2022/11/02 11:59:18 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.7.13 (default, Mar 29 2022, 02:18:16) [GCC 7.5.0] CUDA available: True numpy_random_seed: 335192945 GPU 0: NVIDIA A100-SXM4-80GB CUDA_HOME: /mnt/cache/share/cuda-11.1 NVCC: Cuda compilation tools, release 11.1, V11.1.74 GCC: gcc (GCC) 5.4.0 PyTorch: 1.9.0+cu111 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb) - OpenMP 201511 (a.k.a. OpenMP 4.5) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.1 - 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 8.0.5 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -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 -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.10.0+cu111 OpenCV: 4.6.0 MMEngine: 0.2.0 Runtime environment: cudnn_benchmark: True mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None Distributed launcher: slurm Distributed training: True GPU number: 1 ------------------------------------------------------------ 2022/11/02 11:59:18 - mmengine - INFO - Config: file_client_args = dict(backend='disk') model = dict( type='DBNet', backbone=dict( type='mmdet.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='FPNC', in_channels=[256, 512, 1024, 2048], lateral_channels=256), det_head=dict( type='DBHead', in_channels=256, module_loss=dict(type='DBModuleLoss'), postprocessor=dict(type='DBPostprocessor', text_repr_type='quad')), data_preprocessor=dict( type='TextDetDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], bgr_to_rgb=True, pad_size_divisor=32)) train_pipeline = [ dict( type='LoadImageFromFile', file_client_args=dict(backend='disk'), color_type='color_ignore_orientation'), dict( type='LoadOCRAnnotations', with_bbox=True, with_polygon=True, with_label=True), dict( type='TorchVisionWrapper', op='ColorJitter', brightness=0.12549019607843137, saturation=0.5), dict( type='ImgAugWrapper', args=[['Fliplr', 0.5], { 'cls': 'Affine', 'rotate': [-10, 10] }, ['Resize', [0.5, 3.0]]]), dict(type='RandomCrop', min_side_ratio=0.1), dict(type='Resize', scale=(640, 640), keep_ratio=True), dict(type='Pad', size=(640, 640)), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape')) ] test_pipeline = [ dict( type='LoadImageFromFile', file_client_args=dict(backend='disk'), color_type='color_ignore_orientation'), dict(type='Resize', scale=(4068, 1024), keep_ratio=True), dict( type='LoadOCRAnnotations', with_polygon=True, with_bbox=True, with_label=True), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor')) ] ic15_det_data_root = 'data/det/icdar2015' ic15_det_train = dict( type='OCRDataset', data_root='data/det/icdar2015', ann_file='instances_training.json', data_prefix=dict(img_path='imgs/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=[ dict( type='LoadImageFromFile', file_client_args=dict(backend='disk'), color_type='color_ignore_orientation'), dict( type='LoadOCRAnnotations', with_bbox=True, with_polygon=True, with_label=True), dict( type='TorchVisionWrapper', op='ColorJitter', brightness=0.12549019607843137, saturation=0.5), dict( type='ImgAugWrapper', args=[['Fliplr', 0.5], { 'cls': 'Affine', 'rotate': [-10, 10] }, ['Resize', [0.5, 3.0]]]), dict(type='RandomCrop', min_side_ratio=0.1), dict(type='Resize', scale=(640, 640), keep_ratio=True), dict(type='Pad', size=(640, 640)), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape')) ]) ic15_det_test = dict( type='OCRDataset', data_root='data/det/icdar2015', ann_file='instances_test.json', data_prefix=dict(img_path='imgs/'), test_mode=True, pipeline=[ dict( type='LoadImageFromFile', file_client_args=dict(backend='disk'), color_type='color_ignore_orientation'), dict(type='Resize', scale=(4068, 1024), keep_ratio=True), dict( type='LoadOCRAnnotations', with_polygon=True, with_bbox=True, with_label=True), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor')) ]) default_scope = 'mmocr' env_cfg = dict( cudnn_benchmark=True, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) randomness = dict(seed=None) default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=5), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict( type='CheckpointHook', interval=20, out_dir='sproject:s3://oclip'), sampler_seed=dict(type='DistSamplerSeedHook'), sync_buffer=dict(type='SyncBuffersHook'), visualization=dict( type='VisualizationHook', interval=1, enable=False, show=False, draw_gt=False, draw_pred=False)) log_level = 'INFO' log_processor = dict(type='LogProcessor', window_size=10, by_epoch=True) load_from = None resume = False val_evaluator = dict(type='HmeanIOUMetric') test_evaluator = dict(type='HmeanIOUMetric') vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='TextDetLocalVisualizer', name='visualizer', vis_backends=[dict(type='LocalVisBackend')]) optim_wrapper = dict( type='OptimWrapper', optimizer=dict(type='SGD', lr=0.002, momentum=0.9, weight_decay=0.0001)) train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=1200, val_interval=20) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') param_scheduler = [ dict(type='LinearLR', end=100, start_factor=0.001), dict(type='PolyLR', power=0.9, eta_min=1e-07, begin=100, end=1200) ] train_dataloader = dict( batch_size=16, num_workers=24, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/det/icdar2015', ann_file='instances_training.json', data_prefix=dict(img_path='imgs/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=[ dict( type='LoadImageFromFile', file_client_args=dict(backend='petrel'), color_type='color_ignore_orientation'), dict( type='LoadOCRAnnotations', with_bbox=True, with_polygon=True, with_label=True), dict( type='TorchVisionWrapper', op='ColorJitter', brightness=0.12549019607843137, saturation=0.5), dict( type='ImgAugWrapper', args=[['Fliplr', 0.5], { 'cls': 'Affine', 'rotate': [-10, 10] }, ['Resize', [0.5, 3.0]]]), dict(type='RandomCrop', min_side_ratio=0.1), dict(type='Resize', scale=(640, 640), keep_ratio=True), dict(type='Pad', size=(640, 640)), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape')) ])) val_dataloader = dict( batch_size=1, num_workers=4, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/det/icdar2015', ann_file='instances_test.json', data_prefix=dict(img_path='imgs/'), test_mode=True, pipeline=[ dict( type='LoadImageFromFile', file_client_args=dict(backend='petrel'), color_type='color_ignore_orientation'), dict(type='Resize', scale=(4068, 1024), keep_ratio=True), dict( type='LoadOCRAnnotations', with_polygon=True, with_bbox=True, with_label=True), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor')) ])) test_dataloader = dict( batch_size=1, num_workers=4, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/det/icdar2015', ann_file='instances_test.json', data_prefix=dict(img_path='imgs/'), test_mode=True, pipeline=[ dict( type='LoadImageFromFile', file_client_args=dict(backend='petrel'), color_type='color_ignore_orientation'), dict(type='Resize', scale=(4068, 1024), keep_ratio=True), dict( type='LoadOCRAnnotations', with_polygon=True, with_bbox=True, with_label=True), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor')) ])) auto_scale_lr = dict(base_batch_size=16) launcher = 'slurm' work_dir = './work_dirs/dbnet_resnet50_1200e_icdar2015' 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]): Initialized by user-defined `init_weights` in ConvModule neck.lateral_convs.1.conv.weight - torch.Size([256, 512, 1, 1]): Initialized by user-defined `init_weights` in ConvModule neck.lateral_convs.2.conv.weight - torch.Size([256, 1024, 1, 1]): Initialized by user-defined `init_weights` in ConvModule neck.lateral_convs.3.conv.weight - torch.Size([256, 2048, 1, 1]): Initialized by user-defined `init_weights` in ConvModule neck.smooth_convs.0.conv.weight - torch.Size([64, 256, 3, 3]): Initialized by user-defined `init_weights` in ConvModule neck.smooth_convs.1.conv.weight - torch.Size([64, 256, 3, 3]): Initialized by user-defined `init_weights` in ConvModule neck.smooth_convs.2.conv.weight - torch.Size([64, 256, 3, 3]): Initialized by user-defined `init_weights` in ConvModule neck.smooth_convs.3.conv.weight - torch.Size([64, 256, 3, 3]): Initialized by user-defined `init_weights` in ConvModule det_head.binarize.0.weight - torch.Size([64, 256, 3, 3]): The value is the same before and after calling `init_weights` of DBNet det_head.binarize.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of DBNet det_head.binarize.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DBNet det_head.binarize.3.weight - torch.Size([64, 64, 2, 2]): The value is the same before and after calling `init_weights` of DBNet det_head.binarize.3.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DBNet det_head.binarize.4.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of DBNet det_head.binarize.4.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DBNet det_head.binarize.6.weight - torch.Size([64, 1, 2, 2]): The value is the same before and after calling `init_weights` of DBNet det_head.binarize.6.bias - torch.Size([1]): The value is the same before and after calling `init_weights` of DBNet det_head.threshold.0.weight - torch.Size([64, 256, 3, 3]): The value is the same before and after calling `init_weights` of DBNet det_head.threshold.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of DBNet det_head.threshold.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DBNet det_head.threshold.3.weight - torch.Size([64, 64, 2, 2]): The value is the same before and after calling `init_weights` of DBNet det_head.threshold.3.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DBNet det_head.threshold.4.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of DBNet det_head.threshold.4.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DBNet det_head.threshold.6.weight - torch.Size([64, 1, 2, 2]): The value is the same before and after calling `init_weights` of DBNet det_head.threshold.6.bias - torch.Size([1]): The value is the same before and after calling `init_weights` of DBNet 2022/11/02 12:09:03 - mmengine - INFO - Epoch(train) [1][5/63] lr: 2.0000e-06 memory: 47141 data_time: 86.8242 loss: 23.8279 loss_prob: 19.6291 loss_thr: 3.2120 loss_db: 0.9868 time: 89.0249 2022/11/02 12:09:05 - mmengine - INFO - Epoch(train) [1][10/63] lr: 2.0000e-06 eta: 39 days, 3:37:05 time: 44.7496 data_time: 43.4147 memory: 14901 loss: 23.9391 loss_prob: 19.8101 loss_thr: 3.1407 loss_db: 0.9884 2022/11/02 12:09:08 - mmengine - INFO - Epoch(train) [1][15/63] lr: 2.0000e-06 eta: 39 days, 3:37:05 time: 0.4688 data_time: 0.0047 memory: 14901 loss: 23.4517 loss_prob: 19.3932 loss_thr: 3.0679 loss_db: 0.9905 2022/11/02 12:09:10 - mmengine - INFO - Epoch(train) [1][20/63] lr: 2.0000e-06 eta: 19 days, 18:27:18 time: 0.4485 data_time: 0.0045 memory: 14901 loss: 22.5083 loss_prob: 18.4504 loss_thr: 3.0660 loss_db: 0.9920 2022/11/02 12:09:13 - mmengine - INFO - Epoch(train) [1][25/63] lr: 2.0000e-06 eta: 19 days, 18:27:18 time: 0.4760 data_time: 0.0062 memory: 14901 loss: 21.3476 loss_prob: 17.2543 loss_thr: 3.1008 loss_db: 0.9926 2022/11/02 12:09:15 - mmengine - INFO - Epoch(train) [1][30/63] lr: 2.0000e-06 eta: 13 days, 7:58:03 time: 0.5297 data_time: 0.0177 memory: 14901 loss: 19.6873 loss_prob: 15.6127 loss_thr: 3.0828 loss_db: 0.9918 2022/11/02 12:09:18 - mmengine - INFO - Epoch(train) [1][35/63] lr: 2.0000e-06 eta: 13 days, 7:58:03 time: 0.5498 data_time: 0.0187 memory: 14901 loss: 18.4869 loss_prob: 14.5300 loss_thr: 2.9656 loss_db: 0.9913 2022/11/02 12:09:22 - mmengine - INFO - Epoch(train) [1][40/63] lr: 2.0000e-06 eta: 10 days, 3:18:16 time: 0.6404 data_time: 0.0072 memory: 14901 loss: 17.4091 loss_prob: 13.5187 loss_thr: 2.9005 loss_db: 0.9899 2022/11/02 12:09:24 - mmengine - INFO - Epoch(train) [1][45/63] lr: 2.0000e-06 eta: 10 days, 3:18:16 time: 0.6045 data_time: 0.0052 memory: 14901 loss: 16.6028 loss_prob: 12.7498 loss_thr: 2.8635 loss_db: 0.9894 2022/11/02 12:09:28 - mmengine - INFO - Epoch(train) [1][50/63] lr: 2.0000e-06 eta: 8 days, 5:12:44 time: 0.6182 data_time: 0.0232 memory: 14901 loss: 16.2948 loss_prob: 12.4761 loss_thr: 2.8294 loss_db: 0.9893 2022/11/02 12:09:30 - mmengine - INFO - Epoch(train) [1][55/63] lr: 2.0000e-06 eta: 8 days, 5:12:44 time: 0.6431 data_time: 0.0255 memory: 14901 loss: 16.1171 loss_prob: 12.2657 loss_thr: 2.8611 loss_db: 0.9903 2022/11/02 12:09:33 - mmengine - INFO - Epoch(train) [1][60/63] lr: 2.0000e-06 eta: 6 days, 22:11:47 time: 0.5361 data_time: 0.0073 memory: 14901 loss: 15.6535 loss_prob: 11.8348 loss_thr: 2.8276 loss_db: 0.9911 2022/11/02 12:09:45 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:09:52 - mmengine - INFO - Epoch(train) [2][5/63] lr: 2.2182e-05 eta: 6 days, 22:11:47 time: 1.9886 data_time: 0.2327 memory: 40375 loss: 15.5949 loss_prob: 11.8110 loss_thr: 2.7935 loss_db: 0.9904 2022/11/02 12:09:54 - mmengine - INFO - Epoch(train) [2][10/63] lr: 2.2182e-05 eta: 5 days, 19:15:10 time: 0.9313 data_time: 0.2351 memory: 14901 loss: 15.3425 loss_prob: 11.6522 loss_thr: 2.7006 loss_db: 0.9897 2022/11/02 12:09:57 - mmengine - INFO - Epoch(train) [2][15/63] lr: 2.2182e-05 eta: 5 days, 19:15:10 time: 0.5488 data_time: 0.0347 memory: 14901 loss: 13.4902 loss_prob: 9.9663 loss_thr: 2.5352 loss_db: 0.9887 2022/11/02 12:10:00 - mmengine - INFO - Epoch(train) [2][20/63] lr: 2.2182e-05 eta: 5 days, 3:56:53 time: 0.5891 data_time: 0.0325 memory: 14901 loss: 11.5401 loss_prob: 8.2317 loss_thr: 2.3214 loss_db: 0.9871 2022/11/02 12:10:03 - mmengine - INFO - Epoch(train) [2][25/63] lr: 2.2182e-05 eta: 5 days, 3:56:53 time: 0.6192 data_time: 0.0060 memory: 14901 loss: 10.0334 loss_prob: 6.9381 loss_thr: 2.1106 loss_db: 0.9847 2022/11/02 12:10:06 - mmengine - INFO - Epoch(train) [2][30/63] lr: 2.2182e-05 eta: 4 days, 15:53:44 time: 0.5719 data_time: 0.0191 memory: 14901 loss: 9.1358 loss_prob: 6.2043 loss_thr: 1.9454 loss_db: 0.9862 2022/11/02 12:10:09 - mmengine - INFO - Epoch(train) [2][35/63] lr: 2.2182e-05 eta: 4 days, 15:53:44 time: 0.5334 data_time: 0.0178 memory: 14901 loss: 8.2041 loss_prob: 5.4591 loss_thr: 1.7600 loss_db: 0.9850 2022/11/02 12:10:12 - mmengine - INFO - Epoch(train) [2][40/63] lr: 2.2182e-05 eta: 4 days, 6:10:13 time: 0.5657 data_time: 0.0111 memory: 14901 loss: 7.5904 loss_prob: 4.9837 loss_thr: 1.6260 loss_db: 0.9806 2022/11/02 12:10:14 - mmengine - INFO - Epoch(train) [2][45/63] lr: 2.2182e-05 eta: 4 days, 6:10:13 time: 0.5163 data_time: 0.0108 memory: 14901 loss: 7.2965 loss_prob: 4.7969 loss_thr: 1.5168 loss_db: 0.9828 2022/11/02 12:10:16 - mmengine - INFO - Epoch(train) [2][50/63] lr: 2.2182e-05 eta: 3 days, 22:00:14 time: 0.4783 data_time: 0.0169 memory: 14901 loss: 6.9801 loss_prob: 4.5656 loss_thr: 1.4305 loss_db: 0.9840 2022/11/02 12:10:19 - mmengine - INFO - Epoch(train) [2][55/63] lr: 2.2182e-05 eta: 3 days, 22:00:14 time: 0.4707 data_time: 0.0172 memory: 14901 loss: 6.7248 loss_prob: 4.3745 loss_thr: 1.3693 loss_db: 0.9810 2022/11/02 12:10:21 - mmengine - INFO - Epoch(train) [2][60/63] lr: 2.2182e-05 eta: 3 days, 15:08:41 time: 0.4663 data_time: 0.0056 memory: 14901 loss: 6.5488 loss_prob: 4.2586 loss_thr: 1.3102 loss_db: 0.9800 2022/11/02 12:10:22 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:10:27 - mmengine - INFO - Epoch(train) [3][5/63] lr: 4.2364e-05 eta: 3 days, 15:08:41 time: 0.6660 data_time: 0.1773 memory: 14901 loss: 6.2870 loss_prob: 4.0530 loss_thr: 1.2522 loss_db: 0.9819 2022/11/02 12:10:30 - mmengine - INFO - Epoch(train) [3][10/63] lr: 4.2364e-05 eta: 3 days, 7:56:30 time: 0.7401 data_time: 0.1967 memory: 14901 loss: 6.1447 loss_prob: 3.9320 loss_thr: 1.2273 loss_db: 0.9855 2022/11/02 12:10:32 - mmengine - INFO - Epoch(train) [3][15/63] lr: 4.2364e-05 eta: 3 days, 7:56:30 time: 0.5602 data_time: 0.0258 memory: 14901 loss: 6.0052 loss_prob: 3.8183 loss_thr: 1.2029 loss_db: 0.9840 2022/11/02 12:10:35 - mmengine - INFO - Epoch(train) [3][20/63] lr: 4.2364e-05 eta: 3 days, 3:09:36 time: 0.4901 data_time: 0.0070 memory: 14901 loss: 5.8839 loss_prob: 3.7161 loss_thr: 1.1853 loss_db: 0.9826 2022/11/02 12:10:38 - mmengine - INFO - Epoch(train) [3][25/63] lr: 4.2364e-05 eta: 3 days, 3:09:36 time: 0.5131 data_time: 0.0250 memory: 14901 loss: 5.8197 loss_prob: 3.6521 loss_thr: 1.1845 loss_db: 0.9831 2022/11/02 12:10:40 - mmengine - INFO - Epoch(train) [3][30/63] lr: 4.2364e-05 eta: 2 days, 23:03:02 time: 0.5345 data_time: 0.0324 memory: 14901 loss: 5.7635 loss_prob: 3.6016 loss_thr: 1.1804 loss_db: 0.9816 2022/11/02 12:10:43 - mmengine - INFO - Epoch(train) [3][35/63] lr: 4.2364e-05 eta: 2 days, 23:03:02 time: 0.5127 data_time: 0.0162 memory: 14901 loss: 5.7440 loss_prob: 3.5867 loss_thr: 1.1764 loss_db: 0.9809 2022/11/02 12:10:45 - mmengine - INFO - Epoch(train) [3][40/63] lr: 4.2364e-05 eta: 2 days, 19:22:23 time: 0.4843 data_time: 0.0083 memory: 14901 loss: 5.7137 loss_prob: 3.5542 loss_thr: 1.1747 loss_db: 0.9848 2022/11/02 12:10:47 - mmengine - INFO - Epoch(train) [3][45/63] lr: 4.2364e-05 eta: 2 days, 19:22:23 time: 0.4610 data_time: 0.0062 memory: 14901 loss: 5.6619 loss_prob: 3.5044 loss_thr: 1.1704 loss_db: 0.9871 2022/11/02 12:10:50 - mmengine - INFO - Epoch(train) [3][50/63] lr: 4.2364e-05 eta: 2 days, 16:07:07 time: 0.4891 data_time: 0.0183 memory: 14901 loss: 5.6188 loss_prob: 3.4628 loss_thr: 1.1695 loss_db: 0.9865 2022/11/02 12:10:52 - mmengine - INFO - Epoch(train) [3][55/63] lr: 4.2364e-05 eta: 2 days, 16:07:07 time: 0.5198 data_time: 0.0249 memory: 14901 loss: 5.6305 loss_prob: 3.4737 loss_thr: 1.1706 loss_db: 0.9862 2022/11/02 12:10:55 - mmengine - INFO - Epoch(train) [3][60/63] lr: 4.2364e-05 eta: 2 days, 13:14:26 time: 0.5125 data_time: 0.0116 memory: 14901 loss: 5.6037 loss_prob: 3.4520 loss_thr: 1.1675 loss_db: 0.9843 2022/11/02 12:10:56 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:11:01 - mmengine - INFO - Epoch(train) [4][5/63] lr: 6.2545e-05 eta: 2 days, 13:14:26 time: 0.7393 data_time: 0.1715 memory: 14901 loss: 5.4957 loss_prob: 3.3406 loss_thr: 1.1709 loss_db: 0.9843 2022/11/02 12:11:04 - mmengine - INFO - Epoch(train) [4][10/63] lr: 6.2545e-05 eta: 2 days, 10:03:20 time: 0.7843 data_time: 0.1758 memory: 14901 loss: 5.4611 loss_prob: 3.3057 loss_thr: 1.1677 loss_db: 0.9877 2022/11/02 12:11:07 - mmengine - INFO - Epoch(train) [4][15/63] lr: 6.2545e-05 eta: 2 days, 10:03:20 time: 0.5749 data_time: 0.0109 memory: 14901 loss: 5.4155 loss_prob: 3.2608 loss_thr: 1.1670 loss_db: 0.9877 2022/11/02 12:11:10 - mmengine - INFO - Epoch(train) [4][20/63] lr: 6.2545e-05 eta: 2 days, 7:48:23 time: 0.5347 data_time: 0.0066 memory: 14901 loss: 5.3875 loss_prob: 3.2324 loss_thr: 1.1655 loss_db: 0.9896 2022/11/02 12:11:12 - mmengine - INFO - Epoch(train) [4][25/63] lr: 6.2545e-05 eta: 2 days, 7:48:23 time: 0.5170 data_time: 0.0102 memory: 14901 loss: 5.3708 loss_prob: 3.2164 loss_thr: 1.1616 loss_db: 0.9928 2022/11/02 12:11:15 - mmengine - INFO - Epoch(train) [4][30/63] lr: 6.2545e-05 eta: 2 days, 5:47:13 time: 0.5605 data_time: 0.0303 memory: 14901 loss: 5.3516 loss_prob: 3.1950 loss_thr: 1.1637 loss_db: 0.9929 2022/11/02 12:11:18 - mmengine - INFO - Epoch(train) [4][35/63] lr: 6.2545e-05 eta: 2 days, 5:47:13 time: 0.5473 data_time: 0.0272 memory: 14901 loss: 5.3260 loss_prob: 3.1673 loss_thr: 1.1656 loss_db: 0.9931 2022/11/02 12:11:20 - mmengine - INFO - Epoch(train) [4][40/63] lr: 6.2545e-05 eta: 2 days, 3:53:11 time: 0.4977 data_time: 0.0099 memory: 14901 loss: 5.3128 loss_prob: 3.1540 loss_thr: 1.1654 loss_db: 0.9934 2022/11/02 12:11:23 - mmengine - INFO - Epoch(train) [4][45/63] lr: 6.2545e-05 eta: 2 days, 3:53:11 time: 0.4772 data_time: 0.0073 memory: 14901 loss: 5.3198 loss_prob: 3.1571 loss_thr: 1.1690 loss_db: 0.9937 2022/11/02 12:11:25 - mmengine - INFO - Epoch(train) [4][50/63] lr: 6.2545e-05 eta: 2 days, 2:07:37 time: 0.4776 data_time: 0.0085 memory: 14901 loss: 5.3089 loss_prob: 3.1465 loss_thr: 1.1680 loss_db: 0.9945 2022/11/02 12:11:27 - mmengine - INFO - Epoch(train) [4][55/63] lr: 6.2545e-05 eta: 2 days, 2:07:37 time: 0.4816 data_time: 0.0227 memory: 14901 loss: 5.3127 loss_prob: 3.1543 loss_thr: 1.1642 loss_db: 0.9942 2022/11/02 12:11:30 - mmengine - INFO - Epoch(train) [4][60/63] lr: 6.2545e-05 eta: 2 days, 0:29:58 time: 0.4663 data_time: 0.0187 memory: 14901 loss: 5.2994 loss_prob: 3.1447 loss_thr: 1.1597 loss_db: 0.9950 2022/11/02 12:11:31 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:11:36 - mmengine - INFO - Epoch(train) [5][5/63] lr: 8.2727e-05 eta: 2 days, 0:29:58 time: 0.7263 data_time: 0.2461 memory: 14901 loss: 5.2676 loss_prob: 3.1094 loss_thr: 1.1620 loss_db: 0.9962 2022/11/02 12:11:38 - mmengine - INFO - Epoch(train) [5][10/63] lr: 8.2727e-05 eta: 1 day, 22:41:56 time: 0.7685 data_time: 0.2443 memory: 14901 loss: 5.2765 loss_prob: 3.1163 loss_thr: 1.1634 loss_db: 0.9968 2022/11/02 12:11:41 - mmengine - INFO - Epoch(train) [5][15/63] lr: 8.2727e-05 eta: 1 day, 22:41:56 time: 0.5133 data_time: 0.0050 memory: 14901 loss: 5.2717 loss_prob: 3.1178 loss_thr: 1.1576 loss_db: 0.9963 2022/11/02 12:11:44 - mmengine - INFO - Epoch(train) [5][20/63] lr: 8.2727e-05 eta: 1 day, 21:22:56 time: 0.5278 data_time: 0.0051 memory: 14901 loss: 5.2554 loss_prob: 3.1065 loss_thr: 1.1529 loss_db: 