14:13,623 - mmdet - INFO - Epoch [12][3050/7330] lr: 1.000e-06, eta: 0:44:27, time: 0.612, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0117, loss_rpn_bbox: 0.0367, loss_cls: 0.1412, acc: 94.6580, loss_bbox: 0.1955, loss_mask: 0.2080, loss: 0.5932 +2024-05-31 15:14:44,040 - mmdet - INFO - Epoch [12][3100/7330] lr: 1.000e-06, eta: 0:43:56, time: 0.608, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0392, loss_cls: 0.1497, acc: 94.2703, loss_bbox: 0.2084, loss_mask: 0.2203, loss: 0.6301 +2024-05-31 15:15:14,356 - mmdet - INFO - Epoch [12][3150/7330] lr: 1.000e-06, eta: 0:43:24, time: 0.606, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0396, loss_cls: 0.1535, acc: 94.1323, loss_bbox: 0.2111, loss_mask: 0.2133, loss: 0.6301 +2024-05-31 15:15:44,212 - mmdet - INFO - Epoch [12][3200/7330] lr: 1.000e-06, eta: 0:42:53, time: 0.597, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0108, loss_rpn_bbox: 0.0353, loss_cls: 0.1418, acc: 94.6040, loss_bbox: 0.1941, loss_mask: 0.2098, loss: 0.5919 +2024-05-31 15:16:14,430 - mmdet - INFO - Epoch [12][3250/7330] lr: 1.000e-06, eta: 0:42:22, time: 0.605, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0376, loss_cls: 0.1476, acc: 94.4409, loss_bbox: 0.1995, loss_mask: 0.2144, loss: 0.6116 +2024-05-31 15:16:45,482 - mmdet - INFO - Epoch [12][3300/7330] lr: 1.000e-06, eta: 0:41:51, time: 0.621, data_time: 0.063, memory: 9655, loss_rpn_cls: 0.0135, loss_rpn_bbox: 0.0414, loss_cls: 0.1555, acc: 94.0454, loss_bbox: 0.2117, loss_mask: 0.2142, loss: 0.6363 +2024-05-31 15:17:15,727 - mmdet - INFO - Epoch [12][3350/7330] lr: 1.000e-06, eta: 0:41:20, time: 0.604, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0395, loss_cls: 0.1541, acc: 94.1067, loss_bbox: 0.2156, loss_mask: 0.2200, loss: 0.6434 +2024-05-31 15:17:46,203 - mmdet - INFO - Epoch [12][3400/7330] lr: 1.000e-06, eta: 0:40:48, time: 0.610, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0122, loss_rpn_bbox: 0.0386, loss_cls: 0.1495, acc: 94.3242, loss_bbox: 0.2061, loss_mask: 0.2146, loss: 0.6210 +2024-05-31 15:18:16,821 - mmdet - INFO - Epoch [12][3450/7330] lr: 1.000e-06, eta: 0:40:17, time: 0.613, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0401, loss_cls: 0.1488, acc: 94.3696, loss_bbox: 0.2033, loss_mask: 0.2115, loss: 0.6167 +2024-05-31 15:18:47,477 - mmdet - INFO - Epoch [12][3500/7330] lr: 1.000e-06, eta: 0:39:46, time: 0.613, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0120, loss_rpn_bbox: 0.0383, loss_cls: 0.1508, acc: 94.2803, loss_bbox: 0.2035, loss_mask: 0.2182, loss: 0.6227 +2024-05-31 15:19:17,581 - mmdet - INFO - Epoch [12][3550/7330] lr: 1.000e-06, eta: 0:39:15, time: 0.602, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0385, loss_cls: 0.1510, acc: 94.2310, loss_bbox: 0.2046, loss_mask: 0.2174, loss: 0.6245 +2024-05-31 15:19:54,644 - mmdet - INFO - Epoch [12][3600/7330] lr: 1.000e-06, eta: 0:38:44, time: 0.741, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0383, loss_cls: 0.1490, acc: 94.3203, loss_bbox: 0.2025, loss_mask: 0.2146, loss: 0.6169 +2024-05-31 