diff --git "a/detection/mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.log" "b/detection/mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.log" new file mode 100644--- /dev/null +++ "b/detection/mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.log" @@ -0,0 +1,10593 @@ +2024-05-27 14:30:33,581 - 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+d30b7c1 +------------------------------------------------------------ + +2024-05-27 14:30:35,059 - mmdet - INFO - Distributed training: True +2024-05-27 14:30:36,569 - 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=24, + embed_dim=1024, + num_heads=16, + mlp_ratio=4, + qkv_bias=True, + drop_path_rate=0.4, + init_scale=1.0, + with_fpn=False, + interaction_indexes=[[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], + [10, 11], [12, 13], [14, 15], [16, 17], + [18, 19], [20, 21], [22, 23]], + pretrained='./pretrained/deit_3_large_224_21k.pth', + window_attn=[ + True, True, True, True, True, True, True, True, True, True, + True, True, True, True, True, True, True, True, True, True, + True, True, True, True + ], + window_size=[ + 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, + 28, 28, 28, 28, 28, 28, 28, 28 + ], + 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=672, + 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=[28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28], + 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=1120, + 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=[28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28], + 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=[1024, 1024, 1024, 1024], + 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=(1120, 672), 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=(1120, 672), + 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=(1120, 672), 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=(1120, 672), + 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=(1120, 672), + 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=24, layer_decay_rate=0.85, skip_stride=[2, 2])) +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_sbl_1120_672_448_fpn_1x_coco_bs16' +auto_resume = True +gpu_ids = range(0, 8) + +2024-05-27 14:30:41,569 - mmdet - INFO - Set random seed to 2084664687, deterministic: False +2024-05-27 14:30:49,436 - mmdet - INFO - _IncompatibleKeys(missing_keys=[], unexpected_keys=['cls_token', 'norm.weight', 'norm.bias', 'head.weight', 'head.bias']) +2024-05-27 14:30:50,579 - mmdet - INFO - _IncompatibleKeys(missing_keys=[], unexpected_keys=['cls_token', 'norm.weight', 'norm.bias', 'head.weight', 'head.bias']) +2024-05-27 14:30:52,578 - mmdet - INFO - _IncompatibleKeys(missing_keys=[], unexpected_keys=['cls_token', 'norm.weight', 'norm.bias', 'head.weight', 'head.bias']) +2024-05-27 14:32:08,761 - mmdet - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'} +2024-05-27 14:32:09,211 - mmdet - INFO - initialize RPNHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01} +2024-05-27 14:32:09,248 - 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, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.patch_embed.proj.weight - torch.Size([1024, 3, 16, 16]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.patch_embed.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.0.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.1.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.2.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.3.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.4.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.5.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.6.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.7.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.8.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.9.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.10.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.11.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.12.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.12.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.12.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.12.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.12.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.12.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.12.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.12.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.12.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.12.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.12.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.12.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.12.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.12.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.13.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.13.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.13.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.13.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.13.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.13.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.13.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.13.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.13.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.13.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.13.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.13.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.13.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.13.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.14.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.14.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.14.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.14.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.14.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.14.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.14.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.14.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.14.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.14.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.14.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.14.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.14.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.14.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.15.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.15.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.15.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.15.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.15.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.15.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.15.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.15.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.15.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.15.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.15.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.15.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.15.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.15.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.16.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.16.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.16.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.16.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.16.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.16.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.16.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.16.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.16.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.16.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.16.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.16.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.16.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.16.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.17.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.17.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.17.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.17.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.17.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.17.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.17.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.17.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.17.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.17.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.17.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.17.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.17.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.17.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.18.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.18.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.18.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.18.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.18.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.18.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.18.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.18.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.18.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.18.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.18.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.18.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.18.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.18.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.19.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.19.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.19.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.19.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.19.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.19.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.19.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.19.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.19.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.19.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.19.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.19.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.19.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.19.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.20.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.20.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.20.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.20.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.20.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.20.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.20.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.20.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.20.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.20.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.20.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.20.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.20.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.20.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.21.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.21.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.21.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.21.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.21.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.21.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.21.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.21.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.21.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.21.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.21.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.21.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.21.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.21.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.22.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.22.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.22.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.22.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.22.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.22.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.22.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.22.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.22.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.22.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.22.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.22.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.22.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.22.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.23.gamma_1 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.23.gamma_2 - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.23.norm1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.23.norm1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.23.attn.qkv.weight - torch.Size([3072, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.23.attn.qkv.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.23.attn.proj.weight - torch.Size([1024, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.23.attn.proj.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.23.norm2.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.23.norm2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.23.mlp.fc1.weight - torch.Size([4096, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.23.mlp.fc1.bias - torch.Size([4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.23.mlp.fc2.weight - torch.Size([1024, 4096]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch1.blocks.23.mlp.fc2.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.pos_embed - torch.Size([1, 196, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.patch_embed.proj.weight - torch.Size([768, 3, 16, 16]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.patch_embed.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.0.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.1.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.2.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.3.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.4.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.5.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.6.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.7.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.8.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.9.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.10.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.gamma_1 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.gamma_2 - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.norm1.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.norm1.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.attn.qkv.weight - torch.Size([2304, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.attn.qkv.bias - torch.Size([2304]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.attn.proj.weight - torch.Size([768, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.attn.proj.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.norm2.weight - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.norm2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.mlp.fc1.weight - torch.Size([3072, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.mlp.fc1.bias - torch.Size([3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.mlp.fc2.weight - torch.Size([768, 3072]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch2.blocks.11.mlp.fc2.bias - torch.Size([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.pos_embed - torch.Size([1, 196, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.patch_embed.proj.weight - torch.Size([384, 3, 16, 16]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.patch_embed.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.0.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.1.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.2.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.3.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.4.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.5.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.6.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.7.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.8.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.9.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.10.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.gamma_1 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.gamma_2 - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.norm1.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.norm1.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.attn.qkv.weight - torch.Size([1152, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.attn.qkv.bias - torch.Size([1152]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.attn.proj.weight - torch.Size([384, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.attn.proj.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.norm2.weight - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.norm2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.mlp.fc1.weight - torch.Size([1536, 384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.mlp.fc1.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.mlp.fc2.weight - torch.Size([384, 1536]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.branch3.blocks.11.mlp.fc2.bias - torch.Size([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_proj.weight - torch.Size([1024, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch2to1_proj.bias - torch.Size([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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, 1024]): +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, 1024]): +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([512, 1024]): +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([512]): +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([1024, 512]): +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([1024]): +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([256, 1024]): +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([256]): +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([256, 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([256]): +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([1024, 256]): +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([1024]): +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([1024]): +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([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_proj.weight - torch.Size([768, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_12.branch1to2_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.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_23.branch2to1_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.0.interaction_units_23.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_23.branch1to2_proj.bias - torch.Size([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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, 384]): +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, 384]): +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([192, 384]): +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([192]): +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([384, 192]): +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([384]): +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([96, 384]): +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([96]): +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([96, 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([96]): +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([384, 96]): +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([384]): +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([384]): +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([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_proj.weight - torch.Size([1024, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch2to1_proj.bias - torch.Size([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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, 1024]): +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, 1024]): +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([512, 1024]): +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([512]): +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([1024, 512]): +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([1024]): +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([256, 1024]): +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([256]): +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([256, 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([256]): +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([1024, 256]): +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([1024]): +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([1024]): +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([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_proj.weight - torch.Size([768, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_12.branch1to2_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.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_23.branch2to1_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.1.interaction_units_23.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_23.branch1to2_proj.bias - torch.Size([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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, 384]): +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, 384]): +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([192, 384]): +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([192]): +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([384, 192]): +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([384]): +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([96, 384]): +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([96]): +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([96, 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([96]): +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([384, 96]): +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([384]): +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([384]): +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([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_proj.weight - torch.Size([1024, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch2to1_proj.bias - torch.Size([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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, 1024]): +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, 1024]): +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([512, 1024]): +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([512]): +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([1024, 512]): +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([1024]): +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([256, 1024]): +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([256]): +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([256, 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([256]): +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([1024, 256]): +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([1024]): +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([1024]): +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([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_proj.weight - torch.Size([768, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_12.branch1to2_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.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_23.branch2to1_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.2.interaction_units_23.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_23.branch1to2_proj.bias - torch.Size([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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, 384]): +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, 384]): +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([192, 384]): +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([192]): +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([384, 192]): +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([384]): +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([96, 384]): +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([96]): +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([96, 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([96]): +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([384, 96]): +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([384]): +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([384]): +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([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_proj.weight - torch.Size([1024, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch2to1_proj.bias - torch.Size([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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, 1024]): +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, 1024]): +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([512, 1024]): +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([512]): +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([1024, 512]): +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([1024]): +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([256, 1024]): +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([256]): +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([256, 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([256]): +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([1024, 256]): +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([1024]): +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([1024]): +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([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_proj.weight - torch.Size([768, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_12.branch1to2_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.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_23.branch2to1_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.3.interaction_units_23.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_23.branch1to2_proj.bias - torch.Size([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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, 384]): +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, 384]): +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([192, 384]): +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([192]): +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([384, 192]): +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([384]): +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([96, 384]): +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([96]): +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([96, 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([96]): +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([384, 96]): +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([384]): +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([384]): +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([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_proj.weight - torch.Size([1024, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch2to1_proj.bias - torch.Size([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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, 1024]): +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, 1024]): +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([512, 1024]): +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([512]): +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([1024, 512]): +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([1024]): +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([256, 1024]): +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([256]): +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([256, 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([256]): +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([1024, 256]): +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([1024]): +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([1024]): +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([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_proj.weight - torch.Size([768, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_12.branch1to2_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.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_23.branch2to1_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.4.interaction_units_23.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_23.branch1to2_proj.bias - torch.Size([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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, 384]): +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, 384]): +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([192, 384]): +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([192]): +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([384, 192]): +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([384]): +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([96, 384]): +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([96]): +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([96, 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([96]): +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([384, 96]): +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([384]): +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([384]): +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([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_proj.weight - torch.Size([1024, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch2to1_proj.bias - torch.Size([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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, 1024]): +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, 1024]): +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([512, 1024]): +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([512]): +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([1024, 512]): +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([1024]): +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([256, 1024]): +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([256]): +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([256, 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([256]): +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([1024, 256]): +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([1024]): +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([1024]): +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([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_proj.weight - torch.Size([768, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_12.branch1to2_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.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_23.branch2to1_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.5.interaction_units_23.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_23.branch1to2_proj.bias - torch.Size([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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, 384]): +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, 384]): +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([192, 384]): +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([192]): +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([384, 192]): +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([384]): +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([96, 384]): +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([96]): +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([96, 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([96]): +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([384, 96]): +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([384]): +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([384]): +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([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_proj.weight - torch.Size([1024, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch2to1_proj.bias - torch.Size([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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, 1024]): +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, 1024]): +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([512, 1024]): +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([512]): +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([1024, 512]): +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([1024]): +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([256, 1024]): +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([256]): +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([256, 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([256]): +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([1024, 256]): +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([1024]): +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([1024]): +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([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_proj.weight - torch.Size([768, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_12.branch1to2_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.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_23.branch2to1_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.6.interaction_units_23.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_23.branch1to2_proj.bias - torch.Size([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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, 384]): +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, 384]): +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([192, 384]): +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([192]): +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([384, 192]): +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([384]): +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([96, 384]): +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([96]): +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([96, 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([96]): +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([384, 96]): +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([384]): +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([384]): +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([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_proj.weight - torch.Size([1024, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch2to1_proj.bias - torch.Size([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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, 1024]): +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, 1024]): +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([512, 1024]): +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([512]): +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([1024, 512]): +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([1024]): +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([256, 1024]): +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([256]): +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([256, 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([256]): +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([1024, 256]): +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([1024]): +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([1024]): +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([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_proj.weight - torch.Size([768, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_12.branch1to2_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.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_23.branch2to1_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.7.interaction_units_23.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_23.branch1to2_proj.bias - torch.Size([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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, 384]): +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, 384]): +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([192, 384]): +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([192]): +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([384, 192]): +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([384]): +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([96, 384]): +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([96]): +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([96, 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([96]): +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([384, 96]): +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([384]): +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([384]): +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([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_proj.weight - torch.Size([1024, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch2to1_proj.bias - torch.Size([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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, 1024]): +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, 1024]): +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([512, 1024]): +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([512]): +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([1024, 512]): +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([1024]): +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([256, 1024]): +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([256]): +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([256, 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([256]): +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([1024, 256]): +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([1024]): +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([1024]): +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([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_proj.weight - torch.Size([768, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_12.branch1to2_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.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_23.branch2to1_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.8.interaction_units_23.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_23.branch1to2_proj.bias - torch.Size([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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, 384]): +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, 384]): +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([192, 384]): +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([192]): +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([384, 192]): +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([384]): +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([96, 384]): +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([96]): +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([96, 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([96]): +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([384, 96]): +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([384]): +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([384]): +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([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_proj.weight - torch.Size([1024, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch2to1_proj.bias - torch.Size([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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, 1024]): +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, 1024]): +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([512, 1024]): +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([512]): +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([1024, 512]): +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([1024]): +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([256, 1024]): +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([256]): +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([256, 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([256]): +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([1024, 256]): +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([1024]): +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([1024]): +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([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_proj.weight - torch.Size([768, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_12.branch1to2_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.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_23.branch2to1_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.9.interaction_units_23.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_23.branch1to2_proj.bias - torch.Size([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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, 384]): +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, 384]): +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([192, 384]): +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([192]): +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([384, 192]): +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([384]): +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([96, 384]): +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([96]): +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([96, 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([96]): +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([384, 96]): +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([384]): +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([384]): +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([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_proj.weight - torch.Size([1024, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch2to1_proj.bias - torch.Size([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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, 1024]): +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, 1024]): +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([512, 1024]): +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([512]): +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([1024, 512]): +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([1024]): +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([256, 1024]): +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([256]): +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([256, 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([256]): +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([1024, 256]): +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([1024]): +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([1024]): +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([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_proj.weight - torch.Size([768, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_12.branch1to2_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.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_23.branch2to1_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.10.interaction_units_23.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_23.branch1to2_proj.bias - torch.Size([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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, 384]): +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, 384]): +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([192, 384]): +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([192]): +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([384, 192]): +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([384]): +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([96, 384]): +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([96]): +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([96, 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([96]): +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([384, 96]): +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([384]): +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([384]): +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([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_proj.weight - torch.Size([1024, 768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch2to1_proj.bias - torch.Size([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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([1024]): +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, 1024]): +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, 1024]): +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([512, 1024]): +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([512]): +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([1024, 512]): +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([1024]): +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([256, 1024]): +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([256]): +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([256, 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([256]): +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([1024, 256]): +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([1024]): +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([1024]): +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([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_proj.weight - torch.Size([768, 1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_12.branch1to2_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.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_23.branch2to1_proj.bias - torch.Size([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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([768]): +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, 768]): +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, 768]): +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([384, 768]): +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([384]): +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([768, 384]): +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([768]): +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([192, 768]): +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([192]): +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([192, 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([192]): +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([768, 192]): +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([768]): +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([768]): +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([768]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.interactions.11.interaction_units_23.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_23.branch1to2_proj.bias - torch.Size([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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([384]): +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, 384]): +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, 384]): +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([192, 384]): +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([192]): +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([384, 192]): +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([384]): +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([96, 384]): +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([96]): +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([96, 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([96]): +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([384, 96]): +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([384]): +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([384]): +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([384]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch1.0.weight - torch.Size([1024, 1024, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch1.1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch1.1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch1.3.weight - torch.Size([1024, 1024, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch1.4.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch1.4.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch2.0.weight - torch.Size([1024, 768, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch2.1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch2.1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch2.3.weight - torch.Size([1024, 1024, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch2.4.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch2.4.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch3.0.weight - torch.Size([1024, 384, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch3.1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch3.1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch3.3.weight - torch.Size([1024, 1024, 3, 3]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch3.4.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.merge_branch3.4.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.fpn1.0.weight - torch.Size([1024, 1024, 2, 2]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.fpn1.0.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.fpn1.1.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.fpn1.1.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.fpn1.3.weight - torch.Size([1024, 1024, 2, 2]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.fpn1.3.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.fpn2.0.weight - torch.Size([1024, 1024, 2, 2]): +The value is the same before and after calling `init_weights` of MaskRCNN + +backbone.fpn2.0.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of MaskRCNN + +neck.lateral_convs.0.conv.weight - torch.Size([256, 1024, 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, 1024, 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, 1024, 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, 1024, 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 +2024-05-27 14:32:26,283 - mmdet - INFO - {'num_layers': 24, 'layer_decay_rate': 0.85, 'skip_stride': [2, 2]} +2024-05-27 14:32:26,283 - mmdet - INFO - Build LayerDecayOptimizerConstructor 0.850000 - 26 +2024-05-27 14:32:26,301 - mmdet - INFO - Param groups = { + "layer_25_decay": { + "param_names": [ + "backbone.w1", + "backbone.w2", + "backbone.w3", + "backbone.interactions.0.interaction_units_12.branch2to1_proj.weight", + "backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight", + "backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.attention_weights.weight", + 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"roi_head.mask_head.upsample.bias", + "roi_head.mask_head.conv_logits.bias" + ], + "lr_scale": 1.0, + "lr": 0.0001, + "weight_decay": 0.0 + } +} +2024-05-27 14:32:53,994 - mmdet - INFO - Automatic scaling of learning rate (LR) has been disabled. +2024-05-27 14:32:54,405 - mmdet - INFO - Start running, work_dir: /mnt/petrelfs/PIIP/mmdetection/work_dirs/mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16 +2024-05-27 14:32:54,405 - 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 + -------------------- +2024-05-27 14:32:54,405 - mmdet - INFO - workflow: [('train', 1)], max: 12 epochs +2024-05-27 14:32:54,417 - mmdet - INFO - Checkpoints will be saved to /mnt/petrelfs/PIIP/mmdetection/work_dirs/mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16 by HardDiskBackend. +2024-05-27 14:33:30,186 - mmdet - INFO - Epoch [1][50/7330] lr: 9.890e-06, eta: 17:27:54, time: 0.715, data_time: 0.088, memory: 9039, loss_rpn_cls: 0.6300, loss_rpn_bbox: 0.1054, loss_cls: 1.7987, acc: 64.0596, loss_bbox: 0.0469, loss_mask: 1.3266, loss: 3.9075 +2024-05-27 14:33:58,368 - mmdet - INFO - Epoch [1][100/7330] lr: 1.988e-05, eta: 15:36:15, time: 0.564, data_time: 0.020, memory: 9144, loss_rpn_cls: 0.3183, loss_rpn_bbox: 0.0869, loss_cls: 0.4067, acc: 95.0295, loss_bbox: 0.1630, loss_mask: 0.7574, loss: 1.7324 +2024-05-27 14:34:26,512 - mmdet - INFO - Epoch [1][150/7330] lr: 2.987e-05, eta: 14:58:25, time: 0.563, data_time: 0.017, memory: 9213, loss_rpn_cls: 0.2680, loss_rpn_bbox: 0.0834, loss_cls: 0.3915, acc: 94.3403, loss_bbox: 0.1894, loss_mask: 0.6961, loss: 1.6284 +2024-05-27 14:34:54,828 - mmdet - INFO - Epoch [1][200/7330] lr: 3.986e-05, eta: 14:40:30, time: 0.566, data_time: 0.022, memory: 9337, loss_rpn_cls: 0.2653, loss_rpn_bbox: 0.0888, loss_cls: 0.4090, acc: 93.7290, loss_bbox: 0.2172, loss_mask: 0.6789, loss: 1.6591 +2024-05-27 14:35:29,680 - mmdet - INFO - Epoch [1][250/7330] lr: 4.985e-05, eta: 15:07:47, time: 0.697, data_time: 0.024, memory: 9385, loss_rpn_cls: 0.2190, loss_rpn_bbox: 0.0847, loss_cls: 0.4075, acc: 93.3096, loss_bbox: 0.2383, loss_mask: 0.6576, loss: 1.6070 +2024-05-27 14:35:58,034 - mmdet - INFO - Epoch [1][300/7330] lr: 5.984e-05, eta: 14:54:07, time: 0.567, data_time: 0.017, memory: 9385, loss_rpn_cls: 0.1797, loss_rpn_bbox: 0.0835, loss_cls: 0.4065, acc: 93.1306, loss_bbox: 0.2450, loss_mask: 0.6325, loss: 1.5472 +2024-05-27 14:36:26,625 - mmdet - INFO - Epoch [1][350/7330] lr: 6.983e-05, eta: 14:45:15, time: 0.572, data_time: 0.021, memory: 9403, loss_rpn_cls: 0.1542, loss_rpn_bbox: 0.0854, loss_cls: 0.4393, acc: 92.2900, loss_bbox: 0.2808, loss_mask: 0.6037, loss: 1.5635 +2024-05-27 14:36:55,374 - mmdet - INFO - Epoch [1][400/7330] lr: 7.982e-05, eta: 14:39:02, time: 0.575, data_time: 0.022, memory: 9428, loss_rpn_cls: 0.1233, loss_rpn_bbox: 0.0778, loss_cls: 0.4465, acc: 91.8281, loss_bbox: 0.3061, loss_mask: 0.5665, loss: 1.5202 +2024-05-27 14:37:24,142 - mmdet - INFO - Epoch [1][450/7330] lr: 8.981e-05, eta: 14:34:09, time: 0.575, data_time: 0.018, memory: 9428, loss_rpn_cls: 0.1238, loss_rpn_bbox: 0.0809, loss_cls: 0.4488, acc: 91.2605, loss_bbox: 0.3223, loss_mask: 0.5535, loss: 1.5293 +2024-05-27 14:37:53,150 - mmdet - INFO - Epoch [1][500/7330] lr: 9.980e-05, eta: 14:30:51, time: 0.580, data_time: 0.019, memory: 9428, loss_rpn_cls: 0.1112, loss_rpn_bbox: 0.0771, loss_cls: 0.4386, acc: 91.0405, loss_bbox: 0.3276, loss_mask: 0.5338, loss: 1.4883 +2024-05-27 14:38:21,962 - mmdet - INFO - Epoch [1][550/7330] lr: 1.000e-04, eta: 14:27:33, time: 0.576, data_time: 0.019, memory: 9428, loss_rpn_cls: 0.1043, loss_rpn_bbox: 0.0755, loss_cls: 0.4072, acc: 90.8660, loss_bbox: 0.3324, loss_mask: 0.5030, loss: 1.4223 +2024-05-27 14:38:50,972 - mmdet - INFO - Epoch [1][600/7330] lr: 1.000e-04, eta: 14:25:12, time: 0.580, data_time: 0.019, memory: 9428, loss_rpn_cls: 0.1000, loss_rpn_bbox: 0.0747, loss_cls: 0.4015, acc: 91.0103, loss_bbox: 0.3227, loss_mask: 0.4969, loss: 1.3959 +2024-05-27 14:39:19,922 - mmdet - INFO - Epoch [1][650/7330] lr: 1.000e-04, eta: 14:22:59, time: 0.579, data_time: 0.020, memory: 9456, loss_rpn_cls: 0.1084, loss_rpn_bbox: 0.0800, loss_cls: 0.4064, acc: 90.4146, loss_bbox: 0.3463, loss_mask: 0.4925, loss: 1.4336 +2024-05-27 14:39:51,398 - mmdet - INFO - Epoch [1][700/7330] lr: 1.000e-04, eta: 14:26:17, time: 0.629, data_time: 0.024, memory: 9456, loss_rpn_cls: 0.0978, loss_rpn_bbox: 0.0771, loss_cls: 0.3890, acc: 90.3467, loss_bbox: 0.3445, loss_mask: 0.4784, loss: 1.3868 +2024-05-27 14:40:20,437 - mmdet - INFO - Epoch [1][750/7330] lr: 1.000e-04, eta: 14:24:20, time: 0.581, data_time: 0.021, memory: 9456, loss_rpn_cls: 0.0964, loss_rpn_bbox: 0.0745, loss_cls: 0.3826, acc: 90.3176, loss_bbox: 0.3529, loss_mask: 0.4728, loss: 1.3793 +2024-05-27 14:40:49,559 - mmdet - INFO - Epoch [1][800/7330] lr: 1.000e-04, eta: 14:22:44, time: 0.582, data_time: 0.021, memory: 9458, loss_rpn_cls: 0.0995, loss_rpn_bbox: 0.0777, loss_cls: 0.3955, acc: 89.8149, loss_bbox: 0.3674, loss_mask: 0.4631, loss: 1.4032 +2024-05-27 14:41:18,563 - mmdet - INFO - Epoch [1][850/7330] lr: 1.000e-04, eta: 14:21:03, time: 0.580, data_time: 0.020, memory: 9458, loss_rpn_cls: 0.0929, loss_rpn_bbox: 0.0749, loss_cls: 0.3745, acc: 90.1287, loss_bbox: 0.3577, loss_mask: 0.4616, loss: 1.3615 +2024-05-27 14:41:47,596 - mmdet - INFO - Epoch [1][900/7330] lr: 1.000e-04, eta: 14:19:34, time: 0.581, data_time: 0.021, memory: 9458, loss_rpn_cls: 0.0861, loss_rpn_bbox: 0.0698, loss_cls: 0.3632, acc: 90.0896, loss_bbox: 0.3621, loss_mask: 0.4405, loss: 1.3217 +2024-05-27 14:42:16,612 - mmdet - INFO - Epoch [1][950/7330] lr: 1.000e-04, eta: 14:18:09, time: 0.580, data_time: 0.020, memory: 9458, loss_rpn_cls: 0.0844, loss_rpn_bbox: 0.0714, loss_cls: 0.3539, acc: 90.2410, loss_bbox: 0.3533, loss_mask: 0.4391, loss: 1.3021 +2024-05-27 14:42:46,104 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-27 14:42:46,104 - mmdet - INFO - Epoch [1][1000/7330] lr: 1.000e-04, eta: 14:17:31, time: 0.590, data_time: 0.022, memory: 9458, loss_rpn_cls: 0.0886, loss_rpn_bbox: 0.0746, loss_cls: 0.3580, acc: 89.9414, loss_bbox: 0.3680, loss_mask: 0.4367, loss: 1.3259 +2024-05-27 14:43:15,230 - mmdet - INFO - Epoch [1][1050/7330] lr: 1.000e-04, eta: 14:16:23, time: 0.582, data_time: 0.022, memory: 9458, loss_rpn_cls: 0.0850, loss_rpn_bbox: 0.0758, loss_cls: 0.3533, acc: 90.1699, loss_bbox: 0.3590, loss_mask: 0.4342, loss: 1.3073 +2024-05-27 14:43:44,422 - mmdet - INFO - Epoch [1][1100/7330] lr: 1.000e-04, eta: 14:15:24, time: 0.584, data_time: 0.020, memory: 9458, loss_rpn_cls: 0.0816, loss_rpn_bbox: 0.0745, loss_cls: 0.3473, acc: 90.0059, loss_bbox: 0.3672, loss_mask: 0.4243, loss: 1.2948 +2024-05-27 14:44:16,272 - mmdet - INFO - Epoch [1][1150/7330] lr: 1.000e-04, eta: 14:17:49, time: 0.637, data_time: 0.020, memory: 9458, loss_rpn_cls: 0.0752, loss_rpn_bbox: 0.0677, loss_cls: 0.3482, acc: 90.1072, loss_bbox: 0.3618, loss_mask: 0.4197, loss: 1.2727 +2024-05-27 14:44:47,256 - mmdet - INFO - Epoch [1][1200/7330] lr: 1.000e-04, eta: 14:18:56, time: 0.620, data_time: 0.021, memory: 9458, loss_rpn_cls: 0.0788, loss_rpn_bbox: 0.0705, loss_cls: 0.3498, acc: 89.8948, loss_bbox: 0.3677, loss_mask: 0.4260, loss: 1.2928 +2024-05-27 14:45:16,543 - mmdet - INFO - Epoch [1][1250/7330] lr: 1.000e-04, eta: 14:17:58, time: 0.586, data_time: 0.019, memory: 9458, loss_rpn_cls: 0.0811, loss_rpn_bbox: 0.0680, loss_cls: 0.3437, acc: 89.9792, loss_bbox: 0.3637, loss_mask: 0.4142, loss: 1.2707 +2024-05-27 14:45:45,815 - mmdet - INFO - Epoch [1][1300/7330] lr: 1.000e-04, eta: 14:17:01, time: 0.585, data_time: 0.020, memory: 9458, loss_rpn_cls: 0.0763, loss_rpn_bbox: 0.0668, loss_cls: 0.3478, acc: 89.8113, loss_bbox: 0.3692, loss_mask: 0.4136, loss: 1.2736 +2024-05-27 14:46:14,977 - mmdet - INFO - Epoch [1][1350/7330] lr: 1.000e-04, eta: 14:15:58, time: 0.583, data_time: 0.021, memory: 9458, loss_rpn_cls: 0.0731, loss_rpn_bbox: 0.0638, loss_cls: 0.3308, acc: 90.3684, loss_bbox: 0.3528, loss_mask: 0.4028, loss: 1.2234 +2024-05-27 14:46:44,544 - mmdet - INFO - Epoch [1][1400/7330] lr: 1.000e-04, eta: 14:15:24, time: 0.591, data_time: 0.022, memory: 9458, loss_rpn_cls: 0.0747, loss_rpn_bbox: 0.0713, loss_cls: 0.3532, acc: 89.6108, loss_bbox: 0.3775, loss_mask: 0.4079, loss: 1.2845 +2024-05-27 14:47:13,589 - mmdet - INFO - Epoch [1][1450/7330] lr: 1.000e-04, eta: 14:14:18, time: 0.581, data_time: 0.017, memory: 9458, loss_rpn_cls: 0.0718, loss_rpn_bbox: 0.0676, loss_cls: 0.3364, acc: 90.1707, loss_bbox: 0.3561, loss_mask: 0.4021, loss: 1.2339 +2024-05-27 14:47:42,895 - mmdet - INFO - Epoch [1][1500/7330] lr: 1.000e-04, eta: 14:13:30, time: 0.586, data_time: 0.020, memory: 9458, loss_rpn_cls: 0.0757, loss_rpn_bbox: 0.0687, loss_cls: 0.3367, acc: 89.9487, loss_bbox: 0.3674, loss_mask: 0.3986, loss: 1.2471 +2024-05-27 14:48:12,480 - mmdet - INFO - Epoch [1][1550/7330] lr: 1.000e-04, eta: 14:12:57, time: 0.591, data_time: 0.018, memory: 9458, loss_rpn_cls: 0.0772, loss_rpn_bbox: 0.0713, loss_cls: 0.3533, acc: 89.7000, loss_bbox: 0.3743, loss_mask: 0.3988, loss: 1.2750 +2024-05-27 14:48:44,261 - mmdet - INFO - Epoch [1][1600/7330] lr: 1.000e-04, eta: 14:14:26, time: 0.636, data_time: 0.021, memory: 9458, loss_rpn_cls: 0.0740, loss_rpn_bbox: 0.0682, loss_cls: 0.3270, acc: 90.2913, loss_bbox: 0.3562, loss_mask: 0.3983, loss: 1.2237 +2024-05-27 14:49:13,198 - mmdet - INFO - Epoch [1][1650/7330] lr: 1.000e-04, eta: 14:13:17, time: 0.579, data_time: 0.019, memory: 9458, loss_rpn_cls: 0.0681, loss_rpn_bbox: 0.0604, loss_cls: 0.3210, acc: 90.3191, loss_bbox: 0.3496, loss_mask: 0.3870, loss: 1.1861 +2024-05-27 14:49:42,540 - mmdet - INFO - Epoch [1][1700/7330] lr: 1.000e-04, eta: 14:12:31, time: 0.587, data_time: 0.021, memory: 9458, loss_rpn_cls: 0.0681, loss_rpn_bbox: 0.0672, loss_cls: 0.3230, acc: 90.3086, loss_bbox: 0.3604, loss_mask: 0.3900, loss: 1.2087 +2024-05-27 14:50:12,114 - mmdet - INFO - Epoch [1][1750/7330] lr: 1.000e-04, eta: 14:11:58, time: 0.592, data_time: 0.018, memory: 9458, loss_rpn_cls: 0.0701, loss_rpn_bbox: 0.0663, loss_cls: 0.3346, acc: 89.7732, loss_bbox: 0.3779, loss_mask: 0.4007, loss: 1.2496 +2024-05-27 14:50:41,437 - mmdet - INFO - Epoch [1][1800/7330] lr: 1.000e-04, eta: 14:11:13, time: 0.586, data_time: 0.019, memory: 9458, loss_rpn_cls: 0.0659, loss_rpn_bbox: 0.0605, loss_cls: 0.3170, acc: 90.2529, loss_bbox: 0.3579, loss_mask: 0.3776, loss: 1.1790 +2024-05-27 14:51:10,840 - mmdet - INFO - Epoch [1][1850/7330] lr: 1.000e-04, eta: 14:10:32, time: 0.588, data_time: 0.022, memory: 9458, loss_rpn_cls: 0.0673, loss_rpn_bbox: 0.0632, loss_cls: 0.3224, acc: 90.2729, loss_bbox: 0.3532, loss_mask: 0.3797, loss: 1.1858 +2024-05-27 14:51:40,018 - mmdet - INFO - Epoch [1][1900/7330] lr: 1.000e-04, eta: 14:09:42, time: 0.584, data_time: 0.018, memory: 9458, loss_rpn_cls: 0.0669, loss_rpn_bbox: 0.0626, loss_cls: 0.3107, acc: 90.5486, loss_bbox: 0.3472, loss_mask: 0.3722, loss: 1.1596 +2024-05-27 14:52:09,160 - mmdet - INFO - Epoch [1][1950/7330] lr: 1.000e-04, eta: 14:08:51, time: 0.583, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0607, loss_rpn_bbox: 0.0583, loss_cls: 0.3079, acc: 90.7354, loss_bbox: 0.3362, loss_mask: 0.3762, loss: 1.1393 +2024-05-27 14:52:38,539 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-27 14:52:38,539 - mmdet - INFO - Epoch [1][2000/7330] lr: 1.000e-04, eta: 14:08:11, time: 0.587, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0644, loss_rpn_bbox: 0.0649, loss_cls: 0.3200, acc: 90.2812, loss_bbox: 0.3528, loss_mask: 0.3785, loss: 1.1806 +2024-05-27 14:53:07,732 - mmdet - INFO - Epoch [1][2050/7330] lr: 1.000e-04, eta: 14:07:25, time: 0.584, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0590, loss_rpn_bbox: 0.0594, loss_cls: 0.3164, acc: 90.4414, loss_bbox: 0.3525, loss_mask: 0.3799, loss: 1.1672 +2024-05-27 14:53:42,213 - mmdet - INFO - Epoch [1][2100/7330] lr: 1.000e-04, eta: 14:10:15, time: 0.690, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0712, loss_rpn_bbox: 0.0651, loss_cls: 0.3178, acc: 90.4187, loss_bbox: 0.3477, loss_mask: 0.3789, loss: 1.1808 +2024-05-27 14:54:11,378 - mmdet - INFO - Epoch [1][2150/7330] lr: 1.000e-04, eta: 14:09:23, time: 0.583, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0667, loss_rpn_bbox: 0.0656, loss_cls: 0.3206, acc: 90.0857, loss_bbox: 0.3594, loss_mask: 0.3729, loss: 1.1853 +2024-05-27 14:54:40,774 - mmdet - INFO - Epoch [1][2200/7330] lr: 1.000e-04, eta: 14:08:42, time: 0.588, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0691, loss_rpn_bbox: 0.0675, loss_cls: 0.3226, acc: 89.8401, loss_bbox: 0.3648, loss_mask: 0.3756, loss: 1.1996 +2024-05-27 14:55:09,998 - mmdet - INFO - Epoch [1][2250/7330] lr: 1.000e-04, eta: 14:07:55, time: 0.585, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0643, loss_rpn_bbox: 0.0626, loss_cls: 0.3203, acc: 90.1609, loss_bbox: 0.3557, loss_mask: 0.3697, loss: 1.1725 +2024-05-27 14:55:39,249 - mmdet - INFO - Epoch [1][2300/7330] lr: 1.000e-04, eta: 14:07:09, time: 0.585, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0578, loss_rpn_bbox: 0.0584, loss_cls: 0.3073, acc: 90.5100, loss_bbox: 0.3398, loss_mask: 0.3637, loss: 1.1269 +2024-05-27 14:56:08,476 - mmdet - INFO - Epoch [1][2350/7330] lr: 1.000e-04, eta: 14:06:24, time: 0.585, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0635, loss_rpn_bbox: 0.0635, loss_cls: 0.3091, acc: 90.3247, loss_bbox: 0.3511, loss_mask: 0.3688, loss: 1.1560 +2024-05-27 14:56:37,609 - mmdet - INFO - Epoch [1][2400/7330] lr: 1.000e-04, eta: 14:05:35, time: 0.583, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0625, loss_rpn_bbox: 0.0630, loss_cls: 0.2984, acc: 90.5796, loss_bbox: 0.3470, loss_mask: 0.3577, loss: 1.1286 +2024-05-27 14:57:06,936 - mmdet - INFO - Epoch [1][2450/7330] lr: 1.000e-04, eta: 14:04:54, time: 0.587, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0596, loss_rpn_bbox: 0.0588, loss_cls: 0.3123, acc: 90.4795, loss_bbox: 0.3459, loss_mask: 0.3572, loss: 1.1338 +2024-05-27 14:57:38,834 - mmdet - INFO - Epoch [1][2500/7330] lr: 1.000e-04, eta: 14:05:42, time: 0.638, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0637, loss_rpn_bbox: 0.0628, loss_cls: 0.3192, acc: 90.0037, loss_bbox: 0.3588, loss_mask: 0.3645, loss: 1.1690 +2024-05-27 14:58:08,333 - mmdet - INFO - Epoch [1][2550/7330] lr: 1.000e-04, eta: 14:05:06, time: 0.590, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0636, loss_rpn_bbox: 0.0635, loss_cls: 0.3161, acc: 90.1257, loss_bbox: 0.3580, loss_mask: 0.3629, loss: 1.1641 +2024-05-27 14:58:37,907 - mmdet - INFO - Epoch [1][2600/7330] lr: 1.000e-04, eta: 14:04:32, time: 0.591, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0624, loss_rpn_bbox: 0.0637, loss_cls: 0.3148, acc: 90.2051, loss_bbox: 0.3558, loss_mask: 0.3619, loss: 1.1586 +2024-05-27 14:59:07,211 - mmdet - INFO - Epoch [1][2650/7330] lr: 1.000e-04, eta: 14:03:51, time: 0.586, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0602, loss_rpn_bbox: 0.0627, loss_cls: 0.3116, acc: 90.2139, loss_bbox: 0.3576, loss_mask: 0.3705, loss: 1.1627 +2024-05-27 14:59:36,541 - mmdet - INFO - Epoch [1][2700/7330] lr: 1.000e-04, eta: 14:03:10, time: 0.587, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0577, loss_rpn_bbox: 0.0623, loss_cls: 0.3127, acc: 90.0989, loss_bbox: 0.3521, loss_mask: 0.3461, loss: 1.1308 +2024-05-27 15:00:06,020 - mmdet - INFO - Epoch [1][2750/7330] lr: 1.000e-04, eta: 