2022-12-01 04:19:54,758 - mmdet - INFO - Environment info: ------------------------------------------------------------ sys.platform: linux Python: 3.7.13 (default, Mar 29 2022, 02:18:16) [GCC 7.5.0] CUDA available: True GPU 0,1,2,3: GeForce RTX 3090 CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.1, V11.1.105 GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 PyTorch: 1.12.1+cu113 PyTorch compiling details: PyTorch built with: - GCC 9.3 - C++ Version: 201402 - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 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= -fabi-version=11 -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=1.12.1, 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: 0.13.1+cu113 OpenCV: 4.5.5 MMCV: 1.7.0 MMCV Compiler: GCC 9.3 MMCV CUDA Compiler: 11.3 MMDetection: 2.25.2+8bcaa7c ------------------------------------------------------------ 2022-12-01 04:19:55,194 - mmdet - INFO - Distributed training: False 2022-12-01 04:19:55,466 - mmdet - INFO - Config: optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) 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) log_config = dict( interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)]) dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)] cudnn_benchmark = True sirst_version = 'sirstv2' depth = 50 fpn_strides = [4, 8, 16, 32] split_cfg = dict( train_split='splits/trainval_full.txt', val_split='splits/test_full.txt', test_split='splits/test_full.txt') dataset_type = 'SIRSTDet2NoCoDataset' data_root = 'data/sirst/' img_norm_cfg = dict( mean=[111.89, 111.89, 111.89], std=[27.62, 27.62, 27.62], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict(type='Resize', img_scale=(1000, 600), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict( type='Normalize', mean=[111.89, 111.89, 111.89], std=[27.62, 27.62, 27.62], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1000, 600), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[111.89, 111.89, 111.89], std=[27.62, 27.62, 27.62], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ] data = dict( samples_per_gpu=4, workers_per_gpu=4, train=dict( type='RepeatDataset', times=3, dataset=dict( type='SIRSTDet2NoCoDataset', noco_mode='noco_peak', ann_file=['data/sirst/splits/trainval_full.txt'], img_prefix=['data/sirst/'], pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict(type='Resize', img_scale=(1000, 600), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict( type='Normalize', mean=[111.89, 111.89, 111.89], std=[27.62, 27.62, 27.62], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) ])), val=dict( type='SIRSTDet2NoCoDataset', noco_mode='noco_peak', ann_file='data/sirst/splits/test_full.txt', img_prefix='data/sirst/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1000, 600), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[111.89, 111.89, 111.89], std=[27.62, 27.62, 27.62], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ]), test=dict( type='SIRSTDet2NoCoDataset', noco_mode='noco_peak', ann_file='data/sirst/splits/test_full.txt', img_prefix='data/sirst/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1000, 600), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[111.89, 111.89, 111.89], std=[27.62, 27.62, 27.62], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ])) model = dict( type='FasterRCNN', backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, style='pytorch', init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], 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))), 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=False, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), 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))) checkpoint_config = dict(interval=1) work_dir = 'work_dirs/faster_rcnn_r50_fpn_1x_sirst_gpu_2' auto_resume = False gpu_ids = [2] 2022-12-01 04:19:55,466 - mmdet - INFO - Set random seed to 562812191, deterministic: False 2022-12-01 04:19:56,933 - mmdet - INFO - initialize ResNet with init_cfg {'type': 'Pretrained', 'checkpoint': 'torchvision://resnet50'} 2022-12-01 04:19:58,112 - mmdet - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'} 2022-12-01 04:19:58,220 - mmdet - INFO - initialize RPNHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01} 2022-12-01 04:19:58,228 - 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.conv1.weight - torch.Size([64, 3, 7, 7]): PretrainedInit: load from torchvision://resnet50 backbone.bn1.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.bn1.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn1.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn1.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn2.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn2.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn3.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn3.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.downsample.1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.downsample.1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn1.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn1.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn2.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn2.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn3.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn3.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn1.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn1.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn2.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn2.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn3.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn3.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn1.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn1.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn2.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn2.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn3.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn3.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.downsample.1.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.downsample.1.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn1.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn1.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn2.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn2.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn3.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn3.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn1.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn1.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn2.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn2.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn3.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn3.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn1.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn1.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn2.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn2.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn3.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn3.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.downsample.0.weight - torch.Size([1024, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.downsample.1.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.downsample.1.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn1.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn1.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn2.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn2.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn3.weight - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn3.bias - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.downsample.1.weight - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.downsample.1.bias - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn1.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn1.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn2.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn2.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn3.weight - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn3.bias - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn1.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn1.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn2.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn2.