15:20:33,877 - mmdet - INFO - Epoch [12][3650/7330] lr: 1.000e-06, eta: 0:38:13, time: 0.785, data_time: 0.060, memory: 9655, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0373, loss_cls: 0.1446, acc: 94.4519, loss_bbox: 0.1987, loss_mask: 0.2077, loss: 0.6011 +2024-05-31 15:21:04,436 - mmdet - INFO - Epoch [12][3700/7330] lr: 1.000e-06, eta: 0:37:42, time: 0.611, data_time: 0.064, memory: 9655, loss_rpn_cls: 0.0120, loss_rpn_bbox: 0.0371, loss_cls: 0.1402, acc: 94.6584, loss_bbox: 0.1899, loss_mask: 0.2061, loss: 0.5854 +2024-05-31 15:21:41,046 - mmdet - INFO - Epoch [12][3750/7330] lr: 1.000e-06, eta: 0:37:11, time: 0.732, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0105, loss_rpn_bbox: 0.0378, loss_cls: 0.1398, acc: 94.7510, loss_bbox: 0.1915, loss_mask: 0.2090, loss: 0.5885 +2024-05-31 15:22:10,840 - mmdet - INFO - Epoch [12][3800/7330] lr: 1.000e-06, eta: 0:36:40, time: 0.596, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0372, loss_cls: 0.1424, acc: 94.6206, loss_bbox: 0.1992, loss_mask: 0.2147, loss: 0.6063 +2024-05-31 15:22:41,938 - mmdet - INFO - Epoch [12][3850/7330] lr: 1.000e-06, eta: 0:36:09, time: 0.622, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0405, loss_cls: 0.1474, acc: 94.4458, loss_bbox: 0.2001, loss_mask: 0.2100, loss: 0.6112 +2024-05-31 15:23:11,979 - mmdet - INFO - Epoch [12][3900/7330] lr: 1.000e-06, eta: 0:35:37, time: 0.601, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0108, loss_rpn_bbox: 0.0357, loss_cls: 0.1419, acc: 94.5649, loss_bbox: 0.1975, loss_mask: 0.2063, loss: 0.5923 +2024-05-31 15:23:42,389 - mmdet - INFO - Epoch [12][3950/7330] lr: 1.000e-06, eta: 0:35:06, time: 0.608, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0116, loss_rpn_bbox: 0.0360, loss_cls: 0.1427, acc: 94.5107, loss_bbox: 0.1962, loss_mask: 0.2129, loss: 0.5995 +2024-05-31 15:24:12,749 - mmdet - INFO - Epoch [12][4000/7330] lr: 1.000e-06, eta: 0:34:35, time: 0.607, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0375, loss_cls: 0.1446, acc: 94.4678, loss_bbox: 0.1983, loss_mask: 0.2138, loss: 0.6066 +2024-05-31 15:24:42,683 - mmdet - INFO - Epoch [12][4050/7330] lr: 1.000e-06, eta: 0:34:04, time: 0.599, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0387, loss_cls: 0.1465, acc: 94.4395, loss_bbox: 0.2023, loss_mask: 0.2107, loss: 0.6102 +2024-05-31 15:25:12,823 - mmdet - INFO - Epoch [12][4100/7330] lr: 1.000e-06, eta: 0:33:33, time: 0.603, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0116, loss_rpn_bbox: 0.0382, loss_cls: 0.1450, acc: 94.4910, loss_bbox: 0.1978, loss_mask: 0.2145, loss: 0.6070 +2024-05-31 15:25:44,197 - mmdet - INFO - Epoch [12][4150/7330] lr: 1.000e-06, eta: 0:33:01, time: 0.628, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0439, loss_cls: 0.1549, acc: 94.0601, loss_bbox: 0.2178, loss_mask: 0.2207, loss: 0.6495 +2024-05-31 15:26:14,679 - mmdet - INFO - Epoch [12][4200/7330] lr: 1.000e-06, eta: 0:32:30, time: 0.610, data_time: 0.062, memory: 9655, loss_rpn_cls: 0.0126, loss_rpn_bbox: 0.0368, loss_cls: 0.1396, acc: 