14:02:34, time: 0.590, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0581, loss_rpn_bbox: 0.0609, loss_cls: 0.3122, acc: 90.1450, loss_bbox: 0.3514, loss_mask: 0.3603, loss: 1.1429 +2024-05-27 15:00:35,250 - mmdet - INFO - Epoch [1][2800/7330] lr: 1.000e-04, eta: 14:01:52, time: 0.585, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0605, loss_rpn_bbox: 0.0587, loss_cls: 0.3039, acc: 90.5320, loss_bbox: 0.3382, loss_mask: 0.3536, loss: 1.1149 +2024-05-27 15:01:04,422 - mmdet - INFO - Epoch [1][2850/7330] lr: 1.000e-04, eta: 14:01:07, time: 0.583, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0603, loss_rpn_bbox: 0.0589, loss_cls: 0.3052, acc: 90.5427, loss_bbox: 0.3377, loss_mask: 0.3574, loss: 1.1195 +2024-05-27 15:01:33,754 - mmdet - INFO - Epoch [1][2900/7330] lr: 1.000e-04, eta: 14:00:28, time: 0.587, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0588, loss_rpn_bbox: 0.0599, loss_cls: 0.2954, acc: 90.6995, loss_bbox: 0.3391, loss_mask: 0.3473, loss: 1.1004 +2024-05-27 15:02:02,983 - mmdet - INFO - Epoch [1][2950/7330] lr: 1.000e-04, eta: 13:59:47, time: 0.585, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0533, loss_rpn_bbox: 0.0600, loss_cls: 0.3086, acc: 90.3242, loss_bbox: 0.3516, loss_mask: 0.3512, loss: 1.1248 +2024-05-27 15:02:41,790 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-27 15:02:41,790 - mmdet - INFO - Epoch [1][3000/7330] lr: 1.000e-04, eta: 14:03:37, time: 0.776, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0599, loss_rpn_bbox: 0.0632, loss_cls: 0.3015, acc: 90.4182, loss_bbox: 0.3485, loss_mask: 0.3554, loss: 1.1285 +2024-05-27 15:03:11,039 - mmdet - INFO - Epoch [1][3050/7330] lr: 1.000e-04, eta: 14:02:52, time: 0.585, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0556, loss_rpn_bbox: 0.0567, loss_cls: 0.3018, acc: 90.5969, loss_bbox: 0.3409, loss_mask: 0.3470, loss: 1.1020 +2024-05-27 15:03:40,482 - mmdet - INFO - Epoch [1][3100/7330] lr: 1.000e-04, eta: 14:02:13, time: 0.589, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0621, loss_rpn_bbox: 0.0604, loss_cls: 0.2941, acc: 90.6294, loss_bbox: 0.3415, loss_mask: 0.3476, loss: 1.1058 +2024-05-27 15:04:09,865 - mmdet - INFO - Epoch [1][3150/7330] lr: 1.000e-04, eta: 14:01:33, time: 0.588, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0583, loss_rpn_bbox: 0.0595, loss_cls: 0.2994, acc: 90.5654, loss_bbox: 0.3379, loss_mask: 0.3526, loss: 1.1077 +2024-05-27 15:04:39,294 - mmdet - INFO - Epoch [1][3200/7330] lr: 1.000e-04, eta: 14:00:54, time: 0.589, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0599, loss_rpn_bbox: 0.0595, loss_cls: 0.3066, acc: 90.5930, loss_bbox: 0.3420, loss_mask: 0.3408, loss: 1.1088 +2024-05-27 15:05:08,604 - mmdet - INFO - Epoch [1][3250/7330] lr: 1.000e-04, eta: 14:00:12, time: 0.586, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0597, loss_rpn_bbox: 0.0585, loss_cls: 0.3025, acc: 90.4556, loss_bbox: 0.3410, loss_mask: 0.3484, loss: 1.1102 +2024-05-27 15:05:37,931 - mmdet - INFO - Epoch [1][3300/7330] lr: 1.000e-04, eta: 13:59:32, time: 0.586, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0548, loss_rpn_bbox: 0.0568, loss_cls: 0.2884, acc: 90.7686, loss_bbox: 0.3373, loss_mask: 0.3439, loss: 1.0811 +2024-05-27 15:06:07,165 - mmdet - INFO - Epoch [1][3350/7330] lr: 1.000e-04, eta: 13:58:49, time: 0.585, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0561, loss_rpn_bbox: 0.0550, loss_cls: 0.2916, acc: 90.8682, loss_bbox: 0.3283, loss_mask: 0.3383, loss: 1.0693 +2024-05-27 15:06:39,424 - mmdet - INFO - Epoch [1][3400/7330] lr: 1.000e-04, eta: 13:59:22, time: 0.645, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0576, loss_rpn_bbox: 0.0556, loss_cls: 0.2860, acc: 90.8567, loss_bbox: 0.3341, loss_mask: 0.3438, loss: 1.0772 +2024-05-27 15:07:08,795 - mmdet - INFO - Epoch [1][3450/7330] lr: 1.000e-04, eta: 13:58:42, time: 0.587, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0536, loss_rpn_bbox: 0.0593, loss_cls: 0.2996, acc: 90.5166, loss_bbox: 0.3409, loss_mask: 0.3472, loss: 1.1006 +2024-05-27 15:07:38,095 - mmdet - INFO - Epoch [1][3500/7330] lr: 1.000e-04, eta: 13:58:01, time: 0.586, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0537, loss_rpn_bbox: 0.0568, loss_cls: 0.2972, acc: 90.5872, loss_bbox: 0.3430, loss_mask: 0.3505, loss: 1.1012 +2024-05-27 15:08:07,464 - mmdet - INFO - Epoch [1][3550/7330] lr: 1.000e-04, eta: 13:57:21, time: 0.587, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0556, loss_rpn_bbox: 0.0580, loss_cls: 0.2986, acc: 90.3711, loss_bbox: 0.3471, loss_mask: 0.3439, loss: 1.1031 +2024-05-27 15:08:36,912 - mmdet - INFO - Epoch [1][3600/7330] lr: 1.000e-04, eta: 13:56:44, time: 0.589, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0580, loss_rpn_bbox: 0.0633, loss_cls: 0.2975, acc: 90.5850, loss_bbox: 0.3432, loss_mask: 0.3474, loss: 1.1094 +2024-05-27 15:09:06,268 - mmdet - INFO - Epoch [1][3650/7330] lr: 1.000e-04, eta: 13:56:05, time: 0.587, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0567, loss_rpn_bbox: 0.0588, loss_cls: 0.3155, acc: 90.0657, loss_bbox: 0.3562, loss_mask: 0.3485, loss: 1.1357 +2024-05-27 15:09:35,757 - mmdet - INFO - Epoch [1][3700/7330] lr: 1.000e-04, eta: 13:55:30, time: 0.590, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0574, loss_rpn_bbox: 0.0588, loss_cls: 0.3034, acc: 90.1726, loss_bbox: 0.3569, loss_mask: 0.3444, loss: 1.1209 +2024-05-27 15:10:05,177 - mmdet - INFO - Epoch [1][3750/7330] lr: 1.000e-04, eta: 13:54:53, time: 0.588, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0537, loss_rpn_bbox: 0.0568, loss_cls: 0.2863, acc: 90.8147, loss_bbox: 0.3361, loss_mask: 0.3401, loss: 1.0730 +2024-05-27 15:10:34,487 - mmdet - INFO - Epoch [1][3800/7330] lr: 1.000e-04, eta: 13:54:13, time: 0.586, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0496, loss_rpn_bbox: 0.0552, loss_cls: 0.2869, acc: 90.8264, loss_bbox: 0.3307, loss_mask: 0.3370, loss: 1.0594 +2024-05-27 15:11:03,778 - mmdet - INFO - Epoch [1][3850/7330] lr: 1.000e-04, eta: 13:53:34, time: 0.586, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0510, loss_rpn_bbox: 0.0567, loss_cls: 0.2916, acc: 90.5898, loss_bbox: 0.3430, loss_mask: 0.3408, loss: 1.0830 +2024-05-27 15:11:43,293 - mmdet - INFO - Epoch [1][3900/7330] lr: 1.000e-04, eta: 13:56:34, time: 0.790, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0579, loss_rpn_bbox: 0.0594, loss_cls: 0.2910, acc: 90.6399, loss_bbox: 0.3390, loss_mask: 0.3385, loss: 1.0858 +2024-05-27 15:12:12,818 - mmdet - INFO - Epoch [1][3950/7330] lr: 1.000e-04, eta: 13:55:58, time: 0.591, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0574, loss_rpn_bbox: 0.0584, loss_cls: 0.2837, acc: 90.8535, loss_bbox: 0.3336, loss_mask: 0.3371, loss: 1.0702 +2024-05-27 15:12:42,017 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-27 15:12:42,017 - mmdet - INFO - Epoch [1][4000/7330] lr: 1.000e-04, eta: 13:55:14, time: 0.584, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0523, loss_rpn_bbox: 0.0558, loss_cls: 0.2809, acc: 90.9878, loss_bbox: 0.3281, loss_mask: 0.3340, loss: 1.0511 +2024-05-27 15:13:11,377 - mmdet - INFO - Epoch [1][4050/7330] lr: 1.000e-04, eta: 13:54:34, time: 0.587, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0529, loss_rpn_bbox: 0.0568, loss_cls: 0.2914, acc: 90.6179, loss_bbox: 0.3420, loss_mask: 0.3350, loss: 1.0780 +2024-05-27 15:13:40,661 - mmdet - INFO - Epoch [1][4100/7330] lr: 1.000e-04, eta: 13:53:53, time: 0.586, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0532, loss_rpn_bbox: 0.0579, loss_cls: 0.2937, acc: 90.5464, loss_bbox: 0.3424, loss_mask: 0.3475, loss: 1.0947 +2024-05-27 15:14:10,148 - mmdet - INFO - Epoch [1][4150/7330] lr: 1.000e-04, eta: 13:53:16, time: 0.590, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0542, loss_rpn_bbox: 0.0549, loss_cls: 0.2911, acc: 90.6528, loss_bbox: 0.3390, loss_mask: 0.3282, loss: 1.0674 +2024-05-27 15:14:39,426 - mmdet - INFO - Epoch [1][4200/7330] lr: 1.000e-04, eta: 13:52:36, time: 0.586, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0527, loss_rpn_bbox: 0.0555, loss_cls: 0.2874, acc: 90.9392, loss_bbox: 0.3312, loss_mask: 0.3311, loss: 1.0578 +2024-05-27 15:15:08,720 - mmdet - INFO - Epoch [1][4250/7330] lr: 1.000e-04, eta: 13:51:55, time: 0.586, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0502, loss_rpn_bbox: 0.0574, loss_cls: 0.2898, acc: 90.5881, loss_bbox: 0.3370, loss_mask: 0.3269, loss: 1.0613 +2024-05-27 15:15:40,504 - mmdet - INFO - Epoch [1][4300/7330] lr: 1.000e-04, eta: 13:52:04, time: 0.636, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0526, loss_rpn_bbox: 0.0592, loss_cls: 0.2861, acc: 90.7625, loss_bbox: 0.3346, loss_mask: 0.3295, loss: 1.0619 +2024-05-27 15:16:09,961 - mmdet - INFO - Epoch [1][4350/7330] lr: 1.000e-04, eta: 13:51:27, time: 0.589, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0505, loss_rpn_bbox: 0.0579, loss_cls: 0.2894, acc: 90.5027, loss_bbox: 0.3473, loss_mask: 0.3344, loss: 1.0794 +2024-05-27 15:16:39,413 - mmdet - INFO - Epoch [1][4400/7330] lr: 1.000e-04, eta: 13:50:50, time: 0.589, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0498, loss_rpn_bbox: 0.0571, loss_cls: 0.2941, acc: 90.5000, loss_bbox: 0.3423, loss_mask: 0.3249, loss: 1.0681 +2024-05-27 15:17:08,824 - mmdet - INFO - Epoch [1][4450/7330] lr: 1.000e-04, eta: 13:50:12, time: 0.588, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0545, loss_rpn_bbox: 0.0571, loss_cls: 0.2846, acc: 90.7871, loss_bbox: 0.3326, loss_mask: 0.3287, loss: 1.0575 +2024-05-27 15:17:38,259 - mmdet - INFO - Epoch [1][4500/7330] lr: 1.000e-04, eta: 13:49:35, time: 0.589, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0486, loss_rpn_bbox: 0.0553, loss_cls: 0.2838, acc: 90.7998, loss_bbox: 0.3353, loss_mask: 0.3331, loss: 1.0560 +2024-05-27 15:18:07,678 - mmdet - INFO - Epoch [1][4550/7330] lr: 1.000e-04, eta: 13:48:58, time: 0.588, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0543, loss_rpn_bbox: 0.0570, loss_cls: 0.2903, acc: 90.5898, loss_bbox: 0.3393, loss_mask: 0.3331, loss: 1.0740 +2024-05-27 15:18:37,116 - mmdet - INFO - Epoch [1][4600/7330] lr: 1.000e-04, eta: 13:48:21, time: 0.589, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0553, loss_rpn_bbox: 0.0553, loss_cls: 0.2909, acc: 90.6846, loss_bbox: 0.3315, loss_mask: 0.3246, loss: 1.0576 +2024-05-27 15:19:06,622 - mmdet - INFO - Epoch [1][4650/7330] lr: 1.000e-04, eta: 13:47:46, time: 0.590, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0555, loss_rpn_bbox: 0.0599, loss_cls: 0.2990, acc: 90.3840, loss_bbox: 0.3451, loss_mask: 0.3363, loss: 1.0958 +2024-05-27 15:19:36,011 - mmdet - INFO - Epoch [1][4700/7330] lr: 1.000e-04, eta: 13:47:09, time: 0.588, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0468, loss_rpn_bbox: 0.0523, loss_cls: 0.2782, acc: 90.9927, loss_bbox: 0.3257, loss_mask: 0.3233, loss: 1.0263 +2024-05-27 15:20:05,498 - mmdet - INFO - Epoch [1][4750/7330] lr: 1.000e-04, eta: 13:46:33, time: 0.590, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0528, loss_rpn_bbox: 0.0549, loss_cls: 0.2817, acc: 91.0630, loss_bbox: 0.3241, loss_mask: 0.3254, loss: 1.0387 +2024-05-27 15:20:44,989 - mmdet - INFO - Epoch [1][4800/7330] lr: 1.000e-04, eta: 13:48:51, time: 0.790, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0507, loss_rpn_bbox: 0.0571, loss_cls: 0.2908, acc: 90.4426, loss_bbox: 0.3433, loss_mask: 0.3249, loss: 1.0668 +2024-05-27 15:21:14,558 - mmdet - INFO - Epoch [1][4850/7330] lr: 1.000e-04, eta: 13:48:16, time: 0.591, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0524, loss_rpn_bbox: 0.0578, loss_cls: 0.2830, acc: 90.6929, loss_bbox: 0.3343, loss_mask: 0.3265, loss: 1.0541 +2024-05-27 15:21:43,936 - mmdet - INFO - Epoch [1][4900/7330] lr: 1.000e-04, eta: 13:47:37, time: 0.588, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0520, loss_rpn_bbox: 0.0592, loss_cls: 0.2878, acc: 90.6489, loss_bbox: 0.3384, loss_mask: 0.3311, loss: 1.0686 +2024-05-27 15:22:13,207 - mmdet - INFO - Epoch [1][4950/7330] lr: 1.000e-04, eta: 13:46:57, time: 0.585, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0533, loss_rpn_bbox: 0.0584, loss_cls: 0.3011, acc: 90.2454, loss_bbox: 0.3449, loss_mask: 0.3294, loss: 1.0872 +2024-05-27 15:22:42,621 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-27 15:22:42,621 - mmdet - INFO - Epoch [1][5000/7330] lr: 1.000e-04, eta: 13:46:19, time: 0.588, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0508, loss_rpn_bbox: 0.0555, loss_cls: 0.2763, acc: 91.0493, loss_bbox: 0.3212, loss_mask: 0.3270, loss: 1.0308 +2024-05-27 15:23:12,020 - mmdet - INFO - Epoch [1][5050/7330] lr: 1.000e-04, eta: 13:45:41, time: 0.588, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0483, loss_rpn_bbox: 0.0536, loss_cls: 0.2846, acc: 90.7354, loss_bbox: 0.3369, loss_mask: 0.3286, loss: 1.0520 +2024-05-27 15:23:41,442 - mmdet - INFO - Epoch [1][5100/7330] lr: 1.000e-04, eta: 13:45:04, time: 0.588, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0476, loss_rpn_bbox: 0.0554, loss_cls: 0.2901, acc: 90.6218, loss_bbox: 0.3381, loss_mask: 0.3262, loss: 1.0573 +2024-05-27 15:24:10,618 - mmdet - INFO - Epoch [1][5150/7330] lr: 1.000e-04, eta: 13:44:23, time: 0.583, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0529, loss_rpn_bbox: 0.0570, loss_cls: 0.2872, acc: 90.7290, loss_bbox: 0.3311, loss_mask: 0.3289, loss: 1.0571 +2024-05-27 15:24:40,019 - mmdet - INFO - Epoch [1][5200/7330] lr: 1.000e-04, eta: 13:43:46, time: 0.588, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0496, loss_rpn_bbox: 0.0530, loss_cls: 0.2728, acc: 91.1707, loss_bbox: 0.3199, loss_mask: 0.3277, loss: 1.0231 +2024-05-27 15:25:11,623 - mmdet - INFO - Epoch [1][5250/7330] lr: 1.000e-04, eta: 13:43:43, time: 0.632, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0482, loss_rpn_bbox: 0.0529, loss_cls: 0.2810, acc: 90.8545, loss_bbox: 0.3282, loss_mask: 0.3312, loss: 1.0414 +2024-05-27 15:25:40,966 - mmdet - INFO - Epoch [1][5300/7330] lr: 1.000e-04, eta: 13:43:05, time: 0.587, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0565, loss_rpn_bbox: 0.0562, loss_cls: 0.2935, acc: 90.4983, loss_bbox: 0.3429, loss_mask: 0.3336, loss: 1.0827 +2024-05-27 15:26:10,432 - mmdet - INFO - Epoch [1][5350/7330] lr: 1.000e-04, eta: 13:42:29, time: 0.589, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0565, loss_rpn_bbox: 0.0577, loss_cls: 0.2880, acc: 90.7944, loss_bbox: 0.3348, loss_mask: 0.3243, loss: 1.0613 +2024-05-27 15:26:39,876 - mmdet - INFO - Epoch [1][5400/7330] lr: 1.000e-04, eta: 13:41:52, time: 0.589, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0524, loss_rpn_bbox: 0.0543, loss_cls: 0.2919, acc: 90.6313, loss_bbox: 0.3410, loss_mask: 0.3273, loss: 1.0669 +2024-05-27 15:27:09,341 - mmdet - INFO - Epoch [1][5450/7330] lr: 1.000e-04, eta: 13:41:16, time: 0.589, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0489, loss_rpn_bbox: 0.0566, loss_cls: 0.2868, acc: 90.5588, loss_bbox: 0.3426, loss_mask: 0.3258, loss: 1.0608 +2024-05-27 15:27:38,734 - mmdet - INFO - Epoch [1][5500/7330] lr: 1.000e-04, eta: 13:40:40, time: 0.588, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0489, loss_rpn_bbox: 0.0546, loss_cls: 0.2828, acc: 90.6721, loss_bbox: 0.3327, loss_mask: 0.3269, loss: 1.0458 +2024-05-27 15:28:08,103 - mmdet - INFO - Epoch [1][5550/7330] lr: 1.000e-04, eta: 13:40:02, time: 0.587, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0501, loss_rpn_bbox: 0.0548, loss_cls: 0.2832, acc: 90.9067, loss_bbox: 0.3279, loss_mask: 0.3249, loss: 1.0409 +2024-05-27 15:28:37,513 - mmdet - INFO - Epoch [1][5600/7330] lr: 1.000e-04, eta: 13:39:26, time: 0.588, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0507, loss_rpn_bbox: 0.0530, loss_cls: 0.2736, acc: 91.2197, loss_bbox: 0.3198, loss_mask: 0.3217, loss: 1.0189 +2024-05-27 15:29:06,970 - mmdet - INFO - Epoch [1][5650/7330] lr: 1.000e-04, eta: 13:38:50, time: 0.589, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0502, loss_rpn_bbox: 0.0572, loss_cls: 0.2795, acc: 90.8354, loss_bbox: 0.3252, loss_mask: 0.3251, loss: 1.0373 +2024-05-27 15:29:46,250 - mmdet - INFO - Epoch [1][5700/7330] lr: 1.000e-04, eta: 13:40:37, time: 0.786, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0492, loss_rpn_bbox: 0.0549, loss_cls: 0.2803, acc: 90.9839, loss_bbox: 0.3289, loss_mask: 0.3273, loss: 1.0406 +2024-05-27 15:30:15,899 - mmdet - INFO - Epoch [1][5750/7330] lr: 1.000e-04, eta: 13:40:03, time: 0.593, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0486, loss_rpn_bbox: 0.0544, loss_cls: 0.2772, acc: 90.7803, loss_bbox: 0.3295, loss_mask: 0.3236, loss: 1.0334 +2024-05-27 15:30:45,514 - mmdet - INFO - Epoch [1][5800/7330] lr: 1.000e-04, eta: 13:39:29, time: 0.592, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0498, loss_rpn_bbox: 0.0535, loss_cls: 0.2765, acc: 90.9214, loss_bbox: 0.3277, loss_mask: 0.3246, loss: 1.0322 +2024-05-27 15:31:15,077 - mmdet - INFO - Epoch [1][5850/7330] lr: 1.000e-04, eta: 13:38:54, time: 0.591, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0451, loss_rpn_bbox: 0.0512, loss_cls: 0.2673, acc: 91.1863, loss_bbox: 0.3223, loss_mask: 0.3141, loss: 1.0000 +2024-05-27 15:31:44,629 - mmdet - INFO - Epoch [1][5900/7330] lr: 1.000e-04, eta: 13:38:19, time: 0.591, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0498, loss_rpn_bbox: 0.0576, loss_cls: 0.2874, acc: 90.5811, loss_bbox: 0.3376, loss_mask: 0.3305, loss: 1.0630 +2024-05-27 15:32:14,157 - mmdet - INFO - Epoch [1][5950/7330] lr: 1.000e-04, eta: 13:37:43, time: 0.591, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0504, loss_rpn_bbox: 0.0567, loss_cls: 0.2755, acc: 91.0022, loss_bbox: 0.3240, loss_mask: 0.3190, loss: 1.0257 +2024-05-27 15:32:43,489 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-27 15:32:43,489 - mmdet - INFO - Epoch [1][6000/7330] lr: 1.000e-04, eta: 13:37:06, time: 0.587, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0449, loss_rpn_bbox: 0.0509, loss_cls: 0.2712, acc: 91.2617, loss_bbox: 0.3138, loss_mask: 0.3212, loss: 1.0021 +2024-05-27 15:33:13,122 - mmdet - INFO - Epoch [1][6050/7330] lr: 1.000e-04, eta: 13:36:32, time: 0.592, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0478, loss_rpn_bbox: 0.0536, loss_cls: 0.2664, acc: 91.1843, loss_bbox: 0.3173, loss_mask: 0.3214, loss: 1.0066 +2024-05-27 15:33:42,659 - mmdet - INFO - Epoch [1][6100/7330] lr: 1.000e-04, eta: 13:35:57, time: 0.591, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0463, loss_rpn_bbox: 0.0527, loss_cls: 0.2826, acc: 90.7466, loss_bbox: 0.3291, loss_mask: 0.3243, loss: 1.0350 +2024-05-27 15:34:14,484 - mmdet - INFO - Epoch [1][6150/7330] lr: 1.000e-04, eta: 13:35:53, time: 0.636, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0499, loss_rpn_bbox: 0.0577, loss_cls: 0.2774, acc: 90.8782, loss_bbox: 0.3313, loss_mask: 0.3210, loss: 1.0373 +2024-05-27 15:34:43,762 - mmdet - INFO - Epoch [1][6200/7330] lr: 1.000e-04, eta: 13:35:14, time: 0.586, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0459, loss_rpn_bbox: 0.0538, loss_cls: 0.2710, acc: 91.1492, loss_bbox: 0.3166, loss_mask: 0.3196, loss: 1.0070 +2024-05-27 15:35:13,360 - mmdet - INFO - Epoch [1][6250/7330] lr: 1.000e-04, eta: 13:34:40, time: 0.592, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0550, loss_rpn_bbox: 0.0587, loss_cls: 0.2990, acc: 90.0811, loss_bbox: 0.3507, loss_mask: 0.3276, loss: 1.0911 +2024-05-27 15:35:42,794 - mmdet - INFO - Epoch [1][6300/7330] lr: 1.000e-04, eta: 13:34:04, time: 0.589, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0500, loss_rpn_bbox: 0.0550, loss_cls: 0.2818, acc: 90.7703, loss_bbox: 0.3340, loss_mask: 0.3261, loss: 1.0468 +2024-05-27 15:36:12,521 - mmdet - INFO - Epoch [1][6350/7330] lr: 1.000e-04, eta: 13:33:32, time: 0.594, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0508, loss_rpn_bbox: 0.0570, loss_cls: 0.2753, acc: 91.0015, loss_bbox: 0.3243, loss_mask: 0.3207, loss: 1.0280 +2024-05-27 15:36:41,988 - mmdet - INFO - Epoch [1][6400/7330] lr: 1.000e-04, eta: 13:32:56, time: 0.590, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0497, loss_rpn_bbox: 0.0551, loss_cls: 0.2688, acc: 91.0991, loss_bbox: 0.3200, loss_mask: 0.3234, loss: 1.0169 +2024-05-27 15:37:11,475 - mmdet - INFO - Epoch [1][6450/7330] lr: 1.000e-04, eta: 13:32:21, time: 0.590, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0474, loss_rpn_bbox: 0.0543, loss_cls: 0.2781, acc: 90.8516, loss_bbox: 0.3345, loss_mask: 0.3258, loss: 1.0401 +2024-05-27 15:37:40,838 - mmdet - INFO - Epoch [1][6500/7330] lr: 1.000e-04, eta: 13:31:45, time: 0.587, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0497, loss_rpn_bbox: 0.0536, loss_cls: 0.2756, acc: 90.8008, loss_bbox: 0.3306, loss_mask: 0.3161, loss: 1.0256 +2024-05-27 15:38:10,312 - mmdet - INFO - Epoch [1][6550/7330] lr: 1.000e-04, eta: 13:31:09, time: 0.589, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0463, loss_rpn_bbox: 0.0511, loss_cls: 0.2612, acc: 91.5471, loss_bbox: 0.3042, loss_mask: 0.3185, loss: 0.9813 +2024-05-27 15:38:45,309 - mmdet - INFO - Epoch [1][6600/7330] lr: 1.000e-04, eta: 13:31:43, time: 0.700, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0474, loss_rpn_bbox: 0.0526, loss_cls: 0.2653, acc: 91.2817, loss_bbox: 0.3207, loss_mask: 0.3109, loss: 0.9969 +2024-05-27 15:39:18,736 - mmdet - INFO - Epoch [1][6650/7330] lr: 1.000e-04, eta: 13:31:55, time: 0.669, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0500, loss_rpn_bbox: 0.0556, loss_cls: 0.2801, acc: 90.6929, loss_bbox: 0.3340, loss_mask: 0.3224, loss: 1.0420 +2024-05-27 15:39:48,167 - mmdet - INFO - Epoch [1][6700/7330] lr: 1.000e-04, eta: 13:31:19, time: 0.589, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0441, loss_rpn_bbox: 0.0517, loss_cls: 0.2632, acc: 91.2974, loss_bbox: 0.3158, loss_mask: 0.3139, loss: 0.9888 +2024-05-27 15:40:17,537 - mmdet - INFO - Epoch [1][6750/7330] lr: 1.000e-04, eta: 13:30:42, time: 0.587, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0468, loss_rpn_bbox: 0.0540, loss_cls: 0.2694, acc: 91.1001, loss_bbox: 0.3158, loss_mask: 0.3094, loss: 0.9954 +2024-05-27 15:40:47,148 - mmdet - INFO - Epoch [1][6800/7330] lr: 1.000e-04, eta: 13:30:08, time: 0.592, data_time: 0.025, memory: 9459, loss_rpn_cls: 0.0527, loss_rpn_bbox: 0.0572, loss_cls: 0.2823, acc: 90.6550, loss_bbox: 0.3384, loss_mask: 0.3160, loss: 1.0465 +2024-05-27 15:41:16,622 - mmdet - INFO - Epoch [1][6850/7330] lr: 1.000e-04, eta: 13:29:33, time: 0.589, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0474, loss_rpn_bbox: 0.0527, loss_cls: 0.2692, acc: 91.0439, loss_bbox: 0.3199, loss_mask: 0.3062, loss: 0.9954 +2024-05-27 15:41:46,054 - mmdet - INFO - Epoch [1][6900/7330] lr: 1.000e-04, eta: 13:28:57, time: 0.589, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0455, loss_rpn_bbox: 0.0503, loss_cls: 0.2730, acc: 91.0881, loss_bbox: 0.3202, loss_mask: 0.3087, loss: 0.9977 +2024-05-27 15:42:15,250 - mmdet - INFO - Epoch [1][6950/7330] lr: 1.000e-04, eta: 13:28:18, time: 0.584, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0428, loss_rpn_bbox: 0.0489, loss_cls: 0.2639, acc: 91.4473, loss_bbox: 0.3095, loss_mask: 0.3073, loss: 0.9724 +2024-05-27 15:42:44,604 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-27 15:42:44,604 - mmdet - INFO - Epoch [1][7000/7330] lr: 1.000e-04, eta: 13:27:41, time: 0.587, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0525, loss_rpn_bbox: 0.0549, loss_cls: 0.2759, acc: 90.9390, loss_bbox: 0.3235, loss_mask: 0.3113, loss: 1.0181 +2024-05-27 15:43:16,446 - mmdet - INFO - Epoch [1][7050/7330] lr: 1.000e-04, eta: 13:27:33, time: 0.637, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0462, loss_rpn_bbox: 0.0514, loss_cls: 0.2735, acc: 91.0667, loss_bbox: 0.3187, loss_mask: 0.3148, loss: 1.0045 +2024-05-27 15:43:45,926 - mmdet - INFO - Epoch [1][7100/7330] lr: 1.000e-04, eta: 13:26:58, time: 0.590, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0483, loss_rpn_bbox: 0.0541, loss_cls: 0.2703, acc: 91.0454, loss_bbox: 0.3260, loss_mask: 0.3225, loss: 1.0212 +2024-05-27 15:44:15,310 - mmdet - INFO - Epoch [1][7150/7330] lr: 1.000e-04, eta: 13:26:22, time: 0.588, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0462, loss_rpn_bbox: 0.0541, loss_cls: 0.2648, acc: 91.1836, loss_bbox: 0.3202, loss_mask: 0.3143, loss: 0.9995 +2024-05-27 15:44:44,784 - mmdet - INFO - Epoch [1][7200/7330] lr: 1.000e-04, eta: 13:25:47, time: 0.590, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0473, loss_rpn_bbox: 0.0523, loss_cls: 0.2575, acc: 91.4993, loss_bbox: 0.3033, loss_mask: 0.3015, loss: 0.9619 +2024-05-27 15:45:14,312 - mmdet - INFO - Epoch [1][7250/7330] lr: 1.000e-04, eta: 13:25:12, time: 0.591, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0433, loss_rpn_bbox: 0.0521, loss_cls: 0.2614, acc: 91.4641, loss_bbox: 0.3034, loss_mask: 0.3109, loss: 0.9710 +2024-05-27 15:45:43,644 - mmdet - INFO - Epoch [1][7300/7330] lr: 1.000e-04, eta: 13:24:36, time: 0.587, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0488, loss_rpn_bbox: 0.0530, loss_cls: 0.2657, acc: 91.2437, loss_bbox: 0.3117, loss_mask: 0.3105, loss: 0.9898 +2024-05-27 15:46:02,411 - mmdet - INFO - Saving checkpoint at 1 epochs +2024-05-27 15:47:58,529 - mmdet - INFO - Evaluating bbox... +2024-05-27 15:48:26,805 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.296 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.548 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.290 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.141 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.330 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.443 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.422 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.422 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.422 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.206 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.476 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.610 + +2024-05-27 15:48:26,805 - mmdet - INFO - Evaluating segm... +2024-05-27 15:49:02,288 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.282 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.508 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.278 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.084 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.303 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.502 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.391 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.391 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.391 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.152 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.442 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.617 + +2024-05-27 15:49:02,691 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-27 15:49:02,691 - mmdet - INFO - Epoch(val) [1][625] bbox_mAP: 0.2960, bbox_mAP_50: 0.5480, bbox_mAP_75: 0.2900, bbox_mAP_s: 0.1410, bbox_mAP_m: 0.3300, bbox_mAP_l: 0.4430, bbox_mAP_copypaste: 0.296 0.548 0.290 0.141 0.330 0.443, segm_mAP: 0.2820, segm_mAP_50: 0.5080, segm_mAP_75: 0.2780, segm_mAP_s: 0.0840, segm_mAP_m: 0.3030, segm_mAP_l: 0.5020, segm_mAP_copypaste: 0.282 0.508 0.278 0.084 0.303 0.502 +2024-05-27 15:49:43,417 - mmdet - INFO - Epoch [2][50/7330] lr: 1.000e-04, eta: 13:22:30, time: 0.814, data_time: 0.082, memory: 9459, loss_rpn_cls: 0.0436, loss_rpn_bbox: 0.0531, loss_cls: 0.2696, acc: 90.9292, loss_bbox: 0.3264, loss_mask: 0.3120, loss: 1.0047 +2024-05-27 15:50:13,495 - mmdet - INFO - Epoch [2][100/7330] lr: 1.000e-04, eta: 13:22:02, time: 0.602, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0437, loss_rpn_bbox: 0.0570, loss_cls: 0.2798, acc: 90.6541, loss_bbox: 0.3360, loss_mask: 0.3206, loss: 1.0371 +2024-05-27 15:50:43,119 - mmdet - INFO - Epoch [2][150/7330] lr: 1.000e-04, eta: 13:21:29, time: 0.592, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0434, loss_rpn_bbox: 0.0512, loss_cls: 0.2552, acc: 91.3179, loss_bbox: 0.3121, loss_mask: 0.3008, loss: 0.9628 +2024-05-27 15:51:12,377 - mmdet - INFO - Epoch [2][200/7330] lr: 1.000e-04, eta: 13:20:53, time: 0.585, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0417, loss_rpn_bbox: 0.0528, loss_cls: 0.2580, acc: 91.4507, loss_bbox: 0.3080, loss_mask: 0.3074, loss: 0.9678 +2024-05-27 15:51:41,846 - mmdet - INFO - Epoch [2][250/7330] lr: 1.000e-04, eta: 13:20:19, time: 0.589, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0422, loss_rpn_bbox: 0.0513, loss_cls: 0.2546, acc: 91.3901, loss_bbox: 0.3166, loss_mask: 0.3091, loss: 0.9738 +2024-05-27 15:52:11,445 - mmdet - INFO - Epoch [2][300/7330] lr: 1.000e-04, eta: 13:19:46, time: 0.592, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0417, loss_rpn_bbox: 0.0510, loss_cls: 0.2630, acc: 91.2266, loss_bbox: 0.3162, loss_mask: 0.3069, loss: 0.9787 +2024-05-27 15:52:41,240 - mmdet - INFO - Epoch [2][350/7330] lr: 1.000e-04, eta: 13:19:15, time: 0.596, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0424, loss_rpn_bbox: 0.0535, loss_cls: 0.2671, acc: 91.1101, loss_bbox: 0.3216, loss_mask: 0.3077, loss: 0.9922 +2024-05-27 15:53:10,704 - mmdet - INFO - Epoch [2][400/7330] lr: 1.000e-04, eta: 13:18:41, time: 0.589, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0438, loss_rpn_bbox: 0.0506, loss_cls: 0.2585, acc: 91.3374, loss_bbox: 0.3097, loss_mask: 0.3085, loss: 0.9710 +2024-05-27 15:53:40,364 - mmdet - INFO - Epoch [2][450/7330] lr: 1.000e-04, eta: 13:18:09, time: 0.593, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0409, loss_rpn_bbox: 0.0494, loss_cls: 0.2660, acc: 91.1492, loss_bbox: 0.3159, loss_mask: 0.3040, loss: 0.9762 +2024-05-27 15:54:16,738 - mmdet - INFO - Epoch [2][500/7330] lr: 1.000e-04, eta: 13:18:46, time: 0.727, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0432, loss_rpn_bbox: 0.0495, loss_cls: 0.2567, acc: 91.5273, loss_bbox: 0.3078, loss_mask: 0.3092, loss: 0.9665 +2024-05-27 15:54:51,382 - mmdet - INFO - Epoch [2][550/7330] lr: 1.000e-04, eta: 13:19:04, time: 0.692, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0424, loss_rpn_bbox: 0.0502, loss_cls: 0.2595, acc: 91.2708, loss_bbox: 0.3175, loss_mask: 0.3096, loss: 0.9792 +2024-05-27 15:55:25,565 - mmdet - INFO - Epoch [2][600/7330] lr: 1.000e-04, eta: 13:19:17, time: 0.684, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0385, loss_rpn_bbox: 0.0467, loss_cls: 0.2584, acc: 91.3596, loss_bbox: 0.3069, loss_mask: 0.3085, loss: 0.9590 +2024-05-27 15:55:55,139 - mmdet - INFO - Epoch [2][650/7330] lr: 1.000e-04, eta: 13:18:43, time: 0.591, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0435, loss_rpn_bbox: 0.0524, loss_cls: 0.2662, acc: 91.0132, loss_bbox: 0.3210, loss_mask: 0.3064, loss: 0.9896 +2024-05-27 15:56:24,714 - mmdet - INFO - Epoch [2][700/7330] lr: 1.000e-04, eta: 13:18:09, time: 0.591, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0409, loss_rpn_bbox: 0.0499, loss_cls: 0.2664, acc: 90.9995, loss_bbox: 0.3260, loss_mask: 0.3036, loss: 0.9867 +2024-05-27 15:56:54,301 - mmdet - INFO - Epoch [2][750/7330] lr: 1.000e-04, eta: 13:17:36, time: 0.592, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0463, loss_rpn_bbox: 0.0578, loss_cls: 0.2753, acc: 90.9006, loss_bbox: 0.3315, loss_mask: 0.3064, loss: 1.0173 +2024-05-27 15:57:23,897 - mmdet - INFO - Epoch [2][800/7330] lr: 1.000e-04, eta: 13:17:02, time: 0.592, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0452, loss_rpn_bbox: 0.0515, loss_cls: 0.2614, acc: 91.3174, loss_bbox: 0.3153, loss_mask: 0.3083, loss: 0.9816 +2024-05-27 15:57:53,487 - mmdet - INFO - Epoch [2][850/7330] lr: 1.000e-04, eta: 13:16:29, time: 0.592, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0436, loss_rpn_bbox: 0.0499, loss_cls: 0.2617, acc: 91.1550, loss_bbox: 0.3182, loss_mask: 0.2992, loss: 0.9725 +2024-05-27 15:58:23,232 - mmdet - INFO - Epoch [2][900/7330] lr: 1.000e-04, eta: 13:15:57, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0440, loss_rpn_bbox: 0.0502, loss_cls: 0.2540, acc: 91.4426, loss_bbox: 0.3046, loss_mask: 0.3005, loss: 0.9532 +2024-05-27 15:58:52,866 - mmdet - INFO - Epoch [2][950/7330] lr: 1.000e-04, eta: 13:15:24, time: 0.593, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0460, loss_rpn_bbox: 0.0512, loss_cls: 0.2659, acc: 91.1755, loss_bbox: 0.3146, loss_mask: 0.3064, loss: 0.9841 +2024-05-27 15:59:22,532 - mmdet - INFO - Epoch [2][1000/7330] lr: 1.000e-04, eta: 13:14:51, time: 0.593, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0408, loss_rpn_bbox: 0.0499, loss_cls: 0.2586, acc: 91.2292, loss_bbox: 0.3161, loss_mask: 0.3053, loss: 0.9707 +2024-05-27 15:59:52,480 - mmdet - INFO - Epoch [2][1050/7330] lr: 1.000e-04, eta: 13:14:21, time: 0.599, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0425, loss_rpn_bbox: 0.0548, loss_cls: 0.2767, acc: 90.6914, loss_bbox: 0.3364, loss_mask: 0.3120, loss: 1.0223 +2024-05-27 16:00:22,123 - mmdet - INFO - Epoch [2][1100/7330] lr: 1.000e-04, eta: 13:13:49, time: 0.593, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0453, loss_rpn_bbox: 0.0514, loss_cls: 0.2568, acc: 91.3066, loss_bbox: 0.3123, loss_mask: 0.3116, loss: 0.9774 +2024-05-27 16:00:51,483 - mmdet - INFO - Epoch [2][1150/7330] lr: 1.000e-04, eta: 13:13:13, time: 0.587, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0404, loss_rpn_bbox: 0.0495, loss_cls: 0.2526, acc: 91.5107, loss_bbox: 0.3044, loss_mask: 0.3040, loss: 0.9508 +2024-05-27 16:01:21,224 - mmdet - INFO - Epoch [2][1200/7330] lr: 1.000e-04, eta: 13:12:41, time: 0.594, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0436, loss_rpn_bbox: 0.0516, loss_cls: 0.2588, acc: 91.3660, loss_bbox: 0.3159, loss_mask: 0.3053, loss: 0.9752 +2024-05-27 16:01:50,780 - mmdet - INFO - Epoch [2][1250/7330] lr: 1.000e-04, eta: 13:12:08, time: 0.591, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0440, loss_rpn_bbox: 0.0528, loss_cls: 0.2633, acc: 91.1418, loss_bbox: 0.3158, loss_mask: 0.3047, loss: 0.9807 +2024-05-27 16:02:20,285 - mmdet - INFO - Epoch [2][1300/7330] lr: 1.000e-04, eta: 13:11:34, time: 0.591, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0408, loss_rpn_bbox: 0.0506, loss_cls: 0.2549, acc: 91.3992, loss_bbox: 0.3087, loss_mask: 0.2996, loss: 0.9546 +2024-05-27 16:02:49,866 - mmdet - INFO - Epoch [2][1350/7330] lr: 1.000e-04, eta: 13:11:01, time: 0.591, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0448, loss_rpn_bbox: 0.0476, loss_cls: 0.2498, acc: 91.6270, loss_bbox: 0.3023, loss_mask: 0.2983, loss: 0.9427 +2024-05-27 16:03:19,511 - mmdet - INFO - Epoch [2][1400/7330] lr: 1.000e-04, eta: 13:10:28, time: 0.593, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0406, loss_rpn_bbox: 0.0450, loss_cls: 0.2398, acc: 91.8450, loss_bbox: 0.2943, loss_mask: 0.2873, loss: 0.9070 +2024-05-27 16:03:49,108 - mmdet - INFO - Epoch [2][1450/7330] lr: 1.000e-04, eta: 13:09:55, time: 0.592, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0412, loss_rpn_bbox: 0.0474, loss_cls: 0.2449, acc: 91.6672, loss_bbox: 0.2997, loss_mask: 0.3032, loss: 0.9364 +2024-05-27 16:04:23,016 - mmdet - INFO - Epoch [2][1500/7330] lr: 1.000e-04, eta: 13:10:01, time: 0.678, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0419, loss_rpn_bbox: 0.0513, loss_cls: 0.2576, acc: 91.4363, loss_bbox: 0.3118, loss_mask: 0.3051, loss: 0.9678 +2024-05-27 16:04:52,728 - mmdet - INFO - Epoch [2][1550/7330] lr: 1.000e-04, eta: 13:09:29, time: 0.594, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0434, loss_rpn_bbox: 0.0517, loss_cls: 0.2569, acc: 91.4114, loss_bbox: 0.3104, loss_mask: 0.2984, loss: 0.9607 +2024-05-27 16:05:33,113 - mmdet - INFO - Epoch [2][1600/7330] lr: 1.000e-04, eta: 13:10:31, time: 0.808, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0418, loss_rpn_bbox: 0.0470, loss_cls: 0.2415, acc: 91.9219, loss_bbox: 0.2947, loss_mask: 0.3038, loss: 0.9288 +2024-05-27 16:06:10,278 - mmdet - INFO - Epoch [2][1650/7330] lr: 1.000e-04, eta: 13:11:04, time: 0.743, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0444, loss_rpn_bbox: 0.0542, loss_cls: 0.2610, acc: 91.2397, loss_bbox: 0.3199, loss_mask: 0.3089, loss: 0.9885 +2024-05-27 16:06:40,014 - mmdet - INFO - Epoch [2][1700/7330] lr: 1.000e-04, eta: 13:10:31, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0472, loss_rpn_bbox: 0.0537, loss_cls: 0.2622, acc: 91.2764, loss_bbox: 0.3149, loss_mask: 0.3128, loss: 0.9909 +2024-05-27 16:07:09,876 - mmdet - INFO - Epoch [2][1750/7330] lr: 1.000e-04, eta: 13:10:00, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0440, loss_rpn_bbox: 0.0514, loss_cls: 0.2510, acc: 91.5840, loss_bbox: 0.3029, loss_mask: 0.2998, loss: 0.9491 +2024-05-27 16:07:39,481 - mmdet - INFO - Epoch [2][1800/7330] lr: 1.000e-04, eta: 13:09:26, time: 0.592, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0384, loss_rpn_bbox: 0.0457, loss_cls: 0.2488, acc: 91.6118, loss_bbox: 0.3000, loss_mask: 0.2949, loss: 0.9278 +2024-05-27 16:08:09,222 - mmdet - INFO - Epoch [2][1850/7330] lr: 1.000e-04, eta: 13:08:53, time: 0.594, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0427, loss_rpn_bbox: 0.0494, loss_cls: 0.2587, acc: 91.2307, loss_bbox: 0.3138, loss_mask: 0.3029, loss: 0.9675 +2024-05-27 16:08:39,122 - mmdet - INFO - Epoch [2][1900/7330] lr: 1.000e-04, eta: 13:08:22, time: 0.599, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0411, loss_rpn_bbox: 0.0511, loss_cls: 0.2619, acc: 91.2300, loss_bbox: 0.3165, loss_mask: 0.2983, loss: 0.9690 +2024-05-27 16:09:08,854 - mmdet - INFO - Epoch [2][1950/7330] lr: 1.000e-04, eta: 13:07:49, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0416, loss_rpn_bbox: 0.0492, loss_cls: 0.2559, acc: 91.3643, loss_bbox: 0.3093, loss_mask: 0.3016, loss: 0.9575 +2024-05-27 16:09:38,391 - mmdet - INFO - Epoch [2][2000/7330] lr: 1.000e-04, eta: 13:07:15, time: 0.591, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0432, loss_rpn_bbox: 0.0498, loss_cls: 0.2496, acc: 91.6831, loss_bbox: 0.2980, loss_mask: 0.3025, loss: 0.9432 +2024-05-27 16:10:08,216 - mmdet - INFO - Epoch [2][2050/7330] lr: 1.000e-04, eta: 13:06:43, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0447, loss_rpn_bbox: 0.0513, loss_cls: 0.2423, acc: 91.9236, loss_bbox: 0.2947, loss_mask: 0.2990, loss: 0.9320 +2024-05-27 16:10:37,926 - mmdet - INFO - Epoch [2][2100/7330] lr: 1.000e-04, eta: 13:06:10, time: 0.594, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0439, loss_rpn_bbox: 0.0524, loss_cls: 0.2602, acc: 91.2715, loss_bbox: 0.3107, loss_mask: 0.3043, loss: 0.9716 +2024-05-27 16:11:07,594 - mmdet - INFO - Epoch [2][2150/7330] lr: 1.000e-04, eta: 13:05:37, time: 0.593, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0418, loss_rpn_bbox: 0.0492, loss_cls: 0.2519, acc: 91.4568, loss_bbox: 0.3078, loss_mask: 0.3036, loss: 0.9542 +2024-05-27 16:11:37,288 - mmdet - INFO - Epoch [2][2200/7330] lr: 1.000e-04, eta: 13:05:05, time: 0.594, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0443, loss_rpn_bbox: 0.0493, loss_cls: 0.2499, acc: 91.6072, loss_bbox: 0.3044, loss_mask: 0.3030, loss: 0.9509 +2024-05-27 16:12:06,791 - mmdet - INFO - Epoch [2][2250/7330] lr: 1.000e-04, eta: 13:04:30, time: 0.590, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0426, loss_rpn_bbox: 0.0479, loss_cls: 0.2460, acc: 91.6326, loss_bbox: 0.2985, loss_mask: 0.2993, loss: 0.9342 +2024-05-27 16:12:36,274 - mmdet - INFO - Epoch [2][2300/7330] lr: 1.000e-04, eta: 13:03:56, time: 0.590, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0432, loss_rpn_bbox: 0.0496, loss_cls: 0.2502, acc: 91.6421, loss_bbox: 0.3011, loss_mask: 0.2992, loss: 0.9433 +2024-05-27 16:13:05,942 - mmdet - INFO - Epoch [2][2350/7330] lr: 1.000e-04, eta: 13:03:23, time: 0.593, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0440, loss_rpn_bbox: 0.0498, loss_cls: 0.2547, acc: 91.3242, loss_bbox: 0.3105, loss_mask: 0.2975, loss: 0.9564 +2024-05-27 16:13:35,541 - mmdet - INFO - Epoch [2][2400/7330] lr: 1.000e-04, eta: 13:02:49, time: 0.592, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0435, loss_rpn_bbox: 0.0500, loss_cls: 0.2575, acc: 91.3037, loss_bbox: 0.3108, loss_mask: 0.2978, loss: 0.9596 +2024-05-27 16:14:05,028 - mmdet - INFO - Epoch [2][2450/7330] lr: 1.000e-04, eta: 13:02:15, time: 0.590, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0399, loss_rpn_bbox: 0.0486, loss_cls: 0.2481, acc: 91.5601, loss_bbox: 0.3003, loss_mask: 0.2919, loss: 0.9288 +2024-05-27 16:14:34,548 - mmdet - INFO - Epoch [2][2500/7330] lr: 1.000e-04, eta: 13:01:41, time: 0.590, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0383, loss_rpn_bbox: 0.0464, loss_cls: 0.2392, acc: 91.9841, loss_bbox: 0.2908, loss_mask: 0.2907, loss: 0.9054 +2024-05-27 16:15:07,533 - mmdet - INFO - Epoch [2][2550/7330] lr: 1.000e-04, eta: 13:01:35, time: 0.660, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0415, loss_rpn_bbox: 0.0510, loss_cls: 0.2583, acc: 91.3125, loss_bbox: 0.3108, loss_mask: 0.3010, loss: 0.9625 +2024-05-27 16:15:37,172 - mmdet - INFO - Epoch [2][2600/7330] lr: 1.000e-04, eta: 13:01:02, time: 0.593, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0426, loss_rpn_bbox: 0.0507, loss_cls: 0.2534, acc: 91.4316, loss_bbox: 0.3071, loss_mask: 0.2970, loss: 0.9509 +2024-05-27 16:16:06,794 - mmdet - INFO - Epoch [2][2650/7330] lr: 1.000e-04, eta: 13:00:28, time: 0.592, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0420, loss_rpn_bbox: 0.0473, loss_cls: 0.2467, acc: 91.5913, loss_bbox: 0.3066, loss_mask: 0.2957, loss: 0.9383 +2024-05-27 16:16:50,183 - mmdet - INFO - Epoch [2][2700/7330] lr: 1.000e-04, eta: 13:01:42, time: 0.868, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0409, loss_rpn_bbox: 0.0473, loss_cls: 0.2483, acc: 91.7610, loss_bbox: 0.2953, loss_mask: 0.2913, loss: 0.9231 +2024-05-27 16:17:25,452 - mmdet - INFO - Epoch [2][2750/7330] lr: 1.000e-04, eta: 13:01:52, time: 0.705, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0408, loss_rpn_bbox: 0.0515, loss_cls: 0.2566, acc: 91.2632, loss_bbox: 0.3087, loss_mask: 0.3036, loss: 0.9612 +2024-05-27 16:17:55,209 - mmdet - INFO - Epoch [2][2800/7330] lr: 1.000e-04, eta: 13:01:19, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0438, loss_rpn_bbox: 0.0539, loss_cls: 0.2596, acc: 91.1580, loss_bbox: 0.3132, loss_mask: 0.3024, loss: 0.9730 +2024-05-27 16:18:25,047 - mmdet - INFO - Epoch [2][2850/7330] lr: 1.000e-04, eta: 13:00:47, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0394, loss_rpn_bbox: 0.0505, loss_cls: 0.2528, acc: 91.2769, loss_bbox: 0.3106, loss_mask: 0.3012, loss: 0.9545 +2024-05-27 16:18:54,878 - mmdet - INFO - Epoch [2][2900/7330] lr: 1.000e-04, eta: 13:00:15, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0414, loss_rpn_bbox: 0.0526, loss_cls: 0.2581, acc: 91.2041, loss_bbox: 0.3156, loss_mask: 0.3017, loss: 0.9693 +2024-05-27 16:19:24,582 - mmdet - INFO - Epoch [2][2950/7330] lr: 1.000e-04, eta: 12:59:41, time: 0.594, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0392, loss_rpn_bbox: 0.0495, loss_cls: 0.2559, acc: 91.3840, loss_bbox: 0.3055, loss_mask: 0.2987, loss: 0.9489 +2024-05-27 16:19:54,119 - mmdet - INFO - Epoch [2][3000/7330] lr: 1.000e-04, eta: 12:59:07, time: 0.591, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0377, loss_rpn_bbox: 0.0495, loss_cls: 0.2402, acc: 91.8792, loss_bbox: 0.2918, loss_mask: 0.2867, loss: 0.9059 +2024-05-27 16:20:23,869 - mmdet - INFO - Epoch [2][3050/7330] lr: 1.000e-04, eta: 12:58:34, time: 0.595, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0399, loss_rpn_bbox: 0.0525, loss_cls: 0.2581, acc: 91.1882, loss_bbox: 0.3122, loss_mask: 0.2976, loss: 0.9604 +2024-05-27 16:20:53,501 - mmdet - INFO - Epoch [2][3100/7330] lr: 1.000e-04, eta: 12:58:01, time: 0.593, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0430, loss_rpn_bbox: 0.0506, loss_cls: 0.2430, acc: 91.6926, loss_bbox: 0.2993, loss_mask: 0.2976, loss: 0.9335 +2024-05-27 16:21:23,175 - mmdet - INFO - Epoch [2][3150/7330] lr: 1.000e-04, eta: 12:57:27, time: 0.593, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0383, loss_rpn_bbox: 0.0467, loss_cls: 0.2500, acc: 91.6838, loss_bbox: 0.2947, loss_mask: 0.2921, loss: 0.9218 +2024-05-27 16:21:52,964 - mmdet - INFO - Epoch [2][3200/7330] lr: 1.000e-04, eta: 12:56:55, time: 0.596, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0406, loss_rpn_bbox: 0.0505, loss_cls: 0.2518, acc: 91.5046, loss_bbox: 0.3030, loss_mask: 0.2967, loss: 0.9426 +2024-05-27 16:22:22,562 - mmdet - INFO - Epoch [2][3250/7330] lr: 1.000e-04, eta: 12:56:21, time: 0.592, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0380, loss_rpn_bbox: 0.0487, loss_cls: 0.2434, acc: 91.7073, loss_bbox: 0.3023, loss_mask: 0.2990, loss: 0.9313 +2024-05-27 16:22:52,126 - mmdet - INFO - Epoch [2][3300/7330] lr: 1.000e-04, eta: 12:55:47, time: 0.591, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0368, loss_rpn_bbox: 0.0462, loss_cls: 0.2476, acc: 91.7686, loss_bbox: 0.2959, loss_mask: 0.2935, loss: 0.9199 +2024-05-27 16:23:21,860 - mmdet - INFO - Epoch [2][3350/7330] lr: 1.000e-04, eta: 12:55:14, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0362, loss_rpn_bbox: 0.0474, loss_cls: 0.2478, acc: 91.6975, loss_bbox: 0.2956, loss_mask: 0.2928, loss: 0.9198 +2024-05-27 16:23:51,304 - mmdet - INFO - Epoch [2][3400/7330] lr: 1.000e-04, eta: 12:54:40, time: 0.589, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0376, loss_rpn_bbox: 0.0444, loss_cls: 0.2438, acc: 91.9712, loss_bbox: 0.2922, loss_mask: 0.2868, loss: 0.9048 +2024-05-27 16:24:20,998 - mmdet - INFO - Epoch [2][3450/7330] lr: 1.000e-04, eta: 12:54:07, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0444, loss_rpn_bbox: 0.0496, loss_cls: 0.2492, acc: 91.7009, loss_bbox: 0.2979, loss_mask: 0.2956, loss: 0.9368 +2024-05-27 16:24:50,846 - mmdet - INFO - Epoch [2][3500/7330] lr: 1.000e-04, eta: 12:53:35, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0441, loss_rpn_bbox: 0.0508, loss_cls: 0.2447, acc: 91.6401, loss_bbox: 0.2969, loss_mask: 0.2904, loss: 0.9268 +2024-05-27 16:25:20,656 - mmdet - INFO - Epoch [2][3550/7330] lr: 1.000e-04, eta: 12:53:03, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0397, loss_rpn_bbox: 0.0491, loss_cls: 0.2537, acc: 91.4902, loss_bbox: 0.3018, loss_mask: 0.2964, loss: 0.9407 +2024-05-27 16:25:50,475 - mmdet - INFO - Epoch [2][3600/7330] lr: 1.000e-04, eta: 12:52:31, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0412, loss_rpn_bbox: 0.0489, loss_cls: 0.2534, acc: 91.5247, loss_bbox: 0.2988, loss_mask: 0.2973, loss: 0.9395 +2024-05-27 16:26:22,582 - mmdet - INFO - Epoch [2][3650/7330] lr: 1.000e-04, eta: 12:52:15, time: 0.642, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0414, loss_rpn_bbox: 0.0493, loss_cls: 0.2389, acc: 91.8569, loss_bbox: 0.2956, loss_mask: 0.2937, loss: 0.9189 +2024-05-27 16:26:52,430 - mmdet - INFO - Epoch [2][3700/7330] lr: 1.000e-04, eta: 12:51:43, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0405, loss_rpn_bbox: 0.0489, loss_cls: 0.2478, acc: 91.5391, loss_bbox: 0.3083, loss_mask: 0.2944, loss: 0.9398 +2024-05-27 16:27:29,731 - mmdet - INFO - Epoch [2][3750/7330] lr: 1.000e-04, eta: 12:51:41, time: 0.684, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0412, loss_rpn_bbox: 0.0494, loss_cls: 0.2444, acc: 91.7183, loss_bbox: 0.2900, loss_mask: 0.2847, loss: 0.9098 +2024-05-27 16:28:04,687 - mmdet - INFO - Epoch [2][3800/7330] lr: 1.000e-04, eta: 12:52:06, time: 0.762, data_time: 0.082, memory: 9459, loss_rpn_cls: 0.0429, loss_rpn_bbox: 0.0509, loss_cls: 0.2467, acc: 91.5945, loss_bbox: 0.3019, loss_mask: 0.3013, loss: 0.9437 +2024-05-27 16:28:39,537 - mmdet - INFO - Epoch [2][3850/7330] lr: 1.000e-04, eta: 12:52:08, time: 0.697, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0388, loss_rpn_bbox: 0.0454, loss_cls: 0.2482, acc: 91.5640, loss_bbox: 0.3037, loss_mask: 0.2981, loss: 0.9342 +2024-05-27 16:29:09,456 - mmdet - INFO - Epoch [2][3900/7330] lr: 1.000e-04, eta: 12:51:36, time: 0.598, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0416, loss_rpn_bbox: 0.0482, loss_cls: 0.2450, acc: 91.6426, loss_bbox: 0.2986, loss_mask: 0.2897, loss: 0.9230 +2024-05-27 16:29:38,944 - mmdet - INFO - Epoch [2][3950/7330] lr: 1.000e-04, eta: 12:51:01, time: 0.590, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0331, loss_rpn_bbox: 0.0441, loss_cls: 0.2374, acc: 92.0439, loss_bbox: 0.2866, loss_mask: 0.2855, loss: 0.8868 +2024-05-27 16:30:08,756 - mmdet - INFO - Epoch [2][4000/7330] lr: 1.000e-04, eta: 12:50:29, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0392, loss_rpn_bbox: 0.0498, loss_cls: 0.2497, acc: 91.6353, loss_bbox: 0.3009, loss_mask: 0.2929, loss: 0.9325 +2024-05-27 16:30:38,592 - mmdet - INFO - Epoch [2][4050/7330] lr: 1.000e-04, eta: 12:49:57, time: 0.597, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0387, loss_rpn_bbox: 0.0475, loss_cls: 0.2421, acc: 91.9341, loss_bbox: 0.2919, loss_mask: 0.2913, loss: 0.9115 +2024-05-27 16:31:08,388 - mmdet - INFO - Epoch [2][4100/7330] lr: 1.000e-04, eta: 12:49:24, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0415, loss_rpn_bbox: 0.0477, loss_cls: 0.2417, acc: 91.8625, loss_bbox: 0.2911, loss_mask: 0.2937, loss: 0.9158 +2024-05-27 16:31:38,132 - mmdet - INFO - Epoch [2][4150/7330] lr: 1.000e-04, eta: 12:48:51, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0393, loss_rpn_bbox: 0.0484, loss_cls: 0.2566, acc: 91.3147, loss_bbox: 0.3089, loss_mask: 0.3000, loss: 0.9532 +2024-05-27 16:32:07,718 - mmdet - INFO - Epoch [2][4200/7330] lr: 1.000e-04, eta: 12:48:17, time: 0.592, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0392, loss_rpn_bbox: 0.0474, loss_cls: 0.2388, acc: 91.8938, loss_bbox: 0.2912, loss_mask: 0.2891, loss: 0.9058 +2024-05-27 16:32:37,461 - mmdet - INFO - Epoch [2][4250/7330] lr: 1.000e-04, eta: 12:47:44, time: 0.595, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0372, loss_rpn_bbox: 0.0458, loss_cls: 0.2464, acc: 91.6299, loss_bbox: 0.3037, loss_mask: 0.2880, loss: 0.9212 +2024-05-27 16:33:07,060 - mmdet - INFO - Epoch [2][4300/7330] lr: 1.000e-04, eta: 12:47:10, time: 0.592, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0389, loss_rpn_bbox: 0.0486, loss_cls: 0.2464, acc: 91.6169, loss_bbox: 0.3054, loss_mask: 0.2917, loss: 0.9310 +2024-05-27 16:33:36,651 - mmdet - INFO - Epoch [2][4350/7330] lr: 1.000e-04, eta: 12:46:37, time: 0.592, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0420, loss_rpn_bbox: 0.0471, loss_cls: 0.2420, acc: 91.8311, loss_bbox: 0.2910, loss_mask: 0.2905, loss: 0.9126 +2024-05-27 16:34:06,491 - mmdet - INFO - Epoch [2][4400/7330] lr: 1.000e-04, eta: 12:46:04, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0400, loss_rpn_bbox: 0.0495, loss_cls: 0.2436, acc: 91.8845, loss_bbox: 0.2901, loss_mask: 0.2902, loss: 0.9135 +2024-05-27 16:34:36,134 - mmdet - INFO - Epoch [2][4450/7330] lr: 1.000e-04, eta: 12:45:31, time: 0.593, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0354, loss_rpn_bbox: 0.0474, loss_cls: 0.2448, acc: 91.6504, loss_bbox: 0.2988, loss_mask: 0.2890, loss: 0.9154 +2024-05-27 16:35:05,703 - mmdet - INFO - Epoch [2][4500/7330] lr: 1.000e-04, eta: 12:44:57, time: 0.591, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0368, loss_rpn_bbox: 0.0460, loss_cls: 0.2391, acc: 91.9319, loss_bbox: 0.2872, loss_mask: 0.2924, loss: 0.9015 +2024-05-27 16:35:35,502 - mmdet - INFO - Epoch [2][4550/7330] lr: 1.000e-04, eta: 12:44:25, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0401, loss_rpn_bbox: 0.0503, loss_cls: 0.2548, acc: 91.5215, loss_bbox: 0.3025, loss_mask: 0.2935, loss: 0.9413 +2024-05-27 16:36:05,421 - mmdet - INFO - Epoch [2][4600/7330] lr: 1.000e-04, eta: 12:43:53, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0427, loss_rpn_bbox: 0.0527, loss_cls: 0.2560, acc: 91.3650, loss_bbox: 0.3086, loss_mask: 0.2937, loss: 0.9537 +2024-05-27 16:36:34,942 - mmdet - INFO - Epoch [2][4650/7330] lr: 1.000e-04, eta: 12:43:19, time: 0.590, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0397, loss_rpn_bbox: 0.0483, loss_cls: 0.2463, acc: 91.7439, loss_bbox: 0.2893, loss_mask: 0.2879, loss: 0.9115 +2024-05-27 16:37:04,468 - mmdet - INFO - Epoch [2][4700/7330] lr: 1.000e-04, eta: 12:42:45, time: 0.590, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0381, loss_rpn_bbox: 0.0461, loss_cls: 0.2444, acc: 91.6956, loss_bbox: 0.2953, loss_mask: 0.2923, loss: 0.9161 +2024-05-27 16:37:37,002 - mmdet - INFO - Epoch [2][4750/7330] lr: 1.000e-04, eta: 12:42:30, time: 0.651, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0392, loss_rpn_bbox: 0.0452, loss_cls: 0.2433, acc: 91.7449, loss_bbox: 0.2930, loss_mask: 0.2921, loss: 0.9128 +2024-05-27 16:38:06,755 - mmdet - INFO - Epoch [2][4800/7330] lr: 1.000e-04, eta: 12:41:57, time: 0.595, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0400, loss_rpn_bbox: 0.0479, loss_cls: 0.2391, acc: 91.6843, loss_bbox: 0.2904, loss_mask: 0.2864, loss: 0.9039 +2024-05-27 16:38:46,359 - mmdet - INFO - Epoch [2][4850/7330] lr: 1.000e-04, eta: 12:42:26, time: 0.792, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0391, loss_rpn_bbox: 0.0484, loss_cls: 0.2446, acc: 91.6296, loss_bbox: 0.3002, loss_mask: 0.2909, loss: 0.9231 +2024-05-27 16:39:21,088 - mmdet - INFO - Epoch [2][4900/7330] lr: 1.000e-04, eta: 12:42:24, time: 0.695, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0362, loss_rpn_bbox: 0.0448, loss_cls: 0.2392, acc: 91.6667, loss_bbox: 0.2959, loss_mask: 0.2927, loss: 0.9089 +2024-05-27 16:39:54,053 - mmdet - INFO - Epoch [2][4950/7330] lr: 1.000e-04, eta: 12:42:11, time: 0.659, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0399, loss_rpn_bbox: 0.0491, loss_cls: 0.2405, acc: 91.7791, loss_bbox: 0.2970, loss_mask: 0.2900, loss: 0.9165 +2024-05-27 16:40:23,744 - mmdet - INFO - Epoch [2][5000/7330] lr: 1.000e-04, eta: 12:41:37, time: 0.594, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0380, loss_rpn_bbox: 0.0484, loss_cls: 0.2427, acc: 91.7136, loss_bbox: 0.2941, loss_mask: 0.2900, loss: 0.9131 +2024-05-27 16:40:53,619 - mmdet - INFO - Epoch [2][5050/7330] lr: 1.000e-04, eta: 12:41:05, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0403, loss_rpn_bbox: 0.0483, loss_cls: 0.2413, acc: 91.7664, loss_bbox: 0.2942, loss_mask: 0.2910, loss: 0.9151 +2024-05-27 16:41:23,463 - mmdet - INFO - Epoch [2][5100/7330] lr: 1.000e-04, eta: 12:40:33, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0385, loss_rpn_bbox: 0.0467, loss_cls: 0.2430, acc: 91.7881, loss_bbox: 0.2907, loss_mask: 0.2882, loss: 0.9071 +2024-05-27 16:41:53,196 - mmdet - INFO - Epoch [2][5150/7330] lr: 1.000e-04, eta: 12:40:00, time: 0.595, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0374, loss_rpn_bbox: 0.0439, loss_cls: 0.2326, acc: 92.0085, loss_bbox: 0.2876, loss_mask: 0.2764, loss: 0.8779 +2024-05-27 16:42:22,754 - mmdet - INFO - Epoch [2][5200/7330] lr: 1.000e-04, eta: 12:39:26, time: 0.591, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0382, loss_rpn_bbox: 0.0452, loss_cls: 0.2393, acc: 91.9341, loss_bbox: 0.2887, loss_mask: 0.2906, loss: 0.9020 +2024-05-27 16:42:52,551 - mmdet - INFO - Epoch [2][5250/7330] lr: 1.000e-04, eta: 12:38:53, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0398, loss_rpn_bbox: 0.0477, loss_cls: 0.2480, acc: 91.6694, loss_bbox: 0.2966, loss_mask: 0.2971, loss: 0.9291 +2024-05-27 16:43:22,187 - mmdet - INFO - Epoch [2][5300/7330] lr: 1.000e-04, eta: 12:38:19, time: 0.593, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0414, loss_rpn_bbox: 0.0466, loss_cls: 0.2419, acc: 91.7769, loss_bbox: 0.2947, loss_mask: 0.2856, loss: 0.9102 +2024-05-27 16:43:51,962 - mmdet - INFO - Epoch [2][5350/7330] lr: 1.000e-04, eta: 12:37:47, time: 0.595, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0371, loss_rpn_bbox: 0.0473, loss_cls: 0.2380, acc: 91.8037, loss_bbox: 0.2966, loss_mask: 0.2905, loss: 0.9094 +2024-05-27 16:44:21,466 - mmdet - INFO - Epoch [2][5400/7330] lr: 1.000e-04, eta: 12:37:12, time: 0.590, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0390, loss_rpn_bbox: 0.0484, loss_cls: 0.2460, acc: 91.6460, loss_bbox: 0.2992, loss_mask: 0.2899, loss: 0.9224 +2024-05-27 16:44:51,143 - mmdet - INFO - Epoch [2][5450/7330] lr: 1.000e-04, eta: 12:36:39, time: 0.593, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0414, loss_rpn_bbox: 0.0504, loss_cls: 0.2441, acc: 91.7385, loss_bbox: 0.2990, loss_mask: 0.2934, loss: 0.9282 +2024-05-27 16:45:20,701 - mmdet - INFO - Epoch [2][5500/7330] lr: 1.000e-04, eta: 12:36:05, time: 0.592, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0382, loss_rpn_bbox: 0.0459, loss_cls: 0.2333, acc: 92.0400, loss_bbox: 0.2875, loss_mask: 0.2876, loss: 0.8926 +2024-05-27 16:45:50,432 - mmdet - INFO - Epoch [2][5550/7330] lr: 1.000e-04, eta: 12:35:32, time: 0.595, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0429, loss_rpn_bbox: 0.0518, loss_cls: 0.2373, acc: 91.8899, loss_bbox: 0.2936, loss_mask: 0.2922, loss: 0.9177 +2024-05-27 16:46:20,316 - mmdet - INFO - Epoch [2][5600/7330] lr: 1.000e-04, eta: 12:35:00, time: 0.598, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0394, loss_rpn_bbox: 0.0485, loss_cls: 0.2393, acc: 91.8743, loss_bbox: 0.2898, loss_mask: 0.2886, loss: 0.9056 +2024-05-27 16:46:50,047 - mmdet - INFO - Epoch [2][5650/7330] lr: 1.000e-04, eta: 12:34:27, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0386, loss_rpn_bbox: 0.0468, loss_cls: 0.2322, acc: 92.0200, loss_bbox: 0.2828, loss_mask: 0.2850, loss: 0.8854 +2024-05-27 16:47:19,857 - mmdet - INFO - Epoch [2][5700/7330] lr: 1.000e-04, eta: 12:33:55, time: 0.596, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0358, loss_rpn_bbox: 0.0482, loss_cls: 0.2404, acc: 91.7700, loss_bbox: 0.2964, loss_mask: 0.2909, loss: 0.9117 +2024-05-27 16:47:49,489 - mmdet - INFO - Epoch [2][5750/7330] lr: 1.000e-04, eta: 12:33:22, time: 0.593, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0411, loss_rpn_bbox: 0.0482, loss_cls: 0.2369, acc: 91.9368, loss_bbox: 0.2891, loss_mask: 0.2933, loss: 0.9087 +2024-05-27 16:48:21,601 - mmdet - INFO - Epoch [2][5800/7330] lr: 1.000e-04, eta: 12:33:02, time: 0.642, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0393, loss_rpn_bbox: 0.0473, loss_cls: 0.2449, acc: 91.6848, loss_bbox: 0.2946, loss_mask: 0.2903, loss: 0.9164 +2024-05-27 16:48:51,339 - mmdet - INFO - Epoch [2][5850/7330] lr: 1.000e-04, eta: 12:32:30, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0375, loss_rpn_bbox: 0.0481, loss_cls: 0.2361, acc: 91.8503, loss_bbox: 0.2945, loss_mask: 0.2840, loss: 0.9002 +2024-05-27 16:49:23,363 - mmdet - INFO - Epoch [2][5900/7330] lr: 1.000e-04, eta: 12:32:10, time: 0.641, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0403, loss_rpn_bbox: 0.0490, loss_cls: 0.2453, acc: 91.5723, loss_bbox: 0.2946, loss_mask: 0.2984, loss: 0.9278 +2024-05-27 16:50:00,803 - mmdet - INFO - Epoch [2][5950/7330] lr: 1.000e-04, eta: 12:32:20, time: 0.749, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0397, loss_rpn_bbox: 0.0474, loss_cls: 0.2515, acc: 91.5671, loss_bbox: 0.2974, loss_mask: 0.2897, loss: 0.9257 +2024-05-27 16:50:37,338 - mmdet - INFO - Epoch [2][6000/7330] lr: 1.000e-04, eta: 12:32:26, time: 0.731, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0342, loss_rpn_bbox: 0.0455, loss_cls: 0.2431, acc: 91.7427, loss_bbox: 0.2926, loss_mask: 0.2818, loss: 0.8972 +2024-05-27 16:51:07,119 - mmdet - INFO - Epoch [2][6050/7330] lr: 1.000e-04, eta: 12:31:53, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0356, loss_rpn_bbox: 0.0453, loss_cls: 0.2453, acc: 91.6836, loss_bbox: 0.2901, loss_mask: 0.2812, loss: 0.8975 +2024-05-27 16:51:36,803 - mmdet - INFO - Epoch [2][6100/7330] lr: 1.000e-04, eta: 12:31:19, time: 0.594, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0411, loss_rpn_bbox: 0.0501, loss_cls: 0.2455, acc: 91.7031, loss_bbox: 0.2920, loss_mask: 0.2907, loss: 0.9195 +2024-05-27 16:52:06,348 - mmdet - INFO - Epoch [2][6150/7330] lr: 1.000e-04, eta: 12:30:45, time: 0.591, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0362, loss_rpn_bbox: 0.0459, loss_cls: 0.2325, acc: 92.0061, loss_bbox: 0.2828, loss_mask: 0.2784, loss: 0.8758 +2024-05-27 16:52:36,086 - mmdet - INFO - Epoch [2][6200/7330] lr: 1.000e-04, eta: 12:30:12, time: 0.595, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0331, loss_rpn_bbox: 0.0458, loss_cls: 0.2334, acc: 92.0781, loss_bbox: 0.2800, loss_mask: 0.2801, loss: 0.8725 +2024-05-27 16:53:05,797 - mmdet - INFO - Epoch [2][6250/7330] lr: 1.000e-04, eta: 12:29:39, time: 0.594, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0416, loss_rpn_bbox: 0.0492, loss_cls: 0.2320, acc: 92.0034, loss_bbox: 0.2941, loss_mask: 0.2805, loss: 0.8974 +2024-05-27 16:53:35,518 - mmdet - INFO - Epoch [2][6300/7330] lr: 1.000e-04, eta: 12:29:06, time: 0.594, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0372, loss_rpn_bbox: 0.0487, loss_cls: 0.2365, acc: 91.9937, loss_bbox: 0.2880, loss_mask: 0.2922, loss: 0.9027 +2024-05-27 16:54:05,351 - mmdet - INFO - Epoch [2][6350/7330] lr: 1.000e-04, eta: 12:28:34, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0359, loss_rpn_bbox: 0.0459, loss_cls: 0.2304, acc: 92.1194, loss_bbox: 0.2877, loss_mask: 0.2931, loss: 0.8930 +2024-05-27 16:54:34,908 - mmdet - INFO - Epoch [2][6400/7330] lr: 1.000e-04, eta: 12:28:00, time: 0.591, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0416, loss_rpn_bbox: 0.0486, loss_cls: 0.2463, acc: 91.5649, loss_bbox: 0.2937, loss_mask: 0.2867, loss: 0.9169 +2024-05-27 16:55:04,712 - mmdet - INFO - Epoch [2][6450/7330] lr: 1.000e-04, eta: 12:27:27, time: 0.596, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0410, loss_rpn_bbox: 0.0489, loss_cls: 0.2442, acc: 91.6152, loss_bbox: 0.2904, loss_mask: 0.2905, loss: 0.9150 +2024-05-27 16:55:34,533 - mmdet - INFO - Epoch [2][6500/7330] lr: 1.000e-04, eta: 12:26:55, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0391, loss_rpn_bbox: 0.0485, loss_cls: 0.2382, acc: 91.8652, loss_bbox: 0.2897, loss_mask: 0.2861, loss: 0.9017 +2024-05-27 16:56:04,214 - mmdet - INFO - Epoch [2][6550/7330] lr: 1.000e-04, eta: 12:26:22, time: 0.593, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0346, loss_rpn_bbox: 0.0435, loss_cls: 0.2293, acc: 92.2263, loss_bbox: 0.2806, loss_mask: 0.2736, loss: 0.8616 +2024-05-27 16:56:34,039 - mmdet - INFO - Epoch [2][6600/7330] lr: 1.000e-04, eta: 12:25:49, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0399, loss_rpn_bbox: 0.0484, loss_cls: 0.2373, acc: 91.9099, loss_bbox: 0.2876, loss_mask: 0.2797, loss: 0.8929 +2024-05-27 16:57:03,778 - mmdet - INFO - Epoch [2][6650/7330] lr: 1.000e-04, eta: 12:25:17, time: 0.595, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0410, loss_rpn_bbox: 0.0493, loss_cls: 0.2472, acc: 91.5481, loss_bbox: 0.3013, loss_mask: 0.2919, loss: 0.9308 +2024-05-27 16:57:33,549 - mmdet - INFO - Epoch [2][6700/7330] lr: 1.000e-04, eta: 12:24:44, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0373, loss_rpn_bbox: 0.0488, loss_cls: 0.2481, acc: 91.4998, loss_bbox: 0.2987, loss_mask: 0.2851, loss: 0.9179 +2024-05-27 16:58:03,371 - mmdet - INFO - Epoch [2][6750/7330] lr: 1.000e-04, eta: 12:24:12, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0363, loss_rpn_bbox: 0.0458, loss_cls: 0.2330, acc: 92.0171, loss_bbox: 0.2892, loss_mask: 0.2872, loss: 0.8915 +2024-05-27 16:58:33,260 - mmdet - INFO - Epoch [2][6800/7330] lr: 1.000e-04, eta: 12:23:40, time: 0.598, data_time: 0.025, memory: 9459, loss_rpn_cls: 0.0403, loss_rpn_bbox: 0.0495, loss_cls: 0.2495, acc: 91.5422, loss_bbox: 0.2955, loss_mask: 0.2909, loss: 0.9257 +2024-05-27 16:59:02,940 - mmdet - INFO - Epoch [2][6850/7330] lr: 1.000e-04, eta: 12:23:07, time: 0.594, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0393, loss_rpn_bbox: 0.0477, loss_cls: 0.2483, acc: 91.5547, loss_bbox: 0.2948, loss_mask: 0.2844, loss: 0.9144 +2024-05-27 16:59:35,145 - mmdet - INFO - Epoch [2][6900/7330] lr: 1.000e-04, eta: 12:22:47, time: 0.644, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0343, loss_rpn_bbox: 0.0459, loss_cls: 0.2390, acc: 91.7856, loss_bbox: 0.2912, loss_mask: 0.2843, loss: 0.8947 +2024-05-27 17:00:04,960 - mmdet - INFO - Epoch [2][6950/7330] lr: 1.000e-04, eta: 12:22:14, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0355, loss_rpn_bbox: 0.0460, loss_cls: 0.2342, acc: 91.8821, loss_bbox: 0.2899, loss_mask: 0.2829, loss: 0.8885 +2024-05-27 17:00:40,176 - mmdet - INFO - Epoch [2][7000/7330] lr: 1.000e-04, eta: 12:22:10, time: 0.704, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0386, loss_rpn_bbox: 0.0479, loss_cls: 0.2439, acc: 91.6948, loss_bbox: 0.2973, loss_mask: 0.2847, loss: 0.9124 +2024-05-27 17:01:17,465 - mmdet - INFO - Epoch [2][7050/7330] lr: 1.000e-04, eta: 12:22:16, time: 0.746, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0385, loss_rpn_bbox: 0.0457, loss_cls: 0.2338, acc: 92.0623, loss_bbox: 0.2872, loss_mask: 0.2791, loss: 0.8843 +2024-05-27 17:01:52,032 - mmdet - INFO - Epoch [2][7100/7330] lr: 1.000e-04, eta: 12:22:07, time: 0.691, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0345, loss_rpn_bbox: 0.0436, loss_cls: 0.2338, acc: 91.9695, loss_bbox: 0.2835, loss_mask: 0.2792, loss: 0.8747 +2024-05-27 17:02:21,848 - mmdet - INFO - Epoch [2][7150/7330] lr: 1.000e-04, eta: 12:21:35, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0416, loss_rpn_bbox: 0.0481, loss_cls: 0.2300, acc: 92.1182, loss_bbox: 0.2814, loss_mask: 0.2798, loss: 0.8810 +2024-05-27 17:02:51,844 - mmdet - INFO - Epoch [2][7200/7330] lr: 1.000e-04, eta: 12:21:03, time: 0.600, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0378, loss_rpn_bbox: 0.0445, loss_cls: 0.2341, acc: 91.9932, loss_bbox: 0.2899, loss_mask: 0.2806, loss: 0.8870 +2024-05-27 17:03:21,587 - mmdet - INFO - Epoch [2][7250/7330] lr: 1.000e-04, eta: 12:20:30, time: 0.595, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0376, loss_rpn_bbox: 0.0481, loss_cls: 0.2334, acc: 92.0605, loss_bbox: 0.2836, loss_mask: 0.2805, loss: 0.8832 +2024-05-27 17:03:51,316 - mmdet - INFO - Epoch [2][7300/7330] lr: 1.000e-04, eta: 12:19:57, time: 0.594, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0376, loss_rpn_bbox: 0.0480, loss_cls: 0.2462, acc: 91.6123, loss_bbox: 0.3004, loss_mask: 0.2894, loss: 0.9216 +2024-05-27 17:04:09,944 - mmdet - INFO - Saving checkpoint at 2 epochs +2024-05-27 17:06:09,061 - mmdet - INFO - Evaluating bbox... +2024-05-27 17:06:40,104 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.361 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.611 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.381 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.184 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.400 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.533 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.483 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.483 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.483 + 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 ] = 0.533 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.670 + +2024-05-27 17:06:40,104 - mmdet - INFO - Evaluating segm... +2024-05-27 17:07:10,035 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.327 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.555 