0.9960 2022/11/02 12:11:46 - mmengine - INFO - Epoch(train) [5][25/63] lr: 8.2727e-05 eta: 1 day, 21:22:56 time: 0.5384 data_time: 0.0396 memory: 14901 loss: 5.2459 loss_prob: 3.0932 loss_thr: 1.1555 loss_db: 0.9972 2022/11/02 12:11:49 - mmengine - INFO - Epoch(train) [5][30/63] lr: 8.2727e-05 eta: 1 day, 20:09:24 time: 0.5252 data_time: 0.0417 memory: 14901 loss: 5.2309 loss_prob: 3.0766 loss_thr: 1.1567 loss_db: 0.9976 2022/11/02 12:11:52 - mmengine - INFO - Epoch(train) [5][35/63] lr: 8.2727e-05 eta: 1 day, 20:09:24 time: 0.5364 data_time: 0.0070 memory: 14901 loss: 5.2042 loss_prob: 3.0499 loss_thr: 1.1562 loss_db: 0.9980 2022/11/02 12:11:55 - mmengine - INFO - Epoch(train) [5][40/63] lr: 8.2727e-05 eta: 1 day, 19:05:47 time: 0.6388 data_time: 0.0058 memory: 14901 loss: 5.1818 loss_prob: 3.0272 loss_thr: 1.1562 loss_db: 0.9984 2022/11/02 12:11:59 - mmengine - INFO - Epoch(train) [5][45/63] lr: 8.2727e-05 eta: 1 day, 19:05:47 time: 0.7126 data_time: 0.0062 memory: 14901 loss: 5.1684 loss_prob: 3.0080 loss_thr: 1.1619 loss_db: 0.9985 2022/11/02 12:12:02 - mmengine - INFO - Epoch(train) [5][50/63] lr: 8.2727e-05 eta: 1 day, 18:08:13 time: 0.6831 data_time: 0.0348 memory: 14901 loss: 5.1525 loss_prob: 2.9919 loss_thr: 1.1618 loss_db: 0.9988 2022/11/02 12:12:05 - mmengine - INFO - Epoch(train) [5][55/63] lr: 8.2727e-05 eta: 1 day, 18:08:13 time: 0.5931 data_time: 0.0340 memory: 14901 loss: 5.1406 loss_prob: 2.9786 loss_thr: 1.1629 loss_db: 0.9991 2022/11/02 12:12:08 - mmengine - INFO - Epoch(train) [5][60/63] lr: 8.2727e-05 eta: 1 day, 17:08:44 time: 0.5439 data_time: 0.0044 memory: 14901 loss: 5.1313 loss_prob: 2.9682 loss_thr: 1.1638 loss_db: 0.9994 2022/11/02 12:12:09 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:12:14 - mmengine - INFO - Epoch(train) [6][5/63] lr: 1.0291e-04 eta: 1 day, 17:08:44 time: 0.7483 data_time: 0.2362 memory: 14901 loss: 5.1131 loss_prob: 2.9563 loss_thr: 1.1572 loss_db: 0.9996 2022/11/02 12:12:17 - mmengine - INFO - Epoch(train) [6][10/63] lr: 1.0291e-04 eta: 1 day, 16:02:02 time: 0.8409 data_time: 0.2350 memory: 14901 loss: 5.1083 loss_prob: 2.9544 loss_thr: 1.1544 loss_db: 0.9995 2022/11/02 12:12:21 - mmengine - INFO - Epoch(train) [6][15/63] lr: 1.0291e-04 eta: 1 day, 16:02:02 time: 0.6780 data_time: 0.0044 memory: 14901 loss: 5.1146 loss_prob: 2.9542 loss_thr: 1.1609 loss_db: 0.9995 2022/11/02 12:12:24 - mmengine - INFO - Epoch(train) [6][20/63] lr: 1.0291e-04 eta: 1 day, 15:14:06 time: 0.6431 data_time: 0.0053 memory: 14901 loss: 5.1099 loss_prob: 2.9506 loss_thr: 1.1597 loss_db: 0.9996 2022/11/02 12:12:27 - mmengine - INFO - Epoch(train) [6][25/63] lr: 1.0291e-04 eta: 1 day, 15:14:06 time: 0.6802 data_time: 0.0281 memory: 14901 loss: 5.1015 loss_prob: 2.9470 loss_thr: 1.1548 loss_db: 0.9997 2022/11/02 12:12:30 - mmengine - INFO - Epoch(train) [6][30/63] lr: 1.0291e-04 eta: 1 day, 14:27:05 time: 0.5918 data_time: 0.0346 memory: 14901 loss: 5.0943 loss_prob: 2.9403 loss_thr: 1.1543 loss_db: 0.9997 2022/11/02 12:12:33 - mmengine - INFO - Epoch(train) [6][35/63] lr: 1.0291e-04 eta: 1 day, 14:27:05 time: 0.5298 data_time: 0.0119 memory: 14901 loss: 5.1033 loss_prob: 2.9478 loss_thr: 1.1558 loss_db: 0.9997 2022/11/02 12:12:35 - mmengine - INFO - Epoch(train) [6][40/63] lr: 1.0291e-04 eta: 1 day, 13:40:40 time: 0.5344 data_time: 0.0067 memory: 14901 loss: 5.1114 loss_prob: 2.9535 loss_thr: 1.1582 loss_db: 0.9997 2022/11/02 12:12:38 - mmengine - INFO - Epoch(train) [6][45/63] lr: 1.0291e-04 eta: 1 day, 13:40:40 time: 0.5246 data_time: 0.0066 memory: 14901 loss: 5.1020 loss_prob: 2.9427 loss_thr: 1.1596 loss_db: 0.9997 2022/11/02 12:12:41 - mmengine - INFO - Epoch(train) [6][50/63] lr: 1.0291e-04 eta: 1 day, 12:57:24 time: 0.5516 data_time: 0.0189 memory: 14901 loss: 5.0985 loss_prob: 2.9378 loss_thr: 1.1609 loss_db: 0.9997 2022/11/02 12:12:44 - mmengine - INFO - Epoch(train) [6][55/63] lr: 1.0291e-04 eta: 1 day, 12:57:24 time: 0.5602 data_time: 0.0390 memory: 14901 loss: 5.0880 loss_prob: 2.9295 loss_thr: 1.1587 loss_db: 0.9998 2022/11/02 12:12:46 - mmengine - INFO - Epoch(train) [6][60/63] lr: 1.0291e-04 eta: 1 day, 12:15:59 time: 0.5384 data_time: 0.0248 memory: 14901 loss: 5.0810 loss_prob: 2.9259 loss_thr: 1.1553 loss_db: 0.9998 2022/11/02 12:12:47 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:12:52 - mmengine - INFO - Epoch(train) [7][5/63] lr: 1.2309e-04 eta: 1 day, 12:15:59 time: 0.7017 data_time: 0.2302 memory: 14901 loss: 5.0771 loss_prob: 2.9229 loss_thr: 1.1543 loss_db: 0.9998 2022/11/02 12:12:54 - mmengine - INFO - Epoch(train) [7][10/63] lr: 1.2309e-04 eta: 1 day, 11:25:42 time: 0.7117 data_time: 0.2286 memory: 14901 loss: 5.0729 loss_prob: 2.9181 loss_thr: 1.1550 loss_db: 0.9998 2022/11/02 12:12:57 - mmengine - INFO - Epoch(train) [7][15/63] lr: 1.2309e-04 eta: 1 day, 11:25:42 time: 0.4764 data_time: 0.0047 memory: 14901 loss: 5.0770 loss_prob: 2.9228 loss_thr: 1.1544 loss_db: 0.9998 2022/11/02 12:12:59 - mmengine - INFO - Epoch(train) [7][20/63] lr: 1.2309e-04 eta: 1 day, 10:47:04 time: 0.4782 data_time: 0.0051 memory: 14901 loss: 5.0838 loss_prob: 2.9247 loss_thr: 1.1593 loss_db: 0.9998 2022/11/02 12:13:02 - mmengine - INFO - Epoch(train) [7][25/63] lr: 1.2309e-04 eta: 1 day, 10:47:04 time: 0.5344 data_time: 0.0442 memory: 14901 loss: 5.0891 loss_prob: 2.9306 loss_thr: 1.1587 loss_db: 0.9999 2022/11/02 12:13:05 - mmengine - INFO - Epoch(train) [7][30/63] lr: 1.2309e-04 eta: 1 day, 10:11:48 time: 0.5260 data_time: 0.0482 memory: 14901 loss: 5.1286 loss_prob: 2.9723 loss_thr: 1.1565 loss_db: 0.9998 2022/11/02 12:13:07 - mmengine - INFO - Epoch(train) [7][35/63] lr: 1.2309e-04 eta: 1 day, 10:11:48 time: 0.5055 data_time: 0.0088 memory: 14901 loss: 5.1834 loss_prob: 3.0266 loss_thr: 1.1574 loss_db: 0.9993 2022/11/02 12:13:10 - mmengine - INFO - Epoch(train) [7][40/63] lr: 1.2309e-04 eta: 1 day, 9:38:21 time: 0.5303 data_time: 0.0048 memory: 14901 loss: 5.2158 loss_prob: 3.0554 loss_thr: 1.1621 loss_db: 0.9983 2022/11/02 12:13:12 - mmengine - INFO - Epoch(train) [7][45/63] lr: 1.2309e-04 eta: 1 day, 9:38:21 time: 0.4935 data_time: 0.0047 memory: 14901 loss: 5.2017 loss_prob: 3.0421 loss_thr: 1.1612 loss_db: 0.9984 2022/11/02 12:13:15 - mmengine - INFO - Epoch(train) [7][50/63] lr: 1.2309e-04 eta: 1 day, 9:05:48 time: 0.5082 data_time: 0.0203 memory: 14901 loss: 5.1669 loss_prob: 3.0096 loss_thr: 1.1580 loss_db: 0.9993 2022/11/02 12:13:17 - mmengine - INFO - Epoch(train) [7][55/63] lr: 1.2309e-04 eta: 1 day, 9:05:48 time: 0.5008 data_time: 0.0217 memory: 14901 loss: 5.1454 loss_prob: 2.9870 loss_thr: 1.1590 loss_db: 0.9994 2022/11/02 12:13:20 - mmengine - INFO - Epoch(train) [7][60/63] lr: 1.2309e-04 eta: 1 day, 8:34:23 time: 0.4957 data_time: 0.0060 memory: 14901 loss: 5.1228 loss_prob: 2.9612 loss_thr: 1.1620 loss_db: 0.9995 2022/11/02 12:13:21 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:13:26 - mmengine - INFO - Epoch(train) [8][5/63] lr: 1.4327e-04 eta: 1 day, 8:34:23 time: 0.7349 data_time: 0.2350 memory: 14901 loss: 5.1047 loss_prob: 2.9428 loss_thr: 1.1623 loss_db: 0.9996 2022/11/02 12:13:29 - mmengine - INFO - Epoch(train) [8][10/63] lr: 1.4327e-04 eta: 1 day, 7:58:05 time: 0.7332 data_time: 0.2347 memory: 14901 loss: 5.1361 loss_prob: 2.9770 loss_thr: 1.1596 loss_db: 0.9995 2022/11/02 12:13:31 - mmengine - INFO - Epoch(train) [8][15/63] lr: 1.4327e-04 eta: 1 day, 7:58:05 time: 0.4920 data_time: 0.0055 memory: 14901 loss: 5.1779 loss_prob: 3.0196 loss_thr: 1.1592 loss_db: 0.9991 2022/11/02 12:13:34 - mmengine - INFO - Epoch(train) [8][20/63] lr: 1.4327e-04 eta: 1 day, 7:29:55 time: 0.5041 data_time: 0.0082 memory: 14901 loss: 5.1676 loss_prob: 3.0095 loss_thr: 1.1591 loss_db: 0.9990 2022/11/02 12:13:36 - mmengine - INFO - Epoch(train) [8][25/63] lr: 1.4327e-04 eta: 1 day, 7:29:55 time: 0.4917 data_time: 0.0110 memory: 14901 loss: 5.1291 loss_prob: 2.9708 loss_thr: 1.1591 loss_db: 0.9992 2022/11/02 12:13:39 - mmengine - INFO - Epoch(train) [8][30/63] lr: 1.4327e-04 eta: 1 day, 7:03:02 time: 0.5071 data_time: 0.0289 memory: 14901 loss: 5.1223 loss_prob: 2.9633 loss_thr: 1.1598 loss_db: 0.9992 2022/11/02 12:13:41 - mmengine - INFO - Epoch(train) [8][35/63] lr: 1.4327e-04 eta: 1 day, 7:03:02 time: 0.5300 data_time: 0.0306 memory: 14901 loss: 5.1112 loss_prob: 2.9535 loss_thr: 1.1584 loss_db: 0.9993 2022/11/02 12:13:43 - mmengine - INFO - Epoch(train) [8][40/63] lr: 1.4327e-04 eta: 1 day, 6:36:31 time: 0.4790 data_time: 0.0099 memory: 14901 loss: 5.0879 loss_prob: 2.9324 loss_thr: 1.1560 loss_db: 0.9995 2022/11/02 12:13:46 - mmengine - INFO - Epoch(train) [8][45/63] lr: 1.4327e-04 eta: 1 day, 6:36:31 time: 0.4692 data_time: 0.0064 memory: 14901 loss: 5.0776 loss_prob: 2.9222 loss_thr: 1.1558 loss_db: 0.9996 2022/11/02 12:13:48 - mmengine - INFO - Epoch(train) [8][50/63] lr: 1.4327e-04 eta: 1 day, 6:11:21 time: 0.4893 data_time: 0.0203 memory: 14901 loss: 5.0665 loss_prob: 2.9073 loss_thr: 1.1595 loss_db: 0.9997 2022/11/02 12:13:51 - mmengine - INFO - Epoch(train) [8][55/63] lr: 1.4327e-04 eta: 1 day, 6:11:21 time: 0.4835 data_time: 0.0210 memory: 14901 loss: 5.0487 loss_prob: 2.8945 loss_thr: 1.1544 loss_db: 0.9997 2022/11/02 12:13:54 - mmengine - INFO - Epoch(train) [8][60/63] lr: 1.4327e-04 eta: 1 day, 5:47:49 time: 0.5142 data_time: 0.0098 memory: 14901 loss: 5.0460 loss_prob: 2.8896 loss_thr: 1.1567 loss_db: 0.9998 2022/11/02 12:13:55 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:13:59 - mmengine - INFO - Epoch(train) [9][5/63] lr: 1.6345e-04 eta: 1 day, 5:47:49 time: 0.6877 data_time: 0.2108 memory: 14901 loss: 5.0306 loss_prob: 2.8778 loss_thr: 1.1530 loss_db: 0.9999 2022/11/02 12:14:02 - mmengine - INFO - Epoch(train) [9][10/63] lr: 1.6345e-04 eta: 1 day, 5:19:26 time: 0.7038 data_time: 0.2096 memory: 14901 loss: 5.0239 loss_prob: 2.8688 loss_thr: 1.1553 loss_db: 0.9999 2022/11/02 12:14:04 - mmengine - INFO - Epoch(train) [9][15/63] lr: 1.6345e-04 eta: 1 day, 5:19:26 time: 0.4827 data_time: 0.0049 memory: 14901 loss: 5.0219 loss_prob: 2.8633 loss_thr: 1.1587 loss_db: 0.9999 2022/11/02 12:14:06 - mmengine - INFO - Epoch(train) [9][20/63] lr: 1.6345e-04 eta: 1 day, 4:56:57 time: 0.4744 data_time: 0.0046 memory: 14901 loss: 5.0129 loss_prob: 2.8588 loss_thr: 1.1541 loss_db: 0.9999 2022/11/02 12:14:09 - mmengine - INFO - Epoch(train) [9][25/63] lr: 1.6345e-04 eta: 1 day, 4:56:57 time: 0.4861 data_time: 0.0121 memory: 14901 loss: 5.0038 loss_prob: 2.8523 loss_thr: 1.1516 loss_db: 0.9999 2022/11/02 12:14:12 - mmengine - INFO - Epoch(train) [9][30/63] lr: 1.6345e-04 eta: 1 day, 4:36:35 time: 0.5286 data_time: 0.0337 memory: 14901 loss: 5.0022 loss_prob: 2.8470 loss_thr: 1.1553 loss_db: 0.9999 2022/11/02 12:14:14 - mmengine - INFO - Epoch(train) [9][35/63] lr: 1.6345e-04 eta: 1 day, 4:36:35 time: 0.5263 data_time: 0.0277 memory: 14901 loss: 4.9979 loss_prob: 2.8418 loss_thr: 1.1561 loss_db: 1.0000 2022/11/02 12:14:17 - mmengine - INFO - Epoch(train) [9][40/63] lr: 1.6345e-04 eta: 1 day, 4:16:42 time: 0.5175 data_time: 0.0080 memory: 14901 loss: 4.9937 loss_prob: 2.8400 loss_thr: 1.1538 loss_db: 1.0000 2022/11/02 12:14:19 - mmengine - INFO - Epoch(train) [9][45/63] lr: 1.6345e-04 eta: 1 day, 4:16:42 time: 0.5167 data_time: 0.0084 memory: 14901 loss: 4.9951 loss_prob: 2.8404 loss_thr: 1.1548 loss_db: 1.0000 2022/11/02 12:14:22 - mmengine - INFO - Epoch(train) [9][50/63] lr: 1.6345e-04 eta: 1 day, 3:58:08 time: 0.5443 data_time: 0.0121 memory: 14901 loss: 4.9922 loss_prob: 2.8381 loss_thr: 1.1542 loss_db: 1.0000 2022/11/02 12:14:25 - mmengine - INFO - Epoch(train) [9][55/63] lr: 1.6345e-04 eta: 1 day, 3:58:08 time: 0.5849 data_time: 0.0211 memory: 14901 loss: 4.9842 loss_prob: 2.8327 loss_thr: 1.1515 loss_db: 1.0000 2022/11/02 12:14:28 - mmengine - INFO - Epoch(train) [9][60/63] lr: 1.6345e-04 eta: 1 day, 3:40:32 time: 0.5580 data_time: 0.0177 memory: 14901 loss: 4.9817 loss_prob: 2.8302 loss_thr: 1.1516 loss_db: 1.0000 2022/11/02 12:14:29 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:14:35 - mmengine - INFO - Epoch(train) [10][5/63] lr: 1.8364e-04 eta: 1 day, 3:40:32 time: 0.8335 data_time: 0.2070 memory: 14901 loss: 4.9917 loss_prob: 2.8338 loss_thr: 1.1579 loss_db: 1.0000 2022/11/02 12:14:38 - mmengine - INFO - Epoch(train) [10][10/63] lr: 1.8364e-04 eta: 1 day, 3:21:20 time: 0.8535 data_time: 0.2086 memory: 14901 loss: 5.0023 loss_prob: 2.8427 loss_thr: 1.1597 loss_db: 1.0000 2022/11/02 12:14:42 - mmengine - INFO - Epoch(train) [10][15/63] lr: 1.8364e-04 eta: 1 day, 3:21:20 time: 0.6346 data_time: 0.0182 memory: 14901 loss: 4.9964 loss_prob: 2.8438 loss_thr: 1.1527 loss_db: 0.9999 2022/11/02 12:14:44 - mmengine - INFO - Epoch(train) [10][20/63] lr: 1.8364e-04 eta: 1 day, 3:07:29 time: 0.6726 data_time: 0.0103 memory: 14901 loss: 4.9914 loss_prob: 2.8403 loss_thr: 1.1511 loss_db: 0.9999 2022/11/02 12:14:48 - mmengine - INFO - Epoch(train) [10][25/63] lr: 1.8364e-04 eta: 1 day, 3:07:29 time: 0.6048 data_time: 0.0187 memory: 14901 loss: 4.9836 loss_prob: 2.8379 loss_thr: 1.1458 loss_db: 0.9999 2022/11/02 12:14:50 - mmengine - INFO - Epoch(train) [10][30/63] lr: 1.8364e-04 eta: 1 day, 2:52:14 time: 0.5839 data_time: 0.0256 memory: 14901 loss: 4.9859 loss_prob: 2.8367 loss_thr: 1.1493 loss_db: 0.9999 2022/11/02 12:14:53 - mmengine - INFO - Epoch(train) [10][35/63] lr: 1.8364e-04 eta: 1 day, 2:52:14 time: 0.5439 data_time: 0.0195 memory: 14901 loss: 4.9822 loss_prob: 2.8298 loss_thr: 1.1525 loss_db: 0.9999 2022/11/02 12:14:56 - mmengine - INFO - Epoch(train) [10][40/63] lr: 1.8364e-04 eta: 1 day, 2:37:21 time: 0.5773 data_time: 0.0217 memory: 14901 loss: 4.9785 loss_prob: 2.8289 loss_thr: 1.1497 loss_db: 1.0000 2022/11/02 12:14:59 - mmengine - INFO - Epoch(train) [10][45/63] lr: 1.8364e-04 eta: 1 day, 2:37:21 time: 0.6001 data_time: 0.0140 memory: 14901 loss: 4.9871 loss_prob: 2.8346 loss_thr: 1.1526 loss_db: 0.9999 2022/11/02 12:15:02 - mmengine - INFO - Epoch(train) [10][50/63] lr: 1.8364e-04 eta: 1 day, 2:23:45 time: 0.6168 data_time: 0.0151 memory: 14901 loss: 4.9910 loss_prob: 2.8355 loss_thr: 1.1556 loss_db: 0.9999 2022/11/02 12:15:06 - mmengine - INFO - Epoch(train) [10][55/63] lr: 1.8364e-04 eta: 1 day, 2:23:45 time: 0.7040 data_time: 0.0200 memory: 14901 loss: 4.9934 loss_prob: 2.8352 loss_thr: 1.1583 loss_db: 0.9999 2022/11/02 12:15:09 - mmengine - INFO - Epoch(train) [10][60/63] lr: 1.8364e-04 eta: 1 day, 2:12:38 time: 0.7201 data_time: 0.0116 memory: 14901 loss: 4.9899 loss_prob: 2.8337 loss_thr: 1.1562 loss_db: 0.9999 2022/11/02 12:15:11 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:15:17 - mmengine - INFO - Epoch(train) [11][5/63] lr: 2.0382e-04 eta: 1 day, 2:12:38 time: 0.9087 data_time: 0.2329 memory: 14901 loss: 4.9745 loss_prob: 2.8272 loss_thr: 1.1474 loss_db: 0.9999 2022/11/02 12:15:21 - mmengine - INFO - Epoch(train) [11][10/63] lr: 2.0382e-04 eta: 1 day, 2:00:03 time: 1.0053 data_time: 0.2324 memory: 14901 loss: 4.9633 loss_prob: 2.8189 loss_thr: 1.1445 loss_db: 0.9999 2022/11/02 12:15:24 - mmengine - INFO - Epoch(train) [11][15/63] lr: 2.0382e-04 eta: 1 day, 2:00:03 time: 0.6568 data_time: 0.0167 memory: 14901 loss: 4.9672 loss_prob: 2.8163 loss_thr: 1.1509 loss_db: 0.9999 2022/11/02 12:15:27 - mmengine - INFO - Epoch(train) [11][20/63] lr: 2.0382e-04 eta: 1 day, 1:46:57 time: 0.5782 data_time: 0.0141 memory: 14901 loss: 4.9715 loss_prob: 2.8175 loss_thr: 1.1540 loss_db: 0.9999 2022/11/02 12:15:29 - mmengine - INFO - Epoch(train) [11][25/63] lr: 2.0382e-04 eta: 1 day, 1:46:57 time: 0.5411 data_time: 0.0191 memory: 14901 loss: 4.9777 loss_prob: 2.8202 loss_thr: 1.1575 loss_db: 0.9999 2022/11/02 12:15:32 - mmengine - INFO - Epoch(train) [11][30/63] lr: 2.0382e-04 eta: 1 day, 1:33:02 time: 0.5135 data_time: 0.0283 memory: 14901 loss: 4.9832 loss_prob: 2.8255 loss_thr: 1.1578 loss_db: 1.0000 2022/11/02 12:15:34 - mmengine - INFO - Epoch(train) [11][35/63] lr: 2.0382e-04 eta: 1 day, 1:33:02 time: 0.4797 data_time: 0.0191 memory: 14901 loss: 5.0053 loss_prob: 2.8523 loss_thr: 1.1532 loss_db: 0.9999 2022/11/02 12:15:37 - mmengine - INFO - Epoch(train) [11][40/63] lr: 2.0382e-04 eta: 1 day, 1:19:10 time: 0.4943 data_time: 0.0123 memory: 14901 loss: 5.0389 loss_prob: 2.8840 loss_thr: 1.1554 loss_db: 0.9995 2022/11/02 12:15:39 - mmengine - INFO - Epoch(train) [11][45/63] lr: 2.0382e-04 eta: 1 day, 1:19:10 time: 0.5109 data_time: 0.0088 memory: 14901 loss: 5.0610 loss_prob: 2.9036 loss_thr: 1.1589 loss_db: 0.9986 2022/11/02 12:15:42 - mmengine - INFO - Epoch(train) [11][50/63] lr: 2.0382e-04 eta: 1 day, 1:05:49 time: 0.5009 data_time: 0.0221 memory: 14901 loss: 5.0546 loss_prob: 2.9026 loss_thr: 1.1532 loss_db: 0.9989 2022/11/02 12:15:44 - mmengine - INFO - Epoch(train) [11][55/63] lr: 2.0382e-04 eta: 1 day, 1:05:49 time: 0.5035 data_time: 0.0208 memory: 14901 loss: 5.0309 loss_prob: 2.8823 loss_thr: 1.1489 loss_db: 0.9997 2022/11/02 12:15:47 - mmengine - INFO - Epoch(train) [11][60/63] lr: 2.0382e-04 eta: 1 day, 0:52:43 time: 0.4925 data_time: 0.0062 memory: 14901 loss: 5.0174 loss_prob: 2.8692 loss_thr: 1.1486 loss_db: 0.9996 2022/11/02 12:15:48 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:15:53 - mmengine - INFO - Epoch(train) [12][5/63] lr: 2.2400e-04 eta: 1 day, 0:52:43 time: 0.6643 data_time: 0.2032 memory: 14901 loss: 5.0115 loss_prob: 2.8615 loss_thr: 1.1503 loss_db: 0.9997 2022/11/02 12:15:55 - mmengine - INFO - Epoch(train) [12][10/63] lr: 2.2400e-04 eta: 1 day, 0:37:09 time: 0.6930 data_time: 0.2023 memory: 14901 loss: 4.9986 loss_prob: 2.8466 loss_thr: 1.1522 loss_db: 0.9998 2022/11/02 12:15:57 - mmengine - INFO - Epoch(train) [12][15/63] lr: 2.2400e-04 eta: 1 day, 0:37:09 time: 0.4673 data_time: 0.0069 memory: 14901 loss: 4.9856 loss_prob: 2.8352 loss_thr: 1.1505 loss_db: 0.9999 2022/11/02 12:16:00 - mmengine - INFO - Epoch(train) [12][20/63] lr: 2.2400e-04 eta: 1 day, 0:24:54 time: 0.4947 data_time: 0.0057 memory: 14901 loss: 4.9838 loss_prob: 2.8340 loss_thr: 1.1499 loss_db: 0.9999 2022/11/02 12:16:03 - mmengine - INFO - Epoch(train) [12][25/63] lr: 2.2400e-04 eta: 1 day, 0:24:54 time: 0.5499 data_time: 0.0471 memory: 14901 loss: 4.9809 loss_prob: 2.8303 loss_thr: 1.1510 loss_db: 0.9996 2022/11/02 12:16:05 - mmengine - INFO - Epoch(train) [12][30/63] lr: 2.2400e-04 eta: 1 day, 0:13:31 time: 0.5249 data_time: 0.0478 memory: 14901 loss: 4.9671 loss_prob: 2.8213 loss_thr: 1.1463 loss_db: 0.9995 2022/11/02 12:16:08 - mmengine - INFO - Epoch(train) [12][35/63] lr: 2.2400e-04 eta: 1 day, 0:13:31 time: 0.4799 data_time: 0.0061 memory: 14901 loss: 4.9570 loss_prob: 2.8141 loss_thr: 1.1431 loss_db: 0.9998 2022/11/02 12:16:10 - mmengine - INFO - Epoch(train) [12][40/63] lr: 2.2400e-04 eta: 1 day, 0:01:59 time: 0.4989 data_time: 0.0069 memory: 14901 loss: 4.9376 loss_prob: 2.7996 loss_thr: 1.1386 loss_db: 0.9995 2022/11/02 12:16:12 - mmengine - INFO - Epoch(train) [12][45/63] lr: 2.2400e-04 eta: 1 day, 0:01:59 time: 0.4859 data_time: 0.0069 memory: 14901 loss: 4.9390 loss_prob: 2.7988 loss_thr: 1.1411 loss_db: 0.9991 2022/11/02 12:16:15 - mmengine - INFO - Epoch(train) [12][50/63] lr: 2.2400e-04 eta: 23:50:58 time: 0.5115 data_time: 0.0255 memory: 14901 loss: 4.9642 loss_prob: 2.8153 loss_thr: 1.1496 loss_db: 0.9992 2022/11/02 12:16:18 - mmengine - INFO - Epoch(train) [12][55/63] lr: 2.2400e-04 eta: 23:50:58 time: 0.5316 data_time: 0.0264 memory: 14901 loss: 4.9745 loss_prob: 2.8251 loss_thr: 1.1500 loss_db: 0.9995 2022/11/02 12:16:20 - mmengine - INFO - Epoch(train) [12][60/63] lr: 2.2400e-04 eta: 23:40:21 time: 0.5169 data_time: 0.0056 memory: 14901 loss: 4.9605 loss_prob: 2.8172 loss_thr: 1.1437 loss_db: 0.9997 2022/11/02 12:16:21 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:16:26 - mmengine - INFO - Epoch(train) [13][5/63] lr: 2.4418e-04 eta: 23:40:21 time: 0.6733 data_time: 0.2243 memory: 14901 loss: 4.9407 loss_prob: 2.8041 loss_thr: 1.1388 loss_db: 0.9979 2022/11/02 12:16:28 - mmengine - INFO - Epoch(train) [13][10/63] lr: 2.4418e-04 eta: 23:27:14 time: 0.6900 data_time: 0.2230 memory: 14901 loss: 4.9453 loss_prob: 2.8029 loss_thr: 1.1467 loss_db: 0.9957 2022/11/02 12:16:31 - mmengine - INFO - Epoch(train) [13][15/63] lr: 2.4418e-04 eta: 23:27:14 time: 0.4979 data_time: 0.0066 memory: 14901 loss: 4.9362 loss_prob: 2.8015 loss_thr: 1.1388 loss_db: 0.9960 2022/11/02 12:16:33 - mmengine - INFO - Epoch(train) [13][20/63] lr: 2.4418e-04 eta: 23:16:56 time: 0.4997 data_time: 0.0078 memory: 14901 loss: 4.9334 loss_prob: 2.8024 loss_thr: 1.1344 loss_db: 0.9966 2022/11/02 12:16:36 - mmengine - INFO - Epoch(train) [13][25/63] lr: 2.4418e-04 eta: 23:16:56 time: 0.4663 data_time: 0.0159 memory: 14901 loss: 4.9409 loss_prob: 2.7984 loss_thr: 1.1505 loss_db: 0.9920 2022/11/02 12:16:38 - mmengine - INFO - Epoch(train) [13][30/63] lr: 2.4418e-04 eta: 23:06:49 time: 0.4941 data_time: 0.0286 memory: 14901 loss: 4.9468 loss_prob: 2.8035 loss_thr: 1.1602 loss_db: 0.9831 2022/11/02 12:16:41 - mmengine - INFO - Epoch(train) [13][35/63] lr: 2.4418e-04 eta: 23:06:49 time: 0.4907 data_time: 0.0236 memory: 14901 loss: 4.9406 loss_prob: 2.8129 loss_thr: 1.1459 loss_db: 0.9818 2022/11/02 