94.6873, loss_bbox: 0.1959, loss_mask: 0.2099, loss: 0.5947 +2024-05-31 15:26:45,089 - mmdet - INFO - Epoch [12][4250/7330] lr: 1.000e-06, eta: 0:31:59, time: 0.608, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0403, loss_cls: 0.1528, acc: 94.2241, loss_bbox: 0.2054, loss_mask: 0.2145, loss: 0.6261 +2024-05-31 15:27:15,387 - mmdet - INFO - Epoch [12][4300/7330] lr: 1.000e-06, eta: 0:31:28, time: 0.606, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0388, loss_cls: 0.1539, acc: 94.1785, loss_bbox: 0.2081, loss_mask: 0.2174, loss: 0.6315 +2024-05-31 15:27:45,778 - mmdet - INFO - Epoch [12][4350/7330] lr: 1.000e-06, eta: 0:30:57, time: 0.608, data_time: 0.058, memory: 9655, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0378, loss_cls: 0.1493, acc: 94.2666, loss_bbox: 0.2062, loss_mask: 0.2140, loss: 0.6202 +2024-05-31 15:28:15,595 - mmdet - INFO - Epoch [12][4400/7330] lr: 1.000e-06, eta: 0:30:25, time: 0.596, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0115, loss_rpn_bbox: 0.0361, loss_cls: 0.1333, acc: 94.9355, loss_bbox: 0.1874, loss_mask: 0.2123, loss: 0.5805 +2024-05-31 15:28:48,676 - mmdet - INFO - Epoch [12][4450/7330] lr: 1.000e-06, eta: 0:29:54, time: 0.662, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0378, loss_cls: 0.1494, acc: 94.2849, loss_bbox: 0.2046, loss_mask: 0.2136, loss: 0.6175 +2024-05-31 15:29:23,325 - mmdet - INFO - Epoch [12][4500/7330] lr: 1.000e-06, eta: 0:29:23, time: 0.693, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0366, loss_cls: 0.1451, acc: 94.4377, loss_bbox: 0.2026, loss_mask: 0.2148, loss: 0.6115 +2024-05-31 15:29:57,887 - mmdet - INFO - Epoch [12][4550/7330] lr: 1.000e-06, eta: 0:28:52, time: 0.691, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0119, loss_rpn_bbox: 0.0378, loss_cls: 0.1410, acc: 94.5469, loss_bbox: 0.1964, loss_mask: 0.2146, loss: 0.6018 +2024-05-31 15:30:36,353 - mmdet - INFO - Epoch [12][4600/7330] lr: 1.000e-06, eta: 0:28:21, time: 0.769, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0371, loss_cls: 0.1477, acc: 94.3706, loss_bbox: 0.2017, loss_mask: 0.2120, loss: 0.6116 +2024-05-31 15:31:06,884 - mmdet - INFO - Epoch [12][4650/7330] lr: 1.000e-06, eta: 0:27:50, time: 0.611, data_time: 0.064, memory: 9655, loss_rpn_cls: 0.0122, loss_rpn_bbox: 0.0360, loss_cls: 0.1435, acc: 94.6130, loss_bbox: 0.1939, loss_mask: 0.2092, loss: 0.5947 +2024-05-31 15:31:37,132 - mmdet - INFO - Epoch [12][4700/7330] lr: 1.000e-06, eta: 0:27:19, time: 0.605, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0362, loss_cls: 0.1430, acc: 94.6013, loss_bbox: 0.1955, loss_mask: 0.2103, loss: 0.5977 +2024-05-31 15:32:08,109 - mmdet - INFO - Epoch [12][4750/7330] lr: 1.000e-06, eta: 0:26:48, time: 0.619, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0119, loss_rpn_bbox: 0.0378, loss_cls: 0.1476, acc: 94.3188, loss_bbox: 0.2013, loss_mask: 0.2135, loss: 0.6121 +2024-05-31 15:32:38,772 - mmdet - INFO - Epoch [12][4800/7330] lr: 1.000e-06, eta: 0:26:17, time: 0.614, data_time: 0.059, memory: 9655, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0379, loss_cls: 