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.336 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.109 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.360 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.563 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.433 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.433 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.433 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.190 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.487 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.661 + +2024-05-27 17:07:10,465 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-27 17:07:10,467 - mmdet - INFO - Epoch(val) [2][625] bbox_mAP: 0.3610, bbox_mAP_50: 0.6110, bbox_mAP_75: 0.3810, bbox_mAP_s: 0.1840, bbox_mAP_m: 0.4000, bbox_mAP_l: 0.5330, bbox_mAP_copypaste: 0.361 0.611 0.381 0.184 0.400 0.533, segm_mAP: 0.3270, segm_mAP_50: 0.5550, segm_mAP_75: 0.3360, segm_mAP_s: 0.1090, segm_mAP_m: 0.3600, segm_mAP_l: 0.5630, segm_mAP_copypaste: 0.327 0.555 0.336 0.109 0.360 0.563 +2024-05-27 17:07:51,544 - mmdet - INFO - Epoch [3][50/7330] lr: 1.000e-04, eta: 12:18:32, time: 0.821, data_time: 0.127, memory: 9459, loss_rpn_cls: 0.0336, loss_rpn_bbox: 0.0435, loss_cls: 0.2277, acc: 92.1025, loss_bbox: 0.2773, loss_mask: 0.2779, loss: 0.8601 +2024-05-27 17:08:23,620 - mmdet - INFO - Epoch [3][100/7330] lr: 1.000e-04, eta: 12:18:11, time: 0.642, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0338, loss_rpn_bbox: 0.0439, loss_cls: 0.2263, acc: 92.1069, loss_bbox: 0.2777, loss_mask: 0.2734, loss: 0.8551 +2024-05-27 17:08:53,331 - mmdet - INFO - Epoch [3][150/7330] lr: 1.000e-04, eta: 12:17:38, time: 0.594, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0373, loss_rpn_bbox: 0.0460, loss_cls: 0.2353, acc: 91.8364, loss_bbox: 0.2939, loss_mask: 0.2787, loss: 0.8911 +2024-05-27 17:09:23,139 - mmdet - INFO - Epoch [3][200/7330] lr: 1.000e-04, eta: 12:17:05, time: 0.596, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0339, loss_rpn_bbox: 0.0474, loss_cls: 0.2369, acc: 91.7656, loss_bbox: 0.2968, loss_mask: 0.2785, loss: 0.8935 +2024-05-27 17:09:55,030 - mmdet - INFO - Epoch [3][250/7330] lr: 1.000e-04, eta: 12:16:43, time: 0.638, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0350, loss_rpn_bbox: 0.0457, loss_cls: 0.2265, acc: 92.1433, loss_bbox: 0.2811, loss_mask: 0.2780, loss: 0.8662 +2024-05-27 17:10:29,181 - mmdet - INFO - Epoch [3][300/7330] lr: 1.000e-04, eta: 12:16:32, time: 0.683, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0365, loss_rpn_bbox: 0.0477, loss_cls: 0.2332, acc: 91.9065, loss_bbox: 0.2895, loss_mask: 0.2773, loss: 0.8842 +2024-05-27 17:11:01,054 - mmdet - INFO - Epoch [3][350/7330] lr: 1.000e-04, eta: 12:16:09, time: 0.638, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0364, loss_rpn_bbox: 0.0477, loss_cls: 0.2240, acc: 92.2280, loss_bbox: 0.2835, loss_mask: 0.2820, loss: 0.8735 +2024-05-27 17:11:30,656 - mmdet - INFO - Epoch [3][400/7330] lr: 1.000e-04, eta: 12:15:36, time: 0.592, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0371, loss_rpn_bbox: 0.0494, loss_cls: 0.2350, acc: 91.7341, loss_bbox: 0.2960, loss_mask: 0.2799, loss: 0.8974 +2024-05-27 17:12:00,364 - mmdet - INFO - Epoch [3][450/7330] lr: 1.000e-04, eta: 12:15:03, time: 0.594, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0353, loss_rpn_bbox: 0.0454, loss_cls: 0.2272, acc: 92.1187, loss_bbox: 0.2846, loss_mask: 0.2879, loss: 0.8804 +2024-05-27 17:12:30,064 - mmdet - INFO - Epoch [3][500/7330] lr: 1.000e-04, eta: 12:14:30, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0333, loss_rpn_bbox: 0.0444, loss_cls: 0.2224, acc: 92.3003, loss_bbox: 0.2792, loss_mask: 0.2756, loss: 0.8548 +2024-05-27 17:12:59,802 - mmdet - INFO - Epoch [3][550/7330] lr: 1.000e-04, eta: 12:13:57, time: 0.595, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0359, loss_rpn_bbox: 0.0477, loss_cls: 0.2296, acc: 92.0251, loss_bbox: 0.2880, loss_mask: 0.2781, loss: 0.8794 +2024-05-27 17:13:29,469 - mmdet - INFO - Epoch [3][600/7330] lr: 1.000e-04, eta: 12:13:24, time: 0.593, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0332, loss_rpn_bbox: 0.0444, loss_cls: 0.2329, acc: 91.8735, loss_bbox: 0.2934, loss_mask: 0.2834, loss: 0.8873 +2024-05-27 17:13:59,232 - mmdet - INFO - Epoch [3][650/7330] lr: 1.000e-04, eta: 12:12:51, time: 0.595, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0368, loss_rpn_bbox: 0.0460, loss_cls: 0.2371, acc: 91.6877, loss_bbox: 0.2957, loss_mask: 0.2795, loss: 0.8952 +2024-05-27 17:14:28,724 - mmdet - INFO - Epoch [3][700/7330] lr: 1.000e-04, eta: 12:12:17, time: 0.590, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0345, loss_rpn_bbox: 0.0429, loss_cls: 0.2220, acc: 92.3069, loss_bbox: 0.2695, loss_mask: 0.2692, loss: 0.8380 +2024-05-27 17:14:58,621 - mmdet - INFO - Epoch [3][750/7330] lr: 1.000e-04, eta: 12:11:45, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0375, loss_rpn_bbox: 0.0487, loss_cls: 0.2321, acc: 91.8003, loss_bbox: 0.2942, loss_mask: 0.2856, loss: 0.8981 +2024-05-27 17:15:28,291 - mmdet - INFO - Epoch [3][800/7330] lr: 1.000e-04, eta: 12:11:12, time: 0.593, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0326, loss_rpn_bbox: 0.0442, loss_cls: 0.2270, acc: 92.1648, loss_bbox: 0.2783, loss_mask: 0.2692, loss: 0.8513 +2024-05-27 17:15:58,031 - mmdet - INFO - Epoch [3][850/7330] lr: 1.000e-04, eta: 12:10:40, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0357, loss_rpn_bbox: 0.0476, loss_cls: 0.2260, acc: 92.1755, loss_bbox: 0.2878, loss_mask: 0.2747, loss: 0.8718 +2024-05-27 17:16:27,904 - mmdet - INFO - Epoch [3][900/7330] lr: 1.000e-04, eta: 12:10:08, time: 0.597, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0358, loss_rpn_bbox: 0.0456, loss_cls: 0.2315, acc: 91.9180, loss_bbox: 0.2893, loss_mask: 0.2798, loss: 0.8820 +2024-05-27 17:16:57,701 - mmdet - INFO - Epoch [3][950/7330] lr: 1.000e-04, eta: 12:09:35, time: 0.596, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0378, loss_rpn_bbox: 0.0475, loss_cls: 0.2361, acc: 91.8340, loss_bbox: 0.2885, loss_mask: 0.2802, loss: 0.8901 +2024-05-27 17:17:27,471 - mmdet - INFO - Epoch [3][1000/7330] lr: 1.000e-04, eta: 12:09:03, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0326, loss_rpn_bbox: 0.0419, loss_cls: 0.2220, acc: 92.3052, loss_bbox: 0.2773, loss_mask: 0.2685, loss: 0.8423 +2024-05-27 17:17:57,077 - mmdet - INFO - Epoch [3][1050/7330] lr: 1.000e-04, eta: 12:08:30, time: 0.592, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0358, loss_rpn_bbox: 0.0470, loss_cls: 0.2307, acc: 92.0291, loss_bbox: 0.2817, loss_mask: 0.2793, loss: 0.8745 +2024-05-27 17:18:31,607 - mmdet - INFO - Epoch [3][1100/7330] lr: 1.000e-04, eta: 12:08:19, time: 0.691, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0359, loss_rpn_bbox: 0.0468, loss_cls: 0.2371, acc: 91.8662, loss_bbox: 0.2866, loss_mask: 0.2797, loss: 0.8861 +2024-05-27 17:19:04,280 - mmdet - INFO - Epoch [3][1150/7330] lr: 1.000e-04, eta: 12:08:00, time: 0.653, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0356, loss_rpn_bbox: 0.0462, loss_cls: 0.2285, acc: 92.0479, loss_bbox: 0.2861, loss_mask: 0.2836, loss: 0.8800 +2024-05-27 17:19:36,597 - mmdet - INFO - Epoch [3][1200/7330] lr: 1.000e-04, eta: 12:07:39, time: 0.646, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0347, loss_rpn_bbox: 0.0462, loss_cls: 0.2263, acc: 92.0962, loss_bbox: 0.2836, loss_mask: 0.2791, loss: 0.8698 +2024-05-27 17:20:06,633 - mmdet - INFO - Epoch [3][1250/7330] lr: 1.000e-04, eta: 12:07:07, time: 0.601, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0375, loss_rpn_bbox: 0.0478, loss_cls: 0.2329, acc: 91.8608, loss_bbox: 0.2936, loss_mask: 0.2786, loss: 0.8904 +2024-05-27 17:20:39,789 - mmdet - INFO - Epoch [3][1300/7330] lr: 1.000e-04, eta: 12:06:50, time: 0.663, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0321, loss_rpn_bbox: 0.0445, loss_cls: 0.2177, acc: 92.4504, loss_bbox: 0.2679, loss_mask: 0.2791, loss: 0.8414 +2024-05-27 17:21:09,463 - mmdet - INFO - Epoch [3][1350/7330] lr: 1.000e-04, eta: 12:06:17, time: 0.594, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0387, loss_rpn_bbox: 0.0484, loss_cls: 0.2419, acc: 91.6152, loss_bbox: 0.2969, loss_mask: 0.2885, loss: 0.9145 +2024-05-27 17:21:46,619 - mmdet - INFO - Epoch [3][1400/7330] lr: 1.000e-04, eta: 12:06:17, time: 0.743, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0329, loss_rpn_bbox: 0.0439, loss_cls: 0.2267, acc: 92.2903, loss_bbox: 0.2776, loss_mask: 0.2742, loss: 0.8553 +2024-05-27 17:22:16,275 - mmdet - INFO - Epoch [3][1450/7330] lr: 1.000e-04, eta: 12:05:44, time: 0.593, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0331, loss_rpn_bbox: 0.0450, loss_cls: 0.2251, acc: 92.2532, loss_bbox: 0.2786, loss_mask: 0.2762, loss: 0.8579 +2024-05-27 17:22:45,994 - mmdet - INFO - Epoch [3][1500/7330] lr: 1.000e-04, eta: 12:05:11, time: 0.594, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0320, loss_rpn_bbox: 0.0426, loss_cls: 0.2163, acc: 92.5291, loss_bbox: 0.2729, loss_mask: 0.2693, loss: 0.8330 +2024-05-27 17:23:15,635 - mmdet - INFO - Epoch [3][1550/7330] lr: 1.000e-04, eta: 12:04:38, time: 0.593, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0345, loss_rpn_bbox: 0.0475, loss_cls: 0.2350, acc: 91.9290, loss_bbox: 0.2843, loss_mask: 0.2791, loss: 0.8804 +2024-05-27 17:23:45,408 - mmdet - INFO - Epoch [3][1600/7330] lr: 1.000e-04, eta: 12:04:06, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0329, loss_rpn_bbox: 0.0465, loss_cls: 0.2286, acc: 92.0571, loss_bbox: 0.2861, loss_mask: 0.2784, loss: 0.8725 +2024-05-27 17:24:15,162 - mmdet - INFO - Epoch [3][1650/7330] lr: 1.000e-04, eta: 12:03:33, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0344, loss_rpn_bbox: 0.0459, loss_cls: 0.2234, acc: 92.1299, loss_bbox: 0.2796, loss_mask: 0.2744, loss: 0.8576 +2024-05-27 17:24:44,934 - mmdet - INFO - Epoch [3][1700/7330] lr: 1.000e-04, eta: 12:03:00, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0320, loss_rpn_bbox: 0.0421, loss_cls: 0.2186, acc: 92.3638, loss_bbox: 0.2695, loss_mask: 0.2731, loss: 0.8352 +2024-05-27 17:25:14,584 - mmdet - INFO - Epoch [3][1750/7330] lr: 1.000e-04, eta: 12:02:27, time: 0.593, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0347, loss_rpn_bbox: 0.0465, loss_cls: 0.2330, acc: 91.9045, loss_bbox: 0.2882, loss_mask: 0.2804, loss: 0.8828 +2024-05-27 17:25:44,504 - mmdet - INFO - Epoch [3][1800/7330] lr: 1.000e-04, eta: 12:01:55, time: 0.598, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0350, loss_rpn_bbox: 0.0473, loss_cls: 0.2344, acc: 91.8206, loss_bbox: 0.2892, loss_mask: 0.2791, loss: 0.8850 +2024-05-27 17:26:14,201 - mmdet - INFO - Epoch [3][1850/7330] lr: 1.000e-04, eta: 12:01:22, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0348, loss_rpn_bbox: 0.0451, loss_cls: 0.2233, acc: 92.2236, loss_bbox: 0.2787, loss_mask: 0.2741, loss: 0.8560 +2024-05-27 17:26:44,205 - mmdet - INFO - Epoch [3][1900/7330] lr: 1.000e-04, eta: 12:00:51, time: 0.600, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0343, loss_rpn_bbox: 0.0461, loss_cls: 0.2240, acc: 92.0793, loss_bbox: 0.2863, loss_mask: 0.2785, loss: 0.8692 +2024-05-27 17:27:14,007 - mmdet - INFO - Epoch [3][1950/7330] lr: 1.000e-04, eta: 12:00:18, time: 0.596, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0329, loss_rpn_bbox: 0.0454, loss_cls: 0.2313, acc: 91.9827, loss_bbox: 0.2887, loss_mask: 0.2729, loss: 0.8711 +2024-05-27 17:27:43,791 - mmdet - INFO - Epoch [3][2000/7330] lr: 1.000e-04, eta: 11:59:46, time: 0.596, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0368, loss_rpn_bbox: 0.0469, loss_cls: 0.2292, acc: 92.0474, loss_bbox: 0.2860, loss_mask: 0.2766, loss: 0.8756 +2024-05-27 17:28:13,498 - mmdet - INFO - Epoch [3][2050/7330] lr: 1.000e-04, eta: 11:59:13, time: 0.594, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0358, loss_rpn_bbox: 0.0455, loss_cls: 0.2259, acc: 92.1445, loss_bbox: 0.2778, loss_mask: 0.2751, loss: 0.8601 +2024-05-27 17:28:43,352 - mmdet - INFO - Epoch [3][2100/7330] lr: 1.000e-04, eta: 11:58:41, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0339, loss_rpn_bbox: 0.0446, loss_cls: 0.2231, acc: 92.1731, loss_bbox: 0.2786, loss_mask: 0.2822, loss: 0.8625 +2024-05-27 17:29:16,917 - mmdet - INFO - Epoch [3][2150/7330] lr: 1.000e-04, eta: 11:58:25, time: 0.671, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0347, loss_rpn_bbox: 0.0447, loss_cls: 0.2122, acc: 92.4661, loss_bbox: 0.2790, loss_mask: 0.2688, loss: 0.8394 +2024-05-27 17:29:54,729 - mmdet - INFO - Epoch [3][2200/7330] lr: 1.000e-04, eta: 11:58:26, time: 0.756, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0361, loss_rpn_bbox: 0.0471, loss_cls: 0.2297, acc: 92.0647, loss_bbox: 0.2862, loss_mask: 0.2835, loss: 0.8827 +2024-05-27 17:30:26,881 - mmdet - INFO - Epoch [3][2250/7330] lr: 1.000e-04, eta: 11:58:04, time: 0.643, data_time: 0.025, memory: 9459, loss_rpn_cls: 0.0346, loss_rpn_bbox: 0.0458, loss_cls: 0.2244, acc: 92.1521, loss_bbox: 0.2794, loss_mask: 0.2746, loss: 0.8588 +2024-05-27 17:30:56,483 - mmdet - INFO - Epoch [3][2300/7330] lr: 1.000e-04, eta: 11:57:30, time: 0.592, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0286, loss_rpn_bbox: 0.0420, loss_cls: 0.2106, acc: 92.7571, loss_bbox: 0.2586, loss_mask: 0.2700, loss: 0.8098 +2024-05-27 17:31:26,191 - mmdet - INFO - Epoch [3][2350/7330] lr: 1.000e-04, eta: 11:56:57, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0348, loss_rpn_bbox: 0.0445, loss_cls: 0.2202, acc: 92.3550, loss_bbox: 0.2781, loss_mask: 0.2742, loss: 0.8518 +2024-05-27 17:31:58,949 - mmdet - INFO - Epoch [3][2400/7330] lr: 1.000e-04, eta: 11:56:37, time: 0.655, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0339, loss_rpn_bbox: 0.0478, loss_cls: 0.2409, acc: 91.6399, loss_bbox: 0.2948, loss_mask: 0.2813, loss: 0.8987 +2024-05-27 17:32:33,147 - mmdet - INFO - Epoch [3][2450/7330] lr: 1.000e-04, eta: 11:56:23, time: 0.684, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0362, loss_rpn_bbox: 0.0474, loss_cls: 0.2348, acc: 91.7869, loss_bbox: 0.2923, loss_mask: 0.2799, loss: 0.8907 +2024-05-27 17:33:05,066 - mmdet - INFO - Epoch [3][2500/7330] lr: 1.000e-04, eta: 11:55:59, time: 0.638, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0315, loss_rpn_bbox: 0.0425, loss_cls: 0.2314, acc: 92.0000, loss_bbox: 0.2890, loss_mask: 0.2773, loss: 0.8717 +2024-05-27 17:33:34,932 - mmdet - INFO - Epoch [3][2550/7330] lr: 1.000e-04, eta: 11:55:27, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0321, loss_rpn_bbox: 0.0428, loss_cls: 0.2270, acc: 92.1919, loss_bbox: 0.2772, loss_mask: 0.2654, loss: 0.8444 +2024-05-27 17:34:04,709 - mmdet - INFO - Epoch [3][2600/7330] lr: 1.000e-04, eta: 11:54:54, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0369, loss_rpn_bbox: 0.0461, loss_cls: 0.2319, acc: 91.9756, loss_bbox: 0.2840, loss_mask: 0.2743, loss: 0.8732 +2024-05-27 17:34:34,688 - mmdet - INFO - Epoch [3][2650/7330] lr: 1.000e-04, eta: 11:54:22, time: 0.600, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0356, loss_rpn_bbox: 0.0461, loss_cls: 0.2316, acc: 92.0332, loss_bbox: 0.2837, loss_mask: 0.2743, loss: 0.8713 +2024-05-27 17:35:04,478 - mmdet - INFO - Epoch [3][2700/7330] lr: 1.000e-04, eta: 11:53:50, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0347, loss_rpn_bbox: 0.0445, loss_cls: 0.2150, acc: 92.4871, loss_bbox: 0.2726, loss_mask: 0.2739, loss: 0.8407 +2024-05-27 17:35:34,238 - mmdet - INFO - Epoch [3][2750/7330] lr: 1.000e-04, eta: 11:53:17, time: 0.595, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0353, loss_rpn_bbox: 0.0442, loss_cls: 0.2311, acc: 92.0295, loss_bbox: 0.2854, loss_mask: 0.2785, loss: 0.8745 +2024-05-27 17:36:03,925 - mmdet - INFO - Epoch [3][2800/7330] lr: 1.000e-04, eta: 11:52:44, time: 0.594, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0345, loss_rpn_bbox: 0.0447, loss_cls: 0.2178, acc: 92.4263, loss_bbox: 0.2690, loss_mask: 0.2748, loss: 0.8409 +2024-05-27 17:36:33,679 - mmdet - INFO - Epoch [3][2850/7330] lr: 1.000e-04, eta: 11:52:12, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0335, loss_rpn_bbox: 0.0458, loss_cls: 0.2261, acc: 91.9458, loss_bbox: 0.2840, loss_mask: 0.2760, loss: 0.8655 +2024-05-27 17:37:03,667 - mmdet - INFO - Epoch [3][2900/7330] lr: 1.000e-04, eta: 11:51:40, time: 0.600, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0317, loss_rpn_bbox: 0.0436, loss_cls: 0.2281, acc: 92.0840, loss_bbox: 0.2825, loss_mask: 0.2734, loss: 0.8593 +2024-05-27 17:37:33,526 - mmdet - INFO - Epoch [3][2950/7330] lr: 1.000e-04, eta: 11:51:08, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0369, loss_rpn_bbox: 0.0482, loss_cls: 0.2329, acc: 91.8862, loss_bbox: 0.2880, loss_mask: 0.2818, loss: 0.8878 +2024-05-27 17:38:03,208 - mmdet - INFO - Epoch [3][3000/7330] lr: 1.000e-04, eta: 11:50:35, time: 0.594, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0338, loss_rpn_bbox: 0.0452, loss_cls: 0.2143, acc: 92.4419, loss_bbox: 0.2730, loss_mask: 0.2697, loss: 0.8361 +2024-05-27 17:38:33,021 - mmdet - INFO - Epoch [3][3050/7330] lr: 1.000e-04, eta: 11:50:03, time: 0.596, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0327, loss_rpn_bbox: 0.0453, loss_cls: 0.2258, acc: 92.0793, loss_bbox: 0.2809, loss_mask: 0.2712, loss: 0.8559 +2024-05-27 17:39:02,690 - mmdet - INFO - Epoch [3][3100/7330] lr: 1.000e-04, eta: 11:49:30, time: 0.593, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0341, loss_rpn_bbox: 0.0465, loss_cls: 0.2300, acc: 92.0940, loss_bbox: 0.2830, loss_mask: 0.2766, loss: 0.8702 +2024-05-27 17:39:32,328 - mmdet - INFO - Epoch [3][3150/7330] lr: 1.000e-04, eta: 11:48:57, time: 0.593, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0350, loss_rpn_bbox: 0.0462, loss_cls: 0.2302, acc: 91.9890, loss_bbox: 0.2847, loss_mask: 0.2733, loss: 0.8695 +2024-05-27 17:40:02,134 - mmdet - INFO - Epoch [3][3200/7330] lr: 1.000e-04, eta: 11:48:24, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0355, loss_rpn_bbox: 0.0446, loss_cls: 0.2280, acc: 92.1174, loss_bbox: 0.2797, loss_mask: 0.2730, loss: 0.8607 +2024-05-27 17:40:37,571 - mmdet - INFO - Epoch [3][3250/7330] lr: 1.000e-04, eta: 11:48:14, time: 0.709, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0346, loss_rpn_bbox: 0.0475, loss_cls: 0.2296, acc: 91.9395, loss_bbox: 0.2862, loss_mask: 0.2777, loss: 0.8756 +2024-05-27 17:41:10,218 - mmdet - INFO - Epoch [3][3300/7330] lr: 1.000e-04, eta: 11:47:53, time: 0.653, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0338, loss_rpn_bbox: 0.0438, loss_cls: 0.2271, acc: 91.9614, loss_bbox: 0.2872, loss_mask: 0.2748, loss: 0.8667 +2024-05-27 17:41:42,139 - mmdet - INFO - Epoch [3][3350/7330] lr: 1.000e-04, eta: 11:47:29, time: 0.638, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0320, loss_rpn_bbox: 0.0439, loss_cls: 0.2146, acc: 92.5659, loss_bbox: 0.2658, loss_mask: 0.2717, loss: 0.8279 +2024-05-27 17:42:12,163 - mmdet - INFO - Epoch [3][3400/7330] lr: 1.000e-04, eta: 11:46:57, time: 0.600, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0329, loss_rpn_bbox: 0.0452, loss_cls: 0.2295, acc: 92.0447, loss_bbox: 0.2813, loss_mask: 0.2726, loss: 0.8614 +2024-05-27 17:42:42,222 - mmdet - INFO - Epoch [3][3450/7330] lr: 1.000e-04, eta: 11:46:26, time: 0.602, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0351, loss_rpn_bbox: 0.0460, loss_cls: 0.2264, acc: 92.1089, loss_bbox: 0.2842, loss_mask: 0.2782, loss: 0.8699 +2024-05-27 17:43:14,673 - mmdet - INFO - Epoch [3][3500/7330] lr: 1.000e-04, eta: 11:46:03, time: 0.649, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0362, loss_rpn_bbox: 0.0479, loss_cls: 0.2258, acc: 92.2004, loss_bbox: 0.2789, loss_mask: 0.2763, loss: 0.8651 +2024-05-27 17:43:48,615 - mmdet - INFO - Epoch [3][3550/7330] lr: 1.000e-04, eta: 11:45:47, time: 0.679, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0315, loss_rpn_bbox: 0.0411, loss_cls: 0.2173, acc: 92.4888, loss_bbox: 0.2719, loss_mask: 0.2737, loss: 0.8354 +2024-05-27 17:44:20,426 - mmdet - INFO - Epoch [3][3600/7330] lr: 1.000e-04, eta: 11:45:22, time: 0.636, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0327, loss_rpn_bbox: 0.0432, loss_cls: 0.2151, acc: 92.4402, loss_bbox: 0.2704, loss_mask: 0.2727, loss: 0.8340 +2024-05-27 17:44:55,613 - mmdet - INFO - Epoch [3][3650/7330] lr: 1.000e-04, eta: 11:45:10, time: 0.704, data_time: 0.128, memory: 9459, loss_rpn_cls: 0.0331, loss_rpn_bbox: 0.0429, loss_cls: 0.2252, acc: 92.1365, loss_bbox: 0.2785, loss_mask: 0.2764, loss: 0.8560 +2024-05-27 17:45:25,339 - mmdet - INFO - Epoch [3][3700/7330] lr: 1.000e-04, eta: 11:44:37, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0324, loss_rpn_bbox: 0.0430, loss_cls: 0.2268, acc: 92.1230, loss_bbox: 0.2739, loss_mask: 0.2720, loss: 0.8481 +2024-05-27 17:45:54,903 - mmdet - INFO - Epoch [3][3750/7330] lr: 1.000e-04, eta: 11:44:04, time: 0.591, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0337, loss_rpn_bbox: 0.0428, loss_cls: 0.2218, acc: 92.3118, loss_bbox: 0.2727, loss_mask: 0.2716, loss: 0.8426 +2024-05-27 17:46:24,708 - mmdet - INFO - Epoch [3][3800/7330] lr: 1.000e-04, eta: 11:43:31, time: 0.596, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0327, loss_rpn_bbox: 0.0418, loss_cls: 0.2157, acc: 92.3899, loss_bbox: 0.2703, loss_mask: 0.2705, loss: 0.8311 +2024-05-27 17:46:54,364 - mmdet - INFO - Epoch [3][3850/7330] lr: 1.000e-04, eta: 11:42:58, time: 0.593, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0328, loss_rpn_bbox: 0.0455, loss_cls: 0.2228, acc: 92.1516, loss_bbox: 0.2781, loss_mask: 0.2671, loss: 0.8463 +2024-05-27 17:47:23,844 - mmdet - INFO - Epoch [3][3900/7330] lr: 1.000e-04, eta: 11:42:24, time: 0.590, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0295, loss_rpn_bbox: 0.0416, loss_cls: 0.2135, acc: 92.4277, loss_bbox: 0.2685, loss_mask: 0.2648, loss: 0.8179 +2024-05-27 17:47:53,471 - mmdet - INFO - Epoch [3][3950/7330] lr: 1.000e-04, eta: 11:41:51, time: 0.593, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0330, loss_rpn_bbox: 0.0439, loss_cls: 0.2183, acc: 92.3025, loss_bbox: 0.2727, loss_mask: 0.2690, loss: 0.8369 +2024-05-27 17:48:23,461 - mmdet - INFO - Epoch [3][4000/7330] lr: 1.000e-04, eta: 11:41:20, time: 0.600, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0334, loss_rpn_bbox: 0.0446, loss_cls: 0.2319, acc: 91.9890, loss_bbox: 0.2880, loss_mask: 0.2764, loss: 0.8742 +2024-05-27 17:48:53,193 - mmdet - INFO - Epoch [3][4050/7330] lr: 1.000e-04, eta: 11:40:47, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0343, loss_rpn_bbox: 0.0453, loss_cls: 0.2179, acc: 92.3079, loss_bbox: 0.2763, loss_mask: 0.2655, loss: 0.8392 +2024-05-27 17:49:22,789 - mmdet - INFO - Epoch [3][4100/7330] lr: 1.000e-04, eta: 11:40:14, time: 0.592, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0348, loss_rpn_bbox: 0.0469, loss_cls: 0.2334, acc: 91.9604, loss_bbox: 0.2842, loss_mask: 0.2778, loss: 0.8772 +2024-05-27 17:49:52,503 - mmdet - INFO - Epoch [3][4150/7330] lr: 1.000e-04, eta: 11:39:41, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0312, loss_rpn_bbox: 0.0439, loss_cls: 0.2119, acc: 92.6045, loss_bbox: 0.2703, loss_mask: 0.2625, loss: 0.8198 +2024-05-27 17:50:22,001 - mmdet - INFO - Epoch [3][4200/7330] lr: 1.000e-04, eta: 11:39:07, time: 0.590, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0330, loss_rpn_bbox: 0.0440, loss_cls: 0.2195, acc: 92.3132, loss_bbox: 0.2750, loss_mask: 0.2749, loss: 0.8464 +2024-05-27 17:50:51,574 - mmdet - INFO - Epoch [3][4250/7330] lr: 1.000e-04, eta: 11:38:34, time: 0.591, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0348, loss_rpn_bbox: 0.0441, loss_cls: 0.2211, acc: 92.4097, loss_bbox: 0.2705, loss_mask: 0.2726, loss: 0.8432 +2024-05-27 17:51:21,235 - mmdet - INFO - Epoch [3][4300/7330] lr: 1.000e-04, eta: 11:38:01, time: 0.593, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0327, loss_rpn_bbox: 0.0449, loss_cls: 0.2308, acc: 92.0583, loss_bbox: 0.2756, loss_mask: 0.2739, loss: 0.8579 +2024-05-27 17:51:58,664 - mmdet - INFO - Epoch [3][4350/7330] lr: 1.000e-04, eta: 11:37:57, time: 0.749, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0370, loss_rpn_bbox: 0.0476, loss_cls: 0.2239, acc: 92.2722, loss_bbox: 0.2742, loss_mask: 0.2736, loss: 0.8563 +2024-05-27 17:52:28,481 - mmdet - INFO - Epoch [3][4400/7330] lr: 1.000e-04, eta: 11:37:24, time: 0.596, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0334, loss_rpn_bbox: 0.0421, loss_cls: 0.2270, acc: 92.0168, loss_bbox: 0.2780, loss_mask: 0.2769, loss: 0.8574 +2024-05-27 17:53:00,230 - mmdet - INFO - Epoch [3][4450/7330] lr: 1.000e-04, eta: 11:36:59, time: 0.634, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0354, loss_rpn_bbox: 0.0444, loss_cls: 0.2180, acc: 92.2996, loss_bbox: 0.2700, loss_mask: 0.2647, loss: 0.8325 +2024-05-27 17:53:29,864 - mmdet - INFO - Epoch [3][4500/7330] lr: 1.000e-04, eta: 11:36:26, time: 0.593, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0307, loss_rpn_bbox: 0.0427, loss_cls: 0.2094, acc: 92.6919, loss_bbox: 0.2650, loss_mask: 0.2681, loss: 0.8160 +2024-05-27 17:54:02,249 - mmdet - INFO - Epoch [3][4550/7330] lr: 1.000e-04, eta: 11:36:03, time: 0.648, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0353, loss_rpn_bbox: 0.0479, loss_cls: 0.2352, acc: 91.8130, loss_bbox: 0.2926, loss_mask: 0.2770, loss: 0.8881 +2024-05-27 17:54:36,310 - mmdet - INFO - Epoch [3][4600/7330] lr: 1.000e-04, eta: 11:35:46, time: 0.681, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0342, loss_rpn_bbox: 0.0454, loss_cls: 0.2271, acc: 91.9534, loss_bbox: 0.2840, loss_mask: 0.2708, loss: 0.8615 +2024-05-27 17:55:08,257 - mmdet - INFO - Epoch [3][4650/7330] lr: 1.000e-04, eta: 11:35:21, time: 0.639, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0351, loss_rpn_bbox: 0.0458, loss_cls: 0.2338, acc: 91.7734, loss_bbox: 0.2868, loss_mask: 0.2749, loss: 0.8764 +2024-05-27 17:55:37,927 - mmdet - INFO - Epoch [3][4700/7330] lr: 1.000e-04, eta: 11:34:48, time: 0.593, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0356, loss_rpn_bbox: 0.0450, loss_cls: 0.2287, acc: 92.0908, loss_bbox: 0.2819, loss_mask: 0.2780, loss: 0.8692 +2024-05-27 17:56:07,494 - mmdet - INFO - Epoch [3][4750/7330] lr: 1.000e-04, eta: 11:34:15, time: 0.591, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0335, loss_rpn_bbox: 0.0457, loss_cls: 0.2318, acc: 91.9121, loss_bbox: 0.2871, loss_mask: 0.2722, loss: 0.8702 +2024-05-27 17:56:37,011 - mmdet - INFO - Epoch [3][4800/7330] lr: 1.000e-04, eta: 11:33:41, time: 0.590, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0339, loss_rpn_bbox: 0.0416, loss_cls: 0.2154, acc: 92.4895, loss_bbox: 0.2693, loss_mask: 0.2717, loss: 0.8319 +2024-05-27 17:57:06,866 - mmdet - INFO - Epoch [3][4850/7330] lr: 1.000e-04, eta: 11:33:09, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0320, loss_rpn_bbox: 0.0439, loss_cls: 0.2203, acc: 92.2607, loss_bbox: 0.2748, loss_mask: 0.2739, loss: 0.8449 +2024-05-27 17:57:36,424 - mmdet - INFO - Epoch [3][4900/7330] lr: 1.000e-04, eta: 11:32:36, time: 0.591, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0328, loss_rpn_bbox: 0.0419, loss_cls: 0.2227, acc: 92.3535, loss_bbox: 0.2727, loss_mask: 0.2707, loss: 0.8410 +2024-05-27 17:58:06,289 - mmdet - INFO - Epoch [3][4950/7330] lr: 1.000e-04, eta: 11:32:03, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0339, loss_rpn_bbox: 0.0442, loss_cls: 0.2264, acc: 92.0974, loss_bbox: 0.2806, loss_mask: 0.2748, loss: 0.8599 +2024-05-27 17:58:36,031 - mmdet - INFO - Epoch [3][5000/7330] lr: 1.000e-04, eta: 11:31:31, time: 0.595, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0328, loss_rpn_bbox: 0.0452, loss_cls: 0.2318, acc: 91.8933, loss_bbox: 0.2844, loss_mask: 0.2717, loss: 0.8659 +2024-05-27 17:59:05,552 - mmdet - INFO - Epoch [3][5050/7330] lr: 1.000e-04, eta: 11:30:58, time: 0.590, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0314, loss_rpn_bbox: 0.0439, loss_cls: 0.2241, acc: 92.2776, loss_bbox: 0.2733, loss_mask: 0.2746, loss: 0.8472 +2024-05-27 17:59:35,151 - mmdet - INFO - Epoch [3][5100/7330] lr: 1.000e-04, eta: 11:30:25, time: 0.592, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0323, loss_rpn_bbox: 0.0439, loss_cls: 0.2169, acc: 92.4053, loss_bbox: 0.2684, loss_mask: 0.2663, loss: 0.8279 +2024-05-27 18:00:04,866 - mmdet - INFO - Epoch [3][5150/7330] lr: 1.000e-04, eta: 11:29:52, time: 0.594, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0322, loss_rpn_bbox: 0.0434, loss_cls: 0.2179, acc: 92.3572, loss_bbox: 0.2723, loss_mask: 0.2703, loss: 0.8360 +2024-05-27 18:00:34,456 - mmdet - INFO - Epoch [3][5200/7330] lr: 1.000e-04, eta: 11:29:19, time: 0.592, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0369, loss_rpn_bbox: 0.0445, loss_cls: 0.2249, acc: 92.0999, loss_bbox: 0.2765, loss_mask: 0.2730, loss: 0.8558 +2024-05-27 18:01:03,930 - mmdet - INFO - Epoch [3][5250/7330] lr: 1.000e-04, eta: 11:28:45, time: 0.589, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0324, loss_rpn_bbox: 0.0415, loss_cls: 0.2139, acc: 92.5681, loss_bbox: 0.2625, loss_mask: 0.2681, loss: 0.8184 +2024-05-27 18:01:33,569 - mmdet - INFO - Epoch [3][5300/7330] lr: 1.000e-04, eta: 11:28:13, time: 0.592, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0342, loss_rpn_bbox: 0.0438, loss_cls: 0.2116, acc: 92.6367, loss_bbox: 0.2642, loss_mask: 0.2708, loss: 0.8246 +2024-05-27 18:02:03,393 - mmdet - INFO - Epoch [3][5350/7330] lr: 1.000e-04, eta: 11:27:40, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0304, loss_rpn_bbox: 0.0440, loss_cls: 0.2276, acc: 92.0164, loss_bbox: 0.2773, loss_mask: 0.2716, loss: 0.8509 +2024-05-27 18:02:37,403 - mmdet - INFO - Epoch [3][5400/7330] lr: 1.000e-04, eta: 11:27:22, time: 0.680, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0341, loss_rpn_bbox: 0.0448, loss_cls: 0.2229, acc: 92.2869, loss_bbox: 0.2738, loss_mask: 0.2744, loss: 0.8501 +2024-05-27 18:03:10,991 - mmdet - INFO - Epoch [3][5450/7330] lr: 1.000e-04, eta: 11:27:03, time: 0.672, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0305, loss_rpn_bbox: 0.0440, loss_cls: 0.2237, acc: 92.2578, loss_bbox: 0.2744, loss_mask: 0.2668, loss: 0.8394 +2024-05-27 18:03:42,686 - mmdet - INFO - Epoch [3][5500/7330] lr: 1.000e-04, eta: 11:26:37, time: 0.634, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0318, loss_rpn_bbox: 0.0435, loss_cls: 0.2312, acc: 91.9351, loss_bbox: 0.2839, loss_mask: 0.2689, loss: 0.8592 +2024-05-27 18:04:12,441 - mmdet - INFO - Epoch [3][5550/7330] lr: 1.000e-04, eta: 11:26:04, time: 0.595, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0317, loss_rpn_bbox: 0.0418, loss_cls: 0.2308, acc: 91.9448, loss_bbox: 0.2815, loss_mask: 0.2716, loss: 0.8574 +2024-05-27 18:04:41,977 - mmdet - INFO - Epoch [3][5600/7330] lr: 1.000e-04, eta: 11:25:31, time: 0.591, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0315, loss_rpn_bbox: 0.0424, loss_cls: 0.2164, acc: 92.4651, loss_bbox: 0.2630, loss_mask: 0.2714, loss: 0.8247 +2024-05-27 18:05:15,013 - mmdet - INFO - Epoch [3][5650/7330] lr: 1.000e-04, eta: 11:25:10, time: 0.661, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0327, loss_rpn_bbox: 0.0430, loss_cls: 0.2127, acc: 92.4036, loss_bbox: 0.2702, loss_mask: 0.2727, loss: 0.8313 +2024-05-27 18:05:49,067 - mmdet - INFO - Epoch [3][5700/7330] lr: 1.000e-04, eta: 11:24:52, time: 0.681, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0309, loss_rpn_bbox: 0.0416, loss_cls: 0.2135, acc: 92.5439, loss_bbox: 0.2668, loss_mask: 0.2687, loss: 0.8217 +2024-05-27 18:06:20,994 - mmdet - INFO - Epoch [3][5750/7330] lr: 1.000e-04, eta: 11:24:26, time: 0.639, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0333, loss_rpn_bbox: 0.0428, loss_cls: 0.2223, acc: 92.2959, loss_bbox: 0.2725, loss_mask: 0.2717, loss: 0.8426 +2024-05-27 18:06:50,544 - mmdet - INFO - Epoch [3][5800/7330] lr: 1.000e-04, eta: 11:23:53, time: 0.591, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0326, loss_rpn_bbox: 0.0438, loss_cls: 0.2231, acc: 92.1287, loss_bbox: 0.2774, loss_mask: 0.2681, loss: 0.8450 +2024-05-27 18:07:20,094 - mmdet - INFO - Epoch [3][5850/7330] lr: 1.000e-04, eta: 11:23:20, time: 0.591, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0300, loss_rpn_bbox: 0.0447, loss_cls: 0.2185, acc: 92.1946, loss_bbox: 0.2755, loss_mask: 0.2722, loss: 0.8409 +2024-05-27 18:07:49,742 - mmdet - INFO - Epoch [3][5900/7330] lr: 1.000e-04, eta: 11:22:47, time: 0.593, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0360, loss_rpn_bbox: 0.0446, loss_cls: 0.2278, acc: 92.0750, loss_bbox: 0.2811, loss_mask: 0.2730, loss: 0.8624 +2024-05-27 18:08:19,512 - mmdet - INFO - Epoch [3][5950/7330] lr: 1.000e-04, eta: 11:22:15, time: 0.595, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0314, loss_rpn_bbox: 0.0427, loss_cls: 0.2244, acc: 92.2168, loss_bbox: 0.2780, loss_mask: 0.2691, loss: 0.8455 +2024-05-27 18:08:49,189 - mmdet - INFO - Epoch [3][6000/7330] lr: 1.000e-04, eta: 11:21:42, time: 0.594, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0319, loss_rpn_bbox: 0.0440, loss_cls: 0.2216, acc: 92.3154, loss_bbox: 0.2765, loss_mask: 0.2642, loss: 0.8382 +2024-05-27 18:09:18,934 - mmdet - INFO - Epoch [3][6050/7330] lr: 1.000e-04, eta: 11:21:09, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0353, loss_rpn_bbox: 0.0460, loss_cls: 0.2229, acc: 92.1313, loss_bbox: 0.2789, loss_mask: 0.2756, loss: 0.8587 +2024-05-27 18:09:48,572 - mmdet - INFO - Epoch [3][6100/7330] lr: 1.000e-04, eta: 11:20:37, time: 0.593, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0345, loss_rpn_bbox: 0.0451, loss_cls: 0.2256, acc: 92.2449, loss_bbox: 0.2754, loss_mask: 0.2728, loss: 0.8534 +2024-05-27 18:10:18,174 - mmdet - INFO - Epoch [3][6150/7330] lr: 1.000e-04, eta: 11:20:04, time: 0.592, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0328, loss_rpn_bbox: 0.0427, loss_cls: 0.2229, acc: 92.3049, loss_bbox: 0.2739, loss_mask: 0.2712, loss: 0.8436 +2024-05-27 18:10:47,987 - mmdet - INFO - Epoch [3][6200/7330] lr: 1.000e-04, eta: 11:19:31, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0319, loss_rpn_bbox: 0.0438, loss_cls: 0.2338, acc: 91.9119, loss_bbox: 0.2819, loss_mask: 0.2782, loss: 0.8697 +2024-05-27 18:11:17,554 - mmdet - INFO - Epoch [3][6250/7330] lr: 1.000e-04, eta: 11:18:58, time: 0.591, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0299, loss_rpn_bbox: 0.0418, loss_cls: 0.2119, acc: 92.6301, loss_bbox: 0.2627, loss_mask: 0.2643, loss: 0.8107 +2024-05-27 18:11:47,278 - mmdet - INFO - Epoch [3][6300/7330] lr: 1.000e-04, eta: 11:18:26, time: 0.595, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0332, loss_rpn_bbox: 0.0467, loss_cls: 0.2189, acc: 92.2197, loss_bbox: 0.2749, loss_mask: 0.2696, loss: 0.8434 +2024-05-27 18:12:17,076 - mmdet - INFO - Epoch [3][6350/7330] lr: 1.000e-04, eta: 11:17:54, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0346, loss_rpn_bbox: 0.0460, loss_cls: 0.2219, acc: 92.3032, loss_bbox: 0.2698, loss_mask: 0.2702, loss: 0.8425 +2024-05-27 18:12:46,855 - mmdet - INFO - Epoch [3][6400/7330] lr: 1.000e-04, eta: 11:17:21, time: 0.595, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0323, loss_rpn_bbox: 0.0427, loss_cls: 0.2210, acc: 92.2261, loss_bbox: 0.2781, loss_mask: 0.2702, loss: 0.8442 +2024-05-27 18:13:16,527 - mmdet - INFO - Epoch [3][6450/7330] lr: 1.000e-04, eta: 11:16:49, time: 0.594, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0381, loss_rpn_bbox: 0.0471, loss_cls: 0.2203, acc: 92.2878, loss_bbox: 0.2739, loss_mask: 0.2698, loss: 0.8492 +2024-05-27 18:13:50,984 - mmdet - INFO - Epoch [3][6500/7330] lr: 1.000e-04, eta: 11:16:31, time: 0.689, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0368, loss_rpn_bbox: 0.0458, loss_cls: 0.2250, acc: 92.1016, loss_bbox: 0.2752, loss_mask: 0.2693, loss: 0.8520 +2024-05-27 18:14:22,934 - mmdet - INFO - Epoch [3][6550/7330] lr: 1.000e-04, eta: 11:16:06, time: 0.639, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0343, loss_rpn_bbox: 0.0442, loss_cls: 0.2108, acc: 92.6379, loss_bbox: 0.2644, loss_mask: 0.2654, loss: 0.8191 +2024-05-27 18:14:54,791 - mmdet - INFO - Epoch [3][6600/7330] lr: 1.000e-04, eta: 11:15:40, time: 0.637, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0359, loss_rpn_bbox: 0.0450, loss_cls: 0.2246, acc: 92.2334, loss_bbox: 0.2767, loss_mask: 0.2768, loss: 0.8590 +2024-05-27 18:15:24,298 - mmdet - INFO - Epoch [3][6650/7330] lr: 1.000e-04, eta: 11:15:07, time: 0.590, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0343, loss_rpn_bbox: 0.0450, loss_cls: 0.2139, acc: 92.5405, loss_bbox: 0.2613, loss_mask: 0.2694, loss: 0.8239 +2024-05-27 18:15:56,299 - mmdet - INFO - Epoch [3][6700/7330] lr: 1.000e-04, eta: 11:14:34, time: 0.593, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0344, loss_rpn_bbox: 0.0444, loss_cls: 0.2207, acc: 92.3628, loss_bbox: 0.2698, loss_mask: 0.2738, loss: 0.8431 +2024-05-27 18:16:28,124 - mmdet - INFO - Epoch [3][6750/7330] lr: 1.000e-04, eta: 11:14:16, time: 0.683, data_time: 0.069, memory: 9459, loss_rpn_cls: 0.0312, loss_rpn_bbox: 0.0419, loss_cls: 0.2161, acc: 92.5137, loss_bbox: 0.2688, loss_mask: 0.2669, loss: 0.8249 +2024-05-27 18:17:02,396 - mmdet - INFO - Epoch [3][6800/7330] lr: 1.000e-04, eta: 11:13:57, time: 0.685, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0349, loss_rpn_bbox: 0.0442, loss_cls: 0.2313, acc: 91.8394, loss_bbox: 0.2830, loss_mask: 0.2673, loss: 0.8607 +2024-05-27 18:17:32,033 - mmdet - INFO - Epoch [3][6850/7330] lr: 1.000e-04, eta: 11:13:25, time: 0.593, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0309, loss_rpn_bbox: 0.0441, loss_cls: 0.2183, acc: 92.3096, loss_bbox: 0.2728, loss_mask: 0.2715, loss: 0.8376 +2024-05-27 18:18:01,880 - mmdet - INFO - Epoch [3][6900/7330] lr: 1.000e-04, eta: 11:12:52, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0312, loss_rpn_bbox: 0.0446, loss_cls: 0.2202, acc: 92.3621, loss_bbox: 0.2739, loss_mask: 0.2714, loss: 0.8413 +2024-05-27 18:18:31,677 - mmdet - INFO - Epoch [3][6950/7330] lr: 1.000e-04, eta: 11:12:20, time: 0.596, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0331, loss_rpn_bbox: 0.0445, loss_cls: 0.2140, acc: 92.3794, loss_bbox: 0.2792, loss_mask: 0.2745, loss: 0.8453 +2024-05-27 18:19:01,681 - mmdet - INFO - Epoch [3][7000/7330] lr: 1.000e-04, eta: 11:11:49, time: 0.600, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0344, loss_rpn_bbox: 0.0432, loss_cls: 0.2199, acc: 92.3118, loss_bbox: 0.2726, loss_mask: 0.2639, loss: 0.8341 +2024-05-27 18:19:31,350 - mmdet - INFO - Epoch [3][7050/7330] lr: 1.000e-04, eta: 11:11:16, time: 0.593, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0300, loss_rpn_bbox: 0.0417, loss_cls: 0.2178, acc: 92.3193, loss_bbox: 0.2702, loss_mask: 0.2678, loss: 0.8275 +2024-05-27 18:20:01,061 - mmdet - INFO - Epoch [3][7100/7330] lr: 1.000e-04, eta: 11:10:43, time: 0.594, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0317, loss_rpn_bbox: 0.0424, loss_cls: 0.2200, acc: 92.4824, loss_bbox: 0.2662, loss_mask: 0.2727, loss: 0.8330 +2024-05-27 18:20:30,571 - mmdet - INFO - Epoch [3][7150/7330] lr: 1.000e-04, eta: 11:10:10, time: 0.590, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0301, loss_rpn_bbox: 0.0392, loss_cls: 0.2050, acc: 92.9695, loss_bbox: 0.2552, loss_mask: 0.2623, loss: 0.7918 +2024-05-27 18:21:00,145 - mmdet - INFO - Epoch [3][7200/7330] lr: 1.000e-04, eta: 11:09:38, time: 0.592, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0318, loss_rpn_bbox: 0.0416, loss_cls: 0.2186, acc: 92.2883, loss_bbox: 0.2728, loss_mask: 0.2623, loss: 0.8272 +2024-05-27 18:21:29,756 - mmdet - INFO - Epoch [3][7250/7330] lr: 1.000e-04, eta: 11:09:05, time: 0.592, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0321, loss_rpn_bbox: 0.0423, loss_cls: 0.2169, acc: 92.4692, loss_bbox: 0.2664, loss_mask: 0.2683, loss: 0.8260 +2024-05-27 18:21:59,537 - mmdet - INFO - Epoch [3][7300/7330] lr: 1.000e-04, eta: 11:08:33, time: 0.596, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0336, loss_rpn_bbox: 0.0417, loss_cls: 0.2216, acc: 92.2896, loss_bbox: 0.2703, loss_mask: 0.2633, loss: 0.8305 +2024-05-27 18:22:18,015 - mmdet - INFO - Saving checkpoint at 3 epochs +2024-05-27 18:24:13,106 - mmdet - INFO - Evaluating bbox... +2024-05-27 18:24:41,838 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.393 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.637 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.421 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.211 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.433 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.571 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.516 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.516 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.516 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.294 + 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 ] = 0.704 + +2024-05-27 18:24:41,838 - mmdet - INFO - Evaluating segm... +2024-05-27 18:25:10,004 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.354 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.588 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.369 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.137 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.387 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.590 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.465 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.465 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.465 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.229 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.523 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.684 + +2024-05-27 18:25:10,491 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-27 18:25:10,493 - mmdet - INFO - Epoch(val) [3][625] bbox_mAP: 0.3930, bbox_mAP_50: 0.6370, bbox_mAP_75: 0.4210, bbox_mAP_s: 0.2110, bbox_mAP_m: 0.4330, bbox_mAP_l: 0.5710, bbox_mAP_copypaste: 0.393 0.637 0.421 0.211 0.433 0.571, segm_mAP: 0.3540, segm_mAP_50: 0.5880, segm_mAP_75: 0.3690, segm_mAP_s: 0.1370, segm_mAP_m: 0.3870, segm_mAP_l: 0.5900, segm_mAP_copypaste: 0.354 0.588 0.369 0.137 0.387 0.590 +2024-05-27 18:25:44,865 - mmdet - INFO - Epoch [4][50/7330] lr: 1.000e-04, eta: 11:07:01, time: 0.687, data_time: 0.082, memory: 9459, loss_rpn_cls: 0.0305, loss_rpn_bbox: 0.0432, loss_cls: 0.2166, acc: 92.4316, loss_bbox: 0.2685, loss_mask: 0.2689, loss: 0.8276 +2024-05-27 18:26:17,324 - mmdet - INFO - Epoch [4][100/7330] lr: 1.000e-04, eta: 11:06:37, time: 0.649, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0289, loss_rpn_bbox: 0.0445, loss_cls: 0.2193, acc: 92.2246, loss_bbox: 0.2759, loss_mask: 0.2657, loss: 0.8345 +2024-05-27 18:26:51,943 - mmdet - INFO - Epoch [4][150/7330] lr: 1.000e-04, eta: 11:06:19, time: 0.692, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0287, loss_rpn_bbox: 0.0425, loss_cls: 0.2082, acc: 92.5481, loss_bbox: 0.2693, loss_mask: 0.2616, loss: 0.8104 +2024-05-27 18:27:21,463 - mmdet - INFO - Epoch [4][200/7330] lr: 1.000e-04, eta: 11:05:47, time: 0.590, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0290, loss_rpn_bbox: 0.0409, loss_cls: 0.2156, acc: 92.4851, loss_bbox: 0.2746, loss_mask: 0.2675, loss: 0.8275 +2024-05-27 18:27:50,834 - mmdet - INFO - Epoch [4][250/7330] lr: 1.000e-04, eta: 11:05:13, time: 0.587, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0282, loss_rpn_bbox: 0.0413, loss_cls: 0.2039, acc: 92.8252, loss_bbox: 0.2568, loss_mask: 0.2570, loss: 0.7872 +2024-05-27 18:28:23,430 - mmdet - INFO - Epoch [4][300/7330] lr: 1.000e-04, eta: 11:04:49, time: 0.652, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0317, loss_rpn_bbox: 0.0435, loss_cls: 0.2236, acc: 92.0374, loss_bbox: 0.2800, loss_mask: 0.2686, loss: 0.8475 +2024-05-27 18:28:57,547 - mmdet - INFO - Epoch [4][350/7330] lr: 1.000e-04, eta: 11:04:30, time: 0.682, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0280, loss_rpn_bbox: 0.0393, loss_cls: 0.2052, acc: 92.7117, loss_bbox: 0.2591, loss_mask: 0.2613, loss: 0.7929 +2024-05-27 18:29:27,023 - mmdet - INFO - Epoch [4][400/7330] lr: 1.000e-04, eta: 11:03:57, time: 0.590, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0276, loss_rpn_bbox: 0.0405, loss_cls: 0.2066, acc: 92.6553, loss_bbox: 0.2568, loss_mask: 0.2536, loss: 0.7852 +2024-05-27 18:29:56,596 - mmdet - INFO - Epoch [4][450/7330] lr: 1.000e-04, eta: 11:03:24, time: 0.591, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0326, loss_rpn_bbox: 0.0450, loss_cls: 0.2061, acc: 92.7715, loss_bbox: 0.2544, loss_mask: 0.2560, loss: 0.7940 +2024-05-27 18:30:26,411 - mmdet - INFO - Epoch [4][500/7330] lr: 1.000e-04, eta: 11:02:52, time: 0.596, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0299, loss_rpn_bbox: 0.0457, loss_cls: 0.2218, acc: 92.2146, loss_bbox: 0.2797, loss_mask: 0.2669, loss: 0.8441 +2024-05-27 18:30:55,875 - mmdet - INFO - Epoch [4][550/7330] lr: 1.000e-04, eta: 11:02:19, time: 0.589, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0265, loss_rpn_bbox: 0.0393, loss_cls: 0.2057, acc: 92.8140, loss_bbox: 0.2524, loss_mask: 0.2507, loss: 0.7745 +2024-05-27 18:31:25,711 - mmdet - INFO - Epoch [4][600/7330] lr: 1.000e-04, eta: 11:01:47, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0331, loss_rpn_bbox: 0.0464, loss_cls: 0.2115, acc: 92.5156, loss_bbox: 0.2697, loss_mask: 0.2670, loss: 0.8277 +2024-05-27 18:31:55,516 - mmdet - INFO - Epoch [4][650/7330] lr: 1.000e-04, eta: 11:01:15, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0296, loss_rpn_bbox: 0.0437, loss_cls: 0.2139, acc: 92.3948, loss_bbox: 0.2738, loss_mask: 0.2629, loss: 0.8239 +2024-05-27 18:32:25,127 - mmdet - INFO - Epoch [4][700/7330] lr: 1.000e-04, eta: 11:00:43, time: 0.592, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0297, loss_rpn_bbox: 0.0442, loss_cls: 0.2093, acc: 92.4353, loss_bbox: 0.2728, loss_mask: 0.2652, loss: 0.8212 +2024-05-27 18:32:54,915 - mmdet - INFO - Epoch [4][750/7330] lr: 1.000e-04, eta: 11:00:11, time: 0.596, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0297, loss_rpn_bbox: 0.0415, loss_cls: 0.2103, acc: 92.4807, loss_bbox: 0.2700, loss_mask: 0.2668, loss: 0.8184 +2024-05-27 18:33:24,518 - mmdet - INFO - Epoch [4][800/7330] lr: 1.000e-04, eta: 10:59:38, time: 0.592, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0292, loss_rpn_bbox: 0.0403, loss_cls: 0.2060, acc: 92.7607, loss_bbox: 0.2603, loss_mask: 0.2577, loss: 0.7935 +2024-05-27 18:33:54,161 - mmdet - INFO - Epoch [4][850/7330] lr: 1.000e-04, eta: 10:59:06, time: 0.593, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0316, loss_rpn_bbox: 0.0432, loss_cls: 0.2133, acc: 92.3333, loss_bbox: 0.2687, loss_mask: 0.2676, loss: 0.8244 +2024-05-27 18:34:23,764 - mmdet - INFO - Epoch [4][900/7330] lr: 1.000e-04, eta: 10:58:33, time: 0.592, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0320, loss_rpn_bbox: 0.0441, loss_cls: 0.2203, acc: 92.2454, loss_bbox: 0.2709, loss_mask: 0.2604, loss: 0.8277 +2024-05-27 18:34:53,517 - mmdet - INFO - Epoch [4][950/7330] lr: 1.000e-04, eta: 10:58:01, time: 0.595, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0281, loss_rpn_bbox: 0.0398, loss_cls: 0.2022, acc: 92.8328, loss_bbox: 0.2574, loss_mask: 0.2564, loss: 0.7839 +2024-05-27 18:35:23,270 - mmdet - INFO - Epoch [4][1000/7330] lr: 1.000e-04, eta: 10:57:29, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0267, loss_rpn_bbox: 0.0405, loss_cls: 0.2041, acc: 92.6802, loss_bbox: 0.2616, loss_mask: 0.2550, loss: 0.7878 +2024-05-27 18:35:52,912 - mmdet - INFO - Epoch [4][1050/7330] lr: 1.000e-04, eta: 10:56:57, time: 0.593, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0301, loss_rpn_bbox: 0.0400, loss_cls: 0.2017, acc: 92.9255, loss_bbox: 0.2533, loss_mask: 0.2569, loss: 0.7820 +2024-05-27 18:36:22,886 - mmdet - INFO - Epoch [4][1100/7330] lr: 1.000e-04, eta: 10:56:25, time: 0.599, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0322, loss_rpn_bbox: 0.0431, loss_cls: 0.2192, acc: 92.2363, loss_bbox: 0.2787, loss_mask: 0.2737, loss: 0.8469 +2024-05-27 18:36:57,533 - mmdet - INFO - Epoch [4][1150/7330] lr: 1.000e-04, eta: 10:56:07, time: 0.693, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0309, loss_rpn_bbox: 0.0446, loss_cls: 0.2049, acc: 92.7358, loss_bbox: 0.2606, loss_mask: 0.2710, loss: 0.8120 +2024-05-27 18:37:29,489 - mmdet - INFO - Epoch [4][1200/7330] lr: 1.000e-04, eta: 10:55:41, time: 0.639, data_time: 0.015, memory: 9459, loss_rpn_cls: 0.0289, loss_rpn_bbox: 0.0433, loss_cls: 0.2070, acc: 92.6482, loss_bbox: 0.2602, loss_mask: 0.2599, loss: 0.7991 +2024-05-27 18:38:03,049 - mmdet - INFO - Epoch [4][1250/7330] lr: 1.000e-04, eta: 10:55:19, time: 0.671, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0306, loss_rpn_bbox: 0.0442, loss_cls: 0.2104, acc: 92.5967, loss_bbox: 0.2657, loss_mask: 0.2650, loss: 0.8158 +2024-05-27 18:38:32,691 - mmdet - INFO - Epoch [4][1300/7330] lr: 1.000e-04, eta: 10:54:47, time: 0.593, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0268, loss_rpn_bbox: 0.0386, loss_cls: 0.2019, acc: 92.8845, loss_bbox: 0.2505, loss_mask: 0.2600, loss: 0.7778 +2024-05-27 18:39:07,328 - mmdet - INFO - Epoch [4][1350/7330] lr: 1.000e-04, eta: 10:54:28, time: 0.693, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0313, loss_rpn_bbox: 0.0443, loss_cls: 0.2144, acc: 92.3909, loss_bbox: 0.2727, loss_mask: 0.2721, loss: 0.8348 +2024-05-27 18:39:39,336 - mmdet - INFO - Epoch [4][1400/7330] lr: 1.000e-04, eta: 10:54:03, time: 0.640, data_time: 0.015, memory: 9459, loss_rpn_cls: 0.0271, loss_rpn_bbox: 0.0400, loss_cls: 0.2160, acc: 92.5491, loss_bbox: 0.2648, loss_mask: 0.2603, loss: 0.8082 +2024-05-27 18:40:11,776 - mmdet - INFO - Epoch [4][1450/7330] lr: 1.000e-04, eta: 10:53:38, time: 0.648, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0293, loss_rpn_bbox: 0.0428, loss_cls: 0.2090, acc: 92.6111, loss_bbox: 0.2640, loss_mask: 0.2708, loss: 0.8159 +2024-05-27 18:40:41,478 - mmdet - INFO - Epoch [4][1500/7330] lr: 1.000e-04, eta: 10:53:06, time: 0.595, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0304, loss_rpn_bbox: 0.0443, loss_cls: 0.2161, acc: 92.2283, loss_bbox: 0.2771, loss_mask: 0.2664, loss: 0.8342 +2024-05-27 18:41:11,176 - mmdet - INFO - Epoch [4][1550/7330] lr: 1.000e-04, eta: 10:52:33, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0313, loss_rpn_bbox: 0.0441, loss_cls: 0.2188, acc: 92.2708, loss_bbox: 0.2747, loss_mask: 0.2721, loss: 0.8410 +2024-05-27 18:41:41,057 - mmdet - INFO - Epoch [4][1600/7330] lr: 1.000e-04, eta: 10:52:01, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0317, loss_rpn_bbox: 0.0454, loss_cls: 0.2183, acc: 92.2090, loss_bbox: 0.2763, loss_mask: 0.2679, loss: 0.8396 +2024-05-27 18:42:11,023 - mmdet - INFO - Epoch [4][1650/7330] lr: 1.000e-04, eta: 10:51:30, time: 0.599, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0312, loss_rpn_bbox: 0.0433, loss_cls: 0.2178, acc: 92.3306, loss_bbox: 0.2728, loss_mask: 0.2582, loss: 0.8233 +2024-05-27 18:42:40,728 - mmdet - INFO - Epoch [4][1700/7330] lr: 1.000e-04, eta: 10:50:58, time: 0.594, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0276, loss_rpn_bbox: 0.0400, loss_cls: 0.2042, acc: 92.8245, loss_bbox: 0.2584, loss_mask: 0.2554, loss: 0.7856 +2024-05-27 18:43:10,383 - mmdet - INFO - Epoch [4][1750/7330] lr: 1.000e-04, eta: 10:50:25, time: 0.594, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0303, loss_rpn_bbox: 0.0422, loss_cls: 0.2063, acc: 92.7617, loss_bbox: 0.2640, loss_mask: 0.2629, loss: 0.8057 +2024-05-27 18:43:40,055 - mmdet - INFO - Epoch [4][1800/7330] lr: 1.000e-04, eta: 10:49:53, time: 0.593, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0298, loss_rpn_bbox: 0.0426, loss_cls: 0.2121, acc: 92.4783, loss_bbox: 0.2691, loss_mask: 0.2661, loss: 0.8197 +2024-05-27 18:44:09,805 - mmdet - INFO - Epoch [4][1850/7330] lr: 1.000e-04, eta: 10:49:21, time: 0.594, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0283, loss_rpn_bbox: 0.0408, loss_cls: 0.2000, acc: 92.8618, loss_bbox: 0.2596, loss_mask: 0.2593, loss: 0.7880 +2024-05-27 18:44:39,417 - mmdet - INFO - Epoch [4][1900/7330] lr: 1.000e-04, eta: 10:48:48, time: 0.592, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0279, loss_rpn_bbox: 0.0399, loss_cls: 0.2061, acc: 92.7087, loss_bbox: 0.2593, loss_mask: 0.2611, loss: 0.7942 +2024-05-27 18:45:09,117 - mmdet - INFO - Epoch [4][1950/7330] lr: 1.000e-04, eta: 10:48:16, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0278, loss_rpn_bbox: 0.0418, loss_cls: 0.2082, acc: 92.6060, loss_bbox: 0.2670, loss_mask: 0.2593, loss: 0.8041 +2024-05-27 18:45:38,678 - mmdet - INFO - Epoch [4][2000/7330] lr: 1.000e-04, eta: 10:47:44, time: 0.591, data_time: 0.015, memory: 9459, loss_rpn_cls: 0.0310, loss_rpn_bbox: 0.0420, loss_cls: 0.2145, acc: 92.4656, loss_bbox: 0.2677, loss_mask: 0.2696, loss: 0.8248 +2024-05-27 18:46:08,279 - mmdet - INFO - Epoch [4][2050/7330] lr: 1.000e-04, eta: 10:47:11, time: 0.591, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0294, loss_rpn_bbox: 0.0416, loss_cls: 0.2035, acc: 92.7522, loss_bbox: 0.2590, loss_mask: 0.2635, loss: 0.7970 +2024-05-27 18:46:38,086 - mmdet - INFO - Epoch [4][2100/7330] lr: 1.000e-04, eta: 10:46:39, time: 0.596, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0308, loss_rpn_bbox: 0.0428, loss_cls: 0.2066, acc: 92.7319, loss_bbox: 0.2571, loss_mask: 0.2570, loss: 0.7942 +2024-05-27 18:47:07,886 - mmdet - INFO - Epoch [4][2150/7330] lr: 1.000e-04, eta: 10:46:07, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0308, loss_rpn_bbox: 0.0417, loss_cls: 0.2105, acc: 92.5376, loss_bbox: 0.2641, loss_mask: 0.2607, loss: 0.8078 +2024-05-27 18:47:40,947 - mmdet - INFO - Epoch [4][2200/7330] lr: 1.000e-04, eta: 10:45:44, time: 0.662, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0286, loss_rpn_bbox: 0.0403, loss_cls: 0.2110, acc: 92.5181, loss_bbox: 0.2645, loss_mask: 0.2596, loss: 0.8040 +2024-05-27 18:48:12,817 - mmdet - INFO - Epoch [4][2250/7330] lr: 1.000e-04, eta: 10:45:18, time: 0.637, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0308, loss_rpn_bbox: 0.0413, loss_cls: 0.2157, acc: 92.2954, loss_bbox: 0.2732, loss_mask: 0.2581, loss: 0.8191 +2024-05-27 18:48:54,525 - mmdet - INFO - Epoch [4][2300/7330] lr: 1.000e-04, eta: 10:45:17, time: 0.835, data_time: 0.160, memory: 9459, loss_rpn_cls: 0.0293, loss_rpn_bbox: 0.0397, loss_cls: 0.1966, acc: 93.0339, loss_bbox: 0.2552, loss_mask: 0.2593, loss: 0.7802 +2024-05-27 18:49:24,321 - mmdet - INFO - Epoch [4][2350/7330] lr: 1.000e-04, eta: 10:44:45, time: 0.596, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0314, loss_rpn_bbox: 0.0446, loss_cls: 0.2096, acc: 92.5291, loss_bbox: 0.2690, loss_mask: 0.2614, loss: 0.8161 +2024-05-27 18:49:56,100 - mmdet - INFO - Epoch [4][2400/7330] lr: 1.000e-04, eta: 10:44:18, time: 0.636, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0309, loss_rpn_bbox: 0.0439, loss_cls: 0.2178, acc: 92.2056, loss_bbox: 0.2695, loss_mask: 0.2709, loss: 0.8330 +2024-05-27 18:50:31,483 - mmdet - INFO - Epoch [4][2450/7330] lr: 1.000e-04, eta: 10:44:01, time: 0.708, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0293, loss_rpn_bbox: 0.0413, loss_cls: 0.2029, acc: 92.9011, loss_bbox: 0.2552, loss_mask: 0.2573, loss: 0.7860 +2024-05-27 18:51:03,647 - mmdet - INFO - Epoch [4][2500/7330] lr: 1.000e-04, eta: 10:43:35, time: 0.643, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0310, loss_rpn_bbox: 0.0443, loss_cls: 0.2063, acc: 92.7397, loss_bbox: 0.2599, loss_mask: 0.2591, loss: 0.8005 +2024-05-27 18:51:33,545 - mmdet - INFO - Epoch [4][2550/7330] lr: 1.000e-04, eta: 10:43:03, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0307, loss_rpn_bbox: 0.0458, loss_cls: 0.2199, acc: 92.1956, loss_bbox: 0.2757, loss_mask: 0.2643, loss: 0.8365 +2024-05-27 18:52:03,335 - mmdet - INFO - Epoch [4][2600/7330] lr: 1.000e-04, eta: 10:42:31, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0309, loss_rpn_bbox: 0.0470, loss_cls: 0.2152, acc: 92.4050, loss_bbox: 0.2668, loss_mask: 0.2623, loss: 0.8223 +2024-05-27 18:52:33,142 - mmdet - INFO - Epoch [4][2650/7330] lr: 1.000e-04, eta: 10:41:59, time: 0.596, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0296, loss_rpn_bbox: 0.0425, loss_cls: 0.2059, acc: 92.7090, loss_bbox: 0.2644, loss_mask: 0.2630, loss: 0.8056 +2024-05-27 18:53:03,095 - mmdet - INFO - Epoch [4][2700/7330] lr: 1.000e-04, eta: 10:41:27, time: 0.599, data_time: 0.015, memory: 9459, loss_rpn_cls: 0.0295, loss_rpn_bbox: 0.0430, loss_cls: 0.2139, acc: 92.3635, loss_bbox: 0.2697, loss_mask: 0.2649, loss: 0.8209 +2024-05-27 18:53:32,915 - mmdet - INFO - Epoch [4][2750/7330] lr: 1.000e-04, eta: 10:40:56, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0301, loss_rpn_bbox: 0.0415, loss_cls: 0.2098, acc: 92.5583, loss_bbox: 0.2641, loss_mask: 0.2659, loss: 0.8115 +2024-05-27 18:54:02,506 - mmdet - INFO - Epoch [4][2800/7330] lr: 1.000e-04, eta: 10:40:23, time: 0.592, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0283, loss_rpn_bbox: 0.0419, loss_cls: 0.2081, acc: 92.5874, loss_bbox: 0.2645, loss_mask: 0.2643, loss: 0.8071 +2024-05-27 18:54:32,131 - mmdet - INFO - Epoch [4][2850/7330] lr: 1.000e-04, eta: 10:39:51, time: 0.593, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0293, loss_rpn_bbox: 0.0418, loss_cls: 0.2075, acc: 92.6001, loss_bbox: 0.2609, loss_mask: 0.2615, loss: 0.8010 +2024-05-27 18:55:01,801 - mmdet - INFO - Epoch [4][2900/7330] lr: 1.000e-04, eta: 10:39:18, time: 0.593, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0289, loss_rpn_bbox: 0.0422, loss_cls: 0.2072, acc: 92.5422, loss_bbox: 0.2642, loss_mask: 0.2609, loss: 0.8034 +2024-05-27 18:55:31,449 - mmdet - INFO - Epoch [4][2950/7330] lr: 1.000e-04, eta: 10:38:46, time: 0.593, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0326, loss_rpn_bbox: 0.0409, loss_cls: 0.2106, acc: 92.5444, loss_bbox: 0.2597, loss_mask: 0.2595, loss: 0.8032 +2024-05-27 18:56:01,244 - mmdet - INFO - Epoch [4][3000/7330] lr: 1.000e-04, eta: 10:38:14, time: 0.596, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0282, loss_rpn_bbox: 0.0414, loss_cls: 0.2159, acc: 92.3262, loss_bbox: 0.2709, loss_mask: 0.2626, loss: 0.8190 +2024-05-27 18:56:31,099 - mmdet - INFO - Epoch [4][3050/7330] lr: 1.000e-04, eta: 10:37:42, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0295, loss_rpn_bbox: 0.0428, loss_cls: 0.2198, acc: 92.2876, loss_bbox: 0.2708, loss_mask: 0.2643, loss: 0.8272 +2024-05-27 18:57:00,984 - mmdet - INFO - Epoch [4][3100/7330] lr: 1.000e-04, eta: 10:37:11, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0310, loss_rpn_bbox: 0.0433, loss_cls: 0.2093, acc: 92.4854, loss_bbox: 0.2676, loss_mask: 0.2651, loss: 0.8163 +2024-05-27 18:57:30,821 - mmdet - INFO - Epoch [4][3150/7330] lr: 1.000e-04, eta: 10:36:39, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0302, loss_rpn_bbox: 0.0434, loss_cls: 0.2030, acc: 92.7092, loss_bbox: 0.2580, loss_mask: 0.2643, loss: 0.7989 +2024-05-27 18:58:00,728 - mmdet - INFO - Epoch [4][3200/7330] lr: 1.000e-04, eta: 10:36:07, time: 0.598, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0279, loss_rpn_bbox: 0.0410, loss_cls: 0.2110, acc: 92.5715, loss_bbox: 0.2669, loss_mask: 0.2607, loss: 0.8076 +2024-05-27 18:58:30,711 - mmdet - INFO - Epoch [4][3250/7330] lr: 1.000e-04, eta: 10:35:36, time: 0.600, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0323, loss_rpn_bbox: 0.0449, loss_cls: 0.2249, acc: 92.0488, loss_bbox: 0.2804, loss_mask: 0.2675, loss: 0.8501 +2024-05-27 18:59:04,991 - mmdet - INFO - Epoch [4][3300/7330] lr: 1.000e-04, eta: 10:35:15, time: 0.685, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0304, loss_rpn_bbox: 0.0432, loss_cls: 0.2206, acc: 92.2891, loss_bbox: 0.2687, loss_mask: 0.2602, loss: 0.8231 +2024-05-27 18:59:38,937 - mmdet - INFO - Epoch [4][3350/7330] lr: 1.000e-04, eta: 10:34:53, time: 0.679, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0266, loss_rpn_bbox: 0.0405, loss_cls: 0.2048, acc: 92.7373, loss_bbox: 0.2575, loss_mask: 0.2600, loss: 0.7894 +2024-05-27 19:00:09,026 - mmdet - INFO - Epoch [4][3400/7330] lr: 1.000e-04, eta: 10:34:22, time: 0.601, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0285, loss_rpn_bbox: 0.0418, loss_cls: 0.2090, acc: 92.6416, loss_bbox: 0.2599, loss_mask: 0.2608, loss: 0.8000 +2024-05-27 19:00:41,265 - mmdet - INFO - Epoch [4][3450/7330] lr: 1.000e-04, eta: 10:33:56, time: 0.645, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0328, loss_rpn_bbox: 0.0465, loss_cls: 0.2226, acc: 92.0725, loss_bbox: 0.2829, loss_mask: 0.2728, loss: 0.8575 +2024-05-27 19:01:16,891 - mmdet - INFO - Epoch [4][3500/7330] lr: 1.000e-04, eta: 10:33:38, time: 0.713, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0305, loss_rpn_bbox: 0.0420, loss_cls: 0.2143, acc: 92.4917, loss_bbox: 0.2692, loss_mask: 0.2679, loss: 0.8237 +2024-05-27 19:01:48,946 - mmdet - INFO - Epoch [4][3550/7330] lr: 1.000e-04, eta: 10:33:12, time: 0.641, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0299, loss_rpn_bbox: 0.0446, loss_cls: 0.2135, acc: 92.2866, loss_bbox: 0.2733, loss_mask: 0.2629, loss: 0.8241 +2024-05-27 19:02:18,851 - mmdet - INFO - Epoch [4][3600/7330] lr: 1.000e-04, eta: 10:32:40, time: 0.598, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0314, loss_rpn_bbox: 0.0429, loss_cls: 0.2172, acc: 92.3706, loss_bbox: 0.2691, loss_mask: 0.2651, loss: 0.8256 +2024-05-27 19:02:48,577 - mmdet - INFO - Epoch [4][3650/7330] lr: 1.000e-04, eta: 10:32:08, time: 0.595, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0306, loss_rpn_bbox: 0.0436, loss_cls: 0.2135, acc: 92.3440, loss_bbox: 0.2674, loss_mask: 0.2611, loss: 0.8163 +2024-05-27 19:03:18,443 - mmdet - INFO - Epoch [4][3700/7330] lr: 1.000e-04, eta: 10:31:36, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0290, loss_rpn_bbox: 0.0397, loss_cls: 0.2089, acc: 92.5552, loss_bbox: 0.2580, loss_mask: 0.2587, loss: 0.7943 +2024-05-27 19:03:48,252 - mmdet - INFO - Epoch [4][3750/7330] lr: 1.000e-04, eta: 10:31:04, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0299, loss_rpn_bbox: 0.0404, loss_cls: 0.2060, acc: 92.7375, loss_bbox: 0.2605, loss_mask: 0.2616, loss: 0.7985 +2024-05-27 19:04:18,089 - mmdet - INFO - Epoch [4][3800/7330] lr: 1.000e-04, eta: 10:30:32, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0301, loss_rpn_bbox: 0.0418, loss_cls: 0.2098, acc: 92.5283, loss_bbox: 0.2670, loss_mask: 0.2634, loss: 0.8120 +2024-05-27 19:04:47,817 - mmdet - INFO - Epoch [4][3850/7330] lr: 1.000e-04, eta: 10:30:00, time: 0.595, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0300, loss_rpn_bbox: 0.0416, loss_cls: 0.2119, acc: 92.5713, loss_bbox: 0.2623, loss_mask: 0.2606, loss: 0.8065 +2024-05-27 19:05:17,719 - mmdet - INFO - Epoch [4][3900/7330] lr: 1.000e-04, eta: 10:29:29, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0283, loss_rpn_bbox: 0.0419, loss_cls: 0.2188, acc: 92.1960, loss_bbox: 0.2726, loss_mask: 0.2590, loss: 0.8206 +2024-05-27 19:05:47,439 - mmdet - INFO - Epoch [4][3950/7330] lr: 1.000e-04, eta: 10:28:57, time: 0.594, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0307, loss_rpn_bbox: 0.0440, loss_cls: 0.2094, acc: 92.5339, loss_bbox: 0.2629, loss_mask: 0.2642, loss: 0.8112 +2024-05-27 19:06:17,383 - mmdet - INFO - Epoch [4][4000/7330] lr: 1.000e-04, eta: 10:28:25, time: 0.599, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0281, loss_rpn_bbox: 0.0438, loss_cls: 0.2193, acc: 92.1733, loss_bbox: 0.2718, loss_mask: 0.2628, loss: 0.8258 +2024-05-27 19:06:47,260 - mmdet - INFO - Epoch [4][4050/7330] lr: 1.000e-04, eta: 10:27:53, time: 0.597, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0300, loss_rpn_bbox: 0.0405, loss_cls: 0.2064, acc: 92.6443, loss_bbox: 0.2583, loss_mask: 0.2539, loss: 0.7890 +2024-05-27 19:07:17,066 - mmdet - INFO - Epoch [4][4100/7330] lr: 1.000e-04, eta: 10:27:21, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0276, loss_rpn_bbox: 0.0387, loss_cls: 0.2142, acc: 92.3840, loss_bbox: 0.2662, loss_mask: 0.2565, loss: 0.8033 +2024-05-27 19:07:46,910 - mmdet - INFO - Epoch [4][4150/7330] lr: 1.000e-04, eta: 10:26:50, time: 0.597, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0296, loss_rpn_bbox: 0.0453, loss_cls: 0.2197, acc: 92.2014, loss_bbox: 0.2714, loss_mask: 0.2628, loss: 0.8287 +2024-05-27 19:08:16,859 - mmdet - INFO - Epoch [4][4200/7330] lr: 1.000e-04, eta: 10:26:18, time: 0.599, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0294, loss_rpn_bbox: 0.0430, loss_cls: 0.2191, acc: 92.2629, loss_bbox: 0.2760, loss_mask: 0.2696, loss: 0.8371 +2024-05-27 19:08:46,539 - mmdet - INFO - Epoch [4][4250/7330] lr: 1.000e-04, eta: 10:25:46, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0296, loss_rpn_bbox: 0.0422, loss_cls: 0.2116, acc: 92.5598, loss_bbox: 0.2580, loss_mask: 0.2600, loss: 0.8015 +2024-05-27 19:09:16,218 - mmdet - INFO - Epoch [4][4300/7330] lr: 1.000e-04, eta: 10:25:14, time: 0.594, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0310, loss_rpn_bbox: 0.0445, loss_cls: 0.2100, acc: 92.4900, loss_bbox: 0.2620, loss_mask: 0.2548, loss: 0.8022 +2024-05-27 19:09:51,335 - mmdet - INFO - Epoch [4][4350/7330] lr: 1.000e-04, eta: 10:24:54, time: 0.702, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0294, loss_rpn_bbox: 0.0417, loss_cls: 0.2250, acc: 92.1162, loss_bbox: 0.2718, loss_mask: 0.2623, loss: 0.8302 +2024-05-27 19:10:23,330 - mmdet - INFO - Epoch [4][4400/7330] lr: 1.000e-04, eta: 10:24:28, time: 0.640, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0310, loss_rpn_bbox: 0.0416, loss_cls: 0.2097, acc: 92.4163, loss_bbox: 0.2666, loss_mask: 0.2592, loss: 0.8080 +2024-05-27 19:10:55,559 - mmdet - INFO - Epoch [4][4450/7330] lr: 1.000e-04, eta: 10:24:01, time: 0.645, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0291, loss_rpn_bbox: 0.0409, loss_cls: 0.2027, acc: 92.7751, loss_bbox: 0.2562, loss_mask: 0.2544, loss: 0.7833 +2024-05-27 19:11:25,584 - mmdet - INFO - Epoch [4][4500/7330] lr: 1.000e-04, eta: 10:23:30, time: 0.601, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0305, loss_rpn_bbox: 0.0441, loss_cls: 0.2115, acc: 92.4001, loss_bbox: 0.2628, loss_mask: 0.2587, loss: 0.8077 +2024-05-27 19:12:00,520 - mmdet - INFO - Epoch [4][4550/7330] lr: 1.000e-04, eta: 10:23:10, time: 0.699, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0305, loss_rpn_bbox: 0.0432, loss_cls: 0.2067, acc: 92.5327, loss_bbox: 0.2626, loss_mask: 0.2614, loss: 0.8043 +2024-05-27 19:12:32,613 - mmdet - INFO - Epoch [4][4600/7330] lr: 1.000e-04, eta: 10:22:43, time: 0.642, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0282, loss_rpn_bbox: 0.0414, loss_cls: 0.2143, acc: 92.3894, loss_bbox: 0.2667, loss_mask: 0.2634, loss: 0.8140 +2024-05-27 19:13:04,846 - mmdet - INFO - Epoch [4][4650/7330] lr: 1.000e-04, eta: 10:22:17, time: 0.645, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0268, loss_rpn_bbox: 0.0386, loss_cls: 0.2029, acc: 92.8406, loss_bbox: 0.2552, loss_mask: 0.2582, loss: 0.7817 +2024-05-27 19:13:34,501 - mmdet - INFO - Epoch [4][4700/7330] lr: 1.000e-04, eta: 10:21:45, time: 