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn3.weight - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn3.bias - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 neck.lateral_convs.0.conv.weight - torch.Size([256, 256, 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 FasterRCNN neck.lateral_convs.1.conv.weight - torch.Size([256, 512, 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 FasterRCNN 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 FasterRCNN neck.lateral_convs.3.conv.weight - torch.Size([256, 2048, 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 FasterRCNN 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 FasterRCNN 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 FasterRCNN 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 FasterRCNN 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 FasterRCNN 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 2022-12-01 04:20:13,752 - mmdet - INFO - Automatic scaling of learning rate (LR) has been disabled. 2022-12-01 04:20:17,458 - mmdet - INFO - Start running, host: root@interactive25681, work_dir: /opt/data/private/deepinfrared/work_dirs/faster_rcnn_r50_fpn_1x_sirst_gpu_2 2022-12-01 04:20:17,459 - mmdet - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) StepLrUpdaterHook (NORMAL ) CheckpointHook (LOW ) EvalHook (VERY_LOW ) TextLoggerHook -------------------- before_train_epoch: (VERY_HIGH ) StepLrUpdaterHook (LOW ) IterTimerHook (LOW ) EvalHook (VERY_LOW ) TextLoggerHook -------------------- before_train_iter: (VERY_HIGH ) StepLrUpdaterHook (LOW ) IterTimerHook (LOW ) EvalHook -------------------- after_train_iter: (ABOVE_NORMAL) OptimizerHook (NORMAL ) CheckpointHook (LOW ) IterTimerHook (LOW ) EvalHook (VERY_LOW ) TextLoggerHook -------------------- after_train_epoch: (NORMAL ) CheckpointHook (LOW ) EvalHook (VERY_LOW ) TextLoggerHook -------------------- before_val_epoch: (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 -------------------- 2022-12-01 04:20:17,470 - mmdet - INFO - workflow: [('train', 1)], max: 12 epochs 2022-12-01 04:20:17,471 - mmdet - INFO - Checkpoints will be saved to /opt/data/private/deepinfrared/work_dirs/faster_rcnn_r50_fpn_1x_sirst_gpu_2 by HardDiskBackend. 2022-12-01 04:21:51,716 - mmdet - INFO - Iter [50/6924] lr: 9.890e-04, eta: 3:35:52, time: 1.884, data_time: 0.073, memory: 11836, loss_rpn_cls: 0.3033, loss_rpn_bbox: 0.0045, loss_cls: 0.9873, acc: 86.0557, loss_bbox: 0.0004, loss: 1.2956 2022-12-01 04:22:45,149 - mmdet - INFO - Iter [100/6924] lr: 1.988e-03, eta: 2:47:54, time: 1.068, data_time: 0.021, memory: 11931, loss_rpn_cls: 0.0269, loss_rpn_bbox: 0.0030, loss_cls: 0.0264, acc: 99.8418, loss_bbox: 0.0009, loss: 0.0571 2022-12-01 04:23:15,504 - mmdet - INFO - Iter [150/6924] lr: 2.987e-03, eta: 2:13:56, time: 0.607, data_time: 0.034, memory: 11931, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0030, loss_cls: 0.0142, acc: 99.7744, loss_bbox: 0.0045, loss: 0.0377 2022-12-01 04:23:43,707 - mmdet - INFO - Iter [200/6924] lr: 3.986e-03, eta: 1:55:32, time: 0.565, data_time: 0.025, memory: 11931, loss_rpn_cls: 0.0167, loss_rpn_bbox: 0.0050, loss_cls: 0.0169, acc: 99.7061, loss_bbox: 0.0070, loss: 0.0457 2022-12-01 04:24:24,506 - mmdet - INFO - Iter [250/6924] lr: 4.985e-03, eta: 1:49:54, time: 0.816, data_time: 0.041, memory: 11931, loss_rpn_cls: 0.0098, loss_rpn_bbox: 0.0031, loss_cls: 0.0206, acc: 99.6006, loss_bbox: 0.0118, loss: 0.0453 2022-12-01 04:24:53,622 - mmdet - INFO - Iter [300/6924] lr: 5.984e-03, eta: 1:41:36, time: 0.582, data_time: 0.018, memory: 11931, loss_rpn_cls: 0.0036, loss_rpn_bbox: 0.0022, loss_cls: 0.0135, acc: 99.6660, loss_bbox: 0.0098, loss: 0.0290 2022-12-01 04:25:24,722 - mmdet - INFO - Iter [350/6924] lr: 6.983e-03, eta: 1:36:10, time: 0.622, data_time: 0.022, memory: 11931, loss_rpn_cls: 0.0046, loss_rpn_bbox: 0.0027, loss_cls: 0.0145, acc: 99.5645, loss_bbox: 0.0152, loss: 0.0370 2022-12-01 04:26:01,505 - mmdet - INFO - Iter [400/6924] lr: 7.982e-03, eta: 1:33:29, time: 0.734, data_time: 0.032, memory: 11931, loss_rpn_cls: 0.0074, loss_rpn_bbox: 0.0030, loss_cls: 0.0135, acc: 99.5977, loss_bbox: 0.0121, loss: 0.0361 2022-12-01 04:26:33,305 - mmdet - INFO - Iter [450/6924] lr: 8.981e-03, eta: 1:30:05, time: 0.636, data_time: 0.023, memory: 11931, loss_rpn_cls: 0.0034, loss_rpn_bbox: 0.0016, loss_cls: 0.0125, acc: 99.6709, loss_bbox: 0.0084, loss: 0.0260 2022-12-01 04:27:05,543 - mmdet - INFO - Iter [500/6924] lr: 9.980e-03, eta: 1:27:22, time: 0.646, data_time: 0.037, memory: 11931, loss_rpn_cls: 0.0034, loss_rpn_bbox: 0.0022, loss_cls: 0.0117, acc: 99.6582, loss_bbox: 0.0110, loss: 0.0283 2022-12-01 04:27:38,471 - mmdet - INFO - Iter [550/6924] lr: 1.000e-02, eta: 1:25:10, time: 0.659, data_time: 0.028, memory: 11931, loss_rpn_cls: 0.0022, loss_rpn_bbox: 0.0020, loss_cls: 0.0073, acc: 99.7119, loss_bbox: 0.0120, loss: 0.0235 2022-12-01 04:27:57,038 - mmdet - INFO - Saving checkpoint at 1 epochs 2022-12-01 04:29:03,584 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 1101 | 0.847 | 0.426 | +-------+-----+------+--------+-------+ | mAP | | | | 0.426 | +-------+-----+------+--------+-------+ 2022-12-01 04:29:06,234 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 1101 | 0.827 | 0.418 | +-------+-----+------+--------+-------+ | mAP | | | | 0.418 | +-------+-----+------+--------+-------+ 2022-12-01 04:29:09,098 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 1101 | 0.820 | 0.410 | +-------+-----+------+--------+-------+ | mAP | | | | 0.410 | +-------+-----+------+--------+-------+ 2022-12-01 04:29:12,231 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 1101 | 0.793 | 0.379 | +-------+-----+------+--------+-------+ | mAP | | | | 0.379 | +-------+-----+------+--------+-------+ 2022-12-01 04:29:15,138 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 1101 | 0.773 | 0.368 | +-------+-----+------+--------+-------+ | mAP | | | | 0.368 | +-------+-----+------+--------+-------+ 2022-12-01 04:29:17,817 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 1101 | 0.767 | 0.332 | +-------+-----+------+--------+-------+ | mAP | | | | 0.332 | +-------+-----+------+--------+-------+ 2022-12-01 04:29:20,990 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 1101 | 0.753 | 0.304 | +-------+-----+------+--------+-------+ | mAP | | | | 0.304 | +-------+-----+------+--------+-------+ 2022-12-01 04:29:23,685 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 1101 | 0.647 | 0.245 | +-------+-----+------+--------+-------+ | mAP | | | | 0.245 | +-------+-----+------+--------+-------+ 2022-12-01 04:29:26,311 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 1101 | 0.500 | 0.143 | +-------+-----+------+--------+-------+ | mAP | | | | 0.143 | +-------+-----+------+--------+-------+ 2022-12-01 04:29:26,313 - mmdet - INFO - Iter(val) [256] NoCoAP10: 0.4260, NoCoAP20: 0.4180, NoCoAP30: 0.4100, NoCoAP40: 0.3790, NoCoAP50: 0.3680, NoCoAP60: 0.3320, NoCoAP70: 0.3040, NoCoAP80: 0.2450, NoCoAP90: 0.1430, mNoCoAP: 0.3362 2022-12-01 04:29:35,105 - mmdet - INFO - Iter [600/6924] lr: 1.000e-02, eta: 1:20:44, time: 0.374, data_time: 0.129, memory: 11931, loss_rpn_cls: 0.0025, loss_rpn_bbox: 0.0028, loss_cls: 0.0097, acc: 99.6200, loss_bbox: 0.0136, loss: 0.0286 2022-12-01 04:29:47,608 - mmdet - INFO - Iter [650/6924] lr: 1.000e-02, eta: 1:15:58, time: 0.251, data_time: 0.019, memory: 11931, loss_rpn_cls: 0.0013, loss_rpn_bbox: 0.0019, loss_cls: 0.0114, acc: 99.5938, loss_bbox: 0.0132, loss: 0.0277 2022-12-01 04:30:02,712 - mmdet - INFO - Iter [700/6924] lr: 1.000e-02, eta: 1:12:13, time: 0.302, data_time: 0.014, memory: 11931, loss_rpn_cls: 0.0031, loss_rpn_bbox: 0.0021, loss_cls: 0.0124, acc: 99.5352, loss_bbox: 0.0169, loss: 0.0345 2022-12-01 04:30:15,546 - mmdet - INFO - Iter [750/6924] lr: 1.000e-02, eta: 1:08:37, time: 0.256, data_time: 0.012, memory: 11931, loss_rpn_cls: 0.0032, loss_rpn_bbox: 0.0030, loss_cls: 0.0139, acc: 99.6035, loss_bbox: 0.0142, loss: 0.0344 2022-12-01 04:30:28,216 - mmdet - INFO - Iter [800/6924] lr: 1.000e-02, eta: 1:05:25, time: 0.254, data_time: 0.015, memory: 11931, loss_rpn_cls: 0.0019, loss_rpn_bbox: 0.0015, loss_cls: 0.0091, acc: 99.6611, loss_bbox: 0.0139, loss: 0.0264 2022-12-01 04:31:06,137 - mmdet - INFO - Iter [850/6924] lr: 1.000e-02, eta: 1:05:34, time: 0.756, data_time: 0.031, memory: 11931, loss_rpn_cls: 0.0016, loss_rpn_bbox: 0.0031, loss_cls: 0.0111, acc: 99.5645, loss_bbox: 0.0157, loss: 0.0315 2022-12-01 04:31:38,419 - mmdet - INFO - Iter [900/6924] lr: 1.000e-02, eta: 1:05:02, time: 0.648, data_time: 0.022, memory: 11931, loss_rpn_cls: 0.0014, loss_rpn_bbox: 0.0016, loss_cls: 0.0073, acc: 99.7178, loss_bbox: 0.0135, loss: 0.0238 2022-12-01 04:32:10,814 - mmdet - INFO - Iter [950/6924] lr: 1.000e-02, eta: 1:04:25, time: 0.634, data_time: 0.022, memory: 11931, loss_rpn_cls: 0.0010, loss_rpn_bbox: 0.0017, loss_cls: 0.0062, acc: 99.7783, loss_bbox: 0.0093, loss: 0.0182 2022-12-01 04:32:41,145 - mmdet - INFO - Exp name: faster_rcnn_r50_fpn_1x_sirst.py 2022-12-01 04:32:41,146 - mmdet - INFO - Iter [1000/6924] lr: 1.000e-02, eta: 1:03:45, time: 0.619, data_time: 0.048, memory: 11931, loss_rpn_cls: 0.0017, loss_rpn_bbox: 0.0020, loss_cls: 0.0086, acc: 99.6543, loss_bbox: 0.0148, loss: 0.0271 2022-12-01 04:33:12,918 - mmdet - INFO - Iter [1050/6924] lr: 1.000e-02, eta: 1:03:10, time: 0.635, data_time: 0.027, memory: 11931, loss_rpn_cls: 0.0019, loss_rpn_bbox: 0.0012, loss_cls: 0.0093, acc: 99.6963, loss_bbox: 0.0113, loss: 0.0237 2022-12-01 04:33:47,206 - mmdet - INFO - Iter [1100/6924] lr: 1.000e-02, eta: 1:02:48, time: 0.687, data_time: 0.029, memory: 11931, loss_rpn_cls: 0.0013, loss_rpn_bbox: 0.0019, loss_cls: 0.0074, acc: 99.6914, loss_bbox: 0.0134, loss: 0.0240 2022-12-01 04:34:19,814 - mmdet - INFO - Iter [1150/6924] lr: 1.000e-02, eta: 1:02:17, time: 0.652, data_time: 0.031, memory: 11931, loss_rpn_cls: 0.0017, loss_rpn_bbox: 0.0020, loss_cls: 0.0097, acc: 99.6094, loss_bbox: 0.0160, loss: 0.0293 2022-12-01 04:34:23,706 - mmdet - INFO - Saving checkpoint at 2 epochs 2022-12-01 04:35:05,925 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 169 | 0.813 | 0.755 | +-------+-----+------+--------+-------+ | mAP | | | | 0.755 | +-------+-----+------+--------+-------+ 2022-12-01 04:35:13,811 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 169 | 0.793 | 0.734 | +-------+-----+------+--------+-------+ | mAP | | | | 0.734 | +-------+-----+------+--------+-------+ 2022-12-01 04:35:22,205 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 169 | 0.780 | 0.718 | +-------+-----+------+--------+-------+ | mAP | | | | 0.718 | +-------+-----+------+--------+-------+ 2022-12-01 04:35:30,927 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 169 | 0.747 | 0.684 | +-------+-----+------+--------+-------+ | mAP | | | | 0.684 | +-------+-----+------+--------+-------+ 2022-12-01 04:35:39,237 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 169 | 0.713 | 0.649 | +-------+-----+------+--------+-------+ | mAP | | | | 0.649 | +-------+-----+------+--------+-------+ 2022-12-01 04:35:50,112 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 169 | 0.687 | 0.612 | +-------+-----+------+--------+-------+ | mAP | | | | 0.612 | +-------+-----+------+--------+-------+ 2022-12-01 04:35:58,720 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 169 | 0.640 | 0.558 | +-------+-----+------+--------+-------+ | mAP | | | | 0.558 | +-------+-----+------+--------+-------+ 2022-12-01 04:36:07,143 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 169 | 0.513 | 0.402 | +-------+-----+------+--------+-------+ | mAP | | | | 0.402 | +-------+-----+------+--------+-------+ 2022-12-01 04:36:13,836 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 169 | 0.360 | 0.239 | +-------+-----+------+--------+-------+ | mAP | | | | 0.239 | +-------+-----+------+--------+-------+ 2022-12-01 04:36:13,838 - mmdet - INFO - Iter(val) [256] NoCoAP10: 0.7550, NoCoAP20: 0.7340, NoCoAP30: 0.7180, NoCoAP40: 0.6840, NoCoAP50: 0.6490, NoCoAP60: 0.6120, NoCoAP70: 0.5580, NoCoAP80: 0.4020, NoCoAP90: 0.2390, mNoCoAP: 0.5945 2022-12-01 04:36:38,904 - mmdet - INFO - Iter [1200/6924] lr: 1.000e-02, eta: 1:01:20, time: 0.541, data_time: 0.101, memory: 11931, loss_rpn_cls: 0.0015, loss_rpn_bbox: 0.0018, loss_cls: 0.0079, acc: 99.6826, loss_bbox: 0.0141, loss: 0.0253 2022-12-01 04:36:52,513 - mmdet - INFO - Iter [1250/6924] lr: 1.000e-02, eta: 0:59:24, time: 0.273, data_time: 0.020, memory: 11931, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0015, loss_cls: 0.0064, acc: 99.7510, loss_bbox: 0.0114, loss: 0.0200 2022-12-01 04:37:04,906 - mmdet - INFO - Iter [1300/6924] lr: 1.000e-02, eta: 0:57:30, time: 0.247, data_time: 0.016, memory: 11931, loss_rpn_cls: 0.0016, loss_rpn_bbox: 0.0018, loss_cls: 0.0079, acc: 99.6963, loss_bbox: 0.0140, loss: 0.0253 2022-12-01 04:37:18,143 - mmdet - INFO - Iter [1350/6924] lr: 1.000e-02, eta: 0:55:47, time: 0.265, data_time: 0.024, memory: 11931, loss_rpn_cls: 0.0013, loss_rpn_bbox: 0.0019, loss_cls: 0.0076, acc: 99.6914, loss_bbox: 0.0181, loss: 0.0289 2022-12-01 04:37:37,828 - mmdet - INFO - Iter [1400/6924] lr: 1.000e-02, eta: 0:54:36, time: 0.394, data_time: 0.024, memory: 11931, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0010, loss_cls: 0.0064, acc: 99.7568, loss_bbox: 0.0109, loss: 0.0190 2022-12-01 04:38:08,427 - mmdet - INFO - Iter [1450/6924] lr: 1.000e-02, eta: 0:54:10, time: 0.611, data_time: 0.052, memory: 11931, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0018, loss_cls: 0.0082, acc: 99.6553, loss_bbox: 0.0150, loss: 0.0259 2022-12-01 04:38:39,124 - mmdet - INFO - Iter [1500/6924] lr: 1.000e-02, eta: 0:53:44, time: 0.614, data_time: 0.019, memory: 11931, loss_rpn_cls: 0.0011, loss_rpn_bbox: 0.0015, loss_cls: 0.0078, acc: 99.6748, loss_bbox: 0.0173, loss: 0.0277 2022-12-01 04:39:09,106 - mmdet - INFO - Iter [1550/6924] lr: 1.000e-02, eta: 0:53:15, time: 0.600, data_time: 0.034, memory: 11931, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0010, loss_cls: 0.0071, acc: 99.7012, loss_bbox: 0.0135, loss: 0.0222 2022-12-01 04:39:45,005 - mmdet - INFO - Iter [1600/6924] lr: 1.000e-02, eta: 0:53:06, time: 0.717, data_time: 0.036, memory: 11931, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0014, loss_cls: 0.0060, acc: 99.7539, loss_bbox: 0.0117, loss: 0.0200 2022-12-01 04:40:18,024 - mmdet - INFO - Iter [1650/6924] lr: 1.000e-02, eta: 0:52:46, time: 0.661, data_time: 0.028, memory: 11931, loss_rpn_cls: 0.0017, loss_rpn_bbox: 0.0013, loss_cls: 0.0063, acc: 99.7402, loss_bbox: 0.0115, loss: 0.0209 2022-12-01 04:40:53,517 - mmdet - INFO - Iter [1700/6924] lr: 1.000e-02, eta: 0:52:33, time: 0.710, data_time: 0.050, memory: 11931, loss_rpn_cls: 0.0011, loss_rpn_bbox: 0.0015, loss_cls: 0.0080, acc: 99.6484, loss_bbox: 0.0138, loss: 0.0244 2022-12-01 04:41:16,813 - mmdet - INFO - Saving checkpoint at 3 epochs 2022-12-01 04:41:59,688 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 185 | 0.873 | 0.824 | +-------+-----+------+--------+-------+ | mAP | | | | 0.824 | +-------+-----+------+--------+-------+ 2022-12-01 04:42:08,909 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 185 | 0.860 | 0.810 | +-------+-----+------+--------+-------+ | mAP | | | | 0.810 | +-------+-----+------+--------+-------+ 2022-12-01 04:42:20,630 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 185 | 0.860 | 0.810 | +-------+-----+------+--------+-------+ | mAP | | | | 0.810 | +-------+-----+------+--------+-------+ 2022-12-01 04:42:28,672 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 185 | 0.840 | 0.788 | +-------+-----+------+--------+-------+ | mAP | | | | 0.788 | +-------+-----+------+--------+-------+ 2022-12-01 04:42:36,105 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 185 | 0.813 | 0.763 | +-------+-----+------+--------+-------+ | mAP | | | | 0.763 | +-------+-----+------+--------+-------+ 2022-12-01 04:42:45,151 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 185 | 0.787 | 0.714 | +-------+-----+------+--------+-------+ | mAP | | | | 0.714 | +-------+-----+------+--------+-------+ 2022-12-01 04:42:54,254 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 185 | 0.753 | 0.672 | +-------+-----+------+--------+-------+ | mAP | | | | 0.672 | +-------+-----+------+--------+-------+ 2022-12-01 04:43:04,721 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 185 | 0.693 | 0.596 | +-------+-----+------+--------+-------+ | mAP | | | | 0.596 | +-------+-----+------+--------+-------+ 2022-12-01 04:43:12,627 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 185 | 0.587 | 0.451 | +-------+-----+------+--------+-------+ | mAP | | | | 0.451 | +-------+-----+------+--------+-------+ 2022-12-01 04:43:12,628 - mmdet - INFO - Iter(val) [256] NoCoAP10: 0.8240, NoCoAP20: 0.8100, NoCoAP30: 0.8100, NoCoAP40: 0.7880, NoCoAP50: 0.7630, NoCoAP60: 0.7140, NoCoAP70: 0.6720, NoCoAP80: 0.5960, NoCoAP90: 0.4510, mNoCoAP: 0.7141 2022-12-01 04:43:28,007 - mmdet - INFO - Iter [1750/6924] lr: 1.000e-02, eta: 0:52:33, time: 0.807, data_time: 0.216, memory: 11931, loss_rpn_cls: 0.0012, loss_rpn_bbox: 0.0012, loss_cls: 0.0049, acc: 99.8021, loss_bbox: 0.0095, loss: 0.0168 2022-12-01 04:43:48,334 - mmdet - INFO - Iter [1800/6924] lr: 1.000e-02, eta: 0:51:33, time: 0.406, data_time: 0.030, memory: 11931, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0014, loss_cls: 0.0049, acc: 99.7979, loss_bbox: 0.0098, loss: 0.0170 2022-12-01 04:44:00,904 - mmdet - INFO - Iter [1850/6924] lr: 1.000e-02, eta: 0:50:15, time: 0.251, data_time: 0.017, memory: 11931, loss_rpn_cls: 0.0010, loss_rpn_bbox: 0.0012, loss_cls: 0.0068, acc: 99.7178, loss_bbox: 0.0119, loss: 0.0208 2022-12-01 04:44:13,439 - mmdet - INFO - Iter [1900/6924] lr: 1.000e-02, eta: 0:49:00, time: 0.251, data_time: 0.021, memory: 11931, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0012, loss_cls: 0.0067, acc: 99.7158, loss_bbox: 0.0126, loss: 0.0212 2022-12-01 04:44:26,019 - mmdet - INFO - Iter [1950/6924] lr: 1.000e-02, eta: 0:47:48, time: 0.252, data_time: 0.017, memory: 11931, loss_rpn_cls: 0.0012, loss_rpn_bbox: 0.0013, loss_cls: 0.0041, acc: 