12:16:43 - mmengine - INFO - Epoch(train) [13][40/63] lr: 2.4418e-04 eta: 22:56:44 time: 0.4802 data_time: 0.0096 memory: 14901 loss: 4.9157 loss_prob: 2.8047 loss_thr: 1.1362 loss_db: 0.9748 2022/11/02 12:16:45 - mmengine - INFO - Epoch(train) [13][45/63] lr: 2.4418e-04 eta: 22:56:44 time: 0.4694 data_time: 0.0052 memory: 14901 loss: 4.9052 loss_prob: 2.8031 loss_thr: 1.1461 loss_db: 0.9559 2022/11/02 12:16:48 - mmengine - INFO - Epoch(train) [13][50/63] lr: 2.4418e-04 eta: 22:47:35 time: 0.5244 data_time: 0.0213 memory: 14901 loss: 4.8278 loss_prob: 2.7898 loss_thr: 1.1395 loss_db: 0.8985 2022/11/02 12:16:51 - mmengine - INFO - Epoch(train) [13][55/63] lr: 2.4418e-04 eta: 22:47:35 time: 0.5484 data_time: 0.0255 memory: 14901 loss: 4.7697 loss_prob: 2.7893 loss_thr: 1.1220 loss_db: 0.8583 2022/11/02 12:16:53 - mmengine - INFO - Epoch(train) [13][60/63] lr: 2.4418e-04 eta: 22:38:16 time: 0.4993 data_time: 0.0108 memory: 14901 loss: 4.8280 loss_prob: 2.8094 loss_thr: 1.1560 loss_db: 0.8625 2022/11/02 12:16:55 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:17:00 - mmengine - INFO - Epoch(train) [14][5/63] lr: 2.6436e-04 eta: 22:38:16 time: 0.7697 data_time: 0.2168 memory: 14901 loss: 4.8697 loss_prob: 2.8204 loss_thr: 1.1680 loss_db: 0.8813 2022/11/02 12:17:04 - mmengine - INFO - Epoch(train) [14][10/63] lr: 2.6436e-04 eta: 22:29:57 time: 0.8787 data_time: 0.2221 memory: 14901 loss: 4.7166 loss_prob: 2.8180 loss_thr: 1.1515 loss_db: 0.7471 2022/11/02 12:17:06 - mmengine - INFO - Epoch(train) [14][15/63] lr: 2.6436e-04 eta: 22:29:57 time: 0.6234 data_time: 0.0115 memory: 14901 loss: 4.8481 loss_prob: 2.8676 loss_thr: 1.1762 loss_db: 0.8043 2022/11/02 12:17:09 - mmengine - INFO - Epoch(train) [14][20/63] lr: 2.6436e-04 eta: 22:21:44 time: 0.5425 data_time: 0.0079 memory: 14901 loss: 5.0701 loss_prob: 2.9776 loss_thr: 1.1906 loss_db: 0.9018 2022/11/02 12:17:12 - mmengine - INFO - Epoch(train) [14][25/63] lr: 2.6436e-04 eta: 22:21:44 time: 0.5721 data_time: 0.0079 memory: 14901 loss: 5.2733 loss_prob: 3.2015 loss_thr: 1.2141 loss_db: 0.8577 2022/11/02 12:17:15 - mmengine - INFO - Epoch(train) [14][30/63] lr: 2.6436e-04 eta: 22:14:04 time: 0.5665 data_time: 0.0302 memory: 14901 loss: 5.5489 loss_prob: 3.3872 loss_thr: 1.2634 loss_db: 0.8983 2022/11/02 12:17:18 - mmengine - INFO - Epoch(train) [14][35/63] lr: 2.6436e-04 eta: 22:14:04 time: 0.6277 data_time: 0.0307 memory: 14901 loss: 5.6080 loss_prob: 3.3903 loss_thr: 1.2328 loss_db: 0.9849 2022/11/02 12:17:21 - mmengine - INFO - Epoch(train) [14][40/63] lr: 2.6436e-04 eta: 22:07:29 time: 0.6294 data_time: 0.0089 memory: 14901 loss: 5.5165 loss_prob: 3.3410 loss_thr: 1.1821 loss_db: 0.9934 2022/11/02 12:17:24 - mmengine - INFO - Epoch(train) [14][45/63] lr: 2.6436e-04 eta: 22:07:29 time: 0.5348 data_time: 0.0069 memory: 14901 loss: 5.4311 loss_prob: 3.2931 loss_thr: 1.1752 loss_db: 0.9629 2022/11/02 12:17:26 - mmengine - INFO - Epoch(train) [14][50/63] lr: 2.6436e-04 eta: 21:59:43 time: 0.5357 data_time: 0.0150 memory: 14901 loss: 5.6680 loss_prob: 3.4639 loss_thr: 1.2739 loss_db: 0.9302 2022/11/02 12:17:29 - mmengine - INFO - Epoch(train) [14][55/63] lr: 2.6436e-04 eta: 21:59:43 time: 0.5129 data_time: 0.0183 memory: 14901 loss: 5.8521 loss_prob: 3.5753 loss_thr: 1.3164 loss_db: 0.9605 2022/11/02 12:17:32 - mmengine - INFO - Epoch(train) [14][60/63] lr: 2.6436e-04 eta: 21:52:33 time: 0.5659 data_time: 0.0111 memory: 14901 loss: 5.7063 loss_prob: 3.4508 loss_thr: 1.2639 loss_db: 0.9916 2022/11/02 12:17:33 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:17:39 - mmengine - INFO - Epoch(train) [15][5/63] lr: 2.8455e-04 eta: 21:52:33 time: 0.8500 data_time: 0.3083 memory: 14901 loss: 5.5280 loss_prob: 3.2969 loss_thr: 1.2373 loss_db: 0.9938 2022/11/02 12:17:42 - mmengine - INFO - Epoch(train) [15][10/63] lr: 2.8455e-04 eta: 21:45:46 time: 0.9008 data_time: 0.3072 memory: 14901 loss: 5.4030 loss_prob: 3.2028 loss_thr: 1.2024 loss_db: 0.9978 2022/11/02 12:17:45 - mmengine - INFO - Epoch(train) [15][15/63] lr: 2.8455e-04 eta: 21:45:46 time: 0.6122 data_time: 0.0062 memory: 14901 loss: 5.3201 loss_prob: 3.1386 loss_thr: 1.1821 loss_db: 0.9994 2022/11/02 12:17:48 - mmengine - INFO - Epoch(train) [15][20/63] lr: 2.8455e-04 eta: 21:38:31 time: 0.5360 data_time: 0.0046 memory: 14901 loss: 5.2735 loss_prob: 3.0880 loss_thr: 1.1857 loss_db: 0.9997 2022/11/02 12:17:51 - mmengine - INFO - Epoch(train) [15][25/63] lr: 2.8455e-04 eta: 21:38:31 time: 0.5655 data_time: 0.0378 memory: 14901 loss: 5.2434 loss_prob: 3.0330 loss_thr: 1.2106 loss_db: 0.9998 2022/11/02 12:17:53 - mmengine - INFO - Epoch(train) [15][30/63] lr: 2.8455e-04 eta: 21:31:55 time: 0.5728 data_time: 0.0381 memory: 14901 loss: 5.2404 loss_prob: 3.0312 loss_thr: 1.2094 loss_db: 0.9999 2022/11/02 12:17:56 - mmengine - INFO - Epoch(train) [15][35/63] lr: 2.8455e-04 eta: 21:31:55 time: 0.5526 data_time: 0.0046 memory: 14901 loss: 5.2474 loss_prob: 3.0607 loss_thr: 1.1869 loss_db: 0.9999 2022/11/02 12:17:59 - mmengine - INFO - Epoch(train) [15][40/63] lr: 2.8455e-04 eta: 21:25:10 time: 0.5508 data_time: 0.0044 memory: 14901 loss: 5.2411 loss_prob: 3.0579 loss_thr: 1.1833 loss_db: 0.9999 2022/11/02 12:18:01 - mmengine - INFO - Epoch(train) [15][45/63] lr: 2.8455e-04 eta: 21:25:10 time: 0.4962 data_time: 0.0046 memory: 14901 loss: 5.2484 loss_prob: 3.0485 loss_thr: 1.1999 loss_db: 0.9999 2022/11/02 12:18:04 - mmengine - INFO - Epoch(train) [15][50/63] lr: 2.8455e-04 eta: 21:17:52 time: 0.4983 data_time: 0.0208 memory: 14901 loss: 5.2081 loss_prob: 3.0182 loss_thr: 1.1900 loss_db: 0.9999 2022/11/02 12:18:06 - mmengine - INFO - Epoch(train) [15][55/63] lr: 2.8455e-04 eta: 21:17:52 time: 0.4824 data_time: 0.0207 memory: 14901 loss: 5.1403 loss_prob: 2.9740 loss_thr: 1.1663 loss_db: 0.9999 2022/11/02 12:18:09 - mmengine - INFO - Epoch(train) [15][60/63] lr: 2.8455e-04 eta: 21:10:28 time: 0.4790 data_time: 0.0049 memory: 14901 loss: 5.1272 loss_prob: 2.9612 loss_thr: 1.1661 loss_db: 0.9999 2022/11/02 12:18:10 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:18:16 - mmengine - INFO - Epoch(train) [16][5/63] lr: 3.0473e-04 eta: 21:10:28 time: 0.7793 data_time: 0.2593 memory: 14901 loss: 5.1106 loss_prob: 2.9418 loss_thr: 1.1689 loss_db: 0.9999 2022/11/02 12:18:18 - mmengine - INFO - Epoch(train) [16][10/63] lr: 3.0473e-04 eta: 21:03:17 time: 0.7933 data_time: 0.2608 memory: 14901 loss: 5.0925 loss_prob: 2.9255 loss_thr: 1.1671 loss_db: 0.9999 2022/11/02 12:18:20 - mmengine - INFO - Epoch(train) [16][15/63] lr: 3.0473e-04 eta: 21:03:17 time: 0.4877 data_time: 0.0102 memory: 14901 loss: 5.0780 loss_prob: 2.9141 loss_thr: 1.1639 loss_db: 1.0000 2022/11/02 12:18:23 - mmengine - INFO - Epoch(train) [16][20/63] lr: 3.0473e-04 eta: 20:56:15 time: 0.4827 data_time: 0.0086 memory: 14901 loss: 5.1137 loss_prob: 2.9344 loss_thr: 1.1793 loss_db: 1.0000 2022/11/02 12:18:25 - mmengine - INFO - Epoch(train) [16][25/63] lr: 3.0473e-04 eta: 20:56:15 time: 0.4984 data_time: 0.0282 memory: 14901 loss: 5.2025 loss_prob: 2.9764 loss_thr: 1.2261 loss_db: 1.0000 2022/11/02 12:18:28 - mmengine - INFO - Epoch(train) [16][30/63] lr: 3.0473e-04 eta: 20:50:02 time: 0.5366 data_time: 0.0325 memory: 14901 loss: 5.1923 loss_prob: 2.9800 loss_thr: 1.2124 loss_db: 1.0000 2022/11/02 12:18:31 - mmengine - INFO - Epoch(train) [16][35/63] lr: 3.0473e-04 eta: 20:50:02 time: 0.5097 data_time: 0.0114 memory: 14901 loss: 5.1092 loss_prob: 2.9445 loss_thr: 1.1647 loss_db: 0.9999 2022/11/02 12:18:33 - mmengine - INFO - Epoch(train) [16][40/63] lr: 3.0473e-04 eta: 20:43:32 time: 0.5029 data_time: 0.0112 memory: 14901 loss: 5.0962 loss_prob: 2.9312 loss_thr: 1.1650 loss_db: 0.9999 2022/11/02 12:18:35 - mmengine - INFO - Epoch(train) [16][45/63] lr: 3.0473e-04 eta: 20:43:32 time: 0.4945 data_time: 0.0089 memory: 14901 loss: 5.0913 loss_prob: 2.9311 loss_thr: 1.1602 loss_db: 0.9999 2022/11/02 12:18:38 - mmengine - INFO - Epoch(train) [16][50/63] lr: 3.0473e-04 eta: 20:36:52 time: 0.4795 data_time: 0.0167 memory: 14901 loss: 5.0671 loss_prob: 2.9112 loss_thr: 1.1560 loss_db: 1.0000 2022/11/02 12:18:40 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:18:40 - mmengine - INFO - Epoch(train) [16][55/63] lr: 3.0473e-04 eta: 20:36:52 time: 0.5033 data_time: 0.0213 memory: 14901 loss: 5.0535 loss_prob: 2.8919 loss_thr: 1.1616 loss_db: 1.0000 2022/11/02 12:18:43 - mmengine - INFO - Epoch(train) [16][60/63] lr: 3.0473e-04 eta: 20:30:35 time: 0.5012 data_time: 0.0108 memory: 14901 loss: 5.0493 loss_prob: 2.8853 loss_thr: 1.1640 loss_db: 1.0000 2022/11/02 12:18:44 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:18:50 - mmengine - INFO - Epoch(train) [17][5/63] lr: 3.2491e-04 eta: 20:30:35 time: 0.7823 data_time: 0.2204 memory: 14901 loss: 5.0611 loss_prob: 2.9056 loss_thr: 1.1555 loss_db: 1.0000 2022/11/02 12:18:52 - mmengine - INFO - Epoch(train) [17][10/63] lr: 3.2491e-04 eta: 20:24:37 time: 0.8154 data_time: 0.2195 memory: 14901 loss: 5.0919 loss_prob: 2.9348 loss_thr: 1.1571 loss_db: 1.0000 2022/11/02 12:18:55 - mmengine - INFO - Epoch(train) [17][15/63] lr: 3.2491e-04 eta: 20:24:37 time: 0.4691 data_time: 0.0050 memory: 14901 loss: 5.1079 loss_prob: 2.9506 loss_thr: 1.1574 loss_db: 0.9999 2022/11/02 12:18:57 - mmengine - INFO - Epoch(train) [17][20/63] lr: 3.2491e-04 eta: 20:18:19 time: 0.4777 data_time: 0.0050 memory: 14901 loss: 5.0918 loss_prob: 2.9318 loss_thr: 1.1601 loss_db: 0.9999 2022/11/02 12:18:59 - mmengine - INFO - Epoch(train) [17][25/63] lr: 3.2491e-04 eta: 20:18:19 time: 0.4873 data_time: 0.0119 memory: 14901 loss: 5.0656 loss_prob: 2.9069 loss_thr: 1.1588 loss_db: 1.0000 2022/11/02 12:19:02 - mmengine - INFO - Epoch(train) [17][30/63] lr: 3.2491e-04 eta: 20:12:04 time: 0.4714 data_time: 0.0367 memory: 14901 loss: 5.0409 loss_prob: 2.8838 loss_thr: 1.1571 loss_db: 1.0000 2022/11/02 12:19:04 - mmengine - INFO - Epoch(train) [17][35/63] lr: 3.2491e-04 eta: 20:12:04 time: 0.5055 data_time: 0.0297 memory: 14901 loss: 5.0241 loss_prob: 2.8622 loss_thr: 1.1619 loss_db: 1.0000 2022/11/02 12:19:07 - mmengine - INFO - Epoch(train) [17][40/63] lr: 3.2491e-04 eta: 20:06:07 time: 0.4868 data_time: 0.0047 memory: 14901 loss: 5.0290 loss_prob: 2.8702 loss_thr: 1.1588 loss_db: 1.0000 2022/11/02 12:19:09 - mmengine - INFO - Epoch(train) [17][45/63] lr: 3.2491e-04 eta: 20:06:07 time: 0.4570 data_time: 0.0049 memory: 14901 loss: 5.0400 loss_prob: 2.8832 loss_thr: 1.1569 loss_db: 1.0000 2022/11/02 12:19:12 - mmengine - INFO - Epoch(train) [17][50/63] lr: 3.2491e-04 eta: 20:00:13 time: 0.4832 data_time: 0.0121 memory: 14901 loss: 5.0776 loss_prob: 2.9158 loss_thr: 1.1618 loss_db: 1.0000 2022/11/02 12:19:14 - mmengine - INFO - Epoch(train) [17][55/63] lr: 3.2491e-04 eta: 20:00:13 time: 0.5087 data_time: 0.0232 memory: 14901 loss: 5.1105 loss_prob: 2.9513 loss_thr: 1.1593 loss_db: 1.0000 2022/11/02 12:19:16 - mmengine - INFO - Epoch(train) [17][60/63] lr: 3.2491e-04 eta: 19:54:31 time: 0.4889 data_time: 0.0159 memory: 14901 loss: 5.1056 loss_prob: 2.9535 loss_thr: 1.1522 loss_db: 1.0000 2022/11/02 12:19:18 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:19:22 - mmengine - INFO - Epoch(train) [18][5/63] lr: 3.4509e-04 eta: 19:54:31 time: 0.6783 data_time: 0.1969 memory: 14901 loss: 5.0828 loss_prob: 2.9257 loss_thr: 1.1572 loss_db: 1.0000 2022/11/02 12:19:26 - mmengine - INFO - Epoch(train) [18][10/63] lr: 3.4509e-04 eta: 19:49:13 time: 0.8076 data_time: 0.2104 memory: 14901 loss: 5.0601 loss_prob: 2.9041 loss_thr: 1.1561 loss_db: 1.0000 2022/11/02 12:19:28 - mmengine - INFO - Epoch(train) [18][15/63] lr: 3.4509e-04 eta: 19:49:13 time: 0.5802 data_time: 0.0200 memory: 14901 loss: 5.0396 loss_prob: 2.8862 loss_thr: 1.1535 loss_db: 1.0000 2022/11/02 12:19:31 - mmengine - INFO - Epoch(train) [18][20/63] lr: 3.4509e-04 eta: 19:44:04 time: 0.5188 data_time: 0.0062 memory: 14901 loss: 5.0528 loss_prob: 2.8938 loss_thr: 1.1591 loss_db: 1.0000 2022/11/02 12:19:34 - mmengine - INFO - Epoch(train) [18][25/63] lr: 3.4509e-04 eta: 19:44:04 time: 0.5630 data_time: 0.0094 memory: 14901 loss: 5.0705 loss_prob: 2.9037 loss_thr: 1.1669 loss_db: 1.0000 2022/11/02 12:19:38 - mmengine - INFO - Epoch(train) [18][30/63] lr: 3.4509e-04 eta: 19:40:48 time: 0.6773 data_time: 0.0351 memory: 14901 loss: 5.0687 loss_prob: 2.9034 loss_thr: 1.1653 loss_db: 1.0000 2022/11/02 12:19:41 - mmengine - INFO - Epoch(train) [18][35/63] lr: 3.4509e-04 eta: 19:40:48 time: 0.7121 data_time: 0.0300 memory: 14901 loss: 5.0616 loss_prob: 2.9003 loss_thr: 1.1613 loss_db: 1.0000 2022/11/02 12:19:43 - mmengine - INFO - Epoch(train) [18][40/63] lr: 3.4509e-04 eta: 19:36:25 time: 0.5732 data_time: 0.0064 memory: 14901 loss: 5.0543 loss_prob: 2.8958 loss_thr: 1.1586 loss_db: 1.0000 2022/11/02 12:19:46 - mmengine - INFO - Epoch(train) [18][45/63] lr: 3.4509e-04 eta: 19:36:25 time: 0.4985 data_time: 0.0084 memory: 14901 loss: 5.0381 loss_prob: 2.8840 loss_thr: 1.1541 loss_db: 1.0000 2022/11/02 12:19:50 - mmengine - INFO - Epoch(train) [18][50/63] lr: 3.4509e-04 eta: 19:32:41 time: 0.6244 data_time: 0.0152 memory: 14901 loss: 5.0417 loss_prob: 2.8859 loss_thr: 1.1558 loss_db: 1.0000 2022/11/02 12:19:52 - mmengine - INFO - Epoch(train) [18][55/63] lr: 3.4509e-04 eta: 19:32:41 time: 0.6445 data_time: 0.0261 memory: 14901 loss: 5.0505 loss_prob: 2.8937 loss_thr: 1.1569 loss_db: 1.0000 2022/11/02 12:19:55 - mmengine - INFO - Epoch(train) [18][60/63] lr: 3.4509e-04 eta: 19:27:42 time: 0.5063 data_time: 0.0173 memory: 14901 loss: 5.0557 loss_prob: 2.8940 loss_thr: 1.1617 loss_db: 1.0000 2022/11/02 12:19:56 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:20:04 - mmengine - INFO - Epoch(train) [19][5/63] lr: 3.6527e-04 eta: 19:27:42 time: 0.9867 data_time: 0.3436 memory: 14901 loss: 5.0680 loss_prob: 2.9133 loss_thr: 1.1547 loss_db: 1.0000 2022/11/02 12:20:07 - mmengine - INFO - Epoch(train) [19][10/63] lr: 3.6527e-04 eta: 19:25:49 time: 1.0671 data_time: 0.3432 memory: 14901 loss: 5.0634 loss_prob: 2.9120 loss_thr: 1.1514 loss_db: 1.0000 2022/11/02 12:20:10 - mmengine - INFO - Epoch(train) [19][15/63] lr: 3.6527e-04 eta: 19:25:49 time: 0.6400 data_time: 0.0079 memory: 14901 loss: 5.0488 loss_prob: 2.8979 loss_thr: 1.1510 loss_db: 1.0000 2022/11/02 12:20:13 - mmengine - INFO - Epoch(train) [19][20/63] lr: 3.6527e-04 eta: 19:22:10 time: 0.6145 data_time: 0.0060 memory: 14901 loss: 5.0442 loss_prob: 2.8879 loss_thr: 1.1563 loss_db: 1.0000 2022/11/02 12:20:16 - mmengine - INFO - Epoch(train) [19][25/63] lr: 3.6527e-04 eta: 19:22:10 time: 0.6091 data_time: 0.0260 memory: 14901 loss: 5.0485 loss_prob: 2.8888 loss_thr: 1.1598 loss_db: 1.0000 2022/11/02 12:20:19 - mmengine - INFO - Epoch(train) [19][30/63] lr: 3.6527e-04 eta: 19:18:53 time: 0.6441 data_time: 0.0256 memory: 14901 loss: 5.0533 loss_prob: 2.8938 loss_thr: 1.1595 loss_db: 1.0000 2022/11/02 12:20:22 - mmengine - INFO - Epoch(train) [19][35/63] lr: 3.6527e-04 eta: 19:18:53 time: 0.5935 data_time: 0.0050 memory: 14901 loss: 5.0781 loss_prob: 2.9083 loss_thr: 1.1699 loss_db: 1.0000 2022/11/02 12:20:25 - mmengine - INFO - Epoch(train) [19][40/63] lr: 3.6527e-04 eta: 19:14:13 time: 0.5072 data_time: 0.0050 memory: 14901 loss: 5.1045 loss_prob: 2.9365 loss_thr: 1.1680 loss_db: 1.0000 2022/11/02 12:20:28 - mmengine - INFO - Epoch(train) [19][45/63] lr: 3.6527e-04 eta: 19:14:13 time: 0.5692 data_time: 0.0048 memory: 14901 loss: 5.1515 loss_prob: 2.9932 loss_thr: 1.1583 loss_db: 1.0000 2022/11/02 12:20:31 - mmengine - INFO - Epoch(train) [19][50/63] lr: 3.6527e-04 eta: 19:11:27 time: 0.6803 data_time: 0.0205 memory: 14901 loss: 5.2653 loss_prob: 3.0905 loss_thr: 1.1748 loss_db: 1.0000 2022/11/02 12:20:34 - mmengine - INFO - Epoch(train) [19][55/63] lr: 3.6527e-04 eta: 19:11:27 time: 0.6073 data_time: 0.0229 memory: 14901 loss: 5.3521 loss_prob: 3.1733 loss_thr: 1.1788 loss_db: 1.0000 2022/11/02 12:20:37 - mmengine - INFO - Epoch(train) [19][60/63] lr: 3.6527e-04 eta: 19:07:24 time: 0.5533 data_time: 0.0071 memory: 14901 loss: 5.3926 loss_prob: 3.1881 loss_thr: 1.2045 loss_db: 1.0000 2022/11/02 12:20:38 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:20:43 - mmengine - INFO - Epoch(train) [20][5/63] lr: 3.8545e-04 eta: 19:07:24 time: 0.7284 data_time: 0.1904 memory: 14901 loss: 5.3307 loss_prob: 3.1231 loss_thr: 1.2076 loss_db: 1.0000 2022/11/02 12:20:45 - mmengine - INFO - Epoch(train) [20][10/63] lr: 3.8545e-04 eta: 19:02:06 time: 0.7064 data_time: 0.1927 memory: 14901 loss: 5.2078 loss_prob: 3.0386 loss_thr: 1.1693 loss_db: 1.0000 2022/11/02 12:20:48 - mmengine - INFO - Epoch(train) [20][15/63] lr: 3.8545e-04 eta: 19:02:06 time: 0.4760 data_time: 0.0080 memory: 14901 loss: 5.1671 loss_prob: 3.0002 loss_thr: 1.1669 loss_db: 1.0000 2022/11/02 12:20:51 - mmengine - INFO - Epoch(train) [20][20/63] lr: 3.8545e-04 eta: 18:57:50 time: 0.5186 data_time: 0.0060 memory: 14901 loss: 5.1757 loss_prob: 3.0138 loss_thr: 1.1620 loss_db: 1.0000 2022/11/02 12:20:53 - mmengine - INFO - Epoch(train) [20][25/63] lr: 3.8545e-04 eta: 18:57:50 time: 0.5354 data_time: 0.0070 memory: 14901 loss: 5.2353 loss_prob: 3.0540 loss_thr: 1.1814 loss_db: 1.0000 2022/11/02 12:20:56 - mmengine - INFO - Epoch(train) [20][30/63] lr: 3.8545e-04 eta: 18:53:37 time: 0.5155 data_time: 0.0351 memory: 14901 loss: 5.2395 loss_prob: 3.0583 loss_thr: 1.1812 loss_db: 1.0000 2022/11/02 12:20:58 - mmengine - INFO - Epoch(train) [20][35/63] lr: 3.8545e-04 eta: 18:53:37 time: 0.4870 data_time: 0.0332 memory: 14901 loss: 5.1780 loss_prob: 3.0197 loss_thr: 1.1583 loss_db: 1.0000 2022/11/02 12:21:01 - mmengine - INFO - Epoch(train) [20][40/63] lr: 3.8545e-04 eta: 18:49:12 time: 0.4886 data_time: 0.0044 memory: 14901 loss: 5.1689 loss_prob: 2.9988 loss_thr: 1.1701 loss_db: 1.0000 2022/11/02 12:21:03 - mmengine - INFO - Epoch(train) [20][45/63] lr: 3.8545e-04 eta: 18:49:12 time: 0.5097 data_time: 0.0048 memory: 14901 loss: 5.1476 loss_prob: 2.9754 loss_thr: 1.1723 loss_db: 1.0000 2022/11/02 12:21:06 - mmengine - INFO - Epoch(train) [20][50/63] lr: 3.8545e-04 eta: 18:44:59 time: 0.5013 data_time: 0.0219 memory: 14901 loss: 5.1299 loss_prob: 2.9713 loss_thr: 1.1587 loss_db: 1.0000 2022/11/02 12:21:08 - mmengine - INFO - Epoch(train) [20][55/63] lr: 3.8545e-04 eta: 18:44:59 time: 0.5089 data_time: 0.0214 memory: 14901 loss: 5.1189 loss_prob: 2.9622 loss_thr: 1.1567 loss_db: 1.0000 2022/11/02 12:21:11 - mmengine - INFO - Epoch(train) [20][60/63] lr: 3.8545e-04 eta: 18:40:53 time: 0.5074 data_time: 0.0044 memory: 14901 loss: 5.0973 loss_prob: 2.9309 loss_thr: 1.1664 loss_db: 1.0000 2022/11/02 12:21:12 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:21:12 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/11/02 12:23:27 - mmengine - INFO - Epoch(val) [20][5/500] eta: 18:40:53 time: 26.3208 data_time: 25.0998 memory: 34493 2022/11/02 12:23:27 - mmengine - INFO - Epoch(val) [20][10/500] eta: 1:47:36 time: 13.1769 data_time: 12.5510 memory: 1008 2022/11/02 12:23:27 - mmengine - INFO - Epoch(val) [20][15/500] eta: 1:47:36 time: 0.0334 data_time: 0.0024 memory: 1008 2022/11/02 12:23:27 - mmengine - INFO - Epoch(val) [20][20/500] eta: 0:00:16 time: 0.0339 data_time: 0.0024 memory: 1008 2022/11/02 12:23:28 - mmengine - INFO - Epoch(val) [20][25/500] eta: 0:00:16 time: 0.0340 data_time: 0.0027 memory: 1008 2022/11/02 12:23:28 - mmengine - INFO - Epoch(val) [20][30/500] eta: 0:00:15 time: 0.0337 data_time: 0.0027 memory: 1008 2022/11/02 12:23:28 - mmengine - INFO - Epoch(val) [20][35/500] eta: 0:00:15 time: 0.0330 data_time: 0.0023 memory: 1008 2022/11/02 12:23:28 - mmengine - INFO - Epoch(val) [20][40/500] eta: 0:00:15 time: 0.0345 data_time: 0.0022 memory: 1008 2022/11/02 12:23:28 - mmengine - INFO - Epoch(val) [20][45/500] eta: 0:00:15 time: 0.0338 data_time: 0.0023 memory: 1008 2022/11/02 12:23:28 - mmengine - INFO - Epoch(val) [20][50/500] eta: 0:00:13 time: 0.0303 data_time: 0.0022 memory: 1008 2022/11/02 12:23:29 - mmengine - INFO - Epoch(val) [20][55/500] eta: 0:00:13 time: 0.0296 data_time: 0.0020 memory: 1008 2022/11/02 12:23:29 - mmengine - INFO - Epoch(val) [20][60/500] eta: 0:00:13 time: 0.0298 data_time: 0.0019 memory: 1008 2022/11/02 12:23:29 - mmengine - INFO - Epoch(val) [20][65/500] eta: 0:00:13 time: 0.0299 data_time: 0.0020 memory: 1008 2022/11/02 12:23:29 - mmengine - INFO - Epoch(val) [20][70/500] eta: 0:00:12 time: 0.0298 data_time: 0.0020 memory: 1008 2022/11/02 12:23:29 - mmengine - INFO - Epoch(val) [20][75/500] eta: 0:00:12 time: 0.0304 data_time: 0.0022 memory: 1008 2022/11/02 12:23:29 - mmengine - INFO - Epoch(val) [20][80/500] eta: 0:00:13 time: 0.0315 data_time: 0.0023 memory: 1008 2022/11/02 12:23:30 - mmengine - INFO - Epoch(val) [20][85/500] eta: 0:00:13 time: 0.0318 data_time: 0.0023 memory: 1008 2022/11/02 12:23:30 - mmengine - INFO - Epoch(val) [20][90/500] eta: 0:00:12 time: 0.0317 data_time: 0.0024 memory: 1008 2022/11/02 12:23:30 - mmengine - INFO - Epoch(val) [20][95/500] eta: 0:00:12 time: 0.0315 data_time: 0.0025 memory: 1008 2022/11/02 12:23:30 - mmengine - INFO - Epoch(val) [20][100/500] eta: 0:00:12 time: 0.0311 data_time: 0.0024 memory: 1008 