0.593, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0301, loss_rpn_bbox: 0.0408, loss_cls: 0.2130, acc: 92.4346, loss_bbox: 0.2619, loss_mask: 0.2606, loss: 0.8064 +2024-05-27 19:14:04,314 - mmdet - INFO - Epoch [4][4750/7330] lr: 1.000e-04, eta: 10:21:13, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0305, loss_rpn_bbox: 0.0413, loss_cls: 0.2183, acc: 92.2847, loss_bbox: 0.2667, loss_mask: 0.2596, loss: 0.8164 +2024-05-27 19:14:34,172 - mmdet - INFO - Epoch [4][4800/7330] lr: 1.000e-04, eta: 10:20:41, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0286, loss_rpn_bbox: 0.0424, loss_cls: 0.2115, acc: 92.3984, loss_bbox: 0.2631, loss_mask: 0.2548, loss: 0.8003 +2024-05-27 19:15:04,188 - mmdet - INFO - Epoch [4][4850/7330] lr: 1.000e-04, eta: 10:20:10, time: 0.601, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0330, loss_rpn_bbox: 0.0405, loss_cls: 0.2002, acc: 92.8945, loss_bbox: 0.2537, loss_mask: 0.2540, loss: 0.7815 +2024-05-27 19:15:33,931 - mmdet - INFO - Epoch [4][4900/7330] lr: 1.000e-04, eta: 10:19:38, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0291, loss_rpn_bbox: 0.0416, loss_cls: 0.2069, acc: 92.7168, loss_bbox: 0.2562, loss_mask: 0.2574, loss: 0.7913 +2024-05-27 19:16:03,997 - mmdet - INFO - Epoch [4][4950/7330] lr: 1.000e-04, eta: 10:19:06, time: 0.601, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0324, loss_rpn_bbox: 0.0440, loss_cls: 0.2170, acc: 92.2419, loss_bbox: 0.2723, loss_mask: 0.2603, loss: 0.8260 +2024-05-27 19:16:34,021 - mmdet - INFO - Epoch [4][5000/7330] lr: 1.000e-04, eta: 10:18:35, time: 0.600, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0306, loss_rpn_bbox: 0.0423, loss_cls: 0.2109, acc: 92.4841, loss_bbox: 0.2685, loss_mask: 0.2646, loss: 0.8168 +2024-05-27 19:17:03,764 - mmdet - INFO - Epoch [4][5050/7330] lr: 1.000e-04, eta: 10:18:03, time: 0.595, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0274, loss_rpn_bbox: 0.0396, loss_cls: 0.1991, acc: 92.9700, loss_bbox: 0.2557, loss_mask: 0.2522, loss: 0.7740 +2024-05-27 19:17:33,674 - mmdet - INFO - Epoch [4][5100/7330] lr: 1.000e-04, eta: 10:17:31, time: 0.598, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0264, loss_rpn_bbox: 0.0420, loss_cls: 0.2029, acc: 92.6670, loss_bbox: 0.2574, loss_mask: 0.2586, loss: 0.7874 +2024-05-27 19:18:03,683 - mmdet - INFO - Epoch [4][5150/7330] lr: 1.000e-04, eta: 10:17:00, time: 0.600, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0311, loss_rpn_bbox: 0.0430, loss_cls: 0.2142, acc: 92.3083, loss_bbox: 0.2725, loss_mask: 0.2691, loss: 0.8300 +2024-05-27 19:18:33,479 - mmdet - INFO - Epoch [4][5200/7330] lr: 1.000e-04, eta: 10:16:28, time: 0.596, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0279, loss_rpn_bbox: 0.0401, loss_cls: 0.2025, acc: 92.8955, loss_bbox: 0.2529, loss_mask: 0.2592, loss: 0.7825 +2024-05-27 19:19:03,396 - mmdet - INFO - Epoch [4][5250/7330] lr: 1.000e-04, eta: 10:15:57, time: 0.598, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0295, loss_rpn_bbox: 0.0422, loss_cls: 0.2074, acc: 92.6128, loss_bbox: 0.2624, loss_mask: 0.2608, loss: 0.8023 +2024-05-27 19:19:33,277 - mmdet - INFO - Epoch [4][5300/7330] lr: 1.000e-04, eta: 10:15:25, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0312, loss_rpn_bbox: 0.0445, loss_cls: 0.2202, acc: 92.2656, loss_bbox: 0.2690, loss_mask: 0.2706, loss: 0.8356 +2024-05-27 19:20:03,096 - mmdet - INFO - Epoch [4][5350/7330] lr: 1.000e-04, eta: 10:14:53, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0266, loss_rpn_bbox: 0.0401, loss_cls: 0.2133, acc: 92.5247, loss_bbox: 0.2632, loss_mask: 0.2606, loss: 0.8038 +2024-05-27 19:20:35,116 - mmdet - INFO - Epoch [4][5400/7330] lr: 1.000e-04, eta: 10:14:26, time: 0.640, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0296, loss_rpn_bbox: 0.0421, loss_cls: 0.2080, acc: 92.5278, loss_bbox: 0.2612, loss_mask: 0.2571, loss: 0.7980 +2024-05-27 19:21:09,084 - mmdet - INFO - Epoch [4][5450/7330] lr: 1.000e-04, eta: 10:14:04, time: 0.679, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0284, loss_rpn_bbox: 0.0401, loss_cls: 0.2023, acc: 92.8928, loss_bbox: 0.2513, loss_mask: 0.2600, loss: 0.7821 +2024-05-27 19:21:43,740 - mmdet - INFO - Epoch [4][5500/7330] lr: 1.000e-04, eta: 10:13:42, time: 0.693, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0317, loss_rpn_bbox: 0.0413, loss_cls: 0.2073, acc: 92.7363, loss_bbox: 0.2572, loss_mask: 0.2608, loss: 0.7983 +2024-05-27 19:22:13,608 - mmdet - INFO - Epoch [4][5550/7330] lr: 1.000e-04, eta: 10:13:11, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0261, loss_rpn_bbox: 0.0397, loss_cls: 0.2078, acc: 92.6001, loss_bbox: 0.2625, loss_mask: 0.2539, loss: 0.7901 +2024-05-27 19:22:45,945 - mmdet - INFO - Epoch [4][5600/7330] lr: 1.000e-04, eta: 10:12:44, time: 0.647, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0292, loss_rpn_bbox: 0.0420, loss_cls: 0.2169, acc: 92.3740, loss_bbox: 0.2654, loss_mask: 0.2616, loss: 0.8151 +2024-05-27 19:23:21,864 - mmdet - INFO - Epoch [4][5650/7330] lr: 1.000e-04, eta: 10:12:26, time: 0.718, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0300, loss_rpn_bbox: 0.0417, loss_cls: 0.2098, acc: 92.6567, loss_bbox: 0.2658, loss_mask: 0.2611, loss: 0.8083 +2024-05-27 19:23:54,041 - mmdet - INFO - Epoch [4][5700/7330] lr: 1.000e-04, eta: 10:11:59, time: 0.643, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0308, loss_rpn_bbox: 0.0431, loss_cls: 0.2135, acc: 92.4285, loss_bbox: 0.2702, loss_mask: 0.2625, loss: 0.8201 +2024-05-27 19:24:23,915 - mmdet - INFO - Epoch [4][5750/7330] lr: 1.000e-04, eta: 10:11:27, time: 0.598, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0290, loss_rpn_bbox: 0.0406, loss_cls: 0.2032, acc: 92.8455, loss_bbox: 0.2503, loss_mask: 0.2548, loss: 0.7778 +2024-05-27 19:24:53,919 - mmdet - INFO - Epoch [4][5800/7330] lr: 1.000e-04, eta: 10:10:56, time: 0.600, data_time: 0.025, memory: 9459, loss_rpn_cls: 0.0323, loss_rpn_bbox: 0.0445, loss_cls: 0.2152, acc: 92.3801, loss_bbox: 0.2654, loss_mask: 0.2646, loss: 0.8220 +2024-05-27 19:25:23,850 - mmdet - INFO - Epoch [4][5850/7330] lr: 1.000e-04, eta: 10:10:24, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0278, loss_rpn_bbox: 0.0408, loss_cls: 0.2082, acc: 92.6797, loss_bbox: 0.2515, loss_mask: 0.2531, loss: 0.7815 +2024-05-27 19:25:53,739 - mmdet - INFO - Epoch [4][5900/7330] lr: 1.000e-04, eta: 10:09:53, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0301, loss_rpn_bbox: 0.0426, loss_cls: 0.2075, acc: 92.6558, loss_bbox: 0.2586, loss_mask: 0.2549, loss: 0.7938 +2024-05-27 19:26:23,597 - mmdet - INFO - Epoch [4][5950/7330] lr: 1.000e-04, eta: 10:09:21, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0321, loss_rpn_bbox: 0.0450, loss_cls: 0.2169, acc: 92.2449, loss_bbox: 0.2709, loss_mask: 0.2650, loss: 0.8300 +2024-05-27 19:26:53,398 - mmdet - INFO - Epoch [4][6000/7330] lr: 1.000e-04, eta: 10:08:49, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0291, loss_rpn_bbox: 0.0402, loss_cls: 0.2000, acc: 92.9016, loss_bbox: 0.2536, loss_mask: 0.2592, loss: 0.7821 +2024-05-27 19:27:23,174 - mmdet - INFO - Epoch [4][6050/7330] lr: 1.000e-04, eta: 10:08:17, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0306, loss_rpn_bbox: 0.0441, loss_cls: 0.2116, acc: 92.4404, loss_bbox: 0.2683, loss_mask: 0.2579, loss: 0.8125 +2024-05-27 19:27:53,027 - mmdet - INFO - Epoch [4][6100/7330] lr: 1.000e-04, eta: 10:07:45, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0268, loss_rpn_bbox: 0.0406, loss_cls: 0.2022, acc: 92.7964, loss_bbox: 0.2567, loss_mask: 0.2621, loss: 0.7883 +2024-05-27 19:28:22,880 - mmdet - INFO - Epoch [4][6150/7330] lr: 1.000e-04, eta: 10:07:14, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0286, loss_rpn_bbox: 0.0434, loss_cls: 0.2149, acc: 92.5527, loss_bbox: 0.2670, loss_mask: 0.2602, loss: 0.8141 +2024-05-27 19:28:52,882 - mmdet - INFO - Epoch [4][6200/7330] lr: 1.000e-04, eta: 10:06:42, time: 0.600, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0302, loss_rpn_bbox: 0.0430, loss_cls: 0.2095, acc: 92.5291, loss_bbox: 0.2648, loss_mask: 0.2588, loss: 0.8063 +2024-05-27 19:29:22,764 - mmdet - INFO - Epoch [4][6250/7330] lr: 1.000e-04, eta: 10:06:11, time: 0.598, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0316, loss_rpn_bbox: 0.0441, loss_cls: 0.2136, acc: 92.4346, loss_bbox: 0.2663, loss_mask: 0.2612, loss: 0.8168 +2024-05-27 19:29:52,556 - mmdet - INFO - Epoch [4][6300/7330] lr: 1.000e-04, eta: 10:05:39, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0296, loss_rpn_bbox: 0.0424, loss_cls: 0.2110, acc: 92.4714, loss_bbox: 0.2654, loss_mask: 0.2607, loss: 0.8091 +2024-05-27 19:30:22,349 - mmdet - INFO - Epoch [4][6350/7330] lr: 1.000e-04, eta: 10:05:07, time: 0.596, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0302, loss_rpn_bbox: 0.0420, loss_cls: 0.2065, acc: 92.6440, loss_bbox: 0.2593, loss_mask: 0.2563, loss: 0.7943 +2024-05-27 19:30:52,102 - mmdet - INFO - Epoch [4][6400/7330] lr: 1.000e-04, eta: 10:04:35, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0289, loss_rpn_bbox: 0.0413, loss_cls: 0.2064, acc: 92.5979, loss_bbox: 0.2639, loss_mask: 0.2627, loss: 0.8031 +2024-05-27 19:31:22,200 - mmdet - INFO - Epoch [4][6450/7330] lr: 1.000e-04, eta: 10:04:04, time: 0.602, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0298, loss_rpn_bbox: 0.0410, loss_cls: 0.2066, acc: 92.6799, loss_bbox: 0.2588, loss_mask: 0.2572, loss: 0.7934 +2024-05-27 19:31:56,934 - mmdet - INFO - Epoch [4][6500/7330] lr: 1.000e-04, eta: 10:03:42, time: 0.695, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0272, loss_rpn_bbox: 0.0410, loss_cls: 0.2090, acc: 92.6179, loss_bbox: 0.2624, loss_mask: 0.2546, loss: 0.7941 +2024-05-27 19:32:31,845 - mmdet - INFO - Epoch [4][6550/7330] lr: 1.000e-04, eta: 10:03:21, time: 0.698, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0307, loss_rpn_bbox: 0.0428, loss_cls: 0.2052, acc: 92.7141, loss_bbox: 0.2610, loss_mask: 0.2573, loss: 0.7970 +2024-05-27 19:33:01,700 - mmdet - INFO - Epoch [4][6600/7330] lr: 1.000e-04, eta: 10:02:49, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0333, loss_rpn_bbox: 0.0427, loss_cls: 0.2099, acc: 92.5869, loss_bbox: 0.2610, loss_mask: 0.2625, loss: 0.8094 +2024-05-27 19:33:31,605 - mmdet - INFO - Epoch [4][6650/7330] lr: 1.000e-04, eta: 10:02:18, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0284, loss_rpn_bbox: 0.0416, loss_cls: 0.2081, acc: 92.5713, loss_bbox: 0.2568, loss_mask: 0.2558, loss: 0.7907 +2024-05-27 19:34:06,474 - mmdet - INFO - Epoch [4][6700/7330] lr: 1.000e-04, eta: 10:01:56, time: 0.697, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0252, loss_rpn_bbox: 0.0400, loss_cls: 0.2084, acc: 92.5967, loss_bbox: 0.2620, loss_mask: 0.2579, loss: 0.7936 +2024-05-27 19:34:41,389 - mmdet - INFO - Epoch [4][6750/7330] lr: 1.000e-04, eta: 10:01:35, time: 0.698, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0293, loss_rpn_bbox: 0.0423, loss_cls: 0.2108, acc: 92.5608, loss_bbox: 0.2600, loss_mask: 0.2565, loss: 0.7989 +2024-05-27 19:35:11,150 - mmdet - INFO - Epoch [4][6800/7330] lr: 1.000e-04, eta: 10:01:03, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0279, loss_rpn_bbox: 0.0399, loss_cls: 0.2010, acc: 92.9114, loss_bbox: 0.2572, loss_mask: 0.2554, loss: 0.7814 +2024-05-27 19:35:41,283 - mmdet - INFO - Epoch [4][6850/7330] lr: 1.000e-04, eta: 10:00:32, time: 0.602, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0295, loss_rpn_bbox: 0.0411, loss_cls: 0.2055, acc: 92.6135, loss_bbox: 0.2645, loss_mask: 0.2573, loss: 0.7979 +2024-05-27 19:36:10,996 - mmdet - INFO - Epoch [4][6900/7330] lr: 1.000e-04, eta: 10:00:00, time: 0.595, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0300, loss_rpn_bbox: 0.0419, loss_cls: 0.2080, acc: 92.7529, loss_bbox: 0.2552, loss_mask: 0.2562, loss: 0.7913 +2024-05-27 19:36:40,818 - mmdet - INFO - Epoch [4][6950/7330] lr: 1.000e-04, eta: 9:59:28, time: 0.596, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0295, loss_rpn_bbox: 0.0417, loss_cls: 0.2043, acc: 92.7417, loss_bbox: 0.2556, loss_mask: 0.2569, loss: 0.7880 +2024-05-27 19:37:10,647 - mmdet - INFO - Epoch [4][7000/7330] lr: 1.000e-04, eta: 9:58:56, time: 0.597, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0305, loss_rpn_bbox: 0.0416, loss_cls: 0.2078, acc: 92.6982, loss_bbox: 0.2598, loss_mask: 0.2587, loss: 0.7983 +2024-05-27 19:37:40,611 - mmdet - INFO - Epoch [4][7050/7330] lr: 1.000e-04, eta: 9:58:25, time: 0.599, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0303, loss_rpn_bbox: 0.0418, loss_cls: 0.2143, acc: 92.3259, loss_bbox: 0.2729, loss_mask: 0.2625, loss: 0.8219 +2024-05-27 19:38:10,614 - mmdet - INFO - Epoch [4][7100/7330] lr: 1.000e-04, eta: 9:57:53, time: 0.600, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0293, loss_rpn_bbox: 0.0415, loss_cls: 0.2080, acc: 92.5315, loss_bbox: 0.2689, loss_mask: 0.2632, loss: 0.8109 +2024-05-27 19:38:40,342 - mmdet - INFO - Epoch [4][7150/7330] lr: 1.000e-04, eta: 9:57:21, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0286, loss_rpn_bbox: 0.0378, loss_cls: 0.1921, acc: 93.1863, loss_bbox: 0.2447, loss_mask: 0.2538, loss: 0.7570 +2024-05-27 19:39:10,370 - mmdet - INFO - Epoch [4][7200/7330] lr: 1.000e-04, eta: 9:56:50, time: 0.601, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0298, loss_rpn_bbox: 0.0421, loss_cls: 0.2099, acc: 92.4744, loss_bbox: 0.2654, loss_mask: 0.2569, loss: 0.8041 +2024-05-27 19:39:40,321 - mmdet - INFO - Epoch [4][7250/7330] lr: 1.000e-04, eta: 9:56:19, time: 0.599, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0297, loss_rpn_bbox: 0.0442, loss_cls: 0.2116, acc: 92.5737, loss_bbox: 0.2639, loss_mask: 0.2582, loss: 0.8075 +2024-05-27 19:40:10,416 - mmdet - INFO - Epoch [4][7300/7330] lr: 1.000e-04, eta: 9:55:47, time: 0.602, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0301, loss_rpn_bbox: 0.0404, loss_cls: 0.2036, acc: 92.7998, loss_bbox: 0.2549, loss_mask: 0.2493, loss: 0.7784 +2024-05-27 19:40:29,115 - mmdet - INFO - Saving checkpoint at 4 epochs +2024-05-27 19:42:22,328 - mmdet - INFO - Evaluating bbox... +2024-05-27 19:42:48,881 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.409 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.649 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.444 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.224 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.453 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.587 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.526 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.526 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.526 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.310 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.580 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.713 + +2024-05-27 19:42:48,882 - mmdet - INFO - Evaluating segm... +2024-05-27 19:43:18,018 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.369 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.605 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.388 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.144 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.400 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.602 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.473 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.473 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.473 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.242 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.524 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.689 + +2024-05-27 19:43:18,454 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-27 19:43:18,455 - mmdet - INFO - Epoch(val) [4][625] bbox_mAP: 0.4090, bbox_mAP_50: 0.6490, bbox_mAP_75: 0.4440, bbox_mAP_s: 0.2240, bbox_mAP_m: 0.4530, bbox_mAP_l: 0.5870, bbox_mAP_copypaste: 0.409 0.649 0.444 0.224 0.453 0.587, segm_mAP: 0.3690, segm_mAP_50: 0.6050, segm_mAP_75: 0.3880, segm_mAP_s: 0.1440, segm_mAP_m: 0.4000, segm_mAP_l: 0.6020, segm_mAP_copypaste: 0.369 0.605 0.388 0.144 0.400 0.602 +2024-05-27 19:43:53,285 - mmdet - INFO - Epoch [5][50/7330] lr: 1.000e-04, eta: 9:54:31, time: 0.696, data_time: 0.085, memory: 9459, loss_rpn_cls: 0.0276, loss_rpn_bbox: 0.0404, loss_cls: 0.2021, acc: 92.7192, loss_bbox: 0.2555, loss_mask: 0.2588, loss: 0.7844 +2024-05-27 19:44:22,905 - mmdet - INFO - Epoch [5][100/7330] lr: 1.000e-04, eta: 9:53:59, time: 0.592, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0266, loss_rpn_bbox: 0.0402, loss_cls: 0.1940, acc: 92.9802, loss_bbox: 0.2476, loss_mask: 0.2519, loss: 0.7603 +2024-05-27 19:44:52,433 - mmdet - INFO - Epoch [5][150/7330] lr: 1.000e-04, eta: 9:53:27, time: 0.591, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0250, loss_rpn_bbox: 0.0394, loss_cls: 0.1913, acc: 93.1848, loss_bbox: 0.2456, loss_mask: 0.2510, loss: 0.7522 +2024-05-27 19:45:22,170 - mmdet - INFO - Epoch [5][200/7330] lr: 1.000e-04, eta: 9:52:55, time: 0.595, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0255, loss_rpn_bbox: 0.0398, loss_cls: 0.2002, acc: 92.7444, loss_bbox: 0.2587, loss_mask: 0.2522, loss: 0.7764 +2024-05-27 19:45:51,506 - mmdet - INFO - Epoch [5][250/7330] lr: 1.000e-04, eta: 9:52:22, time: 0.587, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0242, loss_rpn_bbox: 0.0368, loss_cls: 0.1850, acc: 93.3210, loss_bbox: 0.2406, loss_mask: 0.2434, loss: 0.7300 +2024-05-27 19:46:21,112 - mmdet - INFO - Epoch [5][300/7330] lr: 1.000e-04, eta: 9:51:50, time: 0.592, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0297, loss_rpn_bbox: 0.0442, loss_cls: 0.2057, acc: 92.5942, loss_bbox: 0.2662, loss_mask: 0.2616, loss: 0.8074 +2024-05-27 19:46:53,045 - mmdet - INFO - Epoch [5][350/7330] lr: 1.000e-04, eta: 9:51:23, time: 0.639, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0266, loss_rpn_bbox: 0.0390, loss_cls: 0.1926, acc: 93.0613, loss_bbox: 0.2429, loss_mask: 0.2475, loss: 0.7486 +2024-05-27 19:47:25,460 - mmdet - INFO - Epoch [5][400/7330] lr: 1.000e-04, eta: 9:50:56, time: 0.648, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0264, loss_rpn_bbox: 0.0417, loss_cls: 0.2031, acc: 92.6135, loss_bbox: 0.2596, loss_mask: 0.2552, loss: 0.7861 +2024-05-27 19:47:57,310 - mmdet - INFO - Epoch [5][450/7330] lr: 1.000e-04, eta: 9:50:28, time: 0.637, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0264, loss_rpn_bbox: 0.0401, loss_cls: 0.1977, acc: 92.8389, loss_bbox: 0.2543, loss_mask: 0.2560, loss: 0.7745 +2024-05-27 19:48:27,040 - mmdet - INFO - Epoch [5][500/7330] lr: 1.000e-04, eta: 9:49:56, time: 0.595, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0296, loss_rpn_bbox: 0.0442, loss_cls: 0.2033, acc: 92.6343, loss_bbox: 0.2595, loss_mask: 0.2631, loss: 0.7996 +2024-05-27 19:48:59,077 - mmdet - INFO - Epoch [5][550/7330] lr: 1.000e-04, eta: 9:49:29, time: 0.641, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0238, loss_rpn_bbox: 0.0380, loss_cls: 0.1918, acc: 93.1626, loss_bbox: 0.2479, loss_mask: 0.2475, loss: 0.7490 +2024-05-27 19:49:28,889 - mmdet - INFO - Epoch [5][600/7330] lr: 1.000e-04, eta: 9:48:57, time: 0.596, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0284, loss_rpn_bbox: 0.0436, loss_cls: 0.2046, acc: 92.5615, loss_bbox: 0.2654, loss_mask: 0.2550, loss: 0.7970 +2024-05-27 19:49:58,782 - mmdet - INFO - Epoch [5][650/7330] lr: 1.000e-04, eta: 9:48:26, time: 0.597, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0266, loss_rpn_bbox: 0.0410, loss_cls: 0.1990, acc: 92.8345, loss_bbox: 0.2575, loss_mask: 0.2496, loss: 0.7737 +2024-05-27 19:50:28,603 - mmdet - INFO - Epoch [5][700/7330] lr: 1.000e-04, eta: 9:47:54, time: 0.597, data_time: 0.015, memory: 9459, loss_rpn_cls: 0.0258, loss_rpn_bbox: 0.0410, loss_cls: 0.1954, acc: 92.8584, loss_bbox: 0.2526, loss_mask: 0.2504, loss: 0.7653 +2024-05-27 19:50:58,338 - mmdet - INFO - Epoch [5][750/7330] lr: 1.000e-04, eta: 9:47:23, time: 0.595, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0275, loss_rpn_bbox: 0.0415, loss_cls: 0.2065, acc: 92.6240, loss_bbox: 0.2640, loss_mask: 0.2608, loss: 0.8002 +2024-05-27 19:51:27,978 - mmdet - INFO - Epoch [5][800/7330] lr: 1.000e-04, eta: 9:46:51, time: 0.593, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0267, loss_rpn_bbox: 0.0387, loss_cls: 0.1960, acc: 92.8569, loss_bbox: 0.2569, loss_mask: 0.2524, loss: 0.7708 +2024-05-27 19:51:57,794 - mmdet - INFO - Epoch [5][850/7330] lr: 1.000e-04, eta: 9:46:19, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0261, loss_rpn_bbox: 0.0402, loss_cls: 0.1984, acc: 92.9673, loss_bbox: 0.2525, loss_mask: 0.2547, loss: 0.7719 +2024-05-27 19:52:27,334 - mmdet - INFO - Epoch [5][900/7330] lr: 1.000e-04, eta: 9:45:47, time: 0.591, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0260, loss_rpn_bbox: 0.0395, loss_cls: 0.1953, acc: 93.0957, loss_bbox: 0.2467, loss_mask: 0.2508, loss: 0.7583 +2024-05-27 19:52:56,955 - mmdet - INFO - Epoch [5][950/7330] lr: 1.000e-04, eta: 9:45:15, time: 0.592, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0260, loss_rpn_bbox: 0.0396, loss_cls: 0.1905, acc: 93.1179, loss_bbox: 0.2463, loss_mask: 0.2538, loss: 0.7562 +2024-05-27 19:53:26,641 - mmdet - INFO - Epoch [5][1000/7330] lr: 1.000e-04, eta: 9:44:43, time: 0.594, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0284, loss_rpn_bbox: 0.0418, loss_cls: 0.2049, acc: 92.6792, loss_bbox: 0.2643, loss_mask: 0.2611, loss: 0.8005 +2024-05-27 19:53:58,729 - mmdet - INFO - Epoch [5][1050/7330] lr: 1.000e-04, eta: 9:44:16, time: 0.642, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0285, loss_rpn_bbox: 0.0418, loss_cls: 0.2044, acc: 92.6284, loss_bbox: 0.2634, loss_mask: 0.2567, loss: 0.7948 +2024-05-27 19:54:31,157 - mmdet - INFO - Epoch [5][1100/7330] lr: 1.000e-04, eta: 9:43:49, time: 0.649, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0262, loss_rpn_bbox: 0.0421, loss_cls: 0.1987, acc: 92.8252, loss_bbox: 0.2563, loss_mask: 0.2557, loss: 0.7792 +2024-05-27 19:55:00,799 - mmdet - INFO - Epoch [5][1150/7330] lr: 1.000e-04, eta: 9:43:17, time: 0.593, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0253, loss_rpn_bbox: 0.0390, loss_cls: 0.2003, acc: 92.9646, loss_bbox: 0.2487, loss_mask: 0.2506, loss: 0.7639 +2024-05-27 19:55:30,511 - mmdet - INFO - Epoch [5][1200/7330] lr: 1.000e-04, eta: 9:42:45, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0274, loss_rpn_bbox: 0.0387, loss_cls: 0.1893, acc: 93.2043, loss_bbox: 0.2496, loss_mask: 0.2511, loss: 0.7560 +2024-05-27 19:56:00,495 - mmdet - INFO - Epoch [5][1250/7330] lr: 1.000e-04, eta: 9:42:14, time: 0.600, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0286, loss_rpn_bbox: 0.0448, loss_cls: 0.2054, acc: 92.5740, loss_bbox: 0.2631, loss_mask: 0.2576, loss: 0.7994 +2024-05-27 19:56:36,025 - mmdet - INFO - Epoch [5][1300/7330] lr: 1.000e-04, eta: 9:41:53, time: 0.711, data_time: 0.134, memory: 9459, loss_rpn_cls: 0.0272, loss_rpn_bbox: 0.0395, loss_cls: 0.1988, acc: 92.8689, loss_bbox: 0.2522, loss_mask: 0.2483, loss: 0.7660 +2024-05-27 19:57:08,717 - mmdet - INFO - Epoch [5][1350/7330] lr: 1.000e-04, eta: 9:41:27, time: 0.654, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0246, loss_rpn_bbox: 0.0360, loss_cls: 0.1867, acc: 93.3582, loss_bbox: 0.2365, loss_mask: 0.2460, loss: 0.7298 +2024-05-27 19:57:43,661 - mmdet - INFO - Epoch [5][1400/7330] lr: 1.000e-04, eta: 9:41:05, time: 0.699, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0279, loss_rpn_bbox: 0.0414, loss_cls: 0.1969, acc: 92.8875, loss_bbox: 0.2563, loss_mask: 0.2569, loss: 0.7795 +2024-05-27 19:58:15,885 - mmdet - INFO - Epoch [5][1450/7330] lr: 1.000e-04, eta: 9:40:38, time: 0.645, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0271, loss_rpn_bbox: 0.0406, loss_cls: 0.1931, acc: 93.0920, loss_bbox: 0.2497, loss_mask: 0.2496, loss: 0.7601 +2024-05-27 19:58:45,690 - mmdet - INFO - Epoch [5][1500/7330] lr: 1.000e-04, eta: 9:40:06, time: 0.596, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0258, loss_rpn_bbox: 0.0381, loss_cls: 0.1971, acc: 92.9224, loss_bbox: 0.2518, loss_mask: 0.2502, loss: 0.7630 +2024-05-27 19:59:17,553 - mmdet - INFO - Epoch [5][1550/7330] lr: 1.000e-04, eta: 9:39:38, time: 0.637, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0262, loss_rpn_bbox: 0.0386, loss_cls: 0.1940, acc: 92.9348, loss_bbox: 0.2491, loss_mask: 0.2505, loss: 0.7584 +2024-05-27 19:59:49,881 - mmdet - INFO - Epoch [5][1600/7330] lr: 1.000e-04, eta: 9:39:11, time: 0.647, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0273, loss_rpn_bbox: 0.0422, loss_cls: 0.2015, acc: 92.6235, loss_bbox: 0.2571, loss_mask: 0.2502, loss: 0.7782 +2024-05-27 20:00:19,605 - mmdet - INFO - Epoch [5][1650/7330] lr: 1.000e-04, eta: 9:38:39, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0245, loss_rpn_bbox: 0.0404, loss_cls: 0.2005, acc: 92.7063, loss_bbox: 0.2580, loss_mask: 0.2560, loss: 0.7793 +2024-05-27 20:00:49,485 - mmdet - INFO - Epoch [5][1700/7330] lr: 1.000e-04, eta: 9:38:08, time: 0.598, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0246, loss_rpn_bbox: 0.0395, loss_cls: 0.1984, acc: 92.8552, loss_bbox: 0.2496, loss_mask: 0.2432, loss: 0.7554 +2024-05-27 20:01:19,118 - mmdet - INFO - Epoch [5][1750/7330] lr: 1.000e-04, eta: 9:37:36, time: 0.593, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0256, loss_rpn_bbox: 0.0370, loss_cls: 0.1971, acc: 92.9993, loss_bbox: 0.2475, loss_mask: 0.2509, loss: 0.7582 +2024-05-27 20:01:48,936 - mmdet - INFO - Epoch [5][1800/7330] lr: 1.000e-04, eta: 9:37:04, time: 0.596, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0238, loss_rpn_bbox: 0.0380, loss_cls: 0.1979, acc: 92.8452, loss_bbox: 0.2521, loss_mask: 0.2467, loss: 0.7586 +2024-05-27 20:02:18,809 - mmdet - INFO - Epoch [5][1850/7330] lr: 1.000e-04, eta: 9:36:33, time: 0.597, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0272, loss_rpn_bbox: 0.0410, loss_cls: 0.1988, acc: 92.8025, loss_bbox: 0.2529, loss_mask: 0.2496, loss: 0.7695 +2024-05-27 20:02:48,372 - mmdet - INFO - Epoch [5][1900/7330] lr: 1.000e-04, eta: 9:36:00, time: 0.591, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0271, loss_rpn_bbox: 0.0417, loss_cls: 0.2061, acc: 92.6738, loss_bbox: 0.2604, loss_mask: 0.2613, loss: 0.7965 +2024-05-27 20:03:18,156 - mmdet - INFO - Epoch [5][1950/7330] lr: 1.000e-04, eta: 9:35:29, time: 0.596, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0270, loss_rpn_bbox: 0.0407, loss_cls: 0.2024, acc: 92.7336, loss_bbox: 0.2619, loss_mask: 0.2504, loss: 0.7824 +2024-05-27 20:03:47,992 - mmdet - INFO - Epoch [5][2000/7330] lr: 1.000e-04, eta: 9:34:57, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0275, loss_rpn_bbox: 0.0428, loss_cls: 0.2077, acc: 92.5149, loss_bbox: 0.2681, loss_mask: 0.2579, loss: 0.8040 +2024-05-27 20:04:17,541 - mmdet - INFO - Epoch [5][2050/7330] lr: 1.000e-04, eta: 9:34:25, time: 0.591, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0273, loss_rpn_bbox: 0.0399, loss_cls: 0.2020, acc: 92.6804, loss_bbox: 0.2575, loss_mask: 0.2558, loss: 0.7825 +2024-05-27 20:04:47,403 - mmdet - INFO - Epoch [5][2100/7330] lr: 1.000e-04, eta: 9:33:54, time: 0.598, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0284, loss_rpn_bbox: 0.0433, loss_cls: 0.2053, acc: 92.6082, loss_bbox: 0.2632, loss_mask: 0.2551, loss: 0.7953 +2024-05-27 20:05:23,228 - mmdet - INFO - Epoch [5][2150/7330] lr: 1.000e-04, eta: 9:33:33, time: 0.716, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0251, loss_rpn_bbox: 0.0391, loss_cls: 0.2026, acc: 92.6367, loss_bbox: 0.2541, loss_mask: 0.2513, loss: 0.7723 +2024-05-27 20:05:53,020 - mmdet - INFO - Epoch [5][2200/7330] lr: 1.000e-04, eta: 9:33:01, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0251, loss_rpn_bbox: 0.0398, loss_cls: 0.1960, acc: 92.8687, loss_bbox: 0.2517, loss_mask: 0.2522, loss: 0.7647 +2024-05-27 20:06:22,778 - mmdet - INFO - Epoch [5][2250/7330] lr: 1.000e-04, eta: 9:32:29, time: 0.595, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0263, loss_rpn_bbox: 0.0423, loss_cls: 0.1954, acc: 92.9255, loss_bbox: 0.2557, loss_mask: 0.2512, loss: 0.7709 +2024-05-27 20:06:52,588 - mmdet - INFO - Epoch [5][2300/7330] lr: 1.000e-04, eta: 9:31:58, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0261, loss_rpn_bbox: 0.0391, loss_cls: 0.2007, acc: 92.9060, loss_bbox: 0.2534, loss_mask: 0.2520, loss: 0.7714 +2024-05-27 20:07:22,510 - mmdet - INFO - Epoch [5][2350/7330] lr: 1.000e-04, eta: 9:31:26, time: 0.598, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0265, loss_rpn_bbox: 0.0407, loss_cls: 0.2034, acc: 92.7881, loss_bbox: 0.2542, loss_mask: 0.2525, loss: 0.7773 +2024-05-27 20:07:55,562 - mmdet - INFO - Epoch [5][2400/7330] lr: 1.000e-04, eta: 9:31:01, time: 0.661, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0281, loss_rpn_bbox: 0.0421, loss_cls: 0.2001, acc: 92.8467, loss_bbox: 0.2564, loss_mask: 0.2495, loss: 0.7761 +2024-05-27 20:08:31,355 - mmdet - INFO - Epoch [5][2450/7330] lr: 1.000e-04, eta: 9:30:40, time: 0.716, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0268, loss_rpn_bbox: 0.0425, loss_cls: 0.2048, acc: 92.6448, loss_bbox: 0.2625, loss_mask: 0.2641, loss: 0.8007 +2024-05-27 20:09:03,522 - mmdet - INFO - Epoch [5][2500/7330] lr: 1.000e-04, eta: 9:30:12, time: 0.643, data_time: 0.025, memory: 9459, loss_rpn_cls: 0.0289, loss_rpn_bbox: 0.0431, loss_cls: 0.2044, acc: 92.6589, loss_bbox: 0.2576, loss_mask: 0.2547, loss: 0.7887 +2024-05-27 20:09:33,438 - mmdet - INFO - Epoch [5][2550/7330] lr: 1.000e-04, eta: 9:29:41, time: 0.598, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0265, loss_rpn_bbox: 0.0399, loss_cls: 0.1973, acc: 92.7756, loss_bbox: 0.2570, loss_mask: 0.2528, loss: 0.7735 +2024-05-27 20:10:05,488 - mmdet - INFO - Epoch [5][2600/7330] lr: 1.000e-04, eta: 9:29:13, time: 0.641, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0263, loss_rpn_bbox: 0.0423, loss_cls: 0.2068, acc: 92.5371, loss_bbox: 0.2573, loss_mask: 0.2576, loss: 0.7904 +2024-05-27 20:10:35,231 - mmdet - INFO - Epoch [5][2650/7330] lr: 1.000e-04, eta: 9:28:41, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0310, loss_rpn_bbox: 0.0417, loss_cls: 0.2033, acc: 92.8359, loss_bbox: 0.2546, loss_mask: 0.2572, loss: 0.7878 +2024-05-27 20:11:07,721 - mmdet - INFO - Epoch [5][2700/7330] lr: 1.000e-04, eta: 9:28:14, time: 0.650, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0268, loss_rpn_bbox: 0.0396, loss_cls: 0.2046, acc: 92.6626, loss_bbox: 0.2567, loss_mask: 0.2517, loss: 0.7793 +2024-05-27 20:11:37,763 - mmdet - INFO - Epoch [5][2750/7330] lr: 1.000e-04, eta: 9:27:43, time: 0.601, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0282, loss_rpn_bbox: 0.0407, loss_cls: 0.2012, acc: 92.7268, loss_bbox: 0.2526, loss_mask: 0.2508, loss: 0.7734 +2024-05-27 20:12:07,590 - mmdet - INFO - Epoch [5][2800/7330] lr: 1.000e-04, eta: 9:27:11, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0246, loss_rpn_bbox: 0.0378, loss_cls: 0.1973, acc: 92.9292, loss_bbox: 0.2478, loss_mask: 0.2521, loss: 0.7595 +2024-05-27 20:12:37,275 - mmdet - INFO - Epoch [5][2850/7330] lr: 1.000e-04, eta: 9:26:40, time: 0.594, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0260, loss_rpn_bbox: 0.0378, loss_cls: 0.1944, acc: 92.9736, loss_bbox: 0.2486, loss_mask: 0.2458, loss: 0.7526 +2024-05-27 20:13:07,230 - mmdet - INFO - Epoch [5][2900/7330] lr: 1.000e-04, eta: 9:26:08, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0262, loss_rpn_bbox: 0.0409, loss_cls: 0.1999, acc: 92.7522, loss_bbox: 0.2558, loss_mask: 0.2518, loss: 0.7746 +2024-05-27 20:13:36,920 - mmdet - INFO - Epoch [5][2950/7330] lr: 1.000e-04, eta: 9:25:36, time: 0.594, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0260, loss_rpn_bbox: 0.0406, loss_cls: 0.1957, acc: 92.9124, loss_bbox: 0.2487, loss_mask: 0.2520, loss: 0.7631 +2024-05-27 20:14:06,635 - mmdet - INFO - Epoch [5][3000/7330] lr: 1.000e-04, eta: 9:25:05, time: 0.594, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0250, loss_rpn_bbox: 0.0407, loss_cls: 0.1979, acc: 92.8074, loss_bbox: 0.2523, loss_mask: 0.2463, loss: 0.7622 +2024-05-27 20:14:36,453 - mmdet - INFO - Epoch [5][3050/7330] lr: 1.000e-04, eta: 9:24:33, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0303, loss_rpn_bbox: 0.0438, loss_cls: 0.2063, acc: 92.5649, loss_bbox: 0.2656, loss_mask: 0.2587, loss: 0.8047 +2024-05-27 20:15:06,237 - mmdet - INFO - Epoch [5][3100/7330] lr: 1.000e-04, eta: 9:24:01, time: 0.596, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0259, loss_rpn_bbox: 0.0394, loss_cls: 0.2005, acc: 92.7898, loss_bbox: 0.2527, loss_mask: 0.2507, loss: 0.7692 +2024-05-27 20:15:35,859 - mmdet - INFO - Epoch [5][3150/7330] lr: 1.000e-04, eta: 9:23:29, time: 0.592, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0316, loss_rpn_bbox: 0.0410, loss_cls: 0.1973, acc: 92.9292, loss_bbox: 0.2559, loss_mask: 0.2572, loss: 0.7830 +2024-05-27 20:16:10,328 - mmdet - INFO - Epoch [5][3200/7330] lr: 1.000e-04, eta: 9:23:06, time: 0.689, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0273, loss_rpn_bbox: 0.0403, loss_cls: 0.1992, acc: 92.8191, loss_bbox: 0.2541, loss_mask: 0.2566, loss: 0.7776 +2024-05-27 20:16:40,205 - mmdet - INFO - Epoch [5][3250/7330] lr: 1.000e-04, eta: 9:22:34, time: 0.598, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0281, loss_rpn_bbox: 0.0419, loss_cls: 0.1979, acc: 92.9834, loss_bbox: 0.2477, loss_mask: 0.2513, loss: 0.7668 +2024-05-27 20:17:10,033 - mmdet - INFO - Epoch [5][3300/7330] lr: 1.000e-04, eta: 9:22:03, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0276, loss_rpn_bbox: 0.0402, loss_cls: 0.1956, acc: 92.9414, loss_bbox: 0.2536, loss_mask: 0.2536, loss: 0.7706 +2024-05-27 20:17:39,618 - mmdet - INFO - Epoch [5][3350/7330] lr: 1.000e-04, eta: 9:21:31, time: 0.592, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0266, loss_rpn_bbox: 0.0395, loss_cls: 0.1923, acc: 93.0691, loss_bbox: 0.2421, loss_mask: 0.2543, loss: 0.7547 +2024-05-27 20:18:09,274 - mmdet - INFO - Epoch [5][3400/7330] lr: 1.000e-04, eta: 9:20:59, time: 0.593, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0276, loss_rpn_bbox: 0.0410, loss_cls: 0.1999, acc: 92.7280, loss_bbox: 0.2583, loss_mask: 0.2537, loss: 0.7804 +2024-05-27 20:18:42,160 - mmdet - INFO - Epoch [5][3450/7330] lr: 1.000e-04, eta: 9:20:33, time: 0.658, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0275, loss_rpn_bbox: 0.0401, loss_cls: 0.1977, acc: 92.9360, loss_bbox: 0.2493, loss_mask: 0.2486, loss: 0.7633 +2024-05-27 20:19:16,542 - mmdet - INFO - Epoch [5][3500/7330] lr: 1.000e-04, eta: 9:20:09, time: 0.688, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0274, loss_rpn_bbox: 0.0384, loss_cls: 0.1919, acc: 93.0913, loss_bbox: 0.2446, loss_mask: 0.2433, loss: 0.7456 +2024-05-27 20:19:48,575 - mmdet - INFO - Epoch [5][3550/7330] lr: 1.000e-04, eta: 9:19:41, time: 0.640, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0264, loss_rpn_bbox: 0.0388, loss_cls: 0.1939, acc: 92.8735, loss_bbox: 0.2546, loss_mask: 0.2497, loss: 0.7633 +2024-05-27 20:20:18,356 - mmdet - INFO - Epoch [5][3600/7330] lr: 1.000e-04, eta: 9:19:09, time: 0.596, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0280, loss_rpn_bbox: 0.0418, loss_cls: 0.2032, acc: 92.5471, loss_bbox: 0.2616, loss_mask: 0.2578, loss: 0.7925 +2024-05-27 20:20:50,264 - mmdet - INFO - Epoch [5][3650/7330] lr: 1.000e-04, eta: 9:18:41, time: 0.638, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0275, loss_rpn_bbox: 0.0407, loss_cls: 0.2057, acc: 92.5615, loss_bbox: 0.2631, loss_mask: 0.2555, loss: 0.7925 +2024-05-27 20:21:19,964 - mmdet - INFO - Epoch [5][3700/7330] lr: 1.000e-04, eta: 9:18:09, time: 0.594, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0259, loss_rpn_bbox: 0.0379, loss_cls: 0.2032, acc: 92.7273, loss_bbox: 0.2501, loss_mask: 0.2542, loss: 0.7713 +2024-05-27 20:21:52,030 - mmdet - INFO - Epoch [5][3750/7330] lr: 1.000e-04, eta: 9:17:41, time: 0.641, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0279, loss_rpn_bbox: 0.0414, loss_cls: 0.2044, acc: 92.6997, loss_bbox: 0.2569, loss_mask: 0.2509, loss: 0.7816 +2024-05-27 20:22:21,527 - mmdet - INFO - Epoch [5][3800/7330] lr: 1.000e-04, eta: 9:17:09, time: 0.590, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0271, loss_rpn_bbox: 0.0399, loss_cls: 0.1948, acc: 93.0398, loss_bbox: 0.2460, loss_mask: 0.2513, loss: 0.7592 +2024-05-27 20:22:51,276 - mmdet - INFO - Epoch [5][3850/7330] lr: 1.000e-04, eta: 9:16:38, time: 0.595, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0262, loss_rpn_bbox: 0.0398, loss_cls: 0.1936, acc: 93.0320, loss_bbox: 0.2493, loss_mask: 0.2486, loss: 0.7575 +2024-05-27 20:23:20,991 - mmdet - INFO - Epoch [5][3900/7330] lr: 1.000e-04, eta: 9:16:06, time: 0.594, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0281, loss_rpn_bbox: 0.0409, loss_cls: 0.1927, acc: 93.0366, loss_bbox: 0.2450, loss_mask: 0.2475, loss: 0.7543 +2024-05-27 20:23:50,766 - mmdet - INFO - Epoch [5][3950/7330] lr: 1.000e-04, eta: 9:15:34, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0277, loss_rpn_bbox: 0.0429, loss_cls: 0.2045, acc: 92.5984, loss_bbox: 0.2604, loss_mask: 0.2588, loss: 0.7944 +2024-05-27 20:24:20,505 - mmdet - INFO - Epoch [5][4000/7330] lr: 1.000e-04, eta: 9:15:03, time: 0.595, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0294, loss_rpn_bbox: 0.0433, loss_cls: 0.2077, acc: 92.5352, loss_bbox: 0.2584, loss_mask: 0.2560, loss: 0.7947 +2024-05-27 20:24:50,226 - mmdet - INFO - Epoch [5][4050/7330] lr: 1.000e-04, eta: 9:14:31, time: 0.594, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0287, loss_rpn_bbox: 0.0447, loss_cls: 0.2097, acc: 92.4470, loss_bbox: 0.2682, loss_mask: 0.2602, loss: 0.8114 +2024-05-27 20:25:19,929 - mmdet - INFO - Epoch [5][4100/7330] lr: 1.000e-04, eta: 9:13:59, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0267, loss_rpn_bbox: 0.0386, loss_cls: 0.1929, acc: 93.0779, loss_bbox: 0.2487, loss_mask: 0.2531, loss: 0.7601 +2024-05-27 20:25:49,681 - mmdet - INFO - Epoch [5][4150/7330] lr: 1.000e-04, eta: 9:13:27, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0247, loss_rpn_bbox: 0.0379, loss_cls: 0.1924, acc: 93.1501, loss_bbox: 0.2412, loss_mask: 0.2459, loss: 0.7422 +2024-05-27 20:26:19,459 - mmdet - INFO - Epoch [5][4200/7330] lr: 1.000e-04, eta: 9:12:56, time: 0.596, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0278, loss_rpn_bbox: 0.0398, loss_cls: 0.2021, acc: 92.7773, loss_bbox: 0.2565, loss_mask: 0.2566, loss: 0.7827 +2024-05-27 20:26:54,005 - mmdet - INFO - Epoch [5][4250/7330] lr: 1.000e-04, eta: 9:12:32, time: 0.691, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0243, loss_rpn_bbox: 0.0381, loss_cls: 0.1956, acc: 92.9578, loss_bbox: 0.2474, loss_mask: 0.2473, loss: 0.7526 +2024-05-27 20:27:23,785 - mmdet - INFO - Epoch [5][4300/7330] lr: 1.000e-04, eta: 9:12:00, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0274, loss_rpn_bbox: 0.0421, loss_cls: 0.2008, acc: 92.7234, loss_bbox: 0.2583, loss_mask: 0.2559, loss: 0.7846 +2024-05-27 20:27:53,587 - mmdet - INFO - Epoch [5][4350/7330] lr: 1.000e-04, eta: 9:11:29, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0267, loss_rpn_bbox: 0.0386, loss_cls: 0.1998, acc: 92.8445, loss_bbox: 0.2530, loss_mask: 0.2509, loss: 0.7691 +2024-05-27 20:28:23,420 - mmdet - INFO - Epoch [5][4400/7330] lr: 1.000e-04, eta: 9:10:57, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0265, loss_rpn_bbox: 0.0370, loss_cls: 0.2028, acc: 92.8274, loss_bbox: 0.2481, loss_mask: 0.2434, loss: 0.7579 +2024-05-27 20:28:53,284 - mmdet - INFO - Epoch [5][4450/7330] lr: 1.000e-04, eta: 9:10:26, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0269, loss_rpn_bbox: 0.0414, loss_cls: 0.1995, acc: 92.7852, loss_bbox: 0.2589, loss_mask: 0.2617, loss: 0.7883 +2024-05-27 20:29:26,259 - mmdet - INFO - Epoch [5][4500/7330] lr: 1.000e-04, eta: 9:09:59, time: 0.659, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0279, loss_rpn_bbox: 0.0407, loss_cls: 0.2066, acc: 92.6086, loss_bbox: 0.2562, loss_mask: 0.2475, loss: 0.7789 +2024-05-27 20:30:00,968 - mmdet - INFO - Epoch [5][4550/7330] lr: 1.000e-04, eta: 9:09:36, time: 0.694, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0261, loss_rpn_bbox: 0.0398, loss_cls: 0.2015, acc: 92.7971, loss_bbox: 0.2544, loss_mask: 0.2523, loss: 0.7740 +2024-05-27 20:30:32,792 - mmdet - INFO - Epoch [5][4600/7330] lr: 1.000e-04, eta: 9:09:07, time: 0.636, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0245, loss_rpn_bbox: 0.0390, loss_cls: 0.1987, acc: 92.8230, loss_bbox: 0.2522, loss_mask: 0.2517, loss: 0.7661 +2024-05-27 20:31:02,544 - mmdet - INFO - Epoch [5][4650/7330] lr: 1.000e-04, eta: 9:08:36, time: 0.595, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0284, loss_rpn_bbox: 0.0419, loss_cls: 0.2007, acc: 92.8677, loss_bbox: 0.2593, loss_mask: 0.2526, loss: 0.7829 +2024-05-27 20:31:34,470 - mmdet - INFO - Epoch [5][4700/7330] lr: 1.000e-04, eta: 9:08:08, time: 0.638, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0249, loss_rpn_bbox: 0.0387, loss_cls: 0.1952, acc: 93.0457, loss_bbox: 0.2477, loss_mask: 0.2477, loss: 0.7541 +2024-05-27 20:32:04,200 - mmdet - INFO - Epoch [5][4750/7330] lr: 1.000e-04, eta: 9:07:36, time: 0.595, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0259, loss_rpn_bbox: 0.0418, loss_cls: 0.2012, acc: 92.8215, loss_bbox: 0.2535, loss_mask: 0.2543, loss: 0.7766 +2024-05-27 20:32:36,192 - mmdet - INFO - Epoch [5][4800/7330] lr: 1.000e-04, eta: 9:07:08, time: 0.640, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0270, loss_rpn_bbox: 0.0403, loss_cls: 0.2032, acc: 92.6987, loss_bbox: 0.2615, loss_mask: 0.2543, loss: 0.7863 +2024-05-27 20:33:05,960 - mmdet - INFO - Epoch [5][4850/7330] lr: 1.000e-04, eta: 9:06:36, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0289, loss_rpn_bbox: 0.0415, loss_cls: 0.2025, acc: 92.8147, loss_bbox: 0.2568, loss_mask: 0.2568, loss: 0.7865 +2024-05-27 20:33:35,821 - mmdet - INFO - Epoch [5][4900/7330] lr: 1.000e-04, eta: 9:06:05, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0260, loss_rpn_bbox: 0.0387, loss_cls: 0.1949, acc: 93.0095, loss_bbox: 0.2500, loss_mask: 0.2491, loss: 0.7587 +2024-05-27 20:34:05,572 - mmdet - INFO - Epoch [5][4950/7330] lr: 1.000e-04, eta: 9:05:33, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0274, loss_rpn_bbox: 0.0403, loss_cls: 0.1976, acc: 92.7803, loss_bbox: 0.2580, loss_mask: 0.2548, loss: 0.7783 +2024-05-27 20:34:35,489 - mmdet - INFO - Epoch [5][5000/7330] lr: 1.000e-04, eta: 9:05:02, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0279, loss_rpn_bbox: 0.0414, loss_cls: 0.1996, acc: 92.7068, loss_bbox: 0.2539, loss_mask: 0.2576, loss: 0.7803 +2024-05-27 20:35:05,358 - mmdet - INFO - Epoch [5][5050/7330] lr: 1.000e-04, eta: 9:04:30, time: 0.597, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0267, loss_rpn_bbox: 0.0399, loss_cls: 0.2100, acc: 92.5383, loss_bbox: 0.2610, loss_mask: 0.2544, loss: 0.7919 +2024-05-27 20:35:35,030 - mmdet - INFO - Epoch [5][5100/7330] lr: 1.000e-04, eta: 9:03:58, time: 0.593, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0294, loss_rpn_bbox: 0.0417, loss_cls: 0.2036, acc: 92.6116, loss_bbox: 0.2583, loss_mask: 0.2570, loss: 0.7900 +2024-05-27 20:36:04,812 - mmdet - INFO - Epoch [5][5150/7330] lr: 1.000e-04, eta: 9:03:27, time: 0.596, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0277, loss_rpn_bbox: 0.0412, loss_cls: 0.2100, acc: 92.4961, loss_bbox: 0.2646, loss_mask: 0.2503, loss: 0.7937 +2024-05-27 20:36:34,317 - mmdet - INFO - Epoch [5][5200/7330] lr: 1.000e-04, eta: 9:02:55, time: 0.590, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0265, loss_rpn_bbox: 0.0380, loss_cls: 0.1943, acc: 92.9360, loss_bbox: 0.2494, loss_mask: 0.2513, loss: 0.7595 +2024-05-27 20:37:04,055 - mmdet - INFO - Epoch [5][5250/7330] lr: 1.000e-04, eta: 9:02:23, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0275, loss_rpn_bbox: 0.0412, loss_cls: 0.1965, acc: 92.8599, loss_bbox: 0.2533, loss_mask: 0.2564, loss: 0.7750 +2024-05-27 20:37:38,442 - mmdet - INFO - Epoch [5][5300/7330] lr: 1.000e-04, eta: 9:01:59, time: 0.688, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0281, loss_rpn_bbox: 0.0393, loss_cls: 0.2016, acc: 92.7820, loss_bbox: 0.2551, loss_mask: 0.2497, loss: 0.7739 +2024-05-27 20:38:08,141 - mmdet - INFO - Epoch [5][5350/7330] lr: 1.000e-04, eta: 9:01:27, time: 0.594, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0260, loss_rpn_bbox: 0.0385, loss_cls: 0.1987, acc: 92.7976, loss_bbox: 0.2546, loss_mask: 0.2554, loss: 0.7732 +2024-05-27 20:38:37,812 - mmdet - INFO - Epoch [5][5400/7330] lr: 1.000e-04, eta: 9:00:55, time: 0.593, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0288, loss_rpn_bbox: 0.0413, loss_cls: 0.1980, acc: 92.8374, loss_bbox: 0.2517, loss_mask: 0.2511, loss: 0.7709 +2024-05-27 20:39:07,586 - mmdet - INFO - Epoch [5][5450/7330] lr: 1.000e-04, eta: 9:00:24, time: 0.595, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0302, loss_rpn_bbox: 0.0431, loss_cls: 0.2045, acc: 92.7671, loss_bbox: 0.2573, loss_mask: 0.2492, loss: 0.7842 +2024-05-27 20:39:37,480 - mmdet - INFO - Epoch [5][5500/7330] lr: 1.000e-04, eta: 8:59:52, time: 0.598, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0295, loss_rpn_bbox: 0.0411, loss_cls: 0.2111, acc: 92.5483, loss_bbox: 0.2645, loss_mask: 0.2602, loss: 0.8064 +2024-05-27 20:40:09,818 - mmdet - INFO - Epoch [5][5550/7330] lr: 1.000e-04, eta: 8:59:25, time: 0.647, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0251, loss_rpn_bbox: 0.0382, loss_cls: 0.1926, acc: 93.0740, loss_bbox: 0.2515, loss_mask: 0.2468, loss: 0.7542 +2024-05-27 20:40:41,767 - mmdet - INFO - Epoch [5][5600/7330] lr: 1.000e-04, eta: 8:58:57, time: 0.639, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0266, loss_rpn_bbox: 0.0396, loss_cls: 0.1910, acc: 93.0923, loss_bbox: 0.2446, loss_mask: 0.2461, loss: 0.7479 +2024-05-27 20:41:13,577 - mmdet - INFO - Epoch [5][5650/7330] lr: 1.000e-04, eta: 8:58:28, time: 0.636, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0292, loss_rpn_bbox: 0.0403, loss_cls: 0.1985, acc: 92.8545, loss_bbox: 0.2585, loss_mask: 0.2534, loss: 0.7799 +2024-05-27 20:41:45,308 - mmdet - INFO - Epoch [5][5700/7330] lr: 1.000e-04, eta: 8:57:59, time: 0.635, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0241, loss_rpn_bbox: 0.0370, loss_cls: 0.1895, acc: 93.2117, loss_bbox: 0.2378, loss_mask: 0.2488, loss: 0.7373 +2024-05-27 20:42:17,470 - mmdet - INFO - Epoch [5][5750/7330] lr: 1.000e-04, eta: 8:57:32, time: 0.643, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0270, loss_rpn_bbox: 0.0402, loss_cls: 0.1980, acc: 92.8457, loss_bbox: 0.2509, loss_mask: 0.2471, loss: 0.7632 +2024-05-27 20:42:47,300 - mmdet - INFO - Epoch [5][5800/7330] lr: 1.000e-04, eta: 8:57:00, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0262, loss_rpn_bbox: 0.0388, loss_cls: 0.2005, acc: 92.9041, loss_bbox: 0.2552, loss_mask: 0.2493, loss: 0.7700 +2024-05-27 20:43:19,233 - mmdet - INFO - Epoch [5][5850/7330] lr: 1.000e-04, eta: 8:56:32, time: 0.639, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0281, loss_rpn_bbox: 0.0424, loss_cls: 0.2047, acc: 92.5652, loss_bbox: 0.2611, loss_mask: 0.2570, loss: 0.7933 +2024-05-27 20:43:49,079 - mmdet - INFO - Epoch [5][5900/7330] lr: 1.000e-04, eta: 8:56:00, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0255, loss_rpn_bbox: 0.0382, loss_cls: 0.1949, acc: 92.9148, loss_bbox: 0.2528, loss_mask: 0.2507, loss: 0.7621 +2024-05-27 20:44:18,699 - mmdet - INFO - Epoch [5][5950/7330] lr: 1.000e-04, eta: 8:55:28, time: 0.592, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0261, loss_rpn_bbox: 0.0397, loss_cls: 0.2041, acc: 92.6345, loss_bbox: 0.2639, loss_mask: 0.2537, loss: 0.7875 +2024-05-27 20:44:48,380 - mmdet - INFO - Epoch [5][6000/7330] lr: 1.000e-04, eta: 8:54:57, time: 0.594, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0267, loss_rpn_bbox: 0.0401, loss_cls: 0.2041, acc: 92.7061, loss_bbox: 0.2562, loss_mask: 0.2531, loss: 0.7803 +2024-05-27 20:45:18,272 - mmdet - INFO - Epoch [5][6050/7330] lr: 1.000e-04, eta: 8:54:25, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0272, loss_rpn_bbox: 0.0404, loss_cls: 0.1997, acc: 92.7896, loss_bbox: 0.2541, loss_mask: 0.2463, loss: 0.7677 +2024-05-27 20:45:47,934 - mmdet - INFO - Epoch [5][6100/7330] lr: 1.000e-04, eta: 8:53:54, time: 0.593, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0259, loss_rpn_bbox: 0.0382, loss_cls: 0.1935, acc: 93.1396, loss_bbox: 0.2473, loss_mask: 0.2474, loss: 0.7523 +2024-05-27 20:46:17,619 - mmdet - INFO - Epoch [5][6150/7330] lr: 1.000e-04, eta: 8:53:22, time: 0.594, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0254, loss_rpn_bbox: 0.0389, loss_cls: 0.1988, acc: 92.8069, loss_bbox: 0.2522, loss_mask: 0.2477, loss: 0.7629 +2024-05-27 20:46:47,394 - mmdet - INFO - Epoch [5][6200/7330] lr: 1.000e-04, eta: 8:52:50, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0262, loss_rpn_bbox: 0.0403, loss_cls: 0.2015, acc: 92.7905, loss_bbox: 0.2490, loss_mask: 0.2536, loss: 0.7707 +2024-05-27 20:47:17,268 - mmdet - INFO - Epoch [5][6250/7330] lr: 1.000e-04, eta: 8:52:19, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0248, loss_rpn_bbox: 0.0386, loss_cls: 0.1861, acc: 93.2920, loss_bbox: 0.2354, loss_mask: 0.2464, loss: 0.7313 +2024-05-27 20:47:47,077 - mmdet - INFO - Epoch [5][6300/7330] lr: 1.000e-04, eta: 8:51:48, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0240, loss_rpn_bbox: 0.0369, loss_cls: 0.1970, acc: 93.0071, loss_bbox: 0.2455, loss_mask: 0.2436, loss: 0.7469 +2024-05-27 20:48:19,162 - mmdet - INFO - Epoch [5][6350/7330] lr: 1.000e-04, eta: 8:51:19, time: 0.641, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0290, loss_rpn_bbox: 0.0411, loss_cls: 0.1995, acc: 92.8667, loss_bbox: 0.2505, loss_mask: 0.2504, loss: 0.7705 +2024-05-27 20:48:51,277 - mmdet - INFO - Epoch [5][6400/7330] lr: 1.000e-04, eta: 8:50:51, time: 0.643, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0264, loss_rpn_bbox: 0.0399, loss_cls: 0.2028, acc: 92.7771, loss_bbox: 0.2553, loss_mask: 0.2587, loss: 0.7831 +2024-05-27 20:49:21,134 - mmdet - INFO - Epoch [5][6450/7330] lr: 1.000e-04, eta: 8:50:20, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0264, loss_rpn_bbox: 0.0384, loss_cls: 0.1973, acc: 92.8904, loss_bbox: 0.2535, loss_mask: 0.2507, loss: 0.7662 +2024-05-27 20:49:50,967 - mmdet - INFO - Epoch [5][6500/7330] lr: 1.000e-04, eta: 8:49:49, time: 0.597, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0287, loss_rpn_bbox: 0.0426, loss_cls: 0.2018, acc: 92.7351, loss_bbox: 0.2567, loss_mask: 0.2528, loss: 0.7825 +2024-05-27 20:50:20,648 - mmdet - INFO - Epoch [5][6550/7330] lr: 1.000e-04, eta: 8:49:17, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0260, loss_rpn_bbox: 0.0421, loss_cls: 0.2025, acc: 92.7747, loss_bbox: 0.2546, loss_mask: 0.2501, loss: 0.7753 +2024-05-27 20:50:53,460 - mmdet - INFO - Epoch [5][6600/7330] lr: 1.000e-04, eta: 8:48:50, time: 0.656, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0279, loss_rpn_bbox: 0.0418, loss_cls: 0.2043, acc: 92.7544, loss_bbox: 0.2570, loss_mask: 0.2533, loss: 0.7844 +2024-05-27 20:51:23,298 - mmdet - INFO - Epoch [5][6650/7330] lr: 1.000e-04, eta: 8:48:18, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0293, loss_rpn_bbox: 0.0431, loss_cls: 0.2052, acc: 92.6218, loss_bbox: 0.2602, loss_mask: 0.2531, loss: 0.7909 +2024-05-27 20:51:57,780 - mmdet - INFO - Epoch [5][6700/7330] lr: 1.000e-04, eta: 8:47:54, time: 0.690, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0282, loss_rpn_bbox: 0.0396, loss_cls: 0.1995, acc: 92.9055, loss_bbox: 0.2494, loss_mask: 0.2502, loss: 0.7669 +2024-05-27 20:52:30,568 - mmdet - INFO - Epoch [5][6750/7330] lr: 1.000e-04, eta: 8:47:26, time: 0.656, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0281, loss_rpn_bbox: 0.0404, loss_cls: 0.1988, acc: 92.7844, loss_bbox: 0.2499, loss_mask: 0.2494, loss: 0.7666 +2024-05-27 20:53:00,503 - mmdet - INFO - Epoch [5][6800/7330] lr: 1.000e-04, eta: 8:46:55, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0294, loss_rpn_bbox: 0.0400, loss_cls: 0.1993, acc: 92.8469, loss_bbox: 0.2545, loss_mask: 0.2465, loss: 0.7698 +2024-05-27 20:53:33,220 - mmdet - INFO - Epoch [5][6850/7330] lr: 1.000e-04, eta: 8:46:28, time: 0.654, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0295, loss_rpn_bbox: 0.0410, loss_cls: 0.1964, acc: 92.9062, loss_bbox: 0.2516, loss_mask: 0.2457, loss: 0.7643 +2024-05-27 20:54:05,578 - mmdet - INFO - Epoch [5][6900/7330] lr: 1.000e-04, eta: 8:46:00, time: 0.647, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0257, loss_rpn_bbox: 0.0413, loss_cls: 0.1987, acc: 92.7661, loss_bbox: 0.2558, loss_mask: 0.2546, loss: 0.7760 +2024-05-27 20:54:35,395 - mmdet - INFO - Epoch [5][6950/7330] lr: 1.000e-04, eta: 8:45:29, time: 0.596, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0254, loss_rpn_bbox: 0.0393, loss_cls: 0.1966, acc: 92.9636, loss_bbox: 0.2541, loss_mask: 0.2529, loss: 0.7684 +2024-05-27 20:55:05,149 - mmdet - INFO - Epoch [5][7000/7330] lr: 1.000e-04, eta: 8:44:57, time: 0.595, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0269, loss_rpn_bbox: 0.0415, loss_cls: 0.1990, acc: 92.8159, loss_bbox: 0.2523, loss_mask: 0.2548, loss: 0.7745 +2024-05-27 20:55:34,802 - mmdet - INFO - Epoch [5][7050/7330] lr: 1.000e-04, eta: 8:44:25, time: 0.593, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0245, loss_rpn_bbox: 0.0410, loss_cls: 0.1978, acc: 92.8628, loss_bbox: 0.2567, loss_mask: 0.2507, loss: 0.7707 +2024-05-27 20:56:04,650 - mmdet - INFO - Epoch [5][7100/7330] lr: 1.000e-04, eta: 8:43:54, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0249, loss_rpn_bbox: 0.0383, loss_cls: 0.1948, acc: 92.9966, loss_bbox: 0.2442, loss_mask: 0.2480, loss: 0.7503 +2024-05-27 20:56:34,376 - mmdet - INFO - Epoch [5][7150/7330] lr: 1.000e-04, eta: 8:43:22, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0250, loss_rpn_bbox: 0.0387, loss_cls: 0.1974, acc: 92.9080, loss_bbox: 0.2487, loss_mask: 0.2493, loss: 0.7591 +2024-05-27 20:57:04,163 - mmdet - INFO - Epoch [5][7200/7330] lr: 1.000e-04, eta: 8:42:51, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0247, loss_rpn_bbox: 0.0382, loss_cls: 0.2006, acc: 92.8574, loss_bbox: 0.2519, loss_mask: 0.2502, loss: 0.7656 +2024-05-27 20:57:33,754 - mmdet - INFO - Epoch [5][7250/7330] lr: 1.000e-04, eta: 8:42:19, time: 0.592, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0269, loss_rpn_bbox: 0.0381, loss_cls: 0.1874, acc: 93.1704, loss_bbox: 0.2389, loss_mask: 0.2461, loss: 0.7374 +2024-05-27 20:58:03,480 - mmdet - INFO - Epoch [5][7300/7330] lr: 1.000e-04, eta: 8:41:47, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0281, loss_rpn_bbox: 0.0393, loss_cls: 0.1912, acc: 93.1055, loss_bbox: 0.2469, loss_mask: 0.2444, loss: 0.7499 +2024-05-27 20:58:22,084 - mmdet - INFO - Saving checkpoint at 5 epochs +2024-05-27 21:00:16,976 - mmdet - INFO - Evaluating bbox... +2024-05-27 21:00:42,552 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.425 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.665 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.463 + 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 ] = 0.473 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.608 + 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 ] = 0.322 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.595 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.729 + +2024-05-27 21:00:42,553 - mmdet - INFO - Evaluating segm... +2024-05-27 21:01:07,315 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.382 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.624 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.401 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.156 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.422 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.618 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.486 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.486 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.486 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.253 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.542 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.705 + +2024-05-27 21:01:07,754 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-27 21:01:07,756 - mmdet - INFO - Epoch(val) [5][625] bbox_mAP: 0.4250, bbox_mAP_50: 0.6650, bbox_mAP_75: 0.4630, bbox_mAP_s: 0.2340, bbox_mAP_m: 0.4730, bbox_mAP_l: 0.6080, bbox_mAP_copypaste: 0.425 0.665 0.463 0.234 0.473 0.608, segm_mAP: 0.3820, segm_mAP_50: 0.6240, segm_mAP_75: 0.4010, segm_mAP_s: 0.1560, segm_mAP_m: 0.4220, segm_mAP_l: 0.6180, segm_mAP_copypaste: 0.382 0.624 0.401 0.156 0.422 0.618 +2024-05-27 21:01:47,285 - mmdet - INFO - Epoch [6][50/7330] lr: 1.000e-04, eta: 8:40:46, time: 0.790, data_time: 0.081, memory: 9459, loss_rpn_cls: 0.0221, loss_rpn_bbox: 0.0380, loss_cls: 0.1829, acc: 93.3157, loss_bbox: 0.2373, loss_mask: 0.2457, loss: 0.7259 +2024-05-27 21:02:17,312 - mmdet - INFO - Epoch [6][100/7330] lr: 1.000e-04, eta: 8:40:15, time: 0.600, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0250, loss_rpn_bbox: 0.0404, loss_cls: 0.1935, acc: 92.9595, loss_bbox: 0.2560, loss_mask: 0.2464, loss: 0.7613 +2024-05-27 21:02:46,808 - mmdet - INFO - Epoch [6][150/7330] lr: 1.000e-04, eta: 8:39:43, time: 0.590, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0264, loss_rpn_bbox: 0.0421, loss_cls: 0.1956, acc: 92.8433, loss_bbox: 0.2548, loss_mask: 0.2522, loss: 0.7711 +2024-05-27 21:03:19,717 - mmdet - INFO - Epoch [6][200/7330] lr: 1.000e-04, eta: 8:39:16, time: 0.658, data_time: 0.079, memory: 9459, loss_rpn_cls: 0.0220, loss_rpn_bbox: 0.0384, loss_cls: 0.1833, acc: 93.3103, loss_bbox: 0.2377, loss_mask: 0.2432, loss: 0.7246 +2024-05-27 21:03:49,555 - mmdet - INFO - Epoch [6][250/7330] lr: 1.000e-04, eta: 8:38:44, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0240, loss_rpn_bbox: 0.0388, loss_cls: 0.1936, acc: 92.9839, loss_bbox: 0.2458, loss_mask: 0.2497, loss: 0.7521 +2024-05-27 21:04:19,192 - mmdet - INFO - Epoch [6][300/7330] lr: 1.000e-04, eta: 8:38:13, time: 0.593, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0231, loss_rpn_bbox: 0.0395, loss_cls: 0.1846, acc: 93.2114, loss_bbox: 0.2423, loss_mask: 0.2485, loss: 0.7379 +2024-05-27 21:04:48,599 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-27 21:04:48,600 - mmdet - INFO - Epoch [6][350/7330] lr: 1.000e-04, eta: 8:37:41, time: 0.588, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0246, loss_rpn_bbox: 0.0370, loss_cls: 0.1856, acc: 93.3584, loss_bbox: 0.2370, loss_mask: 0.2442, loss: 0.7283 +2024-05-27 21:05:18,320 - mmdet - INFO - Epoch [6][400/7330] lr: 1.000e-04, eta: 8:37:09, time: 0.594, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0234, loss_rpn_bbox: 0.0395, loss_cls: 0.1955, acc: 92.8376, loss_bbox: 0.2511, loss_mask: 0.2502, loss: 0.7596 +2024-05-27 21:05:50,212 - mmdet - INFO - Epoch [6][450/7330] lr: 1.000e-04, eta: 8:36:41, time: 0.638, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0257, loss_rpn_bbox: 0.0409, loss_cls: 0.1975, acc: 92.7949, loss_bbox: 0.2527, loss_mask: 0.2478, loss: 0.7645 +2024-05-27 21:06:19,957 - mmdet - INFO - Epoch [6][500/7330] lr: 1.000e-04, eta: 8:36:09, time: 0.595, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0246, loss_rpn_bbox: 0.0388, loss_cls: 0.1877, acc: 93.1340, loss_bbox: 0.2446, loss_mask: 0.2448, loss: 0.7406 +2024-05-27 21:06:49,712 - mmdet - INFO - Epoch [6][550/7330] lr: 1.000e-04, eta: 8:35:38, time: 0.595, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0237, loss_rpn_bbox: 0.0374, loss_cls: 0.1879, acc: 93.0632, loss_bbox: 0.2469, loss_mask: 0.2431, loss: 0.7389 +2024-05-27 21:07:22,257 - mmdet - INFO - Epoch [6][600/7330] lr: 1.000e-04, eta: 8:35:10, time: 0.651, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0234, loss_rpn_bbox: 0.0385, loss_cls: 0.1912, acc: 92.9885, loss_bbox: 0.2468, loss_mask: 0.2441, loss: 0.7440 +2024-05-27 21:07:54,113 - mmdet - INFO - Epoch [6][650/7330] lr: 1.000e-04, eta: 8:34:41, time: 0.637, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0232, loss_rpn_bbox: 0.0378, loss_cls: 0.1888, acc: 93.1816, loss_bbox: 0.2436, loss_mask: 0.2411, loss: 0.7344 +2024-05-27 21:08:23,819 - mmdet - INFO - Epoch [6][700/7330] lr: 1.000e-04, eta: 8:34:10, time: 0.594, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0242, loss_rpn_bbox: 0.0375, loss_cls: 0.1880, acc: 93.1331, loss_bbox: 0.2464, loss_mask: 0.2393, loss: 0.7355 +2024-05-27 21:08:56,283 - mmdet - INFO - Epoch [6][750/7330] lr: 1.000e-04, eta: 8:33:42, time: 0.649, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0283, loss_rpn_bbox: 0.0417, loss_cls: 0.1929, acc: 92.9275, loss_bbox: 0.2473, loss_mask: 0.2531, loss: 0.7634 +2024-05-27 21:09:25,973 - mmdet - INFO - Epoch [6][800/7330] lr: 1.000e-04, eta: 8:33:11, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0255, loss_rpn_bbox: 0.0371, loss_cls: 0.1924, acc: 93.0852, loss_bbox: 0.2449, loss_mask: 0.2414, loss: 0.7413 +2024-05-27 21:09:55,630 - mmdet - INFO - Epoch [6][850/7330] lr: 1.000e-04, eta: 8:32:39, time: 0.593, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0248, loss_rpn_bbox: 0.0387, loss_cls: 0.1848, acc: 93.1758, loss_bbox: 0.2443, loss_mask: 0.2466, loss: 0.7392 +2024-05-27 21:10:25,340 - mmdet - INFO - Epoch [6][900/7330] lr: 1.000e-04, eta: 8:32:08, time: 0.594, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0247, loss_rpn_bbox: 0.0385, loss_cls: 0.1938, acc: 92.8882, loss_bbox: 0.2525, loss_mask: 0.2522, loss: 0.7616 +2024-05-27 21:10:55,247 - mmdet - INFO - Epoch [6][950/7330] lr: 1.000e-04, eta: 8:31:36, time: 0.598, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0230, loss_rpn_bbox: 0.0365, loss_cls: 0.1887, acc: 93.2017, loss_bbox: 0.2449, loss_mask: 0.2443, loss: 0.7374 +2024-05-27 21:11:27,430 - mmdet - INFO - Epoch [6][1000/7330] lr: 1.000e-04, eta: 8:31:08, time: 0.644, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0244, loss_rpn_bbox: 0.0371, loss_cls: 0.1789, acc: 93.5535, loss_bbox: 0.2292, loss_mask: 0.2343, loss: 0.7039 +2024-05-27 21:11:57,193 - mmdet - INFO - Epoch [6][1050/7330] lr: 1.000e-04, eta: 8:30:37, time: 0.595, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0237, loss_rpn_bbox: 0.0372, loss_cls: 0.1818, acc: 93.4395, loss_bbox: 0.2315, loss_mask: 0.2381, loss: 0.7123 +2024-05-27 21:12:27,228 - mmdet - INFO - Epoch [6][1100/7330] lr: 1.000e-04, eta: 8:30:06, time: 0.601, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0255, loss_rpn_bbox: 0.0396, loss_cls: 0.1900, acc: 93.1062, loss_bbox: 0.2429, loss_mask: 0.2402, loss: 0.7382 +2024-05-27 21:12:59,368 - mmdet - INFO - Epoch [6][1150/7330] lr: 1.000e-04, eta: 8:29:37, time: 0.643, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0236, loss_rpn_bbox: 0.0383, loss_cls: 0.1859, acc: 93.1921, loss_bbox: 0.2410, loss_mask: 0.2391, loss: 0.7280 +2024-05-27 21:13:29,144 - mmdet - INFO - Epoch [6][1200/7330] lr: 1.000e-04, eta: 8:29:06, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0233, loss_rpn_bbox: 0.0365, loss_cls: 0.1884, acc: 93.1169, loss_bbox: 0.2476, loss_mask: 0.2369, loss: 0.7327 +2024-05-27 21:14:01,742 - mmdet - INFO - Epoch [6][1250/7330] lr: 1.000e-04, eta: 8:28:38, time: 0.652, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0242, loss_rpn_bbox: 0.0392, loss_cls: 0.1949, acc: 92.8582, loss_bbox: 0.2484, loss_mask: 0.2463, loss: 0.7530 +2024-05-27 21:14:31,602 - mmdet - INFO - Epoch [6][1300/7330] lr: 1.000e-04, eta: 8:28:07, time: 0.597, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0275, loss_rpn_bbox: 0.0419, loss_cls: 0.1921, acc: 92.9446, loss_bbox: 0.2523, loss_mask: 0.2520, loss: 0.7657 +2024-05-27 21:15:03,827 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-27 21:15:03,827 - mmdet - INFO - Epoch [6][1350/7330] lr: 1.000e-04, eta: 8:27:39, time: 0.645, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0272, loss_rpn_bbox: 0.0426, loss_cls: 0.1968, acc: 92.8257, loss_bbox: 0.2596, loss_mask: 0.2495, loss: 0.7757 +2024-05-27 21:15:33,545 - mmdet - INFO - Epoch [6][1400/7330] lr: 1.000e-04, eta: 8:27:07, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0227, loss_rpn_bbox: 0.0376, loss_cls: 0.1863, acc: 93.1321, loss_bbox: 0.2421, loss_mask: 0.2477, loss: 0.7364 +2024-05-27 21:16:03,496 - mmdet - INFO - Epoch [6][1450/7330] lr: 1.000e-04, eta: 8:26:36, time: 0.599, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0267, loss_rpn_bbox: 0.0410, loss_cls: 0.1917, acc: 92.9905, loss_bbox: 0.2444, loss_mask: 0.2431, loss: 0.7470 +2024-05-27 21:16:35,611 - mmdet - INFO - Epoch [6][1500/7330] lr: 1.000e-04, eta: 8:26:08, time: 0.642, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0247, loss_rpn_bbox: 0.0409, loss_cls: 0.1913, acc: 93.0366, loss_bbox: 0.2453, loss_mask: 0.2421, loss: 0.7442 +2024-05-27 21:17:05,310 - mmdet - INFO - Epoch [6][1550/7330] lr: 1.000e-04, eta: 8:25:36, time: 0.594, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0243, loss_rpn_bbox: 0.0390, loss_cls: 0.1885, acc: 93.1052, loss_bbox: 0.2397, loss_mask: 0.2444, loss: 0.7359 +2024-05-27 21:17:35,276 - mmdet - INFO - Epoch [6][1600/7330] lr: 1.000e-04, eta: 8:25:05, time: 0.599, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0237, loss_rpn_bbox: 0.0388, loss_cls: 0.1886, acc: 93.0833, loss_bbox: 0.2430, loss_mask: 0.2472, loss: 0.7412 +2024-05-27 21:18:07,483 - mmdet - INFO - Epoch [6][1650/7330] lr: 1.000e-04, eta: 8:24:37, time: 0.644, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0251, loss_rpn_bbox: 0.0367, loss_cls: 0.1847, acc: 93.2356, loss_bbox: 0.2425, loss_mask: 0.2453, loss: 0.7341 +2024-05-27 21:18:39,600 - mmdet - INFO - Epoch [6][1700/7330] lr: 1.000e-04, eta: 8:24:08, time: 0.642, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0279, loss_rpn_bbox: 0.0440, loss_cls: 0.2016, acc: 92.6997, loss_bbox: 0.2624, loss_mask: 0.2530, loss: 0.7889 +2024-05-27 21:19:09,452 - mmdet - INFO - Epoch [6][1750/7330] lr: 1.000e-04, eta: 8:23:37, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0243, loss_rpn_bbox: 0.0385, loss_cls: 0.1920, acc: 92.9973, loss_bbox: 0.2459, loss_mask: 0.2438, loss: 0.7445 +2024-05-27 21:19:43,653 - mmdet - INFO - Epoch [6][1800/7330] lr: 1.000e-04, eta: 8:23:11, time: 0.684, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0266, loss_rpn_bbox: 0.0410, loss_cls: 0.1972, acc: 92.7090, loss_bbox: 0.2550, loss_mask: 0.2487, loss: 0.7686 +2024-05-27 21:20:13,386 - mmdet - INFO - Epoch [6][1850/7330] lr: 1.000e-04, eta: 8:22:40, time: 0.595, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0245, loss_rpn_bbox: 0.0391, loss_cls: 0.1894, acc: 93.1343, loss_bbox: 0.2503, loss_mask: 0.2486, loss: 0.7520 +2024-05-27 21:20:43,311 - mmdet - INFO - Epoch [6][1900/7330] lr: 1.000e-04, eta: 8:22:09, time: 0.599, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0227, loss_rpn_bbox: 0.0380, loss_cls: 0.1963, acc: 92.8567, loss_bbox: 0.2519, loss_mask: 0.2410, loss: 0.7500 +2024-05-27 21:21:13,203 - mmdet - INFO - Epoch [6][1950/7330] lr: 1.000e-04, eta: 8:21:37, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0250, loss_rpn_bbox: 0.0400, loss_cls: 0.1883, acc: 