0.1473, acc: 94.4094, loss_bbox: 0.2015, loss_mask: 0.2110, loss: 0.6106 +2024-05-31 15:33:08,587 - mmdet - INFO - Epoch [12][4850/7330] lr: 1.000e-06, eta: 0:25:45, time: 0.596, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0116, loss_rpn_bbox: 0.0353, loss_cls: 0.1429, acc: 94.6411, loss_bbox: 0.1933, loss_mask: 0.2088, loss: 0.5919 +2024-05-31 15:33:39,175 - mmdet - INFO - Epoch [12][4900/7330] lr: 1.000e-06, eta: 0:25:14, time: 0.612, data_time: 0.065, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0376, loss_cls: 0.1491, acc: 94.3901, loss_bbox: 0.2048, loss_mask: 0.2132, loss: 0.6179 +2024-05-31 15:34:09,490 - mmdet - INFO - Epoch [12][4950/7330] lr: 1.000e-06, eta: 0:24:43, time: 0.606, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0375, loss_cls: 0.1475, acc: 94.3223, loss_bbox: 0.2041, loss_mask: 0.2173, loss: 0.6191 +2024-05-31 15:34:39,799 - mmdet - INFO - Epoch [12][5000/7330] lr: 1.000e-06, eta: 0:24:12, time: 0.606, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0381, loss_cls: 0.1491, acc: 94.2959, loss_bbox: 0.2011, loss_mask: 0.2161, loss: 0.6175 +2024-05-31 15:35:10,549 - mmdet - INFO - Epoch [12][5050/7330] lr: 1.000e-06, eta: 0:23:41, time: 0.615, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0401, loss_cls: 0.1541, acc: 94.1309, loss_bbox: 0.2038, loss_mask: 0.2170, loss: 0.6277 +2024-05-31 15:35:40,886 - mmdet - INFO - Epoch [12][5100/7330] lr: 1.000e-06, eta: 0:23:09, time: 0.607, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0114, loss_rpn_bbox: 0.0366, loss_cls: 0.1467, acc: 94.3682, loss_bbox: 0.2018, loss_mask: 0.2164, loss: 0.6129 +2024-05-31 15:36:11,590 - mmdet - INFO - Epoch [12][5150/7330] lr: 1.000e-06, eta: 0:22:38, time: 0.614, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0126, loss_rpn_bbox: 0.0378, loss_cls: 0.1424, acc: 94.5508, loss_bbox: 0.1993, loss_mask: 0.2115, loss: 0.6036 +2024-05-31 15:36:41,775 - mmdet - INFO - Epoch [12][5200/7330] lr: 1.000e-06, eta: 0:22:07, time: 0.604, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0380, loss_cls: 0.1475, acc: 94.3230, loss_bbox: 0.2023, loss_mask: 0.2054, loss: 0.6059 +2024-05-31 15:37:12,114 - mmdet - INFO - Epoch [12][5250/7330] lr: 1.000e-06, eta: 0:21:36, time: 0.607, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0385, loss_cls: 0.1474, acc: 94.4543, loss_bbox: 0.2037, loss_mask: 0.2097, loss: 0.6125 +2024-05-31 15:37:42,046 - mmdet - INFO - Epoch [12][5300/7330] lr: 1.000e-06, eta: 0:21:05, time: 0.599, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0375, loss_cls: 0.1470, acc: 94.4470, loss_bbox: 0.1973, loss_mask: 0.2073, loss: 0.6016 +2024-05-31 15:38:17,857 - mmdet - INFO - Epoch [12][5350/7330] lr: 1.000e-06, eta: 0:20:34, time: 0.716, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0126, loss_rpn_bbox: 0.0373, loss_cls: 0.1470, acc: 94.4128, loss_bbox: 0.2017, loss_mask: 0.2134, loss: 0.6120 +2024-05-31 15:38:53,119 - mmdet - INFO - Epoch [12][5400/7330] lr: 1.000e-06, eta: 0:20:03, time: 0.706, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0380, loss_cls: 