2022/11/02 12:23:30 - mmengine - INFO - Epoch(val) [20][105/500] eta: 0:00:12 time: 0.0317 data_time: 0.0023 memory: 1008 2022/11/02 12:23:30 - mmengine - INFO - Epoch(val) [20][110/500] eta: 0:00:12 time: 0.0317 data_time: 0.0022 memory: 1008 2022/11/02 12:23:30 - mmengine - INFO - Epoch(val) [20][115/500] eta: 0:00:12 time: 0.0308 data_time: 0.0023 memory: 1008 2022/11/02 12:23:31 - mmengine - INFO - Epoch(val) [20][120/500] eta: 0:00:11 time: 0.0307 data_time: 0.0022 memory: 1008 2022/11/02 12:23:31 - mmengine - INFO - Epoch(val) [20][125/500] eta: 0:00:11 time: 0.0305 data_time: 0.0022 memory: 1008 2022/11/02 12:23:31 - mmengine - INFO - Epoch(val) [20][130/500] eta: 0:00:11 time: 0.0307 data_time: 0.0022 memory: 1008 2022/11/02 12:23:31 - mmengine - INFO - Epoch(val) [20][135/500] eta: 0:00:11 time: 0.0318 data_time: 0.0022 memory: 1008 2022/11/02 12:23:31 - mmengine - INFO - Epoch(val) [20][140/500] eta: 0:00:11 time: 0.0325 data_time: 0.0024 memory: 1008 2022/11/02 12:23:31 - mmengine - INFO - Epoch(val) [20][145/500] eta: 0:00:11 time: 0.0325 data_time: 0.0026 memory: 1008 2022/11/02 12:23:32 - mmengine - INFO - Epoch(val) [20][150/500] eta: 0:00:11 time: 0.0328 data_time: 0.0025 memory: 1008 2022/11/02 12:23:32 - mmengine - INFO - Epoch(val) [20][155/500] eta: 0:00:11 time: 0.0313 data_time: 0.0022 memory: 1008 2022/11/02 12:23:32 - mmengine - INFO - Epoch(val) [20][160/500] eta: 0:00:10 time: 0.0298 data_time: 0.0020 memory: 1008 2022/11/02 12:23:32 - mmengine - INFO - Epoch(val) [20][165/500] eta: 0:00:10 time: 0.0300 data_time: 0.0020 memory: 1008 2022/11/02 12:23:32 - mmengine - INFO - Epoch(val) [20][170/500] eta: 0:00:10 time: 0.0313 data_time: 0.0022 memory: 1008 2022/11/02 12:23:32 - mmengine - INFO - Epoch(val) [20][175/500] eta: 0:00:10 time: 0.0317 data_time: 0.0023 memory: 1008 2022/11/02 12:23:33 - mmengine - INFO - Epoch(val) [20][180/500] eta: 0:00:09 time: 0.0310 data_time: 0.0023 memory: 1008 2022/11/02 12:23:33 - mmengine - INFO - Epoch(val) [20][185/500] eta: 0:00:09 time: 0.0309 data_time: 0.0023 memory: 1008 2022/11/02 12:23:33 - mmengine - INFO - Epoch(val) [20][190/500] eta: 0:00:09 time: 0.0307 data_time: 0.0021 memory: 1008 2022/11/02 12:23:33 - mmengine - INFO - Epoch(val) [20][195/500] eta: 0:00:09 time: 0.0302 data_time: 0.0019 memory: 1008 2022/11/02 12:23:33 - mmengine - INFO - Epoch(val) [20][200/500] eta: 0:00:08 time: 0.0298 data_time: 0.0019 memory: 1008 2022/11/02 12:23:33 - mmengine - INFO - Epoch(val) [20][205/500] eta: 0:00:08 time: 0.0300 data_time: 0.0020 memory: 1008 2022/11/02 12:23:33 - mmengine - INFO - Epoch(val) [20][210/500] eta: 0:00:08 time: 0.0304 data_time: 0.0022 memory: 1008 2022/11/02 12:23:34 - mmengine - INFO - Epoch(val) [20][215/500] eta: 0:00:08 time: 0.0307 data_time: 0.0023 memory: 1008 2022/11/02 12:23:34 - mmengine - INFO - Epoch(val) [20][220/500] eta: 0:00:08 time: 0.0307 data_time: 0.0022 memory: 1008 2022/11/02 12:23:34 - mmengine - INFO - Epoch(val) [20][225/500] eta: 0:00:08 time: 0.0316 data_time: 0.0022 memory: 1008 2022/11/02 12:23:34 - mmengine - INFO - Epoch(val) [20][230/500] eta: 0:00:08 time: 0.0325 data_time: 0.0022 memory: 1008 2022/11/02 12:23:34 - mmengine - INFO - Epoch(val) [20][235/500] eta: 0:00:08 time: 0.0317 data_time: 0.0022 memory: 1008 2022/11/02 12:23:34 - mmengine - INFO - Epoch(val) [20][240/500] eta: 0:00:07 time: 0.0307 data_time: 0.0023 memory: 1008 2022/11/02 12:23:35 - mmengine - INFO - Epoch(val) [20][245/500] eta: 0:00:07 time: 0.0310 data_time: 0.0023 memory: 1008 2022/11/02 12:23:35 - mmengine - INFO - Epoch(val) [20][250/500] eta: 0:00:07 time: 0.0310 data_time: 0.0023 memory: 1008 2022/11/02 12:23:35 - mmengine - INFO - Epoch(val) [20][255/500] eta: 0:00:07 time: 0.0307 data_time: 0.0023 memory: 1008 2022/11/02 12:23:35 - mmengine - INFO - Epoch(val) [20][260/500] eta: 0:00:07 time: 0.0308 data_time: 0.0023 memory: 1008 2022/11/02 12:23:35 - mmengine - INFO - Epoch(val) [20][265/500] eta: 0:00:07 time: 0.0310 data_time: 0.0023 memory: 1008 2022/11/02 12:23:35 - mmengine - INFO - Epoch(val) [20][270/500] eta: 0:00:07 time: 0.0311 data_time: 0.0024 memory: 1008 2022/11/02 12:23:35 - mmengine - INFO - Epoch(val) [20][275/500] eta: 0:00:07 time: 0.0310 data_time: 0.0023 memory: 1008 2022/11/02 12:23:36 - mmengine - INFO - Epoch(val) [20][280/500] eta: 0:00:07 time: 0.0329 data_time: 0.0025 memory: 1008 2022/11/02 12:23:36 - mmengine - INFO - Epoch(val) [20][285/500] eta: 0:00:07 time: 0.0332 data_time: 0.0025 memory: 1008 2022/11/02 12:23:36 - mmengine - INFO - Epoch(val) [20][290/500] eta: 0:00:06 time: 0.0309 data_time: 0.0023 memory: 1008 2022/11/02 12:23:36 - mmengine - INFO - Epoch(val) [20][295/500] eta: 0:00:06 time: 0.0306 data_time: 0.0022 memory: 1008 2022/11/02 12:23:36 - mmengine - INFO - Epoch(val) [20][300/500] eta: 0:00:06 time: 0.0307 data_time: 0.0023 memory: 1008 2022/11/02 12:23:36 - mmengine - INFO - Epoch(val) [20][305/500] eta: 0:00:06 time: 0.0308 data_time: 0.0023 memory: 1008 2022/11/02 12:23:37 - mmengine - INFO - Epoch(val) [20][310/500] eta: 0:00:06 time: 0.0333 data_time: 0.0024 memory: 1008 2022/11/02 12:23:37 - mmengine - INFO - Epoch(val) [20][315/500] eta: 0:00:06 time: 0.0335 data_time: 0.0025 memory: 1008 2022/11/02 12:23:37 - mmengine - INFO - Epoch(val) [20][320/500] eta: 0:00:05 time: 0.0318 data_time: 0.0026 memory: 1008 2022/11/02 12:23:37 - mmengine - INFO - Epoch(val) [20][325/500] eta: 0:00:05 time: 0.0319 data_time: 0.0025 memory: 1008 2022/11/02 12:23:37 - mmengine - INFO - Epoch(val) [20][330/500] eta: 0:00:05 time: 0.0311 data_time: 0.0024 memory: 1008 2022/11/02 12:23:37 - mmengine - INFO - Epoch(val) [20][335/500] eta: 0:00:05 time: 0.0306 data_time: 0.0023 memory: 1008 2022/11/02 12:23:38 - mmengine - INFO - Epoch(val) [20][340/500] eta: 0:00:05 time: 0.0315 data_time: 0.0023 memory: 1008 2022/11/02 12:23:38 - mmengine - INFO - Epoch(val) [20][345/500] eta: 0:00:05 time: 0.0319 data_time: 0.0024 memory: 1008 2022/11/02 12:23:38 - mmengine - INFO - Epoch(val) [20][350/500] eta: 0:00:04 time: 0.0313 data_time: 0.0024 memory: 1008 2022/11/02 12:23:38 - mmengine - INFO - Epoch(val) [20][355/500] eta: 0:00:04 time: 0.0313 data_time: 0.0023 memory: 1008 2022/11/02 12:23:38 - mmengine - INFO - Epoch(val) [20][360/500] eta: 0:00:04 time: 0.0319 data_time: 0.0023 memory: 1008 2022/11/02 12:23:38 - mmengine - INFO - Epoch(val) [20][365/500] eta: 0:00:04 time: 0.0318 data_time: 0.0023 memory: 1008 2022/11/02 12:23:38 - mmengine - INFO - Epoch(val) [20][370/500] eta: 0:00:04 time: 0.0308 data_time: 0.0024 memory: 1008 2022/11/02 12:23:39 - mmengine - INFO - Epoch(val) [20][375/500] eta: 0:00:04 time: 0.0309 data_time: 0.0022 memory: 1008 2022/11/02 12:23:39 - mmengine - INFO - Epoch(val) [20][380/500] eta: 0:00:03 time: 0.0323 data_time: 0.0025 memory: 1008 2022/11/02 12:23:39 - mmengine - INFO - Epoch(val) [20][385/500] eta: 0:00:03 time: 0.0324 data_time: 0.0026 memory: 1008 2022/11/02 12:23:39 - mmengine - INFO - Epoch(val) [20][390/500] eta: 0:00:03 time: 0.0311 data_time: 0.0024 memory: 1008 2022/11/02 12:23:39 - mmengine - INFO - Epoch(val) [20][395/500] eta: 0:00:03 time: 0.0311 data_time: 0.0024 memory: 1008 2022/11/02 12:23:39 - mmengine - INFO - Epoch(val) [20][400/500] eta: 0:00:03 time: 0.0307 data_time: 0.0022 memory: 1008 2022/11/02 12:23:40 - mmengine - INFO - Epoch(val) [20][405/500] eta: 0:00:03 time: 0.0310 data_time: 0.0023 memory: 1008 2022/11/02 12:23:40 - mmengine - INFO - Epoch(val) [20][410/500] eta: 0:00:02 time: 0.0315 data_time: 0.0024 memory: 1008 2022/11/02 12:23:40 - mmengine - INFO - Epoch(val) [20][415/500] eta: 0:00:02 time: 0.0314 data_time: 0.0024 memory: 1008 2022/11/02 12:23:40 - mmengine - INFO - Epoch(val) [20][420/500] eta: 0:00:02 time: 0.0327 data_time: 0.0025 memory: 1008 2022/11/02 12:23:40 - mmengine - INFO - Epoch(val) [20][425/500] eta: 0:00:02 time: 0.0330 data_time: 0.0026 memory: 1008 2022/11/02 12:23:40 - mmengine - INFO - Epoch(val) [20][430/500] eta: 0:00:02 time: 0.0320 data_time: 0.0024 memory: 1008 2022/11/02 12:23:41 - mmengine - INFO - Epoch(val) [20][435/500] eta: 0:00:02 time: 0.0317 data_time: 0.0023 memory: 1008 2022/11/02 12:23:41 - mmengine - INFO - Epoch(val) [20][440/500] eta: 0:00:01 time: 0.0313 data_time: 0.0023 memory: 1008 2022/11/02 12:23:41 - mmengine - INFO - Epoch(val) [20][445/500] eta: 0:00:01 time: 0.0310 data_time: 0.0024 memory: 1008 2022/11/02 12:23:41 - mmengine - INFO - Epoch(val) [20][450/500] eta: 0:00:01 time: 0.0307 data_time: 0.0023 memory: 1008 2022/11/02 12:23:41 - mmengine - INFO - Epoch(val) [20][455/500] eta: 0:00:01 time: 0.0312 data_time: 0.0024 memory: 1008 2022/11/02 12:23:41 - mmengine - INFO - Epoch(val) [20][460/500] eta: 0:00:01 time: 0.0316 data_time: 0.0026 memory: 1008 2022/11/02 12:23:41 - mmengine - INFO - Epoch(val) [20][465/500] eta: 0:00:01 time: 0.0308 data_time: 0.0024 memory: 1008 2022/11/02 12:23:42 - mmengine - INFO - Epoch(val) [20][470/500] eta: 0:00:00 time: 0.0307 data_time: 0.0022 memory: 1008 2022/11/02 12:23:42 - mmengine - INFO - Epoch(val) [20][475/500] eta: 0:00:00 time: 0.0306 data_time: 0.0021 memory: 1008 2022/11/02 12:23:42 - mmengine - INFO - Epoch(val) [20][480/500] eta: 0:00:00 time: 0.0313 data_time: 0.0021 memory: 1008 2022/11/02 12:23:42 - mmengine - INFO - Epoch(val) [20][485/500] eta: 0:00:00 time: 0.0318 data_time: 0.0022 memory: 1008 2022/11/02 12:23:42 - mmengine - INFO - Epoch(val) [20][490/500] eta: 0:00:00 time: 0.0349 data_time: 0.0025 memory: 1008 2022/11/02 12:23:42 - mmengine - INFO - Epoch(val) [20][495/500] eta: 0:00:00 time: 0.0371 data_time: 0.0028 memory: 1008 2022/11/02 12:23:43 - mmengine - INFO - Epoch(val) [20][500/500] eta: 0:00:00 time: 0.0328 data_time: 0.0025 memory: 1008 2022/11/02 12:23:43 - mmengine - INFO - Evaluating hmean-iou... 2022/11/02 12:23:43 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.0000, precision: 0.0000, hmean: 0.0000 2022/11/02 12:23:43 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.0000, precision: 0.0000, hmean: 0.0000 2022/11/02 12:23:43 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.0000, precision: 0.0000, hmean: 0.0000 2022/11/02 12:23:43 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.0000, precision: 0.0000, hmean: 0.0000 2022/11/02 12:23:43 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.0000, precision: 0.0000, hmean: 0.0000 2022/11/02 12:23:43 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.0000, precision: 0.0000, hmean: 0.0000 2022/11/02 12:23:43 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0000, precision: 0.0000, hmean: 0.0000 2022/11/02 12:23:43 - mmengine - INFO - Epoch(val) [20][500/500] icdar/precision: 0.0000 icdar/recall: 0.0000 icdar/hmean: 0.0000 2022/11/02 12:23:47 - mmengine - INFO - Epoch(train) [21][5/63] lr: 4.0564e-04 eta: 0:00:00 time: 0.6973 data_time: 0.1957 memory: 14901 loss: 5.0678 loss_prob: 2.9084 loss_thr: 1.1595 loss_db: 1.0000 2022/11/02 12:23:50 - mmengine - INFO - Epoch(train) [21][10/63] lr: 4.0564e-04 eta: 18:36:02 time: 0.6997 data_time: 0.2000 memory: 14901 loss: 5.0712 loss_prob: 2.9134 loss_thr: 1.1579 loss_db: 1.0000 2022/11/02 12:23:52 - mmengine - INFO - Epoch(train) [21][15/63] lr: 4.0564e-04 eta: 18:36:02 time: 0.4641 data_time: 0.0088 memory: 14901 loss: 5.0765 loss_prob: 2.9169 loss_thr: 1.1596 loss_db: 1.0000 2022/11/02 12:23:54 - mmengine - INFO - Epoch(train) [21][20/63] lr: 4.0564e-04 eta: 18:31:35 time: 0.4559 data_time: 0.0045 memory: 14901 loss: 5.0842 loss_prob: 2.9229 loss_thr: 1.1613 loss_db: 1.0000 2022/11/02 12:23:57 - mmengine - INFO - Epoch(train) [21][25/63] lr: 4.0564e-04 eta: 18:31:35 time: 0.4838 data_time: 0.0183 memory: 14901 loss: 5.0586 loss_prob: 2.9018 loss_thr: 1.1568 loss_db: 1.0000 2022/11/02 12:23:59 - mmengine - INFO - Epoch(train) [21][30/63] lr: 4.0564e-04 eta: 18:27:33 time: 0.4934 data_time: 0.0277 memory: 14901 loss: 5.0559 loss_prob: 2.8929 loss_thr: 1.1630 loss_db: 1.0000 2022/11/02 12:24:02 - mmengine - INFO - Epoch(train) [21][35/63] lr: 4.0564e-04 eta: 18:27:33 time: 0.4719 data_time: 0.0209 memory: 14901 loss: 5.0510 loss_prob: 2.8856 loss_thr: 1.1654 loss_db: 1.0000 2022/11/02 12:24:04 - mmengine - INFO - Epoch(train) [21][40/63] lr: 4.0564e-04 eta: 18:23:22 time: 0.4706 data_time: 0.0117 memory: 14901 loss: 5.0180 loss_prob: 2.8622 loss_thr: 1.1558 loss_db: 1.0000 2022/11/02 12:24:06 - mmengine - INFO - Epoch(train) [21][45/63] lr: 4.0564e-04 eta: 18:23:22 time: 0.4689 data_time: 0.0080 memory: 14901 loss: 5.0205 loss_prob: 2.8659 loss_thr: 1.1546 loss_db: 1.0000 2022/11/02 12:24:09 - mmengine - INFO - Epoch(train) [21][50/63] lr: 4.0564e-04 eta: 18:19:16 time: 0.4722 data_time: 0.0150 memory: 14901 loss: 5.0459 loss_prob: 2.8889 loss_thr: 1.1570 loss_db: 1.0000 2022/11/02 12:24:11 - mmengine - INFO - Epoch(train) [21][55/63] lr: 4.0564e-04 eta: 18:19:16 time: 0.4855 data_time: 0.0179 memory: 14901 loss: 5.0558 loss_prob: 2.8962 loss_thr: 1.1596 loss_db: 1.0000 2022/11/02 12:24:13 - mmengine - INFO - Epoch(train) [21][60/63] lr: 4.0564e-04 eta: 18:15:16 time: 0.4781 data_time: 0.0175 memory: 14901 loss: 5.0514 loss_prob: 2.8950 loss_thr: 1.1564 loss_db: 1.0000 2022/11/02 12:24:15 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:24:19 - mmengine - INFO - Epoch(train) [22][5/63] lr: 4.2582e-04 eta: 18:15:16 time: 0.6763 data_time: 0.2196 memory: 14901 loss: 5.0613 loss_prob: 2.8981 loss_thr: 1.1632 loss_db: 1.0000 2022/11/02 12:24:22 - mmengine - INFO - Epoch(train) [22][10/63] lr: 4.2582e-04 eta: 18:11:01 time: 0.7136 data_time: 0.2246 memory: 14901 loss: 5.0651 loss_prob: 2.9077 loss_thr: 1.1575 loss_db: 1.0000 2022/11/02 12:24:24 - mmengine - INFO - Epoch(train) [22][15/63] lr: 4.2582e-04 eta: 18:11:01 time: 0.4760 data_time: 0.0091 memory: 14901 loss: 5.0858 loss_prob: 2.9216 loss_thr: 1.1643 loss_db: 1.0000 2022/11/02 12:24:26 - mmengine - INFO - Epoch(train) [22][20/63] lr: 4.2582e-04 eta: 18:06:53 time: 0.4489 data_time: 0.0051 memory: 14901 loss: 5.0851 loss_prob: 2.9221 loss_thr: 1.1631 loss_db: 1.0000 2022/11/02 12:24:29 - mmengine - INFO - Epoch(train) [22][25/63] lr: 4.2582e-04 eta: 18:06:53 time: 0.4552 data_time: 0.0178 memory: 14901 loss: 5.0848 loss_prob: 2.9289 loss_thr: 1.1559 loss_db: 1.0000 2022/11/02 12:24:31 - mmengine - INFO - Epoch(train) [22][30/63] lr: 4.2582e-04 eta: 18:03:13 time: 0.4918 data_time: 0.0345 memory: 14901 loss: 5.0981 loss_prob: 2.9382 loss_thr: 1.1600 loss_db: 1.0000 2022/11/02 12:24:34 - mmengine - INFO - Epoch(train) [22][35/63] lr: 4.2582e-04 eta: 18:03:13 time: 0.5008 data_time: 0.0219 memory: 14901 loss: 5.0799 loss_prob: 2.9186 loss_thr: 1.1614 loss_db: 1.0000 2022/11/02 12:24:36 - mmengine - INFO - Epoch(train) [22][40/63] lr: 4.2582e-04 eta: 17:59:49 time: 0.5172 data_time: 0.0164 memory: 14901 loss: 5.0483 loss_prob: 2.8900 loss_thr: 1.1584 loss_db: 1.0000 2022/11/02 12:24:39 - mmengine - INFO - Epoch(train) [22][45/63] lr: 4.2582e-04 eta: 17:59:49 time: 0.5247 data_time: 0.0164 memory: 14901 loss: 5.0353 loss_prob: 2.8776 loss_thr: 1.1577 loss_db: 1.0000 2022/11/02 12:24:42 - mmengine - INFO - Epoch(train) [22][50/63] lr: 4.2582e-04 eta: 17:56:45 time: 0.5486 data_time: 0.0049 memory: 14901 loss: 5.0283 loss_prob: 2.8694 loss_thr: 1.1590 loss_db: 1.0000 2022/11/02 12:24:44 - mmengine - INFO - Epoch(train) [22][55/63] lr: 4.2582e-04 eta: 17:56:45 time: 0.5353 data_time: 0.0139 memory: 14901 loss: 5.0289 loss_prob: 2.8674 loss_thr: 1.1615 loss_db: 1.0000 2022/11/02 12:24:46 - mmengine - INFO - Epoch(train) [22][60/63] lr: 4.2582e-04 eta: 17:53:01 time: 0.4703 data_time: 0.0139 memory: 14901 loss: 5.0490 loss_prob: 2.8836 loss_thr: 1.1654 loss_db: 1.0000 2022/11/02 12:24:48 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:24:52 - mmengine - INFO - Epoch(train) [23][5/63] lr: 4.4600e-04 eta: 17:53:01 time: 0.6627 data_time: 0.2080 memory: 14901 loss: 5.0579 loss_prob: 2.8951 loss_thr: 1.1629 loss_db: 1.0000 2022/11/02 12:24:55 - mmengine - INFO - Epoch(train) [23][10/63] lr: 4.4600e-04 eta: 17:48:57 time: 0.6891 data_time: 0.2057 memory: 14901 loss: 5.0345 loss_prob: 2.8766 loss_thr: 1.1580 loss_db: 1.0000 2022/11/02 12:24:57 - mmengine - INFO - Epoch(train) [23][15/63] lr: 4.4600e-04 eta: 17:48:57 time: 0.5024 data_time: 0.0051 memory: 14901 loss: 5.0207 loss_prob: 2.8628 loss_thr: 1.1579 loss_db: 1.0000 2022/11/02 12:24:59 - mmengine - INFO - Epoch(train) [23][20/63] lr: 4.4600e-04 eta: 17:45:31 time: 0.4908 data_time: 0.0048 memory: 14901 loss: 5.0211 loss_prob: 2.8619 loss_thr: 1.1593 loss_db: 1.0000 2022/11/02 12:25:02 - mmengine - INFO - Epoch(train) [23][25/63] lr: 4.4600e-04 eta: 17:45:31 time: 0.5044 data_time: 0.0342 memory: 14901 loss: 5.0081 loss_prob: 2.8500 loss_thr: 1.1581 loss_db: 1.0000 2022/11/02 12:25:05 - mmengine - INFO - Epoch(train) [23][30/63] lr: 4.4600e-04 eta: 17:42:18 time: 0.5090 data_time: 0.0349 memory: 14901 loss: 4.9964 loss_prob: 2.8400 loss_thr: 1.1564 loss_db: 1.0000 2022/11/02 12:25:07 - mmengine - INFO - Epoch(train) [23][35/63] lr: 4.4600e-04 eta: 17:42:18 time: 0.4881 data_time: 0.0065 memory: 14901 loss: 5.0037 loss_prob: 2.8481 loss_thr: 1.1556 loss_db: 1.0000 2022/11/02 12:25:10 - mmengine - INFO - Epoch(train) [23][40/63] lr: 4.4600e-04 eta: 17:39:25 time: 0.5442 data_time: 0.0058 memory: 14901 loss: 5.0221 loss_prob: 2.8693 loss_thr: 1.1528 loss_db: 1.0000 2022/11/02 12:25:14 - mmengine - INFO - Epoch(train) [23][45/63] lr: 4.4600e-04 eta: 17:39:25 time: 0.6477 data_time: 0.0050 memory: 14901 loss: 5.0409 loss_prob: 2.8898 loss_thr: 1.1512 loss_db: 1.0000 2022/11/02 12:25:16 - mmengine - INFO - Epoch(train) [23][50/63] lr: 4.4600e-04 eta: 17:37:06 time: 0.6028 data_time: 0.0241 memory: 14901 loss: 5.0493 loss_prob: 2.8919 loss_thr: 1.1575 loss_db: 1.0000 2022/11/02 12:25:19 - mmengine - INFO - Epoch(train) [23][55/63] lr: 4.4600e-04 eta: 17:37:06 time: 0.5497 data_time: 0.0244 memory: 14901 loss: 5.0467 loss_prob: 2.8787 loss_thr: 1.1680 loss_db: 1.0000 2022/11/02 12:25:21 - mmengine - INFO - Epoch(train) [23][60/63] lr: 4.4600e-04 eta: 17:34:13 time: 0.5359 data_time: 0.0068 memory: 14901 loss: 5.0300 loss_prob: 2.8685 loss_thr: 1.1615 loss_db: 1.0000 2022/11/02 12:25:23 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:25:28 - mmengine - INFO - Epoch(train) [24][5/63] lr: 4.6618e-04 eta: 17:34:13 time: 0.8009 data_time: 0.2872 memory: 14901 loss: 5.0411 loss_prob: 2.8808 loss_thr: 1.1602 loss_db: 1.0000 2022/11/02 12:25:31 - mmengine - INFO - Epoch(train) [24][10/63] lr: 4.6618e-04 eta: 17:32:03 time: 0.8735 data_time: 0.2818 memory: 14901 loss: 5.0817 loss_prob: 2.9031 loss_thr: 1.1786 loss_db: 1.0000 2022/11/02 12:25:34 - mmengine - INFO - Epoch(train) [24][15/63] lr: 4.6618e-04 eta: 17:32:03 time: 0.5492 data_time: 0.0060 memory: 14901 loss: 5.1354 loss_prob: 2.9520 loss_thr: 1.1834 loss_db: 1.0000 2022/11/02 12:25:37 - mmengine - INFO - Epoch(train) [24][20/63] lr: 4.6618e-04 eta: 17:29:40 time: 0.5860 data_time: 0.0060 memory: 14901 loss: 5.1725 loss_prob: 3.0126 loss_thr: 1.1600 loss_db: 1.0000 2022/11/02 12:25:40 - mmengine - INFO - Epoch(train) [24][25/63] lr: 4.6618e-04 eta: 17:29:40 time: 0.6178 data_time: 0.0340 memory: 14901 loss: 5.1929 loss_prob: 3.0310 loss_thr: 1.1619 loss_db: 1.0000 2022/11/02 12:25:43 - mmengine - INFO - Epoch(train) [24][30/63] lr: 4.6618e-04 eta: 17:27:13 time: 0.5735 data_time: 0.0340 memory: 14901 loss: 5.1742 loss_prob: 2.9987 loss_thr: 1.1755 loss_db: 1.0000 2022/11/02 12:25:46 - mmengine - INFO - Epoch(train) [24][35/63] lr: 4.6618e-04 eta: 17:27:13 time: 0.6337 data_time: 0.0047 memory: 14901 loss: 5.1398 loss_prob: 2.9721 loss_thr: 1.1677 loss_db: 1.0000 2022/11/02 12:25:50 - mmengine - INFO - Epoch(train) [24][40/63] lr: 4.6618e-04 eta: 17:25:47 time: 0.6910 data_time: 0.0053 memory: 14901 loss: 5.1272 loss_prob: 2.9573 loss_thr: 1.1699 loss_db: 1.0000 2022/11/02 12:25:52 - mmengine - INFO - Epoch(train) [24][45/63] lr: 4.6618e-04 eta: 17:25:47 time: 0.5990 data_time: 0.0052 memory: 14901 loss: 5.1095 loss_prob: 2.9382 loss_thr: 1.1713 loss_db: 1.0000 2022/11/02 12:25:56 - mmengine - INFO - Epoch(train) [24][50/63] lr: 4.6618e-04 eta: 17:23:31 time: 0.5892 data_time: 0.0306 memory: 14901 loss: 5.0675 loss_prob: 2.9112 loss_thr: 1.1563 loss_db: 1.0000 2022/11/02 12:25:59 - mmengine - INFO - Epoch(train) [24][55/63] lr: 4.6618e-04 eta: 17:23:31 time: 0.6255 data_time: 0.0310 memory: 14901 loss: 5.0552 loss_prob: 2.9004 loss_thr: 1.1549 loss_db: 1.0000 2022/11/02 12:26:01 - mmengine - INFO - Epoch(train) [24][60/63] lr: 4.6618e-04 eta: 17:21:00 time: 0.5538 data_time: 0.0048 memory: 14901 loss: 5.0500 loss_prob: 2.8947 loss_thr: 1.1553 loss_db: 1.0000 2022/11/02 12:26:03 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:26:09 - mmengine - INFO - Epoch(train) [25][5/63] lr: 4.8636e-04 eta: 17:21:00 time: 0.8505 data_time: 0.2053 memory: 14901 loss: 5.0338 loss_prob: 2.8763 loss_thr: 1.1575 loss_db: 1.0000 2022/11/02 12:26:12 - mmengine - INFO - Epoch(train) [25][10/63] lr: 4.8636e-04 eta: 17:18:52 time: 0.8562 data_time: 0.2044 memory: 14901 loss: 5.0425 loss_prob: 