93.0891, loss_bbox: 0.2476, loss_mask: 0.2421, loss: 0.7429 +2024-05-27 21:21:43,113 - mmdet - INFO - Epoch [6][2000/7330] lr: 1.000e-04, eta: 8:21:06, time: 0.598, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0243, loss_rpn_bbox: 0.0376, loss_cls: 0.1835, acc: 93.2524, loss_bbox: 0.2400, loss_mask: 0.2493, loss: 0.7347 +2024-05-27 21:22:15,583 - mmdet - INFO - Epoch [6][2050/7330] lr: 1.000e-04, eta: 8:20:38, time: 0.649, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0257, loss_rpn_bbox: 0.0407, loss_cls: 0.1938, acc: 92.8708, loss_bbox: 0.2586, loss_mask: 0.2480, loss: 0.7669 +2024-05-27 21:22:45,600 - mmdet - INFO - Epoch [6][2100/7330] lr: 1.000e-04, eta: 8:20:07, time: 0.600, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0256, loss_rpn_bbox: 0.0410, loss_cls: 0.1946, acc: 92.9116, loss_bbox: 0.2507, loss_mask: 0.2492, loss: 0.7609 +2024-05-27 21:23:15,233 - mmdet - INFO - Epoch [6][2150/7330] lr: 1.000e-04, eta: 8:19:36, time: 0.593, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0239, loss_rpn_bbox: 0.0367, loss_cls: 0.1905, acc: 93.1118, loss_bbox: 0.2404, loss_mask: 0.2494, loss: 0.7409 +2024-05-27 21:23:47,384 - mmdet - INFO - Epoch [6][2200/7330] lr: 1.000e-04, eta: 8:19:07, time: 0.643, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0252, loss_rpn_bbox: 0.0394, loss_cls: 0.1852, acc: 93.1108, loss_bbox: 0.2472, loss_mask: 0.2518, loss: 0.7487 +2024-05-27 21:24:17,350 - mmdet - INFO - Epoch [6][2250/7330] lr: 1.000e-04, eta: 8:18:36, time: 0.600, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0266, loss_rpn_bbox: 0.0414, loss_cls: 0.1962, acc: 92.7668, loss_bbox: 0.2620, loss_mask: 0.2499, loss: 0.7761 +2024-05-27 21:24:50,756 - mmdet - INFO - Epoch [6][2300/7330] lr: 1.000e-04, eta: 8:18:09, time: 0.668, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0239, loss_rpn_bbox: 0.0373, loss_cls: 0.1956, acc: 92.9875, loss_bbox: 0.2465, loss_mask: 0.2400, loss: 0.7433 +2024-05-27 21:25:20,639 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-27 21:25:20,639 - mmdet - INFO - Epoch [6][2350/7330] lr: 1.000e-04, eta: 8:17:38, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0254, loss_rpn_bbox: 0.0396, loss_cls: 0.1971, acc: 92.9539, loss_bbox: 0.2490, loss_mask: 0.2481, loss: 0.7593 +2024-05-27 21:25:53,326 - mmdet - INFO - Epoch [6][2400/7330] lr: 1.000e-04, eta: 8:17:10, time: 0.654, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0252, loss_rpn_bbox: 0.0380, loss_cls: 0.1886, acc: 93.1021, loss_bbox: 0.2426, loss_mask: 0.2420, loss: 0.7365 +2024-05-27 21:26:23,200 - mmdet - INFO - Epoch [6][2450/7330] lr: 1.000e-04, eta: 8:16:39, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0263, loss_rpn_bbox: 0.0407, loss_cls: 0.1855, acc: 93.1440, loss_bbox: 0.2466, loss_mask: 0.2460, loss: 0.7451 +2024-05-27 21:26:53,124 - mmdet - INFO - Epoch [6][2500/7330] lr: 1.000e-04, eta: 8:16:08, time: 0.599, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0236, loss_rpn_bbox: 0.0378, loss_cls: 0.1860, acc: 93.3503, loss_bbox: 0.2356, loss_mask: 0.2373, loss: 0.7202 +2024-05-27 21:27:25,367 - mmdet - INFO - Epoch [6][2550/7330] lr: 1.000e-04, eta: 8:15:39, time: 0.645, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0248, loss_rpn_bbox: 0.0377, loss_cls: 0.1841, acc: 93.3193, loss_bbox: 0.2356, loss_mask: 0.2408, loss: 0.7230 +2024-05-27 21:27:55,278 - mmdet - INFO - Epoch [6][2600/7330] lr: 1.000e-04, eta: 8:15:08, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0223, loss_rpn_bbox: 0.0367, loss_cls: 0.1832, acc: 93.2827, loss_bbox: 0.2410, loss_mask: 0.2427, loss: 0.7259 +2024-05-27 21:28:25,144 - mmdet - INFO - Epoch [6][2650/7330] lr: 1.000e-04, eta: 8:14:37, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0256, loss_rpn_bbox: 0.0389, loss_cls: 0.1882, acc: 93.2441, loss_bbox: 0.2486, loss_mask: 0.2429, loss: 0.7442 +2024-05-27 21:28:57,782 - mmdet - INFO - Epoch [6][2700/7330] lr: 1.000e-04, eta: 8:14:09, time: 0.653, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0230, loss_rpn_bbox: 0.0375, loss_cls: 0.1893, acc: 93.0837, loss_bbox: 0.2411, loss_mask: 0.2457, loss: 0.7366 +2024-05-27 21:29:30,033 - mmdet - INFO - Epoch [6][2750/7330] lr: 1.000e-04, eta: 8:13:41, time: 0.645, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0230, loss_rpn_bbox: 0.0405, loss_cls: 0.1846, acc: 93.2437, loss_bbox: 0.2403, loss_mask: 0.2423, loss: 0.7306 +2024-05-27 21:29:59,899 - mmdet - INFO - Epoch [6][2800/7330] lr: 1.000e-04, eta: 8:13:09, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0263, loss_rpn_bbox: 0.0426, loss_cls: 0.1983, acc: 92.8462, loss_bbox: 0.2522, loss_mask: 0.2446, loss: 0.7640 +2024-05-27 21:30:32,018 - mmdet - INFO - Epoch [6][2850/7330] lr: 1.000e-04, eta: 8:12:41, time: 0.642, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0401, loss_cls: 0.1902, acc: 92.9453, loss_bbox: 0.2475, loss_mask: 0.2455, loss: 0.7469 +2024-05-27 21:31:01,948 - mmdet - INFO - Epoch [6][2900/7330] lr: 1.000e-04, eta: 8:12:10, time: 0.599, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0248, loss_rpn_bbox: 0.0417, loss_cls: 0.1991, acc: 92.7202, loss_bbox: 0.2562, loss_mask: 0.2486, loss: 0.7704 +2024-05-27 21:31:31,780 - mmdet - INFO - Epoch [6][2950/7330] lr: 1.000e-04, eta: 8:11:38, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0264, loss_rpn_bbox: 0.0415, loss_cls: 0.1961, acc: 92.8430, loss_bbox: 0.2537, loss_mask: 0.2559, loss: 0.7737 +2024-05-27 21:32:01,745 - mmdet - INFO - Epoch [6][3000/7330] lr: 1.000e-04, eta: 8:11:07, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0247, loss_rpn_bbox: 0.0385, loss_cls: 0.1868, acc: 93.1206, loss_bbox: 0.2430, loss_mask: 0.2419, loss: 0.7349 +2024-05-27 21:32:31,586 - mmdet - INFO - Epoch [6][3050/7330] lr: 1.000e-04, eta: 8:10:36, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0252, loss_rpn_bbox: 0.0396, loss_cls: 0.1935, acc: 92.9431, loss_bbox: 0.2491, loss_mask: 0.2445, loss: 0.7519 +2024-05-27 21:33:03,952 - mmdet - INFO - Epoch [6][3100/7330] lr: 1.000e-04, eta: 8:10:08, time: 0.647, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0241, loss_rpn_bbox: 0.0380, loss_cls: 0.1893, acc: 93.2332, loss_bbox: 0.2379, loss_mask: 0.2427, loss: 0.7320 +2024-05-27 21:33:33,743 - mmdet - INFO - Epoch [6][3150/7330] lr: 1.000e-04, eta: 8:09:36, time: 0.595, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0244, loss_rpn_bbox: 0.0380, loss_cls: 0.1889, acc: 93.1257, loss_bbox: 0.2394, loss_mask: 0.2375, loss: 0.7281 +2024-05-27 21:34:03,771 - mmdet - INFO - Epoch [6][3200/7330] lr: 1.000e-04, eta: 8:09:05, time: 0.601, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0258, loss_rpn_bbox: 0.0399, loss_cls: 0.1923, acc: 93.0278, loss_bbox: 0.2520, loss_mask: 0.2525, loss: 0.7624 +2024-05-27 21:34:35,969 - mmdet - INFO - Epoch [6][3250/7330] lr: 1.000e-04, eta: 8:08:37, time: 0.644, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0265, loss_rpn_bbox: 0.0402, loss_cls: 0.1955, acc: 92.8704, loss_bbox: 0.2553, loss_mask: 0.2493, loss: 0.7667 +2024-05-27 21:35:05,896 - mmdet - INFO - Epoch [6][3300/7330] lr: 1.000e-04, eta: 8:08:06, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0253, loss_rpn_bbox: 0.0386, loss_cls: 0.1925, acc: 93.0349, loss_bbox: 0.2455, loss_mask: 0.2456, loss: 0.7475 +2024-05-27 21:35:35,842 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-27 21:35:35,842 - mmdet - INFO - Epoch [6][3350/7330] lr: 1.000e-04, eta: 8:07:35, time: 0.599, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0254, loss_rpn_bbox: 0.0400, loss_cls: 0.1942, acc: 92.8975, loss_bbox: 0.2487, loss_mask: 0.2437, loss: 0.7521 +2024-05-27 21:36:08,440 - mmdet - INFO - Epoch [6][3400/7330] lr: 1.000e-04, eta: 8:07:07, time: 0.652, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0246, loss_rpn_bbox: 0.0375, loss_cls: 0.1865, acc: 93.2664, loss_bbox: 0.2390, loss_mask: 0.2467, loss: 0.7341 +2024-05-27 21:36:40,970 - mmdet - INFO - Epoch [6][3450/7330] lr: 1.000e-04, eta: 8:06:38, time: 0.651, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0232, loss_rpn_bbox: 0.0391, loss_cls: 0.1948, acc: 92.8667, loss_bbox: 0.2532, loss_mask: 0.2503, loss: 0.7606 +2024-05-27 21:37:10,873 - mmdet - INFO - Epoch [6][3500/7330] lr: 1.000e-04, eta: 8:06:07, time: 0.598, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0227, loss_rpn_bbox: 0.0357, loss_cls: 0.1888, acc: 93.2266, loss_bbox: 0.2354, loss_mask: 0.2407, loss: 0.7233 +2024-05-27 21:37:40,987 - mmdet - INFO - Epoch [6][3550/7330] lr: 1.000e-04, eta: 8:05:36, time: 0.602, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0248, loss_rpn_bbox: 0.0418, loss_cls: 0.1950, acc: 92.8389, loss_bbox: 0.2552, loss_mask: 0.2431, loss: 0.7599 +2024-05-27 21:38:15,498 - mmdet - INFO - Epoch [6][3600/7330] lr: 1.000e-04, eta: 8:05:11, time: 0.690, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0265, loss_rpn_bbox: 0.0412, loss_cls: 0.2018, acc: 92.7122, loss_bbox: 0.2563, loss_mask: 0.2481, loss: 0.7738 +2024-05-27 21:38:45,523 - mmdet - INFO - Epoch [6][3650/7330] lr: 1.000e-04, eta: 8:04:39, time: 0.600, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0250, loss_rpn_bbox: 0.0414, loss_cls: 0.1891, acc: 93.0330, loss_bbox: 0.2425, loss_mask: 0.2500, loss: 0.7481 +2024-05-27 21:39:15,301 - mmdet - INFO - Epoch [6][3700/7330] lr: 1.000e-04, eta: 8:04:08, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0252, loss_rpn_bbox: 0.0376, loss_cls: 0.1930, acc: 93.0369, loss_bbox: 0.2431, loss_mask: 0.2477, loss: 0.7466 +2024-05-27 21:39:47,747 - mmdet - INFO - Epoch [6][3750/7330] lr: 1.000e-04, eta: 8:03:40, time: 0.649, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0258, loss_rpn_bbox: 0.0396, loss_cls: 0.1942, acc: 92.9446, loss_bbox: 0.2471, loss_mask: 0.2516, loss: 0.7583 +2024-05-27 21:40:19,988 - mmdet - INFO - Epoch [6][3800/7330] lr: 1.000e-04, eta: 8:03:11, time: 0.645, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0255, loss_rpn_bbox: 0.0385, loss_cls: 0.1875, acc: 93.1919, loss_bbox: 0.2474, loss_mask: 0.2429, loss: 0.7419 +2024-05-27 21:40:49,754 - mmdet - INFO - Epoch [6][3850/7330] lr: 1.000e-04, eta: 8:02:40, time: 0.595, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0226, loss_rpn_bbox: 0.0375, loss_cls: 0.1851, acc: 93.2131, loss_bbox: 0.2416, loss_mask: 0.2408, loss: 0.7276 +2024-05-27 21:41:22,723 - mmdet - INFO - Epoch [6][3900/7330] lr: 1.000e-04, eta: 8:02:12, time: 0.659, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0247, loss_rpn_bbox: 0.0396, loss_cls: 0.1935, acc: 93.0952, loss_bbox: 0.2434, loss_mask: 0.2431, loss: 0.7443 +2024-05-27 21:41:52,470 - mmdet - INFO - Epoch [6][3950/7330] lr: 1.000e-04, eta: 8:01:41, time: 0.595, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0237, loss_rpn_bbox: 0.0376, loss_cls: 0.1835, acc: 93.3298, loss_bbox: 0.2351, loss_mask: 0.2390, loss: 0.7190 +2024-05-27 21:42:22,602 - mmdet - INFO - Epoch [6][4000/7330] lr: 1.000e-04, eta: 8:01:10, time: 0.603, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0219, loss_rpn_bbox: 0.0374, loss_cls: 0.1886, acc: 93.0964, loss_bbox: 0.2490, loss_mask: 0.2448, loss: 0.7418 +2024-05-27 21:42:52,281 - mmdet - INFO - Epoch [6][4050/7330] lr: 1.000e-04, eta: 8:00:39, time: 0.594, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0238, loss_rpn_bbox: 0.0369, loss_cls: 0.1858, acc: 93.3140, loss_bbox: 0.2362, loss_mask: 0.2398, loss: 0.7225 +2024-05-27 21:43:22,085 - mmdet - INFO - Epoch [6][4100/7330] lr: 1.000e-04, eta: 8:00:07, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0250, loss_rpn_bbox: 0.0377, loss_cls: 0.1940, acc: 92.9871, loss_bbox: 0.2524, loss_mask: 0.2511, loss: 0.7602 +2024-05-27 21:43:54,652 - mmdet - INFO - Epoch [6][4150/7330] lr: 1.000e-04, eta: 7:59:39, time: 0.651, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0238, loss_rpn_bbox: 0.0370, loss_cls: 0.1909, acc: 93.1099, loss_bbox: 0.2411, loss_mask: 0.2445, loss: 0.7374 +2024-05-27 21:44:24,394 - mmdet - INFO - Epoch [6][4200/7330] lr: 1.000e-04, eta: 7:59:08, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0205, loss_rpn_bbox: 0.0344, loss_cls: 0.1832, acc: 93.2852, loss_bbox: 0.2374, loss_mask: 0.2382, loss: 0.7136 +2024-05-27 21:44:54,217 - mmdet - INFO - Epoch [6][4250/7330] lr: 1.000e-04, eta: 7:58:36, time: 0.596, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0255, loss_rpn_bbox: 0.0393, loss_cls: 0.1919, acc: 93.0620, loss_bbox: 0.2447, loss_mask: 0.2411, loss: 0.7425 +2024-05-27 21:45:26,338 - mmdet - INFO - Epoch [6][4300/7330] lr: 1.000e-04, eta: 7:58:08, time: 0.642, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0234, loss_rpn_bbox: 0.0375, loss_cls: 0.1949, acc: 92.9905, loss_bbox: 0.2444, loss_mask: 0.2426, loss: 0.7428 +2024-05-27 21:45:56,132 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-27 21:45:56,132 - mmdet - INFO - Epoch [6][4350/7330] lr: 1.000e-04, eta: 7:57:36, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0232, loss_rpn_bbox: 0.0378, loss_cls: 0.1899, acc: 93.0615, loss_bbox: 0.2445, loss_mask: 0.2434, loss: 0.7389 +2024-05-27 21:46:25,976 - mmdet - INFO - Epoch [6][4400/7330] lr: 1.000e-04, eta: 7:57:05, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0261, loss_rpn_bbox: 0.0390, loss_cls: 0.1879, acc: 93.1611, loss_bbox: 0.2425, loss_mask: 0.2458, loss: 0.7413 +2024-05-27 21:46:58,148 - mmdet - INFO - Epoch [6][4450/7330] lr: 1.000e-04, eta: 7:56:36, time: 0.643, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0262, loss_rpn_bbox: 0.0424, loss_cls: 0.2031, acc: 92.5764, loss_bbox: 0.2607, loss_mask: 0.2502, loss: 0.7827 +2024-05-27 21:47:30,253 - mmdet - INFO - Epoch [6][4500/7330] lr: 1.000e-04, eta: 7:56:08, time: 0.642, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0243, loss_rpn_bbox: 0.0384, loss_cls: 0.1926, acc: 93.0200, loss_bbox: 0.2455, loss_mask: 0.2423, loss: 0.7429 +2024-05-27 21:48:00,334 - mmdet - INFO - Epoch [6][4550/7330] lr: 1.000e-04, eta: 7:55:37, time: 0.602, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0273, loss_rpn_bbox: 0.0419, loss_cls: 0.2011, acc: 92.6729, loss_bbox: 0.2615, loss_mask: 0.2478, loss: 0.7796 +2024-05-27 21:48:30,330 - mmdet - INFO - Epoch [6][4600/7330] lr: 1.000e-04, eta: 7:55:06, time: 0.600, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0244, loss_rpn_bbox: 0.0393, loss_cls: 0.1916, acc: 93.0493, loss_bbox: 0.2481, loss_mask: 0.2390, loss: 0.7424 +2024-05-27 21:49:02,594 - mmdet - INFO - Epoch [6][4650/7330] lr: 1.000e-04, eta: 7:54:37, time: 0.645, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0230, loss_rpn_bbox: 0.0367, loss_cls: 0.1858, acc: 93.2446, loss_bbox: 0.2368, loss_mask: 0.2434, loss: 0.7257 +2024-05-27 21:49:32,666 - mmdet - INFO - Epoch [6][4700/7330] lr: 1.000e-04, eta: 7:54:06, time: 0.601, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0250, loss_rpn_bbox: 0.0403, loss_cls: 0.1913, acc: 92.9775, loss_bbox: 0.2536, loss_mask: 0.2521, loss: 0.7623 +2024-05-27 21:50:02,690 - mmdet - INFO - Epoch [6][4750/7330] lr: 1.000e-04, eta: 7:53:35, time: 0.600, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0259, loss_rpn_bbox: 0.0397, loss_cls: 0.1951, acc: 92.9331, loss_bbox: 0.2535, loss_mask: 0.2503, loss: 0.7646 +2024-05-27 21:50:35,037 - mmdet - INFO - Epoch [6][4800/7330] lr: 1.000e-04, eta: 7:53:07, time: 0.647, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0229, loss_rpn_bbox: 0.0361, loss_cls: 0.1720, acc: 93.6660, loss_bbox: 0.2272, loss_mask: 0.2343, loss: 0.6926 +2024-05-27 21:51:07,217 - mmdet - INFO - Epoch [6][4850/7330] lr: 1.000e-04, eta: 7:52:38, time: 0.643, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0260, loss_rpn_bbox: 0.0410, loss_cls: 0.1964, acc: 92.8281, loss_bbox: 0.2514, loss_mask: 0.2496, loss: 0.7644 +2024-05-27 21:51:37,069 - mmdet - INFO - Epoch [6][4900/7330] lr: 1.000e-04, eta: 7:52:07, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0226, loss_rpn_bbox: 0.0365, loss_cls: 0.1863, acc: 93.1453, loss_bbox: 0.2409, loss_mask: 0.2420, loss: 0.7283 +2024-05-27 21:52:07,223 - mmdet - INFO - Epoch [6][4950/7330] lr: 1.000e-04, eta: 7:51:36, time: 0.603, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0253, loss_rpn_bbox: 0.0383, loss_cls: 0.1913, acc: 93.1189, loss_bbox: 0.2465, loss_mask: 0.2412, loss: 0.7426 +2024-05-27 21:52:40,465 - mmdet - INFO - Epoch [6][5000/7330] lr: 1.000e-04, eta: 7:51:08, time: 0.665, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0242, loss_rpn_bbox: 0.0380, loss_cls: 0.1928, acc: 93.0642, loss_bbox: 0.2437, loss_mask: 0.2463, loss: 0.7450 +2024-05-27 21:53:10,580 - mmdet - INFO - Epoch [6][5050/7330] lr: 1.000e-04, eta: 7:50:37, time: 0.602, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0237, loss_rpn_bbox: 0.0384, loss_cls: 0.1892, acc: 93.1819, loss_bbox: 0.2422, loss_mask: 0.2466, loss: 0.7402 +2024-05-27 21:53:40,742 - mmdet - INFO - Epoch [6][5100/7330] lr: 1.000e-04, eta: 7:50:06, time: 0.603, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0236, loss_rpn_bbox: 0.0385, loss_cls: 0.1883, acc: 93.1052, loss_bbox: 0.2460, loss_mask: 0.2435, loss: 0.7398 +2024-05-27 21:54:10,525 - mmdet - INFO - Epoch [6][5150/7330] lr: 1.000e-04, eta: 7:49:35, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0266, loss_rpn_bbox: 0.0407, loss_cls: 0.1915, acc: 93.0300, loss_bbox: 0.2496, loss_mask: 0.2491, loss: 0.7575 +2024-05-27 21:54:44,411 - mmdet - INFO - Epoch [6][5200/7330] lr: 1.000e-04, eta: 7:49:08, time: 0.678, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0257, loss_rpn_bbox: 0.0404, loss_cls: 0.1967, acc: 92.8762, loss_bbox: 0.2514, loss_mask: 0.2488, loss: 0.7629 +2024-05-27 21:55:14,352 - mmdet - INFO - Epoch [6][5250/7330] lr: 1.000e-04, eta: 7:48:37, time: 0.599, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0228, loss_rpn_bbox: 0.0370, loss_cls: 0.1850, acc: 93.3438, loss_bbox: 0.2362, loss_mask: 0.2388, loss: 0.7197 +2024-05-27 21:55:44,379 - mmdet - INFO - Epoch [6][5300/7330] lr: 1.000e-04, eta: 7:48:06, time: 0.601, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0246, loss_rpn_bbox: 0.0382, loss_cls: 0.1884, acc: 93.1484, loss_bbox: 0.2417, loss_mask: 0.2430, loss: 0.7360 +2024-05-27 21:56:16,551 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-27 21:56:16,551 - mmdet - INFO - Epoch [6][5350/7330] lr: 1.000e-04, eta: 7:47:37, time: 0.643, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0214, loss_rpn_bbox: 0.0366, loss_cls: 0.1805, acc: 93.4463, loss_bbox: 0.2339, loss_mask: 0.2423, loss: 0.7147 +2024-05-27 21:56:46,666 - mmdet - INFO - Epoch [6][5400/7330] lr: 1.000e-04, eta: 7:47:06, time: 0.602, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0248, loss_rpn_bbox: 0.0377, loss_cls: 0.1843, acc: 93.2925, loss_bbox: 0.2377, loss_mask: 0.2456, loss: 0.7301 +2024-05-27 21:57:16,599 - mmdet - INFO - Epoch [6][5450/7330] lr: 1.000e-04, eta: 7:46:35, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0259, loss_rpn_bbox: 0.0388, loss_cls: 0.1903, acc: 93.2063, loss_bbox: 0.2453, loss_mask: 0.2461, loss: 0.7464 +2024-05-27 21:57:48,605 - mmdet - INFO - Epoch [6][5500/7330] lr: 1.000e-04, eta: 7:46:06, time: 0.640, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0216, loss_rpn_bbox: 0.0346, loss_cls: 0.1847, acc: 93.3186, loss_bbox: 0.2334, loss_mask: 0.2411, loss: 0.7154 +2024-05-27 21:58:21,079 - mmdet - INFO - Epoch [6][5550/7330] lr: 1.000e-04, eta: 7:45:38, time: 0.649, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0253, loss_rpn_bbox: 0.0393, loss_cls: 0.1863, acc: 93.1201, loss_bbox: 0.2388, loss_mask: 0.2447, loss: 0.7344 +2024-05-27 21:58:50,862 - mmdet - INFO - Epoch [6][5600/7330] lr: 1.000e-04, eta: 7:45:06, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0228, loss_rpn_bbox: 0.0378, loss_cls: 0.1834, acc: 93.2651, loss_bbox: 0.2355, loss_mask: 0.2410, loss: 0.7205 +2024-05-27 21:59:20,847 - mmdet - INFO - Epoch [6][5650/7330] lr: 1.000e-04, eta: 7:44:35, time: 0.600, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0247, loss_rpn_bbox: 0.0374, loss_cls: 0.2014, acc: 92.8000, loss_bbox: 0.2527, loss_mask: 0.2463, loss: 0.7625 +2024-05-27 21:59:53,113 - mmdet - INFO - Epoch [6][5700/7330] lr: 1.000e-04, eta: 7:44:07, time: 0.645, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0254, loss_rpn_bbox: 0.0388, loss_cls: 0.1934, acc: 92.8936, loss_bbox: 0.2487, loss_mask: 0.2434, loss: 0.7496 +2024-05-27 22:00:23,025 - mmdet - INFO - Epoch [6][5750/7330] lr: 1.000e-04, eta: 7:43:35, time: 0.598, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0256, loss_rpn_bbox: 0.0388, loss_cls: 0.1877, acc: 93.1738, loss_bbox: 0.2423, loss_mask: 0.2452, loss: 0.7397 +2024-05-27 22:00:53,077 - mmdet - INFO - Epoch [6][5800/7330] lr: 1.000e-04, eta: 7:43:04, time: 0.600, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0254, loss_rpn_bbox: 0.0401, loss_cls: 0.1976, acc: 92.8872, loss_bbox: 0.2503, loss_mask: 0.2479, loss: 0.7612 +2024-05-27 22:01:25,292 - mmdet - INFO - Epoch [6][5850/7330] lr: 1.000e-04, eta: 7:42:36, time: 0.645, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0297, loss_rpn_bbox: 0.0390, loss_cls: 0.2006, acc: 92.7983, loss_bbox: 0.2501, loss_mask: 0.2468, loss: 0.7662 +2024-05-27 22:01:57,774 - mmdet - INFO - Epoch [6][5900/7330] lr: 1.000e-04, eta: 7:42:07, time: 0.650, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0251, loss_rpn_bbox: 0.0384, loss_cls: 0.1940, acc: 92.9426, loss_bbox: 0.2496, loss_mask: 0.2443, loss: 0.7513 +2024-05-27 22:02:27,797 - mmdet - INFO - Epoch [6][5950/7330] lr: 1.000e-04, eta: 7:41:36, time: 0.600, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0290, loss_rpn_bbox: 0.0418, loss_cls: 0.1940, acc: 92.9143, loss_bbox: 0.2504, loss_mask: 0.2499, loss: 0.7650 +2024-05-27 22:02:57,813 - mmdet - INFO - Epoch [6][6000/7330] lr: 1.000e-04, eta: 7:41:05, time: 0.600, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0261, loss_rpn_bbox: 0.0406, loss_cls: 0.1972, acc: 92.7585, loss_bbox: 0.2541, loss_mask: 0.2490, loss: 0.7669 +2024-05-27 22:03:30,661 - mmdet - INFO - Epoch [6][6050/7330] lr: 1.000e-04, eta: 7:40:37, time: 0.657, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0243, loss_rpn_bbox: 0.0391, loss_cls: 0.1916, acc: 93.0803, loss_bbox: 0.2451, loss_mask: 0.2426, loss: 0.7426 +2024-05-27 22:04:00,649 - mmdet - INFO - Epoch [6][6100/7330] lr: 1.000e-04, eta: 7:40:06, time: 0.600, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0234, loss_rpn_bbox: 0.0373, loss_cls: 0.1927, acc: 92.9868, loss_bbox: 0.2420, loss_mask: 0.2432, loss: 0.7386 +2024-05-27 22:04:30,686 - mmdet - INFO - Epoch [6][6150/7330] lr: 1.000e-04, eta: 7:39:35, time: 0.601, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0248, loss_rpn_bbox: 0.0387, loss_cls: 0.1871, acc: 93.2561, loss_bbox: 0.2396, loss_mask: 0.2427, loss: 0.7328 +2024-05-27 22:05:00,609 - mmdet - INFO - Epoch [6][6200/7330] lr: 1.000e-04, eta: 7:39:04, time: 0.598, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0257, loss_rpn_bbox: 0.0399, loss_cls: 0.1972, acc: 92.9033, loss_bbox: 0.2467, loss_mask: 0.2477, loss: 0.7571 +2024-05-27 22:05:33,174 - mmdet - INFO - Epoch [6][6250/7330] lr: 1.000e-04, eta: 7:38:35, time: 0.651, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0227, loss_rpn_bbox: 0.0364, loss_cls: 0.1856, acc: 93.2305, loss_bbox: 0.2399, loss_mask: 0.2447, loss: 0.7293 +2024-05-27 22:06:03,145 - mmdet - INFO - Epoch [6][6300/7330] lr: 1.000e-04, eta: 7:38:04, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0239, loss_rpn_bbox: 0.0389, loss_cls: 0.1918, acc: 93.0781, loss_bbox: 0.2470, loss_mask: 0.2524, loss: 0.7540 +2024-05-27 22:06:32,856 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-27 22:06:32,856 - mmdet - INFO - Epoch [6][6350/7330] lr: 1.000e-04, eta: 7:37:33, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0364, loss_cls: 0.1856, acc: 93.3635, loss_bbox: 0.2356, loss_mask: 0.2455, loss: 0.7266 +2024-05-27 22:07:04,911 - mmdet - INFO - Epoch [6][6400/7330] lr: 1.000e-04, eta: 7:37:04, time: 0.641, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0239, loss_rpn_bbox: 0.0381, loss_cls: 0.1875, acc: 93.2061, loss_bbox: 0.2376, loss_mask: 0.2453, loss: 0.7323 +2024-05-27 22:07:34,801 - mmdet - INFO - Epoch [6][6450/7330] lr: 1.000e-04, eta: 7:36:33, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0248, loss_rpn_bbox: 0.0375, loss_cls: 0.1856, acc: 93.2539, loss_bbox: 0.2390, loss_mask: 0.2397, loss: 0.7266 +2024-05-27 22:08:04,579 - mmdet - INFO - Epoch [6][6500/7330] lr: 1.000e-04, eta: 7:36:01, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0236, loss_rpn_bbox: 0.0370, loss_cls: 0.1881, acc: 93.2078, loss_bbox: 0.2397, loss_mask: 0.2437, loss: 0.7322 +2024-05-27 22:08:37,054 - mmdet - INFO - Epoch [6][6550/7330] lr: 1.000e-04, eta: 7:35:33, time: 0.649, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0234, loss_rpn_bbox: 0.0404, loss_cls: 0.1981, acc: 92.7971, loss_bbox: 0.2504, loss_mask: 0.2443, loss: 0.7566 +2024-05-27 22:09:14,920 - mmdet - INFO - Epoch [6][6600/7330] lr: 1.000e-04, eta: 7:35:10, time: 0.757, data_time: 0.123, memory: 9459, loss_rpn_cls: 0.0241, loss_rpn_bbox: 0.0388, loss_cls: 0.1879, acc: 93.1528, loss_bbox: 0.2433, loss_mask: 0.2445, loss: 0.7388 +2024-05-27 22:09:44,782 - mmdet - INFO - Epoch [6][6650/7330] lr: 1.000e-04, eta: 7:34:39, time: 0.597, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0240, loss_rpn_bbox: 0.0376, loss_cls: 0.1853, acc: 93.2605, loss_bbox: 0.2380, loss_mask: 0.2448, loss: 0.7297 +2024-05-27 22:10:14,681 - mmdet - INFO - Epoch [6][6700/7330] lr: 1.000e-04, eta: 7:34:07, time: 0.598, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0257, loss_rpn_bbox: 0.0404, loss_cls: 0.1934, acc: 92.9304, loss_bbox: 0.2493, loss_mask: 0.2442, loss: 0.7530 +2024-05-27 22:10:47,169 - mmdet - INFO - Epoch [6][6750/7330] lr: 1.000e-04, eta: 7:33:39, time: 0.650, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0226, loss_rpn_bbox: 0.0381, loss_cls: 0.1904, acc: 93.0325, loss_bbox: 0.2474, loss_mask: 0.2431, loss: 0.7415 +2024-05-27 22:11:17,008 - mmdet - INFO - Epoch [6][6800/7330] lr: 1.000e-04, eta: 7:33:08, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0394, loss_cls: 0.1960, acc: 92.8865, loss_bbox: 0.2521, loss_mask: 0.2495, loss: 0.7604 +2024-05-27 22:11:46,913 - mmdet - INFO - Epoch [6][6850/7330] lr: 1.000e-04, eta: 7:32:36, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0258, loss_rpn_bbox: 0.0401, loss_cls: 0.1988, acc: 92.8345, loss_bbox: 0.2511, loss_mask: 0.2397, loss: 0.7555 +2024-05-27 22:12:19,012 - mmdet - INFO - Epoch [6][6900/7330] lr: 1.000e-04, eta: 7:32:08, time: 0.642, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0397, loss_cls: 0.1877, acc: 93.0808, loss_bbox: 0.2478, loss_mask: 0.2421, loss: 0.7409 +2024-05-27 22:12:51,792 - mmdet - INFO - Epoch [6][6950/7330] lr: 1.000e-04, eta: 7:31:39, time: 0.656, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0256, loss_rpn_bbox: 0.0402, loss_cls: 0.1968, acc: 92.9045, loss_bbox: 0.2434, loss_mask: 0.2453, loss: 0.7513 +2024-05-27 22:13:21,746 - mmdet - INFO - Epoch [6][7000/7330] lr: 1.000e-04, eta: 7:31:08, time: 0.599, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0251, loss_rpn_bbox: 0.0382, loss_cls: 0.1902, acc: 93.1255, loss_bbox: 0.2387, loss_mask: 0.2428, loss: 0.7350 +2024-05-27 22:13:51,615 - mmdet - INFO - Epoch [6][7050/7330] lr: 1.000e-04, eta: 7:30:37, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0258, loss_rpn_bbox: 0.0367, loss_cls: 0.1876, acc: 93.1213, loss_bbox: 0.2441, loss_mask: 0.2460, loss: 0.7401 +2024-05-27 22:14:23,897 - mmdet - INFO - Epoch [6][7100/7330] lr: 1.000e-04, eta: 7:30:08, time: 0.646, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0233, loss_rpn_bbox: 0.0390, loss_cls: 0.1877, acc: 93.2166, loss_bbox: 0.2393, loss_mask: 0.2444, loss: 0.7338 +2024-05-27 22:14:53,783 - mmdet - INFO - Epoch [6][7150/7330] lr: 1.000e-04, eta: 7:29:37, time: 0.598, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0262, loss_rpn_bbox: 0.0409, loss_cls: 0.1974, acc: 92.7920, loss_bbox: 0.2508, loss_mask: 0.2516, loss: 0.7669 +2024-05-27 22:15:23,590 - mmdet - INFO - Epoch [6][7200/7330] lr: 1.000e-04, eta: 7:29:06, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0237, loss_rpn_bbox: 0.0386, loss_cls: 0.1910, acc: 93.0586, loss_bbox: 0.2435, loss_mask: 0.2463, loss: 0.7431 +2024-05-27 22:15:53,443 - mmdet - INFO - Epoch [6][7250/7330] lr: 1.000e-04, eta: 7:28:34, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0223, loss_rpn_bbox: 0.0391, loss_cls: 0.1846, acc: 93.1772, loss_bbox: 0.2375, loss_mask: 0.2341, loss: 0.7176 +2024-05-27 22:16:25,562 - mmdet - INFO - Epoch [6][7300/7330] lr: 1.000e-04, eta: 7:28:05, time: 0.642, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0239, loss_rpn_bbox: 0.0376, loss_cls: 0.1923, acc: 93.0728, loss_bbox: 0.2430, loss_mask: 0.2454, loss: 0.7422 +2024-05-27 22:16:44,295 - mmdet - INFO - Saving checkpoint at 6 epochs +2024-05-27 22:18:37,465 - mmdet - INFO - Evaluating bbox... +2024-05-27 22:19:02,628 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.433 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.666 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.471 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.233 + 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 ] = 0.546 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.546 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.546 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.318 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.603 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.738 + +2024-05-27 22:19:02,628 - mmdet - INFO - Evaluating segm... +2024-05-27 22:19:27,448 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.386 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.626 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.409 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.160 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.422 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.618 + 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 ] = 0.253 + 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 ] = 0.707 + +2024-05-27 22:19:27,871 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-27 22:19:27,873 - mmdet - INFO - Epoch(val) [6][625] bbox_mAP: 0.4330, bbox_mAP_50: 0.6660, bbox_mAP_75: 0.4710, bbox_mAP_s: 0.2330, bbox_mAP_m: 0.4820, bbox_mAP_l: 0.6200, bbox_mAP_copypaste: 0.433 0.666 0.471 0.233 0.482 0.620, segm_mAP: 0.3860, segm_mAP_50: 0.6260, segm_mAP_75: 0.4090, segm_mAP_s: 0.1600, segm_mAP_m: 0.4220, segm_mAP_l: 0.6180, segm_mAP_copypaste: 0.386 0.626 0.409 0.160 0.422 0.618 +2024-05-27 22:20:06,882 - mmdet - INFO - Epoch [7][50/7330] lr: 1.000e-04, eta: 7:27:07, time: 0.780, data_time: 0.089, memory: 9459, loss_rpn_cls: 0.0188, loss_rpn_bbox: 0.0348, loss_cls: 0.1752, acc: 93.5481, loss_bbox: 0.2291, loss_mask: 0.2346, loss: 0.6924 +2024-05-27 22:20:39,433 - mmdet - INFO - Epoch [7][100/7330] lr: 1.000e-04, eta: 7:26:38, time: 0.651, data_time: 0.026, memory: 9459, loss_rpn_cls: 0.0227, loss_rpn_bbox: 0.0401, loss_cls: 0.1869, acc: 93.0513, loss_bbox: 0.2464, loss_mask: 0.2419, loss: 0.7380 +2024-05-27 22:21:11,654 - mmdet - INFO - Epoch [7][150/7330] lr: 1.000e-04, eta: 7:26:09, time: 0.644, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0248, loss_rpn_bbox: 0.0385, loss_cls: 0.1829, acc: 93.1790, loss_bbox: 0.2403, loss_mask: 0.2422, loss: 0.7288 +2024-05-27 22:21:43,763 - mmdet - INFO - Epoch [7][200/7330] lr: 1.000e-04, eta: 7:25:40, time: 0.642, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0216, loss_rpn_bbox: 0.0354, loss_cls: 0.1668, acc: 93.9038, loss_bbox: 0.2256, loss_mask: 0.2290, loss: 0.6783 +2024-05-27 22:22:13,780 - mmdet - INFO - Epoch [7][250/7330] lr: 1.000e-04, eta: 7:25:09, time: 0.600, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0242, loss_rpn_bbox: 0.0398, loss_cls: 0.1789, acc: 93.3979, loss_bbox: 0.2390, loss_mask: 0.2378, loss: 0.7196 +2024-05-27 22:22:43,700 - mmdet - INFO - Epoch [7][300/7330] lr: 1.000e-04, eta: 7:24:38, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0222, loss_rpn_bbox: 0.0365, loss_cls: 0.1748, acc: 93.5132, loss_bbox: 0.2342, loss_mask: 0.2307, loss: 0.6983 +2024-05-27 22:23:13,400 - mmdet - INFO - Epoch [7][350/7330] lr: 1.000e-04, eta: 7:24:07, time: 0.594, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0222, loss_rpn_bbox: 0.0365, loss_cls: 0.1810, acc: 93.3408, loss_bbox: 0.2392, loss_mask: 0.2386, loss: 0.7174 +2024-05-27 22:23:43,145 - mmdet - INFO - Epoch [7][400/7330] lr: 1.000e-04, eta: 7:23:35, time: 0.595, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0240, loss_rpn_bbox: 0.0395, loss_cls: 0.1874, acc: 93.1667, loss_bbox: 0.2407, loss_mask: 0.2427, loss: 0.7343 +2024-05-27 22:24:13,092 - mmdet - INFO - Epoch [7][450/7330] lr: 1.000e-04, eta: 7:23:04, time: 0.599, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0226, loss_rpn_bbox: 0.0357, loss_cls: 0.1752, acc: 93.5520, loss_bbox: 0.2353, loss_mask: 0.2381, loss: 0.7069 +2024-05-27 22:24:42,921 - mmdet - INFO - Epoch [7][500/7330] lr: 1.000e-04, eta: 7:22:33, time: 0.597, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0207, loss_rpn_bbox: 0.0361, loss_cls: 0.1815, acc: 93.3391, loss_bbox: 0.2348, loss_mask: 0.2344, loss: 0.7075 +2024-05-27 22:25:12,827 - mmdet - INFO - Epoch [7][550/7330] lr: 1.000e-04, eta: 7:22:02, time: 0.598, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0229, loss_rpn_bbox: 0.0407, loss_cls: 0.1767, acc: 93.3770, loss_bbox: 0.2403, loss_mask: 0.2361, loss: 0.7167 +2024-05-27 22:25:42,654 - mmdet - INFO - Epoch [7][600/7330] lr: 1.000e-04, eta: 7:21:31, time: 0.596, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0236, loss_rpn_bbox: 0.0380, loss_cls: 0.1833, acc: 93.1741, loss_bbox: 0.2427, loss_mask: 0.2387, loss: 0.7263 +2024-05-27 22:26:14,828 - mmdet - INFO - Epoch [7][650/7330] lr: 1.000e-04, eta: 7:21:02, time: 0.644, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0383, loss_cls: 0.1814, acc: 93.3406, loss_bbox: 0.2353, loss_mask: 0.2413, loss: 0.7175 +2024-05-27 22:26:44,583 - mmdet - INFO - Epoch [7][700/7330] lr: 1.000e-04, eta: 7:20:30, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0223, loss_rpn_bbox: 0.0380, loss_cls: 0.1813, acc: 93.2505, loss_bbox: 0.2391, loss_mask: 0.2394, loss: 0.7201 +2024-05-27 22:27:14,100 - mmdet - INFO - Epoch [7][750/7330] lr: 1.000e-04, eta: 7:19:59, time: 0.590, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0232, loss_rpn_bbox: 0.0388, loss_cls: 0.1698, acc: 93.8010, loss_bbox: 0.2232, loss_mask: 0.2361, loss: 0.6910 +2024-05-27 22:27:43,893 - mmdet - INFO - Epoch [7][800/7330] lr: 1.000e-04, eta: 7:19:28, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0218, loss_rpn_bbox: 0.0378, loss_cls: 0.1793, acc: 93.4092, loss_bbox: 0.2359, loss_mask: 0.2407, loss: 0.7155 +2024-05-27 22:28:13,587 - mmdet - INFO - Epoch [7][850/7330] lr: 1.000e-04, eta: 7:18:56, time: 0.594, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0216, loss_rpn_bbox: 0.0375, loss_cls: 0.1824, acc: 93.2878, loss_bbox: 0.2387, loss_mask: 0.2372, loss: 0.7175 +2024-05-27 22:28:43,472 - mmdet - INFO - Epoch [7][900/7330] lr: 1.000e-04, eta: 7:18:25, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0237, loss_rpn_bbox: 0.0411, loss_cls: 0.1883, acc: 93.0564, loss_bbox: 0.2421, loss_mask: 0.2386, loss: 0.7337 +2024-05-27 22:29:15,935 - mmdet - INFO - Epoch [7][950/7330] lr: 1.000e-04, eta: 7:17:56, time: 0.649, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0215, loss_rpn_bbox: 0.0356, loss_cls: 0.1778, acc: 93.4692, loss_bbox: 0.2272, loss_mask: 0.2338, loss: 0.6958 +2024-05-27 22:29:45,659 - mmdet - INFO - Epoch [7][1000/7330] lr: 1.000e-04, eta: 7:17:25, time: 0.594, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0221, loss_rpn_bbox: 0.0367, loss_cls: 0.1794, acc: 93.3699, loss_bbox: 0.2337, loss_mask: 0.2378, loss: 0.7097 +2024-05-27 22:30:15,856 - mmdet - INFO - Epoch [7][1050/7330] lr: 1.000e-04, eta: 7:16:54, time: 0.603, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0230, loss_rpn_bbox: 0.0409, loss_cls: 0.1877, acc: 93.0652, loss_bbox: 0.2486, loss_mask: 0.2427, loss: 0.7428 +2024-05-27 22:30:45,752 - mmdet - INFO - Epoch [7][1100/7330] lr: 1.000e-04, eta: 7:16:23, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0362, loss_cls: 0.1802, acc: 93.3950, loss_bbox: 0.2313, loss_mask: 0.2394, loss: 0.7084 +2024-05-27 22:31:22,918 - mmdet - INFO - Epoch [7][1150/7330] lr: 1.000e-04, eta: 7:15:59, time: 0.743, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0361, loss_cls: 0.1857, acc: 93.3621, loss_bbox: 0.2348, loss_mask: 0.2358, loss: 0.7136 +2024-05-27 22:31:54,811 - mmdet - INFO - Epoch [7][1200/7330] lr: 1.000e-04, eta: 7:15:30, time: 0.638, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0199, loss_rpn_bbox: 0.0367, loss_cls: 0.1775, acc: 93.4236, loss_bbox: 0.2331, loss_mask: 0.2364, loss: 0.7036 +2024-05-27 22:32:29,732 - mmdet - INFO - Epoch [7][1250/7330] lr: 1.000e-04, eta: 7:15:01, time: 0.641, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0233, loss_rpn_bbox: 0.0379, loss_cls: 0.1812, acc: 93.3899, loss_bbox: 0.2325, loss_mask: 0.2363, loss: 0.7112 +2024-05-27 22:32:59,776 - mmdet - INFO - Epoch [7][1300/7330] lr: 1.000e-04, eta: 7:14:32, time: 0.658, data_time: 0.074, memory: 9459, loss_rpn_cls: 0.0252, loss_rpn_bbox: 0.0392, loss_cls: 0.1823, acc: 93.2598, loss_bbox: 0.2417, loss_mask: 0.2427, loss: 0.7311 +2024-05-27 22:33:29,841 - mmdet - INFO - Epoch [7][1350/7330] lr: 1.000e-04, eta: 7:14:01, time: 0.602, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0229, loss_rpn_bbox: 0.0397, loss_cls: 0.1839, acc: 93.1152, loss_bbox: 0.2467, loss_mask: 0.2517, loss: 0.7449 +2024-05-27 22:33:59,756 - mmdet - INFO - Epoch [7][1400/7330] lr: 1.000e-04, eta: 7:13:30, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0226, loss_rpn_bbox: 0.0379, loss_cls: 0.1807, acc: 93.3528, loss_bbox: 0.2402, loss_mask: 0.2394, loss: 0.7208 +2024-05-27 22:34:29,691 - mmdet - INFO - Epoch [7][1450/7330] lr: 1.000e-04, eta: 7:12:59, time: 0.599, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0211, loss_rpn_bbox: 0.0377, loss_cls: 0.1791, acc: 93.3362, loss_bbox: 0.2374, loss_mask: 0.2398, loss: 0.7152 +2024-05-27 22:34:59,556 - mmdet - INFO - Epoch [7][1500/7330] lr: 1.000e-04, eta: 7:12:28, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0227, loss_rpn_bbox: 0.0399, loss_cls: 0.1877, acc: 93.2302, loss_bbox: 0.2384, loss_mask: 0.2396, loss: 0.7283 +2024-05-27 22:35:29,367 - mmdet - INFO - Epoch [7][1550/7330] lr: 1.000e-04, eta: 7:11:57, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0220, loss_rpn_bbox: 0.0378, loss_cls: 0.1843, acc: 93.2688, loss_bbox: 0.2377, loss_mask: 0.2368, loss: 0.7186 +2024-05-27 22:35:59,283 - mmdet - INFO - Epoch [7][1600/7330] lr: 1.000e-04, eta: 7:11:25, time: 0.598, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0210, loss_rpn_bbox: 0.0384, loss_cls: 0.1747, acc: 93.4785, loss_bbox: 0.2374, loss_mask: 0.2397, loss: 0.7112 +2024-05-27 22:36:29,064 - mmdet - INFO - Epoch [7][1650/7330] lr: 1.000e-04, eta: 7:10:54, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0228, loss_rpn_bbox: 0.0347, loss_cls: 0.1763, acc: 93.5127, loss_bbox: 0.2294, loss_mask: 0.2347, loss: 0.6980 +2024-05-27 22:37:01,447 - mmdet - INFO - Epoch [7][1700/7330] lr: 1.000e-04, eta: 7:10:25, time: 0.647, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0222, loss_rpn_bbox: 0.0361, loss_cls: 0.1828, acc: 93.3027, loss_bbox: 0.2388, loss_mask: 0.2404, loss: 0.7203 +2024-05-27 22:37:31,201 - mmdet - INFO - Epoch [7][1750/7330] lr: 1.000e-04, eta: 7:09:54, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0223, loss_rpn_bbox: 0.0376, loss_cls: 0.1805, acc: 93.4587, loss_bbox: 0.2321, loss_mask: 0.2354, loss: 0.7079 +2024-05-27 22:38:01,104 - mmdet - INFO - Epoch [7][1800/7330] lr: 1.000e-04, eta: 7:09:23, time: 0.598, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0214, loss_rpn_bbox: 0.0368, loss_cls: 0.1804, acc: 93.2852, loss_bbox: 0.2340, loss_mask: 0.2393, loss: 0.7120 +2024-05-27 22:38:30,895 - mmdet - INFO - Epoch [7][1850/7330] lr: 1.000e-04, eta: 7:08:52, time: 0.596, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0206, loss_rpn_bbox: 0.0346, loss_cls: 0.1797, acc: 93.3750, loss_bbox: 0.2346, loss_mask: 0.2368, loss: 0.7064 +2024-05-27 22:39:00,938 - mmdet - INFO - Epoch [7][1900/7330] lr: 1.000e-04, eta: 7:08:21, time: 0.601, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0241, loss_rpn_bbox: 0.0379, loss_cls: 0.1801, acc: 93.4534, loss_bbox: 0.2415, loss_mask: 0.2392, loss: 0.7227 +2024-05-27 22:39:30,874 - mmdet - INFO - Epoch [7][1950/7330] lr: 1.000e-04, eta: 7:07:50, time: 0.599, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0236, loss_rpn_bbox: 0.0403, loss_cls: 0.1825, acc: 93.2490, loss_bbox: 0.2453, loss_mask: 0.2440, loss: 0.7357 +2024-05-27 22:40:03,219 - mmdet - INFO - Epoch [7][2000/7330] lr: 1.000e-04, eta: 7:07:21, time: 0.647, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0228, loss_rpn_bbox: 0.0362, loss_cls: 0.1841, acc: 93.2727, loss_bbox: 0.2440, loss_mask: 0.2405, loss: 0.7276 +2024-05-27 22:40:33,196 - mmdet - INFO - Epoch [7][2050/7330] lr: 1.000e-04, eta: 7:06:50, time: 0.600, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0224, loss_rpn_bbox: 0.0376, loss_cls: 0.1777, acc: 93.3833, loss_bbox: 0.2404, loss_mask: 0.2463, loss: 0.7243 +2024-05-27 22:41:03,098 - mmdet - INFO - Epoch [7][2100/7330] lr: 1.000e-04, eta: 7:06:19, time: 0.598, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0233, loss_rpn_bbox: 0.0394, loss_cls: 0.1908, acc: 92.8774, loss_bbox: 0.2464, loss_mask: 0.2504, loss: 0.7503 +2024-05-27 22:41:32,886 - mmdet - INFO - Epoch [7][2150/7330] lr: 1.000e-04, eta: 7:05:47, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0216, loss_rpn_bbox: 0.0358, loss_cls: 0.1785, acc: 93.3599, loss_bbox: 0.2359, loss_mask: 0.2377, loss: 0.7094 +2024-05-27 22:42:12,044 - mmdet - INFO - Epoch [7][2200/7330] lr: 1.000e-04, eta: 7:05:25, time: 0.783, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0229, loss_rpn_bbox: 0.0365, loss_cls: 0.1836, acc: 93.3196, loss_bbox: 0.2325, loss_mask: 0.2345, loss: 0.7101 +2024-05-27 22:42:44,849 - mmdet - INFO - Epoch [7][2250/7330] lr: 1.000e-04, eta: 7:04:56, time: 0.656, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0218, loss_rpn_bbox: 0.0389, loss_cls: 0.1804, acc: 93.3276, loss_bbox: 0.2396, loss_mask: 0.2345, loss: 0.7151 +2024-05-27 22:43:18,008 - mmdet - INFO - Epoch [7][2300/7330] lr: 1.000e-04, eta: 7:04:28, time: 0.663, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0222, loss_rpn_bbox: 0.0378, loss_cls: 0.1829, acc: 93.3059, loss_bbox: 0.2386, loss_mask: 0.2349, loss: 0.7165 +2024-05-27 22:43:51,665 - mmdet - INFO - Epoch [7][2350/7330] lr: 1.000e-04, eta: 7:04:00, time: 0.673, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0219, loss_rpn_bbox: 0.0371, loss_cls: 0.1837, acc: 93.2493, loss_bbox: 0.2380, loss_mask: 0.2395, loss: 0.7202 +2024-05-27 22:44:21,577 - mmdet - INFO - Epoch [7][2400/7330] lr: 1.000e-04, eta: 7:03:29, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0221, loss_rpn_bbox: 0.0375, loss_cls: 0.1861, acc: 93.2471, loss_bbox: 0.2376, loss_mask: 0.2401, loss: 0.7234 +2024-05-27 22:44:51,429 - mmdet - INFO - Epoch [7][2450/7330] lr: 1.000e-04, eta: 7:02:58, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0216, loss_rpn_bbox: 0.0360, loss_cls: 0.1817, acc: 93.2817, loss_bbox: 0.2348, loss_mask: 0.2364, loss: 0.7105 +2024-05-27 22:45:21,240 - mmdet - INFO - Epoch [7][2500/7330] lr: 1.000e-04, eta: 7:02:27, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0220, loss_rpn_bbox: 0.0388, loss_cls: 0.1850, acc: 93.0759, loss_bbox: 0.2435, loss_mask: 0.2443, loss: 0.7337 +2024-05-27 22:45:51,073 - mmdet - INFO - Epoch [7][2550/7330] lr: 1.000e-04, eta: 7:01:55, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0219, loss_rpn_bbox: 0.0383, loss_cls: 0.1838, acc: 93.3020, loss_bbox: 0.2406, loss_mask: 0.2384, loss: 0.7230 +2024-05-27 22:46:20,917 - mmdet - INFO - Epoch [7][2600/7330] lr: 1.000e-04, eta: 7:01:24, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0355, loss_cls: 0.1742, acc: 93.6218, loss_bbox: 0.2304, loss_mask: 0.2374, loss: 0.6973 +2024-05-27 22:46:50,767 - mmdet - INFO - Epoch [7][2650/7330] lr: 1.000e-04, eta: 7:00:53, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0382, loss_cls: 0.1823, acc: 93.3687, loss_bbox: 0.2366, loss_mask: 0.2378, loss: 0.7157 +2024-05-27 22:47:20,653 - mmdet - INFO - Epoch [7][2700/7330] lr: 1.000e-04, eta: 7:00:22, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0201, loss_rpn_bbox: 0.0360, loss_cls: 0.1796, acc: 93.4128, loss_bbox: 0.2364, loss_mask: 0.2378, loss: 0.7099 +2024-05-27 22:47:52,565 - mmdet - INFO - Epoch [7][2750/7330] lr: 1.000e-04, eta: 6:59:53, time: 0.638, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0221, loss_rpn_bbox: 0.0392, loss_cls: 0.1787, acc: 93.5061, loss_bbox: 0.2337, loss_mask: 0.2417, loss: 0.7154 +2024-05-27 22:48:22,345 - mmdet - INFO - Epoch [7][2800/7330] lr: 1.000e-04, eta: 6:59:21, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0236, loss_rpn_bbox: 0.0379, loss_cls: 0.1878, acc: 93.2947, loss_bbox: 0.2406, loss_mask: 0.2395, loss: 0.7293 +2024-05-27 22:48:52,216 - mmdet - INFO - Epoch [7][2850/7330] lr: 1.000e-04, eta: 6:58:50, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0227, loss_rpn_bbox: 0.0380, loss_cls: 0.1873, acc: 93.2168, loss_bbox: 0.2379, loss_mask: 0.2386, loss: 0.7245 +2024-05-27 22:49:21,895 - mmdet - INFO - Epoch [7][2900/7330] lr: 1.000e-04, eta: 6:58:19, time: 0.594, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0374, loss_cls: 0.1895, acc: 93.1416, loss_bbox: 0.2382, loss_mask: 0.2356, loss: 0.7218 +2024-05-27 22:49:51,728 - mmdet - INFO - Epoch [7][2950/7330] lr: 1.000e-04, eta: 6:57:48, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0219, loss_rpn_bbox: 0.0355, loss_cls: 0.1794, acc: 93.4092, loss_bbox: 0.2336, loss_mask: 0.2378, loss: 0.7082 +2024-05-27 22:50:21,561 - mmdet - INFO - Epoch [7][3000/7330] lr: 1.000e-04, eta: 6:57:17, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0254, loss_rpn_bbox: 0.0389, loss_cls: 0.1841, acc: 93.2407, loss_bbox: 0.2441, loss_mask: 0.2512, loss: 0.7436 +2024-05-27 22:50:53,858 - mmdet - INFO - Epoch [7][3050/7330] lr: 1.000e-04, eta: 6:56:48, time: 0.646, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0237, loss_rpn_bbox: 0.0388, loss_cls: 0.1836, acc: 93.3188, loss_bbox: 0.2380, loss_mask: 0.2385, loss: 0.7227 +2024-05-27 22:51:23,734 - mmdet - INFO - Epoch [7][3100/7330] lr: 1.000e-04, eta: 6:56:16, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0218, loss_rpn_bbox: 0.0388, loss_cls: 0.1849, acc: 93.2026, loss_bbox: 0.2411, loss_mask: 0.2439, loss: 0.7305 +2024-05-27 22:51:53,550 - mmdet - INFO - Epoch [7][3150/7330] lr: 1.000e-04, eta: 6:55:45, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0369, loss_cls: 0.1854, acc: 93.2322, loss_bbox: 0.2384, loss_mask: 0.2401, loss: 0.7243 +2024-05-27 22:52:23,365 - mmdet - INFO - Epoch [7][3200/7330] lr: 1.000e-04, eta: 6:55:14, time: 0.596, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0350, loss_cls: 0.1730, acc: 93.6660, loss_bbox: 0.2270, loss_mask: 0.2318, loss: 0.6876 +2024-05-27 22:53:02,027 - mmdet - INFO - Epoch [7][3250/7330] lr: 1.000e-04, eta: 6:54:51, time: 0.773, data_time: 0.025, memory: 9459, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0380, loss_cls: 0.1816, acc: 93.3259, loss_bbox: 0.2381, loss_mask: 0.2409, loss: 0.7198 +2024-05-27 22:53:34,248 - mmdet - INFO - Epoch [7][3300/7330] lr: 1.000e-04, eta: 6:54:21, time: 0.644, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0213, loss_rpn_bbox: 0.0390, loss_cls: 0.1833, acc: 93.1743, loss_bbox: 0.2443, loss_mask: 0.2435, loss: 0.7313 +2024-05-27 22:54:08,020 - mmdet - INFO - Epoch [7][3350/7330] lr: 1.000e-04, eta: 6:53:54, time: 0.675, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0229, loss_rpn_bbox: 0.0390, loss_cls: 0.1897, acc: 93.0386, loss_bbox: 0.2483, loss_mask: 0.2463, loss: 0.7461 +2024-05-27 22:54:40,378 - mmdet - INFO - Epoch [7][3400/7330] lr: 1.000e-04, eta: 6:53:25, time: 0.647, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0395, loss_cls: 0.1886, acc: 93.1086, loss_bbox: 0.2418, loss_mask: 0.2475, loss: 0.7410 +2024-05-27 22:55:10,105 - mmdet - INFO - Epoch [7][3450/7330] lr: 1.000e-04, eta: 6:52:53, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0350, loss_cls: 0.1723, acc: 93.6331, loss_bbox: 0.2327, loss_mask: 0.2439, loss: 0.7046 +2024-05-27 22:55:39,946 - mmdet - INFO - Epoch [7][3500/7330] lr: 1.000e-04, eta: 6:52:22, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0225, loss_rpn_bbox: 0.0378, loss_cls: 0.1923, acc: 92.9246, loss_bbox: 0.2461, loss_mask: 0.2446, loss: 0.7432 +2024-05-27 22:56:09,820 - mmdet - INFO - Epoch [7][3550/7330] lr: 1.000e-04, eta: 6:51:51, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0385, loss_cls: 0.1792, acc: 93.5027, loss_bbox: 0.2353, loss_mask: 0.2410, loss: 0.7147 +2024-05-27 22:56:39,610 - mmdet - INFO - Epoch [7][3600/7330] lr: 1.000e-04, eta: 6:51:20, time: 0.596, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0351, loss_cls: 0.1725, acc: 93.6545, loss_bbox: 0.2291, loss_mask: 0.2367, loss: 0.6932 +2024-05-27 22:57:09,690 - mmdet - INFO - Epoch [7][3650/7330] lr: 1.000e-04, eta: 6:50:49, time: 0.602, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0260, loss_rpn_bbox: 0.0406, loss_cls: 0.1954, acc: 92.7988, loss_bbox: 0.2520, loss_mask: 0.2487, loss: 0.7627 +2024-05-27 22:57:39,353 - mmdet - INFO - Epoch [7][3700/7330] lr: 1.000e-04, eta: 6:50:17, time: 0.593, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0357, loss_cls: 0.1749, acc: 93.5198, loss_bbox: 0.2255, loss_mask: 0.2375, loss: 0.6972 +2024-05-27 22:58:09,390 - mmdet - INFO - Epoch [7][3750/7330] lr: 1.000e-04, eta: 6:49:46, time: 0.601, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0224, loss_rpn_bbox: 0.0394, loss_cls: 0.1879, acc: 93.1021, loss_bbox: 0.2406, loss_mask: 0.2398, loss: 0.7301 +2024-05-27 22:58:39,331 - mmdet - INFO - Epoch [7][3800/7330] lr: 1.000e-04, eta: 6:49:15, time: 0.599, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0234, loss_rpn_bbox: 0.0395, loss_cls: 0.1835, acc: 93.2996, loss_bbox: 0.2376, loss_mask: 0.2374, loss: 0.7215 +2024-05-27 22:59:11,730 - mmdet - INFO - Epoch [7][3850/7330] lr: 1.000e-04, eta: 6:48:46, time: 0.648, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0239, loss_rpn_bbox: 0.0368, loss_cls: 0.1868, acc: 93.3997, loss_bbox: 0.2340, loss_mask: 0.2371, loss: 0.7185 +2024-05-27 22:59:41,626 - mmdet - INFO - Epoch [7][3900/7330] lr: 1.000e-04, eta: 6:48:15, time: 0.598, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0250, loss_rpn_bbox: 0.0379, loss_cls: 0.1871, acc: 93.0786, loss_bbox: 0.2437, loss_mask: 0.2399, loss: 0.7334 +2024-05-27 23:00:11,745 - mmdet - INFO - Epoch [7][3950/7330] lr: 1.000e-04, eta: 6:47:44, time: 0.602, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0240, loss_rpn_bbox: 0.0352, loss_cls: 0.1829, acc: 93.3291, loss_bbox: 0.2375, loss_mask: 0.2412, loss: 0.7209 +2024-05-27 23:00:41,666 - mmdet - INFO - Epoch [7][4000/7330] lr: 1.000e-04, eta: 6:47:13, time: 0.598, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0240, loss_rpn_bbox: 0.0388, loss_cls: 0.1864, acc: 93.2385, loss_bbox: 0.2435, loss_mask: 0.2409, loss: 0.7335 +2024-05-27 23:01:11,721 - mmdet - INFO - Epoch [7][4050/7330] lr: 1.000e-04, eta: 6:46:42, time: 0.601, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0250, loss_rpn_bbox: 0.0403, loss_cls: 0.1830, acc: 93.1748, loss_bbox: 0.2450, loss_mask: 0.2427, loss: 0.7360 +2024-05-27 23:01:43,805 - mmdet - INFO - Epoch [7][4100/7330] lr: 1.000e-04, eta: 6:46:13, time: 0.642, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0230, loss_rpn_bbox: 0.0393, loss_cls: 0.1816, acc: 93.3230, loss_bbox: 0.2355, loss_mask: 0.2336, loss: 0.7130 +2024-05-27 23:02:13,758 - mmdet - INFO - Epoch [7][4150/7330] lr: 1.000e-04, eta: 6:45:42, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0244, loss_rpn_bbox: 0.0380, loss_cls: 0.1925, acc: 92.9150, loss_bbox: 0.2471, loss_mask: 0.2425, loss: 0.7445 +2024-05-27 23:02:43,630 - mmdet - INFO - Epoch [7][4200/7330] lr: 1.000e-04, eta: 6:45:11, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0217, loss_rpn_bbox: 0.0381, loss_cls: 0.1883, acc: 93.0820, loss_bbox: 0.2488, loss_mask: 0.2482, loss: 0.7450 +2024-05-27 23:03:13,468 - mmdet - INFO - Epoch [7][4250/7330] lr: 1.000e-04, eta: 6:44:40, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0242, loss_rpn_bbox: 0.0406, loss_cls: 0.1846, acc: 93.2363, loss_bbox: 0.2393, loss_mask: 0.2420, loss: 0.7307 +2024-05-27 23:03:50,954 - mmdet - INFO - Epoch [7][4300/7330] lr: 1.000e-04, eta: 6:44:15, time: 0.750, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0244, loss_rpn_bbox: 0.0410, loss_cls: 0.1876, acc: 93.0173, loss_bbox: 0.2433, loss_mask: 0.2402, loss: 0.7364 +2024-05-27 23:04:23,357 - mmdet - INFO - Epoch [7][4350/7330] lr: 1.000e-04, eta: 6:43:46, time: 0.648, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0213, loss_rpn_bbox: 0.0360, loss_cls: 0.1777, acc: 93.4014, loss_bbox: 0.2311, loss_mask: 0.2322, loss: 0.6984 +2024-05-27 23:04:56,010 - mmdet - INFO - Epoch [7][4400/7330] lr: 1.000e-04, eta: 6:43:17, time: 0.653, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0365, loss_cls: 0.1880, acc: 93.1167, loss_bbox: 0.2442, loss_mask: 0.2415, loss: 0.7336 +2024-05-27 23:05:27,872 - mmdet - INFO - Epoch [7][4450/7330] lr: 1.000e-04, eta: 6:42:47, time: 0.637, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0221, loss_rpn_bbox: 0.0365, loss_cls: 0.1832, acc: 93.2651, loss_bbox: 0.2441, loss_mask: 0.2410, loss: 0.7269 +2024-05-27 23:05:57,825 - mmdet - INFO - Epoch [7][4500/7330] lr: 1.000e-04, eta: 6:42:16, time: 0.599, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0234, loss_rpn_bbox: 0.0380, loss_cls: 0.1829, acc: 93.2739, loss_bbox: 0.2418, loss_mask: 0.2416, loss: 0.7276 +2024-05-27 23:06:27,680 - mmdet - INFO - Epoch [7][4550/7330] lr: 1.000e-04, eta: 6:41:45, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0213, loss_rpn_bbox: 0.0365, loss_cls: 0.1872, acc: 93.0012, loss_bbox: 0.2456, loss_mask: 0.2419, loss: 0.7324 +2024-05-27 23:06:57,398 - mmdet - INFO - Epoch [7][4600/7330] lr: 1.000e-04, eta: 6:41:14, time: 0.594, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0242, loss_rpn_bbox: 0.0383, loss_cls: 0.1856, acc: 93.1934, loss_bbox: 0.2427, loss_mask: 0.2410, loss: 0.7318 +2024-05-27 23:07:27,165 - mmdet - INFO - Epoch [7][4650/7330] lr: 1.000e-04, eta: 6:40:43, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0246, loss_rpn_bbox: 0.0381, loss_cls: 0.1874, acc: 93.1838, loss_bbox: 0.2380, loss_mask: 0.2407, loss: 0.7288 +2024-05-27 23:07:56,930 - mmdet - INFO - Epoch [7][4700/7330] lr: 1.000e-04, eta: 6:40:11, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0238, loss_rpn_bbox: 0.0383, loss_cls: 0.1864, acc: 93.1484, loss_bbox: 0.2421, loss_mask: 0.2419, loss: 0.7325 +2024-05-27 23:08:26,586 - mmdet - INFO - Epoch [7][4750/7330] lr: 1.000e-04, eta: 6:39:40, time: 0.593, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0228, loss_rpn_bbox: 0.0382, loss_cls: 0.1831, acc: 93.3040, loss_bbox: 0.2405, loss_mask: 0.2390, loss: 0.7236 +2024-05-27 23:08:56,426 - mmdet - INFO - Epoch [7][4800/7330] lr: 1.000e-04, eta: 6:39:09, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0219, loss_rpn_bbox: 0.0382, loss_cls: 0.1831, acc: 93.3818, loss_bbox: 0.2355, loss_mask: 0.2322, loss: 0.7109 +2024-05-27 23:09:26,102 - mmdet - INFO - Epoch [7][4850/7330] lr: 1.000e-04, eta: 6:38:38, time: 0.593, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0211, loss_rpn_bbox: 0.0356, loss_cls: 0.1767, acc: 93.5378, loss_bbox: 0.2283, loss_mask: 0.2371, loss: 0.6989 +2024-05-27 23:09:58,572 - mmdet - INFO - Epoch [7][4900/7330] lr: 1.000e-04, eta: 6:38:09, time: 0.649, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0229, loss_rpn_bbox: 0.0383, loss_cls: 0.1800, acc: 93.4121, loss_bbox: 0.2356, loss_mask: 0.2376, loss: 0.7143 +2024-05-27 23:10:28,557 - mmdet - INFO - Epoch [7][4950/7330] lr: 1.000e-04, eta: 6:37:38, time: 0.600, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0229, loss_rpn_bbox: 0.0392, loss_cls: 0.1881, acc: 93.1755, loss_bbox: 0.2470, loss_mask: 0.2403, loss: 0.7375 +2024-05-27 23:10:58,384 - mmdet - INFO - Epoch [7][5000/7330] lr: 1.000e-04, eta: 6:37:07, time: 0.596, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0395, loss_cls: 0.1873, acc: 93.1145, loss_bbox: 0.2469, loss_mask: 0.2469, loss: 0.7441 +2024-05-27 23:11:28,255 - mmdet - INFO - Epoch [7][5050/7330] lr: 1.000e-04, eta: 6:36:35, time: 0.597, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0224, loss_rpn_bbox: 0.0366, loss_cls: 0.1826, acc: 93.3792, loss_bbox: 0.2330, loss_mask: 0.2376, loss: 0.7122 +2024-05-27 23:11:58,209 - mmdet - INFO - Epoch [7][5100/7330] lr: 1.000e-04, eta: 6:36:04, time: 0.599, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0236, loss_rpn_bbox: 0.0375, loss_cls: 0.1856, acc: 93.2029, loss_bbox: 0.2404, loss_mask: 0.2379, loss: 0.7250 +2024-05-27 23:12:30,172 - mmdet - INFO - Epoch [7][5150/7330] lr: 1.000e-04, eta: 6:35:35, time: 0.639, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0226, loss_rpn_bbox: 0.0374, loss_cls: 0.1827, acc: 93.3147, loss_bbox: 0.2393, loss_mask: 0.2431, loss: 0.7252 +2024-05-27 23:12:59,863 - mmdet - INFO - Epoch [7][5200/7330] lr: 1.000e-04, eta: 6:35:04, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0229, loss_rpn_bbox: 0.0367, loss_cls: 0.1806, acc: 93.3994, loss_bbox: 0.2352, loss_mask: 0.2426, loss: 0.7181 +2024-05-27 23:13:29,711 - mmdet - INFO - Epoch [7][5250/7330] lr: 1.000e-04, eta: 6:34:33, time: 0.597, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0222, loss_rpn_bbox: 0.0345, loss_cls: 0.1770, acc: 93.5518, loss_bbox: 0.2271, loss_mask: 0.2298, loss: 0.6905 +2024-05-27 23:13:59,633 - mmdet - INFO - Epoch [7][5300/7330] lr: 1.000e-04, eta: 6:34:01, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0219, loss_rpn_bbox: 0.0358, loss_cls: 0.1789, acc: 93.3596, loss_bbox: 0.2314, loss_mask: 0.2375, loss: 0.7055 +2024-05-27 23:14:39,970 - mmdet - INFO - Epoch [7][5350/7330] lr: 1.000e-04, eta: 6:33:39, time: 0.807, data_time: 0.123, memory: 9459, loss_rpn_cls: 0.0233, loss_rpn_bbox: 0.0364, loss_cls: 0.1796, acc: 93.4209, loss_bbox: 0.2379, loss_mask: 0.2411, loss: 0.7182 +2024-05-27 23:15:14,899 - mmdet - INFO - Epoch [7][5400/7330] lr: 1.000e-04, eta: 6:33:11, time: 0.699, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0249, loss_rpn_bbox: 0.0372, loss_cls: 0.1834, acc: 93.2725, loss_bbox: 0.2348, loss_mask: 0.2403, loss: 0.7206 +2024-05-27 23:15:44,689 - mmdet - INFO - Epoch [7][5450/7330] lr: 1.000e-04, eta: 6:32:40, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0232, loss_rpn_bbox: 0.0374, loss_cls: 0.1771, acc: 93.5142, loss_bbox: 0.2294, loss_mask: 0.2372, loss: 0.7042 +2024-05-27 23:16:19,353 - mmdet - INFO - Epoch [7][5500/7330] lr: 1.000e-04, eta: 6:32:13, time: 0.693, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0204, loss_rpn_bbox: 0.0377, loss_cls: 0.1789, acc: 93.3188, loss_bbox: 0.2352, loss_mask: 0.2389, loss: 0.7112 +2024-05-27 23:16:49,142 - mmdet - INFO - Epoch [7][5550/7330] lr: 1.000e-04, eta: 6:31:42, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0217, loss_rpn_bbox: 0.0387, loss_cls: 0.1838, acc: 93.2595, loss_bbox: 0.2388, loss_mask: 0.2379, loss: 0.7209 +2024-05-27 23:17:19,040 - mmdet - INFO - Epoch [7][5600/7330] lr: 1.000e-04, eta: 6:31:11, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0245, loss_rpn_bbox: 0.0373, loss_cls: 0.1836, acc: 93.3416, loss_bbox: 0.2331, loss_mask: 0.2370, loss: 0.7154 +2024-05-27 23:17:49,025 - mmdet - INFO - Epoch [7][5650/7330] lr: 1.000e-04, eta: 6:30:39, time: 0.600, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0219, loss_rpn_bbox: 0.0361, loss_cls: 0.1849, acc: 93.2317, loss_bbox: 0.2424, loss_mask: 0.2405, loss: 0.7258 +2024-05-27 23:18:18,810 - mmdet - INFO - Epoch [7][5700/7330] lr: 1.000e-04, eta: 6:30:08, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0230, loss_rpn_bbox: 0.0381, loss_cls: 0.1811, acc: 93.3696, loss_bbox: 0.2346, loss_mask: 0.2379, loss: 0.7147 +2024-05-27 23:18:48,785 - mmdet - INFO - Epoch [7][5750/7330] lr: 1.000e-04, eta: 6:29:37, time: 0.600, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0236, loss_rpn_bbox: 0.0395, loss_cls: 0.1868, acc: 93.1660, loss_bbox: 0.2413, loss_mask: 0.2450, loss: 0.7362 +2024-05-27 23:19:18,612 - mmdet - INFO - Epoch [7][5800/7330] lr: 1.000e-04, eta: 6:29:06, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0222, loss_rpn_bbox: 0.0373, loss_cls: 0.1853, acc: 93.3184, loss_bbox: 0.2340, loss_mask: 0.2383, loss: 0.7171 +2024-05-27 23:19:48,636 - mmdet - INFO - Epoch [7][5850/7330] lr: 1.000e-04, eta: 6:28:35, time: 0.600, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0249, loss_rpn_bbox: 0.0391, loss_cls: 0.1961, acc: 93.0269, loss_bbox: 0.2460, loss_mask: 0.2412, loss: 0.7472 +2024-05-27 23:20:18,507 - mmdet - INFO - Epoch [7][5900/7330] lr: 1.000e-04, eta: 6:28:04, time: 0.597, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0222, loss_rpn_bbox: 0.0364, loss_cls: 0.1778, acc: 93.5225, loss_bbox: 0.2324, loss_mask: 0.2373, loss: 0.7061 +2024-05-27 23:20:50,617 - mmdet - INFO - Epoch [7][5950/7330] lr: 1.000e-04, eta: 6:27:35, time: 0.642, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0238, loss_rpn_bbox: 0.0387, loss_cls: 0.1880, acc: 93.0425, loss_bbox: 0.2437, loss_mask: 0.2425, loss: 0.7367 +2024-05-27 23:21:20,614 - mmdet - INFO - Epoch [7][6000/7330] lr: 1.000e-04, eta: 6:27:04, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0236, loss_rpn_bbox: 0.0382, loss_cls: 0.1799, acc: 93.4619, loss_bbox: 0.2366, loss_mask: 0.2337, loss: 0.7119 +2024-05-27 23:21:50,599 - mmdet - INFO - Epoch [7][6050/7330] lr: 1.000e-04, eta: 6:26:33, time: 0.600, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0246, loss_rpn_bbox: 0.0397, loss_cls: 0.1910, acc: 93.1423, loss_bbox: 0.2428, loss_mask: 0.2399, loss: 0.7380 +2024-05-27 23:22:20,520 - mmdet - INFO - Epoch [7][6100/7330] lr: 1.000e-04, eta: 6:26:02, time: 0.598, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0351, loss_cls: 0.1826, acc: 93.3669, loss_bbox: 0.2298, loss_mask: 0.2327, loss: 0.7037 +2024-05-27 23:22:50,763 - mmdet - INFO - Epoch [7][6150/7330] lr: 1.000e-04, eta: 6:25:31, time: 0.605, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0247, loss_rpn_bbox: 0.0392, loss_cls: 0.1912, acc: 93.0618, loss_bbox: 0.2420, loss_mask: 0.2424, loss: 0.7394 +2024-05-27 23:23:22,732 - mmdet - INFO - Epoch [7][6200/7330] lr: 1.000e-04, eta: 6:25:01, time: 0.639, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0203, loss_rpn_bbox: 0.0343, loss_cls: 0.1714, acc: 93.6196, loss_bbox: 0.2254, loss_mask: 0.2343, loss: 0.6858 +2024-05-27 23:23:52,769 - mmdet - INFO - Epoch [7][6250/7330] lr: 1.000e-04, eta: 6:24:30, time: 0.601, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0232, loss_rpn_bbox: 0.0371, loss_cls: 0.1877, acc: 93.0493, loss_bbox: 0.2430, loss_mask: 0.2402, loss: 0.7312 +2024-05-27 23:24:22,861 - mmdet - INFO - Epoch [7][6300/7330] lr: 1.000e-04, eta: 6:23:59, time: 0.602, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0260, loss_rpn_bbox: 0.0411, loss_cls: 0.1917, acc: 92.9399, loss_bbox: 0.2521, loss_mask: 0.2414, loss: 0.7523 +2024-05-27 23:24:52,668 - mmdet - INFO - Epoch [7][6350/7330] lr: 1.000e-04, eta: 6:23:28, time: 0.596, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0215, loss_rpn_bbox: 0.0361, loss_cls: 0.1767, acc: 93.5171, loss_bbox: 0.2320, loss_mask: 0.2397, loss: 0.7060 +2024-05-27 23:25:27,131 - mmdet - INFO - Epoch [7][6400/7330] lr: 1.000e-04, eta: 6:23:00, time: 0.689, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0233, loss_rpn_bbox: 0.0369, loss_cls: 0.1877, acc: 93.1880, loss_bbox: 0.2381, loss_mask: 0.2400, loss: 0.7260 +2024-05-27 23:26:03,368 - mmdet - INFO - Epoch [7][6450/7330] lr: 1.000e-04, eta: 6:22:34, time: 0.725, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0228, loss_rpn_bbox: 0.0366, loss_cls: 0.1917, acc: 93.0088, loss_bbox: 0.2421, loss_mask: 0.2444, loss: 0.7376 +2024-05-27 23:26:33,096 - mmdet - INFO - Epoch [7][6500/7330] lr: 1.000e-04, eta: 6:22:03, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0203, loss_rpn_bbox: 0.0372, loss_cls: 0.1766, acc: 93.5654, loss_bbox: 0.2305, loss_mask: 0.2360, loss: 0.7007 +2024-05-27 23:27:07,640 - mmdet - INFO - Epoch [7][6550/7330] lr: 1.000e-04, eta: 6:21:35, time: 0.691, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0226, loss_rpn_bbox: 0.0382, loss_cls: 0.1884, acc: 93.1013, loss_bbox: 0.2457, loss_mask: 0.2433, loss: 0.7382 +2024-05-27 23:27:37,666 - mmdet - INFO - Epoch [7][6600/7330] lr: 1.000e-04, eta: 6:21:04, time: 0.601, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0232, loss_rpn_bbox: 0.0404, loss_cls: 0.1922, acc: 92.9468, loss_bbox: 0.2477, loss_mask: 0.2430, loss: 0.7464 +2024-05-27 23:28:07,692 - mmdet - INFO - Epoch [7][6650/7330] lr: 1.000e-04, eta: 6:20:33, time: 0.601, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0216, loss_rpn_bbox: 0.0376, loss_cls: 0.1865, acc: 93.0945, loss_bbox: 0.2365, loss_mask: 0.2328, loss: 0.7149 +2024-05-27 23:28:37,572 - mmdet - INFO - Epoch [7][6700/7330] lr: 1.000e-04, eta: 6:20:02, time: 0.598, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0258, loss_rpn_bbox: 0.0388, loss_cls: 0.1892, acc: 93.0776, loss_bbox: 0.2426, loss_mask: 0.2435, loss: 0.7399 +2024-05-27 23:29:07,552 - mmdet - INFO - Epoch [7][6750/7330] lr: 1.000e-04, eta: 6:19:31, time: 0.600, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0245, loss_rpn_bbox: 0.0369, loss_cls: 0.1843, acc: 93.2847, loss_bbox: 0.2336, loss_mask: 0.2369, loss: 0.7162 +2024-05-27 23:29:37,373 - mmdet - INFO - Epoch [7][6800/7330] lr: 1.000e-04, eta: 6:19:00, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0230, loss_rpn_bbox: 0.0363, loss_cls: 0.1846, acc: 93.2473, loss_bbox: 0.2365, loss_mask: 0.2335, loss: 0.7139 +2024-05-27 23:30:07,192 - mmdet - INFO - Epoch [7][6850/7330] lr: 1.000e-04, eta: 6:18:29, time: 0.596, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0220, loss_rpn_bbox: 0.0362, loss_cls: 0.1823, acc: 93.4282, loss_bbox: 0.2348, loss_mask: 0.2428, loss: 0.7180 +2024-05-27 23:30:37,152 - mmdet - INFO - Epoch [7][6900/7330] lr: 1.000e-04, eta: 6:17:58, time: 0.599, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0243, loss_rpn_bbox: 0.0389, loss_cls: 0.1821, acc: 93.4109, loss_bbox: 0.2366, loss_mask: 0.2379, loss: 0.7198 +2024-05-27 23:31:06,961 - mmdet - INFO - Epoch [7][6950/7330] lr: 1.000e-04, eta: 6:17:27, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0256, loss_rpn_bbox: 0.0400, loss_cls: 0.1843, acc: 93.1228, loss_bbox: 0.2444, loss_mask: 0.2426, loss: 0.7368 +2024-05-27 23:31:38,911 - mmdet - INFO - Epoch [7][7000/7330] lr: 1.000e-04, eta: 6:16:57, time: 0.639, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0229, loss_rpn_bbox: 0.0371, loss_cls: 0.1821, acc: 93.3098, loss_bbox: 