0.1444, acc: 94.5171, loss_bbox: 0.2047, loss_mask: 0.2148, loss: 0.6145 +2024-05-31 15:39:25,508 - mmdet - INFO - Epoch [12][5450/7330] lr: 1.000e-06, eta: 0:19:31, time: 0.648, data_time: 0.053, memory: 9655, loss_rpn_cls: 0.0140, loss_rpn_bbox: 0.0392, loss_cls: 0.1483, acc: 94.3875, loss_bbox: 0.2047, loss_mask: 0.2197, loss: 0.6258 +2024-05-31 15:40:03,860 - mmdet - INFO - Epoch [12][5500/7330] lr: 1.000e-06, eta: 0:19:00, time: 0.767, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0120, loss_rpn_bbox: 0.0349, loss_cls: 0.1432, acc: 94.5818, loss_bbox: 0.1977, loss_mask: 0.2096, loss: 0.5975 +2024-05-31 15:40:34,425 - mmdet - INFO - Epoch [12][5550/7330] lr: 1.000e-06, eta: 0:18:29, time: 0.612, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0394, loss_cls: 0.1499, acc: 94.3018, loss_bbox: 0.2054, loss_mask: 0.2142, loss: 0.6220 +2024-05-31 15:41:04,644 - mmdet - INFO - Epoch [12][5600/7330] lr: 1.000e-06, eta: 0:17:58, time: 0.604, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0120, loss_rpn_bbox: 0.0358, loss_cls: 0.1453, acc: 94.4036, loss_bbox: 0.2010, loss_mask: 0.2191, loss: 0.6132 +2024-05-31 15:41:35,799 - mmdet - INFO - Epoch [12][5650/7330] lr: 1.000e-06, eta: 0:17:27, time: 0.623, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0404, loss_cls: 0.1527, acc: 94.2539, loss_bbox: 0.2046, loss_mask: 0.2151, loss: 0.6272 +2024-05-31 15:42:06,454 - mmdet - INFO - Epoch [12][5700/7330] lr: 1.000e-06, eta: 0:16:56, time: 0.613, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0386, loss_cls: 0.1443, acc: 94.4985, loss_bbox: 0.2001, loss_mask: 0.2106, loss: 0.6069 +2024-05-31 15:42:36,782 - mmdet - INFO - Epoch [12][5750/7330] lr: 1.000e-06, eta: 0:16:25, time: 0.607, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0384, loss_cls: 0.1482, acc: 94.3857, loss_bbox: 0.2027, loss_mask: 0.2152, loss: 0.6172 +2024-05-31 15:43:07,079 - mmdet - INFO - Epoch [12][5800/7330] lr: 1.000e-06, eta: 0:15:53, time: 0.606, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0406, loss_cls: 0.1533, acc: 94.1587, loss_bbox: 0.2092, loss_mask: 0.2170, loss: 0.6335 +2024-05-31 15:43:37,400 - mmdet - INFO - Epoch [12][5850/7330] lr: 1.000e-06, eta: 0:15:22, time: 0.606, data_time: 0.043, memory: 9655, loss_rpn_cls: 0.0106, loss_rpn_bbox: 0.0358, loss_cls: 0.1413, acc: 94.5615, loss_bbox: 0.1979, loss_mask: 0.2127, loss: 0.5984 +2024-05-31 15:44:08,001 - mmdet - INFO - Epoch [12][5900/7330] lr: 1.000e-06, eta: 0:14:51, time: 0.612, data_time: 0.045, memory: 9655, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0414, loss_cls: 0.1537, acc: 94.1035, loss_bbox: 0.2126, loss_mask: 0.2204, loss: 0.6410 +2024-05-31 15:44:38,671 - mmdet - INFO - Epoch [12][5950/7330] lr: 1.000e-06, eta: 0:14:20, time: 0.613, data_time: 0.065, memory: 9655, loss_rpn_cls: 0.0138, loss_rpn_bbox: 0.0393, loss_cls: 0.1503, acc: 94.2617, loss_bbox: 0.2088, loss_mask: 0.2148, loss: 0.6270 +2024-05-31 15:45:09,096 - mmdet - INFO - Epoch [12][6000/7330] lr: 1.000e-06, eta: 0:13:49, time: 