99.8301, loss_bbox: 0.0101, loss: 0.0167 2022-12-01 04:44:51,286 - mmdet - INFO - Exp name: faster_rcnn_r50_fpn_1x_sirst.py 2022-12-01 04:44:51,287 - mmdet - INFO - Iter [2000/6924] lr: 1.000e-02, eta: 0:47:10, time: 0.505, data_time: 0.030, memory: 11931, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0008, loss_cls: 0.0058, acc: 99.7578, loss_bbox: 0.0115, loss: 0.0188 2022-12-01 04:45:21,306 - mmdet - INFO - Iter [2050/6924] lr: 1.000e-02, eta: 0:46:45, time: 0.601, data_time: 0.024, memory: 11931, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0011, loss_cls: 0.0054, acc: 99.7871, loss_bbox: 0.0120, loss: 0.0191 2022-12-01 04:45:53,116 - mmdet - INFO - Iter [2100/6924] lr: 1.000e-02, eta: 0:46:23, time: 0.636, data_time: 0.016, memory: 11931, loss_rpn_cls: 0.0015, loss_rpn_bbox: 0.0019, loss_cls: 0.0077, acc: 99.6680, loss_bbox: 0.0169, loss: 0.0280 2022-12-01 04:46:26,304 - mmdet - INFO - Iter [2150/6924] lr: 1.000e-02, eta: 0:46:03, time: 0.662, data_time: 0.024, memory: 11931, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0014, loss_cls: 0.0058, acc: 99.7705, loss_bbox: 0.0126, loss: 0.0208 2022-12-01 04:47:01,605 - mmdet - INFO - Iter [2200/6924] lr: 1.000e-02, eta: 0:45:48, time: 0.706, data_time: 0.037, memory: 11931, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0012, loss_cls: 0.0052, acc: 99.7764, loss_bbox: 0.0114, loss: 0.0187 2022-12-01 04:47:33,919 - mmdet - INFO - Iter [2250/6924] lr: 1.000e-02, eta: 0:45:26, time: 0.647, data_time: 0.035, memory: 11931, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0009, loss_cls: 0.0052, acc: 99.7822, loss_bbox: 0.0127, loss: 0.0194 2022-12-01 04:48:07,108 - mmdet - INFO - Iter [2300/6924] lr: 1.000e-02, eta: 0:45:05, time: 0.664, data_time: 0.041, memory: 11931, loss_rpn_cls: 0.0015, loss_rpn_bbox: 0.0014, loss_cls: 0.0074, acc: 99.7051, loss_bbox: 0.0139, loss: 0.0241 2022-12-01 04:48:12,313 - mmdet - INFO - Saving checkpoint at 4 epochs 2022-12-01 04:48:46,620 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 160 | 0.867 | 0.833 | +-------+-----+------+--------+-------+ | mAP | | | | 0.833 | +-------+-----+------+--------+-------+ 2022-12-01 04:48:54,135 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 160 | 0.860 | 0.821 | +-------+-----+------+--------+-------+ | mAP | | | | 0.821 | +-------+-----+------+--------+-------+ 2022-12-01 04:49:02,307 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 160 | 0.853 | 0.812 | +-------+-----+------+--------+-------+ | mAP | | | | 0.812 | +-------+-----+------+--------+-------+ 2022-12-01 04:49:10,215 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 160 | 0.840 | 0.796 | +-------+-----+------+--------+-------+ | mAP | | | | 0.796 | +-------+-----+------+--------+-------+ 2022-12-01 04:49:19,810 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 160 | 0.800 | 0.752 | +-------+-----+------+--------+-------+ | mAP | | | | 0.752 | +-------+-----+------+--------+-------+ 2022-12-01 04:49:28,038 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 160 | 0.773 | 0.702 | +-------+-----+------+--------+-------+ | mAP | | | | 0.702 | +-------+-----+------+--------+-------+ 2022-12-01 04:49:36,028 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 160 | 0.733 | 0.650 | +-------+-----+------+--------+-------+ | mAP | | | | 0.650 | +-------+-----+------+--------+-------+ 2022-12-01 04:49:43,509 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 160 | 0.627 | 0.518 | +-------+-----+------+--------+-------+ | mAP | | | | 0.518 | +-------+-----+------+--------+-------+ 2022-12-01 04:49:50,033 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 160 | 0.480 | 0.328 | +-------+-----+------+--------+-------+ | mAP | | | | 0.328 | +-------+-----+------+--------+-------+ 2022-12-01 04:49:50,035 - mmdet - INFO - Iter(val) [256] NoCoAP10: 0.8330, NoCoAP20: 0.8210, NoCoAP30: 0.8120, NoCoAP40: 0.7960, NoCoAP50: 0.7520, NoCoAP60: 0.7020, NoCoAP70: 0.6500, NoCoAP80: 0.5180, NoCoAP90: 0.3280, mNoCoAP: 0.6902 2022-12-01 04:50:19,443 - mmdet - INFO - Iter [2350/6924] lr: 1.000e-02, eta: 0:44:46, time: 0.696, data_time: 0.102, memory: 11931, loss_rpn_cls: 0.0011, loss_rpn_bbox: 0.0011, loss_cls: 0.0050, acc: 99.7907, loss_bbox: 0.0101, loss: 0.0173 2022-12-01 04:50:51,906 - mmdet - INFO - Iter [2400/6924] lr: 1.000e-02, eta: 0:44:23, time: 0.650, data_time: 0.034, memory: 11931, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0011, loss_cls: 0.0056, acc: 99.7637, loss_bbox: 0.0123, loss: 0.0197 2022-12-01 04:51:09,353 - mmdet - INFO - Iter [2450/6924] lr: 1.000e-02, eta: 0:43:31, time: 0.349, data_time: 0.019, memory: 11931, loss_rpn_cls: 0.0014, loss_rpn_bbox: 0.0013, loss_cls: 0.0067, acc: 99.7363, loss_bbox: 0.0132, loss: 0.0226 2022-12-01 04:51:21,704 - mmdet - INFO - Iter [2500/6924] lr: 1.000e-02, eta: 0:42:32, time: 0.246, data_time: 0.015, memory: 11931, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0009, loss_cls: 0.0045, acc: 99.8252, loss_bbox: 0.0086, loss: 0.0146 2022-12-01 04:51:36,589 - mmdet - INFO - Iter [2550/6924] lr: 1.000e-02, eta: 0:41:40, time: 0.298, data_time: 0.016, memory: 11931, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0011, loss_cls: 0.0053, acc: 99.7812, loss_bbox: 0.0109, loss: 0.0179 2022-12-01 04:51:56,225 - mmdet - INFO - Iter [2600/6924] lr: 1.000e-02, eta: 0:40:56, time: 0.393, data_time: 0.013, memory: 11931, loss_rpn_cls: 0.0004, loss_rpn_bbox: 0.0010, loss_cls: 0.0056, acc: 99.7520, loss_bbox: 0.0113, loss: 0.0183 2022-12-01 04:52:25,617 - mmdet - INFO - Iter [2650/6924] lr: 1.000e-02, eta: 0:40:29, time: 0.588, data_time: 0.027, memory: 11931, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0014, loss_cls: 0.0059, acc: 99.7578, loss_bbox: 0.0132, loss: 0.0215 2022-12-01 04:52:59,736 - mmdet - INFO - Iter [2700/6924] lr: 1.000e-02, eta: 0:40:10, time: 0.682, data_time: 0.035, memory: 11931, loss_rpn_cls: 0.0004, loss_rpn_bbox: 0.0008, loss_cls: 0.0052, acc: 99.7881, loss_bbox: 0.0095, loss: 0.0159 2022-12-01 04:53:32,038 - mmdet - INFO - Iter [2750/6924] lr: 1.000e-02, eta: 0:39:47, time: 0.646, data_time: 0.040, memory: 11931, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0011, loss_cls: 0.0058, acc: 99.7578, loss_bbox: 0.0127, loss: 0.0200 2022-12-01 04:54:03,436 - mmdet - INFO - Iter [2800/6924] lr: 1.000e-02, eta: 0:39:23, time: 0.628, data_time: 0.037, memory: 11931, loss_rpn_cls: 0.0012, loss_rpn_bbox: 0.0010, loss_cls: 0.0082, acc: 99.6719, loss_bbox: 0.0134, loss: 0.0238 2022-12-01 04:54:38,910 - mmdet - INFO - Iter [2850/6924] lr: 1.000e-02, eta: 0:39:04, time: 0.709, data_time: 0.037, memory: 11931, loss_rpn_cls: 0.0011, loss_rpn_bbox: 0.0016, loss_cls: 0.0066, acc: 99.7314, loss_bbox: 0.0114, loss: 0.0206 2022-12-01 04:55:03,319 - mmdet - INFO - Saving checkpoint at 5 epochs 2022-12-01 04:55:40,322 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 177 | 0.867 | 0.805 | +-------+-----+------+--------+-------+ | mAP | | | | 0.805 | +-------+-----+------+--------+-------+ 2022-12-01 04:55:47,328 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 177 | 0.860 | 0.794 | +-------+-----+------+--------+-------+ | mAP | | | | 0.794 | +-------+-----+------+--------+-------+ 2022-12-01 04:55:54,667 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 177 | 0.847 | 0.776 | +-------+-----+------+--------+-------+ | mAP | | | | 0.776 | +-------+-----+------+--------+-------+ 2022-12-01 04:56:02,750 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 177 | 0.827 | 0.751 | +-------+-----+------+--------+-------+ | mAP | | | | 0.751 | +-------+-----+------+--------+-------+ 2022-12-01 04:56:10,628 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 177 | 0.793 | 0.718 | +-------+-----+------+--------+-------+ | mAP | | | | 0.718 | +-------+-----+------+--------+-------+ 2022-12-01 04:56:18,724 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 177 | 0.760 | 0.659 | +-------+-----+------+--------+-------+ | mAP | | | | 0.659 | +-------+-----+------+--------+-------+ 2022-12-01 04:56:27,705 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 177 | 0.713 | 0.603 | +-------+-----+------+--------+-------+ | mAP | | | | 0.603 | +-------+-----+------+--------+-------+ 2022-12-01 04:56:36,015 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 177 | 0.640 | 0.511 | +-------+-----+------+--------+-------+ | mAP | | | | 0.511 | +-------+-----+------+--------+-------+ 2022-12-01 04:56:43,847 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 177 | 0.473 | 0.299 | +-------+-----+------+--------+-------+ | mAP | | | | 0.299 | +-------+-----+------+--------+-------+ 2022-12-01 04:56:43,849 - mmdet - INFO - Iter(val) [256] NoCoAP10: 0.8050, NoCoAP20: 0.7940, NoCoAP30: 0.7760, NoCoAP40: 0.7510, NoCoAP50: 0.7180, NoCoAP60: 0.6590, NoCoAP70: 0.6030, NoCoAP80: 0.5110, NoCoAP90: 0.2990, mNoCoAP: 0.6573 2022-12-01 