0.2374, loss_mask: 0.2398, loss: 0.7192 +2024-05-27 23:32:08,767 - mmdet - INFO - Epoch [7][7050/7330] lr: 1.000e-04, eta: 6:16:26, time: 0.597, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0240, loss_rpn_bbox: 0.0382, loss_cls: 0.1955, acc: 92.8333, loss_bbox: 0.2515, loss_mask: 0.2450, loss: 0.7541 +2024-05-27 23:32:38,552 - mmdet - INFO - Epoch [7][7100/7330] lr: 1.000e-04, eta: 6:15:55, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0219, loss_rpn_bbox: 0.0359, loss_cls: 0.1804, acc: 93.4163, loss_bbox: 0.2347, loss_mask: 0.2415, loss: 0.7144 +2024-05-27 23:33:08,541 - mmdet - INFO - Epoch [7][7150/7330] lr: 1.000e-04, eta: 6:15:24, time: 0.600, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0214, loss_rpn_bbox: 0.0355, loss_cls: 0.1767, acc: 93.5024, loss_bbox: 0.2329, loss_mask: 0.2367, loss: 0.7031 +2024-05-27 23:33:38,489 - mmdet - INFO - Epoch [7][7200/7330] lr: 1.000e-04, eta: 6:14:53, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0352, loss_cls: 0.1745, acc: 93.6582, loss_bbox: 0.2289, loss_mask: 0.2320, loss: 0.6940 +2024-05-27 23:34:11,047 - mmdet - INFO - Epoch [7][7250/7330] lr: 1.000e-04, eta: 6:14:24, time: 0.651, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0215, loss_rpn_bbox: 0.0357, loss_cls: 0.1834, acc: 93.3735, loss_bbox: 0.2370, loss_mask: 0.2365, loss: 0.7140 +2024-05-27 23:34:40,874 - mmdet - INFO - Epoch [7][7300/7330] lr: 1.000e-04, eta: 6:13:52, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0232, loss_rpn_bbox: 0.0368, loss_cls: 0.1810, acc: 93.3818, loss_bbox: 0.2308, loss_mask: 0.2404, loss: 0.7123 +2024-05-27 23:34:59,385 - mmdet - INFO - Saving checkpoint at 7 epochs +2024-05-27 23:36:55,730 - mmdet - INFO - Evaluating bbox... +2024-05-27 23:37:18,863 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.436 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.673 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.474 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.248 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.483 + 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 ] = 0.549 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.549 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.549 + 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 ] = 0.603 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.735 + +2024-05-27 23:37:18,864 - mmdet - INFO - Evaluating segm... +2024-05-27 23:37:46,839 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.390 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.635 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.411 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.170 + 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 ] = 0.625 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.493 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.493 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.493 + 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 ] = 0.548 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.707 + +2024-05-27 23:37:47,280 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-27 23:37:47,282 - mmdet - INFO - Epoch(val) [7][625] bbox_mAP: 0.4360, bbox_mAP_50: 0.6730, bbox_mAP_75: 0.4740, bbox_mAP_s: 0.2480, bbox_mAP_m: 0.4830, bbox_mAP_l: 0.6190, bbox_mAP_copypaste: 0.436 0.673 0.474 0.248 0.483 0.619, segm_mAP: 0.3900, segm_mAP_50: 0.6350, segm_mAP_75: 0.4110, segm_mAP_s: 0.1700, segm_mAP_m: 0.4290, segm_mAP_l: 0.6250, segm_mAP_copypaste: 0.390 0.635 0.411 0.170 0.429 0.625 +2024-05-27 23:38:22,623 - mmdet - INFO - Epoch [8][50/7330] lr: 1.000e-04, eta: 6:12:54, time: 0.707, data_time: 0.084, memory: 9459, loss_rpn_cls: 0.0183, loss_rpn_bbox: 0.0360, loss_cls: 0.1729, acc: 93.5879, loss_bbox: 0.2274, loss_mask: 0.2306, loss: 0.6853 +2024-05-27 23:38:54,736 - mmdet - INFO - Epoch [8][100/7330] lr: 1.000e-04, eta: 6:12:24, time: 0.642, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0209, loss_rpn_bbox: 0.0380, loss_cls: 0.1791, acc: 93.4443, loss_bbox: 0.2365, loss_mask: 0.2348, loss: 0.7092 +2024-05-27 23:39:29,619 - mmdet - INFO - Epoch [8][150/7330] lr: 1.000e-04, eta: 6:11:57, time: 0.697, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0378, loss_cls: 0.1744, acc: 93.4128, loss_bbox: 0.2346, loss_mask: 0.2372, loss: 0.7053 +2024-05-27 23:39:59,394 - mmdet - INFO - Epoch [8][200/7330] lr: 1.000e-04, eta: 6:11:26, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0195, loss_rpn_bbox: 0.0365, loss_cls: 0.1758, acc: 93.4199, loss_bbox: 0.2385, loss_mask: 0.2401, loss: 0.7104 +2024-05-27 23:40:29,290 - mmdet - INFO - Epoch [8][250/7330] lr: 1.000e-04, eta: 6:10:55, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0210, loss_rpn_bbox: 0.0366, loss_cls: 0.1762, acc: 93.4773, loss_bbox: 0.2368, loss_mask: 0.2371, loss: 0.7077 +2024-05-27 23:40:59,108 - mmdet - INFO - Epoch [8][300/7330] lr: 1.000e-04, eta: 6:10:24, time: 0.596, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0218, loss_rpn_bbox: 0.0366, loss_cls: 0.1775, acc: 93.5198, loss_bbox: 0.2319, loss_mask: 0.2300, loss: 0.6978 +2024-05-27 23:41:29,067 - mmdet - INFO - Epoch [8][350/7330] lr: 1.000e-04, eta: 6:09:53, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0386, loss_cls: 0.1854, acc: 93.2197, loss_bbox: 0.2384, loss_mask: 0.2353, loss: 0.7184 +2024-05-27 23:42:01,417 - mmdet - INFO - Epoch [8][400/7330] lr: 1.000e-04, eta: 6:09:23, time: 0.647, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0187, loss_rpn_bbox: 0.0351, loss_cls: 0.1691, acc: 93.6145, loss_bbox: 0.2263, loss_mask: 0.2295, loss: 0.6787 +2024-05-27 23:42:33,416 - mmdet - INFO - Epoch [8][450/7330] lr: 1.000e-04, eta: 6:08:54, time: 0.640, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0370, loss_cls: 0.1777, acc: 93.4756, loss_bbox: 0.2317, loss_mask: 0.2344, loss: 0.7007 +2024-05-27 23:43:03,161 - mmdet - INFO - Epoch [8][500/7330] lr: 1.000e-04, eta: 6:08:23, time: 0.595, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0366, loss_cls: 0.1776, acc: 93.4231, loss_bbox: 0.2350, loss_mask: 0.2323, loss: 0.7014 +2024-05-27 23:43:33,081 - mmdet - INFO - Epoch [8][550/7330] lr: 1.000e-04, eta: 6:07:52, time: 0.598, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0209, loss_rpn_bbox: 0.0375, loss_cls: 0.1757, acc: 93.3799, loss_bbox: 0.2346, loss_mask: 0.2335, loss: 0.7022 +2024-05-27 23:44:02,901 - mmdet - INFO - Epoch [8][600/7330] lr: 1.000e-04, eta: 6:07:20, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0188, loss_rpn_bbox: 0.0334, loss_cls: 0.1624, acc: 93.8887, loss_bbox: 0.2197, loss_mask: 0.2308, loss: 0.6650 +2024-05-27 23:44:32,691 - mmdet - INFO - Epoch [8][650/7330] lr: 1.000e-04, eta: 6:06:49, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0203, loss_rpn_bbox: 0.0368, loss_cls: 0.1769, acc: 93.3093, loss_bbox: 0.2344, loss_mask: 0.2368, loss: 0.7052 +2024-05-27 23:45:02,378 - mmdet - INFO - Epoch [8][700/7330] lr: 1.000e-04, eta: 6:06:18, time: 0.594, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0202, loss_rpn_bbox: 0.0374, loss_cls: 0.1746, acc: 93.5513, loss_bbox: 0.2332, loss_mask: 0.2347, loss: 0.7001 +2024-05-27 23:45:32,035 - mmdet - INFO - Epoch [8][750/7330] lr: 1.000e-04, eta: 6:05:47, time: 0.593, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0197, loss_rpn_bbox: 0.0320, loss_cls: 0.1708, acc: 93.7649, loss_bbox: 0.2219, loss_mask: 0.2287, loss: 0.6732 +2024-05-27 23:46:01,730 - mmdet - INFO - Epoch [8][800/7330] lr: 1.000e-04, eta: 6:05:16, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0221, loss_rpn_bbox: 0.0364, loss_cls: 0.1727, acc: 93.6431, loss_bbox: 0.2275, loss_mask: 0.2342, loss: 0.6928 +2024-05-27 23:46:31,528 - mmdet - INFO - Epoch [8][850/7330] lr: 1.000e-04, eta: 6:04:45, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0211, loss_rpn_bbox: 0.0389, loss_cls: 0.1785, acc: 93.3687, loss_bbox: 0.2368, loss_mask: 0.2336, loss: 0.7090 +2024-05-27 23:47:01,619 - mmdet - INFO - Epoch [8][900/7330] lr: 1.000e-04, eta: 6:04:14, time: 0.602, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0191, loss_rpn_bbox: 0.0358, loss_cls: 0.1785, acc: 93.2695, loss_bbox: 0.2374, loss_mask: 0.2345, loss: 0.7053 +2024-05-27 23:47:31,530 - mmdet - INFO - Epoch [8][950/7330] lr: 1.000e-04, eta: 6:03:43, time: 0.598, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0204, loss_rpn_bbox: 0.0363, loss_cls: 0.1770, acc: 93.4377, loss_bbox: 0.2325, loss_mask: 0.2317, loss: 0.6979 +2024-05-27 23:48:01,459 - mmdet - INFO - Epoch [8][1000/7330] lr: 1.000e-04, eta: 6:03:12, time: 0.599, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0210, loss_rpn_bbox: 0.0392, loss_cls: 0.1803, acc: 93.2507, loss_bbox: 0.2427, loss_mask: 0.2337, loss: 0.7169 +2024-05-27 23:48:31,470 - mmdet - INFO - Epoch [8][1050/7330] lr: 1.000e-04, eta: 6:02:41, time: 0.600, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0243, loss_rpn_bbox: 0.0401, loss_cls: 0.1830, acc: 93.2117, loss_bbox: 0.2408, loss_mask: 0.2430, loss: 0.7311 +2024-05-27 23:49:04,928 - mmdet - INFO - Epoch [8][1100/7330] lr: 1.000e-04, eta: 6:02:12, time: 0.669, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0219, loss_rpn_bbox: 0.0373, loss_cls: 0.1769, acc: 93.4175, loss_bbox: 0.2351, loss_mask: 0.2343, loss: 0.7056 +2024-05-27 23:49:36,748 - mmdet - INFO - Epoch [8][1150/7330] lr: 1.000e-04, eta: 6:01:43, time: 0.636, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0199, loss_rpn_bbox: 0.0356, loss_cls: 0.1775, acc: 93.5010, loss_bbox: 0.2308, loss_mask: 0.2286, loss: 0.6923 +2024-05-27 23:50:13,459 - mmdet - INFO - Epoch [8][1200/7330] lr: 1.000e-04, eta: 6:01:16, time: 0.734, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0209, loss_rpn_bbox: 0.0360, loss_cls: 0.1820, acc: 93.2676, loss_bbox: 0.2401, loss_mask: 0.2428, loss: 0.7218 +2024-05-27 23:50:45,563 - mmdet - INFO - Epoch [8][1250/7330] lr: 1.000e-04, eta: 6:00:47, time: 0.641, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0227, loss_rpn_bbox: 0.0367, loss_cls: 0.1806, acc: 93.3215, loss_bbox: 0.2339, loss_mask: 0.2393, loss: 0.7132 +2024-05-27 23:51:15,230 - mmdet - INFO - Epoch [8][1300/7330] lr: 1.000e-04, eta: 6:00:15, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0210, loss_rpn_bbox: 0.0370, loss_cls: 0.1749, acc: 93.5264, loss_bbox: 0.2312, loss_mask: 0.2356, loss: 0.6997 +2024-05-27 23:51:45,114 - mmdet - INFO - Epoch [8][1350/7330] lr: 1.000e-04, eta: 5:59:44, time: 0.598, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0211, loss_rpn_bbox: 0.0386, loss_cls: 0.1760, acc: 93.4519, loss_bbox: 0.2342, loss_mask: 0.2407, loss: 0.7106 +2024-05-27 23:52:14,923 - mmdet - INFO - Epoch [8][1400/7330] lr: 1.000e-04, eta: 5:59:13, time: 0.596, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0206, loss_rpn_bbox: 0.0356, loss_cls: 0.1696, acc: 93.7024, loss_bbox: 0.2268, loss_mask: 0.2306, loss: 0.6833 +2024-05-27 23:52:46,999 - mmdet - INFO - Epoch [8][1450/7330] lr: 1.000e-04, eta: 5:58:44, time: 0.641, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0207, loss_rpn_bbox: 0.0362, loss_cls: 0.1713, acc: 93.6545, loss_bbox: 0.2277, loss_mask: 0.2298, loss: 0.6858 +2024-05-27 23:53:18,865 - mmdet - INFO - Epoch [8][1500/7330] lr: 1.000e-04, eta: 5:58:14, time: 0.637, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0186, loss_rpn_bbox: 0.0337, loss_cls: 0.1712, acc: 93.7268, loss_bbox: 0.2258, loss_mask: 0.2364, loss: 0.6856 +2024-05-27 23:53:48,734 - mmdet - INFO - Epoch [8][1550/7330] lr: 1.000e-04, eta: 5:57:43, time: 0.597, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0216, loss_rpn_bbox: 0.0359, loss_cls: 0.1643, acc: 93.9377, loss_bbox: 0.2184, loss_mask: 0.2325, loss: 0.6728 +2024-05-27 23:54:18,512 - mmdet - INFO - Epoch [8][1600/7330] lr: 1.000e-04, eta: 5:57:12, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0195, loss_rpn_bbox: 0.0364, loss_cls: 0.1708, acc: 93.7268, loss_bbox: 0.2235, loss_mask: 0.2357, loss: 0.6860 +2024-05-27 23:54:48,312 - mmdet - INFO - Epoch [8][1650/7330] lr: 1.000e-04, eta: 5:56:41, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0199, loss_rpn_bbox: 0.0361, loss_cls: 0.1817, acc: 93.3274, loss_bbox: 0.2362, loss_mask: 0.2336, loss: 0.7076 +2024-05-27 23:55:18,141 - mmdet - INFO - Epoch [8][1700/7330] lr: 1.000e-04, eta: 5:56:10, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0216, loss_rpn_bbox: 0.0378, loss_cls: 0.1814, acc: 93.3086, loss_bbox: 0.2398, loss_mask: 0.2427, loss: 0.7233 +2024-05-27 23:55:48,167 - mmdet - INFO - Epoch [8][1750/7330] lr: 1.000e-04, eta: 5:55:39, time: 0.601, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0217, loss_rpn_bbox: 0.0399, loss_cls: 0.1845, acc: 93.2087, loss_bbox: 0.2390, loss_mask: 0.2358, loss: 0.7210 +2024-05-27 23:56:17,973 - mmdet - INFO - Epoch [8][1800/7330] lr: 1.000e-04, eta: 5:55:08, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0209, loss_rpn_bbox: 0.0379, loss_cls: 0.1839, acc: 93.1733, loss_bbox: 0.2398, loss_mask: 0.2399, loss: 0.7225 +2024-05-27 23:56:48,066 - mmdet - INFO - Epoch [8][1850/7330] lr: 1.000e-04, eta: 5:54:37, time: 0.602, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0219, loss_rpn_bbox: 0.0378, loss_cls: 0.1804, acc: 93.2439, loss_bbox: 0.2395, loss_mask: 0.2350, loss: 0.7146 +2024-05-27 23:57:17,968 - mmdet - INFO - Epoch [8][1900/7330] lr: 1.000e-04, eta: 5:54:06, time: 0.598, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0381, loss_cls: 0.1742, acc: 93.5273, loss_bbox: 0.2309, loss_mask: 0.2398, loss: 0.7037 +2024-05-27 23:57:47,767 - mmdet - INFO - Epoch [8][1950/7330] lr: 1.000e-04, eta: 5:53:35, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0199, loss_rpn_bbox: 0.0356, loss_cls: 0.1762, acc: 93.4783, loss_bbox: 0.2363, loss_mask: 0.2383, loss: 0.7063 +2024-05-27 23:58:17,577 - mmdet - INFO - Epoch [8][2000/7330] lr: 1.000e-04, eta: 5:53:04, time: 0.596, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0205, loss_rpn_bbox: 0.0362, loss_cls: 0.1721, acc: 93.6230, loss_bbox: 0.2310, loss_mask: 0.2341, loss: 0.6940 +2024-05-27 23:58:47,425 - mmdet - INFO - Epoch [8][2050/7330] lr: 1.000e-04, eta: 5:52:33, time: 0.597, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0211, loss_rpn_bbox: 0.0348, loss_cls: 0.1665, acc: 93.8599, loss_bbox: 0.2239, loss_mask: 0.2303, loss: 0.6766 +2024-05-27 23:59:17,311 - mmdet - INFO - Epoch [8][2100/7330] lr: 1.000e-04, eta: 5:52:02, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0225, loss_rpn_bbox: 0.0393, loss_cls: 0.1790, acc: 93.3955, loss_bbox: 0.2383, loss_mask: 0.2420, loss: 0.7211 +2024-05-27 23:59:50,775 - mmdet - INFO - Epoch [8][2150/7330] lr: 1.000e-04, eta: 5:51:33, time: 0.670, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0196, loss_rpn_bbox: 0.0332, loss_cls: 0.1645, acc: 94.0049, loss_bbox: 0.2146, loss_mask: 0.2279, loss: 0.6598 +2024-05-28 00:00:23,768 - mmdet - INFO - Epoch [8][2200/7330] lr: 1.000e-04, eta: 5:51:04, time: 0.660, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0202, loss_rpn_bbox: 0.0355, loss_cls: 0.1803, acc: 93.3311, loss_bbox: 0.2346, loss_mask: 0.2395, loss: 0.7101 +2024-05-28 00:01:00,317 - mmdet - INFO - Epoch [8][2250/7330] lr: 1.000e-04, eta: 5:50:37, time: 0.731, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0210, loss_rpn_bbox: 0.0398, loss_cls: 0.1824, acc: 93.2900, loss_bbox: 0.2368, loss_mask: 0.2357, loss: 0.7156 +2024-05-28 00:01:33,653 - mmdet - INFO - Epoch [8][2300/7330] lr: 1.000e-04, eta: 5:50:08, time: 0.667, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0194, loss_rpn_bbox: 0.0342, loss_cls: 0.1736, acc: 93.5920, loss_bbox: 0.2257, loss_mask: 0.2324, loss: 0.6853 +2024-05-28 00:02:03,288 - mmdet - INFO - Epoch [8][2350/7330] lr: 1.000e-04, eta: 5:49:37, time: 0.593, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0202, loss_rpn_bbox: 0.0342, loss_cls: 0.1651, acc: 93.8425, loss_bbox: 0.2166, loss_mask: 0.2264, loss: 0.6626 +2024-05-28 00:02:33,025 - mmdet - INFO - Epoch [8][2400/7330] lr: 1.000e-04, eta: 5:49:06, time: 0.595, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0219, loss_rpn_bbox: 0.0370, loss_cls: 0.1730, acc: 93.5415, loss_bbox: 0.2263, loss_mask: 0.2274, loss: 0.6856 +2024-05-28 00:03:02,917 - mmdet - INFO - Epoch [8][2450/7330] lr: 1.000e-04, eta: 5:48:35, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0214, loss_rpn_bbox: 0.0358, loss_cls: 0.1740, acc: 93.6133, loss_bbox: 0.2255, loss_mask: 0.2331, loss: 0.6897 +2024-05-28 00:03:34,998 - mmdet - INFO - Epoch [8][2500/7330] lr: 1.000e-04, eta: 5:48:05, time: 0.642, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0357, loss_cls: 0.1768, acc: 93.4136, loss_bbox: 0.2347, loss_mask: 0.2343, loss: 0.7027 +2024-05-28 00:04:06,743 - mmdet - INFO - Epoch [8][2550/7330] lr: 1.000e-04, eta: 5:47:36, time: 0.635, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0343, loss_cls: 0.1704, acc: 93.7581, loss_bbox: 0.2212, loss_mask: 0.2278, loss: 0.6734 +2024-05-28 00:04:36,589 - mmdet - INFO - Epoch [8][2600/7330] lr: 1.000e-04, eta: 5:47:05, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0187, loss_rpn_bbox: 0.0368, loss_cls: 0.1754, acc: 93.4763, loss_bbox: 0.2320, loss_mask: 0.2340, loss: 0.6969 +2024-05-28 00:05:06,337 - mmdet - INFO - Epoch [8][2650/7330] lr: 1.000e-04, eta: 5:46:33, time: 0.595, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0200, loss_rpn_bbox: 0.0357, loss_cls: 0.1735, acc: 93.6008, loss_bbox: 0.2287, loss_mask: 0.2335, loss: 0.6913 +2024-05-28 00:05:36,160 - mmdet - INFO - Epoch [8][2700/7330] lr: 1.000e-04, eta: 5:46:02, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0355, loss_cls: 0.1730, acc: 93.6099, loss_bbox: 0.2290, loss_mask: 0.2315, loss: 0.6898 +2024-05-28 00:06:06,058 - mmdet - INFO - Epoch [8][2750/7330] lr: 1.000e-04, eta: 5:45:31, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0209, loss_rpn_bbox: 0.0360, loss_cls: 0.1721, acc: 93.6382, loss_bbox: 0.2258, loss_mask: 0.2312, loss: 0.6860 +2024-05-28 00:06:35,797 - mmdet - INFO - Epoch [8][2800/7330] lr: 1.000e-04, eta: 5:45:00, time: 0.595, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0220, loss_rpn_bbox: 0.0386, loss_cls: 0.1774, acc: 93.4304, loss_bbox: 0.2323, loss_mask: 0.2329, loss: 0.7031 +2024-05-28 00:07:05,490 - mmdet - INFO - Epoch [8][2850/7330] lr: 1.000e-04, eta: 5:44:29, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0216, loss_rpn_bbox: 0.0360, loss_cls: 0.1723, acc: 93.5598, loss_bbox: 0.2281, loss_mask: 0.2332, loss: 0.6911 +2024-05-28 00:07:35,393 - mmdet - INFO - Epoch [8][2900/7330] lr: 1.000e-04, eta: 5:43:58, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0200, loss_rpn_bbox: 0.0378, loss_cls: 0.1769, acc: 93.4175, loss_bbox: 0.2307, loss_mask: 0.2380, loss: 0.7034 +2024-05-28 00:08:05,071 - mmdet - INFO - Epoch [8][2950/7330] lr: 1.000e-04, eta: 5:43:27, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0190, loss_rpn_bbox: 0.0349, loss_cls: 0.1674, acc: 93.8562, loss_bbox: 0.2198, loss_mask: 0.2306, loss: 0.6717 +2024-05-28 00:08:34,847 - mmdet - INFO - Epoch [8][3000/7330] lr: 1.000e-04, eta: 5:42:56, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0201, loss_rpn_bbox: 0.0369, loss_cls: 0.1728, acc: 93.5471, loss_bbox: 0.2336, loss_mask: 0.2363, loss: 0.6998 +2024-05-28 00:09:04,681 - mmdet - INFO - Epoch [8][3050/7330] lr: 1.000e-04, eta: 5:42:25, time: 0.597, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0211, loss_rpn_bbox: 0.0344, loss_cls: 0.1730, acc: 93.5479, loss_bbox: 0.2271, loss_mask: 0.2330, loss: 0.6886 +2024-05-28 00:09:34,440 - mmdet - INFO - Epoch [8][3100/7330] lr: 1.000e-04, eta: 5:41:54, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0195, loss_rpn_bbox: 0.0341, loss_cls: 0.1702, acc: 93.6921, loss_bbox: 0.2251, loss_mask: 0.2377, loss: 0.6866 +2024-05-28 00:10:04,534 - mmdet - INFO - Epoch [8][3150/7330] lr: 1.000e-04, eta: 5:41:23, time: 0.602, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0228, loss_rpn_bbox: 0.0385, loss_cls: 0.1814, acc: 93.2483, loss_bbox: 0.2352, loss_mask: 0.2318, loss: 0.7098 +2024-05-28 00:10:36,475 - mmdet - INFO - Epoch [8][3200/7330] lr: 1.000e-04, eta: 5:40:53, time: 0.639, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0211, loss_rpn_bbox: 0.0390, loss_cls: 0.1821, acc: 93.2800, loss_bbox: 0.2361, loss_mask: 0.2330, loss: 0.7113 +2024-05-28 00:11:08,531 - mmdet - INFO - Epoch [8][3250/7330] lr: 1.000e-04, eta: 5:40:24, time: 0.641, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0201, loss_rpn_bbox: 0.0352, loss_cls: 0.1786, acc: 93.4807, loss_bbox: 0.2267, loss_mask: 0.2391, loss: 0.6998 +2024-05-28 00:11:44,920 - mmdet - INFO - Epoch [8][3300/7330] lr: 1.000e-04, eta: 5:39:57, time: 0.728, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0213, loss_rpn_bbox: 0.0353, loss_cls: 0.1694, acc: 93.7671, loss_bbox: 0.2235, loss_mask: 0.2346, loss: 0.6841 +2024-05-28 00:12:17,116 - mmdet - INFO - Epoch [8][3350/7330] lr: 1.000e-04, eta: 5:39:27, time: 0.644, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0223, loss_rpn_bbox: 0.0393, loss_cls: 0.1806, acc: 93.3298, loss_bbox: 0.2370, loss_mask: 0.2327, loss: 0.7119 +2024-05-28 00:12:47,125 - mmdet - INFO - Epoch [8][3400/7330] lr: 1.000e-04, eta: 5:38:56, time: 0.600, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0216, loss_rpn_bbox: 0.0380, loss_cls: 0.1773, acc: 93.3884, loss_bbox: 0.2326, loss_mask: 0.2352, loss: 0.7047 +2024-05-28 00:13:16,910 - mmdet - INFO - Epoch [8][3450/7330] lr: 1.000e-04, eta: 5:38:25, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0226, loss_rpn_bbox: 0.0390, loss_cls: 0.1833, acc: 93.2378, loss_bbox: 0.2373, loss_mask: 0.2360, loss: 0.7183 +2024-05-28 00:13:46,689 - mmdet - INFO - Epoch [8][3500/7330] lr: 1.000e-04, eta: 5:37:54, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0344, loss_cls: 0.1709, acc: 93.6660, loss_bbox: 0.2227, loss_mask: 0.2279, loss: 0.6772 +2024-05-28 00:14:18,954 - mmdet - INFO - Epoch [8][3550/7330] lr: 1.000e-04, eta: 5:37:24, time: 0.645, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0413, loss_cls: 0.1836, acc: 93.2422, loss_bbox: 0.2462, loss_mask: 0.2454, loss: 0.7400 +2024-05-28 00:14:50,953 - mmdet - INFO - Epoch [8][3600/7330] lr: 1.000e-04, eta: 5:36:55, time: 0.640, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0226, loss_rpn_bbox: 0.0369, loss_cls: 0.1762, acc: 93.4507, loss_bbox: 0.2284, loss_mask: 0.2307, loss: 0.6947 +2024-05-28 00:15:20,862 - mmdet - INFO - Epoch [8][3650/7330] lr: 1.000e-04, eta: 5:36:24, time: 0.598, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0231, loss_rpn_bbox: 0.0415, loss_cls: 0.1888, acc: 93.0632, loss_bbox: 0.2501, loss_mask: 0.2494, loss: 0.7529 +2024-05-28 00:15:50,434 - mmdet - INFO - Epoch [8][3700/7330] lr: 1.000e-04, eta: 5:35:52, time: 0.591, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0376, loss_cls: 0.1843, acc: 93.2488, loss_bbox: 0.2364, loss_mask: 0.2379, loss: 0.7174 +2024-05-28 00:16:20,236 - mmdet - INFO - Epoch [8][3750/7330] lr: 1.000e-04, eta: 5:35:21, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0219, loss_rpn_bbox: 0.0357, loss_cls: 0.1757, acc: 93.6296, loss_bbox: 0.2254, loss_mask: 0.2269, loss: 0.6857 +2024-05-28 00:16:50,006 - mmdet - INFO - Epoch [8][3800/7330] lr: 1.000e-04, eta: 5:34:50, time: 0.595, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0209, loss_rpn_bbox: 0.0376, loss_cls: 0.1838, acc: 93.2986, loss_bbox: 0.2367, loss_mask: 0.2355, loss: 0.7146 +2024-05-28 00:17:19,829 - mmdet - INFO - Epoch [8][3850/7330] lr: 1.000e-04, eta: 5:34:19, time: 0.596, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0362, loss_cls: 0.1768, acc: 93.5522, loss_bbox: 0.2310, loss_mask: 0.2323, loss: 0.6972 +2024-05-28 00:17:49,662 - mmdet - INFO - Epoch [8][3900/7330] lr: 1.000e-04, eta: 5:33:48, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0220, loss_rpn_bbox: 0.0367, loss_cls: 0.1786, acc: 93.3621, loss_bbox: 0.2329, loss_mask: 0.2438, loss: 0.7139 +2024-05-28 00:18:19,314 - mmdet - INFO - Epoch [8][3950/7330] lr: 1.000e-04, eta: 5:33:17, time: 0.593, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0194, loss_rpn_bbox: 0.0362, loss_cls: 0.1762, acc: 93.4768, loss_bbox: 0.2307, loss_mask: 0.2345, loss: 0.6970 +2024-05-28 00:18:49,074 - mmdet - INFO - Epoch [8][4000/7330] lr: 1.000e-04, eta: 5:32:46, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0196, loss_rpn_bbox: 0.0346, loss_cls: 0.1670, acc: 93.7737, loss_bbox: 0.2196, loss_mask: 0.2291, loss: 0.6698 +2024-05-28 00:19:18,810 - mmdet - INFO - Epoch [8][4050/7330] lr: 1.000e-04, eta: 5:32:15, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0209, loss_rpn_bbox: 0.0363, loss_cls: 0.1762, acc: 93.5232, loss_bbox: 0.2306, loss_mask: 0.2325, loss: 0.6966 +2024-05-28 00:19:48,581 - mmdet - INFO - Epoch [8][4100/7330] lr: 1.000e-04, eta: 5:31:44, time: 0.595, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0216, loss_rpn_bbox: 0.0379, loss_cls: 0.1798, acc: 93.3760, loss_bbox: 0.2384, loss_mask: 0.2410, loss: 0.7187 +2024-05-28 00:20:18,337 - mmdet - INFO - Epoch [8][4150/7330] lr: 1.000e-04, eta: 5:31:13, time: 0.595, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0229, loss_rpn_bbox: 0.0385, loss_cls: 0.1843, acc: 93.2634, loss_bbox: 0.2357, loss_mask: 0.2368, loss: 0.7182 +2024-05-28 00:20:48,172 - mmdet - INFO - Epoch [8][4200/7330] lr: 1.000e-04, eta: 5:30:42, time: 0.597, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0218, loss_rpn_bbox: 0.0364, loss_cls: 0.1778, acc: 93.5247, loss_bbox: 0.2325, loss_mask: 0.2364, loss: 0.7049 +2024-05-28 00:21:20,579 - mmdet - INFO - Epoch [8][4250/7330] lr: 1.000e-04, eta: 5:30:12, time: 0.648, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0217, loss_rpn_bbox: 0.0374, loss_cls: 0.1757, acc: 93.4304, loss_bbox: 0.2312, loss_mask: 0.2342, loss: 0.7002 +2024-05-28 00:21:52,700 - mmdet - INFO - Epoch [8][4300/7330] lr: 1.000e-04, eta: 5:29:43, time: 0.642, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0215, loss_rpn_bbox: 0.0377, loss_cls: 0.1811, acc: 93.3184, loss_bbox: 0.2339, loss_mask: 0.2376, loss: 0.7119 +2024-05-28 00:22:29,230 - mmdet - INFO - Epoch [8][4350/7330] lr: 1.000e-04, eta: 5:29:16, time: 0.731, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0223, loss_rpn_bbox: 0.0362, loss_cls: 0.1796, acc: 93.2419, loss_bbox: 0.2377, loss_mask: 0.2411, loss: 0.7170 +2024-05-28 00:23:01,968 - mmdet - INFO - Epoch [8][4400/7330] lr: 1.000e-04, eta: 5:28:46, time: 0.655, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0201, loss_rpn_bbox: 0.0371, loss_cls: 0.1789, acc: 93.3296, loss_bbox: 0.2393, loss_mask: 0.2400, loss: 0.7154 +2024-05-28 00:23:31,737 - mmdet - INFO - Epoch [8][4450/7330] lr: 1.000e-04, eta: 5:28:15, time: 0.595, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0236, loss_rpn_bbox: 0.0394, loss_cls: 0.1780, acc: 93.4111, loss_bbox: 0.2364, loss_mask: 0.2403, loss: 0.7176 +2024-05-28 00:24:01,638 - mmdet - INFO - Epoch [8][4500/7330] lr: 1.000e-04, eta: 5:27:44, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0224, loss_rpn_bbox: 0.0359, loss_cls: 0.1787, acc: 93.4209, loss_bbox: 0.2321, loss_mask: 0.2385, loss: 0.7077 +2024-05-28 00:24:31,402 - mmdet - INFO - Epoch [8][4550/7330] lr: 1.000e-04, eta: 5:27:13, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0228, loss_rpn_bbox: 0.0377, loss_cls: 0.1807, acc: 93.4514, loss_bbox: 0.2342, loss_mask: 0.2375, loss: 0.7130 +2024-05-28 00:25:03,400 - mmdet - INFO - Epoch [8][4600/7330] lr: 1.000e-04, eta: 5:26:43, time: 0.640, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0236, loss_rpn_bbox: 0.0383, loss_cls: 0.1808, acc: 93.3596, loss_bbox: 0.2382, loss_mask: 0.2389, loss: 0.7196 +2024-05-28 00:25:36,321 - mmdet - INFO - Epoch [8][4650/7330] lr: 1.000e-04, eta: 5:26:14, time: 0.658, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0201, loss_rpn_bbox: 0.0362, loss_cls: 0.1799, acc: 93.3718, loss_bbox: 0.2357, loss_mask: 0.2358, loss: 0.7076 +2024-05-28 00:26:11,564 - mmdet - INFO - Epoch [8][4700/7330] lr: 1.000e-04, eta: 5:25:46, time: 0.705, data_time: 0.127, memory: 9459, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0351, loss_cls: 0.1751, acc: 93.5396, loss_bbox: 0.2304, loss_mask: 0.2326, loss: 0.6921 +2024-05-28 00:26:41,425 - mmdet - INFO - Epoch [8][4750/7330] lr: 1.000e-04, eta: 5:25:15, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0215, loss_rpn_bbox: 0.0402, loss_cls: 0.1892, acc: 92.9871, loss_bbox: 0.2417, loss_mask: 0.2350, loss: 0.7277 +2024-05-28 00:27:11,310 - mmdet - INFO - Epoch [8][4800/7330] lr: 1.000e-04, eta: 5:24:44, time: 0.598, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0383, loss_cls: 0.1788, acc: 93.3601, loss_bbox: 0.2336, loss_mask: 0.2378, loss: 0.7120 +2024-05-28 00:27:41,046 - mmdet - INFO - Epoch [8][4850/7330] lr: 1.000e-04, eta: 5:24:13, time: 0.595, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0201, loss_rpn_bbox: 0.0383, loss_cls: 0.1807, acc: 93.2380, loss_bbox: 0.2346, loss_mask: 0.2379, loss: 0.7116 +2024-05-28 00:28:10,670 - mmdet - INFO - Epoch [8][4900/7330] lr: 1.000e-04, eta: 5:23:42, time: 0.592, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0221, loss_rpn_bbox: 0.0356, loss_cls: 0.1788, acc: 93.5444, loss_bbox: 0.2280, loss_mask: 0.2285, loss: 0.6930 +2024-05-28 00:28:40,530 - mmdet - INFO - Epoch [8][4950/7330] lr: 1.000e-04, eta: 5:23:11, time: 0.597, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0225, loss_rpn_bbox: 0.0360, loss_cls: 0.1711, acc: 93.7361, loss_bbox: 0.2235, loss_mask: 0.2305, loss: 0.6835 +2024-05-28 00:29:10,246 - mmdet - INFO - Epoch [8][5000/7330] lr: 1.000e-04, eta: 5:22:40, time: 0.594, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0204, loss_rpn_bbox: 0.0331, loss_cls: 0.1681, acc: 93.8721, loss_bbox: 0.2219, loss_mask: 0.2338, loss: 0.6775 +2024-05-28 00:29:39,852 - mmdet - INFO - Epoch [8][5050/7330] lr: 1.000e-04, eta: 5:22:09, time: 0.592, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0339, loss_cls: 0.1760, acc: 93.5830, loss_bbox: 0.2232, loss_mask: 0.2264, loss: 0.6793 +2024-05-28 00:30:09,795 - mmdet - INFO - Epoch [8][5100/7330] lr: 1.000e-04, eta: 5:21:38, time: 0.599, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0219, loss_rpn_bbox: 0.0357, loss_cls: 0.1753, acc: 93.6624, loss_bbox: 0.2312, loss_mask: 0.2372, loss: 0.7013 +2024-05-28 00:30:39,676 - mmdet - INFO - Epoch [8][5150/7330] lr: 1.000e-04, eta: 5:21:07, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0210, loss_rpn_bbox: 0.0370, loss_cls: 0.1777, acc: 93.4326, loss_bbox: 0.2338, loss_mask: 0.2357, loss: 0.7051 +2024-05-28 00:31:09,700 - mmdet - INFO - Epoch [8][5200/7330] lr: 1.000e-04, eta: 5:20:36, time: 0.601, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0200, loss_rpn_bbox: 0.0358, loss_cls: 0.1750, acc: 93.4849, loss_bbox: 0.2300, loss_mask: 0.2333, loss: 0.6941 +2024-05-28 00:31:39,405 - mmdet - INFO - Epoch [8][5250/7330] lr: 1.000e-04, eta: 5:20:05, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0222, loss_rpn_bbox: 0.0364, loss_cls: 0.1818, acc: 93.3887, loss_bbox: 0.2306, loss_mask: 0.2349, loss: 0.7059 +2024-05-28 00:32:11,711 - mmdet - INFO - Epoch [8][5300/7330] lr: 1.000e-04, eta: 5:19:35, time: 0.646, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0197, loss_rpn_bbox: 0.0361, loss_cls: 0.1705, acc: 93.7119, loss_bbox: 0.2251, loss_mask: 0.2285, loss: 0.6799 +2024-05-28 00:32:43,769 - mmdet - INFO - Epoch [8][5350/7330] lr: 1.000e-04, eta: 5:19:06, time: 0.641, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0197, loss_rpn_bbox: 0.0353, loss_cls: 0.1745, acc: 93.6353, loss_bbox: 0.2265, loss_mask: 0.2306, loss: 0.6866 +2024-05-28 00:33:20,351 - mmdet - INFO - Epoch [8][5400/7330] lr: 1.000e-04, eta: 5:18:38, time: 0.732, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0222, loss_rpn_bbox: 0.0381, loss_cls: 0.1861, acc: 93.1863, loss_bbox: 0.2384, loss_mask: 0.2388, loss: 0.7237 +2024-05-28 00:33:52,632 - mmdet - INFO - Epoch [8][5450/7330] lr: 1.000e-04, eta: 5:18:09, time: 0.646, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0206, loss_rpn_bbox: 0.0361, loss_cls: 0.1816, acc: 93.2847, loss_bbox: 0.2360, loss_mask: 0.2340, loss: 0.7083 +2024-05-28 00:34:22,471 - mmdet - INFO - Epoch [8][5500/7330] lr: 1.000e-04, eta: 5:17:38, time: 0.597, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0342, loss_cls: 0.1705, acc: 93.6885, loss_bbox: 0.2264, loss_mask: 0.2336, loss: 0.6856 +2024-05-28 00:34:52,252 - mmdet - INFO - Epoch [8][5550/7330] lr: 1.000e-04, eta: 5:17:07, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0195, loss_rpn_bbox: 0.0351, loss_cls: 0.1741, acc: 93.5779, loss_bbox: 0.2278, loss_mask: 0.2298, loss: 0.6863 +2024-05-28 00:35:21,950 - mmdet - INFO - Epoch [8][5600/7330] lr: 1.000e-04, eta: 5:16:35, time: 0.594, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0206, loss_rpn_bbox: 0.0360, loss_cls: 0.1806, acc: 93.4402, loss_bbox: 0.2308, loss_mask: 0.2331, loss: 0.7010 +2024-05-28 00:35:54,225 - mmdet - INFO - Epoch [8][5650/7330] lr: 1.000e-04, eta: 5:16:06, time: 0.646, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0251, loss_rpn_bbox: 0.0392, loss_cls: 0.1828, acc: 93.2920, loss_bbox: 0.2411, loss_mask: 0.2448, loss: 0.7330 +2024-05-28 00:36:26,680 - mmdet - INFO - Epoch [8][5700/7330] lr: 1.000e-04, eta: 5:15:36, time: 0.649, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0203, loss_rpn_bbox: 0.0368, loss_cls: 0.1718, acc: 93.5344, loss_bbox: 0.2293, loss_mask: 0.2350, loss: 0.6931 +2024-05-28 00:36:56,623 - mmdet - INFO - Epoch [8][5750/7330] lr: 1.000e-04, eta: 5:15:05, time: 0.599, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0219, loss_rpn_bbox: 0.0366, loss_cls: 0.1760, acc: 93.5129, loss_bbox: 0.2319, loss_mask: 0.2340, loss: 0.7003 +2024-05-28 00:37:26,210 - mmdet - INFO - Epoch [8][5800/7330] lr: 1.000e-04, eta: 5:14:34, time: 0.592, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0228, loss_rpn_bbox: 0.0378, loss_cls: 0.1817, acc: 93.2893, loss_bbox: 0.2386, loss_mask: 0.2379, loss: 0.7188 +2024-05-28 00:37:56,205 - mmdet - INFO - Epoch [8][5850/7330] lr: 1.000e-04, eta: 5:14:03, time: 0.600, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0210, loss_rpn_bbox: 0.0362, loss_cls: 0.1722, acc: 93.6519, loss_bbox: 0.2295, loss_mask: 0.2389, loss: 0.6978 +2024-05-28 00:38:26,185 - mmdet - INFO - Epoch [8][5900/7330] lr: 1.000e-04, eta: 5:13:32, time: 0.600, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0246, loss_rpn_bbox: 0.0401, loss_cls: 0.1901, acc: 93.0466, loss_bbox: 0.2438, loss_mask: 0.2400, loss: 0.7386 +2024-05-28 00:38:55,856 - mmdet - INFO - Epoch [8][5950/7330] lr: 1.000e-04, eta: 5:13:01, time: 0.593, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0194, loss_rpn_bbox: 0.0345, loss_cls: 0.1704, acc: 93.8416, loss_bbox: 0.2236, loss_mask: 0.2299, loss: 0.6777 +2024-05-28 00:39:25,686 - mmdet - INFO - Epoch [8][6000/7330] lr: 1.000e-04, eta: 5:12:30, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0205, loss_rpn_bbox: 0.0382, loss_cls: 0.1794, acc: 93.3318, loss_bbox: 0.2403, loss_mask: 0.2377, loss: 0.7162 +2024-05-28 00:39:55,645 - mmdet - INFO - Epoch [8][6050/7330] lr: 1.000e-04, eta: 5:11:59, time: 0.599, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0192, loss_rpn_bbox: 0.0352, loss_cls: 0.1728, acc: 93.6023, loss_bbox: 0.2216, loss_mask: 0.2303, loss: 0.6791 +2024-05-28 00:40:25,676 - mmdet - INFO - Epoch [8][6100/7330] lr: 1.000e-04, eta: 5:11:28, time: 0.601, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0227, loss_rpn_bbox: 0.0376, loss_cls: 0.1847, acc: 93.3599, loss_bbox: 0.2369, loss_mask: 0.2392, loss: 0.7210 +2024-05-28 00:40:55,586 - mmdet - INFO - Epoch [8][6150/7330] lr: 1.000e-04, eta: 5:10:57, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0214, loss_rpn_bbox: 0.0371, loss_cls: 0.1746, acc: 93.5110, loss_bbox: 0.2345, loss_mask: 0.2406, loss: 0.7082 +2024-05-28 00:41:25,437 - mmdet - INFO - Epoch [8][6200/7330] lr: 1.000e-04, eta: 5:10:26, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0223, loss_rpn_bbox: 0.0382, loss_cls: 0.1806, acc: 93.2312, loss_bbox: 0.2402, loss_mask: 0.2373, loss: 0.7186 +2024-05-28 00:41:55,264 - mmdet - INFO - Epoch [8][6250/7330] lr: 1.000e-04, eta: 5:09:55, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0209, loss_rpn_bbox: 0.0364, loss_cls: 0.1799, acc: 93.3123, loss_bbox: 0.2327, loss_mask: 0.2290, loss: 0.6990 +2024-05-28 00:42:25,241 - mmdet - INFO - Epoch [8][6300/7330] lr: 1.000e-04, eta: 5:09:25, time: 0.600, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0206, loss_rpn_bbox: 0.0361, loss_cls: 0.1742, acc: 93.6670, loss_bbox: 0.2308, loss_mask: 0.2291, loss: 0.6908 +2024-05-28 00:42:57,707 - mmdet - INFO - Epoch [8][6350/7330] lr: 1.000e-04, eta: 5:08:55, time: 0.649, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0363, loss_cls: 0.1794, acc: 93.3879, loss_bbox: 0.2322, loss_mask: 0.2311, loss: 0.6998 +2024-05-28 00:43:32,526 - mmdet - INFO - Epoch [8][6400/7330] lr: 1.000e-04, eta: 5:08:27, time: 0.696, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0199, loss_rpn_bbox: 0.0344, loss_cls: 0.1715, acc: 93.6763, loss_bbox: 0.2224, loss_mask: 0.2268, loss: 0.6749 +2024-05-28 00:44:07,045 - mmdet - INFO - Epoch [8][6450/7330] lr: 1.000e-04, eta: 5:07:58, time: 0.690, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0223, loss_rpn_bbox: 0.0373, loss_cls: 0.1791, acc: 93.3428, loss_bbox: 0.2381, loss_mask: 0.2359, loss: 0.7126 +2024-05-28 00:44:39,471 - mmdet - INFO - Epoch [8][6500/7330] lr: 1.000e-04, eta: 5:07:28, time: 0.648, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0226, loss_rpn_bbox: 0.0359, loss_cls: 0.1810, acc: 93.3918, loss_bbox: 0.2269, loss_mask: 0.2359, loss: 0.7023 +2024-05-28 00:45:09,217 - mmdet - INFO - Epoch [8][6550/7330] lr: 1.000e-04, eta: 5:06:57, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0208, loss_rpn_bbox: 0.0349, loss_cls: 0.1665, acc: 93.7327, loss_bbox: 0.2239, loss_mask: 0.2330, loss: 0.6790 +2024-05-28 00:45:39,008 - mmdet - INFO - Epoch [8][6600/7330] lr: 1.000e-04, eta: 5:06:26, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0209, loss_rpn_bbox: 0.0360, loss_cls: 0.1806, acc: 93.5300, loss_bbox: 0.2255, loss_mask: 0.2288, loss: 0.6919 +2024-05-28 00:46:08,954 - mmdet - INFO - Epoch [8][6650/7330] lr: 1.000e-04, eta: 5:05:55, time: 0.599, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0216, loss_rpn_bbox: 0.0348, loss_cls: 0.1764, acc: 93.4800, loss_bbox: 0.2316, loss_mask: 0.2333, loss: 0.6976 +2024-05-28 00:46:41,124 - mmdet - INFO - Epoch [8][6700/7330] lr: 1.000e-04, eta: 5:05:26, time: 0.643, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0210, loss_rpn_bbox: 0.0347, loss_cls: 0.1723, acc: 93.6858, loss_bbox: 0.2252, loss_mask: 0.2350, loss: 0.6883 +2024-05-28 00:47:14,407 - mmdet - INFO - Epoch [8][6750/7330] lr: 1.000e-04, eta: 5:04:56, time: 0.666, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0219, loss_rpn_bbox: 0.0371, loss_cls: 0.1830, acc: 93.2185, loss_bbox: 0.2407, loss_mask: 0.2367, loss: 0.7194 +2024-05-28 00:47:44,218 - mmdet - INFO - Epoch [8][6800/7330] lr: 1.000e-04, eta: 5:04:25, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0217, loss_rpn_bbox: 0.0374, loss_cls: 0.1853, acc: 93.1348, loss_bbox: 0.2366, loss_mask: 0.2404, loss: 0.7214 +2024-05-28 00:48:14,060 - mmdet - INFO - Epoch [8][6850/7330] lr: 1.000e-04, eta: 5:03:54, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0226, loss_rpn_bbox: 0.0374, loss_cls: 0.1834, acc: 93.2473, loss_bbox: 0.2392, loss_mask: 0.2401, loss: 0.7228 +2024-05-28 00:48:44,052 - mmdet - INFO - Epoch [8][6900/7330] lr: 1.000e-04, eta: 5:03:24, time: 0.600, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0375, loss_cls: 0.1805, acc: 93.3423, loss_bbox: 0.2346, loss_mask: 0.2317, loss: 0.7078 +2024-05-28 00:49:13,828 - mmdet - INFO - Epoch [8][6950/7330] lr: 1.000e-04, eta: 5:02:53, time: 0.596, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0202, loss_rpn_bbox: 0.0353, loss_cls: 0.1741, acc: 93.4973, loss_bbox: 0.2250, loss_mask: 0.2345, loss: 0.6891 +2024-05-28 00:49:43,510 - mmdet - INFO - Epoch [8][7000/7330] lr: 1.000e-04, eta: 5:02:22, time: 0.594, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0202, loss_rpn_bbox: 0.0355, loss_cls: 0.1676, acc: 93.7598, loss_bbox: 0.2223, loss_mask: 0.2306, loss: 0.6762 +2024-05-28 00:50:13,377 - mmdet - INFO - Epoch [8][7050/7330] lr: 1.000e-04, eta: 5:01:51, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0194, loss_rpn_bbox: 0.0356, loss_cls: 0.1767, acc: 93.5332, loss_bbox: 0.2321, loss_mask: 0.2313, loss: 0.6950 +2024-05-28 00:50:43,180 - mmdet - INFO - Epoch [8][7100/7330] lr: 1.000e-04, eta: 5:01:20, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0216, loss_rpn_bbox: 0.0383, loss_cls: 0.1729, acc: 93.6541, loss_bbox: 0.2250, loss_mask: 0.2308, loss: 0.6886 +2024-05-28 00:51:13,104 - mmdet - INFO - Epoch [8][7150/7330] lr: 1.000e-04, eta: 5:00:49, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0201, loss_rpn_bbox: 0.0354, loss_cls: 0.1725, acc: 93.7034, loss_bbox: 0.2256, loss_mask: 0.2288, loss: 0.6824 +2024-05-28 00:51:42,793 - mmdet - INFO - Epoch [8][7200/7330] lr: 1.000e-04, eta: 5:00:18, time: 0.594, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0205, loss_rpn_bbox: 0.0373, loss_cls: 0.1729, acc: 93.5928, loss_bbox: 0.2264, loss_mask: 0.2365, loss: 0.6936 +2024-05-28 00:52:12,449 - mmdet - INFO - Epoch [8][7250/7330] lr: 1.000e-04, eta: 4:59:47, time: 0.593, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0209, loss_rpn_bbox: 0.0357, loss_cls: 0.1687, acc: 93.8108, loss_bbox: 0.2255, loss_mask: 0.2329, loss: 0.6836 +2024-05-28 00:52:42,426 - mmdet - INFO - Epoch [8][7300/7330] lr: 1.000e-04, eta: 4:59:16, time: 0.600, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0230, loss_rpn_bbox: 0.0389, loss_cls: 0.1873, acc: 93.0623, loss_bbox: 0.2463, loss_mask: 0.2407, loss: 0.7362 +2024-05-28 00:53:01,245 - mmdet - INFO - Saving checkpoint at 8 epochs +2024-05-28 00:54:56,817 - mmdet - INFO - Evaluating bbox... +2024-05-28 00:55:22,944 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.445 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.679 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.483 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.256 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.487 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.632 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.560 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.560 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.560 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.351 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.612 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.749 + +2024-05-28 00:55:22,944 - mmdet - INFO - Evaluating segm... +2024-05-28 00:55:47,066 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.395 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.638 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.417 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.171 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.437 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.628 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.500 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.500 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.500 + 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 ] = 0.555 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.713 + +2024-05-28 00:55:47,419 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-28 00:55:47,420 - mmdet - INFO - Epoch(val) [8][625] bbox_mAP: 0.4450, bbox_mAP_50: 0.6790, bbox_mAP_75: 0.4830, bbox_mAP_s: 0.2560, bbox_mAP_m: 0.4870, bbox_mAP_l: 0.6320, bbox_mAP_copypaste: 0.445 0.679 0.483 0.256 0.487 0.632, segm_mAP: 0.3950, segm_mAP_50: 0.6380, segm_mAP_75: 0.4170, segm_mAP_s: 0.1710, segm_mAP_m: 0.4370, segm_mAP_l: 0.6280, segm_mAP_copypaste: 0.395 0.638 0.417 0.171 0.437 0.628 +2024-05-28 00:56:23,733 - mmdet - INFO - Epoch [9][50/7330] lr: 1.000e-05, eta: 4:58:20, time: 0.726, data_time: 0.126, memory: 9459, loss_rpn_cls: 0.0190, loss_rpn_bbox: 0.0348, loss_cls: 0.1633, acc: 93.8640, loss_bbox: 0.2201, loss_mask: 0.2243, loss: 0.6615 +2024-05-28 00:56:53,695 - mmdet - INFO - Epoch [9][100/7330] lr: 1.000e-05, eta: 4:57:49, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0185, loss_rpn_bbox: 0.0355, loss_cls: 0.1630, acc: 93.8076, loss_bbox: 0.2205, loss_mask: 0.2253, loss: 0.6629 +2024-05-28 00:57:23,453 - mmdet - INFO - Epoch [9][150/7330] lr: 1.000e-05, eta: 4:57:19, time: 0.595, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0185, loss_rpn_bbox: 0.0350, loss_cls: 0.1656, acc: 93.8218, loss_bbox: 0.2193, loss_mask: 0.2283, loss: 0.6667 +2024-05-28 00:57:53,428 - mmdet - INFO - Epoch [9][200/7330] lr: 1.000e-05, eta: 4:56:48, time: 0.600, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0376, loss_cls: 0.1718, acc: 93.4517, loss_bbox: 0.2279, loss_mask: 0.2308, loss: 0.6893 +2024-05-28 00:58:23,223 - mmdet - INFO - Epoch [9][250/7330] lr: 1.000e-05, eta: 4:56:17, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0355, loss_cls: 0.1617, acc: 93.8562, loss_bbox: 0.2196, loss_mask: 0.2286, loss: 0.6636 +2024-05-28 00:58:54,481 - mmdet - INFO - Epoch [9][300/7330] lr: 1.000e-05, eta: 4:55:46, time: 0.625, data_time: 0.047, memory: 9459, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0372, loss_cls: 0.1644, acc: 93.8157, loss_bbox: 0.2190, loss_mask: 0.2276, loss: 0.6671 +2024-05-28 00:59:24,069 - mmdet - INFO - Epoch [9][350/7330] lr: 1.000e-05, eta: 4:55:15, time: 0.592, data_time: 0.015, memory: 9459, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0310, loss_cls: 0.1516, acc: 94.2786, loss_bbox: 0.2068, loss_mask: 0.2213, loss: 0.6272 +2024-05-28 00:59:54,158 - mmdet - INFO - Epoch [9][400/7330] lr: 1.000e-05, eta: 4:54:45, time: 0.602, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0184, loss_rpn_bbox: 0.0337, loss_cls: 0.1670, acc: 93.8523, loss_bbox: 0.2188, loss_mask: 0.2279, loss: 0.6657 +2024-05-28 01:00:24,022 - mmdet - INFO - Epoch [9][450/7330] lr: 1.000e-05, eta: 4:54:14, time: 0.597, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0188, loss_rpn_bbox: 0.0352, loss_cls: 0.1652, acc: 93.8044, loss_bbox: 0.2238, loss_mask: 0.2300, loss: 0.6730 +2024-05-28 01:00:53,905 - mmdet - INFO - Epoch [9][500/7330] lr: 1.000e-05, eta: 4:53:43, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0176, loss_rpn_bbox: 0.0334, loss_cls: 0.1631, acc: 93.8896, loss_bbox: 0.2161, loss_mask: 0.2203, loss: 0.6504 +2024-05-28 01:01:25,889 - mmdet - INFO - Epoch [9][550/7330] lr: 1.000e-05, eta: 4:53:13, time: 0.640, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0172, loss_rpn_bbox: 0.0347, loss_cls: 0.1615, acc: 93.8918, loss_bbox: 0.2139, loss_mask: 0.2252, loss: 0.6524 +2024-05-28 01:01:55,753 - mmdet - INFO - Epoch [9][600/7330] lr: 1.000e-05, eta: 4:52:42, time: 0.597, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0187, loss_rpn_bbox: 0.0352, loss_cls: 0.1610, acc: 93.9348, loss_bbox: 0.2174, loss_mask: 0.2259, loss: 0.6581 +2024-05-28 01:02:25,679 - mmdet - INFO - Epoch [9][650/7330] lr: 1.000e-05, eta: 4:52:11, time: 0.598, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0176, loss_rpn_bbox: 0.0341, loss_cls: 0.1676, acc: 93.6711, loss_bbox: 0.2271, loss_mask: 0.2278, loss: 0.6742 +2024-05-28 01:02:59,090 - mmdet - INFO - Epoch [9][700/7330] lr: 1.000e-05, eta: 4:51:42, time: 0.668, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0193, loss_rpn_bbox: 0.0382, loss_cls: 0.1664, acc: 93.7156, loss_bbox: 0.2276, loss_mask: 0.2277, loss: 0.6792 +2024-05-28 01:03:29,094 - mmdet - INFO - Epoch [9][750/7330] lr: 1.000e-05, eta: 4:51:11, time: 0.600, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0202, loss_rpn_bbox: 0.0367, loss_cls: 0.1660, acc: 93.7107, loss_bbox: 0.2223, loss_mask: 0.2287, loss: 0.6738 +2024-05-28 01:03:59,087 - mmdet - INFO - Epoch [9][800/7330] lr: 1.000e-05, eta: 4:50:40, time: 0.600, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0176, loss_rpn_bbox: 0.0340, loss_cls: 0.1606, acc: 93.9387, loss_bbox: 0.2180, loss_mask: 0.2214, loss: 0.6515 +2024-05-28 01:04:29,011 - mmdet - INFO - Epoch [9][850/7330] lr: 1.000e-05, eta: 4:50:09, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0190, loss_rpn_bbox: 0.0362, loss_cls: 0.1739, acc: 93.4851, loss_bbox: 0.2342, loss_mask: 0.2329, loss: 0.6962 +2024-05-28 01:04:59,153 - mmdet - INFO - Epoch [9][900/7330] lr: 1.000e-05, eta: 4:49:38, time: 0.603, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0195, loss_rpn_bbox: 0.0374, loss_cls: 0.1707, acc: 93.5701, loss_bbox: 0.2308, loss_mask: 0.2336, loss: 0.6919 +2024-05-28 01:05:28,950 - mmdet - INFO - Epoch [9][950/7330] lr: 1.000e-05, eta: 4:49:07, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0176, loss_rpn_bbox: 0.0321, loss_cls: 0.1533, acc: 94.2554, loss_bbox: 0.2074, loss_mask: 0.2230, loss: 0.6334 +2024-05-28 01:05:58,878 - mmdet - INFO - Epoch [9][1000/7330] lr: 1.000e-05, eta: 4:48:37, time: 0.598, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0174, loss_rpn_bbox: 0.0334, loss_cls: 0.1574, acc: 94.0410, loss_bbox: 0.2133, loss_mask: 0.2251, loss: 0.6467 +2024-05-28 01:06:31,043 - mmdet - INFO - Epoch [9][1050/7330] lr: 1.000e-05, eta: 4:48:07, time: 0.643, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0175, loss_rpn_bbox: 0.0344, loss_cls: 0.1590, acc: 93.9343, loss_bbox: 0.2163, loss_mask: 0.2282, loss: 0.6553 +2024-05-28 01:07:03,979 - mmdet - INFO - Epoch [9][1100/7330] lr: 1.000e-05, eta: 4:47:37, time: 0.659, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0317, loss_cls: 0.1522, acc: 94.2510, loss_bbox: 0.2029, loss_mask: 0.2157, loss: 0.6176 +2024-05-28 01:07:33,672 - mmdet - INFO - Epoch [9][1150/7330] lr: 1.000e-05, eta: 4:47:06, time: 0.594, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0181, loss_rpn_bbox: 0.0332, loss_cls: 0.1584, acc: 94.0437, loss_bbox: 0.2112, loss_mask: 0.2209, loss: 0.6418 +2024-05-28 01:08:05,962 - mmdet - INFO - Epoch [9][1200/7330] lr: 1.000e-05, eta: 4:46:37, time: 0.646, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0167, loss_rpn_bbox: 0.0334, loss_cls: 0.1550, acc: 94.1646, loss_bbox: 0.2088, loss_mask: 0.2200, loss: 0.6339 +2024-05-28 01:08:35,985 - mmdet - INFO - Epoch [9][1250/7330] lr: 1.000e-05, eta: 4:46:06, time: 0.600, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0323, loss_cls: 0.1523, acc: 94.2332, loss_bbox: 0.2046, loss_mask: 0.2186, loss: 0.6231 +2024-05-28 01:09:07,871 - mmdet - INFO - Epoch [9][1300/7330] lr: 1.000e-05, eta: 4:45:36, time: 0.638, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0174, loss_rpn_bbox: 0.0335, loss_cls: 0.1597, acc: 93.9990, loss_bbox: 0.2163, loss_mask: 0.2237, loss: 0.6506 +2024-05-28 01:09:37,922 - mmdet - INFO - Epoch [9][1350/7330] lr: 1.000e-05, eta: 4:45:05, time: 0.601, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0177, loss_rpn_bbox: 0.0353, loss_cls: 0.1658, acc: 93.7373, loss_bbox: 0.2206, loss_mask: 0.2292, loss: 0.6686 +2024-05-28 01:10:10,969 - mmdet - INFO - Epoch [9][1400/7330] lr: 1.000e-05, eta: 4:44:35, time: 0.661, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0173, loss_rpn_bbox: 0.0338, loss_cls: 0.1599, acc: 93.9673, loss_bbox: 0.2170, loss_mask: 0.2199, loss: 0.6480 +2024-05-28 01:10:43,346 - mmdet - INFO - Epoch [9][1450/7330] lr: 1.000e-05, eta: 4:44:06, time: 0.648, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0197, loss_rpn_bbox: 0.0357, loss_cls: 0.1623, acc: 93.9087, loss_bbox: 0.2188, loss_mask: 0.2244, loss: 0.6609 +2024-05-28 01:11:13,028 - mmdet - INFO - Epoch [9][1500/7330] lr: 1.000e-05, eta: 4:43:35, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0173, loss_rpn_bbox: 0.0341, loss_cls: 0.1606, acc: 94.0212, loss_bbox: 0.2143, loss_mask: 0.2241, loss: 0.6505 +2024-05-28 01:11:42,803 - mmdet - INFO - Epoch [9][1550/7330] lr: 1.000e-05, eta: 4:43:04, time: 0.595, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0169, loss_rpn_bbox: 0.0344, loss_cls: 0.1606, acc: 93.9258, loss_bbox: 0.2199, loss_mask: 0.2250, loss: 0.6569 +2024-05-28 01:12:14,654 - mmdet - INFO - Epoch [9][1600/7330] lr: 1.000e-05, eta: 4:42:34, time: 0.637, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0172, loss_rpn_bbox: 0.0328, loss_cls: 0.1613, acc: 93.8330, loss_bbox: 0.2177, loss_mask: 0.2225, loss: 0.6515 +2024-05-28 01:12:44,358 - mmdet - INFO - Epoch [9][1650/7330] lr: 1.000e-05, eta: 4:42:03, time: 0.594, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0157, loss_rpn_bbox: 0.0320, loss_cls: 0.1534, acc: 94.1978, loss_bbox: 0.2048, loss_mask: 0.2205, loss: 0.6264 +2024-05-28 01:13:14,433 - mmdet - INFO - Epoch [9][1700/7330] lr: 1.000e-05, eta: 4:41:32, time: 0.601, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0168, loss_rpn_bbox: 0.0342, loss_cls: 0.1575, acc: 94.0386, loss_bbox: 0.2133, loss_mask: 0.2220, loss: 0.6438 +2024-05-28 01:13:48,249 - mmdet - INFO - Epoch [9][1750/7330] lr: 1.000e-05, eta: 4:41:03, time: 0.677, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0367, loss_cls: 0.1597, acc: 93.9734, loss_bbox: 0.2171, loss_mask: 0.2297, loss: 0.6622 +2024-05-28 01:14:18,155 - mmdet - INFO - Epoch [9][1800/7330] lr: 1.000e-05, eta: 4:40:32, time: 0.598, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0358, loss_cls: 0.1662, acc: 93.7114, loss_bbox: 0.2241, loss_mask: 0.2285, loss: 0.6735 +2024-05-28 01:14:48,103 - mmdet - INFO - Epoch [9][1850/7330] lr: 1.000e-05, eta: 4:40:01, time: 0.599, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0314, loss_cls: 0.1501, acc: 94.2839, loss_bbox: 0.2097, loss_mask: 0.2185, loss: 0.6250 +2024-05-28 01:15:17,995 - mmdet - INFO - Epoch [9][1900/7330] lr: 1.000e-05, eta: 4:39:30, time: 0.598, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0171, loss_rpn_bbox: 0.0324, loss_cls: 0.1546, acc: 94.1433, loss_bbox: 0.2077, loss_mask: 0.2132, loss: 0.6250 +2024-05-28 01:15:47,839 - mmdet - INFO - Epoch [9][1950/7330] lr: 1.000e-05, eta: 4:38:59, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0328, loss_cls: 0.1604, acc: 94.0188, loss_bbox: 0.2105, loss_mask: 0.2228, loss: 0.6424 +2024-05-28 01:16:17,638 - mmdet - INFO - Epoch [9][2000/7330] lr: 1.000e-05, eta: 4:38:28, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0168, loss_rpn_bbox: 0.0327, loss_cls: 0.1539, acc: 94.1387, loss_bbox: 0.2090, loss_mask: 0.2186, loss: 0.6310 +2024-05-28 01:16:47,500 - mmdet - INFO - Epoch [9][2050/7330] lr: 1.000e-05, eta: 4:37:57, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0156, loss_rpn_bbox: 0.0311, loss_cls: 0.1514, acc: 94.2600, loss_bbox: 0.2078, loss_mask: 0.2189, loss: 0.6248 +2024-05-28 01:17:17,244 - mmdet - INFO - Epoch [9][2100/7330] lr: 1.000e-05, eta: 4:37:27, time: 0.595, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0291, loss_cls: 0.1517, acc: 94.2778, loss_bbox: 0.2070, loss_mask: 0.2130, loss: 0.6170 +2024-05-28 01:17:53,192 - mmdet - INFO - Epoch [9][2150/7330] lr: 1.000e-05, eta: 4:36:58, time: 0.719, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0336, loss_cls: 0.1588, acc: 93.9944, loss_bbox: 0.2143, loss_mask: 0.2247, loss: 0.6473 +2024-05-28 01:18:23,165 - mmdet - INFO - Epoch [9][2200/7330] lr: 1.000e-05, eta: 4:36:28, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0167, loss_rpn_bbox: 0.0327, loss_cls: 0.1598, acc: 94.0339, loss_bbox: 0.2157, loss_mask: 0.2174, loss: 0.6422 +2024-05-28 01:18:53,130 - mmdet - INFO - Epoch [9][2250/7330] lr: 1.000e-05, eta: 4:35:57, time: 0.599, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0176, loss_rpn_bbox: 0.0348, loss_cls: 0.1566, acc: 94.0249, loss_bbox: 0.2143, loss_mask: 0.2239, loss: 0.6471 +2024-05-28 01:19:25,475 - mmdet - INFO - Epoch [9][2300/7330] lr: 1.000e-05, eta: 4:35:27, time: 0.647, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0325, loss_cls: 0.1523, acc: 94.1399, loss_bbox: 0.2091, loss_mask: 0.2193, loss: 0.6295 +2024-05-28 01:19:57,827 - mmdet - INFO - Epoch [9][2350/7330] lr: 1.000e-05, eta: 4:34:57, time: 0.647, data_time: 0.026, memory: 9459, loss_rpn_cls: 0.0186, loss_rpn_bbox: 0.0345, loss_cls: 0.1644, acc: 93.8152, loss_bbox: 0.2233, loss_mask: 0.2291, loss: 0.6699 +2024-05-28 01:20:27,746 - mmdet - INFO - Epoch [9][2400/7330] lr: 1.000e-05, eta: 4:34:26, time: 0.598, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0326, loss_cls: 0.1538, acc: 94.1582, loss_bbox: 0.2106, loss_mask: 0.2195, loss: 0.6331 +2024-05-28 01:20:59,958 - mmdet - INFO - Epoch [9][2450/7330] lr: 1.000e-05, eta: 4:33:56, time: 0.644, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0338, loss_cls: 0.1530, acc: 94.1846, loss_bbox: 0.2140, loss_mask: 0.2202, loss: 0.6393 +2024-05-28 01:21:32,268 - mmdet - INFO - Epoch [9][2500/7330] lr: 1.000e-05, eta: 4:33:26, time: 0.646, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0170, loss_rpn_bbox: 0.0335, loss_cls: 0.1561, acc: 94.0566, loss_bbox: 0.2138, loss_mask: 0.2244, loss: 0.6448 +2024-05-28 01:22:02,358 - mmdet - INFO - Epoch [9][2550/7330] lr: 1.000e-05, eta: 4:32:56, time: 0.602, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0176, loss_rpn_bbox: 0.0326, loss_cls: 0.1521, acc: 94.2390, loss_bbox: 0.2058, loss_mask: 0.2155, loss: 0.6237 +2024-05-28 01:22:32,526 - mmdet - INFO - Epoch [9][2600/7330] lr: 1.000e-05, eta: 4:32:25, time: 0.603, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0337, loss_cls: 0.1553, acc: 94.1453, loss_bbox: 0.2096, loss_mask: 0.2175, loss: 0.6327 +2024-05-28 01:23:04,723 - mmdet - INFO - Epoch [9][2650/7330] lr: 1.000e-05, eta: 4:31:55, time: 0.644, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0315, loss_cls: 0.1552, acc: 94.1436, loss_bbox: 0.2068, loss_mask: 0.2157, loss: 0.6255 +2024-05-28 01:23:34,789 - mmdet - INFO - Epoch [9][2700/7330] lr: 1.000e-05, eta: 4:31:24, time: 0.601, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0332, loss_cls: 0.1568, acc: 94.1099, loss_bbox: 0.2118, loss_mask: 0.2195, loss: 0.6374 +2024-05-28 01:24:04,846 - mmdet - INFO - Epoch [9][2750/7330] lr: 1.000e-05, eta: 4:30:53, time: 0.601, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0316, loss_cls: 0.1543, acc: 94.1831, loss_bbox: 0.2092, loss_mask: 0.2211, loss: 0.6328 +2024-05-28 01:24:37,186 - mmdet - INFO - Epoch [9][2800/7330] lr: 1.000e-05, eta: 4:30:24, time: 0.647, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0206, loss_rpn_bbox: 0.0347, loss_cls: 0.1577, acc: 94.0784, loss_bbox: 0.2161, loss_mask: 0.2259, loss: 0.6551 +2024-05-28 01:25:07,459 - mmdet - INFO - Epoch [9][2850/7330] lr: 1.000e-05, eta: 4:29:53, time: 0.605, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0342, loss_cls: 0.1628, acc: 93.7781, loss_bbox: 0.2170, loss_mask: 0.2203, loss: 0.6507 +2024-05-28 01:25:37,695 - mmdet - INFO - Epoch [9][2900/7330] lr: 1.000e-05, eta: 4:29:22, time: 0.605, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0184, loss_rpn_bbox: 0.0338, loss_cls: 0.1586, acc: 93.9478, loss_bbox: 0.2168, loss_mask: 0.2248, loss: 0.6523 +2024-05-28 01:26:07,895 - mmdet - INFO - Epoch [9][2950/7330] lr: 1.000e-05, eta: 4:28:51, time: 0.604, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0172, loss_rpn_bbox: 0.0342, loss_cls: 0.1618, acc: 93.8289, loss_bbox: 0.2220, loss_mask: 0.2230, loss: 0.6582 +2024-05-28 01:26:37,919 - mmdet - INFO - Epoch [9][3000/7330] lr: 1.000e-05, eta: 4:28:20, time: 0.600, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0181, loss_rpn_bbox: 0.0364, loss_cls: 0.1675, acc: 93.5886, loss_bbox: 0.2252, loss_mask: 0.2284, loss: 0.6755 +2024-05-28 01:27:08,065 - mmdet - INFO - Epoch [9][3050/7330] lr: 1.000e-05, eta: 4:27:50, time: 0.603, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0335, loss_cls: 0.1567, acc: 94.1277, loss_bbox: 0.2076, loss_mask: 0.2150, loss: 0.6285 +2024-05-28 01:27:38,223 - mmdet - INFO - Epoch [9][3100/7330] lr: 1.000e-05, eta: 4:27:19, time: 0.603, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0185, loss_rpn_bbox: 0.0355, loss_cls: 0.1618, acc: 93.8025, loss_bbox: 0.2177, loss_mask: 0.2193, loss: 0.6529 +2024-05-28 01:28:08,408 - mmdet - INFO - Epoch [9][3150/7330] lr: 1.000e-05, eta: 4:26:48, time: 0.604, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0178, loss_rpn_bbox: 0.0337, loss_cls: 0.1582, acc: 93.9600, loss_bbox: 0.2186, loss_mask: 0.2238, loss: 0.6521 +2024-05-28 01:28:42,835 - mmdet - INFO - Epoch [9][3200/7330] lr: 1.000e-05, eta: 4:26:19, time: 0.689, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0177, loss_rpn_bbox: 0.0360, loss_cls: 0.1664, acc: 93.8311, loss_bbox: 0.2216, loss_mask: 0.2265, loss: 0.6680 +2024-05-28 01:29:12,802 - mmdet - INFO - Epoch [9][3250/7330] lr: 1.000e-05, eta: 4:25:48, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0164, loss_rpn_bbox: 0.0329, loss_cls: 0.1539, acc: 94.0564, loss_bbox: 0.2106, loss_mask: 0.2200, loss: 0.6338 +2024-05-28 01:29:42,930 - mmdet - INFO - Epoch [9][3300/7330] lr: 1.000e-05, eta: 4:25:18, time: 0.603, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0169, loss_rpn_bbox: 0.0327, loss_cls: 0.1535, acc: 94.1477, loss_bbox: 0.2116, loss_mask: 0.2200, loss: 0.6348 +2024-05-28 01:30:17,096 - mmdet - INFO - Epoch [9][3350/7330] lr: 1.000e-05, eta: 4:24:49, time: 0.683, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0312, loss_cls: 0.1569, acc: 94.0571, loss_bbox: 0.2119, loss_mask: 0.2234, loss: 0.6394 +2024-05-28 01:30:49,268 - mmdet - INFO - Epoch [9][3400/7330] lr: 1.000e-05, eta: 4:24:19, time: 0.643, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0181, loss_rpn_bbox: 0.0329, loss_cls: 0.1564, acc: 94.0117, loss_bbox: 0.2159, loss_mask: 0.2209, loss: 0.6442 +2024-05-28 01:31:19,337 - mmdet - INFO - Epoch [9][3450/7330] lr: 1.000e-05, eta: 4:23:48, time: 0.601, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0344, loss_cls: 0.1564, acc: 94.0974, loss_bbox: 0.2141, loss_mask: 0.2231, loss: 0.6446 +2024-05-28 01:31:51,854 - mmdet - INFO - Epoch [9][3500/7330] lr: 1.000e-05, eta: 4:23:18, time: 0.650, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0174, loss_rpn_bbox: 0.0340, loss_cls: 0.1574, acc: 93.9812, loss_bbox: 0.2146, loss_mask: 0.2251, loss: 0.6484 +2024-05-28 01:32:24,020 - mmdet - INFO - Epoch [9][3550/7330] lr: 1.000e-05, eta: 4:22:48, time: 0.643, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0342, loss_cls: 0.1641, acc: 93.9468, loss_bbox: 0.2167, loss_mask: 0.2261, loss: 0.6592 +2024-05-28 01:32:54,329 - mmdet - INFO - Epoch [9][3600/7330] lr: 1.000e-05, eta: 4:22:17, time: 0.606, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0181, loss_rpn_bbox: 0.0360, loss_cls: 0.1687, acc: 93.5935, loss_bbox: 0.2257, loss_mask: 0.2300, loss: 0.6784 +2024-05-28 01:33:24,436 - mmdet - INFO - Epoch [9][3650/7330] lr: 1.000e-05, eta: 4:21:47, time: 0.602, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0172, loss_rpn_bbox: 0.0326, loss_cls: 0.1559, acc: 94.0957, loss_bbox: 0.2113, loss_mask: 0.2216, loss: 0.6387 +2024-05-28 01:33:56,655 - mmdet - INFO - Epoch [9][3700/7330] lr: 1.000e-05, eta: 4:21:17, time: 0.644, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0345, loss_cls: 0.1527, acc: 94.2678, loss_bbox: 0.2098, loss_mask: 0.2227, loss: 0.6363 +2024-05-28 01:34:26,774 - mmdet - INFO - Epoch [9][3750/7330] lr: 1.000e-05, eta: 4:20:46, time: 0.602, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0309, loss_cls: 0.1518, acc: 94.2097, loss_bbox: 0.2051, loss_mask: 0.2175, loss: 0.6215 +2024-05-28 01:34:57,076 - mmdet - INFO - Epoch [9][3800/7330] lr: 1.000e-05, eta: 4:20:15, time: 0.606, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0340, loss_cls: 0.1526, acc: 94.2268, loss_bbox: 0.2114, loss_mask: 0.2227, loss: 0.6372 +2024-05-28 01:35:29,337 - mmdet - INFO - Epoch [9][3850/7330] lr: 1.000e-05, eta: 4:19:45, time: 0.645, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0163, loss_rpn_bbox: 0.0327, loss_cls: 0.1527, acc: 94.2034, loss_bbox: 0.2123, loss_mask: 0.2214, loss: 0.6354 +2024-05-28 01:35:59,390 - mmdet - INFO - Epoch [9][3900/7330] lr: 1.000e-05, eta: 4:19:14, time: 0.601, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0170, loss_rpn_bbox: 0.0340, loss_cls: 0.1584, acc: 94.0186, loss_bbox: 0.2113, loss_mask: 0.2233, loss: 0.6440 +2024-05-28 01:36:29,357 - mmdet - INFO - Epoch [9][3950/7330] lr: 1.000e-05, eta: 4:18:44, time: 0.599, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0329, loss_cls: 0.1535, acc: 94.1724, loss_bbox: 0.2122, loss_mask: 0.2220, loss: 0.6359 +2024-05-28 01:36:59,150 - mmdet - INFO - Epoch [9][4000/7330] lr: 1.000e-05, eta: 4:18:13, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0324, loss_cls: 0.1513, acc: 94.2524, loss_bbox: 0.2095, loss_mask: 0.2197, loss: 0.6288 +2024-05-28 01:37:29,176 - mmdet - INFO - Epoch [9][4050/7330] lr: 1.000e-05, eta: 4:17:42, time: 0.600, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0164, loss_rpn_bbox: 0.0318, loss_cls: 0.1522, acc: 94.2366, loss_bbox: 0.2087, loss_mask: 0.2213, loss: 0.6303 +2024-05-28 01:37:59,148 - mmdet - INFO - Epoch [9][4100/7330] lr: 1.000e-05, eta: 4:17:11, time: 0.599, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0169, loss_rpn_bbox: 0.0344, loss_cls: 0.1578, acc: 93.9756, loss_bbox: 0.2114, loss_mask: 0.2213, loss: 0.6418 +2024-05-28 01:38:29,170 - mmdet - INFO - Epoch [9][4150/7330] lr: 1.000e-05, eta: 4:16:40, time: 0.600, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0329, loss_cls: 0.1584, acc: 94.0984, loss_bbox: 0.2083, loss_mask: 0.2209, loss: 0.6370 +2024-05-28 01:39:01,544 - mmdet - INFO - Epoch [9][4200/7330] lr: 1.000e-05, eta: 4:16:10, time: 0.648, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0167, loss_rpn_bbox: 0.0335, loss_cls: 0.1581, acc: 94.0295, loss_bbox: 0.2120, loss_mask: 0.2227, loss: 0.6430 +2024-05-28 01:39:33,639 - mmdet - INFO - Epoch [9][4250/7330] lr: 1.000e-05, eta: 4:15:40, time: 0.642, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0320, loss_cls: 0.1516, acc: 94.1733, loss_bbox: 0.2072, loss_mask: 0.2187, loss: 0.6255 +2024-05-28 01:40:03,680 - mmdet - INFO - Epoch [9][4300/7330] lr: 1.000e-05, eta: 4:15:09, time: 0.601, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0330, loss_cls: 0.1599, acc: 93.9185, loss_bbox: 0.2126, loss_mask: 0.2199, loss: 0.6416 +2024-05-28 01:40:33,561 - mmdet - INFO - Epoch [9][4350/7330] lr: 1.000e-05, eta: 4:14:39, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0340, loss_cls: 0.1599, acc: 93.9575, loss_bbox: 0.2151, loss_mask: 0.2204, loss: 0.6459 +2024-05-28 01:41:06,293 - mmdet - INFO - Epoch [9][4400/7330] lr: 1.000e-05, eta: 4:14:09, time: 0.655, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0168, loss_rpn_bbox: 0.0336, loss_cls: 0.1523, acc: 94.2871, loss_bbox: 0.2077, loss_mask: 0.2222, loss: 0.6326 +2024-05-28 01:41:38,561 - mmdet - INFO - Epoch [9][4450/7330] lr: 1.000e-05, eta: 4:13:39, time: 0.645, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0178, loss_rpn_bbox: 0.0340, loss_cls: 0.1573, acc: 94.0906, loss_bbox: 0.2137, loss_mask: 0.2162, loss: 0.6390 +2024-05-28 01:42:08,297 - mmdet - INFO - Epoch [9][4500/7330] lr: 1.000e-05, eta: 4:13:08, time: 0.595, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0172, loss_rpn_bbox: 0.0329, loss_cls: 0.1584, acc: 94.0571, loss_bbox: 0.2130, loss_mask: 