2.8804 loss_thr: 1.1621 loss_db: 1.0000 2022/11/02 12:26:14 - mmengine - INFO - Epoch(train) [25][15/63] lr: 4.8636e-04 eta: 17:18:52 time: 0.5447 data_time: 0.0107 memory: 14901 loss: 5.0349 loss_prob: 2.8707 loss_thr: 1.1642 loss_db: 1.0000 2022/11/02 12:26:17 - mmengine - INFO - Epoch(train) [25][20/63] lr: 4.8636e-04 eta: 17:16:03 time: 0.5089 data_time: 0.0065 memory: 14901 loss: 5.0272 loss_prob: 2.8695 loss_thr: 1.1577 loss_db: 1.0000 2022/11/02 12:26:19 - mmengine - INFO - Epoch(train) [25][25/63] lr: 4.8636e-04 eta: 17:16:03 time: 0.4748 data_time: 0.0129 memory: 14901 loss: 5.0367 loss_prob: 2.8811 loss_thr: 1.1556 loss_db: 1.0000 2022/11/02 12:26:22 - mmengine - INFO - Epoch(train) [25][30/63] lr: 4.8636e-04 eta: 17:13:10 time: 0.4973 data_time: 0.0310 memory: 14901 loss: 5.0372 loss_prob: 2.8796 loss_thr: 1.1577 loss_db: 1.0000 2022/11/02 12:26:24 - mmengine - INFO - Epoch(train) [25][35/63] lr: 4.8636e-04 eta: 17:13:10 time: 0.4971 data_time: 0.0267 memory: 14901 loss: 5.0302 loss_prob: 2.8737 loss_thr: 1.1565 loss_db: 1.0000 2022/11/02 12:26:27 - mmengine - INFO - Epoch(train) [25][40/63] lr: 4.8636e-04 eta: 17:10:16 time: 0.4897 data_time: 0.0088 memory: 14901 loss: 5.0196 loss_prob: 2.8645 loss_thr: 1.1551 loss_db: 1.0000 2022/11/02 12:26:29 - mmengine - INFO - Epoch(train) [25][45/63] lr: 4.8636e-04 eta: 17:10:16 time: 0.4874 data_time: 0.0048 memory: 14901 loss: 5.0138 loss_prob: 2.8558 loss_thr: 1.1580 loss_db: 1.0000 2022/11/02 12:26:31 - mmengine - INFO - Epoch(train) [25][50/63] lr: 4.8636e-04 eta: 17:07:14 time: 0.4691 data_time: 0.0101 memory: 14901 loss: 5.0059 loss_prob: 2.8492 loss_thr: 1.1567 loss_db: 1.0000 2022/11/02 12:26:34 - mmengine - INFO - Epoch(train) [25][55/63] lr: 4.8636e-04 eta: 17:07:14 time: 0.4876 data_time: 0.0246 memory: 14901 loss: 4.9978 loss_prob: 2.8430 loss_thr: 1.1548 loss_db: 1.0000 2022/11/02 12:26:36 - mmengine - INFO - Epoch(train) [25][60/63] lr: 4.8636e-04 eta: 17:04:25 time: 0.4912 data_time: 0.0198 memory: 14901 loss: 4.9978 loss_prob: 2.8418 loss_thr: 1.1561 loss_db: 1.0000 2022/11/02 12:26:37 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:26:42 - mmengine - INFO - Epoch(train) [26][5/63] lr: 5.0655e-04 eta: 17:04:25 time: 0.6651 data_time: 0.2295 memory: 14901 loss: 5.0092 loss_prob: 2.8555 loss_thr: 1.1537 loss_db: 1.0000 2022/11/02 12:26:45 - mmengine - INFO - Epoch(train) [26][10/63] lr: 5.0655e-04 eta: 17:01:31 time: 0.7284 data_time: 0.2293 memory: 14901 loss: 5.0173 loss_prob: 2.8622 loss_thr: 1.1551 loss_db: 1.0000 2022/11/02 12:26:47 - mmengine - INFO - Epoch(train) [26][15/63] lr: 5.0655e-04 eta: 17:01:31 time: 0.5258 data_time: 0.0049 memory: 14901 loss: 5.0227 loss_prob: 2.8660 loss_thr: 1.1568 loss_db: 1.0000 2022/11/02 12:26:50 - mmengine - INFO - Epoch(train) [26][20/63] lr: 5.0655e-04 eta: 16:59:10 time: 0.5420 data_time: 0.0049 memory: 14901 loss: 5.0265 loss_prob: 2.8674 loss_thr: 1.1591 loss_db: 1.0000 2022/11/02 12:26:53 - mmengine - INFO - Epoch(train) [26][25/63] lr: 5.0655e-04 eta: 16:59:10 time: 0.5391 data_time: 0.0242 memory: 14901 loss: 5.0191 loss_prob: 2.8605 loss_thr: 1.1586 loss_db: 1.0000 2022/11/02 12:26:55 - mmengine - INFO - Epoch(train) [26][30/63] lr: 5.0655e-04 eta: 16:56:30 time: 0.4981 data_time: 0.0334 memory: 14901 loss: 5.0088 loss_prob: 2.8549 loss_thr: 1.1540 loss_db: 1.0000 2022/11/02 12:26:57 - mmengine - INFO - Epoch(train) [26][35/63] lr: 5.0655e-04 eta: 16:56:30 time: 0.4819 data_time: 0.0140 memory: 14901 loss: 5.0081 loss_prob: 2.8505 loss_thr: 1.1576 loss_db: 1.0000 2022/11/02 12:27:00 - mmengine - INFO - Epoch(train) [26][40/63] lr: 5.0655e-04 eta: 16:53:46 time: 0.4833 data_time: 0.0059 memory: 14901 loss: 5.0061 loss_prob: 2.8485 loss_thr: 1.1577 loss_db: 1.0000 2022/11/02 12:27:02 - mmengine - INFO - Epoch(train) [26][45/63] lr: 5.0655e-04 eta: 16:53:46 time: 0.4824 data_time: 0.0075 memory: 14901 loss: 5.0050 loss_prob: 2.8520 loss_thr: 1.1530 loss_db: 1.0000 2022/11/02 12:27:05 - mmengine - INFO - Epoch(train) [26][50/63] lr: 5.0655e-04 eta: 16:51:28 time: 0.5387 data_time: 0.0269 memory: 14901 loss: 5.0210 loss_prob: 2.8665 loss_thr: 1.1545 loss_db: 1.0000 2022/11/02 12:27:08 - mmengine - INFO - Epoch(train) [26][55/63] lr: 5.0655e-04 eta: 16:51:28 time: 0.5646 data_time: 0.0286 memory: 14901 loss: 5.0398 loss_prob: 2.8841 loss_thr: 1.1557 loss_db: 1.0000 2022/11/02 12:27:11 - mmengine - INFO - Epoch(train) [26][60/63] lr: 5.0655e-04 eta: 16:49:13 time: 0.5402 data_time: 0.0081 memory: 14901 loss: 5.0366 loss_prob: 2.8836 loss_thr: 1.1531 loss_db: 1.0000 2022/11/02 12:27:12 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:27:17 - mmengine - INFO - Epoch(train) [27][5/63] lr: 5.2673e-04 eta: 16:49:13 time: 0.7102 data_time: 0.2076 memory: 14901 loss: 5.0212 loss_prob: 2.8644 loss_thr: 1.1569 loss_db: 1.0000 2022/11/02 12:27:19 - mmengine - INFO - Epoch(train) [27][10/63] lr: 5.2673e-04 eta: 16:46:30 time: 0.7243 data_time: 0.2176 memory: 14901 loss: 5.0100 loss_prob: 2.8553 loss_thr: 1.1547 loss_db: 1.0000 2022/11/02 12:27:22 - mmengine - INFO - Epoch(train) [27][15/63] lr: 5.2673e-04 eta: 16:46:30 time: 0.4960 data_time: 0.0160 memory: 14901 loss: 5.0044 loss_prob: 2.8492 loss_thr: 1.1553 loss_db: 1.0000 2022/11/02 12:27:24 - mmengine - INFO - Epoch(train) [27][20/63] lr: 5.2673e-04 eta: 16:43:48 time: 0.4706 data_time: 0.0060 memory: 14901 loss: 5.0026 loss_prob: 2.8466 loss_thr: 1.1560 loss_db: 1.0000 2022/11/02 12:27:27 - mmengine - INFO - Epoch(train) [27][25/63] lr: 5.2673e-04 eta: 16:43:48 time: 0.5092 data_time: 0.0225 memory: 14901 loss: 4.9986 loss_prob: 2.8421 loss_thr: 1.1565 loss_db: 1.0000 2022/11/02 12:27:29 - mmengine - INFO - Epoch(train) [27][30/63] lr: 5.2673e-04 eta: 16:41:37 time: 0.5379 data_time: 0.0468 memory: 14901 loss: 4.9987 loss_prob: 2.8403 loss_thr: 1.1584 loss_db: 1.0000 2022/11/02 12:27:32 - mmengine - INFO - Epoch(train) [27][35/63] lr: 5.2673e-04 eta: 16:41:37 time: 0.4950 data_time: 0.0291 memory: 14901 loss: 5.0114 loss_prob: 2.8513 loss_thr: 1.1602 loss_db: 1.0000 2022/11/02 12:27:34 - mmengine - INFO - Epoch(train) [27][40/63] lr: 5.2673e-04 eta: 16:39:06 time: 0.4884 data_time: 0.0056 memory: 14901 loss: 5.0218 loss_prob: 2.8676 loss_thr: 1.1543 loss_db: 1.0000 2022/11/02 12:27:37 - mmengine - INFO - Epoch(train) [27][45/63] lr: 5.2673e-04 eta: 16:39:06 time: 0.4934 data_time: 0.0055 memory: 14901 loss: 5.0233 loss_prob: 2.8706 loss_thr: 1.1528 loss_db: 0.9999 2022/11/02 12:27:39 - mmengine - INFO - Epoch(train) [27][50/63] lr: 5.2673e-04 eta: 16:36:44 time: 0.5048 data_time: 0.0190 memory: 14901 loss: 5.0169 loss_prob: 2.8639 loss_thr: 1.1530 loss_db: 0.9999 2022/11/02 12:27:42 - mmengine - INFO - Epoch(train) [27][55/63] lr: 5.2673e-04 eta: 16:36:44 time: 0.5275 data_time: 0.0240 memory: 14901 loss: 5.0129 loss_prob: 2.8584 loss_thr: 1.1545 loss_db: 1.0000 2022/11/02 12:27:44 - mmengine - INFO - Epoch(train) [27][60/63] lr: 5.2673e-04 eta: 16:34:15 time: 0.4872 data_time: 0.0093 memory: 14901 loss: 5.0107 loss_prob: 2.8544 loss_thr: 1.1563 loss_db: 1.0000 2022/11/02 12:27:45 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:27:51 - mmengine - INFO - Epoch(train) [28][5/63] lr: 5.4691e-04 eta: 16:34:15 time: 0.7557 data_time: 0.2151 memory: 14901 loss: 5.0074 loss_prob: 2.8504 loss_thr: 1.1570 loss_db: 1.0000 2022/11/02 12:27:53 - mmengine - INFO - Epoch(train) [28][10/63] lr: 5.4691e-04 eta: 16:32:09 time: 0.7819 data_time: 0.2156 memory: 14901 loss: 5.0007 loss_prob: 2.8462 loss_thr: 1.1545 loss_db: 1.0000 2022/11/02 12:27:56 - mmengine - INFO - Epoch(train) [28][15/63] lr: 5.4691e-04 eta: 16:32:09 time: 0.4963 data_time: 0.0069 memory: 14901 loss: 4.9920 loss_prob: 2.8446 loss_thr: 1.1475 loss_db: 1.0000 2022/11/02 12:27:58 - mmengine - INFO - Epoch(train) [28][20/63] lr: 5.4691e-04 eta: 16:29:41 time: 0.4782 data_time: 0.0087 memory: 14901 loss: 4.9966 loss_prob: 2.8457 loss_thr: 1.1510 loss_db: 1.0000 2022/11/02 12:28:01 - mmengine - INFO - Epoch(train) [28][25/63] lr: 5.4691e-04 eta: 16:29:41 time: 0.4867 data_time: 0.0125 memory: 14901 loss: 5.0122 loss_prob: 2.8529 loss_thr: 1.1593 loss_db: 0.9999 2022/11/02 12:28:03 - mmengine - INFO - Epoch(train) [28][30/63] lr: 5.4691e-04 eta: 16:27:29 time: 0.5141 data_time: 0.0275 memory: 14901 loss: 5.0204 loss_prob: 2.8611 loss_thr: 1.1594 loss_db: 0.9999 2022/11/02 12:28:06 - mmengine - INFO - Epoch(train) [28][35/63] lr: 5.4691e-04 eta: 16:27:29 time: 0.5198 data_time: 0.0237 memory: 14901 loss: 5.0179 loss_prob: 2.8589 loss_thr: 1.1590 loss_db: 0.9999 2022/11/02 12:28:08 - mmengine - INFO - Epoch(train) [28][40/63] lr: 5.4691e-04 eta: 16:25:13 time: 0.5007 data_time: 0.0112 memory: 14901 loss: 5.0121 loss_prob: 2.8541 loss_thr: 1.1580 loss_db: 0.9999 2022/11/02 12:28:10 - mmengine - INFO - Epoch(train) [28][45/63] lr: 5.4691e-04 eta: 16:25:13 time: 0.4682 data_time: 0.0135 memory: 14901 loss: 5.0050 loss_prob: 2.8488 loss_thr: 1.1562 loss_db: 0.9999 2022/11/02 12:28:13 - mmengine - INFO - Epoch(train) [28][50/63] lr: 5.4691e-04 eta: 16:22:57 time: 0.4961 data_time: 0.0171 memory: 14901 loss: 4.9999 loss_prob: 2.8434 loss_thr: 1.1566 loss_db: 0.9999 2022/11/02 12:28:16 - mmengine - INFO - Epoch(train) [28][55/63] lr: 5.4691e-04 eta: 16:22:57 time: 0.5292 data_time: 0.0217 memory: 14901 loss: 4.9932 loss_prob: 2.8404 loss_thr: 1.1529 loss_db: 0.9999 2022/11/02 12:28:18 - mmengine - INFO - Epoch(train) [28][60/63] lr: 5.4691e-04 eta: 16:20:44 time: 0.5010 data_time: 0.0117 memory: 14901 loss: 4.9896 loss_prob: 2.8368 loss_thr: 1.1529 loss_db: 0.9999 2022/11/02 12:28:19 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:28:24 - mmengine - INFO - Epoch(train) [29][5/63] lr: 5.6709e-04 eta: 16:20:44 time: 0.7047 data_time: 0.2235 memory: 14901 loss: 4.9827 loss_prob: 2.8302 loss_thr: 1.1525 loss_db: 0.9999 2022/11/02 12:28:27 - mmengine - INFO - Epoch(train) [29][10/63] lr: 5.6709e-04 eta: 16:18:33 time: 0.7460 data_time: 0.2234 memory: 14901 loss: 4.9769 loss_prob: 2.8267 loss_thr: 1.1502 loss_db: 0.9999 2022/11/02 12:28:29 - mmengine - INFO - Epoch(train) [29][15/63] lr: 5.6709e-04 eta: 16:18:33 time: 0.4962 data_time: 0.0074 memory: 14901 loss: 4.9918 loss_prob: 2.8362 loss_thr: 1.1556 loss_db: 0.9999 2022/11/02 12:28:31 - mmengine - INFO - Epoch(train) [29][20/63] lr: 5.6709e-04 eta: 16:16:14 time: 0.4786 data_time: 0.0092 memory: 14901 loss: 5.0080 loss_prob: 2.8529 loss_thr: 1.1551 loss_db: 0.9999 2022/11/02 12:28:34 - mmengine - INFO - Epoch(train) [29][25/63] lr: 5.6709e-04 eta: 16:16:14 time: 0.4862 data_time: 0.0204 memory: 14901 loss: 5.0079 loss_prob: 2.8592 loss_thr: 1.1487 loss_db: 0.9999 2022/11/02 12:28:37 - mmengine - INFO - Epoch(train) [29][30/63] lr: 5.6709e-04 eta: 16:14:12 time: 0.5162 data_time: 0.0333 memory: 14901 loss: 5.0079 loss_prob: 2.8568 loss_thr: 1.1511 loss_db: 0.9999 2022/11/02 12:28:39 - mmengine - INFO - Epoch(train) [29][35/63] lr: 5.6709e-04 eta: 16:14:12 time: 0.5074 data_time: 0.0201 memory: 14901 loss: 5.0040 loss_prob: 2.8521 loss_thr: 1.1519 loss_db: 1.0000 2022/11/02 12:28:41 - mmengine - INFO - Epoch(train) [29][40/63] lr: 5.6709e-04 eta: 16:11:57 time: 0.4798 data_time: 0.0057 memory: 14901 loss: 4.9972 loss_prob: 2.8439 loss_thr: 1.1534 loss_db: 0.9999 2022/11/02 12:28:44 - mmengine - INFO - Epoch(train) [29][45/63] lr: 5.6709e-04 eta: 16:11:57 time: 0.4830 data_time: 0.0082 memory: 14901 loss: 4.9964 loss_prob: 2.8383 loss_thr: 1.1581 loss_db: 1.0000 2022/11/02 12:28:47 - mmengine - INFO - Epoch(train) [29][50/63] lr: 5.6709e-04 eta: 16:10:01 time: 0.5251 data_time: 0.0242 memory: 14901 loss: 4.9963 loss_prob: 2.8374 loss_thr: 1.1589 loss_db: 1.0000 2022/11/02 12:28:49 - mmengine - INFO - Epoch(train) [29][55/63] lr: 5.6709e-04 eta: 16:10:01 time: 0.5306 data_time: 0.0218 memory: 14901 loss: 4.9894 loss_prob: 2.8341 loss_thr: 1.1554 loss_db: 1.0000 2022/11/02 12:28:52 - mmengine - INFO - Epoch(train) [29][60/63] lr: 5.6709e-04 eta: 16:08:05 time: 0.5214 data_time: 0.0061 memory: 14901 loss: 4.9928 loss_prob: 2.8366 loss_thr: 1.1562 loss_db: 0.9999 2022/11/02 12:28:54 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:28:59 - mmengine - INFO - Epoch(train) [30][5/63] lr: 5.8727e-04 eta: 16:08:05 time: 0.8180 data_time: 0.2429 memory: 14901 loss: 4.9877 loss_prob: 2.8422 loss_thr: 1.1457 loss_db: 0.9998 2022/11/02 12:29:02 - mmengine - INFO - Epoch(train) [30][10/63] lr: 5.8727e-04 eta: 16:06:37 time: 0.8295 data_time: 0.2427 memory: 14901 loss: 4.9956 loss_prob: 2.8460 loss_thr: 1.1498 loss_db: 0.9998 2022/11/02 12:29:05 - mmengine - INFO - Epoch(train) [30][15/63] lr: 5.8727e-04 eta: 16:06:37 time: 0.5873 data_time: 0.0052 memory: 14901 loss: 4.9932 loss_prob: 2.8411 loss_thr: 1.1522 loss_db: 0.9999 2022/11/02 12:29:08 - mmengine - INFO - Epoch(train) [30][20/63] lr: 5.8727e-04 eta: 16:04:59 time: 0.5624 data_time: 0.0053 memory: 14901 loss: 5.0121 loss_prob: 2.8588 loss_thr: 1.1534 loss_db: 1.0000 2022/11/02 12:29:11 - mmengine - INFO - Epoch(train) [30][25/63] lr: 5.8727e-04 eta: 16:04:59 time: 0.5673 data_time: 0.0082 memory: 14901 loss: 5.0361 loss_prob: 2.8734 loss_thr: 1.1627 loss_db: 1.0000 2022/11/02 12:29:13 - mmengine - INFO - Epoch(train) [30][30/63] lr: 5.8727e-04 eta: 16:03:18 time: 0.5484 data_time: 0.0355 memory: 14901 loss: 5.0758 loss_prob: 2.9123 loss_thr: 1.1636 loss_db: 1.0000 2022/11/02 12:29:16 - mmengine - INFO - Epoch(train) [30][35/63] lr: 5.8727e-04 eta: 16:03:18 time: 0.5285 data_time: 0.0330 memory: 14901 loss: 5.1634 loss_prob: 2.9887 loss_thr: 1.1747 loss_db: 0.9999 2022/11/02 12:29:19 - mmengine - INFO - Epoch(train) [30][40/63] lr: 5.8727e-04 eta: 16:01:36 time: 0.5460 data_time: 0.0053 memory: 14901 loss: 5.2047 loss_prob: 3.0130 loss_thr: 1.1917 loss_db: 1.0000 2022/11/02 12:29:21 - mmengine - INFO - Epoch(train) [30][45/63] lr: 5.8727e-04 eta: 16:01:36 time: 0.5490 data_time: 0.0046 memory: 14901 loss: 5.1927 loss_prob: 3.0183 loss_thr: 1.1744 loss_db: 1.0000 2022/11/02 12:29:24 - mmengine - INFO - Epoch(train) [30][50/63] lr: 5.8727e-04 eta: 16:00:00 time: 0.5590 data_time: 0.0247 memory: 14901 loss: 5.1627 loss_prob: 3.0063 loss_thr: 1.1565 loss_db: 1.0000 2022/11/02 12:29:27 - mmengine - INFO - Epoch(train) [30][55/63] lr: 5.8727e-04 eta: 16:00:00 time: 0.6052 data_time: 0.0249 memory: 14901 loss: 5.1559 loss_prob: 2.9978 loss_thr: 1.1581 loss_db: 1.0000 2022/11/02 12:29:30 - mmengine - INFO - Epoch(train) [30][60/63] lr: 5.8727e-04 eta: 15:58:46 time: 0.6112 data_time: 0.0052 memory: 14901 loss: 5.1660 loss_prob: 3.0046 loss_thr: 1.1615 loss_db: 1.0000 2022/11/02 12:29:32 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:29:38 - mmengine - INFO - Epoch(train) [31][5/63] lr: 6.0745e-04 eta: 15:58:46 time: 0.8639 data_time: 0.2165 memory: 14901 loss: 5.1335 loss_prob: 2.9671 loss_thr: 1.1664 loss_db: 1.0000 2022/11/02 12:29:40 - mmengine - INFO - Epoch(train) [31][10/63] lr: 6.0745e-04 eta: 15:57:11 time: 0.7965 data_time: 0.2250 memory: 14901 loss: 5.1049 loss_prob: 2.9442 loss_thr: 1.1607 loss_db: 1.0000 2022/11/02 12:29:43 - mmengine - INFO - Epoch(train) [31][15/63] lr: 6.0745e-04 eta: 15:57:11 time: 0.5678 data_time: 0.0149 memory: 14901 loss: 5.0725 loss_prob: 2.9124 loss_thr: 1.1601 loss_db: 1.0000 2022/11/02 12:29:46 - mmengine - INFO - Epoch(train) [31][20/63] lr: 6.0745e-04 eta: 15:56:03 time: 0.6226 data_time: 0.0106 memory: 14901 loss: 5.0783 loss_prob: 2.9147 loss_thr: 1.1637 loss_db: 1.0000 2022/11/02 12:29:49 - mmengine - INFO - Epoch(train) [31][25/63] lr: 6.0745e-04 eta: 15:56:03 time: 0.5576 data_time: 0.0166 memory: 14901 loss: 5.0758 loss_prob: 2.9157 loss_thr: 1.1602 loss_db: 1.0000 2022/11/02 12:29:51 - mmengine - INFO - Epoch(train) [31][30/63] lr: 6.0745e-04 eta: 15:54:08 time: 0.4983 data_time: 0.0236 memory: 14901 loss: 5.0672 loss_prob: 2.9106 loss_thr: 1.1566 loss_db: 1.0000 2022/11/02 12:29:54 - mmengine - INFO - Epoch(train) [31][35/63] lr: 6.0745e-04 eta: 15:54:08 time: 0.4848 data_time: 0.0240 memory: 14901 loss: 5.0748 loss_prob: 2.9131 loss_thr: 1.1617 loss_db: 1.0000 2022/11/02 12:29:56 - mmengine - INFO - Epoch(train) [31][40/63] lr: 6.0745e-04 eta: 15:52:08 time: 0.4850 data_time: 0.0143 memory: 14901 loss: 5.0692 loss_prob: 2.9042 loss_thr: 1.1651 loss_db: 0.9999 2022/11/02 12:29:58 - mmengine - INFO - Epoch(train) [31][45/63] lr: 6.0745e-04 eta: 15:52:08 time: 0.4722 data_time: 0.0075 memory: 14901 loss: 5.0759 loss_prob: 2.9182 loss_thr: 1.1578 loss_db: 1.0000 2022/11/02 12:30:01 - mmengine - INFO - Epoch(train) [31][50/63] lr: 6.0745e-04 eta: 15:50:08 time: 0.4787 data_time: 0.0153 memory: 14901 loss: 5.0724 loss_prob: 2.9190 loss_thr: 1.1534 loss_db: 1.0000 2022/11/02 12:30:03 - mmengine - INFO - Epoch(train) [31][55/63] lr: 6.0745e-04 eta: 15:50:08 time: 0.5006 data_time: 0.0340 memory: 14901 loss: 5.0479 loss_prob: 2.8902 loss_thr: 1.1577 loss_db: 1.0000 2022/11/02 12:30:06 - mmengine - INFO - Epoch(train) [31][60/63] lr: 6.0745e-04 eta: 15:48:16 time: 0.4974 data_time: 0.0262 memory: 14901 loss: 5.0391 loss_prob: 2.8861 loss_thr: 1.1530 loss_db: 1.0000 2022/11/02 12:30:07 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:30:12 - mmengine - INFO - Epoch(train) [32][5/63] lr: 6.2764e-04 eta: 15:48:16 time: 0.6823 data_time: 0.2012 memory: 14901 loss: 5.0291 loss_prob: 2.8784 loss_thr: 1.1507 loss_db: 0.9999 2022/11/02 12:30:15 - mmengine - INFO - Epoch(train) [32][10/63] lr: 6.2764e-04 eta: 15:46:28 time: 0.7438 data_time: 0.2021 memory: 14901 loss: 5.0151 loss_prob: 2.8623 loss_thr: 1.1528 loss_db: 1.0000 2022/11/02 12:30:17 - mmengine - INFO - Epoch(train) [32][15/63] lr: 6.2764e-04 eta: 15:46:28 time: 0.5416 data_time: 0.0067 memory: 14901 loss: 5.0090 loss_prob: 2.8546 loss_thr: 1.1544 loss_db: 0.9999 2022/11/02 12:30:20 - mmengine - INFO - Epoch(train) [32][20/63] lr: 6.2764e-04 eta: 15:44:57 time: 0.5479 data_time: 0.0048 memory: 14901 loss: 5.0064 loss_prob: 2.8516 loss_thr: 1.1549 loss_db: 0.9999 2022/11/02 12:30:22 - mmengine - INFO - Epoch(train) [32][25/63] lr: 6.2764e-04 eta: 15:44:57 time: 0.5297 data_time: 0.0159 memory: 14901 loss: 5.0004 loss_prob: 2.8414 loss_thr: 1.1590 loss_db: 1.0000 2022/11/02 12:30:25 - mmengine - INFO - Epoch(train) [32][30/63] lr: 6.2764e-04 eta: 15:43:16 time: 0.5184 data_time: 0.0551 memory: 14901 loss: 4.9955 loss_prob: 2.8375 loss_thr: 1.1580 loss_db: 0.9999 2022/11/02 12:30:28 - mmengine - INFO - Epoch(train) [32][35/63] lr: 6.2764e-04 eta: 15:43:16 time: 0.5172 data_time: 0.0441 memory: 14901 loss: 4.9923 loss_prob: 2.8383 loss_thr: 1.1540 loss_db: 0.9999 2022/11/02 12:30:30 - mmengine - INFO - Epoch(train) [32][40/63] lr: 6.2764e-04 eta: 15:41:21 time: 0.4791 data_time: 0.0060 memory: 14901 loss: 4.9914 loss_prob: 2.8387 loss_thr: 1.1528 loss_db: 1.0000 2022/11/02 12:30:33 - mmengine - INFO - Epoch(train) [32][45/63] lr: 6.2764e-04 eta: 15:41:21 time: 0.5073 data_time: 0.0057 memory: 14901 loss: 4.9853 loss_prob: 2.8361 loss_thr: 1.1493 loss_db: 0.9999 2022/11/02 12:30:33 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:30:35 - mmengine - INFO - Epoch(train) [32][50/63] lr: 6.2764e-04 eta: 15:39:38 time: 0.5078 data_time: 0.0137 memory: 14901 loss: 4.9829 loss_prob: 2.8331 loss_thr: 1.1500 loss_db: 0.9999 2022/11/02 12:30:37 - mmengine - INFO - Epoch(train) [32][55/63] lr: 6.2764e-04 eta: 15:39:38 time: 0.4834 data_time: 0.0253 memory: 14901 loss: 4.9828 loss_prob: 2.8316 loss_thr: 1.1513 loss_db: 0.9999 2022/11/02 12:30:40 - mmengine - INFO - Epoch(train) [32][60/63] lr: 6.2764e-04 eta: 15:37:45 time: 0.4779 data_time: 0.0159 memory: 14901 loss: 4.9779 loss_prob: 2.8291 loss_thr: 1.1488 loss_db: 0.9999 2022/11/02 12:30:41 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:30:46 - mmengine - INFO - Epoch(train) [33][5/63] lr: 6.4782e-04 eta: 15:37:45 time: 0.6718 data_time: 0.2215 memory: 14901 loss: 4.9729 loss_prob: 2.8291 loss_thr: 1.1439 loss_db: 0.9998 2022/11/02 12:30:48 - mmengine - INFO - Epoch(train) [33][10/63] lr: 6.4782e-04 eta: 15:35:54 time: 0.7156 data_time: 0.2252 memory: 14901 loss: 4.9729 loss_prob: 2.8295 loss_thr: 1.1436 loss_db: 0.9998 2022/11/02 12:30:51 - mmengine - INFO - Epoch(train) [33][15/63] lr: 6.4782e-04 eta: 15:35:54 time: 0.5140 data_time: 0.0080 memory: 14901 loss: 4.9713 loss_prob: 2.8280 loss_thr: 1.1435 loss_db: 0.9998 2022/11/02 12:30:53 - mmengine - INFO - Epoch(train) [33][20/63] lr: 6.4782e-04 eta: 15:34:22 time: 0.5281 data_time: 0.0054 memory: 14901 loss: 4.9724 loss_prob: 2.8280 loss_thr: 1.1448 loss_db: 0.9997 2022/11/02 12:30:56 - mmengine - INFO - Epoch(train) [33][25/63] lr: 6.4782e-04 eta: 15:34:22 time: 0.5483 data_time: 0.0340 memory: 14901 loss: 4.9731 loss_prob: 2.8271 loss_thr: 1.1463 loss_db: 0.9997 2022/11/02 12:30:59 - mmengine - INFO - Epoch(train) [33][30/63] lr: 6.4782e-04 eta: 15:32:49 time: 0.5268 data_time: 0.0329 memory: 14901 loss: 4.9707 loss_prob: 2.8252 loss_thr: 1.1456 loss_db: 0.9999 2022/11/02 12:31:01 - mmengine - INFO - Epoch(train) [33][35/63] lr: 6.4782e-04 eta: 15:32:49 time: 0.4788 data_time: 0.0087 memory: 14901 loss: 4.9753 loss_prob: 2.8270 loss_thr: 1.1484 loss_db: 0.9999 2022/11/02 