0.609, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0391, loss_cls: 0.1458, acc: 94.3748, loss_bbox: 0.1996, loss_mask: 0.2097, loss: 0.6071 +2024-05-31 15:45:39,352 - mmdet - INFO - Epoch [12][6050/7330] lr: 1.000e-06, eta: 0:13:17, time: 0.605, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0119, loss_rpn_bbox: 0.0356, loss_cls: 0.1456, acc: 94.4448, loss_bbox: 0.2024, loss_mask: 0.2174, loss: 0.6130 +2024-05-31 15:46:09,550 - mmdet - INFO - Epoch [12][6100/7330] lr: 1.000e-06, eta: 0:12:46, time: 0.604, data_time: 0.038, memory: 9655, loss_rpn_cls: 0.0126, loss_rpn_bbox: 0.0365, loss_cls: 0.1413, acc: 94.5659, loss_bbox: 0.1961, loss_mask: 0.2092, loss: 0.5958 +2024-05-31 15:46:40,305 - mmdet - INFO - Epoch [12][6150/7330] lr: 1.000e-06, eta: 0:12:15, time: 0.615, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0395, loss_cls: 0.1519, acc: 94.0942, loss_bbox: 0.2075, loss_mask: 0.2168, loss: 0.6290 +2024-05-31 15:47:12,996 - mmdet - INFO - Epoch [12][6200/7330] lr: 1.000e-06, eta: 0:11:44, time: 0.654, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0390, loss_cls: 0.1494, acc: 94.2620, loss_bbox: 0.2083, loss_mask: 0.2152, loss: 0.6251 +2024-05-31 15:47:47,848 - mmdet - INFO - Epoch [12][6250/7330] lr: 1.000e-06, eta: 0:11:13, time: 0.697, data_time: 0.041, memory: 9655, loss_rpn_cls: 0.0122, loss_rpn_bbox: 0.0363, loss_cls: 0.1461, acc: 94.4353, loss_bbox: 0.2011, loss_mask: 0.2147, loss: 0.6103 +2024-05-31 15:48:23,731 - mmdet - INFO - Epoch [12][6300/7330] lr: 1.000e-06, eta: 0:10:42, time: 0.718, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0385, loss_cls: 0.1453, acc: 94.4685, loss_bbox: 0.2029, loss_mask: 0.2163, loss: 0.6158 +2024-05-31 15:48:56,842 - mmdet - INFO - Epoch [12][6350/7330] lr: 1.000e-06, eta: 0:10:11, time: 0.662, data_time: 0.047, memory: 9655, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0381, loss_cls: 0.1482, acc: 94.3264, loss_bbox: 0.2010, loss_mask: 0.2127, loss: 0.6128 +2024-05-31 15:49:31,667 - mmdet - INFO - Epoch [12][6400/7330] lr: 1.000e-06, eta: 0:09:39, time: 0.696, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0389, loss_cls: 0.1437, acc: 94.4954, loss_bbox: 0.2006, loss_mask: 0.2142, loss: 0.6104 +2024-05-31 15:50:01,968 - mmdet - INFO - Epoch [12][6450/7330] lr: 1.000e-06, eta: 0:09:08, time: 0.606, data_time: 0.044, memory: 9655, loss_rpn_cls: 0.0115, loss_rpn_bbox: 0.0373, loss_cls: 0.1429, acc: 94.5720, loss_bbox: 0.1980, loss_mask: 0.2112, loss: 0.6009 +2024-05-31 15:50:32,296 - mmdet - INFO - Epoch [12][6500/7330] lr: 1.000e-06, eta: 0:08:37, time: 0.607, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0120, loss_rpn_bbox: 0.0382, loss_cls: 0.1486, acc: 94.3628, loss_bbox: 0.2016, loss_mask: 0.2171, loss: 0.6175 +2024-05-31 15:51:02,893 - mmdet - INFO - Epoch [12][6550/7330] lr: 1.000e-06, eta: 0:08:06, time: 0.612, data_time: 0.054, memory: 9655, loss_rpn_cls: 0.0113, loss_rpn_bbox: 0.0367, loss_cls: 0.1406, acc: 94.6221, loss_bbox: 0.1951, loss_mask: 0.2088, loss: 0.5925 +2024-05-31 15:51:33,745 - mmdet - INFO - Epoch [12][6600/7330] lr: 