0.2213, loss: 0.6427 +2024-05-28 01:42:40,564 - mmdet - INFO - Epoch [9][4550/7330] lr: 1.000e-05, eta: 4:12:38, time: 0.645, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0334, loss_cls: 0.1523, acc: 94.2266, loss_bbox: 0.2088, loss_mask: 0.2169, loss: 0.6274 +2024-05-28 01:43:13,018 - mmdet - INFO - Epoch [9][4600/7330] lr: 1.000e-05, eta: 4:12:08, time: 0.649, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0168, loss_rpn_bbox: 0.0345, loss_cls: 0.1532, acc: 94.1790, loss_bbox: 0.2107, loss_mask: 0.2206, loss: 0.6357 +2024-05-28 01:43:42,950 - mmdet - INFO - Epoch [9][4650/7330] lr: 1.000e-05, eta: 4:11:37, time: 0.599, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0188, loss_rpn_bbox: 0.0333, loss_cls: 0.1518, acc: 94.2832, loss_bbox: 0.2059, loss_mask: 0.2202, loss: 0.6300 +2024-05-28 01:44:12,998 - mmdet - INFO - Epoch [9][4700/7330] lr: 1.000e-05, eta: 4:11:06, time: 0.601, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0179, loss_rpn_bbox: 0.0344, loss_cls: 0.1561, acc: 94.0520, loss_bbox: 0.2170, loss_mask: 0.2221, loss: 0.6476 +2024-05-28 01:44:45,320 - mmdet - INFO - Epoch [9][4750/7330] lr: 1.000e-05, eta: 4:10:36, time: 0.646, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0157, loss_rpn_bbox: 0.0327, loss_cls: 0.1531, acc: 94.1270, loss_bbox: 0.2061, loss_mask: 0.2233, loss: 0.6310 +2024-05-28 01:45:15,217 - mmdet - INFO - Epoch [9][4800/7330] lr: 1.000e-05, eta: 4:10:06, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0171, loss_rpn_bbox: 0.0337, loss_cls: 0.1559, acc: 94.1404, loss_bbox: 0.2112, loss_mask: 0.2168, loss: 0.6346 +2024-05-28 01:45:45,250 - mmdet - INFO - Epoch [9][4850/7330] lr: 1.000e-05, eta: 4:09:35, time: 0.600, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0170, loss_rpn_bbox: 0.0336, loss_cls: 0.1563, acc: 94.1208, loss_bbox: 0.2128, loss_mask: 0.2253, loss: 0.6449 +2024-05-28 01:46:15,363 - mmdet - INFO - Epoch [9][4900/7330] lr: 1.000e-05, eta: 4:09:04, time: 0.603, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0171, loss_rpn_bbox: 0.0348, loss_cls: 0.1610, acc: 93.8660, loss_bbox: 0.2184, loss_mask: 0.2254, loss: 0.6568 +2024-05-28 01:46:47,469 - mmdet - INFO - Epoch [9][4950/7330] lr: 1.000e-05, eta: 4:08:34, time: 0.642, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0178, loss_rpn_bbox: 0.0335, loss_cls: 0.1587, acc: 94.0212, loss_bbox: 0.2186, loss_mask: 0.2247, loss: 0.6533 +2024-05-28 01:47:17,551 - mmdet - INFO - Epoch [9][5000/7330] lr: 1.000e-05, eta: 4:08:03, time: 0.602, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0163, loss_rpn_bbox: 0.0332, loss_cls: 0.1592, acc: 94.0767, loss_bbox: 0.2146, loss_mask: 0.2253, loss: 0.6486 +2024-05-28 01:47:47,485 - mmdet - INFO - Epoch [9][5050/7330] lr: 1.000e-05, eta: 4:07:32, time: 0.599, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0321, loss_cls: 0.1557, acc: 94.0664, loss_bbox: 0.2117, loss_mask: 0.2225, loss: 0.6375 +2024-05-28 01:48:17,395 - mmdet - INFO - Epoch [9][5100/7330] lr: 1.000e-05, eta: 4:07:01, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0172, loss_rpn_bbox: 0.0355, loss_cls: 0.1611, acc: 93.9370, loss_bbox: 0.2143, loss_mask: 0.2260, loss: 0.6542 +2024-05-28 01:48:47,258 - mmdet - INFO - Epoch [9][5150/7330] lr: 1.000e-05, eta: 4:06:31, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0183, loss_rpn_bbox: 0.0332, loss_cls: 0.1568, acc: 94.1294, loss_bbox: 0.2116, loss_mask: 0.2211, loss: 0.6410 +2024-05-28 01:49:17,235 - mmdet - INFO - Epoch [9][5200/7330] lr: 1.000e-05, eta: 4:06:00, time: 0.600, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0167, loss_rpn_bbox: 0.0341, loss_cls: 0.1603, acc: 93.8376, loss_bbox: 0.2178, loss_mask: 0.2263, loss: 0.6552 +2024-05-28 01:49:49,585 - mmdet - INFO - Epoch [9][5250/7330] lr: 1.000e-05, eta: 4:05:30, time: 0.647, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0171, loss_rpn_bbox: 0.0334, loss_cls: 0.1562, acc: 93.9875, loss_bbox: 0.2113, loss_mask: 0.2229, loss: 0.6410 +2024-05-28 01:50:21,885 - mmdet - INFO - Epoch [9][5300/7330] lr: 1.000e-05, eta: 4:05:00, time: 0.646, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0176, loss_rpn_bbox: 0.0346, loss_cls: 0.1619, acc: 93.8933, loss_bbox: 0.2153, loss_mask: 0.2237, loss: 0.6531 +2024-05-28 01:50:51,933 - mmdet - INFO - Epoch [9][5350/7330] lr: 1.000e-05, eta: 4:04:29, time: 0.601, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0178, loss_rpn_bbox: 0.0352, loss_cls: 0.1545, acc: 94.1079, loss_bbox: 0.2174, loss_mask: 0.2218, loss: 0.6466 +2024-05-28 01:51:21,856 - mmdet - INFO - Epoch [9][5400/7330] lr: 1.000e-05, eta: 4:03:58, time: 0.599, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0310, loss_cls: 0.1531, acc: 94.2812, loss_bbox: 0.2073, loss_mask: 0.2189, loss: 0.6268 +2024-05-28 01:51:54,582 - mmdet - INFO - Epoch [9][5450/7330] lr: 1.000e-05, eta: 4:03:28, time: 0.654, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0174, loss_rpn_bbox: 0.0344, loss_cls: 0.1546, acc: 94.1497, loss_bbox: 0.2108, loss_mask: 0.2234, loss: 0.6406 +2024-05-28 01:52:26,595 - mmdet - INFO - Epoch [9][5500/7330] lr: 1.000e-05, eta: 4:02:58, time: 0.640, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0172, loss_rpn_bbox: 0.0326, loss_cls: 0.1564, acc: 94.0540, loss_bbox: 0.2073, loss_mask: 0.2173, loss: 0.6308 +2024-05-28 01:52:56,681 - mmdet - INFO - Epoch [9][5550/7330] lr: 1.000e-05, eta: 4:02:28, time: 0.601, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0174, loss_rpn_bbox: 0.0331, loss_cls: 0.1585, acc: 93.9050, loss_bbox: 0.2168, loss_mask: 0.2256, loss: 0.6514 +2024-05-28 01:53:29,227 - mmdet - INFO - Epoch [9][5600/7330] lr: 1.000e-05, eta: 4:01:58, time: 0.651, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0177, loss_rpn_bbox: 0.0338, loss_cls: 0.1574, acc: 94.0376, loss_bbox: 0.2131, loss_mask: 0.2229, loss: 0.6450 +2024-05-28 01:54:02,211 - mmdet - INFO - Epoch [9][5650/7330] lr: 1.000e-05, eta: 4:01:28, time: 0.660, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0171, loss_rpn_bbox: 0.0326, loss_cls: 0.1591, acc: 93.9758, loss_bbox: 0.2120, loss_mask: 0.2176, loss: 0.6384 +2024-05-28 01:54:32,337 - mmdet - INFO - Epoch [9][5700/7330] lr: 1.000e-05, eta: 4:00:57, time: 0.602, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0176, loss_rpn_bbox: 0.0349, loss_cls: 0.1593, acc: 93.9473, loss_bbox: 0.2187, loss_mask: 0.2251, loss: 0.6556 +2024-05-28 01:55:02,451 - mmdet - INFO - Epoch [9][5750/7330] lr: 1.000e-05, eta: 4:00:26, time: 0.602, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0172, loss_rpn_bbox: 0.0358, loss_cls: 0.1625, acc: 93.8813, loss_bbox: 0.2181, loss_mask: 0.2226, loss: 0.6563 +2024-05-28 01:55:34,964 - mmdet - INFO - Epoch [9][5800/7330] lr: 1.000e-05, eta: 3:59:56, time: 0.650, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0177, loss_rpn_bbox: 0.0333, loss_cls: 0.1560, acc: 94.1011, loss_bbox: 0.2134, loss_mask: 0.2261, loss: 0.6465 +2024-05-28 01:56:05,043 - mmdet - INFO - Epoch [9][5850/7330] lr: 1.000e-05, eta: 3:59:26, time: 0.602, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0170, loss_rpn_bbox: 0.0338, loss_cls: 0.1588, acc: 93.9177, loss_bbox: 0.2148, loss_mask: 0.2202, loss: 0.6446 +2024-05-28 01:56:35,162 - mmdet - INFO - Epoch [9][5900/7330] lr: 1.000e-05, eta: 3:58:55, time: 0.602, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0334, loss_cls: 0.1550, acc: 94.1101, loss_bbox: 0.2106, loss_mask: 0.2192, loss: 0.6342 +2024-05-28 01:57:05,132 - mmdet - INFO - Epoch [9][5950/7330] lr: 1.000e-05, eta: 3:58:24, time: 0.599, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0332, loss_cls: 0.1510, acc: 94.2583, loss_bbox: 0.2089, loss_mask: 0.2196, loss: 0.6278 +2024-05-28 01:57:37,274 - mmdet - INFO - Epoch [9][6000/7330] lr: 1.000e-05, eta: 3:57:54, time: 0.643, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0174, loss_rpn_bbox: 0.0341, loss_cls: 0.1549, acc: 94.1094, loss_bbox: 0.2099, loss_mask: 0.2200, loss: 0.6362 +2024-05-28 01:58:07,176 - mmdet - INFO - Epoch [9][6050/7330] lr: 1.000e-05, eta: 3:57:23, time: 0.598, data_time: 0.015, memory: 9459, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0324, loss_cls: 0.1523, acc: 94.2095, loss_bbox: 0.2051, loss_mask: 0.2174, loss: 0.6222 +2024-05-28 01:58:37,214 - mmdet - INFO - Epoch [9][6100/7330] lr: 1.000e-05, eta: 3:56:52, time: 0.601, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0346, loss_cls: 0.1592, acc: 93.9104, loss_bbox: 0.2180, loss_mask: 0.2236, loss: 0.6516 +2024-05-28 01:59:07,254 - mmdet - INFO - Epoch [9][6150/7330] lr: 1.000e-05, eta: 3:56:22, time: 0.601, data_time: 0.015, memory: 9459, loss_rpn_cls: 0.0178, loss_rpn_bbox: 0.0337, loss_cls: 0.1499, acc: 94.3191, loss_bbox: 0.2037, loss_mask: 0.2224, loss: 0.6275 +2024-05-28 01:59:37,267 - mmdet - INFO - Epoch [9][6200/7330] lr: 1.000e-05, eta: 3:55:51, time: 0.600, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0163, loss_rpn_bbox: 0.0323, loss_cls: 0.1535, acc: 94.1670, loss_bbox: 0.2071, loss_mask: 0.2219, loss: 0.6311 +2024-05-28 02:00:07,354 - mmdet - INFO - Epoch [9][6250/7330] lr: 1.000e-05, eta: 3:55:20, time: 0.602, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0170, loss_rpn_bbox: 0.0339, loss_cls: 0.1592, acc: 93.9077, loss_bbox: 0.2148, loss_mask: 0.2268, loss: 0.6517 +2024-05-28 02:00:44,674 - mmdet - INFO - Epoch [9][6300/7330] lr: 1.000e-05, eta: 3:54:52, time: 0.746, data_time: 0.125, memory: 9459, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0332, loss_cls: 0.1573, acc: 94.1602, loss_bbox: 0.2096, loss_mask: 0.2185, loss: 0.6352 +2024-05-28 02:01:16,575 - mmdet - INFO - Epoch [9][6350/7330] lr: 1.000e-05, eta: 3:54:22, time: 0.639, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0347, loss_cls: 0.1601, acc: 93.8772, loss_bbox: 0.2165, loss_mask: 0.2277, loss: 0.6571 +2024-05-28 02:01:46,602 - mmdet - INFO - Epoch [9][6400/7330] lr: 1.000e-05, eta: 3:53:51, time: 0.600, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0173, loss_rpn_bbox: 0.0338, loss_cls: 0.1517, acc: 94.2405, loss_bbox: 0.2062, loss_mask: 0.2152, loss: 0.6241 +2024-05-28 02:02:16,620 - mmdet - INFO - Epoch [9][6450/7330] lr: 1.000e-05, eta: 3:53:20, time: 0.600, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0317, loss_cls: 0.1555, acc: 94.0710, loss_bbox: 0.2120, loss_mask: 0.2213, loss: 0.6363 +2024-05-28 02:02:49,861 - mmdet - INFO - Epoch [9][6500/7330] lr: 1.000e-05, eta: 3:52:50, time: 0.665, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0171, loss_rpn_bbox: 0.0345, loss_cls: 0.1580, acc: 93.9819, loss_bbox: 0.2127, loss_mask: 0.2261, loss: 0.6483 +2024-05-28 02:03:21,839 - mmdet - INFO - Epoch [9][6550/7330] lr: 1.000e-05, eta: 3:52:20, time: 0.640, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0169, loss_rpn_bbox: 0.0324, loss_cls: 0.1491, acc: 94.3372, loss_bbox: 0.2069, loss_mask: 0.2208, loss: 0.6261 +2024-05-28 02:03:51,883 - mmdet - INFO - Epoch [9][6600/7330] lr: 1.000e-05, eta: 3:51:49, time: 0.600, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0148, loss_rpn_bbox: 0.0313, loss_cls: 0.1496, acc: 94.3276, loss_bbox: 0.2076, loss_mask: 0.2178, loss: 0.6212 +2024-05-28 02:04:24,351 - mmdet - INFO - Epoch [9][6650/7330] lr: 1.000e-05, eta: 3:51:19, time: 0.650, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0168, loss_rpn_bbox: 0.0322, loss_cls: 0.1489, acc: 94.3579, loss_bbox: 0.2033, loss_mask: 0.2172, loss: 0.6185 +2024-05-28 02:04:56,831 - mmdet - INFO - Epoch [9][6700/7330] lr: 1.000e-05, eta: 3:50:49, time: 0.650, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0164, loss_rpn_bbox: 0.0342, loss_cls: 0.1537, acc: 94.1199, loss_bbox: 0.2147, loss_mask: 0.2179, loss: 0.6370 +2024-05-28 02:05:26,904 - mmdet - INFO - Epoch [9][6750/7330] lr: 1.000e-05, eta: 3:50:19, time: 0.602, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0325, loss_cls: 0.1518, acc: 94.2107, loss_bbox: 0.2107, loss_mask: 0.2205, loss: 0.6310 +2024-05-28 02:05:56,733 - mmdet - INFO - Epoch [9][6800/7330] lr: 1.000e-05, eta: 3:49:48, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0331, loss_cls: 0.1551, acc: 94.1003, loss_bbox: 0.2117, loss_mask: 0.2182, loss: 0.6343 +2024-05-28 02:06:28,918 - mmdet - INFO - Epoch [9][6850/7330] lr: 1.000e-05, eta: 3:49:18, time: 0.644, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0176, loss_rpn_bbox: 0.0352, loss_cls: 0.1598, acc: 93.9143, loss_bbox: 0.2149, loss_mask: 0.2197, loss: 0.6473 +2024-05-28 02:06:58,752 - mmdet - INFO - Epoch [9][6900/7330] lr: 1.000e-05, eta: 3:48:47, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0174, loss_rpn_bbox: 0.0340, loss_cls: 0.1590, acc: 93.9763, loss_bbox: 0.2129, loss_mask: 0.2268, loss: 0.6501 +2024-05-28 02:07:28,537 - mmdet - INFO - Epoch [9][6950/7330] lr: 1.000e-05, eta: 3:48:16, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0180, loss_rpn_bbox: 0.0348, loss_cls: 0.1557, acc: 94.0298, loss_bbox: 0.2156, loss_mask: 0.2271, loss: 0.6512 +2024-05-28 02:07:58,323 - mmdet - INFO - Epoch [9][7000/7330] lr: 1.000e-05, eta: 3:47:45, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0320, loss_cls: 0.1571, acc: 94.0012, loss_bbox: 0.2113, loss_mask: 0.2198, loss: 0.6362 +2024-05-28 02:08:30,694 - mmdet - INFO - Epoch [9][7050/7330] lr: 1.000e-05, eta: 3:47:15, time: 0.647, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0333, loss_cls: 0.1510, acc: 94.2261, loss_bbox: 0.2080, loss_mask: 0.2207, loss: 0.6285 +2024-05-28 02:09:00,817 - mmdet - INFO - Epoch [9][7100/7330] lr: 1.000e-05, eta: 3:46:44, time: 0.602, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0352, loss_cls: 0.1623, acc: 93.7471, loss_bbox: 0.2219, loss_mask: 0.2276, loss: 0.6630 +2024-05-28 02:09:30,807 - mmdet - INFO - Epoch [9][7150/7330] lr: 1.000e-05, eta: 3:46:13, time: 0.600, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0314, loss_cls: 0.1497, acc: 94.3948, loss_bbox: 0.2048, loss_mask: 0.2153, loss: 0.6178 +2024-05-28 02:10:00,587 - mmdet - INFO - Epoch [9][7200/7330] lr: 1.000e-05, eta: 3:45:42, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0140, loss_rpn_bbox: 0.0323, loss_cls: 0.1499, acc: 94.3535, loss_bbox: 0.2053, loss_mask: 0.2175, loss: 0.6190 +2024-05-28 02:10:30,457 - mmdet - INFO - Epoch [9][7250/7330] lr: 1.000e-05, eta: 3:45:12, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0148, loss_rpn_bbox: 0.0297, loss_cls: 0.1465, acc: 94.4373, loss_bbox: 0.2028, loss_mask: 0.2220, loss: 0.6158 +2024-05-28 02:11:00,343 - mmdet - INFO - Epoch [9][7300/7330] lr: 1.000e-05, eta: 3:44:41, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0329, loss_cls: 0.1531, acc: 94.2278, loss_bbox: 0.2043, loss_mask: 0.2135, loss: 0.6191 +2024-05-28 02:11:19,031 - mmdet - INFO - Saving checkpoint at 9 epochs +2024-05-28 02:13:11,598 - mmdet - INFO - Evaluating bbox... +2024-05-28 02:13:35,121 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.462 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.689 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.504 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.266 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.505 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.657 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.571 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.571 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.571 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.353 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.624 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.764 + +2024-05-28 02:13:35,121 - mmdet - INFO - Evaluating segm... +2024-05-28 02:14:00,953 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.406 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.649 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.431 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.180 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.443 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.645 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.507 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.507 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.507 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.281 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.560 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.722 + +2024-05-28 02:14:01,256 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-28 02:14:01,258 - mmdet - INFO - Epoch(val) [9][625] bbox_mAP: 0.4620, bbox_mAP_50: 0.6890, bbox_mAP_75: 0.5040, bbox_mAP_s: 0.2660, bbox_mAP_m: 0.5050, bbox_mAP_l: 0.6570, bbox_mAP_copypaste: 0.462 0.689 0.504 0.266 0.505 0.657, segm_mAP: 0.4060, segm_mAP_50: 0.6490, segm_mAP_75: 0.4310, segm_mAP_s: 0.1800, segm_mAP_m: 0.4430, segm_mAP_l: 0.6450, segm_mAP_copypaste: 0.406 0.649 0.431 0.180 0.443 0.645 +2024-05-28 02:14:39,206 - mmdet - INFO - Epoch [10][50/7330] lr: 1.000e-05, eta: 3:43:48, time: 0.759, data_time: 0.092, memory: 9459, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0323, loss_cls: 0.1495, acc: 94.3403, loss_bbox: 0.2057, loss_mask: 0.2151, loss: 0.6186 +2024-05-28 02:15:09,137 - mmdet - INFO - Epoch [10][100/7330] lr: 1.000e-05, eta: 3:43:17, time: 0.599, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0347, loss_cls: 0.1521, acc: 94.0955, loss_bbox: 0.2111, loss_mask: 0.2198, loss: 0.6344 +2024-05-28 02:15:38,805 - mmdet - INFO - Epoch [10][150/7330] lr: 1.000e-05, eta: 3:42:46, time: 0.593, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0298, loss_cls: 0.1519, acc: 94.3120, loss_bbox: 0.2001, loss_mask: 0.2165, loss: 0.6142 +2024-05-28 02:16:08,893 - mmdet - INFO - Epoch [10][200/7330] lr: 1.000e-05, eta: 3:42:16, time: 0.602, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0362, loss_cls: 0.1603, acc: 93.8877, loss_bbox: 0.2243, loss_mask: 0.2268, loss: 0.6641 +2024-05-28 02:16:38,728 - mmdet - INFO - Epoch [10][250/7330] lr: 1.000e-05, eta: 3:41:45, time: 0.597, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0340, loss_cls: 0.1591, acc: 93.9312, loss_bbox: 0.2156, loss_mask: 0.2282, loss: 0.6530 +2024-05-28 02:17:08,515 - mmdet - INFO - Epoch [10][300/7330] lr: 1.000e-05, eta: 3:41:14, time: 0.596, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0322, loss_cls: 0.1479, acc: 94.3677, loss_bbox: 0.2009, loss_mask: 0.2149, loss: 0.6124 +2024-05-28 02:17:38,191 - mmdet - INFO - Epoch [10][350/7330] lr: 1.000e-05, eta: 3:40:43, time: 0.593, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0169, loss_rpn_bbox: 0.0348, loss_cls: 0.1580, acc: 93.9629, loss_bbox: 0.2156, loss_mask: 0.2233, loss: 0.6486 +2024-05-28 02:18:07,836 - mmdet - INFO - Epoch [10][400/7330] lr: 1.000e-05, eta: 3:40:12, time: 0.593, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0331, loss_cls: 0.1532, acc: 94.1814, loss_bbox: 0.2076, loss_mask: 0.2208, loss: 0.6307 +2024-05-28 02:18:39,856 - mmdet - INFO - Epoch [10][450/7330] lr: 1.000e-05, eta: 3:39:42, time: 0.640, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0277, loss_cls: 0.1368, acc: 94.7151, loss_bbox: 0.1878, loss_mask: 0.2111, loss: 0.5765 +2024-05-28 02:19:09,531 - mmdet - INFO - Epoch [10][500/7330] lr: 1.000e-05, eta: 3:39:11, time: 0.593, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0337, loss_cls: 0.1558, acc: 94.0664, loss_bbox: 0.2110, loss_mask: 0.2206, loss: 0.6366 +2024-05-28 02:19:39,256 - mmdet - INFO - Epoch [10][550/7330] lr: 1.000e-05, eta: 3:38:40, time: 0.594, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0323, loss_cls: 0.1490, acc: 94.4004, loss_bbox: 0.2014, loss_mask: 0.2218, loss: 0.6210 +2024-05-28 02:20:11,119 - mmdet - INFO - Epoch [10][600/7330] lr: 1.000e-05, eta: 3:38:10, time: 0.637, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0314, loss_cls: 0.1502, acc: 94.2788, loss_bbox: 0.2015, loss_mask: 0.2163, loss: 0.6148 +2024-05-28 02:20:40,730 - mmdet - INFO - Epoch [10][650/7330] lr: 1.000e-05, eta: 3:37:39, time: 0.593, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0333, loss_cls: 0.1532, acc: 94.0630, loss_bbox: 0.2112, loss_mask: 0.2192, loss: 0.6329 +2024-05-28 02:21:10,497 - mmdet - INFO - Epoch [10][700/7330] lr: 1.000e-05, eta: 3:37:08, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0323, loss_cls: 0.1534, acc: 94.1726, loss_bbox: 0.2096, loss_mask: 0.2148, loss: 0.6263 +2024-05-28 02:21:40,361 - mmdet - INFO - Epoch [10][750/7330] lr: 1.000e-05, eta: 3:36:37, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0171, loss_rpn_bbox: 0.0342, loss_cls: 0.1543, acc: 94.0549, loss_bbox: 0.2130, loss_mask: 0.2272, loss: 0.6459 +2024-05-28 02:22:12,864 - mmdet - INFO - Epoch [10][800/7330] lr: 1.000e-05, eta: 3:36:07, time: 0.651, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0369, loss_cls: 0.1632, acc: 93.9189, loss_bbox: 0.2187, loss_mask: 0.2237, loss: 0.6613 +2024-05-28 02:22:42,396 - mmdet - INFO - Epoch [10][850/7330] lr: 1.000e-05, eta: 3:35:36, time: 0.591, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0309, loss_cls: 0.1505, acc: 94.3511, loss_bbox: 0.2031, loss_mask: 0.2150, loss: 0.6140 +2024-05-28 02:23:14,221 - mmdet - INFO - Epoch [10][900/7330] lr: 1.000e-05, eta: 3:35:06, time: 0.637, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0322, loss_cls: 0.1457, acc: 94.4333, loss_bbox: 0.2023, loss_mask: 0.2139, loss: 0.6097 +2024-05-28 02:23:44,160 - mmdet - INFO - Epoch [10][950/7330] lr: 1.000e-05, eta: 3:34:35, time: 0.599, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0150, loss_rpn_bbox: 0.0324, loss_cls: 0.1510, acc: 94.2769, loss_bbox: 0.2036, loss_mask: 0.2194, loss: 0.6215 +2024-05-28 02:24:13,839 - mmdet - INFO - Epoch [10][1000/7330] lr: 1.000e-05, eta: 3:34:04, time: 0.594, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0317, loss_cls: 0.1485, acc: 94.2937, loss_bbox: 0.2050, loss_mask: 0.2167, loss: 0.6178 +2024-05-28 02:24:43,622 - mmdet - INFO - Epoch [10][1050/7330] lr: 1.000e-05, eta: 3:33:34, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0163, loss_rpn_bbox: 0.0329, loss_cls: 0.1556, acc: 94.1765, loss_bbox: 0.2131, loss_mask: 0.2167, loss: 0.6347 +2024-05-28 02:25:15,581 - mmdet - INFO - Epoch [10][1100/7330] lr: 1.000e-05, eta: 3:33:03, time: 0.639, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0156, loss_rpn_bbox: 0.0318, loss_cls: 0.1461, acc: 94.4521, loss_bbox: 0.2029, loss_mask: 0.2156, loss: 0.6120 +2024-05-28 02:25:45,591 - mmdet - INFO - Epoch [10][1150/7330] lr: 1.000e-05, eta: 3:32:33, time: 0.600, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0328, loss_cls: 0.1568, acc: 94.0315, loss_bbox: 0.2122, loss_mask: 0.2190, loss: 0.6373 +2024-05-28 02:26:15,445 - mmdet - INFO - Epoch [10][1200/7330] lr: 1.000e-05, eta: 3:32:02, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0319, loss_cls: 0.1534, acc: 94.1895, loss_bbox: 0.2124, loss_mask: 0.2182, loss: 0.6319 +2024-05-28 02:26:45,498 - mmdet - INFO - Epoch [10][1250/7330] lr: 1.000e-05, eta: 3:31:31, time: 0.601, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0187, loss_rpn_bbox: 0.0357, loss_cls: 0.1543, acc: 94.1160, loss_bbox: 0.2102, loss_mask: 0.2181, loss: 0.6369 +2024-05-28 02:27:15,583 - mmdet - INFO - Epoch [10][1300/7330] lr: 1.000e-05, eta: 3:31:00, time: 0.602, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0164, loss_rpn_bbox: 0.0353, loss_cls: 0.1588, acc: 93.9182, loss_bbox: 0.2208, loss_mask: 0.2254, loss: 0.6568 +2024-05-28 02:27:48,271 - mmdet - INFO - Epoch [10][1350/7330] lr: 1.000e-05, eta: 3:30:30, time: 0.654, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0329, loss_cls: 0.1532, acc: 94.2524, loss_bbox: 0.2087, loss_mask: 0.2219, loss: 0.6334 +2024-05-28 02:28:21,316 - mmdet - INFO - Epoch [10][1400/7330] lr: 1.000e-05, eta: 3:30:00, time: 0.661, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0174, loss_rpn_bbox: 0.0340, loss_cls: 0.1576, acc: 94.0764, loss_bbox: 0.2128, loss_mask: 0.2238, loss: 0.6457 +2024-05-28 02:28:55,517 - mmdet - INFO - Epoch [10][1450/7330] lr: 1.000e-05, eta: 3:29:31, time: 0.684, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0168, loss_rpn_bbox: 0.0353, loss_cls: 0.1559, acc: 93.9871, loss_bbox: 0.2099, loss_mask: 0.2209, loss: 0.6387 +2024-05-28 02:29:25,446 - mmdet - INFO - Epoch [10][1500/7330] lr: 1.000e-05, eta: 3:29:00, time: 0.599, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0332, loss_cls: 0.1543, acc: 94.1372, loss_bbox: 0.2122, loss_mask: 0.2238, loss: 0.6390 +2024-05-28 02:29:55,123 - mmdet - INFO - Epoch [10][1550/7330] lr: 1.000e-05, eta: 3:28:29, time: 0.594, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0291, loss_cls: 0.1419, acc: 94.6052, loss_bbox: 0.1960, loss_mask: 0.2116, loss: 0.5917 +2024-05-28 02:30:25,012 - mmdet - INFO - Epoch [10][1600/7330] lr: 1.000e-05, eta: 3:27:58, time: 0.598, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0329, loss_cls: 0.1523, acc: 94.1492, loss_bbox: 0.2079, loss_mask: 0.2202, loss: 0.6285 +2024-05-28 02:30:56,809 - mmdet - INFO - Epoch [10][1650/7330] lr: 1.000e-05, eta: 3:27:28, time: 0.635, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0307, loss_cls: 0.1489, acc: 94.3850, loss_bbox: 0.2033, loss_mask: 0.2133, loss: 0.6121 +2024-05-28 02:31:26,582 - mmdet - INFO - Epoch [10][1700/7330] lr: 1.000e-05, eta: 3:26:57, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0320, loss_cls: 0.1517, acc: 94.2266, loss_bbox: 0.2100, loss_mask: 0.2188, loss: 0.6287 +2024-05-28 02:31:56,606 - mmdet - INFO - Epoch [10][1750/7330] lr: 1.000e-05, eta: 3:26:27, time: 0.600, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0356, loss_cls: 0.1602, acc: 93.8660, loss_bbox: 0.2186, loss_mask: 0.2260, loss: 0.6569 +2024-05-28 02:32:26,377 - mmdet - INFO - Epoch [10][1800/7330] lr: 1.000e-05, eta: 3:25:56, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0167, loss_rpn_bbox: 0.0349, loss_cls: 0.1581, acc: 93.9761, loss_bbox: 0.2161, loss_mask: 0.2216, loss: 0.6473 +2024-05-28 02:32:58,532 - mmdet - INFO - Epoch [10][1850/7330] lr: 1.000e-05, eta: 3:25:26, time: 0.643, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0164, loss_rpn_bbox: 0.0340, loss_cls: 0.1550, acc: 94.2017, loss_bbox: 0.2093, loss_mask: 0.2279, loss: 0.6427 +2024-05-28 02:33:28,412 - mmdet - INFO - Epoch [10][1900/7330] lr: 1.000e-05, eta: 3:24:55, time: 0.598, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0183, loss_rpn_bbox: 0.0340, loss_cls: 0.1628, acc: 93.8733, loss_bbox: 0.2192, loss_mask: 0.2227, loss: 0.6569 +2024-05-28 02:34:00,323 - mmdet - INFO - Epoch [10][1950/7330] lr: 1.000e-05, eta: 3:24:25, time: 0.638, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0169, loss_rpn_bbox: 0.0330, loss_cls: 0.1496, acc: 94.3633, loss_bbox: 0.2094, loss_mask: 0.2206, loss: 0.6295 +2024-05-28 02:34:30,167 - mmdet - INFO - Epoch [10][2000/7330] lr: 1.000e-05, eta: 3:23:54, time: 0.597, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0334, loss_cls: 0.1523, acc: 94.1841, loss_bbox: 0.2082, loss_mask: 0.2205, loss: 0.6299 +2024-05-28 02:35:00,123 - mmdet - INFO - Epoch [10][2050/7330] lr: 1.000e-05, eta: 3:23:23, time: 0.599, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0318, loss_cls: 0.1495, acc: 94.2861, loss_bbox: 0.2086, loss_mask: 0.2195, loss: 0.6253 +2024-05-28 02:35:29,730 - mmdet - INFO - Epoch [10][2100/7330] lr: 1.000e-05, eta: 3:22:52, time: 0.592, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0146, loss_rpn_bbox: 0.0300, loss_cls: 0.1456, acc: 94.4626, loss_bbox: 0.2038, loss_mask: 0.2212, loss: 0.6153 +2024-05-28 02:36:01,680 - mmdet - INFO - Epoch [10][2150/7330] lr: 1.000e-05, eta: 3:22:22, time: 0.639, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0332, loss_cls: 0.1487, acc: 94.3689, loss_bbox: 0.2048, loss_mask: 0.2157, loss: 0.6189 +2024-05-28 02:36:31,472 - mmdet - INFO - Epoch [10][2200/7330] lr: 1.000e-05, eta: 3:21:51, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0148, loss_rpn_bbox: 0.0330, loss_cls: 0.1495, acc: 94.3206, loss_bbox: 0.2073, loss_mask: 0.2205, loss: 0.6252 +2024-05-28 02:37:01,344 - mmdet - INFO - Epoch [10][2250/7330] lr: 1.000e-05, eta: 3:21:20, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0164, loss_rpn_bbox: 0.0319, loss_cls: 0.1508, acc: 94.2544, loss_bbox: 0.2057, loss_mask: 0.2197, loss: 0.6244 +2024-05-28 02:37:31,046 - mmdet - INFO - Epoch [10][2300/7330] lr: 1.000e-05, eta: 3:20:49, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0163, loss_rpn_bbox: 0.0324, loss_cls: 0.1495, acc: 94.3074, loss_bbox: 0.2049, loss_mask: 0.2205, loss: 0.6236 +2024-05-28 02:38:00,989 - mmdet - INFO - Epoch [10][2350/7330] lr: 1.000e-05, eta: 3:20:19, time: 0.599, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0164, loss_rpn_bbox: 0.0336, loss_cls: 0.1558, acc: 94.1648, loss_bbox: 0.2113, loss_mask: 0.2219, loss: 0.6390 +2024-05-28 02:38:34,040 - mmdet - INFO - Epoch [10][2400/7330] lr: 1.000e-05, eta: 3:19:49, time: 0.661, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0316, loss_cls: 0.1505, acc: 94.3242, loss_bbox: 0.2021, loss_mask: 0.2146, loss: 0.6147 +2024-05-28 02:39:07,349 - mmdet - INFO - Epoch [10][2450/7330] lr: 1.000e-05, eta: 3:19:19, time: 0.666, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0337, loss_cls: 0.1513, acc: 94.2629, loss_bbox: 0.2097, loss_mask: 0.2160, loss: 0.6267 +2024-05-28 02:39:45,823 - mmdet - INFO - Epoch [10][2500/7330] lr: 1.000e-05, eta: 3:18:50, time: 0.770, data_time: 0.071, memory: 9459, loss_rpn_cls: 0.0157, loss_rpn_bbox: 0.0316, loss_cls: 0.1479, acc: 94.3879, loss_bbox: 0.2007, loss_mask: 0.2165, loss: 0.6124 +2024-05-28 02:40:15,962 - mmdet - INFO - Epoch [10][2550/7330] lr: 1.000e-05, eta: 3:18:20, time: 0.603, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0168, loss_rpn_bbox: 0.0350, loss_cls: 0.1534, acc: 94.1538, loss_bbox: 0.2104, loss_mask: 0.2191, loss: 0.6347 +2024-05-28 02:40:45,760 - mmdet - INFO - Epoch [10][2600/7330] lr: 1.000e-05, eta: 3:17:49, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0316, loss_cls: 0.1468, acc: 94.4255, loss_bbox: 0.2039, loss_mask: 0.2188, loss: 0.6166 +2024-05-28 02:41:15,795 - mmdet - INFO - Epoch [10][2650/7330] lr: 1.000e-05, eta: 3:17:18, time: 0.601, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0150, loss_rpn_bbox: 0.0330, loss_cls: 0.1545, acc: 94.1084, loss_bbox: 0.2057, loss_mask: 0.2175, loss: 0.6257 +2024-05-28 02:41:47,942 - mmdet - INFO - Epoch [10][2700/7330] lr: 1.000e-05, eta: 3:16:48, time: 0.643, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0179, loss_rpn_bbox: 0.0343, loss_cls: 0.1555, acc: 94.1028, loss_bbox: 0.2144, loss_mask: 0.2205, loss: 0.6426 +2024-05-28 02:42:17,790 - mmdet - INFO - Epoch [10][2750/7330] lr: 1.000e-05, eta: 3:16:17, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0179, loss_rpn_bbox: 0.0346, loss_cls: 0.1545, acc: 94.1033, loss_bbox: 0.2133, loss_mask: 0.2207, loss: 0.6411 +2024-05-28 02:42:47,568 - mmdet - INFO - Epoch [10][2800/7330] lr: 1.000e-05, eta: 3:15:46, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0310, loss_cls: 0.1487, acc: 94.2896, loss_bbox: 0.2081, loss_mask: 0.2173, loss: 0.6209 +2024-05-28 02:43:17,509 - mmdet - INFO - Epoch [10][2850/7330] lr: 1.000e-05, eta: 3:15:16, time: 0.599, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0332, loss_cls: 0.1490, acc: 94.3164, loss_bbox: 0.2063, loss_mask: 0.2177, loss: 0.6220 +2024-05-28 02:43:49,862 - mmdet - INFO - Epoch [10][2900/7330] lr: 1.000e-05, eta: 3:14:45, time: 0.647, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0322, loss_cls: 0.1552, acc: 94.1604, loss_bbox: 0.2062, loss_mask: 0.2189, loss: 0.6283 +2024-05-28 02:44:19,596 - mmdet - INFO - Epoch [10][2950/7330] lr: 1.000e-05, eta: 3:14:15, time: 0.595, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0176, loss_rpn_bbox: 0.0358, loss_cls: 0.1548, acc: 94.0879, loss_bbox: 0.2140, loss_mask: 0.2245, loss: 0.6468 +2024-05-28 02:44:51,296 - mmdet - INFO - Epoch [10][3000/7330] lr: 1.000e-05, eta: 3:13:44, time: 0.634, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0312, loss_cls: 0.1478, acc: 94.3081, loss_bbox: 0.2009, loss_mask: 0.2169, loss: 0.6130 +2024-05-28 02:45:20,873 - mmdet - INFO - Epoch [10][3050/7330] lr: 1.000e-05, eta: 3:13:13, time: 0.592, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0313, loss_cls: 0.1451, acc: 94.4390, loss_bbox: 0.1975, loss_mask: 0.2121, loss: 0.6015 +2024-05-28 02:45:50,687 - mmdet - INFO - Epoch [10][3100/7330] lr: 1.000e-05, eta: 3:12:43, time: 0.596, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0329, loss_cls: 0.1503, acc: 94.1687, loss_bbox: 0.2053, loss_mask: 0.2187, loss: 0.6222 +2024-05-28 02:46:20,616 - mmdet - INFO - Epoch [10][3150/7330] lr: 1.000e-05, eta: 3:12:12, time: 0.599, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0340, loss_cls: 0.1551, acc: 94.1074, loss_bbox: 0.2089, loss_mask: 0.2190, loss: 0.6335 +2024-05-28 02:46:52,965 - mmdet - INFO - Epoch [10][3200/7330] lr: 1.000e-05, eta: 3:11:42, time: 0.647, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0179, loss_rpn_bbox: 0.0344, loss_cls: 0.1541, acc: 94.1013, loss_bbox: 0.2142, loss_mask: 0.2222, loss: 0.6427 +2024-05-28 02:47:22,769 - mmdet - INFO - Epoch [10][3250/7330] lr: 1.000e-05, eta: 3:11:11, time: 0.596, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0310, loss_cls: 0.1484, acc: 94.3127, loss_bbox: 0.2102, loss_mask: 0.2204, loss: 0.6260 +2024-05-28 02:47:52,516 - mmdet - INFO - Epoch [10][3300/7330] lr: 1.000e-05, eta: 3:10:40, time: 0.595, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0338, loss_cls: 0.1477, acc: 94.4058, loss_bbox: 0.2082, loss_mask: 0.2214, loss: 0.6261 +2024-05-28 02:48:22,217 - mmdet - INFO - Epoch [10][3350/7330] lr: 1.000e-05, eta: 3:10:09, time: 0.594, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0156, loss_rpn_bbox: 0.0314, loss_cls: 0.1498, acc: 94.3096, loss_bbox: 0.2026, loss_mask: 0.2198, loss: 0.6192 +2024-05-28 02:48:52,010 - mmdet - INFO - Epoch [10][3400/7330] lr: 1.000e-05, eta: 3:09:38, time: 0.596, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0318, loss_cls: 0.1492, acc: 94.3328, loss_bbox: 0.2046, loss_mask: 0.2135, loss: 0.6150 +2024-05-28 02:49:24,852 - mmdet - INFO - Epoch [10][3450/7330] lr: 1.000e-05, eta: 3:09:08, time: 0.657, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0328, loss_cls: 0.1539, acc: 94.1438, loss_bbox: 0.2134, loss_mask: 0.2196, loss: 0.6363 +2024-05-28 02:49:57,036 - mmdet - INFO - Epoch [10][3500/7330] lr: 1.000e-05, eta: 3:08:38, time: 0.644, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0150, loss_rpn_bbox: 0.0309, loss_cls: 0.1433, acc: 94.5618, loss_bbox: 0.1966, loss_mask: 0.2115, loss: 0.5972 +2024-05-28 02:50:32,160 - mmdet - INFO - Epoch [10][3550/7330] lr: 1.000e-05, eta: 3:08:09, time: 0.702, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0323, loss_cls: 0.1426, acc: 94.4644, loss_bbox: 0.2030, loss_mask: 0.2178, loss: 0.6106 +2024-05-28 02:51:01,969 - mmdet - INFO - Epoch [10][3600/7330] lr: 1.000e-05, eta: 3:07:38, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0323, loss_cls: 0.1547, acc: 94.0674, loss_bbox: 0.2106, loss_mask: 0.2194, loss: 0.6331 +2024-05-28 02:51:32,091 - mmdet - INFO - Epoch [10][3650/7330] lr: 1.000e-05, eta: 3:07:07, time: 0.602, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0179, loss_rpn_bbox: 0.0367, loss_cls: 0.1655, acc: 93.7546, loss_bbox: 0.2215, loss_mask: 0.2282, loss: 0.6697 +2024-05-28 02:52:01,733 - mmdet - INFO - Epoch [10][3700/7330] lr: 1.000e-05, eta: 3:06:36, time: 0.593, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0169, loss_rpn_bbox: 0.0335, loss_cls: 0.1537, acc: 94.0698, loss_bbox: 0.2085, loss_mask: 0.2206, loss: 0.6332 +2024-05-28 02:52:33,855 - mmdet - INFO - Epoch [10][3750/7330] lr: 1.000e-05, eta: 3:06:06, time: 0.642, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0169, loss_rpn_bbox: 0.0338, loss_cls: 0.1517, acc: 94.2114, loss_bbox: 0.2119, loss_mask: 0.2176, loss: 0.6318 +2024-05-28 02:53:03,804 - mmdet - INFO - Epoch [10][3800/7330] lr: 1.000e-05, eta: 3:05:35, time: 0.599, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0320, loss_cls: 0.1485, acc: 94.4282, loss_bbox: 0.2045, loss_mask: 0.2138, loss: 0.6141 +2024-05-28 02:53:33,517 - mmdet - INFO - Epoch [10][3850/7330] lr: 1.000e-05, eta: 3:05:04, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0164, loss_rpn_bbox: 0.0311, loss_cls: 0.1501, acc: 94.2913, loss_bbox: 0.2049, loss_mask: 0.2168, loss: 0.6192 +2024-05-28 02:54:03,521 - mmdet - INFO - Epoch [10][3900/7330] lr: 1.000e-05, eta: 3:04:34, time: 0.600, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0324, loss_cls: 0.1533, acc: 94.0979, loss_bbox: 0.2130, loss_mask: 0.2244, loss: 0.6383 +2024-05-28 02:54:35,618 - mmdet - INFO - Epoch [10][3950/7330] lr: 1.000e-05, eta: 3:04:03, time: 0.642, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0320, loss_cls: 0.1514, acc: 94.2603, loss_bbox: 0.2030, loss_mask: 0.2202, loss: 0.6226 +2024-05-28 02:55:05,430 - mmdet - INFO - Epoch [10][4000/7330] lr: 1.000e-05, eta: 3:03:33, time: 0.596, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0169, loss_rpn_bbox: 0.0327, loss_cls: 0.1542, acc: 94.1160, loss_bbox: 0.2105, loss_mask: 0.2243, loss: 0.6386 +2024-05-28 02:55:37,287 - mmdet - INFO - Epoch [10][4050/7330] lr: 1.000e-05, eta: 3:03:02, time: 0.637, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0323, loss_cls: 0.1508, acc: 94.2393, loss_bbox: 0.2100, loss_mask: 0.2202, loss: 0.6299 +2024-05-28 02:56:07,091 - mmdet - INFO - Epoch [10][4100/7330] lr: 1.000e-05, eta: 3:02:31, time: 0.596, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0163, loss_rpn_bbox: 0.0324, loss_cls: 0.1529, acc: 94.1877, loss_bbox: 0.2097, loss_mask: 0.2207, loss: 0.6319 +2024-05-28 02:56:36,988 - mmdet - INFO - Epoch [10][4150/7330] lr: 1.000e-05, eta: 3:02:01, time: 0.598, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0170, loss_rpn_bbox: 0.0351, loss_cls: 0.1577, acc: 94.0649, loss_bbox: 0.2144, loss_mask: 0.2233, loss: 0.6474 +2024-05-28 02:57:06,901 - mmdet - INFO - Epoch [10][4200/7330] lr: 1.000e-05, eta: 3:01:30, time: 0.598, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0169, loss_rpn_bbox: 0.0327, loss_cls: 0.1531, acc: 94.2512, loss_bbox: 0.2101, loss_mask: 0.2222, loss: 0.6349 +2024-05-28 02:57:39,046 - mmdet - INFO - Epoch [10][4250/7330] lr: 1.000e-05, eta: 3:01:00, time: 0.643, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0179, loss_rpn_bbox: 0.0345, loss_cls: 0.1546, acc: 94.1301, loss_bbox: 0.2121, loss_mask: 0.2218, loss: 0.6409 +2024-05-28 02:58:08,787 - mmdet - INFO - Epoch [10][4300/7330] lr: 1.000e-05, eta: 3:00:29, time: 0.595, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0176, loss_rpn_bbox: 0.0331, loss_cls: 0.1556, acc: 94.1030, loss_bbox: 0.2092, loss_mask: 0.2185, loss: 0.6341 +2024-05-28 02:58:38,681 - mmdet - INFO - Epoch [10][4350/7330] lr: 1.000e-05, eta: 2:59:58, time: 0.598, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0318, loss_cls: 0.1491, acc: 94.2710, loss_bbox: 0.2064, loss_mask: 0.2183, loss: 0.6209 +2024-05-28 02:59:08,588 - mmdet - INFO - Epoch [10][4400/7330] lr: 1.000e-05, eta: 2:59:27, time: 0.598, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0317, loss_cls: 0.1426, acc: 94.5176, loss_bbox: 0.2013, loss_mask: 0.2145, loss: 0.6046 +2024-05-28 02:59:38,469 - mmdet - INFO - Epoch [10][4450/7330] lr: 1.000e-05, eta: 2:58:57, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0150, loss_rpn_bbox: 0.0326, loss_cls: 0.1507, acc: 94.2971, loss_bbox: 0.2060, loss_mask: 0.2200, loss: 0.6243 +2024-05-28 03:00:12,514 - mmdet - INFO - Epoch [10][4500/7330] lr: 1.000e-05, eta: 2:58:27, time: 0.681, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0364, loss_cls: 0.1527, acc: 94.1367, loss_bbox: 0.2103, loss_mask: 0.2243, loss: 0.6399 +2024-05-28 03:00:44,368 - mmdet - INFO - Epoch [10][4550/7330] lr: 1.000e-05, eta: 2:57:56, time: 0.637, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0327, loss_cls: 0.1577, acc: 93.9226, loss_bbox: 0.2144, loss_mask: 0.2222, loss: 0.6431 +2024-05-28 03:01:19,762 - mmdet - INFO - Epoch [10][4600/7330] lr: 1.000e-05, eta: 2:57:27, time: 0.708, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0330, loss_cls: 0.1511, acc: 94.3347, loss_bbox: 0.2091, loss_mask: 0.2225, loss: 0.6322 +2024-05-28 03:01:49,836 - mmdet - INFO - Epoch [10][4650/7330] lr: 1.000e-05, eta: 2:56:56, time: 0.601, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0175, loss_rpn_bbox: 0.0354, loss_cls: 0.1575, acc: 93.9929, loss_bbox: 0.2203, loss_mask: 0.2227, loss: 0.6535 +2024-05-28 03:02:19,989 - mmdet - INFO - Epoch [10][4700/7330] lr: 1.000e-05, eta: 2:56:26, time: 0.603, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0336, loss_cls: 0.1507, acc: 94.2722, loss_bbox: 0.2076, loss_mask: 0.2188, loss: 0.6261 +2024-05-28 03:02:49,833 - mmdet - INFO - Epoch [10][4750/7330] lr: 1.000e-05, eta: 2:55:55, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0345, loss_cls: 0.1547, acc: 94.0173, loss_bbox: 0.2118, loss_mask: 0.2203, loss: 0.6379 +2024-05-28 03:03:21,761 - mmdet - INFO - Epoch [10][4800/7330] lr: 1.000e-05, eta: 2:55:24, time: 0.639, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0169, loss_rpn_bbox: 0.0337, loss_cls: 0.1560, acc: 94.1013, loss_bbox: 0.2107, loss_mask: 0.2234, loss: 0.6407 +2024-05-28 03:03:51,546 - mmdet - INFO - Epoch [10][4850/7330] lr: 1.000e-05, eta: 2:54:54, time: 0.596, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0327, loss_cls: 0.1475, acc: 94.4089, loss_bbox: 0.2045, loss_mask: 0.2185, loss: 0.6194 +2024-05-28 03:04:21,258 - mmdet - INFO - Epoch [10][4900/7330] lr: 1.000e-05, eta: 2:54:23, time: 0.594, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0157, loss_rpn_bbox: 0.0324, loss_cls: 0.1494, acc: 94.2664, loss_bbox: 0.2052, loss_mask: 0.2146, loss: 0.6172 +2024-05-28 03:04:50,939 - mmdet - INFO - Epoch [10][4950/7330] lr: 1.000e-05, eta: 2:53:52, time: 0.594, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0308, loss_cls: 0.1482, acc: 94.3274, loss_bbox: 0.2015, loss_mask: 0.2174, loss: 0.6123 +2024-05-28 03:05:23,162 - mmdet - INFO - Epoch [10][5000/7330] lr: 1.000e-05, eta: 2:53:22, time: 0.644, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0169, loss_rpn_bbox: 0.0339, loss_cls: 0.1546, acc: 94.1541, loss_bbox: 0.2116, loss_mask: 0.2154, loss: 0.6323 +2024-05-28 03:05:52,716 - mmdet - INFO - Epoch [10][5050/7330] lr: 1.000e-05, eta: 2:52:51, time: 0.591, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0327, loss_cls: 0.1439, acc: 94.5518, loss_bbox: 0.1989, loss_mask: 0.2181, loss: 0.6097 +2024-05-28 03:06:24,580 - mmdet - INFO - Epoch [10][5100/7330] lr: 1.000e-05, eta: 2:52:21, time: 0.637, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0156, loss_rpn_bbox: 0.0311, loss_cls: 0.1467, acc: 94.3584, loss_bbox: 0.1998, loss_mask: 0.2157, loss: 0.6088 +2024-05-28 03:06:54,142 - mmdet - INFO - Epoch [10][5150/7330] lr: 1.000e-05, eta: 2:51:50, time: 0.591, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0167, loss_rpn_bbox: 0.0316, loss_cls: 0.1538, acc: 94.1111, loss_bbox: 0.2079, loss_mask: 0.2213, loss: 0.6312 +2024-05-28 03:07:23,927 - mmdet - INFO - Epoch [10][5200/7330] lr: 1.000e-05, eta: 2:51:19, time: 0.596, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0345, loss_cls: 0.1503, acc: 94.3193, loss_bbox: 0.2048, loss_mask: 0.2206, loss: 0.6261 +2024-05-28 03:07:53,840 - mmdet - INFO - Epoch [10][5250/7330] lr: 1.000e-05, eta: 2:50:48, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0170, loss_rpn_bbox: 0.0328, loss_cls: 0.1545, acc: 94.2129, loss_bbox: 0.2082, loss_mask: 0.2168, loss: 0.6292 +2024-05-28 03:08:26,070 - mmdet - INFO - Epoch [10][5300/7330] lr: 1.000e-05, eta: 2:50:18, time: 0.645, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0339, loss_cls: 0.1520, acc: 94.1785, loss_bbox: 0.2132, loss_mask: 0.2212, loss: 0.6358 +2024-05-28 03:08:56,120 - mmdet - INFO - Epoch [10][5350/7330] lr: 1.000e-05, eta: 2:49:47, time: 0.601, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0170, loss_rpn_bbox: 0.0350, loss_cls: 0.1562, acc: 94.0503, loss_bbox: 0.2126, loss_mask: 0.2226, loss: 0.6434 +2024-05-28 03:09:25,956 - mmdet - INFO - Epoch [10][5400/7330] lr: 1.000e-05, eta: 2:49:16, time: 0.597, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0329, loss_cls: 0.1475, acc: 94.3906, loss_bbox: 0.2041, loss_mask: 0.2188, loss: 0.6191 +2024-05-28 03:09:55,783 - mmdet - INFO - Epoch [10][5450/7330] lr: 1.000e-05, eta: 2:48:46, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0170, loss_rpn_bbox: 0.0353, loss_cls: 0.1628, acc: 93.7705, loss_bbox: 0.2240, loss_mask: 0.2276, loss: 0.6667 +2024-05-28 03:10:25,631 - mmdet - INFO - Epoch [10][5500/7330] lr: 1.000e-05, eta: 2:48:15, time: 0.597, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0157, loss_rpn_bbox: 0.0332, loss_cls: 0.1563, acc: 94.0361, loss_bbox: 0.2140, loss_mask: 0.2227, loss: 0.6418 +2024-05-28 03:10:58,264 - mmdet - INFO - Epoch [10][5550/7330] lr: 1.000e-05, eta: 2:47:45, time: 0.653, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0174, loss_rpn_bbox: 0.0339, loss_cls: 0.1580, acc: 93.9927, loss_bbox: 0.2153, loss_mask: 0.2203, loss: 0.6449 +2024-05-28 03:11:30,389 - mmdet - INFO - Epoch [10][5600/7330] lr: 1.000e-05, eta: 2:47:14, time: 0.642, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0167, loss_rpn_bbox: 0.0336, loss_cls: 0.1530, acc: 94.1763, loss_bbox: 0.2088, loss_mask: 0.2201, loss: 0.6321 +2024-05-28 03:12:04,800 - mmdet - INFO - Epoch [10][5650/7330] lr: 1.000e-05, eta: 2:46:45, time: 0.688, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0342, loss_cls: 0.1497, acc: 94.3555, loss_bbox: 0.2057, loss_mask: 0.2143, loss: 0.6200 +2024-05-28 03:12:34,756 - mmdet - INFO - Epoch [10][5700/7330] lr: 1.000e-05, eta: 2:46:14, time: 0.599, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0164, loss_rpn_bbox: 0.0335, loss_cls: 0.1552, acc: 94.2275, loss_bbox: 0.2097, loss_mask: 0.2159, loss: 0.6306 +2024-05-28 03:13:04,796 - mmdet - INFO - Epoch [10][5750/7330] lr: 1.000e-05, eta: 2:45:43, time: 0.601, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0164, loss_rpn_bbox: 0.0348, loss_cls: 0.1555, acc: 94.1223, loss_bbox: 0.2124, loss_mask: 0.2209, loss: 0.6401 +2024-05-28 03:13:34,559 - mmdet - INFO - Epoch [10][5800/7330] lr: 1.000e-05, eta: 2:45:12, time: 0.595, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0323, loss_cls: 0.1486, acc: 94.2822, loss_bbox: 0.2058, loss_mask: 0.2212, loss: 0.6221 +2024-05-28 03:14:06,769 - mmdet - INFO - Epoch [10][5850/7330] lr: 1.000e-05, eta: 2:44:42, time: 0.645, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0329, loss_cls: 0.1505, acc: 94.2190, loss_bbox: 0.2092, loss_mask: 0.2191, loss: 0.6268 +2024-05-28 03:14:36,615 - mmdet - INFO - Epoch [10][5900/7330] lr: 1.000e-05, eta: 2:44:11, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0168, loss_rpn_bbox: 0.0325, loss_cls: 0.1513, acc: 94.3247, loss_bbox: 0.2044, loss_mask: 0.2218, loss: 0.6268 +2024-05-28 03:15:06,438 - mmdet - INFO - Epoch [10][5950/7330] lr: 1.000e-05, eta: 2:43:40, time: 0.597, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0156, loss_rpn_bbox: 0.0324, loss_cls: 0.1487, acc: 94.2690, loss_bbox: 0.2066, loss_mask: 0.2138, loss: 0.6172 +2024-05-28 03:15:36,390 - mmdet - INFO - Epoch [10][6000/7330] lr: 1.000e-05, eta: 2:43:10, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0167, loss_rpn_bbox: 0.0353, loss_cls: 0.1557, acc: 94.0842, loss_bbox: 0.2172, loss_mask: 0.2203, loss: 0.6451 +2024-05-28 03:16:08,902 - mmdet - INFO - Epoch [10][6050/7330] lr: 1.000e-05, eta: 2:42:40, time: 0.650, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0330, loss_cls: 0.1480, acc: 94.3484, loss_bbox: 0.2037, loss_mask: 0.2165, loss: 0.6171 +2024-05-28 03:16:38,737 - mmdet - INFO - Epoch [10][6100/7330] lr: 1.000e-05, eta: 2:42:09, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0156, loss_rpn_bbox: 0.0325, loss_cls: 0.1512, acc: 94.2354, loss_bbox: 0.2062, loss_mask: 0.2146, loss: 0.6201 +2024-05-28 03:17:11,011 - mmdet - INFO - Epoch [10][6150/7330] lr: 1.000e-05, eta: 2:41:38, time: 0.646, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0334, loss_cls: 0.1531, acc: 94.1479, loss_bbox: 0.2128, loss_mask: 0.2232, loss: 0.6391 +2024-05-28 03:17:41,004 - mmdet - INFO - Epoch [10][6200/7330] lr: 1.000e-05, eta: 2:41:08, time: 0.600, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0163, loss_rpn_bbox: 0.0338, loss_cls: 0.1564, acc: 94.0530, loss_bbox: 0.2171, loss_mask: 0.2269, loss: 0.6505 +2024-05-28 03:18:10,996 - mmdet - INFO - Epoch [10][6250/7330] lr: 1.000e-05, eta: 2:40:37, time: 0.600, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0164, loss_rpn_bbox: 0.0352, loss_cls: 0.1529, acc: 94.1489, loss_bbox: 0.2115, loss_mask: 0.2152, loss: 0.6313 +2024-05-28 03:18:40,798 - mmdet - INFO - Epoch [10][6300/7330] lr: 1.000e-05, eta: 2:40:06, time: 0.596, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0169, loss_rpn_bbox: 0.0339, loss_cls: 0.1553, acc: 94.1221, loss_bbox: 0.2137, loss_mask: 0.2216, loss: 0.6414 +2024-05-28 03:19:12,919 - mmdet - INFO - Epoch [10][6350/7330] lr: 1.000e-05, eta: 2:39:36, time: 0.642, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0157, loss_rpn_bbox: 0.0325, loss_cls: 0.1525, acc: 94.2393, loss_bbox: 0.2067, loss_mask: 0.2164, loss: 0.6237 +2024-05-28 03:19:42,689 - mmdet - INFO - Epoch [10][6400/7330] lr: 1.000e-05, eta: 2:39:05, time: 0.595, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0321, loss_cls: 0.1595, acc: 93.9573, loss_bbox: 0.2147, loss_mask: 0.2243, loss: 0.6466 +2024-05-28 03:20:12,609 - mmdet - INFO - Epoch [10][6450/7330] lr: 1.000e-05, eta: 2:38:34, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0322, loss_cls: 0.1513, acc: 94.2810, loss_bbox: 0.2068, loss_mask: 0.2211, loss: 0.6276 +2024-05-28 03:20:42,344 - mmdet - INFO - Epoch [10][6500/7330] lr: 1.000e-05, eta: 2:38:04, time: 0.595, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0168, loss_rpn_bbox: 0.0335, loss_cls: 0.1467, acc: 94.4236, loss_bbox: 0.2017, loss_mask: 0.2179, loss: 0.6166 +2024-05-28 03:21:12,133 - mmdet - INFO - Epoch [10][6550/7330] lr: 1.000e-05, eta: 2:37:33, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0331, loss_cls: 0.1513, acc: 94.2876, loss_bbox: 0.2083, loss_mask: 0.2246, loss: 0.6333 +2024-05-28 03:21:44,719 - mmdet - INFO - Epoch [10][6600/7330] lr: 1.000e-05, eta: 2:37:03, time: 0.652, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0163, loss_rpn_bbox: 0.0341, loss_cls: 0.1528, acc: 94.1968, loss_bbox: 0.2090, loss_mask: 0.2230, loss: 0.6352 +2024-05-28 03:22:16,854 - mmdet - INFO - Epoch [10][6650/7330] lr: 1.000e-05, eta: 2:36:32, time: 0.643, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0156, loss_rpn_bbox: 0.0310, loss_cls: 0.1569, acc: 94.0552, loss_bbox: 0.2143, loss_mask: 0.2170, loss: 0.6347 +2024-05-28 03:22:51,238 - mmdet - INFO - Epoch [10][6700/7330] lr: 1.000e-05, eta: 2:36:02, time: 0.687, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0306, loss_cls: 0.1459, acc: 94.4709, loss_bbox: 0.2025, loss_mask: 0.2158, loss: 0.6101 +2024-05-28 03:23:21,149 - mmdet - INFO - Epoch [10][6750/7330] lr: 1.000e-05, eta: 2:35:32, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0318, loss_cls: 0.1442, acc: 94.4653, loss_bbox: 0.2004, loss_mask: 0.2151, loss: 0.6063 +2024-05-28 03:23:50,888 - mmdet - INFO - Epoch [10][6800/7330] lr: 1.000e-05, eta: 2:35:01, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0320, loss_cls: 0.1504, acc: 94.3135, loss_bbox: 0.2039, loss_mask: 0.2158, loss: 0.6168 +2024-05-28 03:24:20,629 - mmdet - INFO - Epoch [10][6850/7330] lr: 1.000e-05, eta: 2:34:30, time: 0.595, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0304, loss_cls: 0.1487, acc: 94.3057, loss_bbox: 0.2010, loss_mask: 0.2185, loss: 0.6144 +2024-05-28 03:24:52,669 - mmdet - INFO - Epoch [10][6900/7330] lr: 1.000e-05, eta: 2:34:00, time: 0.641, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0329, loss_cls: 0.1518, acc: 94.1360, loss_bbox: 0.2096, loss_mask: 0.2202, loss: 0.6299 +2024-05-28 03:25:22,574 - mmdet - INFO - Epoch [10][6950/7330] lr: 1.000e-05, eta: 2:33:29, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0167, loss_rpn_bbox: 0.0337, loss_cls: 0.1491, acc: 94.2288, loss_bbox: 0.2086, loss_mask: 0.2198, loss: 0.6278 +2024-05-28 03:25:52,470 - mmdet - INFO - Epoch [10][7000/7330] lr: 1.000e-05, eta: 2:32:58, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0157, loss_rpn_bbox: 0.0327, loss_cls: 0.1536, acc: 94.1438, loss_bbox: 0.2099, loss_mask: 0.2177, loss: 0.6296 +2024-05-28 03:26:24,849 - mmdet - INFO - Epoch [10][7050/7330] lr: 1.000e-05, eta: 2:32:28, time: 0.648, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0337, loss_cls: 0.1519, acc: 94.2141, loss_bbox: 0.2042, loss_mask: 0.2195, loss: 0.6255 +2024-05-28 03:26:54,924 - mmdet - INFO - Epoch [10][7100/7330] lr: 1.000e-05, eta: 2:31:57, time: 0.601, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0326, loss_cls: 0.1523, acc: 94.1973, loss_bbox: 0.2077, loss_mask: 0.2195, loss: 0.6281 +2024-05-28 03:27:25,027 - mmdet - INFO - Epoch [10][7150/7330] lr: 1.000e-05, eta: 2:31:27, time: 0.602, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0187, loss_rpn_bbox: 0.0355, loss_cls: 0.1574, acc: 93.9709, loss_bbox: 0.2166, loss_mask: 0.2218, loss: 0.6499 +2024-05-28 03:27:56,887 - mmdet - INFO - Epoch [10][7200/7330] lr: 1.000e-05, eta: 2:30:56, time: 0.637, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0343, loss_cls: 0.1511, acc: 94.2822, loss_bbox: 0.2075, loss_mask: 0.2218, loss: 0.6306 +2024-05-28 03:28:26,700 - mmdet - INFO - Epoch [10][7250/7330] lr: 1.000e-05, eta: 2:30:25, time: 0.596, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0140, loss_rpn_bbox: 0.0310, loss_cls: 0.1471, acc: 94.3818, loss_bbox: 0.2066, loss_mask: 0.2192, loss: 0.6179 +2024-05-28 03:28:56,737 - mmdet - INFO - Epoch [10][7300/7330] lr: 1.000e-05, eta: 2:29:55, time: 0.601, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0163, loss_rpn_bbox: 0.0332, loss_cls: 0.1552, acc: 94.0024, loss_bbox: 0.2176, loss_mask: 0.2241, loss: 0.6464 +2024-05-28 03:29:15,508 - mmdet - INFO - Saving checkpoint at 10 epochs +2024-05-28 03:31:07,976 - mmdet - INFO - Evaluating bbox... +2024-05-28 03:31:33,022 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.464 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.692 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.506 + 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 ] = 0.507 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.661 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.571 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.571 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.571 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.354 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.620 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.767 + +2024-05-28 03:31:33,022 - mmdet - INFO - Evaluating segm... +2024-05-28 03:31:55,732 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.407 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.651 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.430 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.180 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.446 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.643 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.506 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.506 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.506 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.280 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.559 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.722 + +2024-05-28 03:31:56,066 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-28 03:31:56,067 - mmdet - INFO - Epoch(val) [10][625] bbox_mAP: 0.4640, bbox_mAP_50: 0.6920, bbox_mAP_75: 0.5060, bbox_mAP_s: 0.2650, bbox_mAP_m: 0.5070, bbox_mAP_l: 0.6610, bbox_mAP_copypaste: 0.464 0.692 0.506 0.265 0.507 0.661, segm_mAP: 0.4070, segm_mAP_50: 0.6510, segm_mAP_75: 0.4300, segm_mAP_s: 0.1800, segm_mAP_m: 0.4460, segm_mAP_l: 0.6430, segm_mAP_copypaste: 0.407 0.651 0.430 0.180 0.446 0.643 +2024-05-28 03:32:29,386 - mmdet - INFO - Epoch [11][50/7330] lr: 1.000e-05, eta: 2:29:03, time: 0.666, data_time: 0.085, memory: 9459, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0318, loss_cls: 0.1457, acc: 94.4319, loss_bbox: 0.2037, loss_mask: 0.2152, loss: 0.6108 +2024-05-28 03:33:02,036 - mmdet - INFO - Epoch [11][100/7330] lr: 1.000e-05, eta: 2:28:32, time: 0.653, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0334, loss_cls: 0.1524, acc: 94.1836, loss_bbox: 0.2087, loss_mask: 0.2214, loss: 0.6324 +2024-05-28 03:33:33,893 - mmdet - INFO - Epoch [11][150/7330] lr: 1.000e-05, eta: 2:28:02, time: 0.637, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0301, loss_cls: 0.1393, acc: 94.6677, loss_bbox: 0.1958, loss_mask: 0.2119, loss: 0.5910 +2024-05-28 03:34:03,721 - mmdet - INFO - Epoch [11][200/7330] lr: 1.000e-05, eta: 2:27:31, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0172, loss_rpn_bbox: 0.0334, loss_cls: 0.1503, acc: 94.2417, loss_bbox: 0.2105, loss_mask: 0.2202, loss: 0.6317 +2024-05-28 03:34:33,558 - mmdet - INFO - Epoch [11][250/7330] lr: 1.000e-05, eta: 2:27:00, time: 0.597, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0324, loss_cls: 0.1478, acc: 94.3420, loss_bbox: 0.2030, loss_mask: 0.2160, loss: 0.6135 +2024-05-28 03:35:03,267 - mmdet - INFO - Epoch [11][300/7330] lr: 1.000e-05, eta: 2:26:30, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0350, loss_cls: 0.1528, acc: 94.1863, loss_bbox: 0.2101, loss_mask: 0.2185, loss: 0.6324 +2024-05-28 03:35:33,145 - mmdet - INFO - Epoch [11][350/7330] lr: 1.000e-05, eta: 2:25:59, time: 0.598, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0294, loss_cls: 0.1366, acc: 94.7449, loss_bbox: 0.1936, loss_mask: 0.2073, loss: 0.5813 +2024-05-28 03:36:03,252 - mmdet - INFO - Epoch [11][400/7330] lr: 1.000e-05, eta: 2:25:28, time: 0.602, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0163, loss_rpn_bbox: 0.0349, loss_cls: 0.1496, acc: 94.3054, loss_bbox: 0.2099, loss_mask: 0.2186, loss: 0.6293 +2024-05-28 03:36:33,224 - mmdet - INFO - Epoch [11][450/7330] lr: 1.000e-05, eta: 2:24:57, time: 0.599, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0169, loss_rpn_bbox: 0.0352, loss_cls: 0.1568, acc: 93.9846, loss_bbox: 0.2178, loss_mask: 0.2312, loss: 0.6579 +2024-05-28 03:37:03,119 - mmdet - INFO - Epoch [11][500/7330] lr: 1.000e-05, eta: 2:24:27, time: 0.598, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0148, loss_rpn_bbox: 0.0309, loss_cls: 0.1430, acc: 94.4929, loss_bbox: 0.1999, loss_mask: 0.2150, loss: 0.6035 +2024-05-28 03:37:37,981 - mmdet - INFO - Epoch [11][550/7330] lr: 1.000e-05, eta: 2:23:57, time: 0.697, data_time: 0.125, memory: 9459, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0312, loss_cls: 0.1473, acc: 94.4238, loss_bbox: 0.2013, loss_mask: 0.2156, loss: 0.6113 +2024-05-28 03:38:07,622 - mmdet - INFO - Epoch [11][600/7330] lr: 1.000e-05, eta: 2:23:26, time: 0.593, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0319, loss_cls: 0.1443, acc: 94.4109, loss_bbox: 0.1991, loss_mask: 0.2179, loss: 0.6086 +2024-05-28 03:38:37,288 - mmdet - INFO - Epoch [11][650/7330] lr: 1.000e-05, eta: 2:22:55, time: 0.593, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0311, loss_cls: 0.1406, acc: 94.6428, loss_bbox: 0.2000, loss_mask: 0.2106, loss: 0.5972 +2024-05-28 03:39:06,928 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-28 03:39:06,928 - mmdet - INFO - Epoch [11][700/7330] lr: 1.000e-05, eta: 2:22:25, time: 0.593, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0331, loss_cls: 0.1498, acc: 94.3115, loss_bbox: 0.2087, loss_mask: 0.2193, loss: 0.6260 +2024-05-28 03:39:38,752 - mmdet - INFO - Epoch [11][750/7330] lr: 1.000e-05, eta: 2:21:54, time: 0.636, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0138, loss_rpn_bbox: 0.0297, loss_cls: 0.1395, acc: 94.7180, loss_bbox: 0.1939, loss_mask: 0.2095, loss: 0.5865 +2024-05-28 03:40:08,666 - mmdet - INFO - Epoch [11][800/7330] lr: 1.000e-05, eta: 2:21:23, time: 0.598, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0167, loss_rpn_bbox: 0.0343, loss_cls: 0.1475, acc: 94.3958, loss_bbox: 0.2034, loss_mask: 0.2158, loss: 0.6178 +2024-05-28 03:40:38,407 - mmdet - INFO - Epoch [11][850/7330] lr: 1.000e-05, eta: 2:20:53, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0140, loss_rpn_bbox: 0.0310, loss_cls: 0.1424, acc: 94.5576, loss_bbox: 0.1982, loss_mask: 0.2173, loss: 0.6029 +2024-05-28 03:41:14,893 - mmdet - INFO - Epoch [11][900/7330] lr: 1.000e-05, eta: 2:20:23, time: 0.730, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0150, loss_rpn_bbox: 0.0309, loss_cls: 0.1437, acc: 94.5332, loss_bbox: 0.1992, loss_mask: 0.2118, loss: 0.6006 +2024-05-28 03:41:47,171 - mmdet - INFO - Epoch [11][950/7330] lr: 1.000e-05, eta: 2:19:53, time: 0.646, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0298, loss_cls: 0.1411, acc: 94.6318, loss_bbox: 0.1974, loss_mask: 0.2099, loss: 0.5915 +2024-05-28 03:42:16,883 - mmdet - INFO - Epoch [11][1000/7330] lr: 1.000e-05, eta: 2:19:22, time: 0.594, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0316, loss_cls: 0.1482, acc: 94.3474, loss_bbox: 0.2011, loss_mask: 0.2118, loss: 0.6079 +2024-05-28 03:42:48,949 - mmdet - INFO - Epoch [11][1050/7330] lr: 1.000e-05, eta: 2:18:52, time: 0.641, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0326, loss_cls: 0.1529, acc: 94.1572, loss_bbox: 0.2114, loss_mask: 0.2202, loss: 0.6329 +2024-05-28 03:43:18,810 - mmdet - INFO - Epoch [11][1100/7330] lr: 1.000e-05, eta: 2:18:21, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0296, loss_cls: 0.1419, acc: 94.6006, loss_bbox: 0.1954, loss_mask: 0.2078, loss: 0.5891 +2024-05-28 03:43:53,577 - mmdet - INFO - Epoch [11][1150/7330] lr: 1.000e-05, eta: 2:17:51, time: 0.695, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0169, loss_rpn_bbox: 0.0346, loss_cls: 0.1585, acc: 93.9729, loss_bbox: 0.2132, loss_mask: 0.2198, loss: 0.6429 +2024-05-28 03:44:23,285 - mmdet - INFO - Epoch [11][1200/7330] lr: 1.000e-05, eta: 2:17:20, time: 0.594, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0337, loss_cls: 0.1557, acc: 94.0979, loss_bbox: 0.2181, loss_mask: 0.2224, loss: 0.6454 +2024-05-28 03:44:53,124 - mmdet - INFO - Epoch [11][1250/7330] lr: 1.000e-05, eta: 2:16:50, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0339, loss_cls: 0.1501, acc: 94.3296, loss_bbox: 0.2047, loss_mask: 0.2222, loss: 0.6253 +2024-05-28 03:45:22,775 - mmdet - INFO - Epoch [11][1300/7330] lr: 1.000e-05, eta: 2:16:19, time: 0.593, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0322, loss_cls: 0.1499, acc: 94.2961, loss_bbox: 0.2032, loss_mask: 0.2163, loss: 0.6172 +2024-05-28 03:45:54,840 - mmdet - INFO - Epoch [11][1350/7330] lr: 1.000e-05, eta: 2:15:48, time: 0.641, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0327, loss_cls: 0.1501, acc: 94.2356, loss_bbox: 0.2076, loss_mask: 0.2213, loss: 0.6276 +2024-05-28 03:46:24,804 - mmdet - INFO - Epoch [11][1400/7330] lr: 1.000e-05, eta: 2:15:18, time: 0.599, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0341, loss_cls: 0.1537, acc: 94.1455, loss_bbox: 0.2098, loss_mask: 0.2198, loss: 0.6327 +2024-05-28 03:46:54,533 - mmdet - INFO - Epoch [11][1450/7330] lr: 1.000e-05, eta: 2:14:47, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0321, loss_cls: 0.1447, acc: 94.5203, loss_bbox: 0.1997, loss_mask: 0.2196, loss: 0.6119 +2024-05-28 03:47:24,273 - mmdet - INFO - Epoch [11][1500/7330] lr: 1.000e-05, eta: 2:14:16, time: 0.595, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0171, loss_rpn_bbox: 0.0344, loss_cls: 0.1498, acc: 94.1926, loss_bbox: 0.2082, loss_mask: 0.2180, loss: 0.6275 +2024-05-28 03:47:54,142 - mmdet - INFO - Epoch [11][1550/7330] lr: 1.000e-05, eta: 2:13:45, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0340, loss_cls: 0.1454, acc: 94.4255, loss_bbox: 0.2023, loss_mask: 0.2165, loss: 0.6142 +2024-05-28 03:48:23,973 - mmdet - INFO - Epoch [11][1600/7330] lr: 1.000e-05, eta: 2:13:15, time: 0.597, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0146, loss_rpn_bbox: 0.0324, loss_cls: 0.1450, acc: 94.4795, loss_bbox: 0.2034, loss_mask: 0.2179, loss: 0.6133 +2024-05-28 03:48:53,753 - mmdet - INFO - Epoch [11][1650/7330] lr: 1.000e-05, eta: 2:12:44, time: 0.596, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0148, loss_rpn_bbox: 0.0314, loss_cls: 0.1475, acc: 94.3647, loss_bbox: 0.2037, loss_mask: 0.2191, loss: 0.6164 +2024-05-28 03:49:23,747 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-28 03:49:23,748 - mmdet - INFO - Epoch [11][1700/7330] lr: 1.000e-05, eta: 2:12:13, time: 0.600, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0175, loss_rpn_bbox: 0.0337, loss_cls: 0.1515, acc: 94.2678, loss_bbox: 0.2088, loss_mask: 0.2175, loss: 0.6291 +2024-05-28 03:49:53,692 - mmdet - INFO - Epoch [11][1750/7330] lr: 1.000e-05, eta: 2:11:43, time: 0.599, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0328, loss_cls: 0.1476, acc: 94.3291, loss_bbox: 0.2040, loss_mask: 0.2138, loss: 0.6144 +2024-05-28 03:50:25,804 - mmdet - INFO - Epoch [11][1800/7330] lr: 1.000e-05, eta: 2:11:12, time: 0.642, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0351, loss_cls: 0.1615, acc: 93.8933, loss_bbox: 0.2132, loss_mask: 0.2214, loss: 0.6477 +2024-05-28 03:50:55,504 - mmdet - INFO - Epoch [11][1850/7330] lr: 1.000e-05, eta: 2:10:41, time: 0.594, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0313, loss_cls: 0.1424, acc: 94.5300, loss_bbox: 0.1990, loss_mask: 0.2162, loss: 0.6032 +2024-05-28 03:51:25,348 - mmdet - INFO - Epoch [11][1900/7330] lr: 1.000e-05, eta: 2:10:11, time: 0.596, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0171, loss_rpn_bbox: 0.0357, loss_cls: 0.1514, acc: 94.1912, loss_bbox: 0.2131, loss_mask: 0.2189, loss: 0.6362 +2024-05-28 03:52:02,097 - mmdet - INFO - Epoch [11][1950/7330] lr: 1.000e-05, eta: 2:09:41, time: 0.735, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0170, loss_rpn_bbox: 0.0343, loss_cls: 0.1528, acc: 94.1101, loss_bbox: 0.2108, loss_mask: 0.2243, loss: 0.6392 +2024-05-28 03:52:33,908 - mmdet - INFO - Epoch [11][2000/7330] lr: 1.000e-05, eta: 2:09:11, time: 0.636, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0354, loss_cls: 0.1496, acc: 94.3596, loss_bbox: 0.2082, loss_mask: 0.2190, loss: 0.6284 +2024-05-28 03:53:03,687 - mmdet - INFO - Epoch [11][2050/7330] lr: 1.000e-05, eta: 2:08:40, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0320, loss_cls: 0.1442, acc: 94.4561, loss_bbox: 0.2049, loss_mask: 0.2146, loss: 0.6101 +2024-05-28 03:53:35,834 - mmdet - INFO - Epoch [11][2100/7330] lr: 1.000e-05, eta: 2:08:10, time: 0.643, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0316, loss_cls: 0.1473, acc: 94.3926, loss_bbox: 0.2027, loss_mask: 0.2175, loss: 0.6143 +2024-05-28 03:54:05,723 - mmdet - INFO - Epoch [11][2150/7330] lr: 1.000e-05, eta: 2:07:39, time: 0.598, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0167, loss_rpn_bbox: 0.0337, loss_cls: 0.1510, acc: 94.3054, loss_bbox: 0.2056, loss_mask: 0.2196, loss: 0.6267 +2024-05-28 03:54:39,698 - mmdet - INFO - Epoch [11][2200/7330] lr: 1.000e-05, eta: 2:07:09, time: 0.680, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0156, loss_rpn_bbox: 0.0324, loss_cls: 0.1477, acc: 94.3806, loss_bbox: 0.2043, loss_mask: 0.2160, loss: 0.6161 +2024-05-28 03:55:09,786 - mmdet - INFO - Epoch [11][2250/7330] lr: 1.000e-05, eta: 2:06:38, time: 0.601, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0328, loss_cls: 0.1530, acc: 94.1321, loss_bbox: 0.2093, loss_mask: 0.2168, loss: 0.6285 +2024-05-28 03:55:39,592 - mmdet - INFO - Epoch [11][2300/7330] lr: 1.000e-05, eta: 2:06:07, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0318, loss_cls: 0.1467, acc: 94.3042, loss_bbox: 0.2011, loss_mask: 0.2201, loss: 0.6152 +2024-05-28 03:56:09,520 - mmdet - INFO - Epoch [11][2350/7330] lr: 1.000e-05, eta: 2:05:37, time: 0.599, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0312, loss_cls: 0.1449, acc: 94.5159, loss_bbox: 0.1984, loss_mask: 0.2136, loss: 0.6039 +2024-05-28 03:56:41,486 - mmdet - INFO - Epoch [11][2400/7330] lr: 1.000e-05, eta: 2:05:06, time: 0.639, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0309, loss_cls: 0.1465, acc: 94.3750, loss_bbox: 0.2044, loss_mask: 0.2154, loss: 0.6125 +2024-05-28 03:57:11,441 - mmdet - INFO - Epoch [11][2450/7330] lr: 1.000e-05, eta: 2:04:36, time: 0.599, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0313, loss_cls: 0.1445, acc: 94.4834, loss_bbox: 0.2018, loss_mask: 0.2177, loss: 0.6095 +2024-05-28 03:57:41,407 - mmdet - INFO - Epoch [11][2500/7330] lr: 1.000e-05, eta: 2:04:05, time: 0.599, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0323, loss_cls: 0.1453, acc: 94.3782, loss_bbox: 0.2076, loss_mask: 0.2179, loss: 0.6182 +2024-05-28 03:58:11,346 - mmdet - INFO - Epoch [11][2550/7330] lr: 1.000e-05, eta: 2:03:34, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0337, loss_cls: 0.1499, acc: 94.2793, loss_bbox: 0.2102, loss_mask: 0.2202, loss: 0.6290 +2024-05-28 03:58:41,373 - mmdet - INFO - Epoch [11][2600/7330] lr: 1.000e-05, eta: 2:03:03, time: 0.601, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0171, loss_rpn_bbox: 0.0362, loss_cls: 0.1549, acc: 94.0798, loss_bbox: 0.2134, loss_mask: 0.2236, loss: 0.6451 +2024-05-28 03:59:11,281 - mmdet - INFO - Epoch [11][2650/7330] lr: 1.000e-05, eta: 2:02:33, time: 0.598, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0327, loss_cls: 0.1502, acc: 94.2986, loss_bbox: 0.2088, loss_mask: 0.2215, loss: 0.6293 +2024-05-28 03:59:41,239 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-28 03:59:41,239 - mmdet - INFO - Epoch [11][2700/7330] lr: 1.000e-05, eta: 2:02:02, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0323, loss_cls: 0.1517, acc: 94.2722, loss_bbox: 0.2065, loss_mask: 0.2202, loss: 0.6254 +2024-05-28 04:00:11,178 - mmdet - INFO - Epoch [11][2750/7330] lr: 1.000e-05, eta: 2:01:31, time: 0.599, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0336, loss_cls: 0.1477, acc: 94.3845, loss_bbox: 0.2041, loss_mask: 0.2175, loss: 0.6171 +2024-05-28 04:00:40,962 - mmdet - INFO - Epoch [11][2800/7330] lr: 1.000e-05, eta: 2:01:00, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0340, loss_cls: 0.1539, acc: 94.0403, loss_bbox: 0.2118, loss_mask: 0.2208, loss: 0.6356 +2024-05-28 04:01:12,863 - mmdet - INFO - Epoch [11][2850/7330] lr: 1.000e-05, eta: 2:00:30, time: 0.638, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0148, loss_rpn_bbox: 0.0316, loss_cls: 0.1466, acc: 94.3110, loss_bbox: 0.2013, loss_mask: 0.2175, loss: 0.6118 +2024-05-28 04:01:42,709 - mmdet - INFO - Epoch [11][2900/7330] lr: 1.000e-05, eta: 1:59:59, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0340, loss_cls: 0.1501, acc: 94.3694, loss_bbox: 0.2069, loss_mask: 0.2174, loss: 0.6245 +2024-05-28 04:02:12,495 - mmdet - INFO - Epoch [11][2950/7330] lr: 1.000e-05, eta: 1:59:29, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0177, loss_rpn_bbox: 0.0348, loss_cls: 0.1479, acc: 94.3521, loss_bbox: 0.2033, loss_mask: 0.2153, loss: 0.6190 +2024-05-28 04:02:48,154 - mmdet - INFO - Epoch [11][3000/7330] lr: 1.000e-05, eta: 1:58:59, time: 0.713, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0311, loss_cls: 0.1437, acc: 94.4866, loss_bbox: 0.2015, loss_mask: 0.2193, loss: 0.6110 +2024-05-28 04:03:20,170 - mmdet - INFO - Epoch [11][3050/7330] lr: 1.000e-05, eta: 1:58:28, time: 0.640, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0306, loss_cls: 0.1475, acc: 94.2949, loss_bbox: 0.1978, loss_mask: 0.2138, loss: 0.6038 +2024-05-28 04:03:52,429 - mmdet - INFO - Epoch [11][3100/7330] lr: 1.000e-05, eta: 1:57:58, time: 0.645, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0336, loss_cls: 0.1495, acc: 94.2649, loss_bbox: 0.2074, loss_mask: 0.2186, loss: 0.6253 +2024-05-28 04:04:22,162 - mmdet - INFO - Epoch [11][3150/7330] lr: 1.000e-05, eta: 1:57:27, time: 0.594, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0156, loss_rpn_bbox: 0.0319, loss_cls: 0.1437, acc: 94.5530, loss_bbox: 0.1982, loss_mask: 0.2159, loss: 0.6052 +2024-05-28 04:04:51,861 - mmdet - INFO - Epoch [11][3200/7330] lr: 1.000e-05, eta: 1:56:56, time: 0.594, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0146, loss_rpn_bbox: 0.0323, loss_cls: 0.1426, acc: 94.5251, loss_bbox: 0.2006, loss_mask: 0.2190, loss: 0.6091 +2024-05-28 04:05:26,293 - mmdet - INFO - Epoch [11][3250/7330] lr: 1.000e-05, eta: 1:56:26, time: 0.689, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0325, loss_cls: 0.1499, acc: 94.2446, loss_bbox: 0.2114, loss_mask: 0.2192, loss: 0.6277 +2024-05-28 04:05:56,066 - mmdet - INFO - Epoch [11][3300/7330] lr: 1.000e-05, eta: 1:55:56, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0313, loss_cls: 0.1455, acc: 94.5186, loss_bbox: 0.2015, loss_mask: 0.2183, loss: 0.6128 +2024-05-28 04:06:26,024 - mmdet - INFO - Epoch [11][3350/7330] lr: 1.000e-05, eta: 1:55:25, time: 0.599, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0157, loss_rpn_bbox: 0.0317, loss_cls: 0.1516, acc: 94.3467, loss_bbox: 0.2074, loss_mask: 0.2196, loss: 0.6259 +2024-05-28 04:06:55,966 - mmdet - INFO - Epoch [11][3400/7330] lr: 1.000e-05, eta: 1:54:54, time: 0.599, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0342, loss_cls: 0.1543, acc: 94.1482, loss_bbox: 0.2103, loss_mask: 0.2206, loss: 0.6352 +2024-05-28 04:07:28,807 - mmdet - INFO - Epoch [11][3450/7330] lr: 1.000e-05, eta: 1:54:24, time: 0.657, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0321, loss_cls: 0.1522, acc: 94.1550, loss_bbox: 0.2105, loss_mask: 0.2209, loss: 0.6307 +2024-05-28 04:07:58,787 - mmdet - INFO - Epoch [11][3500/7330] lr: 1.000e-05, eta: 1:53:53, time: 0.600, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0318, loss_cls: 0.1419, acc: 94.6060, loss_bbox: 0.1978, loss_mask: 0.2151, loss: 0.6026 +2024-05-28 04:08:28,660 - mmdet - INFO - Epoch [11][3550/7330] lr: 1.000e-05, eta: 1:53:23, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0312, loss_cls: 0.1453, acc: 94.4924, loss_bbox: 0.2029, loss_mask: 0.2160, loss: 0.6095 +2024-05-28 04:08:59,558 - mmdet - INFO - Epoch [11][3600/7330] lr: 1.000e-05, eta: 1:52:52, time: 0.618, data_time: 0.039, memory: 9459, loss_rpn_cls: 0.0163, loss_rpn_bbox: 0.0319, loss_cls: 0.1467, acc: 94.3142, loss_bbox: 0.1980, loss_mask: 0.2144, loss: 0.6073 +2024-05-28 04:09:29,241 - mmdet - INFO - Epoch [11][3650/7330] lr: 1.000e-05, eta: 1:52:21, time: 0.594, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0334, loss_cls: 0.1449, acc: 94.4207, loss_bbox: 0.2054, loss_mask: 0.2167, loss: 0.6153 +2024-05-28 04:09:59,285 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-28 04:09:59,285 - mmdet - INFO - Epoch [11][3700/7330] lr: 1.000e-05, eta: 1:51:51, time: 0.601, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0339, loss_cls: 0.1542, acc: 94.1270, loss_bbox: 0.2084, loss_mask: 0.2192, loss: 0.6321 +2024-05-28 04:10:29,295 - mmdet - INFO - Epoch [11][3750/7330] lr: 1.000e-05, eta: 1:51:20, time: 0.600, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0171, loss_rpn_bbox: 0.0352, loss_cls: 0.1568, acc: 93.9492, loss_bbox: 0.2143, loss_mask: 0.2253, loss: 0.6486 +2024-05-28 04:10:59,103 - mmdet - INFO - Epoch [11][3800/7330] lr: 1.000e-05, eta: 1:50:49, time: 0.596, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0328, loss_cls: 0.1478, acc: 94.3772, loss_bbox: 0.2052, loss_mask: 0.2191, loss: 0.6198 +2024-05-28 04:11:29,009 - mmdet - INFO - Epoch [11][3850/7330] lr: 1.000e-05, eta: 1:50:18, time: 0.598, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0164, loss_rpn_bbox: 0.0317, loss_cls: 0.1430, acc: 94.5039, loss_bbox: 0.1962, loss_mask: 0.2117, loss: 0.5990 +2024-05-28 04:12:00,809 - mmdet - INFO - Epoch [11][3900/7330] lr: 1.000e-05, eta: 1:49:48, time: 0.636, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0322, loss_cls: 0.1449, acc: 94.3943, loss_bbox: 0.2044, loss_mask: 0.2118, loss: 0.6084 +2024-05-28 04:12:30,578 - mmdet - INFO - Epoch [11][3950/7330] lr: 1.000e-05, eta: 1:49:17, time: 0.595, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0334, loss_cls: 0.1507, acc: 94.2454, loss_bbox: 0.2071, loss_mask: 0.2149, loss: 0.6223 +2024-05-28 04:13:00,501 - mmdet - INFO - Epoch [11][4000/7330] lr: 1.000e-05, eta: 1:48:46, time: 0.598, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0150, loss_rpn_bbox: 0.0324, loss_cls: 0.1475, acc: 94.3159, loss_bbox: 0.2057, loss_mask: 0.2207, loss: 0.6213 +2024-05-28 04:13:35,717 - mmdet - INFO - Epoch [11][4050/7330] lr: 1.000e-05, eta: 1:48:17, time: 0.704, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0315, loss_cls: 0.1514, acc: 94.3013, loss_bbox: 0.2018, loss_mask: 0.2136, loss: 0.6135 +2024-05-28 04:14:07,834 - mmdet - INFO - Epoch [11][4100/7330] lr: 1.000e-05, eta: 1:47:46, time: 0.642, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0156, loss_rpn_bbox: 0.0330, loss_cls: 0.1469, acc: 94.3806, loss_bbox: 0.2041, loss_mask: 0.2147, loss: 0.6144 +2024-05-28 04:14:40,083 - mmdet - INFO - Epoch [11][4150/7330] lr: 1.000e-05, eta: 1:47:16, time: 0.644, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0174, loss_rpn_bbox: 0.0335, loss_cls: 0.1518, acc: 94.2354, loss_bbox: 0.2119, loss_mask: 0.2236, loss: 0.6382 +2024-05-28 04:15:10,180 - mmdet - INFO - Epoch [11][4200/7330] lr: 1.000e-05, eta: 1:46:45, time: 0.602, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0352, loss_cls: 0.1561, acc: 94.1038, loss_bbox: 0.2132, loss_mask: 0.2205, loss: 0.6416 +2024-05-28 04:15:42,193 - mmdet - INFO - Epoch [11][4250/7330] lr: 1.000e-05, eta: 1:46:15, time: 0.640, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0330, loss_cls: 0.1489, acc: 94.2275, loss_bbox: 0.2088, loss_mask: 0.2183, loss: 0.6242 +2024-05-28 04:16:14,256 - mmdet - INFO - Epoch [11][4300/7330] lr: 1.000e-05, eta: 1:45:44, time: 0.641, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0355, loss_cls: 0.1532, acc: 94.0806, loss_bbox: 0.2098, loss_mask: 0.2207, loss: 0.6352 +2024-05-28 04:16:44,162 - mmdet - INFO - Epoch [11][4350/7330] lr: 1.000e-05, eta: 1:45:13, time: 0.598, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0339, loss_cls: 0.1521, acc: 94.2327, loss_bbox: 0.2095, loss_mask: 0.2212, loss: 0.6318 +2024-05-28 04:17:13,893 - mmdet - INFO - Epoch [11][4400/7330] lr: 1.000e-05, eta: 1:44:43, time: 0.595, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0335, loss_cls: 0.1485, acc: 94.3157, loss_bbox: 0.2070, loss_mask: 0.2160, loss: 0.6204 +2024-05-28 04:17:43,787 - mmdet - INFO - Epoch [11][4450/7330] lr: 1.000e-05, eta: 1:44:12, time: 0.598, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0164, loss_rpn_bbox: 0.0323, loss_cls: 0.1536, acc: 94.1958, loss_bbox: 0.2076, loss_mask: 0.2171, loss: 0.6270 +2024-05-28 04:18:16,318 - mmdet - INFO - Epoch [11][4500/7330] lr: 1.000e-05, eta: 1:43:42, time: 0.651, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0168, loss_rpn_bbox: 0.0330, loss_cls: 0.1553, acc: 94.2002, loss_bbox: 0.2122, loss_mask: 0.2187, loss: 0.6360 +2024-05-28 04:18:46,132 - mmdet - INFO - Epoch [11][4550/7330] lr: 1.000e-05, eta: 1:43:11, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0332, loss_cls: 0.1509, acc: 94.2576, loss_bbox: 0.2088, loss_mask: 0.2191, loss: 0.6285 +2024-05-28 04:19:15,856 - mmdet - INFO - Epoch [11][4600/7330] lr: 1.000e-05, eta: 1:42:40, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0321, loss_cls: 0.1495, acc: 94.2534, loss_bbox: 0.2067, loss_mask: 0.2223, loss: 0.6258 +2024-05-28 04:19:45,591 - mmdet - INFO - Epoch [11][4650/7330] lr: 1.000e-05, eta: 1:42:09, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0326, loss_cls: 0.1531, acc: 94.1758, loss_bbox: 0.2095, loss_mask: 0.2176, loss: 0.6288 +2024-05-28 04:20:15,548 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-28 04:20:15,548 - mmdet - INFO - Epoch [11][4700/7330] lr: 1.000e-05, eta: 1:41:39, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0168, loss_rpn_bbox: 0.0317, loss_cls: 0.1522, acc: 94.2053, loss_bbox: 0.2109, loss_mask: 0.2180, loss: 0.6297 +2024-05-28 04:20:45,269 - mmdet - INFO - Epoch [11][4750/7330] lr: 1.000e-05, eta: 1:41:08, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0322, loss_cls: 0.1492, acc: 94.2954, loss_bbox: 0.2099, loss_mask: 0.2176, loss: 0.6250 +2024-05-28 04:21:15,130 - mmdet - INFO - Epoch [11][4800/7330] lr: 1.000e-05, eta: 1:40:37, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0317, loss_cls: 0.1447, acc: 94.4651, loss_bbox: 0.2029, loss_mask: 0.2156, loss: 0.6097 +2024-05-28 04:21:44,992 - mmdet - INFO - Epoch [11][4850/7330] lr: 1.000e-05, eta: 1:40:07, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0330, loss_cls: 0.1507, acc: 94.2717, loss_bbox: 0.2087, loss_mask: 0.2158, loss: 0.6240 +2024-05-28 04:22:15,093 - mmdet - INFO - Epoch [11][4900/7330] lr: 1.000e-05, eta: 1:39:36, time: 0.602, data_time: 0.025, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0328, loss_cls: 0.1526, acc: 94.2317, loss_bbox: 0.2112, loss_mask: 0.2156, loss: 0.6283 +2024-05-28 04:22:47,235 - mmdet - INFO - Epoch [11][4950/7330] lr: 1.000e-05, eta: 1:39:05, time: 0.643, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0347, loss_cls: 0.1560, acc: 94.1169, loss_bbox: 0.2169, loss_mask: 0.2250, loss: 0.6483 +2024-05-28 04:23:17,217 - mmdet - INFO - Epoch [11][5000/7330] lr: 1.000e-05, eta: 1:38:35, time: 0.600, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0167, loss_rpn_bbox: 0.0336, loss_cls: 0.1544, acc: 94.1191, loss_bbox: 0.2113, loss_mask: 0.2149, loss: 0.6308 +2024-05-28 04:23:47,023 - mmdet - INFO - Epoch [11][5050/7330] lr: 1.000e-05, eta: 1:38:04, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0316, loss_cls: 0.1465, acc: 94.4961, loss_bbox: 0.1993, loss_mask: 0.2150, loss: 0.6075 +2024-05-28 04:24:22,934 - mmdet - INFO - Epoch [11][5100/7330] lr: 1.000e-05, eta: 1:37:34, time: 0.718, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0298, loss_cls: 0.1425, acc: 94.5647, loss_bbox: 0.1962, loss_mask: 0.2141, loss: 0.5971 +2024-05-28 04:24:54,896 - mmdet - INFO - Epoch [11][5150/7330] lr: 1.000e-05, eta: 1:37:04, time: 0.639, data_time: 0.025, memory: 9459, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0352, loss_cls: 0.1566, acc: 94.0522, loss_bbox: 0.2157, loss_mask: 0.2252, loss: 0.6491 +2024-05-28 04:25:26,747 - mmdet - INFO - Epoch [11][5200/7330] lr: 1.000e-05, eta: 1:36:33, time: 0.637, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0302, loss_cls: 0.1419, acc: 94.6116, loss_bbox: 0.1937, loss_mask: 0.2108, loss: 0.5907 +2024-05-28 04:25:56,425 - mmdet - INFO - Epoch [11][5250/7330] lr: 1.000e-05, eta: 1:36:02, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0148, loss_rpn_bbox: 0.0310, loss_cls: 0.1469, acc: 94.3682, loss_bbox: 0.2017, loss_mask: 0.2173, loss: 0.6116 +2024-05-28 04:26:28,723 - mmdet - INFO - Epoch [11][5300/7330] lr: 1.000e-05, eta: 1:35:32, time: 0.646, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0326, loss_cls: 0.1510, acc: 94.2957, loss_bbox: 0.2097, loss_mask: 0.2208, loss: 0.6288 +2024-05-28 04:27:00,753 - mmdet - INFO - Epoch [11][5350/7330] lr: 1.000e-05, eta: 1:35:02, time: 0.641, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0312, loss_cls: 0.1418, acc: 94.5886, loss_bbox: 0.1947, loss_mask: 0.2138, loss: 0.5960 +2024-05-28 04:27:30,603 - mmdet - INFO - Epoch [11][5400/7330] lr: 1.000e-05, eta: 1:34:31, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0317, loss_cls: 0.1420, acc: 94.5559, loss_bbox: 0.1995, loss_mask: 0.2166, loss: 0.6051 +2024-05-28 04:28:00,421 - mmdet - INFO - Epoch [11][5450/7330] lr: 1.000e-05, eta: 1:34:00, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0168, loss_rpn_bbox: 0.0328, loss_cls: 0.1536, acc: 94.2195, loss_bbox: 0.2083, loss_mask: 0.2215, loss: 0.6330 +2024-05-28 04:28:30,554 - mmdet - INFO - Epoch [11][5500/7330] lr: 1.000e-05, eta: 1:33:29, time: 0.602, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0164, loss_rpn_bbox: 0.0339, loss_cls: 0.1567, acc: 94.0627, loss_bbox: 0.2126, loss_mask: 0.2179, loss: 0.6376 +2024-05-28 04:29:04,081 - mmdet - INFO - Epoch [11][5550/7330] lr: 1.000e-05, eta: 1:32:59, time: 0.671, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0344, loss_cls: 0.1550, acc: 94.0227, loss_bbox: 0.2120, loss_mask: 0.2211, loss: 0.6390 +2024-05-28 04:29:33,941 - mmdet - INFO - Epoch [11][5600/7330] lr: 1.000e-05, eta: 1:32:28, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0343, loss_cls: 0.1548, acc: 93.9807, loss_bbox: 0.2164, loss_mask: 0.2168, loss: 0.6384 +2024-05-28 04:30:04,103 - mmdet - INFO - Epoch [11][5650/7330] lr: 1.000e-05, eta: 1:31:58, time: 0.603, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0306, loss_cls: 0.1513, acc: 94.2170, loss_bbox: 0.2057, loss_mask: 0.2214, loss: 0.6240 +2024-05-28 04:30:34,157 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-28 04:30:34,157 - mmdet - INFO - Epoch [11][5700/7330] lr: 1.000e-05, eta: 1:31:27, time: 0.601, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0337, loss_cls: 0.1524, acc: 94.1489, loss_bbox: 0.2108, loss_mask: 0.2212, loss: 0.6340 +2024-05-28 04:31:04,271 - mmdet - INFO - Epoch [11][5750/7330] lr: 1.000e-05, eta: 1:30:56, time: 0.602, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0164, loss_rpn_bbox: 0.0336, loss_cls: 0.1515, acc: 94.1228, loss_bbox: 0.2105, loss_mask: 0.2226, loss: 0.6346 +2024-05-28 04:31:34,198 - mmdet - INFO - Epoch [11][5800/7330] lr: 1.000e-05, eta: 1:30:26, time: 0.599, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0293, loss_cls: 0.1421, acc: 94.5879, loss_bbox: 0.1956, loss_mask: 0.2078, loss: 0.5889 +2024-05-28 04:32:04,241 - mmdet - INFO - Epoch [11][5850/7330] lr: 1.000e-05, eta: 1:29:55, time: 0.601, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0329, loss_cls: 0.1527, acc: 94.1831, loss_bbox: 0.2065, loss_mask: 0.2146, loss: 0.6230 +2024-05-28 04:32:34,206 - mmdet - INFO - Epoch [11][5900/7330] lr: 1.000e-05, eta: 1:29:24, time: 0.599, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0157, loss_rpn_bbox: 0.0323, loss_cls: 0.1484, acc: 94.3096, loss_bbox: 0.2056, loss_mask: 0.2157, loss: 0.6178 +2024-05-28 04:33:06,956 - mmdet - INFO - Epoch [11][5950/7330] lr: 1.000e-05, eta: 1:28:54, time: 0.655, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0320, loss_cls: 0.1460, acc: 94.4265, loss_bbox: 0.2054, loss_mask: 0.2185, loss: 0.6172 +2024-05-28 04:33:36,947 - mmdet - INFO - Epoch [11][6000/7330] lr: 1.000e-05, eta: 1:28:23, time: 0.600, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0322, loss_cls: 0.1466, acc: 94.4233, loss_bbox: 0.2037, loss_mask: 0.2187, loss: 0.6164 +2024-05-28 04:34:06,979 - mmdet - INFO - Epoch [11][6050/7330] lr: 1.000e-05, eta: 1:27:53, time: 0.601, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0352, loss_cls: 0.1530, acc: 94.0918, loss_bbox: 0.2111, loss_mask: 0.2179, loss: 0.6331 +2024-05-28 04:34:37,132 - mmdet - INFO - Epoch [11][6100/7330] lr: 1.000e-05, eta: 1:27:22, time: 0.603, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0324, loss_cls: 0.1546, acc: 94.1394, loss_bbox: 0.2088, loss_mask: 0.2207, loss: 0.6326 +2024-05-28 04:35:12,220 - mmdet - INFO - Epoch [11][6150/7330] lr: 1.000e-05, eta: 1:26:52, time: 0.702, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0340, loss_cls: 0.1483, acc: 94.3689, loss_bbox: 0.2079, loss_mask: 0.2198, loss: 0.6255 +2024-05-28 04:35:44,646 - mmdet - INFO - Epoch [11][6200/7330] lr: 1.000e-05, eta: 1:26:21, time: 0.649, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0315, loss_cls: 0.1520, acc: 94.2344, loss_bbox: 0.2116, loss_mask: 0.2198, loss: 0.6304 +2024-05-28 04:36:16,808 - mmdet - INFO - Epoch [11][6250/7330] lr: 1.000e-05, eta: 1:25:51, time: 0.643, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0150, loss_rpn_bbox: 0.0320, loss_cls: 0.1466, acc: 94.3875, loss_bbox: 0.2027, loss_mask: 0.2214, loss: 0.6177 +2024-05-28 04:36:46,942 - mmdet - INFO - Epoch [11][6300/7330] lr: 1.000e-05, eta: 1:25:20, time: 0.603, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0175, loss_rpn_bbox: 0.0355, loss_cls: 0.1572, acc: 94.0200, loss_bbox: 0.2130, loss_mask: 0.2240, loss: 0.6473 +2024-05-28 04:37:19,027 - mmdet - INFO - Epoch [11][6350/7330] lr: 1.000e-05, eta: 1:24:50, time: 0.642, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0318, loss_cls: 0.1489, acc: 94.3391, loss_bbox: 0.2077, loss_mask: 0.2223, loss: 0.6258 +2024-05-28 04:37:51,445 - mmdet - INFO - Epoch [11][6400/7330] lr: 1.000e-05, eta: 1:24:19, time: 0.648, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0311, loss_cls: 0.1503, acc: 94.1995, loss_bbox: 0.2062, loss_mask: 0.2180, loss: 0.6206 +2024-05-28 04:38:21,481 - mmdet - INFO - Epoch [11][6450/7330] lr: 1.000e-05, eta: 1:23:49, time: 0.601, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0314, loss_cls: 0.1474, acc: 94.3853, loss_bbox: 0.2042, loss_mask: 0.2155, loss: 0.6126 +2024-05-28 04:38:51,699 - mmdet - INFO - Epoch [11][6500/7330] lr: 1.000e-05, eta: 1:23:18, time: 0.604, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0334, loss_cls: 0.1505, acc: 94.2808, loss_bbox: 0.2071, loss_mask: 0.2147, loss: 0.6210 +2024-05-28 04:39:21,492 - mmdet - INFO - Epoch [11][6550/7330] lr: 1.000e-05, eta: 1:22:47, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0324, loss_cls: 0.1525, acc: 94.1401, loss_bbox: 0.2090, loss_mask: 0.2178, loss: 0.6266 +2024-05-28 04:39:54,400 - mmdet - INFO - Epoch [11][6600/7330] lr: 1.000e-05, eta: 1:22:17, time: 0.658, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0316, loss_cls: 0.1486, acc: 94.3416, loss_bbox: 0.2019, loss_mask: 0.2174, loss: 0.6158 +2024-05-28 04:40:24,414 - mmdet - INFO - Epoch [11][6650/7330] lr: 1.000e-05, eta: 1:21:46, time: 0.600, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0163, loss_rpn_bbox: 0.0331, loss_cls: 0.1527, acc: 94.1130, loss_bbox: 0.2106, loss_mask: 0.2247, loss: 0.6374 +2024-05-28 04:40:54,315 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-28 04:40:54,315 - mmdet - INFO - Epoch [11][6700/7330] lr: 1.000e-05, eta: 1:21:15, time: 0.598, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0168, loss_rpn_bbox: 0.0328, loss_cls: 0.1552, acc: 94.1599, loss_bbox: 0.2099, loss_mask: 0.2194, loss: 0.6340 +2024-05-28 04:41:24,200 - mmdet - INFO - Epoch [11][6750/7330] lr: 1.000e-05, eta: 1:20:45, time: 0.598, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0156, loss_rpn_bbox: 0.0336, loss_cls: 0.1514, acc: 94.1545, loss_bbox: 0.2153, loss_mask: 0.2263, loss: 0.6422 +2024-05-28 04:41:54,012 - mmdet - INFO - Epoch [11][6800/7330] lr: 1.000e-05, eta: 1:20:14, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0320, loss_cls: 0.1476, acc: 94.2949, loss_bbox: 0.2076, loss_mask: 0.2185, loss: 0.6205 +2024-05-28 04:42:23,870 - mmdet - INFO - Epoch [11][6850/7330] lr: 1.000e-05, eta: 1:19:43, time: 0.597, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0309, loss_cls: 0.1427, acc: 94.4746, loss_bbox: 0.2005, loss_mask: 0.2155, loss: 0.6041 +2024-05-28 04:42:53,858 - mmdet - INFO - Epoch [11][6900/7330] lr: 1.000e-05, eta: 1:19:13, time: 0.600, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0163, loss_rpn_bbox: 0.0325, loss_cls: 0.1519, acc: 94.1948, loss_bbox: 0.2034, loss_mask: 0.2185, loss: 0.6226 +2024-05-28 04:43:23,532 - mmdet - INFO - Epoch [11][6950/7330] lr: 1.000e-05, eta: 1:18:42, time: 0.593, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0320, loss_cls: 0.1469, acc: 94.3232, loss_bbox: 0.2008, loss_mask: 0.2172, loss: 0.6128 +2024-05-28 04:43:55,437 - mmdet - INFO - Epoch [11][7000/7330] lr: 1.000e-05, eta: 1:18:11, time: 0.638, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0170, loss_rpn_bbox: 0.0343, loss_cls: 0.1500, acc: 94.2649, loss_bbox: 0.2112, loss_mask: 0.2192, loss: 0.6318 +2024-05-28 04:44:25,143 - mmdet - INFO - Epoch [11][7050/7330] lr: 1.000e-05, eta: 1:17:41, time: 0.594, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0297, loss_cls: 0.1390, acc: 94.6123, loss_bbox: 0.1920, loss_mask: 0.2112, loss: 0.5856 +2024-05-28 04:44:55,200 - mmdet - INFO - Epoch [11][7100/7330] lr: 1.000e-05, eta: 1:17:10, time: 0.601, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0163, loss_rpn_bbox: 0.0324, loss_cls: 0.1482, acc: 94.2932, loss_bbox: 0.2099, loss_mask: 0.2204, loss: 0.6271 +2024-05-28 04:45:25,076 - mmdet - INFO - Epoch [11][7150/7330] lr: 1.000e-05, eta: 1:16:39, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0326, loss_cls: 0.1470, acc: 94.4385, loss_bbox: 0.2038, loss_mask: 0.2154, loss: 0.6149 +2024-05-28 04:46:01,267 - mmdet - INFO - Epoch [11][7200/7330] lr: 1.000e-05, eta: 1:16:09, time: 0.724, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0293, loss_cls: 0.1376, acc: 94.7646, loss_bbox: 0.1932, loss_mask: 0.2102, loss: 0.5829 +2024-05-28 04:46:33,736 - mmdet - INFO - Epoch [11][7250/7330] lr: 1.000e-05, eta: 1:15:39, time: 0.649, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0333, loss_cls: 0.1484, acc: 94.3528, loss_bbox: 0.2053, loss_mask: 0.2169, loss: 0.6194 +2024-05-28 04:47:06,354 - mmdet - INFO - Epoch [11][7300/7330] lr: 1.000e-05, eta: 1:15:08, time: 0.652, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0328, loss_cls: 0.1481, acc: 94.4263, loss_bbox: 0.1994, loss_mask: 0.2152, loss: 0.6120 +2024-05-28 04:47:25,046 - mmdet - INFO - Saving checkpoint at 11 epochs +2024-05-28 04:49:18,932 - mmdet - INFO - Evaluating bbox... +2024-05-28 04:49:40,340 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.465 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.692 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.507 + 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 ] = 0.507 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.661 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.573 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.573 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.573 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.366 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.622 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.768 + +2024-05-28 04:49:40,340 - mmdet - INFO - Evaluating segm... +2024-05-28 04:50:08,776 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.408 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.652 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.432 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.184 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.446 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.644 + 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 ] = 0.292 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.559 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.721 + +2024-05-28 04:50:09,290 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-28 04:50:09,292 - mmdet - INFO - Epoch(val) [11][625] bbox_mAP: 0.4650, bbox_mAP_50: 0.6920, bbox_mAP_75: 0.5070, bbox_mAP_s: 0.2710, bbox_mAP_m: 0.5070, bbox_mAP_l: 0.6610, bbox_mAP_copypaste: 0.465 0.692 0.507 0.271 0.507 0.661, segm_mAP: 0.4080, segm_mAP_50: 0.6520, segm_mAP_75: 0.4320, segm_mAP_s: 0.1840, segm_mAP_m: 0.4460, segm_mAP_l: 0.6440, segm_mAP_copypaste: 0.408 0.652 0.432 0.184 0.446 0.644 +2024-05-28 04:50:42,389 - mmdet - INFO - Epoch [12][50/7330] lr: 1.000e-06, eta: 1:14:18, time: 0.662, data_time: 0.090, memory: 9459, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0308, loss_cls: 0.1449, acc: 94.4849, loss_bbox: 0.1997, loss_mask: 0.2102, loss: 0.5993 +2024-05-28 04:51:14,628 - mmdet - INFO - Epoch [12][100/7330] lr: 1.000e-06, eta: 1:13:47, time: 0.645, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0318, loss_cls: 0.1475, acc: 94.3716, loss_bbox: 0.2010, loss_mask: 0.2133, loss: 0.6090 +2024-05-28 04:51:44,304 - mmdet - INFO - Epoch [12][150/7330] lr: 1.000e-06, eta: 1:13:17, time: 0.594, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0325, loss_cls: 0.1436, acc: 94.4119, loss_bbox: 0.2036, loss_mask: 0.2163, loss: 0.6104 +2024-05-28 04:52:16,655 - mmdet - INFO - Epoch [12][200/7330] lr: 1.000e-06, eta: 1:12:46, time: 0.647, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0333, loss_cls: 0.1480, acc: 94.3535, loss_bbox: 0.2050, loss_mask: 0.2181, loss: 0.6197 +2024-05-28 04:52:46,590 - mmdet - INFO - Epoch [12][250/7330] lr: 1.000e-06, eta: 1:12:15, time: 0.599, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0329, loss_cls: 0.1444, acc: 94.4412, loss_bbox: 0.2027, loss_mask: 0.2146, loss: 0.6099 +2024-05-28 04:53:18,787 - mmdet - INFO - Epoch [12][300/7330] lr: 1.000e-06, eta: 1:11:45, time: 0.644, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0331, loss_cls: 0.1437, acc: 94.5171, loss_bbox: 0.2014, loss_mask: 0.2155, loss: 0.6092 +2024-05-28 04:53:48,685 - mmdet - INFO - Epoch [12][350/7330] lr: 1.000e-06, eta: 1:11:14, time: 0.598, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0320, loss_cls: 0.1454, acc: 94.4834, loss_bbox: 0.2013, loss_mask: 0.2113, loss: 0.6059 +2024-05-28 04:54:18,460 - mmdet - INFO - Epoch [12][400/7330] lr: 1.000e-06, eta: 1:10:44, time: 0.595, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0325, loss_cls: 0.1411, acc: 94.6216, loss_bbox: 0.2007, loss_mask: 0.2145, loss: 0.6032 +2024-05-28 04:54:48,448 - mmdet - INFO - Epoch [12][450/7330] lr: 1.000e-06, eta: 1:10:13, time: 0.600, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0157, loss_rpn_bbox: 0.0328, loss_cls: 0.1490, acc: 94.2751, loss_bbox: 0.2110, loss_mask: 0.2149, loss: 0.6234 +2024-05-28 04:55:18,231 - mmdet - INFO - Epoch [12][500/7330] lr: 1.000e-06, eta: 1:09:42, time: 0.596, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0313, loss_cls: 0.1411, acc: 94.5989, loss_bbox: 0.1970, loss_mask: 0.2095, loss: 0.5931 +2024-05-28 04:55:48,075 - mmdet - INFO - Epoch [12][550/7330] lr: 1.000e-06, eta: 1:09:12, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0311, loss_cls: 0.1439, acc: 94.4624, loss_bbox: 0.2047, loss_mask: 0.2157, loss: 0.6095 +2024-05-28 04:56:18,042 - mmdet - INFO - Epoch [12][600/7330] lr: 1.000e-06, eta: 1:08:41, time: 0.599, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0364, loss_cls: 0.1604, acc: 93.8613, loss_bbox: 0.2168, loss_mask: 0.2219, loss: 0.6521 +2024-05-28 04:56:47,977 - mmdet - INFO - Epoch [12][650/7330] lr: 1.000e-06, eta: 1:08:10, time: 0.599, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0316, loss_cls: 0.1423, acc: 94.5034, loss_bbox: 0.2017, loss_mask: 0.2177, loss: 0.6075 +2024-05-28 04:57:17,679 - mmdet - INFO - Epoch [12][700/7330] lr: 1.000e-06, eta: 1:07:40, time: 0.594, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0148, loss_rpn_bbox: 0.0336, loss_cls: 0.1447, acc: 94.3962, loss_bbox: 0.2052, loss_mask: 0.2210, loss: 0.6192 +2024-05-28 04:57:50,404 - mmdet - INFO - Epoch [12][750/7330] lr: 1.000e-06, eta: 1:07:09, time: 0.655, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0138, loss_rpn_bbox: 0.0336, loss_cls: 0.1419, acc: 94.4741, loss_bbox: 0.2011, loss_mask: 0.2132, loss: 0.6037 +2024-05-28 04:58:20,266 - mmdet - INFO - Epoch [12][800/7330] lr: 1.000e-06, eta: 1:06:38, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0334, loss_cls: 0.1489, acc: 94.2024, loss_bbox: 0.2124, loss_mask: 0.2200, loss: 0.6294 +2024-05-28 04:58:50,266 - mmdet - INFO - Epoch [12][850/7330] lr: 1.000e-06, eta: 1:06:08, time: 0.600, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0150, loss_rpn_bbox: 0.0339, loss_cls: 0.1545, acc: 94.0554, loss_bbox: 0.2129, loss_mask: 0.2207, loss: 0.6370 +2024-05-28 04:59:19,955 - mmdet - INFO - Epoch [12][900/7330] lr: 1.000e-06, eta: 1:05:37, time: 0.594, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0335, loss_cls: 0.1467, acc: 94.3887, loss_bbox: 0.2043, loss_mask: 0.2170, loss: 0.6165 +2024-05-28 04:59:49,829 - mmdet - INFO - Epoch [12][950/7330] lr: 1.000e-06, eta: 1:05:06, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0334, loss_cls: 0.1496, acc: 94.3831, loss_bbox: 0.2069, loss_mask: 0.2157, loss: 0.6198 +2024-05-28 05:00:19,688 - mmdet - INFO - Epoch [12][1000/7330] lr: 1.000e-06, eta: 1:04:36, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0140, loss_rpn_bbox: 0.0330, loss_cls: 0.1492, acc: 94.3149, loss_bbox: 0.2068, loss_mask: 0.2205, loss: 0.6235 +2024-05-28 05:00:53,559 - mmdet - INFO - Epoch [12][1050/7330] lr: 1.000e-06, eta: 1:04:05, time: 0.677, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0342, loss_cls: 0.1460, acc: 94.4004, loss_bbox: 0.2040, loss_mask: 0.2193, loss: 0.6195 +2024-05-28 05:01:23,217 - mmdet - INFO - Epoch [12][1100/7330] lr: 1.000e-06, eta: 1:03:35, time: 0.593, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0298, loss_cls: 0.1392, acc: 94.6086, loss_bbox: 0.1946, loss_mask: 0.2106, loss: 0.5870 +2024-05-28 05:01:55,324 - mmdet - INFO - Epoch [12][1150/7330] lr: 1.000e-06, eta: 1:03:04, time: 0.642, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0303, loss_cls: 0.1444, acc: 94.4111, loss_bbox: 0.2005, loss_mask: 0.2133, loss: 0.6038 +2024-05-28 05:02:25,115 - mmdet - INFO - Epoch [12][1200/7330] lr: 1.000e-06, eta: 1:02:33, time: 0.596, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0332, loss_cls: 0.1482, acc: 94.3206, loss_bbox: 0.2054, loss_mask: 0.2183, loss: 0.6210 +2024-05-28 05:02:57,625 - mmdet - INFO - Epoch [12][1250/7330] lr: 1.000e-06, eta: 1:02:03, time: 0.650, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0344, loss_cls: 0.1544, acc: 94.0386, loss_bbox: 0.2100, loss_mask: 0.2192, loss: 0.6340 +2024-05-28 05:03:32,944 - mmdet - INFO - Epoch [12][1300/7330] lr: 1.000e-06, eta: 1:01:33, time: 0.706, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0327, loss_cls: 0.1500, acc: 94.2476, loss_bbox: 0.2102, loss_mask: 0.2175, loss: 0.6258 +2024-05-28 05:04:04,938 - mmdet - INFO - Epoch [12][1350/7330] lr: 1.000e-06, eta: 1:01:02, time: 0.640, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0325, loss_cls: 0.1512, acc: 94.1855, loss_bbox: 0.2100, loss_mask: 0.2194, loss: 0.6278 +2024-05-28 05:04:35,107 - mmdet - INFO - Epoch [12][1400/7330] lr: 1.000e-06, eta: 1:00:31, time: 0.603, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0320, loss_cls: 0.1470, acc: 94.3560, loss_bbox: 0.2063, loss_mask: 0.2127, loss: 0.6133 +2024-05-28 05:05:04,912 - mmdet - INFO - Epoch [12][1450/7330] lr: 1.000e-06, eta: 1:00:01, time: 0.596, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0324, loss_cls: 0.1414, acc: 94.5796, loss_bbox: 0.1969, loss_mask: 0.2106, loss: 0.5962 +2024-05-28 05:05:34,793 - mmdet - INFO - Epoch [12][1500/7330] lr: 1.000e-06, eta: 0:59:30, time: 0.598, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0318, loss_cls: 0.1459, acc: 94.3250, loss_bbox: 0.2049, loss_mask: 0.2158, loss: 0.6135 +2024-05-28 05:06:04,580 - mmdet - INFO - Epoch [12][1550/7330] lr: 1.000e-06, eta: 0:58:59, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0336, loss_cls: 0.1476, acc: 94.3167, loss_bbox: 0.2043, loss_mask: 0.2181, loss: 0.6185 +2024-05-28 05:06:34,589 - mmdet - INFO - Epoch [12][1600/7330] lr: 1.000e-06, eta: 0:58:29, time: 0.600, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0156, loss_rpn_bbox: 0.0351, loss_cls: 0.1466, acc: 94.3486, loss_bbox: 0.2040, loss_mask: 0.2192, loss: 0.6204 +2024-05-28 05:07:04,406 - mmdet - INFO - Epoch [12][1650/7330] lr: 1.000e-06, eta: 0:57:58, time: 0.596, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0316, loss_cls: 0.1424, acc: 94.5889, loss_bbox: 0.2027, loss_mask: 0.2159, loss: 0.6059 +2024-05-28 05:07:34,027 - mmdet - INFO - Epoch [12][1700/7330] lr: 1.000e-06, eta: 0:57:27, time: 0.592, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0140, loss_rpn_bbox: 0.0300, loss_cls: 0.1428, acc: 94.5989, loss_bbox: 0.1981, loss_mask: 0.2122, loss: 0.5970 +2024-05-28 05:08:03,651 - mmdet - INFO - Epoch [12][1750/7330] lr: 1.000e-06, eta: 0:56:57, time: 0.592, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0156, loss_rpn_bbox: 0.0320, loss_cls: 0.1449, acc: 94.5176, loss_bbox: 0.2033, loss_mask: 0.2176, loss: 0.6134 +2024-05-28 05:08:35,848 - mmdet - INFO - Epoch [12][1800/7330] lr: 1.000e-06, eta: 0:56:26, time: 0.644, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0148, loss_rpn_bbox: 0.0317, loss_cls: 0.1490, acc: 94.3535, loss_bbox: 0.2064, loss_mask: 0.2205, loss: 0.6224 +2024-05-28 05:09:05,511 - mmdet - INFO - Epoch [12][1850/7330] lr: 1.000e-06, eta: 0:55:56, time: 0.593, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0288, loss_cls: 0.1404, acc: 94.6975, loss_bbox: 0.1936, loss_mask: 0.2153, loss: 0.5914 +2024-05-28 05:09:35,410 - mmdet - INFO - Epoch [12][1900/7330] lr: 1.000e-06, eta: 0:55:25, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0316, loss_cls: 0.1486, acc: 94.2910, loss_bbox: 0.2061, loss_mask: 0.2167, loss: 0.6178 +2024-05-28 05:10:05,430 - mmdet - INFO - Epoch [12][1950/7330] lr: 1.000e-06, eta: 0:54:54, time: 0.600, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0331, loss_cls: 0.1508, acc: 94.1968, loss_bbox: 0.2108, loss_mask: 0.2263, loss: 0.6365 +2024-05-28 05:10:35,514 - mmdet - INFO - Epoch [12][2000/7330] lr: 1.000e-06, eta: 0:54:24, time: 0.602, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0138, loss_rpn_bbox: 0.0321, loss_cls: 0.1496, acc: 94.2937, loss_bbox: 0.2083, loss_mask: 0.2167, loss: 0.6205 +2024-05-28 05:11:07,589 - mmdet - INFO - Epoch [12][2050/7330] lr: 1.000e-06, eta: 0:53:53, time: 0.641, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0344, loss_cls: 0.1500, acc: 94.2756, loss_bbox: 0.2051, loss_mask: 0.2165, loss: 0.6215 +2024-05-28 05:11:39,751 - mmdet - INFO - Epoch [12][2100/7330] lr: 1.000e-06, eta: 0:53:22, time: 0.643, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0343, loss_cls: 0.1503, acc: 94.2380, loss_bbox: 0.2094, loss_mask: 0.2202, loss: 0.6302 +2024-05-28 05:12:09,617 - mmdet - INFO - Epoch [12][2150/7330] lr: 1.000e-06, eta: 0:52:52, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0312, loss_cls: 0.1425, acc: 94.6060, loss_bbox: 0.1977, loss_mask: 0.2143, loss: 0.6006 +2024-05-28 05:12:41,555 - mmdet - INFO - Epoch [12][2200/7330] lr: 1.000e-06, eta: 0:52:21, time: 0.638, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0303, loss_cls: 0.1430, acc: 94.5757, loss_bbox: 0.1959, loss_mask: 0.2105, loss: 0.5933 +2024-05-28 05:13:15,088 - mmdet - INFO - Epoch [12][2250/7330] lr: 1.000e-06, eta: 0:51:51, time: 0.671, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0323, loss_cls: 0.1469, acc: 94.3562, loss_bbox: 0.2004, loss_mask: 0.2164, loss: 0.6097 +2024-05-28 05:13:45,144 - mmdet - INFO - Epoch [12][2300/7330] lr: 1.000e-06, eta: 0:51:20, time: 0.601, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0146, loss_rpn_bbox: 0.0313, loss_cls: 0.1439, acc: 94.4143, loss_bbox: 0.1997, loss_mask: 0.2167, loss: 0.6063 +2024-05-28 05:14:22,626 - mmdet - INFO - Epoch [12][2350/7330] lr: 1.000e-06, eta: 0:50:50, time: 0.750, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0126, loss_rpn_bbox: 0.0286, loss_cls: 0.1353, acc: 94.8005, loss_bbox: 0.1919, loss_mask: 0.2083, loss: 0.5766 +2024-05-28 05:14:52,619 - mmdet - INFO - Epoch [12][2400/7330] lr: 1.000e-06, eta: 0:50:19, time: 0.600, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0164, loss_rpn_bbox: 0.0351, loss_cls: 0.1520, acc: 94.1440, loss_bbox: 0.2140, loss_mask: 0.2261, loss: 0.6437 +2024-05-28 05:15:22,605 - mmdet - INFO - Epoch [12][2450/7330] lr: 1.000e-06, eta: 0:49:49, time: 0.600, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0157, loss_rpn_bbox: 0.0326, loss_cls: 0.1490, acc: 94.3230, loss_bbox: 0.2111, loss_mask: 0.2189, loss: 0.6273 +2024-05-28 05:15:52,664 - mmdet - INFO - Epoch [12][2500/7330] lr: 1.000e-06, eta: 0:49:18, time: 0.601, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0330, loss_cls: 0.1458, acc: 94.4211, loss_bbox: 0.2029, loss_mask: 0.2184, loss: 0.6155 +2024-05-28 05:16:22,645 - mmdet - INFO - Epoch [12][2550/7330] lr: 1.000e-06, eta: 0:48:47, time: 0.600, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0311, loss_cls: 0.1406, acc: 94.6101, loss_bbox: 0.1985, loss_mask: 0.2137, loss: 0.5992 +2024-05-28 05:16:52,466 - mmdet - INFO - Epoch [12][2600/7330] lr: 1.000e-06, eta: 0:48:17, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0304, loss_cls: 0.1402, acc: 94.6169, loss_bbox: 0.1929, loss_mask: 0.2078, loss: 0.5862 +2024-05-28 05:17:22,379 - mmdet - INFO - Epoch [12][2650/7330] lr: 1.000e-06, eta: 0:47:46, time: 0.599, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0323, loss_cls: 0.1446, acc: 94.4604, loss_bbox: 0.2065, loss_mask: 0.2163, loss: 0.6146 +2024-05-28 05:17:52,433 - mmdet - INFO - Epoch [12][2700/7330] lr: 1.000e-06, eta: 0:47:15, time: 0.601, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0150, loss_rpn_bbox: 0.0319, loss_cls: 0.1486, acc: 94.3330, loss_bbox: 0.2034, loss_mask: 0.2123, loss: 0.6112 +2024-05-28 05:18:22,430 - mmdet - INFO - Epoch [12][2750/7330] lr: 1.000e-06, eta: 0:46:45, time: 0.600, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0170, loss_rpn_bbox: 0.0343, loss_cls: 0.1540, acc: 94.1653, loss_bbox: 0.2087, loss_mask: 0.2223, loss: 0.6364 +2024-05-28 05:18:54,836 - mmdet - INFO - Epoch [12][2800/7330] lr: 1.000e-06, eta: 0:46:14, time: 0.648, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0305, loss_cls: 0.1437, acc: 94.5374, loss_bbox: 0.2011, loss_mask: 0.2094, loss: 0.5988 +2024-05-28 05:19:24,711 - mmdet - INFO - Epoch [12][2850/7330] lr: 1.000e-06, eta: 0:45:43, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0328, loss_cls: 0.1521, acc: 94.1514, loss_bbox: 0.2117, loss_mask: 0.2246, loss: 0.6356 +2024-05-28 05:19:54,646 - mmdet - INFO - Epoch [12][2900/7330] lr: 1.000e-06, eta: 0:45:13, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0331, loss_cls: 0.1501, acc: 94.2112, loss_bbox: 0.2091, loss_mask: 0.2186, loss: 0.6264 +2024-05-28 05:20:24,643 - mmdet - INFO - Epoch [12][2950/7330] lr: 1.000e-06, eta: 0:44:42, time: 0.600, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0344, loss_cls: 0.1549, acc: 94.0234, loss_bbox: 0.2124, loss_mask: 0.2176, loss: 0.6346 +2024-05-28 05:20:54,330 - mmdet - INFO - Epoch [12][3000/7330] lr: 1.000e-06, eta: 0:44:11, time: 0.594, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0308, loss_cls: 0.1391, acc: 94.6497, loss_bbox: 0.1962, loss_mask: 0.2086, loss: 0.5887 +2024-05-28 05:21:24,115 - mmdet - INFO - Epoch [12][3050/7330] lr: 1.000e-06, eta: 0:43:41, time: 0.596, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0327, loss_cls: 0.1465, acc: 94.4910, loss_bbox: 0.2002, loss_mask: 0.2137, loss: 0.6083 +2024-05-28 05:21:57,230 - mmdet - INFO - Epoch [12][3100/7330] lr: 1.000e-06, eta: 0:43:10, time: 0.662, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0337, loss_cls: 0.1449, acc: 94.4253, loss_bbox: 0.2005, loss_mask: 0.2158, loss: 0.6102 +2024-05-28 05:22:29,729 - mmdet - INFO - Epoch [12][3150/7330] lr: 1.000e-06, eta: 0:42:40, time: 0.650, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0156, loss_rpn_bbox: 0.0327, loss_cls: 0.1462, acc: 94.3401, loss_bbox: 0.2067, loss_mask: 0.2177, loss: 0.6188 +2024-05-28 05:22:59,593 - mmdet - INFO - Epoch [12][3200/7330] lr: 1.000e-06, eta: 0:42:09, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0311, loss_cls: 0.1452, acc: 94.5513, loss_bbox: 0.1991, loss_mask: 0.2147, loss: 0.6054 +2024-05-28 05:23:31,515 - mmdet - INFO - Epoch [12][3250/7330] lr: 1.000e-06, eta: 0:41:39, time: 0.638, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0306, loss_cls: 0.1445, acc: 94.4768, loss_bbox: 0.1990, loss_mask: 0.2143, loss: 0.6039 +2024-05-28 05:24:03,837 - mmdet - INFO - Epoch [12][3300/7330] lr: 1.000e-06, eta: 0:41:08, time: 0.647, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0316, loss_cls: 0.1471, acc: 94.3132, loss_bbox: 0.2012, loss_mask: 0.2097, loss: 0.6047 +2024-05-28 05:24:35,954 - mmdet - INFO - Epoch [12][3350/7330] lr: 1.000e-06, eta: 0:40:37, time: 0.642, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0295, loss_cls: 0.1412, acc: 94.5696, loss_bbox: 0.1952, loss_mask: 0.2118, loss: 0.5906 +2024-05-28 05:25:10,687 - mmdet - INFO - Epoch [12][3400/7330] lr: 1.000e-06, eta: 0:40:07, time: 0.695, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0308, loss_cls: 0.1420, acc: 94.5327, loss_bbox: 0.1983, loss_mask: 0.2083, loss: 0.5936 +2024-05-28 05:25:40,537 - mmdet - INFO - Epoch [12][3450/7330] lr: 1.000e-06, eta: 0:39:36, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0335, loss_cls: 0.1472, acc: 94.3203, loss_bbox: 0.2062, loss_mask: 0.2155, loss: 0.6175 +2024-05-28 05:26:10,570 - mmdet - INFO - Epoch [12][3500/7330] lr: 1.000e-06, eta: 0:39:06, time: 0.601, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0325, loss_cls: 0.1523, acc: 94.2532, loss_bbox: 0.2026, loss_mask: 0.2174, loss: 0.6206 +2024-05-28 05:26:40,674 - mmdet - INFO - Epoch [12][3550/7330] lr: 1.000e-06, eta: 0:38:35, time: 0.602, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0333, loss_cls: 0.1516, acc: 94.2131, loss_bbox: 0.2123, loss_mask: 0.2208, loss: 0.6334 +2024-05-28 05:27:10,644 - mmdet - INFO - Epoch [12][3600/7330] lr: 1.000e-06, eta: 0:38:04, time: 0.600, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0170, loss_rpn_bbox: 0.0349, loss_cls: 0.1563, acc: 94.0354, loss_bbox: 0.2166, loss_mask: 0.2224, loss: 0.6473 +2024-05-28 05:27:40,717 - mmdet - INFO - Epoch [12][3650/7330] lr: 1.000e-06, eta: 0:37:34, time: 0.601, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0323, loss_cls: 0.1443, acc: 94.4470, loss_bbox: 0.2001, loss_mask: 0.2157, loss: 0.6072 +2024-05-28 05:28:10,415 - mmdet - INFO - Epoch [12][3700/7330] lr: 1.000e-06, eta: 0:37:03, time: 0.594, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0303, loss_cls: 0.1426, acc: 94.5720, loss_bbox: 0.1971, loss_mask: 0.2095, loss: 0.5940 +2024-05-28 05:28:40,504 - mmdet - INFO - Epoch [12][3750/7330] lr: 1.000e-06, eta: 0:36:32, time: 0.602, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0317, loss_cls: 0.1488, acc: 94.2947, loss_bbox: 0.2042, loss_mask: 0.2185, loss: 0.6185 +2024-05-28 05:29:10,340 - mmdet - INFO - Epoch [12][3800/7330] lr: 1.000e-06, eta: 0:36:02, time: 0.596, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0310, loss_cls: 0.1435, acc: 94.4775, loss_bbox: 0.2006, loss_mask: 0.2124, loss: 0.6026 +2024-05-28 05:29:42,586 - mmdet - INFO - Epoch [12][3850/7330] lr: 1.000e-06, eta: 0:35:31, time: 0.645, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0329, loss_cls: 0.1515, acc: 94.1753, loss_bbox: 0.2079, loss_mask: 0.2167, loss: 0.6256 +2024-05-28 05:30:12,440 - mmdet - INFO - Epoch [12][3900/7330] lr: 1.000e-06, eta: 0:35:01, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0309, loss_cls: 0.1460, acc: 94.4382, loss_bbox: 0.2016, loss_mask: 0.2101, loss: 0.6032 +2024-05-28 05:30:42,471 - mmdet - INFO - Epoch [12][3950/7330] lr: 1.000e-06, eta: 0:34:30, time: 0.601, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0323, loss_cls: 0.1510, acc: 94.2339, loss_bbox: 0.2085, loss_mask: 0.2156, loss: 0.6230 +2024-05-28 05:31:12,411 - mmdet - INFO - Epoch [12][4000/7330] lr: 1.000e-06, eta: 0:33:59, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0339, loss_cls: 0.1490, acc: 94.3098, loss_bbox: 0.2040, loss_mask: 0.2208, loss: 0.6227 +2024-05-28 05:31:42,350 - mmdet - INFO - Epoch [12][4050/7330] lr: 1.000e-06, eta: 0:33:29, time: 0.599, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0157, loss_rpn_bbox: 0.0325, loss_cls: 0.1499, acc: 94.1846, loss_bbox: 0.2087, loss_mask: 0.2193, loss: 0.6261 +2024-05-28 05:32:12,194 - mmdet - INFO - Epoch [12][4100/7330] lr: 1.000e-06, eta: 0:32:58, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0324, loss_cls: 0.1471, acc: 94.3979, loss_bbox: 0.2027, loss_mask: 0.2143, loss: 0.6120 +2024-05-28 05:32:45,822 - mmdet - INFO - Epoch [12][4150/7330] lr: 1.000e-06, eta: 0:32:27, time: 0.673, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0321, loss_cls: 0.1459, acc: 94.3560, loss_bbox: 0.2010, loss_mask: 0.2108, loss: 0.6042 +2024-05-28 05:33:20,031 - mmdet - INFO - Epoch [12][4200/7330] lr: 1.000e-06, eta: 0:31:57, time: 0.684, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0324, loss_cls: 0.1453, acc: 94.4500, loss_bbox: 0.2040, loss_mask: 0.2139, loss: 0.6110 +2024-05-28 05:33:49,999 - mmdet - INFO - Epoch [12][4250/7330] lr: 1.000e-06, eta: 0:31:26, time: 0.599, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0300, loss_cls: 0.1420, acc: 94.5776, loss_bbox: 0.2000, loss_mask: 0.2140, loss: 0.5998 +2024-05-28 05:34:22,125 - mmdet - INFO - Epoch [12][4300/7330] lr: 1.000e-06, eta: 0:30:56, time: 0.642, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0343, loss_cls: 0.1460, acc: 94.3276, loss_bbox: 0.2042, loss_mask: 0.2189, loss: 0.6196 +2024-05-28 05:34:55,319 - mmdet - INFO - Epoch [12][4350/7330] lr: 1.000e-06, eta: 0:30:25, time: 0.664, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0146, loss_rpn_bbox: 0.0328, loss_cls: 0.1487, acc: 94.2720, loss_bbox: 0.2083, loss_mask: 0.2179, loss: 0.6224 +2024-05-28 05:35:27,545 - mmdet - INFO - Epoch [12][4400/7330] lr: 1.000e-06, eta: 0:29:55, time: 0.644, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0315, loss_cls: 0.1458, acc: 94.4219, loss_bbox: 0.1985, loss_mask: 0.2183, loss: 0.6089 +2024-05-28 05:36:02,170 - mmdet - INFO - Epoch [12][4450/7330] lr: 1.000e-06, eta: 0:29:24, time: 0.693, data_time: 0.023, memory: 9459, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0312, loss_cls: 0.1447, acc: 94.4937, loss_bbox: 0.1980, loss_mask: 0.2142, loss: 0.6023 +2024-05-28 05:36:32,191 - mmdet - INFO - Epoch [12][4500/7330] lr: 1.000e-06, eta: 0:28:53, time: 0.600, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0303, loss_cls: 0.1456, acc: 94.4536, loss_bbox: 0.1974, loss_mask: 0.2131, loss: 0.6018 +2024-05-28 05:37:02,114 - mmdet - INFO - Epoch [12][4550/7330] lr: 1.000e-06, eta: 0:28:23, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0348, loss_cls: 0.1516, acc: 94.2300, loss_bbox: 0.2113, loss_mask: 0.2211, loss: 0.6343 +2024-05-28 05:37:31,914 - mmdet - INFO - Epoch [12][4600/7330] lr: 1.000e-06, eta: 0:27:52, time: 0.596, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0318, loss_cls: 0.1479, acc: 94.4104, loss_bbox: 0.2022, loss_mask: 0.2186, loss: 0.6158 +2024-05-28 05:38:01,899 - mmdet - INFO - Epoch [12][4650/7330] lr: 1.000e-06, eta: 0:27:21, time: 0.600, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0307, loss_cls: 0.1399, acc: 94.7002, loss_bbox: 0.1951, loss_mask: 0.2145, loss: 0.5944 +2024-05-28 05:38:31,933 - mmdet - INFO - Epoch [12][4700/7330] lr: 1.000e-06, eta: 0:26:51, time: 0.601, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0156, loss_rpn_bbox: 0.0333, loss_cls: 0.1542, acc: 94.1392, loss_bbox: 0.2107, loss_mask: 0.2200, loss: 0.6337 +2024-05-28 05:39:01,994 - mmdet - INFO - Epoch [12][4750/7330] lr: 1.000e-06, eta: 0:26:20, time: 0.601, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0315, loss_cls: 0.1467, acc: 94.3740, loss_bbox: 0.2048, loss_mask: 0.2127, loss: 0.6112 +2024-05-28 05:39:31,934 - mmdet - INFO - Epoch [12][4800/7330] lr: 1.000e-06, eta: 0:25:49, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0296, loss_cls: 0.1362, acc: 94.7976, loss_bbox: 0.1885, loss_mask: 0.2095, loss: 0.5777 +2024-05-28 05:40:01,853 - mmdet - INFO - Epoch [12][4850/7330] lr: 1.000e-06, eta: 0:25:19, time: 0.599, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0331, loss_cls: 0.1465, acc: 94.3384, loss_bbox: 0.2029, loss_mask: 0.2195, loss: 0.6164 +2024-05-28 05:40:34,320 - mmdet - INFO - Epoch [12][4900/7330] lr: 1.000e-06, eta: 0:24:48, time: 0.649, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0157, loss_rpn_bbox: 0.0323, loss_cls: 0.1462, acc: 94.3418, loss_bbox: 0.2054, loss_mask: 0.2181, loss: 0.6176 +2024-05-28 05:41:04,285 - mmdet - INFO - Epoch [12][4950/7330] lr: 1.000e-06, eta: 0:24:18, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0332, loss_cls: 0.1445, acc: 94.4512, loss_bbox: 0.2026, loss_mask: 0.2174, loss: 0.6125 +2024-05-28 05:41:34,145 - mmdet - INFO - Epoch [12][5000/7330] lr: 1.000e-06, eta: 0:23:47, time: 0.597, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0328, loss_cls: 0.1456, acc: 94.3921, loss_bbox: 0.1989, loss_mask: 0.2128, loss: 0.6055 +2024-05-28 05:42:04,077 - mmdet - INFO - Epoch [12][5050/7330] lr: 1.000e-06, eta: 0:23:16, time: 0.599, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0311, loss_cls: 0.1417, acc: 94.5837, loss_bbox: 0.1956, loss_mask: 0.2130, loss: 0.5963 +2024-05-28 05:42:33,815 - mmdet - INFO - Epoch [12][5100/7330] lr: 1.000e-06, eta: 0:22:46, time: 0.595, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0150, loss_rpn_bbox: 0.0323, loss_cls: 0.1459, acc: 94.4207, loss_bbox: 0.2022, loss_mask: 0.2141, loss: 0.6095 +2024-05-28 05:43:03,699 - mmdet - INFO - Epoch [12][5150/7330] lr: 1.000e-06, eta: 0:22:15, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0317, loss_cls: 0.1428, acc: 94.5461, loss_bbox: 0.2021, loss_mask: 0.2159, loss: 0.6068 +2024-05-28 05:43:36,179 - mmdet - INFO - Epoch [12][5200/7330] lr: 1.000e-06, eta: 0:21:44, time: 0.650, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0335, loss_cls: 0.1504, acc: 94.2583, loss_bbox: 0.2071, loss_mask: 0.2174, loss: 0.6244 +2024-05-28 05:44:08,455 - mmdet - INFO - Epoch [12][5250/7330] lr: 1.000e-06, eta: 0:21:14, time: 0.645, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0148, loss_rpn_bbox: 0.0306, loss_cls: 0.1424, acc: 94.5488, loss_bbox: 0.1973, loss_mask: 0.2165, loss: 0.6016 +2024-05-28 05:44:38,460 - mmdet - INFO - Epoch [12][5300/7330] lr: 1.000e-06, eta: 0:20:43, time: 0.600, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0150, loss_rpn_bbox: 0.0318, loss_cls: 0.1430, acc: 94.4763, loss_bbox: 0.2018, loss_mask: 0.2172, loss: 0.6088 +2024-05-28 05:45:10,702 - mmdet - INFO - Epoch [12][5350/7330] lr: 1.000e-06, eta: 0:20:13, time: 0.645, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0342, loss_cls: 0.1486, acc: 94.3440, loss_bbox: 0.2071, loss_mask: 0.2165, loss: 0.6226 +2024-05-28 05:45:43,072 - mmdet - INFO - Epoch [12][5400/7330] lr: 1.000e-06, eta: 0:19:42, time: 0.647, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0148, loss_rpn_bbox: 0.0306, loss_cls: 0.1439, acc: 94.6216, loss_bbox: 0.1968, loss_mask: 0.2096, loss: 0.5957 +2024-05-28 05:46:15,084 - mmdet - INFO - Epoch [12][5450/7330] lr: 1.000e-06, eta: 0:19:11, time: 0.640, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0320, loss_cls: 0.1395, acc: 94.6479, loss_bbox: 0.1972, loss_mask: 0.2094, loss: 0.5928 +2024-05-28 05:46:49,525 - mmdet - INFO - Epoch [12][5500/7330] lr: 1.000e-06, eta: 0:18:41, time: 0.689, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0311, loss_cls: 0.1397, acc: 94.7063, loss_bbox: 0.1948, loss_mask: 0.2109, loss: 0.5904 +2024-05-28 05:47:19,294 - mmdet - INFO - Epoch [12][5550/7330] lr: 1.000e-06, eta: 0:18:10, time: 0.595, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0148, loss_rpn_bbox: 0.0328, loss_cls: 0.1419, acc: 94.5344, loss_bbox: 0.1984, loss_mask: 0.2133, loss: 0.6012 +2024-05-28 05:47:49,130 - mmdet - INFO - Epoch [12][5600/7330] lr: 1.000e-06, eta: 0:17:40, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0332, loss_cls: 0.1482, acc: 94.3630, loss_bbox: 0.2018, loss_mask: 0.2159, loss: 0.6152 +2024-05-28 05:48:19,115 - mmdet - INFO - Epoch [12][5650/7330] lr: 1.000e-06, eta: 0:17:09, time: 0.600, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0312, loss_cls: 0.1459, acc: 94.3845, loss_bbox: 0.2030, loss_mask: 0.2130, loss: 0.6072 +2024-05-28 05:48:49,218 - mmdet - INFO - Epoch [12][5700/7330] lr: 1.000e-06, eta: 0:16:38, time: 0.602, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0157, loss_rpn_bbox: 0.0347, loss_cls: 0.1541, acc: 94.0835, loss_bbox: 0.2153, loss_mask: 0.2223, loss: 0.6420 +2024-05-28 05:49:19,140 - mmdet - INFO - Epoch [12][5750/7330] lr: 1.000e-06, eta: 0:16:08, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0323, loss_cls: 0.1425, acc: 94.5315, loss_bbox: 0.2011, loss_mask: 0.2119, loss: 0.6031 +2024-05-28 05:49:49,008 - mmdet - INFO - Epoch [12][5800/7330] lr: 1.000e-06, eta: 0:15:37, time: 0.597, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0146, loss_rpn_bbox: 0.0305, loss_cls: 0.1401, acc: 94.6177, loss_bbox: 0.1994, loss_mask: 0.2104, loss: 0.5950 +2024-05-28 05:50:18,880 - mmdet - INFO - Epoch [12][5850/7330] lr: 1.000e-06, eta: 0:15:06, time: 0.597, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0140, loss_rpn_bbox: 0.0305, loss_cls: 0.1419, acc: 94.6270, loss_bbox: 0.1984, loss_mask: 0.2107, loss: 0.5956 +2024-05-28 05:50:48,830 - mmdet - INFO - Epoch [12][5900/7330] lr: 1.000e-06, eta: 0:14:36, time: 0.599, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0323, loss_cls: 0.1428, acc: 94.5076, loss_bbox: 0.2030, loss_mask: 0.2153, loss: 0.6093 +2024-05-28 05:51:21,157 - mmdet - INFO - Epoch [12][5950/7330] lr: 1.000e-06, eta: 0:14:05, time: 0.647, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0142, loss_rpn_bbox: 0.0311, loss_cls: 0.1440, acc: 94.4661, loss_bbox: 0.1989, loss_mask: 0.2110, loss: 0.5991 +2024-05-28 05:51:51,123 - mmdet - INFO - Epoch [12][6000/7330] lr: 1.000e-06, eta: 0:13:34, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0345, loss_cls: 0.1493, acc: 94.2959, loss_bbox: 0.2048, loss_mask: 0.2156, loss: 0.6204 +2024-05-28 05:52:21,117 - mmdet - INFO - Epoch [12][6050/7330] lr: 1.000e-06, eta: 0:13:04, time: 0.600, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0332, loss_cls: 0.1500, acc: 94.2698, loss_bbox: 0.2038, loss_mask: 0.2189, loss: 0.6216 +2024-05-28 05:52:51,171 - mmdet - INFO - Epoch [12][6100/7330] lr: 1.000e-06, eta: 0:12:33, time: 0.601, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0138, loss_rpn_bbox: 0.0292, loss_cls: 0.1411, acc: 94.5703, loss_bbox: 0.1948, loss_mask: 0.2087, loss: 0.5876 +2024-05-28 05:53:21,110 - mmdet - INFO - Epoch [12][6150/7330] lr: 1.000e-06, eta: 0:12:02, time: 0.599, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0327, loss_cls: 0.1473, acc: 94.4060, loss_bbox: 0.2037, loss_mask: 0.2146, loss: 0.6136 +2024-05-28 05:53:51,084 - mmdet - INFO - Epoch [12][6200/7330] lr: 1.000e-06, eta: 0:11:32, time: 0.599, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0150, loss_rpn_bbox: 0.0328, loss_cls: 0.1487, acc: 94.3328, loss_bbox: 0.2051, loss_mask: 0.2180, loss: 0.6196 +2024-05-28 05:54:27,264 - mmdet - INFO - Epoch [12][6250/7330] lr: 1.000e-06, eta: 0:11:01, time: 0.724, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0337, loss_cls: 0.1475, acc: 94.3577, loss_bbox: 0.2051, loss_mask: 0.2209, loss: 0.6222 +2024-05-28 05:54:57,141 - mmdet - INFO - Epoch [12][6300/7330] lr: 1.000e-06, eta: 0:10:31, time: 0.598, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0318, loss_cls: 0.1448, acc: 94.4897, loss_bbox: 0.1986, loss_mask: 0.2174, loss: 0.6076 +2024-05-28 05:55:27,103 - mmdet - INFO - Epoch [12][6350/7330] lr: 1.000e-06, eta: 0:10:00, time: 0.599, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0320, loss_cls: 0.1477, acc: 94.3240, loss_bbox: 0.2017, loss_mask: 0.2157, loss: 0.6130 +2024-05-28 05:56:00,052 - mmdet - INFO - Epoch [12][6400/7330] lr: 1.000e-06, eta: 0:09:29, time: 0.659, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0150, loss_rpn_bbox: 0.0302, loss_cls: 0.1417, acc: 94.5747, loss_bbox: 0.1954, loss_mask: 0.2126, loss: 0.5949 +2024-05-28 05:56:32,345 - mmdet - INFO - Epoch [12][6450/7330] lr: 1.000e-06, eta: 0:08:59, time: 0.646, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0321, loss_cls: 0.1500, acc: 94.2119, loss_bbox: 0.2041, loss_mask: 0.2164, loss: 0.6169 +2024-05-28 05:57:04,779 - mmdet - INFO - Epoch [12][6500/7330] lr: 1.000e-06, eta: 0:08:28, time: 0.649, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0307, loss_cls: 0.1463, acc: 94.3918, loss_bbox: 0.1981, loss_mask: 0.2100, loss: 0.5996 +2024-05-28 05:57:39,695 - mmdet - INFO - Epoch [12][6550/7330] lr: 1.000e-06, eta: 0:07:58, time: 0.698, data_time: 0.024, memory: 9459, loss_rpn_cls: 0.0150, loss_rpn_bbox: 0.0325, loss_cls: 0.1454, acc: 94.4167, loss_bbox: 0.2020, loss_mask: 0.2164, loss: 0.6113 +2024-05-28 05:58:09,593 - mmdet - INFO - Epoch [12][6600/7330] lr: 1.000e-06, eta: 0:07:27, time: 0.597, data_time: 0.022, memory: 9459, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0325, loss_cls: 0.1461, acc: 94.4050, loss_bbox: 0.2018, loss_mask: 0.2173, loss: 0.6137 +2024-05-28 05:58:39,403 - mmdet - INFO - Epoch [12][6650/7330] lr: 1.000e-06, eta: 0:06:56, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0319, loss_cls: 0.1477, acc: 94.3540, loss_bbox: 0.2033, loss_mask: 0.2137, loss: 0.6116 +2024-05-28 05:59:09,202 - mmdet - INFO - Epoch [12][6700/7330] lr: 1.000e-06, eta: 0:06:26, time: 0.595, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0300, loss_cls: 0.1468, acc: 94.4111, loss_bbox: 0.1994, loss_mask: 0.2175, loss: 0.6076 +2024-05-28 05:59:38,953 - mmdet - INFO - Epoch [12][6750/7330] lr: 1.000e-06, eta: 0:05:55, time: 0.595, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0302, loss_cls: 0.1417, acc: 94.5225, loss_bbox: 0.1959, loss_mask: 0.2098, loss: 0.5914 +2024-05-28 06:00:08,883 - mmdet - INFO - Epoch [12][6800/7330] lr: 1.000e-06, eta: 0:05:24, time: 0.599, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0329, loss_cls: 0.1519, acc: 94.2410, loss_bbox: 0.2111, loss_mask: 0.2247, loss: 0.6361 +2024-05-28 06:00:38,567 - mmdet - INFO - Epoch [12][6850/7330] lr: 1.000e-06, eta: 0:04:54, time: 0.594, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0307, loss_cls: 0.1418, acc: 94.5291, loss_bbox: 0.1967, loss_mask: 0.2145, loss: 0.5992 +2024-05-28 06:01:08,458 - mmdet - INFO - Epoch [12][6900/7330] lr: 1.000e-06, eta: 0:04:23, time: 0.598, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0148, loss_rpn_bbox: 0.0318, loss_cls: 0.1487, acc: 94.2258, loss_bbox: 0.2074, loss_mask: 0.2182, loss: 0.6207 +2024-05-28 06:01:38,430 - mmdet - INFO - Epoch [12][6950/7330] lr: 1.000e-06, eta: 0:03:52, time: 0.599, data_time: 0.017, memory: 9459, loss_rpn_cls: 0.0146, loss_rpn_bbox: 0.0312, loss_cls: 0.1398, acc: 94.6411, loss_bbox: 0.1953, loss_mask: 0.2134, loss: 0.5943 +2024-05-28 06:02:10,661 - mmdet - INFO - Epoch [12][7000/7330] lr: 1.000e-06, eta: 0:03:22, time: 0.645, data_time: 0.020, memory: 9459, loss_rpn_cls: 0.0165, loss_rpn_bbox: 0.0344, loss_cls: 0.1555, acc: 94.0894, loss_bbox: 0.2129, loss_mask: 0.2223, loss: 0.6415 +2024-05-28 06:02:40,547 - mmdet - INFO - Epoch [12][7050/7330] lr: 1.000e-06, eta: 0:02:51, time: 0.598, data_time: 0.021, memory: 9459, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0315, loss_cls: 0.1478, acc: 94.3489, loss_bbox: 0.2088, loss_mask: 0.2218, loss: 0.6250 +2024-05-28 06:03:10,448 - mmdet - INFO - Epoch [12][7100/7330] lr: 1.000e-06, eta: 0:02:20, time: 0.598, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0332, loss_cls: 0.1471, acc: 94.3423, loss_bbox: 0.2031, loss_mask: 0.2170, loss: 0.6171 +2024-05-28 06:03:40,532 - mmdet - INFO - Epoch [12][7150/7330] lr: 1.000e-06, eta: 0:01:50, time: 0.601, data_time: 0.016, memory: 9459, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0318, loss_cls: 0.1414, acc: 94.5544, loss_bbox: 0.1995, loss_mask: 0.2160, loss: 0.6028 +2024-05-28 06:04:10,348 - mmdet - INFO - Epoch [12][7200/7330] lr: 1.000e-06, eta: 0:01:19, time: 0.597, data_time: 0.019, memory: 9459, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0302, loss_cls: 0.1420, acc: 94.5828, loss_bbox: 0.1985, loss_mask: 0.2142, loss: 0.5990 +2024-05-28 06:04:40,198 - mmdet - INFO - Epoch [12][7250/7330] lr: 1.000e-06, eta: 0:00:49, time: 0.597, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0150, loss_rpn_bbox: 0.0333, loss_cls: 0.1457, acc: 94.3787, loss_bbox: 0.2039, loss_mask: 0.2168, loss: 0.6148 +2024-05-28 06:05:15,107 - mmdet - INFO - Epoch [12][7300/7330] lr: 1.000e-06, eta: 0:00:18, time: 0.698, data_time: 0.018, memory: 9459, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0304, loss_cls: 0.1379, acc: 94.7717, loss_bbox: 0.1910, loss_mask: 0.2124, loss: 0.5861 +2024-05-28 06:05:33,774 - mmdet - INFO - Saving checkpoint at 12 epochs +2024-05-28 06:07:28,091 - mmdet - INFO - Evaluating bbox... +2024-05-28 06:07:52,572 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.465 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.693 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.506 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.276 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.506 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.665 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.572 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.572 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.572 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.364 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.621 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.770 + +2024-05-28 06:07:52,572 - mmdet - INFO - Evaluating segm... +2024-05-28 06:08:14,861 - mmdet - INFO - + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.408 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.653 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.432 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.184 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.446 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.646 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.507 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.507 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.507 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.290 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.559 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.723 + +2024-05-28 06:08:15,215 - mmdet - INFO - Exp name: mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py +2024-05-28 06:08:15,217 - mmdet - INFO - Epoch(val) [12][625] bbox_mAP: 0.4650, bbox_mAP_50: 0.6930, bbox_mAP_75: 0.5060, bbox_mAP_s: 0.2760, bbox_mAP_m: 0.5060, bbox_mAP_l: 0.6650, bbox_mAP_copypaste: 0.465 0.693 0.506 0.276 0.506 0.665, segm_mAP: 0.4080, segm_mAP_50: 0.6530, segm_mAP_75: 0.4320, segm_mAP_s: 0.1840, segm_mAP_m: 0.4460, segm_mAP_l: 0.6460, segm_mAP_copypaste: 0.408 0.653 0.432 0.184 0.446 0.646