12:31:04 - mmengine - INFO - Epoch(train) [33][40/63] lr: 6.4782e-04 eta: 15:31:08 time: 0.4988 data_time: 0.0139 memory: 14901 loss: 4.9785 loss_prob: 2.8302 loss_thr: 1.1484 loss_db: 0.9999 2022/11/02 12:31:06 - mmengine - INFO - Epoch(train) [33][45/63] lr: 6.4782e-04 eta: 15:31:08 time: 0.4917 data_time: 0.0097 memory: 14901 loss: 4.9688 loss_prob: 2.8283 loss_thr: 1.1406 loss_db: 0.9999 2022/11/02 12:31:09 - mmengine - INFO - Epoch(train) [33][50/63] lr: 6.4782e-04 eta: 15:29:23 time: 0.4852 data_time: 0.0191 memory: 14901 loss: 4.9653 loss_prob: 2.8248 loss_thr: 1.1407 loss_db: 0.9998 2022/11/02 12:31:11 - mmengine - INFO - Epoch(train) [33][55/63] lr: 6.4782e-04 eta: 15:29:23 time: 0.5245 data_time: 0.0217 memory: 14901 loss: 4.9773 loss_prob: 2.8261 loss_thr: 1.1513 loss_db: 0.9999 2022/11/02 12:31:14 - mmengine - INFO - Epoch(train) [33][60/63] lr: 6.4782e-04 eta: 15:27:50 time: 0.5174 data_time: 0.0071 memory: 14901 loss: 4.9745 loss_prob: 2.8250 loss_thr: 1.1496 loss_db: 0.9999 2022/11/02 12:31:15 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:31:20 - mmengine - INFO - Epoch(train) [34][5/63] lr: 6.6800e-04 eta: 15:27:50 time: 0.7096 data_time: 0.2211 memory: 14901 loss: 4.9714 loss_prob: 2.8234 loss_thr: 1.1481 loss_db: 0.9999 2022/11/02 12:31:22 - mmengine - INFO - Epoch(train) [34][10/63] lr: 6.6800e-04 eta: 15:26:19 time: 0.7529 data_time: 0.2210 memory: 14901 loss: 4.9642 loss_prob: 2.8220 loss_thr: 1.1423 loss_db: 0.9998 2022/11/02 12:31:26 - mmengine - INFO - Epoch(train) [34][15/63] lr: 6.6800e-04 eta: 15:26:19 time: 0.5787 data_time: 0.0066 memory: 14901 loss: 4.9648 loss_prob: 2.8221 loss_thr: 1.1431 loss_db: 0.9997 2022/11/02 12:31:28 - mmengine - INFO - Epoch(train) [34][20/63] lr: 6.6800e-04 eta: 15:25:09 time: 0.5791 data_time: 0.0093 memory: 14901 loss: 4.9740 loss_prob: 2.8284 loss_thr: 1.1460 loss_db: 0.9996 2022/11/02 12:31:32 - mmengine - INFO - Epoch(train) [34][25/63] lr: 6.6800e-04 eta: 15:25:09 time: 0.6442 data_time: 0.0438 memory: 14901 loss: 4.9738 loss_prob: 2.8325 loss_thr: 1.1417 loss_db: 0.9997 2022/11/02 12:31:36 - mmengine - INFO - Epoch(train) [34][30/63] lr: 6.6800e-04 eta: 15:25:07 time: 0.7720 data_time: 0.0419 memory: 14901 loss: 4.9799 loss_prob: 2.8325 loss_thr: 1.1477 loss_db: 0.9997 2022/11/02 12:31:38 - mmengine - INFO - Epoch(train) [34][35/63] lr: 6.6800e-04 eta: 15:25:07 time: 0.6345 data_time: 0.0081 memory: 14901 loss: 4.9813 loss_prob: 2.8308 loss_thr: 1.1511 loss_db: 0.9994 2022/11/02 12:31:42 - mmengine - INFO - Epoch(train) [34][40/63] lr: 6.6800e-04 eta: 15:23:56 time: 0.5701 data_time: 0.0077 memory: 14901 loss: 4.9708 loss_prob: 2.8268 loss_thr: 1.1445 loss_db: 0.9994 2022/11/02 12:31:44 - mmengine - INFO - Epoch(train) [34][45/63] lr: 6.6800e-04 eta: 15:23:56 time: 0.5907 data_time: 0.0050 memory: 14901 loss: 4.9559 loss_prob: 2.8219 loss_thr: 1.1345 loss_db: 0.9996 2022/11/02 12:31:47 - mmengine - INFO - Epoch(train) [34][50/63] lr: 6.6800e-04 eta: 15:22:40 time: 0.5565 data_time: 0.0205 memory: 14901 loss: 4.9553 loss_prob: 2.8222 loss_thr: 1.1339 loss_db: 0.9992 2022/11/02 12:31:50 - mmengine - INFO - Epoch(train) [34][55/63] lr: 6.6800e-04 eta: 15:22:40 time: 0.5379 data_time: 0.0205 memory: 14901 loss: 4.9696 loss_prob: 2.8252 loss_thr: 1.1453 loss_db: 0.9991 2022/11/02 12:31:52 - mmengine - INFO - Epoch(train) [34][60/63] lr: 6.6800e-04 eta: 15:21:08 time: 0.5096 data_time: 0.0067 memory: 14901 loss: 4.9645 loss_prob: 2.8259 loss_thr: 1.1392 loss_db: 0.9994 2022/11/02 12:31:54 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:31:59 - mmengine - INFO - Epoch(train) [35][5/63] lr: 6.8818e-04 eta: 15:21:08 time: 0.8032 data_time: 0.2720 memory: 14901 loss: 4.9513 loss_prob: 2.8266 loss_thr: 1.1735 loss_db: 0.9512 2022/11/02 12:32:02 - mmengine - INFO - Epoch(train) [35][10/63] lr: 6.8818e-04 eta: 15:20:16 time: 0.8540 data_time: 0.2700 memory: 14901 loss: 4.9748 loss_prob: 2.8308 loss_thr: 1.1794 loss_db: 0.9646 2022/11/02 12:32:05 - mmengine - INFO - Epoch(train) [35][15/63] lr: 6.8818e-04 eta: 15:20:16 time: 0.5815 data_time: 0.0058 memory: 14901 loss: 5.0183 loss_prob: 2.8485 loss_thr: 1.1702 loss_db: 0.9996 2022/11/02 12:32:08 - mmengine - INFO - Epoch(train) [35][20/63] lr: 6.8818e-04 eta: 15:19:01 time: 0.5510 data_time: 0.0053 memory: 14901 loss: 5.0130 loss_prob: 2.8485 loss_thr: 1.1667 loss_db: 0.9978 2022/11/02 12:32:11 - mmengine - INFO - Epoch(train) [35][25/63] lr: 6.8818e-04 eta: 15:19:01 time: 0.6030 data_time: 0.0360 memory: 14901 loss: 4.9730 loss_prob: 2.8316 loss_thr: 1.1521 loss_db: 0.9893 2022/11/02 12:32:14 - mmengine - INFO - Epoch(train) [35][30/63] lr: 6.8818e-04 eta: 15:18:06 time: 0.6105 data_time: 0.0373 memory: 14901 loss: 4.9695 loss_prob: 2.8313 loss_thr: 1.1640 loss_db: 0.9742 2022/11/02 12:32:16 - mmengine - INFO - Epoch(train) [35][35/63] lr: 6.8818e-04 eta: 15:18:06 time: 0.5342 data_time: 0.0071 memory: 14901 loss: 4.9431 loss_prob: 2.8253 loss_thr: 1.1542 loss_db: 0.9635 2022/11/02 12:32:19 - mmengine - INFO - Epoch(train) [35][40/63] lr: 6.8818e-04 eta: 15:16:45 time: 0.5327 data_time: 0.0052 memory: 14901 loss: 4.9196 loss_prob: 2.8216 loss_thr: 1.1585 loss_db: 0.9394 2022/11/02 12:32:22 - mmengine - INFO - Epoch(train) [35][45/63] lr: 6.8818e-04 eta: 15:16:45 time: 0.5311 data_time: 0.0045 memory: 14901 loss: 4.9249 loss_prob: 2.8333 loss_thr: 1.1842 loss_db: 0.9073 2022/11/02 12:32:24 - mmengine - INFO - Epoch(train) [35][50/63] lr: 6.8818e-04 eta: 15:15:22 time: 0.5255 data_time: 0.0262 memory: 14901 loss: 4.9639 loss_prob: 2.8404 loss_thr: 1.1752 loss_db: 0.9482 2022/11/02 12:32:27 - mmengine - INFO - Epoch(train) [35][55/63] lr: 6.8818e-04 eta: 15:15:22 time: 0.5152 data_time: 0.0266 memory: 14901 loss: 5.0157 loss_prob: 2.8707 loss_thr: 1.1528 loss_db: 0.9921 2022/11/02 12:32:29 - mmengine - INFO - Epoch(train) [35][60/63] lr: 6.8818e-04 eta: 15:13:53 time: 0.5023 data_time: 0.0048 memory: 14901 loss: 5.0244 loss_prob: 2.9171 loss_thr: 1.1663 loss_db: 0.9410 2022/11/02 12:32:31 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:32:35 - mmengine - INFO - Epoch(train) [36][5/63] lr: 7.0836e-04 eta: 15:13:53 time: 0.6861 data_time: 0.1659 memory: 14901 loss: 4.9575 loss_prob: 2.9210 loss_thr: 1.1883 loss_db: 0.8482 2022/11/02 12:32:38 - mmengine - INFO - Epoch(train) [36][10/63] lr: 7.0836e-04 eta: 15:12:22 time: 0.7245 data_time: 0.1706 memory: 14901 loss: 5.0360 loss_prob: 2.9080 loss_thr: 1.1871 loss_db: 0.9409 2022/11/02 12:32:40 - mmengine - INFO - Epoch(train) [36][15/63] lr: 7.0836e-04 eta: 15:12:22 time: 0.4890 data_time: 0.0093 memory: 14901 loss: 5.0642 loss_prob: 2.8981 loss_thr: 1.1665 loss_db: 0.9996 2022/11/02 12:32:43 - mmengine - INFO - Epoch(train) [36][20/63] lr: 7.0836e-04 eta: 15:10:50 time: 0.4896 data_time: 0.0047 memory: 14901 loss: 5.0377 loss_prob: 2.8822 loss_thr: 1.1557 loss_db: 0.9997 2022/11/02 12:32:45 - mmengine - INFO - Epoch(train) [36][25/63] lr: 7.0836e-04 eta: 15:10:50 time: 0.4992 data_time: 0.0083 memory: 14901 loss: 5.0146 loss_prob: 2.8771 loss_thr: 1.1395 loss_db: 0.9980 2022/11/02 12:32:48 - mmengine - INFO - Epoch(train) [36][30/63] lr: 7.0836e-04 eta: 15:09:29 time: 0.5212 data_time: 0.0346 memory: 14901 loss: 4.9983 loss_prob: 2.8691 loss_thr: 1.1317 loss_db: 0.9975 2022/11/02 12:32:51 - mmengine - INFO - Epoch(train) [36][35/63] lr: 7.0836e-04 eta: 15:09:29 time: 0.5652 data_time: 0.0314 memory: 14901 loss: 4.9743 loss_prob: 2.8624 loss_thr: 1.1321 loss_db: 0.9799 2022/11/02 12:32:54 - mmengine - INFO - Epoch(train) [36][40/63] lr: 7.0836e-04 eta: 15:08:26 time: 0.5750 data_time: 0.0052 memory: 14901 loss: 4.9004 loss_prob: 2.8593 loss_thr: 1.1464 loss_db: 0.8947 2022/11/02 12:32:56 - mmengine - INFO - Epoch(train) [36][45/63] lr: 7.0836e-04 eta: 15:08:26 time: 0.5173 data_time: 0.0047 memory: 14901 loss: 4.8834 loss_prob: 2.8582 loss_thr: 1.1879 loss_db: 0.8373 2022/11/02 12:32:58 - mmengine - INFO - Epoch(train) [36][50/63] lr: 7.0836e-04 eta: 15:06:47 time: 0.4621 data_time: 0.0102 memory: 14901 loss: 4.9185 loss_prob: 2.8540 loss_thr: 1.2086 loss_db: 0.8560 2022/11/02 12:33:01 - mmengine - INFO - Epoch(train) [36][55/63] lr: 7.0836e-04 eta: 15:06:47 time: 0.5165 data_time: 0.0250 memory: 14901 loss: 4.8828 loss_prob: 2.8522 loss_thr: 1.1977 loss_db: 0.8330 2022/11/02 12:33:04 - mmengine - INFO - Epoch(train) [36][60/63] lr: 7.0836e-04 eta: 15:05:37 time: 0.5485 data_time: 0.0194 memory: 14901 loss: 4.8410 loss_prob: 2.8623 loss_thr: 1.1826 loss_db: 0.7961 2022/11/02 12:33:05 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:33:10 - mmengine - INFO - Epoch(train) [37][5/63] lr: 7.2855e-04 eta: 15:05:37 time: 0.6818 data_time: 0.2213 memory: 14901 loss: 4.8915 loss_prob: 2.8696 loss_thr: 1.1809 loss_db: 0.8410 2022/11/02 12:33:12 - mmengine - INFO - Epoch(train) [37][10/63] lr: 7.2855e-04 eta: 15:04:07 time: 0.7123 data_time: 0.2221 memory: 14901 loss: 4.8124 loss_prob: 2.8403 loss_thr: 1.1557 loss_db: 0.8163 2022/11/02 12:33:15 - mmengine - INFO - Epoch(train) [37][15/63] lr: 7.2855e-04 eta: 15:04:07 time: 0.4970 data_time: 0.0083 memory: 14901 loss: 4.7007 loss_prob: 2.8337 loss_thr: 1.1371 loss_db: 0.7299 2022/11/02 12:33:17 - mmengine - INFO - Epoch(train) [37][20/63] lr: 7.2855e-04 eta: 15:02:45 time: 0.5065 data_time: 0.0087 memory: 14901 loss: 4.7254 loss_prob: 2.8443 loss_thr: 1.1541 loss_db: 0.7270 2022/11/02 12:33:20 - mmengine - INFO - Epoch(train) [37][25/63] lr: 7.2855e-04 eta: 15:02:45 time: 0.5209 data_time: 0.0195 memory: 14901 loss: 4.7268 loss_prob: 2.8411 loss_thr: 1.1657 loss_db: 0.7200 2022/11/02 12:33:23 - mmengine - INFO - Epoch(train) [37][30/63] lr: 7.2855e-04 eta: 15:01:35 time: 0.5446 data_time: 0.0369 memory: 14901 loss: 4.7463 loss_prob: 2.8545 loss_thr: 1.1604 loss_db: 0.7313 2022/11/02 12:33:25 - mmengine - INFO - Epoch(train) [37][35/63] lr: 7.2855e-04 eta: 15:01:35 time: 0.5248 data_time: 0.0233 memory: 14901 loss: 4.7989 loss_prob: 2.8649 loss_thr: 1.1558 loss_db: 0.7782 2022/11/02 12:33:28 - mmengine - INFO - Epoch(train) [37][40/63] lr: 7.2855e-04 eta: 15:00:21 time: 0.5275 data_time: 0.0063 memory: 14901 loss: 4.7960 loss_prob: 2.8425 loss_thr: 1.1534 loss_db: 0.8001 2022/11/02 12:33:30 - mmengine - INFO - Epoch(train) [37][45/63] lr: 7.2855e-04 eta: 15:00:21 time: 0.5236 data_time: 0.0067 memory: 14901 loss: 4.8132 loss_prob: 2.8460 loss_thr: 1.1608 loss_db: 0.8064 2022/11/02 12:33:33 - mmengine - INFO - Epoch(train) [37][50/63] lr: 7.2855e-04 eta: 14:59:05 time: 0.5192 data_time: 0.0227 memory: 14901 loss: 4.7915 loss_prob: 2.8579 loss_thr: 1.1704 loss_db: 0.7632 2022/11/02 12:33:36 - mmengine - INFO - Epoch(train) [37][55/63] lr: 7.2855e-04 eta: 14:59:05 time: 0.5224 data_time: 0.0254 memory: 14901 loss: 4.7936 loss_prob: 2.8624 loss_thr: 1.1993 loss_db: 0.7319 2022/11/02 12:33:38 - mmengine - INFO - Epoch(train) [37][60/63] lr: 7.2855e-04 eta: 14:57:40 time: 0.4898 data_time: 0.0077 memory: 14901 loss: 4.8749 loss_prob: 2.8472 loss_thr: 1.1715 loss_db: 0.8563 2022/11/02 12:33:39 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:33:44 - mmengine - INFO - Epoch(train) [38][5/63] lr: 7.4873e-04 eta: 14:57:40 time: 0.6821 data_time: 0.1921 memory: 14901 loss: 4.7455 loss_prob: 2.8146 loss_thr: 1.1188 loss_db: 0.8121 2022/11/02 12:33:47 - mmengine - INFO - Epoch(train) [38][10/63] lr: 7.4873e-04 eta: 14:56:26 time: 0.7492 data_time: 0.2283 memory: 14901 loss: 4.7488 loss_prob: 2.8366 loss_thr: 1.1524 loss_db: 0.7598 2022/11/02 12:33:49 - mmengine - INFO - Epoch(train) [38][15/63] lr: 7.4873e-04 eta: 14:56:26 time: 0.5473 data_time: 0.0422 memory: 14901 loss: 4.9235 loss_prob: 2.8519 loss_thr: 1.1737 loss_db: 0.8979 2022/11/02 12:33:52 - mmengine - INFO - Epoch(train) [38][20/63] lr: 7.4873e-04 eta: 14:55:06 time: 0.5011 data_time: 0.0047 memory: 14901 loss: 4.9745 loss_prob: 2.8478 loss_thr: 1.1622 loss_db: 0.9646 2022/11/02 12:33:55 - mmengine - INFO - Epoch(train) [38][25/63] lr: 7.4873e-04 eta: 14:55:06 time: 0.5141 data_time: 0.0086 memory: 14901 loss: 4.9529 loss_prob: 2.8477 loss_thr: 1.1343 loss_db: 0.9710 2022/11/02 12:33:57 - mmengine - INFO - Epoch(train) [38][30/63] lr: 7.4873e-04 eta: 14:54:10 time: 0.5772 data_time: 0.0290 memory: 14901 loss: 4.9428 loss_prob: 2.8665 loss_thr: 1.1349 loss_db: 0.9414 2022/11/02 12:34:00 - mmengine - INFO - Epoch(train) [38][35/63] lr: 7.4873e-04 eta: 14:54:10 time: 0.5617 data_time: 0.0318 memory: 14901 loss: 4.9856 loss_prob: 2.8707 loss_thr: 1.1462 loss_db: 0.9686 2022/11/02 12:34:03 - mmengine - INFO - Epoch(train) [38][40/63] lr: 7.4873e-04 eta: 14:53:16 time: 0.5803 data_time: 0.0111 memory: 14901 loss: 5.0109 loss_prob: 2.8711 loss_thr: 1.1404 loss_db: 0.9994 2022/11/02 12:34:07 - mmengine - INFO - Epoch(train) [38][45/63] lr: 7.4873e-04 eta: 14:53:16 time: 0.6967 data_time: 0.0050 memory: 14901 loss: 4.9956 loss_prob: 2.8926 loss_thr: 1.1530 loss_db: 0.9501 2022/11/02 12:34:11 - mmengine - INFO - Epoch(train) [38][50/63] lr: 7.4873e-04 eta: 14:53:11 time: 0.7388 data_time: 0.0162 memory: 14901 loss: 4.9406 loss_prob: 2.8918 loss_thr: 1.1847 loss_db: 0.8641 2022/11/02 12:34:14 - mmengine - INFO - Epoch(train) [38][55/63] lr: 7.4873e-04 eta: 14:53:11 time: 0.6412 data_time: 0.0195 memory: 14901 loss: 4.8985 loss_prob: 2.8768 loss_thr: 1.1703 loss_db: 0.8514 2022/11/02 12:34:16 - mmengine - INFO - Epoch(train) [38][60/63] lr: 7.4873e-04 eta: 14:52:09 time: 0.5557 data_time: 0.0156 memory: 14901 loss: 4.8666 loss_prob: 2.8724 loss_thr: 1.1716 loss_db: 0.8226 2022/11/02 12:34:17 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:34:23 - mmengine - INFO - Epoch(train) [39][5/63] lr: 7.6891e-04 eta: 14:52:09 time: 0.7921 data_time: 0.2001 memory: 14901 loss: 4.7489 loss_prob: 2.8795 loss_thr: 1.1406 loss_db: 0.7289 2022/11/02 12:34:26 - mmengine - INFO - Epoch(train) [39][10/63] lr: 7.6891e-04 eta: 14:51:29 time: 0.8483 data_time: 0.2133 memory: 14901 loss: 4.9354 loss_prob: 2.8990 loss_thr: 1.1473 loss_db: 0.8891 2022/11/02 12:34:29 - mmengine - INFO - Epoch(train) [39][15/63] lr: 7.6891e-04 eta: 14:51:29 time: 0.5946 data_time: 0.0180 memory: 14901 loss: 4.8492 loss_prob: 2.8396 loss_thr: 1.1457 loss_db: 0.8639 2022/11/02 12:34:32 - mmengine - INFO - Epoch(train) [39][20/63] lr: 7.6891e-04 eta: 14:50:40 time: 0.5942 data_time: 0.0052 memory: 14901 loss: 4.7750 loss_prob: 2.8325 loss_thr: 1.1893 loss_db: 0.7533 2022/11/02 12:34:35 - mmengine - INFO - Epoch(train) [39][25/63] lr: 7.6891e-04 eta: 14:50:40 time: 0.5692 data_time: 0.0233 memory: 14901 loss: 4.8750 loss_prob: 2.8667 loss_thr: 1.2034 loss_db: 0.8049 2022/11/02 12:34:38 - mmengine - INFO - Epoch(train) [39][30/63] lr: 7.6891e-04 eta: 14:50:02 time: 0.6294 data_time: 0.0232 memory: 14901 loss: 4.8328 loss_prob: 2.8650 loss_thr: 1.2099 loss_db: 0.7579 2022/11/02 12:34:41 - mmengine - INFO - Epoch(train) [39][35/63] lr: 7.6891e-04 eta: 14:50:02 time: 0.5951 data_time: 0.0179 memory: 14901 loss: 4.6877 loss_prob: 2.8405 loss_thr: 1.1731 loss_db: 0.6741 2022/11/02 12:34:43 - mmengine - INFO - Epoch(train) [39][40/63] lr: 7.6891e-04 eta: 14:48:42 time: 0.4857 data_time: 0.0175 memory: 14901 loss: 4.6870 loss_prob: 2.8378 loss_thr: 1.1079 loss_db: 0.7413 2022/11/02 12:34:46 - mmengine - INFO - Epoch(train) [39][45/63] lr: 7.6891e-04 eta: 14:48:42 time: 0.5628 data_time: 0.0043 memory: 14901 loss: 4.7099 loss_prob: 2.8272 loss_thr: 1.1455 loss_db: 0.7371 2022/11/02 12:34:50 - mmengine - INFO - Epoch(train) [39][50/63] lr: 7.6891e-04 eta: 14:48:27 time: 0.7034 data_time: 0.0183 memory: 14901 loss: 4.6560 loss_prob: 2.8246 loss_thr: 1.1595 loss_db: 0.6719 2022/11/02 12:34:53 - mmengine - INFO - Epoch(train) [39][55/63] lr: 7.6891e-04 eta: 14:48:27 time: 0.6869 data_time: 0.0186 memory: 14901 loss: 4.5972 loss_prob: 2.8025 loss_thr: 1.1148 loss_db: 0.6799 2022/11/02 12:34:56 - mmengine - INFO - Epoch(train) [39][60/63] lr: 7.6891e-04 eta: 14:47:33 time: 0.5734 data_time: 0.0085 memory: 14901 loss: 4.5690 loss_prob: 2.7904 loss_thr: 1.1165 loss_db: 0.6620 2022/11/02 12:34:57 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:35:02 - mmengine - INFO - Epoch(train) [40][5/63] lr: 7.8909e-04 eta: 14:47:33 time: 0.6885 data_time: 0.2178 memory: 14901 loss: 4.6890 loss_prob: 2.8462 loss_thr: 1.1394 loss_db: 0.7034 2022/11/02 12:35:04 - mmengine - INFO - Epoch(train) [40][10/63] lr: 7.8909e-04 eta: 14:46:14 time: 0.7102 data_time: 0.2192 memory: 14901 loss: 4.5794 loss_prob: 2.7921 loss_thr: 1.1009 loss_db: 0.6865 2022/11/02 12:35:07 - mmengine - INFO - Epoch(train) [40][15/63] lr: 7.8909e-04 eta: 14:46:14 time: 0.5294 data_time: 0.0066 memory: 14901 loss: 4.4765 loss_prob: 2.7505 loss_thr: 1.0819 loss_db: 0.6441 2022/11/02 12:35:10 - mmengine - INFO - Epoch(train) [40][20/63] lr: 7.8909e-04 eta: 14:45:12 time: 0.5441 data_time: 0.0097 memory: 14901 loss: 4.3902 loss_prob: 2.7393 loss_thr: 1.0644 loss_db: 0.5865 2022/11/02 12:35:12 - mmengine - INFO - Epoch(train) [40][25/63] lr: 7.8909e-04 eta: 14:45:12 time: 0.4955 data_time: 0.0143 memory: 14901 loss: 4.3858 loss_prob: 2.7222 loss_thr: 1.0521 loss_db: 0.6115 2022/11/02 12:35:14 - mmengine - INFO - Epoch(train) [40][30/63] lr: 7.8909e-04 eta: 14:43:51 time: 0.4754 data_time: 0.0285 memory: 14901 loss: 4.3973 loss_prob: 2.7220 loss_thr: 1.0717 loss_db: 0.6036 2022/11/02 12:35:17 - mmengine - INFO - Epoch(train) [40][35/63] lr: 7.8909e-04 eta: 14:43:51 time: 0.4865 data_time: 0.0236 memory: 14901 loss: 4.2807 loss_prob: 2.6882 loss_thr: 1.0518 loss_db: 0.5407 2022/11/02 12:35:19 - mmengine - INFO - Epoch(train) [40][40/63] lr: 7.8909e-04 eta: 14:42:33 time: 0.4827 data_time: 0.0046 memory: 14901 loss: 4.2465 loss_prob: 2.6722 loss_thr: 1.0315 loss_db: 0.5428 2022/11/02 12:35:22 - mmengine - INFO - Epoch(train) [40][45/63] lr: 7.8909e-04 eta: 14:42:33 time: 0.4866 data_time: 0.0080 memory: 14901 loss: 4.3254 loss_prob: 2.6979 loss_thr: 1.0555 loss_db: 0.5720 2022/11/02 12:35:25 - mmengine - INFO - Epoch(train) [40][50/63] lr: 7.8909e-04 eta: 14:41:31 time: 0.5375 data_time: 0.0201 memory: 14901 loss: 4.2148 loss_prob: 2.6316 loss_thr: 1.0542 loss_db: 0.5289 2022/11/02 12:35:28 - mmengine - INFO - Epoch(train) [40][55/63] lr: 7.8909e-04 eta: 14:41:31 time: 0.6076 data_time: 0.0235 memory: 14901 loss: 4.2255 loss_prob: 2.6599 loss_thr: 1.0450 loss_db: 0.5206 2022/11/02 12:35:30 - mmengine - INFO - Epoch(train) [40][60/63] lr: 7.8909e-04 eta: 14:40:37 time: 0.5607 data_time: 0.0117 memory: 14901 loss: 4.3959 loss_prob: 2.7408 loss_thr: 1.0651 loss_db: 0.5900 2022/11/02 12:35:32 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:35:32 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/11/02 12:35:35 - mmengine - INFO - Epoch(val) [40][5/500] eta: 14:40:37 time: 0.0564 data_time: 0.0065 memory: 14901 2022/11/02 12:35:36 - mmengine - INFO - Epoch(val) [40][10/500] eta: 0:00:39 time: 0.0815 data_time: 0.0063 memory: 1008 2022/11/02 12:35:36 - mmengine - INFO - Epoch(val) [40][15/500] eta: 0:00:39 time: 0.0865 data_time: 0.0029 memory: 1008 2022/11/02 12:35:37 - mmengine - INFO - Epoch(val) [40][20/500] eta: 0:00:41 time: 0.0868 data_time: 0.0032 memory: 1008 2022/11/02 12:35:37 - mmengine - INFO - Epoch(val) [40][25/500] eta: 0:00:41 time: 0.0687 data_time: 0.0024 memory: 1008 2022/11/02 12:35:37 - mmengine - INFO - Epoch(val) [40][30/500] eta: 0:00:36 time: 0.0780 data_time: 0.0024 memory: 1008 2022/11/02 12:35:38 - mmengine - INFO - Epoch(val) [40][35/500] eta: 0:00:36 time: 0.0964 data_time: 0.0024 memory: 1008 2022/11/02 12:35:39 - mmengine - INFO - Epoch(val) [40][40/500] eta: 0:00:55 time: 0.1201 data_time: 0.0027 memory: 1008 2022/11/02 12:35:39 - mmengine - INFO - Epoch(val) [40][45/500] eta: 0:00:55 time: 0.1340 data_time: 0.0028 memory: 1008 2022/11/02 12:35:40 - mmengine - INFO - Epoch(val) [40][50/500] eta: 0:00:46 time: 0.1034 data_time: 0.0026 memory: 1008 2022/11/02 12:35:40 - mmengine - INFO - Epoch(val) [40][55/500] eta: 0:00:46 time: 0.0753 data_time: 0.0025 memory: 1008 2022/11/02 12:35:40 - mmengine - INFO - Epoch(val) [40][60/500] eta: 0:00:35 time: 0.0814 data_time: 0.0024 memory: 1008 2022/11/02 12:35:41 - mmengine - INFO - Epoch(val) [40][65/500] eta: 0:00:35 time: 0.0840 data_time: 0.0026 memory: 1008 2022/11/02 12:35:41 - mmengine - INFO - Epoch(val) [40][70/500] eta: 0:00:35 time: 0.0828 data_time: 0.0027 memory: 1008 2022/11/02 12:35:42 - mmengine - INFO - Epoch(val) [40][75/500] eta: 0:00:35 time: 0.0855 data_time: 0.0024 memory: 1008 2022/11/02 12:35:42 - mmengine - INFO - Epoch(val) [40][80/500] eta: 0:00:25 time: 0.0597 data_time: 0.0024 memory: 1008 2022/11/02 12:35:42 - mmengine - INFO - Epoch(val) [40][85/500] eta: 0:00:25 time: 0.0555 data_time: 0.0025 memory: 1008 2022/11/02 12:35:43 - mmengine - INFO - Epoch(val) [40][90/500] eta: 0:00:33 time: 0.0828 data_time: 0.0024 memory: 1008 2022/11/02 12:35:43 - mmengine - INFO - Epoch(val) [40][95/500] eta: 0:00:33 time: 0.0823 data_time: 0.0025 memory: 1008 2022/11/02 12:35:43 - mmengine - INFO - Epoch(val) [40][100/500] eta: 0:00:30 time: 0.0760 data_time: 0.0025 memory: 1008 2022/11/02 12:35:44 - mmengine - INFO - Epoch(val) [40][105/500] eta: 