1.000e-06, eta: 0:07:35, time: 0.617, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0385, loss_cls: 0.1495, acc: 94.4119, loss_bbox: 0.2014, loss_mask: 0.2102, loss: 0.6125 +2024-05-31 15:52:04,180 - mmdet - INFO - Epoch [12][6650/7330] lr: 1.000e-06, eta: 0:07:03, time: 0.609, data_time: 0.055, memory: 9655, loss_rpn_cls: 0.0119, loss_rpn_bbox: 0.0369, loss_cls: 0.1435, acc: 94.4873, loss_bbox: 0.1953, loss_mask: 0.2092, loss: 0.5968 +2024-05-31 15:52:34,389 - mmdet - INFO - Epoch [12][6700/7330] lr: 1.000e-06, eta: 0:06:32, time: 0.604, data_time: 0.048, memory: 9655, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0374, loss_cls: 0.1408, acc: 94.5691, loss_bbox: 0.1967, loss_mask: 0.2133, loss: 0.6016 +2024-05-31 15:53:05,219 - mmdet - INFO - Epoch [12][6750/7330] lr: 1.000e-06, eta: 0:06:01, time: 0.617, data_time: 0.061, memory: 9655, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0378, loss_cls: 0.1464, acc: 94.5393, loss_bbox: 0.1971, loss_mask: 0.2100, loss: 0.6049 +2024-05-31 15:53:35,129 - mmdet - INFO - Epoch [12][6800/7330] lr: 1.000e-06, eta: 0:05:30, time: 0.598, data_time: 0.049, memory: 9655, loss_rpn_cls: 0.0116, loss_rpn_bbox: 0.0364, loss_cls: 0.1409, acc: 94.5906, loss_bbox: 0.1892, loss_mask: 0.2054, loss: 0.5836 +2024-05-31 15:54:05,020 - mmdet - INFO - Epoch [12][6850/7330] lr: 1.000e-06, eta: 0:04:59, time: 0.597, data_time: 0.040, memory: 9655, loss_rpn_cls: 0.0110, loss_rpn_bbox: 0.0364, loss_cls: 0.1393, acc: 94.6782, loss_bbox: 0.1899, loss_mask: 0.2088, loss: 0.5854 +2024-05-31 15:54:35,815 - mmdet - INFO - Epoch [12][6900/7330] lr: 1.000e-06, eta: 0:04:28, time: 0.616, data_time: 0.067, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0382, loss_cls: 0.1503, acc: 94.2544, loss_bbox: 0.2032, loss_mask: 0.2115, loss: 0.6165 +2024-05-31 15:55:05,644 - mmdet - INFO - Epoch [12][6950/7330] lr: 1.000e-06, eta: 0:03:56, time: 0.597, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0119, loss_rpn_bbox: 0.0350, loss_cls: 0.1425, acc: 94.6311, loss_bbox: 0.1929, loss_mask: 0.2071, loss: 0.5895 +2024-05-31 15:55:36,001 - mmdet - INFO - Epoch [12][7000/7330] lr: 1.000e-06, eta: 0:03:25, time: 0.607, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0386, loss_cls: 0.1482, acc: 94.3225, loss_bbox: 0.2051, loss_mask: 0.2147, loss: 0.6187 +2024-05-31 15:56:06,460 - mmdet - INFO - Epoch [12][7050/7330] lr: 1.000e-06, eta: 0:02:54, time: 0.610, data_time: 0.051, memory: 9655, loss_rpn_cls: 0.0135, loss_rpn_bbox: 0.0387, loss_cls: 0.1537, acc: 94.1812, loss_bbox: 0.2120, loss_mask: 0.2224, loss: 0.6404 +2024-05-31 15:56:41,840 - mmdet - INFO - Epoch [12][7100/7330] lr: 1.000e-06, eta: 0:02:23, time: 0.707, data_time: 0.056, memory: 9655, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0380, loss_cls: 0.1406, acc: 94.5645, loss_bbox: 0.1942, loss_mask: 0.2137, loss: 0.5987 +2024-05-31 15:57:14,923 - mmdet - INFO - Epoch [12][7150/7330] lr: 1.000e-06, eta: 0:01:52, time: 0.662, data_time: 0.052, memory: 9655, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0387, loss_cls: 