04:56:58,705 - mmdet - INFO - Iter [2900/6924] lr: 1.000e-02, eta: 0:39:03, time: 0.984, data_time: 0.305, memory: 11931, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0004, loss_cls: 0.0047, acc: 99.7884, loss_bbox: 0.0090, loss: 0.0147 2022-12-01 04:57:29,204 - mmdet - INFO - Iter [2950/6924] lr: 1.000e-02, eta: 0:38:36, time: 0.607, data_time: 0.035, memory: 11931, loss_rpn_cls: 0.0012, loss_rpn_bbox: 0.0011, loss_cls: 0.0073, acc: 99.6865, loss_bbox: 0.0140, loss: 0.0237 2022-12-01 04:57:59,482 - mmdet - INFO - Exp name: faster_rcnn_r50_fpn_1x_sirst.py 2022-12-01 04:57:59,483 - mmdet - INFO - Iter [3000/6924] lr: 1.000e-02, eta: 0:38:08, time: 0.607, data_time: 0.026, memory: 11931, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0014, loss_cls: 0.0053, acc: 99.7842, loss_bbox: 0.0128, loss: 0.0203 2022-12-01 04:58:12,305 - mmdet - INFO - Iter [3050/6924] lr: 1.000e-02, eta: 0:37:18, time: 0.257, data_time: 0.013, memory: 11931, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0009, loss_cls: 0.0044, acc: 99.8125, loss_bbox: 0.0092, loss: 0.0150 2022-12-01 04:58:24,580 - mmdet - INFO - Iter [3100/6924] lr: 1.000e-02, eta: 0:36:29, time: 0.246, data_time: 0.021, memory: 11931, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0012, loss_cls: 0.0050, acc: 99.8027, loss_bbox: 0.0114, loss: 0.0182 2022-12-01 04:58:37,004 - mmdet - INFO - Iter [3150/6924] lr: 1.000e-02, eta: 0:35:41, time: 0.248, data_time: 0.020, memory: 11931, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0008, loss_cls: 0.0042, acc: 99.8438, loss_bbox: 0.0086, loss: 0.0145 2022-12-01 04:58:55,467 - mmdet - INFO - Iter [3200/6924] lr: 1.000e-02, eta: 0:35:01, time: 0.369, data_time: 0.016, memory: 11931, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0007, loss_cls: 0.0043, acc: 99.8252, loss_bbox: 0.0091, loss: 0.0148 2022-12-01 04:59:23,404 - mmdet - INFO - Iter [3250/6924] lr: 1.000e-02, eta: 0:34:32, time: 0.558, data_time: 0.018, memory: 11931, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0011, loss_cls: 0.0055, acc: 99.7725, loss_bbox: 0.0130, loss: 0.0205 2022-12-01 04:59:51,704 - mmdet - INFO - Iter [3300/6924] lr: 1.000e-02, eta: 0:34:04, time: 0.566, data_time: 0.044, memory: 11931, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0016, loss_cls: 0.0047, acc: 99.8193, loss_bbox: 0.0116, loss: 0.0186 2022-12-01 05:00:18,933 - mmdet - INFO - Iter [3350/6924] lr: 1.000e-02, eta: 0:33:35, time: 0.546, data_time: 0.018, memory: 11931, loss_rpn_cls: 0.0011, loss_rpn_bbox: 0.0007, loss_cls: 0.0046, acc: 99.8037, loss_bbox: 0.0091, loss: 0.0155 2022-12-01 05:00:54,674 - mmdet - INFO - Iter [3400/6924] lr: 1.000e-02, eta: 0:33:15, time: 0.715, data_time: 0.028, memory: 11931, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0011, loss_cls: 0.0052, acc: 99.7783, loss_bbox: 0.0103, loss: 0.0172 2022-12-01 05:01:27,922 - mmdet - INFO - Iter [3450/6924] lr: 1.000e-02, eta: 0:32:51, time: 0.665, data_time: 0.040, memory: 11931, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0011, loss_cls: 0.0052, acc: 99.7725, loss_bbox: 0.0098, loss: 0.0166 2022-12-01 05:01:36,907 - mmdet - INFO - Saving checkpoint at 6 epochs 2022-12-01 05:02:20,025 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 165 | 0.880 | 0.831 | +-------+-----+------+--------+-------+ | mAP | | | | 0.831 | +-------+-----+------+--------+-------+ 2022-12-01 05:02:32,152 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 165 | 0.880 | 0.824 | +-------+-----+------+--------+-------+ | mAP | | | | 0.824 | +-------+-----+------+--------+-------+ 2022-12-01 05:02:44,327 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 165 | 0.867 | 0.810 | +-------+-----+------+--------+-------+ | mAP | | | | 0.810 | +-------+-----+------+--------+-------+ 2022-12-01 05:02:53,108 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 165 | 0.833 | 0.765 | +-------+-----+------+--------+-------+ | mAP | | | | 0.765 | +-------+-----+------+--------+-------+ 2022-12-01 05:03:03,412 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 165 | 0.807 | 0.734 | +-------+-----+------+--------+-------+ | mAP | | | | 0.734 | +-------+-----+------+--------+-------+ 2022-12-01 05:03:14,450 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 165 | 0.760 | 0.673 | +-------+-----+------+--------+-------+ | mAP | | | | 0.673 | +-------+-----+------+--------+-------+ 2022-12-01 05:03:22,913 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 165 | 0.720 | 0.621 | +-------+-----+------+--------+-------+ | mAP | | | | 0.621 | +-------+-----+------+--------+-------+ 2022-12-01 05:03:32,513 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 165 | 0.613 | 0.491 | +-------+-----+------+--------+-------+ | mAP | | | | 0.491 | +-------+-----+------+--------+-------+ 2022-12-01 05:03:42,882 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 165 | 0.460 | 0.322 | +-------+-----+------+--------+-------+ | mAP | | | | 0.322 | +-------+-----+------+--------+-------+ 2022-12-01 05:03:42,886 - mmdet - INFO - Iter(val) [256] NoCoAP10: 0.8310, NoCoAP20: 0.8240, NoCoAP30: 0.8100, NoCoAP40: 0.7650, NoCoAP50: 0.7340, NoCoAP60: 0.6730, NoCoAP70: 0.6210, NoCoAP80: 0.4910, NoCoAP90: 0.3220, mNoCoAP: 0.6745 2022-12-01 05:04:09,720 - mmdet - INFO - Iter [3500/6924] lr: 1.000e-02, eta: 0:32:30, time: 0.705, data_time: 0.127, memory: 11931, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0014, loss_cls: 0.0060, acc: 99.7559, loss_bbox: 0.0131, loss: 0.0214 2022-12-01 05:04:45,706 - mmdet - INFO - Iter [3550/6924] lr: 1.000e-02, eta: 0:32:08, time: 0.720, data_time: 0.020, memory: 11931, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0011, loss_cls: 0.0056, acc: 99.7725, loss_bbox: 0.0112, loss: 0.0188 2022-12-01 05:05:06,531 - mmdet - INFO - Iter [3600/6924] lr: 1.000e-02, eta: 0:31:33, time: 0.416, data_time: 0.028, memory: 11931, loss_rpn_cls: 0.0010, loss_rpn_bbox: 0.0008, loss_cls: 0.0057, acc: 99.7832, loss_bbox: 0.0098, loss: 0.0173 2022-12-01 05:05:28,107 - mmdet - INFO - Iter [3650/6924] lr: 1.000e-02, eta: 0:30:58, time: 0.432, data_time: 0.022, memory: 11931, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0005, loss_cls: 0.0042, acc: 99.8301, loss_bbox: 0.0075, loss: 0.0130 2022-12-01 05:05:43,906 - mmdet - INFO - Iter [3700/6924] lr: 1.000e-02, eta: 0:30:19, time: 0.316, data_time: 0.026, memory: 11931, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0009, loss_cls: 0.0041, acc: 99.8320, loss_bbox: 0.0088, loss: 0.0144 2022-12-01 05:06:06,804 - mmdet - INFO - Iter [3750/6924] lr: 1.000e-02, eta: 0:29:46, time: 0.457, data_time: 0.020, memory: 11931, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0009, loss_cls: 0.0043, acc: 99.8193, loss_bbox: 0.0083, loss: 0.0139 2022-12-01 05:06:32,727 - mmdet - INFO - Iter [3800/6924] lr: 1.000e-02, eta: 0:29:16, time: 0.519, data_time: 0.018, memory: 11931, loss_rpn_cls: 0.0003, loss_rpn_bbox: 0.0007, loss_cls: 0.0049, acc: 99.7920, loss_bbox: 0.0096, loss: 0.0156 2022-12-01 05:07:01,825 - mmdet - INFO - Iter [3850/6924] lr: 1.000e-02, eta: 0:28:49, time: 0.583, data_time: 0.038, memory: 11931, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0010, loss_cls: 0.0054, acc: 99.7871, loss_bbox: 0.0104, loss: 0.0176 2022-12-01 05:07:30,104 - mmdet - INFO - Iter [3900/6924] lr: 1.000e-02, eta: 0:28:21, time: 0.565, data_time: 0.039, memory: 11931, loss_rpn_cls: 0.0015, loss_rpn_bbox: 0.0008, loss_cls: 0.0053, acc: 99.7930, loss_bbox: 0.0093, loss: 0.0169 2022-12-01 05:08:00,409 - mmdet - INFO - Iter [3950/6924] lr: 1.000e-02, eta: 0:27:54, time: 0.607, data_time: 0.019, memory: 11931, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0009, loss_cls: 0.0048, acc: 99.8037, loss_bbox: 0.0091, loss: 0.0153 2022-12-01 05:08:25,508 - mmdet - INFO - Exp name: faster_rcnn_r50_fpn_1x_sirst.py 2022-12-01 05:08:25,508 - mmdet - INFO - Iter [4000/6924] lr: 1.000e-02, eta: 0:27:24, time: 0.502, data_time: 0.019, memory: 11931, loss_rpn_cls: 0.0004, loss_rpn_bbox: 0.0013, loss_cls: 0.0050, acc: 99.7842, loss_bbox: 0.0119, loss: 0.0186 2022-12-01 05:08:50,963 - mmdet - INFO - Saving checkpoint at 7 epochs 2022-12-01 05:09:43,578 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 163 | 0.887 | 0.858 | +-------+-----+------+--------+-------+ | mAP | | | | 0.858 | +-------+-----+------+--------+-------+ 2022-12-01 05:10:04,233 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 163 | 0.880 | 0.852 | +-------+-----+------+--------+-------+ | mAP | | | | 0.852 | +-------+-----+------+--------+-------+ 2022-12-01 05:10:15,011 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 163 | 0.867 | 0.824 | +-------+-----+------+--------+-------+ | mAP | | | | 0.824 | +-------+-----+------+--------+-------+ 2022-12-01 05:10:28,217 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 163 | 0.840 | 0.794 | +-------+-----+------+--------+-------+ | mAP | | | | 0.794 | +-------+-----+------+--------+-------+ 2022-12-01 05:10:44,812 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 163 | 0.813 | 0.760 | +-------+-----+------+--------+-------+ | mAP | | | | 0.760 | +-------+-----+------+--------+-------+ 2022-12-01 05:10:59,893 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 163 | 0.767 | 0.694 | +-------+-----+------+--------+-------+ | mAP | | | | 0.694 | +-------+-----+------+--------+-------+ 2022-12-01 05:11:06,150 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 163 | 0.740 | 0.635 | +-------+-----+------+--------+-------+ | mAP | | | | 0.635 | +-------+-----+------+--------+-------+ 2022-12-01 05:11:10,257 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 163 | 0.627 | 0.491 | +-------+-----+------+--------+-------+ | mAP | | | | 0.491 | +-------+-----+------+--------+-------+ 2022-12-01 05:11:14,787 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 163 | 0.487 | 0.330 | +-------+-----+------+--------+-------+ | mAP | | | | 0.330 | +-------+-----+------+--------+-------+ 2022-12-01 05:11:14,789 - mmdet - INFO - Iter(val) [256] NoCoAP10: 0.8580, NoCoAP20: 0.8520, NoCoAP30: 0.8240, NoCoAP40: 0.7940, NoCoAP50: 0.7600, NoCoAP60: 0.6940, NoCoAP70: 0.6350, NoCoAP80: 0.4910, NoCoAP90: 0.3300, mNoCoAP: 0.6931 2022-12-01 05:11:22,838 - mmdet - INFO - Iter [4050/6924] lr: 1.000e-02, eta: 0:27:01, time: 0.710, data_time: 0.282, memory: 11931, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0008, loss_cls: 0.0070, acc: 99.6982, loss_bbox: 0.0141, loss: 0.0226 2022-12-01 05:11:44,720 - mmdet - INFO - Iter [4100/6924] lr: 1.000e-02, eta: 0:26:28, time: 0.438, data_time: 0.016, memory: 11931, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0010, loss_cls: 0.0049, acc: 99.7822, loss_bbox: 0.0103, loss: 0.0170 2022-12-01 05:12:10,321 - mmdet - INFO - Iter [4150/6924] lr: 1.000e-02, eta: 0:25:58, time: 0.512, data_time: 0.027, memory: 11931, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0010, loss_cls: 0.0045, acc: 99.8291, loss_bbox: 0.0098, loss: 0.0159 2022-12-01 05:12:35,722 - mmdet - INFO - Iter [4200/6924] lr: 1.000e-02, eta: 0:25:29, time: 0.508, data_time: 0.021, memory: 11931, loss_rpn_cls: 0.0011, loss_rpn_bbox: 0.0012, loss_cls: 0.0051, acc: 99.7949, loss_bbox: 0.0115, loss: 0.0189 2022-12-01 05:13:01,426 - mmdet - INFO - Iter [4250/6924] lr: 1.000e-02, eta: 0:24:59, time: 0.514, data_time: 0.033, memory: 11931, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0007, loss_cls: 0.0045, acc: 99.7949, loss_bbox: 0.0102, loss: 0.0160 2022-12-01 05:13:35,607 - mmdet - INFO - Iter [4300/6924] lr: 1.000e-02, eta: 0:24:35, time: 0.684, data_time: 0.031, memory: 11931, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0007, loss_cls: 0.0043, acc: 99.8281, loss_bbox: 0.0092, loss: 0.0149 2022-12-01 05:14:10,545 - mmdet - INFO - Iter [4350/6924] lr: 1.000e-02, eta: 0:24:11, time: 0.698, data_time: 0.059, memory: 11931, loss_rpn_cls: 0.0004, loss_rpn_bbox: 0.0006, loss_cls: 0.0046, acc: 99.8066, loss_bbox: 0.0094, loss: 0.0151 2022-12-01 05:14:39,752 - mmdet - INFO - Iter [4400/6924] lr: 1.000e-02, eta: 0:23:43, time: 0.585, data_time: 0.042, memory: 11931, loss_rpn_cls: 0.0004, loss_rpn_bbox: 0.0006, loss_cls: 0.0049, acc: 99.7959, loss_bbox: 0.0104, loss: 0.0162 2022-12-01 05:15:09,105 - mmdet - INFO - Iter [4450/6924] lr: 1.000e-02, eta: 0:23:16, time: 0.586, data_time: 0.018, memory: 11931, loss_rpn_cls: 0.0004, loss_rpn_bbox: 0.0008, loss_cls: 0.0041, acc: 99.8223, loss_bbox: 0.0089, loss: 0.0141 2022-12-01 05:15:46,049 - mmdet - INFO - Iter [4500/6924] lr: 1.000e-02, eta: 0:22:52, time: 0.740, data_time: 0.063, memory: 11931, loss_rpn_cls: 0.0003, loss_rpn_bbox: 0.0006, loss_cls: 0.0043, acc: 99.8213, loss_bbox: 0.0091, loss: 0.0143 2022-12-01 05:16:16,116 - mmdet - INFO - Iter [4550/6924] lr: 1.000e-02, eta: 0:22:25, time: 0.601, data_time: 0.030, memory: 11931, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0008, loss_cls: 0.0043, acc: 99.8301, loss_bbox: 0.0108, loss: 0.0167 2022-12-01 05:16:43,805 - mmdet - INFO - Iter [4600/6924] lr: 1.000e-02, eta: 0:21:56, time: 0.552, data_time: 0.023, memory: 11931, loss_rpn_cls: 0.0014, loss_rpn_bbox: 0.0011, loss_cls: 0.0059, acc: 99.7930, loss_bbox: 0.0091, loss: 0.0176 2022-12-01 05:16:51,969 - mmdet - INFO - Saving checkpoint at 8 epochs 2022-12-01 05:17:37,212 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 144 | 0.800 | 0.763 | +-------+-----+------+--------+-------+ | mAP | | | | 0.763 | +-------+-----+------+--------+-------+ 2022-12-01 05:17:55,066 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 144 | 0.780 | 0.737 | +-------+-----+------+--------+-------+ | mAP | | | | 0.737 | +-------+-----+------+--------+-------+ 2022-12-01 05:18:04,224 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 144 | 0.767 | 0.721 | +-------+-----+------+--------+-------+ | mAP | | | | 0.721 | +-------+-----+------+--------+-------+ 2022-12-01 05:18:18,246 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 144 | 0.733 | 0.679 | +-------+-----+------+--------+-------+ | mAP | | | | 0.679 | +-------+-----+------+--------+-------+ 2022-12-01 05:18:23,422 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 144 | 0.720 | 0.661 | +-------+-----+------+--------+-------+ | mAP | | | | 0.661 | +-------+-----+------+--------+-------+ 2022-12-01 05:18:28,720 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 144 | 0.667 | 0.594 | +-------+-----+------+--------+-------+ | mAP | | | | 0.594 | +-------+-----+------+--------+-------+ 2022-12-01 05:18:33,426 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 144 | 0.627 | 0.542 | +-------+-----+------+--------+-------+ | mAP | | | | 0.542 | +-------+-----+------+--------+-------+ 2022-12-01 05:18:38,145 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 144 | 0.500 | 0.393 | +-------+-----+------+--------+-------+ | mAP | | | | 0.393 | +-------+-----+------+--------+-------+ 2022-12-01 05:18:42,407 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 144 | 0.380 | 0.248 | +-------+-----+------+--------+-------+ | mAP | | | | 0.248 | +-------+-----+------+--------+-------+ 2022-12-01 05:18:42,410 - mmdet - INFO - Iter(val) [256] NoCoAP10: 0.7630, NoCoAP20: 0.7370, NoCoAP30: 0.7210, NoCoAP40: 0.6790, NoCoAP50: 0.6610, NoCoAP60: 0.5940, NoCoAP70: 0.5420, NoCoAP80: 0.3930, NoCoAP90: 0.2480, mNoCoAP: 0.5930 2022-12-01 05:19:01,208 - mmdet - INFO - Iter [4650/6924] lr: 1.000e-03, eta: 0:21:27, time: 0.551, data_time: 0.111, memory: 11931, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0008, loss_cls: 0.0034, acc: 99.8521, loss_bbox: 0.0080, loss: 0.0131 2022-12-01 05:19:29,205 - mmdet - INFO - Iter [4700/6924] lr: 1.000e-03, eta: 0:20:59, time: 0.559, data_time: 0.043, memory: 11931, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0008, loss_cls: 0.0033, acc: 99.8555, loss_bbox: 0.0065, loss: 0.0113 2022-12-01 05:20:01,422 - mmdet - INFO - Iter [4750/6924] lr: 1.000e-03, eta: 0:20:32, time: 0.646, data_time: 0.029, memory: 11931, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0007, loss_cls: 0.0038, acc: 99.8477, loss_bbox: 0.0079, loss: 0.0130 2022-12-01 05:20:27,110 - mmdet - INFO - Iter [4800/6924] lr: 1.000e-03, eta: 0:20:03, time: 0.513, data_time: 0.024, memory: 11931, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0004, loss_cls: 0.0042, acc: 99.8633, loss_bbox: 0.0063, loss: 0.0115 2022-12-01 05:20:56,135 - mmdet - INFO - Iter [4850/6924] lr: 1.000e-03, eta: 0:19:35, time: 0.581, data_time: 0.020, memory: 11931, loss_rpn_cls: 0.0004, loss_rpn_bbox: 0.0006, loss_cls: 0.0037, acc: 99.8525, loss_bbox: 0.0078, loss: 0.0125 2022-12-01 05:21:33,064 - mmdet - INFO - Iter [4900/6924] lr: 1.000e-03, eta: 0:19:10, time: 0.738, data_time: 0.042, memory: 11931, loss_rpn_cls: 0.0003, loss_rpn_bbox: 0.0005, loss_cls: 0.0034, acc: 99.8652, loss_bbox: 0.0064, loss: 0.0106 2022-12-01 05:22:05,117 - mmdet - INFO - Iter [4950/6924] lr: 1.000e-03, eta: 0:18:43, time: 0.642, data_time: 0.024, memory: 11931, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0006, loss_cls: 0.0035, acc: 99.8525, loss_bbox: 0.0072, loss: 0.0117 2022-12-01 05:22:44,513 - mmdet - INFO - Exp name: faster_rcnn_r50_fpn_1x_sirst.py 2022-12-01 05:22:44,513 - mmdet - INFO - Iter [5000/6924] lr: 1.000e-03, eta: 0:18:19, time: 0.788, data_time: 0.041, memory: 11931, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0006, loss_cls: 0.0049, acc: 99.8535, loss_bbox: 0.0078, loss: 0.0139 2022-12-01 05:23:15,107 - mmdet - INFO - Iter [5050/6924] lr: 1.000e-03, eta: 0:17:51, time: 0.611, data_time: 0.046, memory: 11931, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0005, loss_cls: 0.0043, acc: 99.8291, loss_bbox: 0.0078, loss: 0.0134 2022-12-01 05:23:45,012 - mmdet - INFO - Iter [5100/6924] lr: 1.000e-03, eta: 0:17:23, time: 0.598, data_time: 