0.1496, acc: 94.2356, loss_bbox: 0.2029, loss_mask: 0.2100, loss: 0.6144 +2024-05-31 15:57:49,975 - mmdet - INFO - Epoch [12][7200/7330] lr: 1.000e-06, eta: 0:01:21, time: 0.701, data_time: 0.050, memory: 9655, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0378, loss_cls: 0.1475, acc: 94.4216, loss_bbox: 0.2037, loss_mask: 0.2101, loss: 0.6118 +2024-05-31 15:58:26,837 - mmdet - INFO - Epoch [12][7250/7330] lr: 1.000e-06, eta: 0:00:49, time: 0.737, data_time: 0.046, memory: 9655, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0362, loss_cls: 0.1449, acc: 94.4846, loss_bbox: 0.2006, loss_mask: 0.2135, loss: 0.6075 +2024-05-31 15:58:57,144 - mmdet - INFO - Epoch [12][7300/7330] lr: 1.000e-06, eta: 0:00:18, time: 0.606, data_time: 0.057, memory: 9655, loss_rpn_cls: 0.0138, loss_rpn_bbox: 0.0402, loss_cls: 0.1525, acc: 94.2068, loss_bbox: 0.2138, loss_mask: 0.2187, loss: 0.6390 +2024-05-31 15:59:16,219 - mmdet - INFO - Saving checkpoint at 12 epochs +2024-05-31 16:00:50,206 - mmdet - INFO - Evaluating bbox... +2024-05-31 16:01:13,526 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.450 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.670 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.489 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.269 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.482 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.620 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.567 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.567 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.567 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.373 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.605 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.730 + +2024-05-31 16:01:13,526 - mmdet - INFO - Evaluating segm... +2024-05-31 16:01:41,512 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.403 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.637 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.428 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.194 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.429 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.613 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.509 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.509 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.509 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.307 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.552 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.693 + +2024-05-31 16:01:41,869 - mmdet - INFO - Exp name: mask_rcnn_deit_tsb_1568_896_448_fpn_1x_coco_bs16.py +2024-05-31 16:01:41,871 - mmdet - INFO - Epoch(val) [12][625] bbox_mAP: 0.4500, bbox_mAP_50: 0.6700, bbox_mAP_75: 0.4890, bbox_mAP_s: 0.2690, bbox_mAP_m: 0.4820, bbox_mAP_l: 0.6200, bbox_mAP_copypaste: 0.450 0.670 0.489 0.269 0.482 0.620, segm_mAP: 0.4030, segm_mAP_50: 0.6370, segm_mAP_75: 0.4280, segm_mAP_s: 0.1940, segm_mAP_m: 0.4290, segm_mAP_l: 0.6130, segm_mAP_copypaste: 0.403 0.637 0.428 0.194 0.429 0.613