0.029, memory: 11931, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0006, loss_cls: 0.0034, acc: 99.8604, loss_bbox: 0.0066, loss: 0.0111 2022-12-01 05:24:15,435 - mmdet - INFO - Iter [5150/6924] lr: 1.000e-03, eta: 0:16:55, time: 0.609, data_time: 0.028, memory: 11931, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0004, loss_cls: 0.0037, acc: 99.8828, loss_bbox: 0.0056, loss: 0.0105 2022-12-01 05:24:39,789 - mmdet - INFO - Saving checkpoint at 9 epochs 2022-12-01 05:25:06,709 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 154 | 0.867 | 0.837 | +-------+-----+------+--------+-------+ | mAP | | | | 0.837 | +-------+-----+------+--------+-------+ 2022-12-01 05:25:10,754 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 154 | 0.867 | 0.837 | +-------+-----+------+--------+-------+ | mAP | | | | 0.837 | +-------+-----+------+--------+-------+ 2022-12-01 05:25:15,547 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 154 | 0.847 | 0.814 | +-------+-----+------+--------+-------+ | mAP | | | | 0.814 | +-------+-----+------+--------+-------+ 2022-12-01 05:25:21,765 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 154 | 0.820 | 0.781 | +-------+-----+------+--------+-------+ | mAP | | | | 0.781 | +-------+-----+------+--------+-------+ 2022-12-01 05:25:37,881 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 154 | 0.787 | 0.737 | +-------+-----+------+--------+-------+ | mAP | | | | 0.737 | +-------+-----+------+--------+-------+ 2022-12-01 05:25:52,359 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 154 | 0.740 | 0.674 | +-------+-----+------+--------+-------+ | mAP | | | | 0.674 | +-------+-----+------+--------+-------+ 2022-12-01 05:25:57,117 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 154 | 0.720 | 0.650 | +-------+-----+------+--------+-------+ | mAP | | | | 0.650 | +-------+-----+------+--------+-------+ 2022-12-01 05:26:22,058 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 154 | 0.620 | 0.507 | +-------+-----+------+--------+-------+ | mAP | | | | 0.507 | +-------+-----+------+--------+-------+ 2022-12-01 05:26:41,383 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 154 | 0.487 | 0.347 | +-------+-----+------+--------+-------+ | mAP | | | | 0.347 | +-------+-----+------+--------+-------+ 2022-12-01 05:26:41,386 - mmdet - INFO - Iter(val) [256] NoCoAP10: 0.8370, NoCoAP20: 0.8370, NoCoAP30: 0.8140, NoCoAP40: 0.7810, NoCoAP50: 0.7370, NoCoAP60: 0.6740, NoCoAP70: 0.6500, NoCoAP80: 0.5070, NoCoAP90: 0.3470, mNoCoAP: 0.6872 2022-12-01 05:26:48,404 - mmdet - INFO - Iter [5200/6924] lr: 1.000e-03, eta: 0:16:33, time: 0.977, data_time: 0.530, memory: 11931, loss_rpn_cls: 0.0004, loss_rpn_bbox: 0.0002, loss_cls: 0.0019, acc: 99.9163, loss_bbox: 0.0034, loss: 0.0059 2022-12-01 05:27:12,714 - mmdet - INFO - Iter [5250/6924] lr: 1.000e-03, eta: 0:16:03, time: 0.487, data_time: 0.022, memory: 11931, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0006, loss_cls: 0.0043, acc: 99.8281, loss_bbox: 0.0077, loss: 0.0133 2022-12-01 05:27:40,710 - mmdet - INFO - Iter [5300/6924] lr: 1.000e-03, eta: 0:15:34, time: 0.560, data_time: 0.017, memory: 11931, loss_rpn_cls: 0.0004, loss_rpn_bbox: 0.0005, loss_cls: 0.0036, acc: 99.8438, loss_bbox: 0.0069, loss: 0.0114 2022-12-01 05:28:07,938 - mmdet - INFO - Iter [5350/6924] lr: 1.000e-03, eta: 0:15:04, time: 0.544, data_time: 0.015, memory: 11931, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0007, loss_cls: 0.0040, acc: 99.8594, loss_bbox: 0.0073, loss: 0.0126 2022-12-01 05:28:34,227 - mmdet - INFO - Iter [5400/6924] lr: 1.000e-03, eta: 0:14:35, time: 0.526, data_time: 0.013, memory: 11931, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0006, loss_cls: 0.0034, acc: 99.8652, loss_bbox: 0.0068, loss: 0.0115 2022-12-01 05:29:02,559 - mmdet - INFO - Iter [5450/6924] lr: 1.000e-03, eta: 0:14:06, time: 0.567, data_time: 0.027, memory: 11931, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0004, loss_cls: 0.0033, acc: 99.8887, loss_bbox: 0.0061, loss: 0.0103 2022-12-01 05:29:40,007 - mmdet - INFO - Iter [5500/6924] lr: 1.000e-03, eta: 0:13:40, time: 0.749, data_time: 0.029, memory: 11931, loss_rpn_cls: 0.0003, loss_rpn_bbox: 0.0006, loss_cls: 0.0033, acc: 99.8818, loss_bbox: 0.0072, loss: 0.0115 2022-12-01 05:30:12,710 - mmdet - INFO - Iter [5550/6924] lr: 1.000e-03, eta: 0:13:12, time: 0.654, data_time: 0.040, memory: 11931, loss_rpn_cls: 0.0004, loss_rpn_bbox: 0.0003, loss_cls: 0.0030, acc: 99.8711, loss_bbox: 0.0056, loss: 0.0093 2022-12-01 05:30:40,509 - mmdet - INFO - Iter [5600/6924] lr: 1.000e-03, eta: 0:12:43, time: 0.556, data_time: 0.025, memory: 11931, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0004, loss_cls: 0.0034, acc: 99.8555, loss_bbox: 0.0062, loss: 0.0104 2022-12-01 05:31:07,212 - mmdet - INFO - Iter [5650/6924] lr: 1.000e-03, eta: 0:12:13, time: 0.534, data_time: 0.026, memory: 11931, loss_rpn_cls: 0.0003, loss_rpn_bbox: 0.0004, loss_cls: 0.0035, acc: 99.8496, loss_bbox: 0.0067, loss: 0.0110 2022-12-01 05:31:29,806 - mmdet - INFO - Iter [5700/6924] lr: 1.000e-03, eta: 0:11:43, time: 0.452, data_time: 0.014, memory: 11931, loss_rpn_cls: 0.0003, loss_rpn_bbox: 0.0006, loss_cls: 0.0041, acc: 99.8359, loss_bbox: 0.0074, loss: 0.0124 2022-12-01 05:31:53,226 - mmdet - INFO - Iter [5750/6924] lr: 1.000e-03, eta: 0:11:14, time: 0.468, data_time: 0.017, memory: 11931, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0006, loss_cls: 0.0036, acc: 99.8525, loss_bbox: 0.0071, loss: 0.0122 2022-12-01 05:32:03,048 - mmdet - INFO - Saving checkpoint at 10 epochs 2022-12-01 05:32:49,134 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 157 | 0.880 | 0.855 | +-------+-----+------+--------+-------+ | mAP | | | | 0.855 | +-------+-----+------+--------+-------+ 2022-12-01 05:33:14,237 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 157 | 0.880 | 0.855 | +-------+-----+------+--------+-------+ | mAP | | | | 0.855 | +-------+-----+------+--------+-------+ 2022-12-01 05:33:36,416 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 157 | 0.853 | 0.824 | +-------+-----+------+--------+-------+ | mAP | | | | 0.824 | +-------+-----+------+--------+-------+ 2022-12-01 05:34:00,219 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 157 | 0.833 | 0.798 | +-------+-----+------+--------+-------+ | mAP | | | | 0.798 | +-------+-----+------+--------+-------+ 2022-12-01 05:34:19,967 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 157 | 0.793 | 0.744 | +-------+-----+------+--------+-------+ | mAP | | | | 0.744 | +-------+-----+------+--------+-------+ 2022-12-01 05:34:40,408 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 157 | 0.740 | 0.670 | +-------+-----+------+--------+-------+ | mAP | | | | 0.670 | +-------+-----+------+--------+-------+ 2022-12-01 05:35:02,204 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 157 | 0.713 | 0.633 | +-------+-----+------+--------+-------+ | mAP | | | | 0.633 | +-------+-----+------+--------+-------+ 2022-12-01 05:35:27,454 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 157 | 0.613 | 0.508 | +-------+-----+------+--------+-------+ | mAP | | | | 0.508 | +-------+-----+------+--------+-------+ 2022-12-01 05:35:52,436 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 157 | 0.487 | 0.339 | +-------+-----+------+--------+-------+ | mAP | | | | 0.339 | +-------+-----+------+--------+-------+ 2022-12-01 05:35:52,438 - mmdet - INFO - Iter(val) [256] NoCoAP10: 0.8550, NoCoAP20: 0.8550, NoCoAP30: 0.8240, NoCoAP40: 0.7980, NoCoAP50: 0.7440, NoCoAP60: 0.6700, NoCoAP70: 0.6330, NoCoAP80: 0.5080, NoCoAP90: 0.3390, mNoCoAP: 0.6918 2022-12-01 05:36:12,305 - mmdet - INFO - Iter [5800/6924] lr: 1.000e-03, eta: 0:10:46, time: 0.658, data_time: 0.137, memory: 11931, loss_rpn_cls: 0.0003, loss_rpn_bbox: 0.0004, loss_cls: 0.0034, acc: 99.8682, loss_bbox: 0.0060, loss: 0.0101 2022-12-01 05:36:39,617 - mmdet - INFO - Iter [5850/6924] lr: 1.000e-03, eta: 0:10:17, time: 0.548, data_time: 0.026, memory: 11931, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0005, loss_cls: 0.0047, acc: 99.8164, loss_bbox: 0.0074, loss: 0.0134 2022-12-01 05:37:08,804 - mmdet - INFO - Iter [5900/6924] lr: 1.000e-03, eta: 0:09:48, time: 0.583, data_time: 0.014, memory: 11931, loss_rpn_cls: 0.0003, loss_rpn_bbox: 0.0005, loss_cls: 0.0029, acc: 99.8857, loss_bbox: 0.0062, loss: 0.0100 2022-12-01 05:37:30,449 - mmdet - INFO - Iter [5950/6924] lr: 1.000e-03, eta: 0:09:18, time: 0.434, data_time: 0.033, memory: 11931, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0004, loss_cls: 0.0043, acc: 99.8311, loss_bbox: 0.0073, loss: 0.0124 2022-12-01 05:37:52,441 - mmdet - INFO - Exp name: faster_rcnn_r50_fpn_1x_sirst.py 2022-12-01 05:37:52,441 - mmdet - INFO - Iter [6000/6924] lr: 1.000e-03, eta: 0:08:48, time: 0.440, data_time: 0.031, memory: 11931, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0005, loss_cls: 0.0033, acc: 99.8555, loss_bbox: 0.0060, loss: 0.0104 2022-12-01 05:38:15,593 - mmdet - INFO - Iter [6050/6924] lr: 1.000e-03, eta: 0:08:19, time: 0.463, data_time: 0.052, memory: 11931, loss_rpn_cls: 0.0004, loss_rpn_bbox: 0.0003, loss_cls: 0.0029, acc: 99.8789, loss_bbox: 0.0048, loss: 0.0085 2022-12-01 05:38:40,332 - mmdet - INFO - Iter [6100/6924] lr: 1.000e-03, eta: 0:07:50, time: 0.494, data_time: 0.029, memory: 11931, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0004, loss_cls: 0.0043, acc: 99.8389, loss_bbox: 0.0073, loss: 0.0127 2022-12-01 05:39:10,217 - mmdet - INFO - Iter [6150/6924] lr: 1.000e-03, eta: 0:07:21, time: 0.598, data_time: 0.034, memory: 11931, loss_rpn_cls: 0.0002, loss_rpn_bbox: 0.0004, loss_cls: 0.0029, acc: 99.8770, loss_bbox: 0.0057, loss: 0.0092 2022-12-01 05:39:36,804 - mmdet - INFO - Iter [6200/6924] lr: 1.000e-03, eta: 0:06:53, time: 0.530, data_time: 0.025, memory: 11931, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0005, loss_cls: 0.0030, acc: 99.8730, loss_bbox: 0.0052, loss: 0.0092 2022-12-01 05:40:04,816 - mmdet - INFO - Iter [6250/6924] lr: 1.000e-03, eta: 0:06:24, time: 0.562, data_time: 0.024, memory: 11931, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0007, loss_cls: 0.0046, acc: 99.7998, loss_bbox: 0.0078, loss: 0.0136 2022-12-01 05:40:37,215 - mmdet - INFO - Iter [6300/6924] lr: 1.000e-03, eta: 0:05:56, time: 0.647, data_time: 0.029, memory: 11931, loss_rpn_cls: 0.0004, loss_rpn_bbox: 0.0005, loss_cls: 0.0034, acc: 99.8662, loss_bbox: 0.0071, loss: 0.0114 2022-12-01 05:41:10,836 - mmdet - INFO - Saving checkpoint at 11 epochs 2022-12-01 05:42:11,605 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 154 | 0.873 | 0.847 | +-------+-----+------+--------+-------+ | mAP | | | | 0.847 | +-------+-----+------+--------+-------+ 2022-12-01 05:42:30,557 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 154 | 0.873 | 0.847 | +-------+-----+------+--------+-------+ | mAP | | | | 0.847 | +-------+-----+------+--------+-------+ 2022-12-01 05:42:54,343 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 154 | 0.840 | 0.809 | +-------+-----+------+--------+-------+ | mAP | | | | 0.809 | +-------+-----+------+--------+-------+ 2022-12-01 05:43:16,546 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 154 | 0.820 | 0.784 | +-------+-----+------+--------+-------+ | mAP | | | | 0.784 | +-------+-----+------+--------+-------+ 2022-12-01 05:43:41,055 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 154 | 0.787 | 0.737 | +-------+-----+------+--------+-------+ | mAP | | | | 0.737 | +-------+-----+------+--------+-------+ 2022-12-01 05:43:49,431 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 154 | 0.733 | 0.660 | +-------+-----+------+--------+-------+ | mAP | | | | 0.660 | +-------+-----+------+--------+-------+ 2022-12-01 05:43:54,436 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 154 | 0.707 | 0.626 | +-------+-----+------+--------+-------+ | mAP | | | | 0.626 | +-------+-----+------+--------+-------+ 2022-12-01 05:43:59,654 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 154 | 0.600 | 0.497 | +-------+-----+------+--------+-------+ | mAP | | | | 0.497 | +-------+-----+------+--------+-------+ 2022-12-01 05:44:06,305 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 154 | 0.467 | 0.324 | +-------+-----+------+--------+-------+ | mAP | | | | 0.324 | +-------+-----+------+--------+-------+ 2022-12-01 05:44:06,308 - mmdet - INFO - Iter(val) [256] NoCoAP10: 0.8470, NoCoAP20: 0.8470, NoCoAP30: 0.8090, NoCoAP40: 0.7840, NoCoAP50: 0.7370, NoCoAP60: 0.6600, NoCoAP70: 0.6260, NoCoAP80: 0.4970, NoCoAP90: 0.3240, mNoCoAP: 0.6812 2022-12-01 05:44:10,422 - mmdet - INFO - Iter [6350/6924] lr: 1.000e-04, eta: 0:05:31, time: 1.351, data_time: 1.026, memory: 11931, loss_rpn_cls: 0.0000, loss_rpn_bbox: 0.0003, loss_cls: 0.0047, acc: 99.8698, loss_bbox: 0.0095, loss: 0.0145 2022-12-01 05:44:41,813 - mmdet - INFO - Iter [6400/6924] lr: 1.000e-04, eta: 0:05:02, time: 0.628, data_time: 0.025, memory: 11931, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0004, loss_cls: 0.0038, acc: 99.8398, loss_bbox: 0.0062, loss: 0.0109 2022-12-01 05:45:08,517 - mmdet - INFO - Iter [6450/6924] lr: 1.000e-04, eta: 0:04:33, time: 0.534, data_time: 0.035, memory: 11931, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0005, loss_cls: 0.0040, acc: 99.8389, loss_bbox: 0.0068, loss: 0.0118 2022-12-01 05:45:30,127 - mmdet - INFO - Iter [6500/6924] lr: 1.000e-04, eta: 0:04:04, time: 0.432, data_time: 0.017, memory: 11931, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0004, loss_cls: 0.0028, acc: 99.8799, loss_bbox: 0.0055, loss: 0.0093 2022-12-01 05:45:49,904 - mmdet - INFO - Iter [6550/6924] lr: 1.000e-04, eta: 0:03:35, time: 0.395, data_time: 0.023, memory: 11931, loss_rpn_cls: 0.0003, loss_rpn_bbox: 0.0005, loss_cls: 0.0035, acc: 99.8594, loss_bbox: 0.0063, loss: 0.0107 2022-12-01 05:46:09,704 - mmdet - INFO - Iter [6600/6924] lr: 1.000e-04, eta: 0:03:05, time: 0.396, data_time: 0.015, memory: 11931, loss_rpn_cls: 0.0005, loss_rpn_bbox: 0.0005, loss_cls: 0.0033, acc: 99.8643, loss_bbox: 0.0067, loss: 0.0111 2022-12-01 05:46:32,120 - mmdet - INFO - Iter [6650/6924] lr: 1.000e-04, eta: 0:02:36, time: 0.449, data_time: 0.028, memory: 11931, loss_rpn_cls: 0.0004, loss_rpn_bbox: 0.0004, loss_cls: 0.0036, acc: 99.8574, loss_bbox: 0.0054, loss: 0.0098 2022-12-01 05:47:02,842 - mmdet - INFO - Iter [6700/6924] lr: 1.000e-04, eta: 0:02:08, time: 0.615, data_time: 0.036, memory: 11931, loss_rpn_cls: 0.0004, loss_rpn_bbox: 0.0004, loss_cls: 0.0031, acc: 99.8760, loss_bbox: 0.0066, loss: 0.0106 2022-12-01 05:47:33,404 - mmdet - INFO - Iter [6750/6924] lr: 1.000e-04, eta: 0:01:39, time: 0.610, data_time: 0.016, memory: 11931, loss_rpn_cls: 0.0003, loss_rpn_bbox: 0.0004, loss_cls: 0.0035, acc: 99.8643, loss_bbox: 0.0066, loss: 0.0107 2022-12-01 05:48:02,074 - mmdet - INFO - Iter [6800/6924] lr: 1.000e-04, eta: 0:01:11, time: 0.574, data_time: 0.046, memory: 11931, loss_rpn_cls: 0.0002, loss_rpn_bbox: 0.0004, loss_cls: 0.0030, acc: 99.8848, loss_bbox: 0.0053, loss: 0.0089 2022-12-01 05:48:29,708 - mmdet - INFO - Iter [6850/6924] lr: 1.000e-04, eta: 0:00:42, time: 0.552, data_time: 0.017, memory: 11931, loss_rpn_cls: 0.0006, loss_rpn_bbox: 0.0005, loss_cls: 0.0038, acc: 99.8535, loss_bbox: 0.0064, loss: 0.0114 2022-12-01 05:48:58,005 - mmdet - INFO - Iter [6900/6924] lr: 1.000e-04, eta: 0:00:13, time: 0.563, data_time: 0.022, memory: 11931, loss_rpn_cls: 0.0004, loss_rpn_bbox: 0.0003, loss_cls: 0.0034, acc: 99.8672, loss_bbox: 0.0062, loss: 0.0105 2022-12-01 05:49:11,135 - mmdet - INFO - Saving checkpoint at 12 epochs 2022-12-01 05:49:30,536 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 151 | 0.847 | 0.826 | +-------+-----+------+--------+-------+ | mAP | | | | 0.826 | +-------+-----+------+--------+-------+ 2022-12-01 05:49:34,516 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 151 | 0.847 | 0.826 | +-------+-----+------+--------+-------+ | mAP | | | | 0.826 | +-------+-----+------+--------+-------+ 2022-12-01 05:49:38,923 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 151 | 0.820 | 0.796 | +-------+-----+------+--------+-------+ | mAP | | | | 0.796 | +-------+-----+------+--------+-------+ 2022-12-01 05:49:43,023 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 151 | 0.793 | 0.765 | +-------+-----+------+--------+-------+ | mAP | | | | 0.765 | +-------+-----+------+--------+-------+ 2022-12-01 05:49:47,133 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 151 | 0.767 | 0.724 | +-------+-----+------+--------+-------+ | mAP | | | | 0.724 | +-------+-----+------+--------+-------+ 2022-12-01 05:49:51,738 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 151 | 0.720 | 0.656 | +-------+-----+------+--------+-------+ | mAP | | | | 0.656 | +-------+-----+------+--------+-------+ 2022-12-01 05:49:55,860 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 151 | 0.687 | 0.608 | +-------+-----+------+--------+-------+ | mAP | | | | 0.608 | +-------+-----+------+--------+-------+ 2022-12-01 05:49:59,928 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 151 | 0.593 | 0.494 | +-------+-----+------+--------+-------+ | mAP | | | | 0.494 | +-------+-----+------+--------+-------+ 2022-12-01 05:50:04,431 - mmdet - INFO - +-------+-----+------+--------+-------+ | class | gts | dets | recall | ap | +-------+-----+------+--------+-------+ | 0 | 150 | 151 | 0.460 | 0.332 | +-------+-----+------+--------+-------+ | mAP | | | | 0.332 | +-------+-----+------+--------+-------+ 2022-12-01 05:50:04,623 - mmdet - INFO - Iter(val) [256] NoCoAP10: 0.8260, NoCoAP20: 0.8260, NoCoAP30: 0.7960, NoCoAP40: 0.7650, NoCoAP50: 0.7240, NoCoAP60: 0.6560, NoCoAP70: 0.6080, NoCoAP80: 0.4940, NoCoAP90: 0.3320, mNoCoAP: 0.6696