0:00:30 time: 0.0801 data_time: 0.0026 memory: 1008 2022/11/02 12:35:44 - mmengine - INFO - Epoch(val) [40][110/500] eta: 0:00:32 time: 0.0829 data_time: 0.0026 memory: 1008 2022/11/02 12:35:45 - mmengine - INFO - Epoch(val) [40][115/500] eta: 0:00:32 time: 0.1004 data_time: 0.0026 memory: 1008 2022/11/02 12:35:45 - mmengine - INFO - Epoch(val) [40][120/500] eta: 0:00:33 time: 0.0879 data_time: 0.0026 memory: 1008 2022/11/02 12:35:46 - mmengine - INFO - Epoch(val) [40][125/500] eta: 0:00:33 time: 0.0682 data_time: 0.0029 memory: 1008 2022/11/02 12:35:46 - mmengine - INFO - Epoch(val) [40][130/500] eta: 0:00:32 time: 0.0878 data_time: 0.0026 memory: 1008 2022/11/02 12:35:46 - mmengine - INFO - Epoch(val) [40][135/500] eta: 0:00:32 time: 0.0845 data_time: 0.0024 memory: 1008 2022/11/02 12:35:47 - mmengine - INFO - Epoch(val) [40][140/500] eta: 0:00:30 time: 0.0841 data_time: 0.0028 memory: 1008 2022/11/02 12:35:47 - mmengine - INFO - Epoch(val) [40][145/500] eta: 0:00:30 time: 0.0988 data_time: 0.0030 memory: 1008 2022/11/02 12:35:48 - mmengine - INFO - Epoch(val) [40][150/500] eta: 0:00:33 time: 0.0958 data_time: 0.0027 memory: 1008 2022/11/02 12:35:48 - mmengine - INFO - Epoch(val) [40][155/500] eta: 0:00:33 time: 0.0915 data_time: 0.0025 memory: 1008 2022/11/02 12:35:49 - mmengine - INFO - Epoch(val) [40][160/500] eta: 0:00:35 time: 0.1046 data_time: 0.0024 memory: 1008 2022/11/02 12:35:50 - mmengine - INFO - Epoch(val) [40][165/500] eta: 0:00:35 time: 0.1343 data_time: 0.0024 memory: 1008 2022/11/02 12:35:50 - mmengine - INFO - Epoch(val) [40][170/500] eta: 0:00:50 time: 0.1520 data_time: 0.0025 memory: 1008 2022/11/02 12:35:51 - mmengine - INFO - Epoch(val) [40][175/500] eta: 0:00:50 time: 0.1140 data_time: 0.0024 memory: 1008 2022/11/02 12:35:51 - mmengine - INFO - Epoch(val) [40][180/500] eta: 0:00:25 time: 0.0785 data_time: 0.0024 memory: 1008 2022/11/02 12:35:51 - mmengine - INFO - Epoch(val) [40][185/500] eta: 0:00:25 time: 0.0735 data_time: 0.0025 memory: 1008 2022/11/02 12:35:52 - mmengine - INFO - Epoch(val) [40][190/500] eta: 0:00:19 time: 0.0640 data_time: 0.0024 memory: 1008 2022/11/02 12:35:52 - mmengine - INFO - Epoch(val) [40][195/500] eta: 0:00:19 time: 0.0675 data_time: 0.0024 memory: 1008 2022/11/02 12:35:53 - mmengine - INFO - Epoch(val) [40][200/500] eta: 0:00:28 time: 0.0952 data_time: 0.0027 memory: 1008 2022/11/02 12:35:53 - mmengine - INFO - Epoch(val) [40][205/500] eta: 0:00:28 time: 0.0989 data_time: 0.0028 memory: 1008 2022/11/02 12:35:54 - mmengine - INFO - Epoch(val) [40][210/500] eta: 0:00:22 time: 0.0784 data_time: 0.0025 memory: 1008 2022/11/02 12:35:54 - mmengine - INFO - Epoch(val) [40][215/500] eta: 0:00:22 time: 0.0886 data_time: 0.0029 memory: 1008 2022/11/02 12:35:54 - mmengine - INFO - Epoch(val) [40][220/500] eta: 0:00:20 time: 0.0749 data_time: 0.0030 memory: 1008 2022/11/02 12:35:55 - mmengine - INFO - Epoch(val) [40][225/500] eta: 0:00:20 time: 0.0815 data_time: 0.0026 memory: 1008 2022/11/02 12:35:55 - mmengine - INFO - Epoch(val) [40][230/500] eta: 0:00:26 time: 0.0969 data_time: 0.0026 memory: 1008 2022/11/02 12:35:56 - mmengine - INFO - Epoch(val) [40][235/500] eta: 0:00:26 time: 0.0846 data_time: 0.0025 memory: 1008 2022/11/02 12:35:56 - mmengine - INFO - Epoch(val) [40][240/500] eta: 0:00:21 time: 0.0823 data_time: 0.0028 memory: 1008 2022/11/02 12:35:57 - mmengine - INFO - Epoch(val) [40][245/500] eta: 0:00:21 time: 0.0848 data_time: 0.0028 memory: 1008 2022/11/02 12:35:57 - mmengine - INFO - Epoch(val) [40][250/500] eta: 0:00:28 time: 0.1142 data_time: 0.0024 memory: 1008 2022/11/02 12:35:58 - mmengine - INFO - Epoch(val) [40][255/500] eta: 0:00:28 time: 0.1094 data_time: 0.0029 memory: 1008 2022/11/02 12:35:58 - mmengine - INFO - Epoch(val) [40][260/500] eta: 0:00:16 time: 0.0699 data_time: 0.0028 memory: 1008 2022/11/02 12:35:59 - mmengine - INFO - Epoch(val) [40][265/500] eta: 0:00:16 time: 0.1080 data_time: 0.0037 memory: 1008 2022/11/02 12:35:59 - mmengine - INFO - Epoch(val) [40][270/500] eta: 0:00:26 time: 0.1156 data_time: 0.0038 memory: 1008 2022/11/02 12:36:00 - mmengine - INFO - Epoch(val) [40][275/500] eta: 0:00:26 time: 0.0769 data_time: 0.0031 memory: 1008 2022/11/02 12:36:00 - mmengine - INFO - Epoch(val) [40][280/500] eta: 0:00:16 time: 0.0755 data_time: 0.0032 memory: 1008 2022/11/02 12:36:00 - mmengine - INFO - Epoch(val) [40][285/500] eta: 0:00:16 time: 0.0685 data_time: 0.0029 memory: 1008 2022/11/02 12:36:01 - mmengine - INFO - Epoch(val) [40][290/500] eta: 0:00:18 time: 0.0879 data_time: 0.0027 memory: 1008 2022/11/02 12:36:01 - mmengine - INFO - Epoch(val) [40][295/500] eta: 0:00:18 time: 0.1065 data_time: 0.0029 memory: 1008 2022/11/02 12:36:02 - mmengine - INFO - Epoch(val) [40][300/500] eta: 0:00:18 time: 0.0903 data_time: 0.0029 memory: 1008 2022/11/02 12:36:02 - mmengine - INFO - Epoch(val) [40][305/500] eta: 0:00:18 time: 0.0978 data_time: 0.0029 memory: 1008 2022/11/02 12:36:03 - mmengine - INFO - Epoch(val) [40][310/500] eta: 0:00:17 time: 0.0925 data_time: 0.0029 memory: 1008 2022/11/02 12:36:03 - mmengine - INFO - Epoch(val) [40][315/500] eta: 0:00:17 time: 0.0728 data_time: 0.0028 memory: 1008 2022/11/02 12:36:03 - mmengine - INFO - Epoch(val) [40][320/500] eta: 0:00:12 time: 0.0681 data_time: 0.0028 memory: 1008 2022/11/02 12:36:04 - mmengine - INFO - Epoch(val) [40][325/500] eta: 0:00:12 time: 0.0895 data_time: 0.0025 memory: 1008 2022/11/02 12:36:04 - mmengine - INFO - Epoch(val) [40][330/500] eta: 0:00:16 time: 0.0980 data_time: 0.0027 memory: 1008 2022/11/02 12:36:04 - mmengine - INFO - Epoch(val) [40][335/500] eta: 0:00:16 time: 0.0640 data_time: 0.0028 memory: 1008 2022/11/02 12:36:05 - mmengine - INFO - Epoch(val) [40][340/500] eta: 0:00:15 time: 0.0966 data_time: 0.0026 memory: 1008 2022/11/02 12:36:06 - mmengine - INFO - Epoch(val) [40][345/500] eta: 0:00:15 time: 0.1033 data_time: 0.0026 memory: 1008 2022/11/02 12:36:06 - mmengine - INFO - Epoch(val) [40][350/500] eta: 0:00:13 time: 0.0882 data_time: 0.0026 memory: 1008 2022/11/02 12:36:06 - mmengine - INFO - Epoch(val) [40][355/500] eta: 0:00:13 time: 0.0935 data_time: 0.0025 memory: 1008 2022/11/02 12:36:07 - mmengine - INFO - Epoch(val) [40][360/500] eta: 0:00:10 time: 0.0752 data_time: 0.0026 memory: 1008 2022/11/02 12:36:07 - mmengine - INFO - Epoch(val) [40][365/500] eta: 0:00:10 time: 0.0850 data_time: 0.0026 memory: 1008 2022/11/02 12:36:08 - mmengine - INFO - Epoch(val) [40][370/500] eta: 0:00:10 time: 0.0814 data_time: 0.0026 memory: 1008 2022/11/02 12:36:08 - mmengine - INFO - Epoch(val) [40][375/500] eta: 0:00:10 time: 0.0767 data_time: 0.0033 memory: 1008 2022/11/02 12:36:09 - mmengine - INFO - Epoch(val) [40][380/500] eta: 0:00:12 time: 0.1003 data_time: 0.0033 memory: 1008 2022/11/02 12:36:09 - mmengine - INFO - Epoch(val) [40][385/500] eta: 0:00:12 time: 0.1016 data_time: 0.0026 memory: 1008 2022/11/02 12:36:10 - mmengine - INFO - Epoch(val) [40][390/500] eta: 0:00:12 time: 0.1160 data_time: 0.0027 memory: 1008 2022/11/02 12:36:10 - mmengine - INFO - Epoch(val) [40][395/500] eta: 0:00:12 time: 0.1202 data_time: 0.0028 memory: 1008 2022/11/02 12:36:11 - mmengine - INFO - Epoch(val) [40][400/500] eta: 0:00:08 time: 0.0871 data_time: 0.0027 memory: 1008 2022/11/02 12:36:11 - mmengine - INFO - Epoch(val) [40][405/500] eta: 0:00:08 time: 0.0904 data_time: 0.0028 memory: 1008 2022/11/02 12:36:12 - mmengine - INFO - Epoch(val) [40][410/500] eta: 0:00:08 time: 0.0915 data_time: 0.0026 memory: 1008 2022/11/02 12:36:12 - mmengine - INFO - Epoch(val) [40][415/500] eta: 0:00:08 time: 0.0775 data_time: 0.0025 memory: 1008 2022/11/02 12:36:13 - mmengine - INFO - Epoch(val) [40][420/500] eta: 0:00:09 time: 0.1136 data_time: 0.0490 memory: 1008 2022/11/02 12:36:13 - mmengine - INFO - Epoch(val) [40][425/500] eta: 0:00:09 time: 0.1364 data_time: 0.0487 memory: 1008 2022/11/02 12:36:14 - mmengine - INFO - Epoch(val) [40][430/500] eta: 0:00:07 time: 0.1061 data_time: 0.0022 memory: 1008 2022/11/02 12:36:14 - mmengine - INFO - Epoch(val) [40][435/500] eta: 0:00:07 time: 0.0751 data_time: 0.0026 memory: 1008 2022/11/02 12:36:14 - mmengine - INFO - Epoch(val) [40][440/500] eta: 0:00:03 time: 0.0574 data_time: 0.0025 memory: 1008 2022/11/02 12:36:15 - mmengine - INFO - Epoch(val) [40][445/500] eta: 0:00:03 time: 0.0703 data_time: 0.0024 memory: 1008 2022/11/02 12:36:15 - mmengine - INFO - Epoch(val) [40][450/500] eta: 0:00:04 time: 0.0874 data_time: 0.0024 memory: 1008 2022/11/02 12:36:16 - mmengine - INFO - Epoch(val) [40][455/500] eta: 0:00:04 time: 0.0876 data_time: 0.0024 memory: 1008 2022/11/02 12:36:16 - mmengine - INFO - Epoch(val) [40][460/500] eta: 0:00:03 time: 0.0759 data_time: 0.0029 memory: 1008 2022/11/02 12:36:17 - mmengine - INFO - Epoch(val) [40][465/500] eta: 0:00:03 time: 0.0834 data_time: 0.0033 memory: 1008 2022/11/02 12:36:17 - mmengine - INFO - Epoch(val) [40][470/500] eta: 0:00:02 time: 0.0892 data_time: 0.0031 memory: 1008 2022/11/02 12:36:17 - mmengine - INFO - Epoch(val) [40][475/500] eta: 0:00:02 time: 0.0691 data_time: 0.0029 memory: 1008 2022/11/02 12:36:18 - mmengine - INFO - Epoch(val) [40][480/500] eta: 0:00:01 time: 0.0783 data_time: 0.0027 memory: 1008 2022/11/02 12:36:18 - mmengine - INFO - Epoch(val) [40][485/500] eta: 0:00:01 time: 0.0896 data_time: 0.0025 memory: 1008 2022/11/02 12:36:19 - mmengine - INFO - Epoch(val) [40][490/500] eta: 0:00:01 time: 0.1153 data_time: 0.0026 memory: 1008 2022/11/02 12:36:19 - mmengine - INFO - Epoch(val) [40][495/500] eta: 0:00:01 time: 0.1164 data_time: 0.0026 memory: 1008 2022/11/02 12:36:20 - mmengine - INFO - Epoch(val) [40][500/500] eta: 0:00:00 time: 0.0791 data_time: 0.0032 memory: 1008 2022/11/02 12:36:20 - mmengine - INFO - Evaluating hmean-iou... 2022/11/02 12:36:20 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.1993, precision: 0.0488, hmean: 0.0784 2022/11/02 12:36:20 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.0934, precision: 0.5389, hmean: 0.1592 2022/11/02 12:36:20 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.0029, precision: 0.6000, hmean: 0.0057 2022/11/02 12:36:20 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.0000, precision: 0.0000, hmean: 0.0000 2022/11/02 12:36:20 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.0000, precision: 0.0000, hmean: 0.0000 2022/11/02 12:36:20 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.0000, precision: 0.0000, hmean: 0.0000 2022/11/02 12:36:20 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0000, precision: 0.0000, hmean: 0.0000 2022/11/02 12:36:20 - mmengine - INFO - Epoch(val) [40][500/500] icdar/precision: 0.5389 icdar/recall: 0.0934 icdar/hmean: 0.1592 2022/11/02 12:36:25 - mmengine - INFO - Epoch(train) [41][5/63] lr: 8.0927e-04 eta: 0:00:00 time: 0.7648 data_time: 0.1995 memory: 14901 loss: 4.4229 loss_prob: 2.7257 loss_thr: 1.0775 loss_db: 0.6197 2022/11/02 12:36:27 - mmengine - INFO - Epoch(train) [41][10/63] lr: 8.0927e-04 eta: 14:39:34 time: 0.7538 data_time: 0.2022 memory: 14901 loss: 4.3644 loss_prob: 2.6862 loss_thr: 1.0657 loss_db: 0.6125 2022/11/02 12:36:30 - mmengine - INFO - Epoch(train) [41][15/63] lr: 8.0927e-04 eta: 14:39:34 time: 0.4741 data_time: 0.0113 memory: 14901 loss: 4.3063 loss_prob: 2.6457 loss_thr: 1.0681 loss_db: 0.5925 2022/11/02 12:36:32 - mmengine - INFO - Epoch(train) [41][20/63] lr: 8.0927e-04 eta: 14:38:17 time: 0.4807 data_time: 0.0096 memory: 14901 loss: 4.2846 loss_prob: 2.6574 loss_thr: 1.0663 loss_db: 0.5609 2022/11/02 12:36:36 - mmengine - INFO - Epoch(train) [41][25/63] lr: 8.0927e-04 eta: 14:38:17 time: 0.6300 data_time: 0.0346 memory: 14901 loss: 4.2860 loss_prob: 2.6931 loss_thr: 1.0411 loss_db: 0.5518 2022/11/02 12:36:40 - mmengine - INFO - Epoch(train) [41][30/63] lr: 8.0927e-04 eta: 14:38:42 time: 0.8354 data_time: 0.0374 memory: 14901 loss: 4.4211 loss_prob: 2.7287 loss_thr: 1.0494 loss_db: 0.6429 2022/11/02 12:36:43 - mmengine - INFO - Epoch(train) [41][35/63] lr: 8.0927e-04 eta: 14:38:42 time: 0.6992 data_time: 0.0116 memory: 14901 loss: 4.3852 loss_prob: 2.6782 loss_thr: 1.0688 loss_db: 0.6382 2022/11/02 12:36:47 - mmengine - INFO - Epoch(train) [41][40/63] lr: 8.0927e-04 eta: 14:38:12 time: 0.6407 data_time: 0.0110 memory: 14901 loss: 4.1499 loss_prob: 2.5834 loss_thr: 1.0462 loss_db: 0.5204 2022/11/02 12:36:50 - mmengine - INFO - Epoch(train) [41][45/63] lr: 8.0927e-04 eta: 14:38:12 time: 0.7079 data_time: 0.0097 memory: 14901 loss: 4.0464 loss_prob: 2.5363 loss_thr: 1.0231 loss_db: 0.4871 2022/11/02 12:36:53 - mmengine - INFO - Epoch(train) [41][50/63] lr: 8.0927e-04 eta: 14:37:41 time: 0.6371 data_time: 0.0161 memory: 14901 loss: 4.0903 loss_prob: 2.5510 loss_thr: 1.0166 loss_db: 0.5227 2022/11/02 12:36:56 - mmengine - INFO - Epoch(train) [41][55/63] lr: 8.0927e-04 eta: 14:37:41 time: 0.5655 data_time: 0.0185 memory: 14901 loss: 4.1627 loss_prob: 2.5913 loss_thr: 1.0302 loss_db: 0.5412 2022/11/02 12:36:58 - mmengine - INFO - Epoch(train) [41][60/63] lr: 8.0927e-04 eta: 14:36:34 time: 0.5089 data_time: 0.0118 memory: 14901 loss: 4.2201 loss_prob: 2.6268 loss_thr: 1.0416 loss_db: 0.5517 2022/11/02 12:37:00 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:37:06 - mmengine - INFO - Epoch(train) [42][5/63] lr: 8.2945e-04 eta: 14:36:34 time: 0.8512 data_time: 0.2204 memory: 14901 loss: 3.8167 loss_prob: 2.4045 loss_thr: 0.9652 loss_db: 0.4470 2022/11/02 12:37:08 - mmengine - INFO - Epoch(train) [42][10/63] lr: 8.2945e-04 eta: 14:36:04 time: 0.8640 data_time: 0.2185 memory: 14901 loss: 3.9585 loss_prob: 2.4922 loss_thr: 0.9785 loss_db: 0.4878 2022/11/02 12:37:11 - mmengine - INFO - Epoch(train) [42][15/63] lr: 8.2945e-04 eta: 14:36:04 time: 0.4997 data_time: 0.0051 memory: 14901 loss: 4.1546 loss_prob: 2.6095 loss_thr: 1.0206 loss_db: 0.5246 2022/11/02 12:37:14 - mmengine - INFO - Epoch(train) [42][20/63] lr: 8.2945e-04 eta: 14:35:03 time: 0.5287 data_time: 0.0053 memory: 14901 loss: 4.1581 loss_prob: 2.5904 loss_thr: 1.0353 loss_db: 0.5323 2022/11/02 12:37:17 - mmengine - INFO - Epoch(train) [42][25/63] lr: 8.2945e-04 eta: 14:35:03 time: 0.6173 data_time: 0.0193 memory: 14901 loss: 4.0749 loss_prob: 2.5378 loss_thr: 1.0193 loss_db: 0.5178 2022/11/02 12:37:20 - mmengine - INFO - Epoch(train) [42][30/63] lr: 8.2945e-04 eta: 14:34:25 time: 0.6095 data_time: 0.0379 memory: 14901 loss: 3.9885 loss_prob: 2.4998 loss_thr: 1.0009 loss_db: 0.4878 2022/11/02 12:37:22 - mmengine - INFO - Epoch(train) [42][35/63] lr: 8.2945e-04 eta: 14:34:25 time: 0.5116 data_time: 0.0236 memory: 14901 loss: 3.9000 loss_prob: 2.4514 loss_thr: 0.9745 loss_db: 0.4741 2022/11/02 12:37:25 - mmengine - INFO - Epoch(train) [42][40/63] lr: 8.2945e-04 eta: 14:33:14 time: 0.4881 data_time: 0.0044 memory: 14901 loss: 3.9620 loss_prob: 2.4901 loss_thr: 0.9832 loss_db: 0.4888 2022/11/02 12:37:27 - mmengine - INFO - Epoch(train) [42][45/63] lr: 8.2945e-04 eta: 14:33:14 time: 0.4850 data_time: 0.0041 memory: 14901 loss: 4.0935 loss_prob: 2.5749 loss_thr: 1.0032 loss_db: 0.5155 2022/11/02 12:37:30 - mmengine - INFO - Epoch(train) [42][50/63] lr: 8.2945e-04 eta: 14:32:12 time: 0.5191 data_time: 0.0222 memory: 14901 loss: 3.8569 loss_prob: 2.4386 loss_thr: 0.9691 loss_db: 0.4491 2022/11/02 12:37:32 - mmengine - INFO - Epoch(train) [42][55/63] lr: 8.2945e-04 eta: 14:32:12 time: 0.5378 data_time: 0.0244 memory: 14901 loss: 3.8329 loss_prob: 2.4254 loss_thr: 0.9577 loss_db: 0.4498 2022/11/02 12:37:35 - mmengine - INFO - Epoch(train) [42][60/63] lr: 8.2945e-04 eta: 14:31:08 time: 0.5113 data_time: 0.0066 memory: 14901 loss: 4.0424 loss_prob: 2.5212 loss_thr: 0.9997 loss_db: 0.5215 2022/11/02 12:37:36 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:37:40 - mmengine - INFO - Epoch(train) [43][5/63] lr: 8.4964e-04 eta: 14:31:08 time: 0.6591 data_time: 0.2105 memory: 14901 loss: 4.1694 loss_prob: 2.6144 loss_thr: 0.9763 loss_db: 0.5787 2022/11/02 12:37:43 - mmengine - INFO - Epoch(train) [43][10/63] lr: 8.4964e-04 eta: 14:29:54 time: 0.6965 data_time: 0.2175 memory: 14901 loss: 4.2718 loss_prob: 2.6947 loss_thr: 1.0054 loss_db: 0.5717 2022/11/02 12:37:45 - mmengine - INFO - Epoch(train) [43][15/63] lr: 8.4964e-04 eta: 14:29:54 time: 0.4990 data_time: 0.0120 memory: 14901 loss: 4.1644 loss_prob: 2.6309 loss_thr: 1.0195 loss_db: 0.5140 2022/11/02 12:37:48 - mmengine - INFO - Epoch(train) [43][20/63] lr: 8.4964e-04 eta: 14:28:46 time: 0.4950 data_time: 0.0054 memory: 14901 loss: 3.9377 loss_prob: 2.4945 loss_thr: 0.9760 loss_db: 0.4672 2022/11/02 12:37:51 - mmengine - INFO - Epoch(train) [43][25/63] lr: 8.4964e-04 eta: 14:28:46 time: 0.5643 data_time: 0.0301 memory: 14901 loss: 3.8591 loss_prob: 2.4422 loss_thr: 0.9570 loss_db: 0.4599 2022/11/02 12:37:54 - mmengine - INFO - Epoch(train) [43][30/63] lr: 8.4964e-04 eta: 14:28:04 time: 0.5843 data_time: 0.0394 memory: 14901 loss: 3.8583 loss_prob: 2.4322 loss_thr: 0.9709 loss_db: 0.4552 2022/11/02 12:37:56 - mmengine - INFO - Epoch(train) [43][35/63] lr: 8.4964e-04 eta: 14:28:04 time: 0.5386 data_time: 0.0155 memory: 14901 loss: 3.7877 loss_prob: 2.3836 loss_thr: 0.9601 loss_db: 0.4440 2022/11/02 12:37:59 - mmengine - INFO - Epoch(train) [43][40/63] lr: 8.4964e-04 eta: 14:27:00 time: 0.5068 data_time: 0.0060 memory: 14901 loss: 3.7337 loss_prob: 2.3567 loss_thr: 0.9434 loss_db: 0.4336 2022/11/02 12:38:01 - mmengine - INFO - Epoch(train) [43][45/63] lr: 8.4964e-04 eta: 14:27:00 time: 0.4908 data_time: 0.0046 memory: 14901 loss: 3.6798 loss_prob: 2.3102 loss_thr: 0.9474 loss_db: 0.4221 2022/11/02 12:38:04 - mmengine - INFO - Epoch(train) [43][50/63] lr: 8.4964e-04 eta: 14:26:00 time: 0.5162 data_time: 0.0185 memory: 14901 loss: 3.6003 loss_prob: 2.2610 loss_thr: 0.9351 loss_db: 0.4043 2022/11/02 12:38:06 - mmengine - INFO - Epoch(train) [43][55/63] lr: 8.4964e-04 eta: 14:26:00 time: 0.4968 data_time: 0.0240 memory: 14901 loss: 3.6423 loss_prob: 2.2926 loss_thr: 0.9412 loss_db: 0.4084 2022/11/02 12:38:09 - mmengine - INFO - Epoch(train) [43][60/63] lr: 8.4964e-04 eta: 14:24:49 time: 0.4785 data_time: 0.0106 memory: 14901 loss: 3.5720 loss_prob: 2.2288 loss_thr: 0.9505 loss_db: 0.3927 2022/11/02 12:38:10 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:38:14 - mmengine - INFO - Epoch(train) [44][5/63] lr: 8.6982e-04 eta: 14:24:49 time: 0.6383 data_time: 0.1923 memory: 14901 loss: 3.5487 loss_prob: 2.2198 loss_thr: 0.9390 loss_db: 0.3899 2022/11/02 12:38:17 - mmengine - INFO - Epoch(train) [44][10/63] lr: 8.6982e-04 eta: 14:23:38 time: 0.6924 data_time: 0.1933 memory: 14901 loss: 3.6625 loss_prob: 2.2946 loss_thr: 0.9457 loss_db: 0.4222 2022/11/02 12:38:19 - mmengine - INFO - Epoch(train) [44][15/63] lr: 8.6982e-04 eta: 14:23:38 time: 0.5325 data_time: 0.0110 memory: 14901 loss: 3.5562 loss_prob: 2.2265 loss_thr: 0.9376 loss_db: 0.3921 2022/11/02 12:38:22 - mmengine - INFO - Epoch(train) [44][20/63] lr: 8.6982e-04 eta: 14:22:35 time: 0.5026 data_time: 0.0097 memory: 14901 loss: 3.5550 loss_prob: 2.2369 loss_thr: 0.9333 loss_db: 0.3848 2022/11/02 12:38:24 - mmengine - INFO - Epoch(train) [44][25/63] lr: 8.6982e-04 eta: 14:22:35 time: 0.4936 data_time: 0.0282 memory: 14901 loss: 3.7876 loss_prob: 2.3926 loss_thr: 0.9439 loss_db: 0.4511 2022/11/02 12:38:27 - mmengine - INFO - Epoch(train) [44][30/63] lr: 8.6982e-04 eta: 14:21:41 time: 0.5326 data_time: 0.0322 memory: 14901 loss: 4.2113 loss_prob: 2.6155 loss_thr: 0.9807 loss_db: 0.6151 2022/11/02 12:38:30 - mmengine - INFO - Epoch(train) [44][35/63] lr: 8.6982e-04 eta: 14:21:41 time: 0.5562 data_time: 0.0170 memory: 14901 loss: 4.2308 loss_prob: 2.6200 loss_thr: 0.9952 loss_db: 0.6156 2022/11/02 12:38:33 - mmengine - INFO - Epoch(train) [44][40/63] lr: 8.6982e-04 eta: 14:20:52 time: 0.5532 data_time: 0.0132 memory: 14901 loss: 3.9551 loss_prob: 2.4908 loss_thr: 0.9677 loss_db: 0.4967 2022/11/02 12:38:35 - mmengine - INFO - Epoch(train) [44][45/63] lr: 8.6982e-04 eta: 14:20:52 time: 0.4960 data_time: 0.0083 memory: 14901 loss: 3.9157 loss_prob: 2.4848 loss_thr: 0.9488 loss_db: 0.4821 2022/11/02 12:38:38 - mmengine - INFO - Epoch(train) [44][50/63] lr: 8.6982e-04 eta: 14:19:49 time: 0.4978 data_time: 0.0172 memory: 14901 loss: 3.7785 loss_prob: 2.4017 loss_thr: 0.9226 loss_db: 0.4542 2022/11/02 12:38:40 - mmengine - INFO - Epoch(train) [44][55/63] lr: 8.6982e-04 eta: 14:19:49 time: 0.5175 data_time: 0.0168 memory: 14901 loss: 3.7632 loss_prob: 2.3744 loss_thr: 0.9167 loss_db: 0.4721 2022/11/02 12:38:43 - mmengine - INFO - Epoch(train) [44][60/63] lr: 8.6982e-04 eta: 14:18:47 time: 0.4987 data_time: 0.0106 memory: 14901 loss: 3.6760 loss_prob: 2.3156 loss_thr: 0.9183 loss_db: 0.4421 2022/11/02 12:38:44 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:38:49 - mmengine - INFO - Epoch(train) [45][5/63] lr: 8.9000e-04 eta: 14:18:47 time: 0.7267 data_time: 0.2569 memory: 14901 loss: 3.5715 loss_prob: 2.2651 loss_thr: 0.9022 loss_db: 0.4042 2022/11/02 12:38:52 - mmengine - INFO - Epoch(train) [45][10/63] lr: 8.9000e-04 eta: 14:18:00 time: 0.7747 data_time: 0.2569 memory: 14901 loss: 3.8139 loss_prob: 2.4076 loss_thr: 0.9244 loss_db: 0.4819 2022/11/02 12:38:54 - mmengine - INFO - Epoch(train) [45][15/63] lr: 8.9000e-04 eta: 14:18:00 time: 0.5038 data_time: 0.0058 memory: 14901 loss: 3.9226 loss_prob: 2.4629 loss_thr: 0.9397 loss_db: 0.5200 2022/11/02 12:38:57 - mmengine - INFO - Epoch(train) [45][20/63] lr: 8.9000e-04 eta: 14:17:08 time: 0.5363 data_time: 0.0217 memory: 14901 loss: 3.6952 loss_prob: 2.3416 loss_thr: 0.9195 loss_db: 0.4341 2022/11/02 12:38:59 - mmengine - INFO - Epoch(train) [45][25/63] lr: 8.9000e-04 eta: 14:17:08 time: 0.5197 data_time: 0.0258 memory: 14901 loss: 3.5660 loss_prob: 2.2572 loss_thr: 0.9150 loss_db: 0.3938 2022/11/02 12:39:02 - mmengine - INFO - Epoch(train) [45][30/63] lr: 8.9000e-04 eta: 14:16:04 time: 0.4856 data_time: 0.0175 memory: 14901 loss: 3.5678 loss_prob: 2.2398 loss_thr: 0.9260 loss_db: 0.4020 2022/11/02 12:39:04 - mmengine - INFO - Epoch(train) [45][35/63] lr: 8.9000e-04 eta: 14:16:04 time: 0.5234 data_time: 0.0140 memory: 14901 loss: 3.4569 loss_prob: 2.1668 loss_thr: 0.9151 loss_db: 0.3751 2022/11/02 12:39:07 - mmengine - INFO - Epoch(train) [45][40/63] lr: 8.9000e-04 eta: 14:15:15 time: 0.5436 data_time: 0.0082 memory: 14901 loss: 3.3906 loss_prob: 2.1367 loss_thr: 0.8848 loss_db: 0.3692 2022/11/02 12:39:10 - mmengine - INFO - Epoch(train) [45][45/63] lr: 8.9000e-04 eta: 14:15:15 time: 0.6037 data_time: 0.0169 memory: 14901 loss: 3.2918 loss_prob: 2.0658 loss_thr: 0.8705 loss_db: 0.3555 2022/11/02 12:39:14 - mmengine - INFO - Epoch(train) [45][50/63] lr: 8.9000e-04 eta: 14:14:59 time: 0.6707 data_time: 0.0223 memory: 14901 loss: 3.5680 loss_prob: 2.2605 loss_thr: 0.9028 loss_db: 0.4047 2022/11/02 12:39:17 - mmengine - INFO - Epoch(train) [45][55/63] lr: 8.9000e-04 eta: 14:14:59 time: 0.6797 data_time: 0.0165 memory: 14901 loss: 3.6259 loss_prob: 2.3054 loss_thr: 0.9156 loss_db: 0.4049 2022/11/02 12:39:20 - mmengine - INFO - Epoch(train) [45][60/63] lr: 8.9000e-04 eta: 14:14:30 time: 0.6196 data_time: 0.0104 memory: 14901 loss: 3.4219 loss_prob: 2.1515 loss_thr: 0.8959 loss_db: 0.3746 2022/11/02 12:39:21 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:39:27 - mmengine - INFO - Epoch(train) [46][5/63] lr: 9.1018e-04 eta: 14:14:30 time: 0.8232 data_time: 0.2322 memory: 14901 loss: 3.5294 loss_prob: 2.2336 loss_thr: 0.8875 loss_db: 0.4082 2022/11/02 12:39:30 - mmengine - INFO - Epoch(train) [46][10/63] lr: 9.1018e-04 eta: 14:13:59 time: 0.8284 data_time: 0.2310 memory: 14901 loss: 3.5895 loss_prob: 2.2820 loss_thr: 0.8909 loss_db: 0.4167 2022/11/02 12:39:33 - mmengine - INFO - Epoch(train) [46][15/63] lr: 9.1018e-04 eta: 14:13:59 time: 0.5953 data_time: 0.0051 memory: 14901 loss: 3.8838 loss_prob: 2.4541 loss_thr: 0.9301 loss_db: 0.4995 2022/11/02 12:39:36 - mmengine - INFO - Epoch(train) [46][20/63] lr: 9.1018e-04 eta: 14:13:23 time: 0.5937 data_time: 0.0046 memory: 14901 loss: 3.9022 loss_prob: 2.4725 loss_thr: 0.9204 loss_db: 0.5093 2022/11/02 12:39:39 - mmengine - INFO - Epoch(train) [46][25/63] lr: 9.1018e-04 eta: 14:13:23 time: 0.6443 data_time: 0.0281 memory: 14901 loss: 3.5956 loss_prob: 2.2853 loss_thr: 0.8893 loss_db: 0.4209 2022/11/02 12:39:42 - mmengine - INFO - Epoch(train) [46][30/63] lr: 9.1018e-04 eta: 14:13:07 time: 0.6681 data_time: 0.0344 memory: 14901 loss: 3.4201 loss_prob: 2.1550 loss_thr: 0.8915 loss_db: 0.3736 2022/11/02 12:39:46 - mmengine - INFO - Epoch(train) [46][35/63] lr: 9.1018e-04 eta: 14:13:07 time: 0.6926 data_time: 0.0117 memory: 14901 loss: 3.5386 loss_prob: 2.2501 loss_thr: 0.8759 loss_db: 0.4126 2022/11/02 12:39:49 - mmengine - INFO - Epoch(train) [46][40/63] lr: 9.1018e-04 eta: 14:12:59 time: 0.7009 data_time: 0.0051 memory: 14901 loss: 3.5232 loss_prob: 2.2466 loss_thr: 0.8700 loss_db: 0.4066 2022/11/02 12:39:52 - mmengine - INFO - Epoch(train) [46][45/63] lr: 9.1018e-04 eta: 14:12:59 time: 0.6147 data_time: 0.0047 memory: 14901 loss: 3.4862 loss_prob: 2.1769 loss_thr: 0.9129 loss_db: 0.3964 2022/11/02 12:39:55 - mmengine - INFO - Epoch(train) [46][50/63] lr: 9.1018e-04 eta: 14:12:21 time: 0.5778 data_time: 0.0237 memory: 14901 loss: 3.6664 loss_prob: 2.2903 loss_thr: 0.9368 loss_db: 0.4393 2022/11/02 12:39:58 - mmengine - INFO - Epoch(train) [46][55/63] lr: 9.1018e-04 eta: 14:12:21 time: 0.5492 data_time: 0.0237 memory: 14901 loss: 3.6914 loss_prob: 2.3149 loss_thr: 0.9298 loss_db: 0.4468 2022/11/02 12:40:01 - mmengine - INFO - Epoch(train) [46][60/63] lr: 9.1018e-04 eta: 14:11:35 time: 0.5480 data_time: 0.0049 memory: 14901 loss: 3.3906 loss_prob: 2.1240 loss_thr: 0.8868 loss_db: 0.3798 2022/11/02 12:40:02 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:40:07 - mmengine - INFO - Epoch(train) [47][5/63] lr: 9.3036e-04 eta: 14:11:35 time: 0.7859 data_time: 0.2121 memory: 14901 loss: 3.3816 loss_prob: 2.1385 loss_thr: 0.8665 loss_db: 0.3766 2022/11/02 12:40:10 - mmengine - INFO - Epoch(train) [47][10/63] lr: 9.3036e-04 eta: 14:10:52 time: 0.7799 data_time: 0.2123 memory: 14901 loss: 3.5763 loss_prob: 2.2557 loss_thr: 0.9094 loss_db: 0.4112 2022/11/02 12:40:12 - mmengine - INFO - Epoch(train) [47][15/63] lr: 9.3036e-04 eta: 14:10:52 time: 0.4738 data_time: 0.0068 memory: 14901 loss: 3.4649 loss_prob: 2.1674 loss_thr: 0.9070 loss_db: 0.3905 2022/11/02 12:40:15 - mmengine - INFO - Epoch(train) [47][20/63] lr: 9.3036e-04 eta: 14:09:51 time: 0.4843 data_time: 0.0067 memory: 14901 loss: 3.2899 loss_prob: 2.0637 loss_thr: 0.8783 loss_db: 0.3479 2022/11/02 12:40:17 - mmengine - INFO - Epoch(train) [47][25/63] lr: 9.3036e-04 eta: 14:09:51 time: 0.5037 data_time: 0.0103 memory: 14901 loss: 3.2836 loss_prob: 2.0445 loss_thr: 0.8896 loss_db: 0.3495 2022/11/02 12:40:20 - mmengine - INFO - Epoch(train) [47][30/63] lr: 9.3036e-04 eta: 14:09:01 time: 0.5281 data_time: 0.0357 memory: 14901 loss: 3.3502 loss_prob: 2.0849 loss_thr: 0.8908 loss_db: 0.3745 2022/11/02 12:40:23 - mmengine - INFO - Epoch(train) [47][35/63] lr: 9.3036e-04 eta: 14:09:01 time: 0.5403 data_time: 0.0306 memory: 14901 loss: 3.3378 loss_prob: 2.0829 loss_thr: 0.8780 loss_db: 0.3769 2022/11/02 12:40:25 - mmengine - INFO - Epoch(train) [47][40/63] lr: 9.3036e-04 eta: 14:08:12 time: 0.5331 data_time: 0.0053 memory: 14901 loss: 3.3171 loss_prob: 2.0630 loss_thr: 0.8879 loss_db: 0.3661 2022/11/02 12:40:28 - mmengine - INFO - Epoch(train) [47][45/63] lr: 9.3036e-04 eta: 14:08:12 time: 0.5428 data_time: 0.0051 memory: 14901 loss: 3.3534 loss_prob: 2.0841 loss_thr: 0.9002 loss_db: 0.3691 2022/11/02 12:40:31 - mmengine - INFO - Epoch(train) [47][50/63] lr: 9.3036e-04 eta: 14:07:27 time: 0.5455 data_time: 0.0201 memory: 14901 loss: 3.3109 loss_prob: 2.0609 loss_thr: 0.8849 loss_db: 0.3651 2022/11/02 12:40:33 - mmengine - INFO - Epoch(train) [47][55/63] lr: 9.3036e-04 eta: 14:07:27 time: 0.5107 data_time: 0.0210 memory: 14901 loss: 3.4252 loss_prob: 2.1444 loss_thr: 0.8851 loss_db: 0.3956 2022/11/02 12:40:36 - mmengine - INFO - Epoch(train) [47][60/63] lr: 9.3036e-04 eta: 14:06:28 time: 0.4870 data_time: 0.0060 memory: 14901 loss: 3.2678 loss_prob: 2.0378 loss_thr: 0.8694 loss_db: 0.3606 2022/11/02 12:40:37 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:40:42 - mmengine - INFO - Epoch(train) [48][5/63] lr: 9.5055e-04 eta: 14:06:28 time: 0.6916 data_time: 0.1949 memory: 14901 loss: 3.2394 loss_prob: 2.0285 loss_thr: 0.8543 loss_db: 0.3566 2022/11/02 12:40:44 - mmengine - INFO - Epoch(train) [48][10/63] lr: 9.5055e-04 eta: 14:05:29 time: 0.7048 data_time: 0.2005 memory: 14901 loss: 3.1156 loss_prob: 1.9457 loss_thr: 0.8331 loss_db: 0.3367 2022/11/02 12:40:47 - mmengine - INFO - Epoch(train) [48][15/63] lr: 9.5055e-04 eta: 14:05:29 time: 0.4876 data_time: 0.0114 memory: 14901 loss: 3.0354 loss_prob: 1.8768 loss_thr: 0.8391 loss_db: 0.3195 2022/11/02 12:40:49 - mmengine - INFO - Epoch(train) [48][20/63] lr: 9.5055e-04 eta: 14:04:31 time: 0.4888 data_time: 0.0056 memory: 14901 loss: 3.1051 loss_prob: 1.9360 loss_thr: 0.8453 loss_db: 0.3238 2022/11/02 12:40:51 - mmengine - INFO - Epoch(train) [48][25/63] lr: 9.5055e-04 eta: 14:04:31 time: 0.4901 data_time: 0.0104 memory: 14901 loss: 3.1765 loss_prob: 1.9746 loss_thr: 0.8721 loss_db: 0.3298 2022/11/02 12:40:54 - mmengine - INFO - Epoch(train) [48][30/63] lr: 9.5055e-04 eta: 14:03:38 time: 0.5103 data_time: 0.0316 memory: 14901 loss: 3.1277 loss_prob: 1.9249 loss_thr: 0.8743 loss_db: 0.3286 2022/11/02 12:40:56 - mmengine - INFO - Epoch(train) [48][35/63] lr: 9.5055e-04 eta: 14:03:38 time: 0.4900 data_time: 0.0307 memory: 14901 loss: 3.2576 loss_prob: 2.0373 loss_thr: 0.8674 loss_db: 0.3529 2022/11/02 12:40:58 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:40:59 - mmengine - INFO - Epoch(train) [48][40/63] lr: 9.5055e-04 eta: 14:02:34 time: 0.4629 data_time: 0.0090 memory: 14901 loss: 3.2291 loss_prob: 2.0169 loss_thr: 0.8622 loss_db: 0.3500 2022/11/02 12:41:01 - mmengine - INFO - Epoch(train) [48][45/63] lr: 9.5055e-04 eta: 14:02:34 time: 0.5082 data_time: 0.0077 memory: 14901 loss: 3.2462 loss_prob: 2.0190 loss_thr: 0.8795 loss_db: 0.3477 2022/11/02 12:41:04 - mmengine - INFO - Epoch(train) [48][50/63] lr: 9.5055e-04 eta: 14:01:50 time: 0.5394 data_time: 0.0168 memory: 14901 loss: 3.2931 loss_prob: 2.0708 loss_thr: 0.8681 loss_db: 0.3543 2022/11/02 12:41:06 - mmengine - INFO - Epoch(train) [48][55/63] lr: 9.5055e-04 eta: 14:01:50 time: 0.5045 data_time: 0.0245 memory: 14901 loss: 3.2880 loss_prob: 2.0697 loss_thr: 0.8529 loss_db: 0.3654 2022/11/02 12:41:09 - mmengine - INFO - Epoch(train) [48][60/63] lr: 9.5055e-04 eta: 14:00:53 time: 0.4904 data_time: 0.0165 memory: 14901 loss: 3.2211 loss_prob: 2.0110 loss_thr: 0.8529 loss_db: 0.3572 2022/11/02 12:41:10 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:41:15 - mmengine - INFO - Epoch(train) [49][5/63] lr: 9.7073e-04 eta: 14:00:53 time: 0.6740 data_time: 0.1933 memory: 14901 loss: 3.1734 loss_prob: 1.9835 loss_thr: 0.8439 loss_db: 0.3460 2022/11/02 12:41:17 - mmengine - INFO - Epoch(train) [49][10/63] lr: 9.7073e-04 eta: 13:59:59 time: 0.7163 data_time: 0.1964 memory: 14901 loss: 3.3277 loss_prob: 2.0750 loss_thr: 0.8726 loss_db: 0.3800 2022/11/02 12:41:20 - mmengine - INFO - Epoch(train) [49][15/63] lr: 9.7073e-04 eta: 13:59:59 time: 0.5141 data_time: 0.0076 memory: 14901 loss: 3.0940 loss_prob: 1.9281 loss_thr: 0.8323 loss_db: 0.3336 2022/11/02 12:41:22 - mmengine - INFO - Epoch(train) [49][20/63] lr: 9.7073e-04 eta: 13:59:02 time: 0.4812 data_time: 0.0051 memory: 14901 loss: 3.0919 loss_prob: 1.9255 loss_thr: 0.8346 loss_db: 0.3318 2022/11/02 12:41:25 - mmengine - INFO - Epoch(train) [49][25/63] lr: 9.7073e-04 eta: 13:59:02 time: 0.5013 data_time: 0.0309 memory: 14901 loss: 3.0950 loss_prob: 1.9201 loss_thr: 0.8395 loss_db: 0.3353 2022/11/02 12:41:27 - mmengine - INFO - Epoch(train) [49][30/63] lr: 9.7073e-04 eta: 13:58:16 time: 0.5293 data_time: 0.0445 memory: 14901 loss: 2.9437 loss_prob: 1.8125 loss_thr: 0.8316 loss_db: 0.2996 2022/11/02 12:41:30 - mmengine - INFO - Epoch(train) [49][35/63] lr: 9.7073e-04 eta: 13:58:16 time: 0.5174 data_time: 0.0206 memory: 14901 loss: 2.9970 loss_prob: 1.8525 loss_thr: 0.8385 loss_db: 0.3060 2022/11/02 12:41:32 - mmengine - INFO - Epoch(train) [49][40/63] lr: 9.7073e-04 eta: 13:57:20 time: 0.4886 data_time: 0.0065 memory: 14901 loss: 3.1688 loss_prob: 1.9715 loss_thr: 0.8668 loss_db: 0.3306 2022/11/02 12:41:35 - mmengine - INFO - Epoch(train) [49][45/63] lr: 9.7073e-04 eta: 13:57:20 time: 0.4697 data_time: 0.0047 memory: 14901 loss: 3.3985 loss_prob: 2.1510 loss_thr: 0.8777 loss_db: 0.3698 2022/11/02 12:41:37 - mmengine - INFO - Epoch(train) [49][50/63] lr: 9.7073e-04 eta: 13:56:29 time: 0.5044 data_time: 0.0208 memory: 14901 loss: 3.5432 loss_prob: 2.2692 loss_thr: 0.8725 loss_db: 0.4015 2022/11/02 12:41:40 - mmengine - INFO - Epoch(train) [49][55/63] lr: 9.7073e-04 eta: 13:56:29 time: 0.5398 data_time: 0.0258 memory: 14901 loss: 3.6282 loss_prob: 2.2959 loss_thr: 0.8906 loss_db: 0.4416 2022/11/02 12:41:43 - mmengine - INFO - Epoch(train) [49][60/63] lr: 9.7073e-04 eta: 13:55:43 time: 0.5281 data_time: 0.0109 memory: 14901 loss: 3.5510 loss_prob: 2.2346 loss_thr: 0.8925 loss_db: 0.4238 2022/11/02 12:41:44 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:41:49 - mmengine - INFO - Epoch(train) [50][5/63] lr: 9.9091e-04 eta: 13:55:43 time: 0.7594 data_time: 0.2120 memory: 14901 loss: 3.5388 loss_prob: 2.2266 loss_thr: 0.8987 loss_db: 0.4134 2022/11/02 12:41:53 - mmengine - INFO - Epoch(train) [50][10/63] lr: 9.9091e-04 eta: 13:55:25 time: 0.8573 data_time: 0.2146 memory: 14901 loss: 3.4918 loss_prob: 2.2076 loss_thr: 0.8868 loss_db: 0.3975 2022/11/02 12:41:56 - mmengine - INFO - Epoch(train) [50][15/63] lr: 9.9091e-04 eta: 13:55:25 time: 0.6255 data_time: 0.0078 memory: 14901 loss: 3.3037 loss_prob: 2.0793 loss_thr: 0.8550 loss_db: 0.3694 2022/11/02 12:41:59 - mmengine - INFO - Epoch(train) [50][20/63] lr: 9.9091e-04 eta: 13:54:59 time: 0.6109 data_time: 0.0061 memory: 14901 loss: 3.1769 loss_prob: 1.9941 loss_thr: 0.8424 loss_db: 0.3404 2022/11/02 12:42:02 - mmengine - INFO - Epoch(train) [50][25/63] lr: 9.9091e-04 eta: 13:54:59 time: 0.6080 data_time: 0.0124 memory: 14901 loss: 3.1432 loss_prob: 1.9568 loss_thr: 0.8471 loss_db: 0.3392 2022/11/02 12:42:05 - mmengine - INFO - Epoch(train) [50][30/63] lr: 9.9091e-04 eta: 13:54:41 time: 0.6442 data_time: 0.0343 memory: 14901 loss: 3.2555 loss_prob: 2.0241 loss_thr: 0.8627 loss_db: 0.3687 2022/11/02 12:42:08 - mmengine - INFO - Epoch(train) [50][35/63] lr: 9.9091e-04 eta: 13:54:41 time: 0.6835 data_time: 0.0277 memory: 14901 loss: 3.2222 loss_prob: 2.0044 loss_thr: 0.8534 loss_db: 0.3644 2022/11/02 12:42:11 - mmengine - INFO - Epoch(train) [50][40/63] lr: 9.9091e-04 eta: 13:54:06 time: 0.5703 data_time: 0.0055 memory: 14901 loss: 3.0019 loss_prob: 1.8651 loss_thr: 0.8247 loss_db: 0.3121 2022/11/02 12:42:13 - mmengine - INFO - Epoch(train) [50][45/63] lr: 9.9091e-04 eta: 13:54:06 time: 0.4993 data_time: 0.0058 memory: 14901 loss: 3.0097 loss_prob: 1.8714 loss_thr: 0.8283 loss_db: 0.3100 2022/11/02 12:42:16 - mmengine - INFO - Epoch(train) [50][50/63] lr: 9.9091e-04 eta: 13:53:30 time: 0.5634 data_time: 0.0117 memory: 14901 loss: 2.9697 loss_prob: 1.8383 loss_thr: 0.8136 loss_db: 0.3177 2022/11/02 12:42:19 - mmengine - INFO - Epoch(train) [50][55/63] lr: 9.9091e-04 eta: 13:53:30 time: 0.5888 data_time: 0.0224 memory: 14901 loss: 3.2963 loss_prob: 2.0756 loss_thr: 0.8317 loss_db: 0.3890 2022/11/02 12:42:23 - mmengine - INFO - Epoch(train) [50][60/63] lr: 9.9091e-04 eta: 13:53:18 time: 0.6659 data_time: 0.0162 memory: 14901 loss: 3.5221 loss_prob: 2.2535 loss_thr: 0.8496 loss_db: 0.4190 2022/11/02 12:42:24 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:42:30 - mmengine - INFO - Epoch(train) [51][5/63] lr: 1.0111e-03 eta: 13:53:18 time: 0.7666 data_time: 0.2345 memory: 14901 loss: 3.6052 loss_prob: 2.2887 loss_thr: 0.8962 loss_db: 0.4203 2022/11/02 12:42:33 - mmengine - INFO - Epoch(train) [51][10/63] lr: 1.0111e-03 eta: 13:52:51 time: 0.8189 data_time: 0.2352 memory: 14901 loss: 3.3042 loss_prob: 2.0705 loss_thr: 0.8680 loss_db: 0.3657 2022/11/02 12:42:35 - mmengine - INFO - Epoch(train) [51][15/63] lr: 1.0111e-03 eta: 13:52:51 time: 0.5274 data_time: 0.0080 memory: 14901 loss: 3.0601 loss_prob: 1.8965 loss_thr: 0.8418 loss_db: 0.3218 2022/11/02 12:42:38 - mmengine - INFO - Epoch(train) [51][20/63] lr: 1.0111e-03 eta: 13:52:02 time: 0.5070 data_time: 0.0073 memory: 14901 loss: 3.0832 loss_prob: 1.9381 loss_thr: 0.8233 loss_db: 0.3219 2022/11/02 12:42:40 - mmengine - INFO - Epoch(train) [51][25/63] lr: 1.0111e-03 eta: 13:52:02 time: 0.5424 data_time: 0.0150 memory: 14901 loss: 3.3183 loss_prob: 2.1084 loss_thr: 0.8347 loss_db: 0.3752 2022/11/02 12:42:43 - mmengine - INFO - Epoch(train) [51][30/63] lr: 1.0111e-03 eta: 13:51:21 time: 0.5381 data_time: 0.0317 memory: 14901 loss: 3.4551 loss_prob: 2.1922 loss_thr: 0.8712 loss_db: 0.3917 2022/11/02 12:42:45 - mmengine - INFO - Epoch(train) [51][35/63] lr: 1.0111e-03 eta: 13:51:21 time: 0.4838 data_time: 0.0231 memory: 14901 loss: 3.2112 loss_prob: 2.0113 loss_thr: 0.8560 loss_db: 0.3439 2022/11/02 12:42:48 - mmengine - INFO - Epoch(train) [51][40/63] lr: 1.0111e-03 eta: 13:50:22 time: 0.4591 data_time: 0.0072 memory: 14901 loss: 3.0985 loss_prob: 1.9324 loss_thr: 0.8302 loss_db: 0.3360 2022/11/02 12:42:50 - mmengine - INFO - Epoch(train) [51][45/63] lr: 1.0111e-03 eta: 13:50:22 time: 0.4640 data_time: 0.0053 memory: 14901 loss: 3.3898 loss_prob: 2.1411 loss_thr: 0.8606 loss_db: 0.3881 2022/11/02 12:42:52 - mmengine - INFO - Epoch(train) [51][50/63] lr: 1.0111e-03 eta: 13:49:27 time: 0.4759 data_time: 0.0184 memory: 14901 loss: 3.2606 loss_prob: 2.0402 loss_thr: 0.8577 loss_db: 0.3626 2022/11/02 12:42:55 - mmengine - INFO - Epoch(train) [51][55/63] lr: 1.0111e-03 eta: 13:49:27 time: 0.4808 data_time: 0.0185 memory: 14901 loss: 2.9686 loss_prob: 1.8450 loss_thr: 0.8139 loss_db: 0.3097 2022/11/02 12:42:57 - mmengine - INFO - Epoch(train) [51][60/63] lr: 1.0111e-03 eta: 13:48:34 time: 0.4815 data_time: 0.0090 memory: 14901 loss: 3.0219 loss_prob: 1.8871 loss_thr: 0.8169 loss_db: 0.3179 2022/11/02 12:42:59 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:43:03 - mmengine - INFO - Epoch(train) [52][5/63] lr: 1.0313e-03 eta: 13:48:34 time: 0.7099 data_time: 0.1855 memory: 14901 loss: 3.0001 loss_prob: 1.8539 loss_thr: 0.8358 loss_db: 0.3103 2022/11/02 12:43:06 - mmengine - INFO - Epoch(train) [52][10/63] lr: 1.0313e-03 eta: 13:47:40 time: 0.6923 data_time: 0.1808 memory: 14901 loss: 3.1062 loss_prob: 1.9337 loss_thr: 0.8419 loss_db: 0.3306 2022/11/02 12:43:08 - mmengine - INFO - Epoch(train) [52][15/63] lr: 1.0313e-03 eta: 13:47:40 time: 0.5113 data_time: 0.0132 memory: 14901 loss: 3.1590 loss_prob: 1.9571 loss_thr: 0.8568 loss_db: 0.3451 2022/11/02 12:43:11 - mmengine - INFO - Epoch(train) [52][20/63] lr: 1.0313e-03 eta: 13:46:50 time: 0.4957 data_time: 0.0131 memory: 14901 loss: 3.2086 loss_prob: 1.9951 loss_thr: 0.8587 loss_db: 0.3548 2022/11/02 12:43:13 - mmengine - INFO - Epoch(train) [52][25/63] lr: 1.0313e-03 eta: 13:46:50 time: 0.5085 data_time: 0.0190 memory: 14901 loss: 3.2130 loss_prob: 2.0297 loss_thr: 0.8348 loss_db: 0.3485 2022/11/02 12:43:16 - mmengine - INFO - Epoch(train) [52][30/63] lr: 1.0313e-03 eta: 13:46:03 time: 0.5071 data_time: 0.0233 memory: 14901 loss: 3.0133 loss_prob: 1.8857 loss_thr: 0.8130 loss_db: 0.3145 2022/11/02 12:43:18 - mmengine - INFO - Epoch(train) [52][35/63] lr: 1.0313e-03 eta: 13:46:03 time: 0.4610 data_time: 0.0166 memory: 14901 loss: 3.0204 loss_prob: 1.8919 loss_thr: 0.8048 loss_db: 0.3237 2022/11/02 12:43:20 - mmengine - INFO - Epoch(train) [52][40/63] lr: 1.0313e-03 eta: 13:45:10 time: 0.4758 data_time: 0.0132 memory: 14901 loss: 2.9174 loss_prob: 1.8072 loss_thr: 0.8070 loss_db: 0.3032 2022/11/02 12:43:23 - mmengine - INFO - Epoch(train) [52][45/63] lr: 1.0313e-03 eta: 13:45:10 time: 0.4970 data_time: 0.0055 memory: 14901 loss: 2.8300 loss_prob: 1.7296 loss_thr: 0.8123 loss_db: 0.2882 2022/11/02 12:43:26 - mmengine - INFO - Epoch(train) [52][50/63] lr: 1.0313e-03 eta: 13:44:25 time: 0.5105 data_time: 0.0216 memory: 14901 loss: 3.0135 loss_prob: 1.8594 loss_thr: 0.8322 loss_db: 0.3219 2022/11/02 12:43:28 - mmengine - INFO - Epoch(train) [52][55/63] lr: 1.0313e-03 eta: 13:44:25 time: 0.4986 data_time: 0.0247 memory: 14901 loss: 3.1118 loss_prob: 1.9382 loss_thr: 0.8390 loss_db: 0.3346 2022/11/02 12:43:30 - mmengine - INFO - Epoch(train) [52][60/63] lr: 1.0313e-03 eta: 13:43:29 time: 0.4634 data_time: 0.0105 memory: 14901 loss: 3.0532 loss_prob: 1.8993 loss_thr: 0.8276 loss_db: 0.3263 2022/11/02 12:43:31 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:43:37 - mmengine - INFO - Epoch(train) [53][5/63] lr: 1.0515e-03 eta: 13:43:29 time: 0.7624 data_time: 0.2302 memory: 14901 loss: 2.9457 loss_prob: 1.8256 loss_thr: 0.8060 loss_db: 0.3141 2022/11/02 12:43:39 - mmengine - INFO - Epoch(train) [53][10/63] lr: 1.0515e-03 eta: 13:42:58 time: 0.7891 data_time: 0.2303 memory: 14901 loss: 2.9016 loss_prob: 1.7732 loss_thr: 0.8332 loss_db: 0.2951 2022/11/02 12:43:42 - mmengine - INFO - Epoch(train) [53][15/63] lr: 1.0515e-03 eta: 13:42:58 time: 0.5081 data_time: 0.0084 memory: 14901 loss: 2.9887 loss_prob: 1.8315 loss_thr: 0.8522 loss_db: 0.3049 2022/11/02 12:43:44 - mmengine - INFO - Epoch(train) [53][20/63] lr: 1.0515e-03 eta: 13:42:12 time: 0.5022 data_time: 0.0058 memory: 14901 loss: 2.9335 loss_prob: 1.8037 loss_thr: 0.8244 loss_db: 0.3054 2022/11/02 12:43:47 - mmengine - INFO - Epoch(train) [53][25/63] lr: 1.0515e-03 eta: 13:42:12 time: 0.4993 data_time: 0.0323 memory: 14901 loss: 2.8550 loss_prob: 1.7649 loss_thr: 0.7911 loss_db: 0.2990 2022/11/02 12:43:50 - mmengine - INFO - Epoch(train) [53][30/63] lr: 1.0515e-03 eta: 13:41:34 time: 0.5415 data_time: 0.0347 memory: 14901 loss: 2.9322 loss_prob: 1.8228 loss_thr: 0.7998 loss_db: 0.3095 2022/11/02 12:43:52 - mmengine - INFO - Epoch(train) [53][35/63] lr: 1.0515e-03 eta: 13:41:34 time: 0.5124 data_time: 0.0070 memory: 14901 loss: 3.0154 loss_prob: 1.8826 loss_thr: 0.8117 loss_db: 0.3210 2022/11/02 12:43:54 - mmengine - INFO - Epoch(train) [53][40/63] lr: 1.0515e-03 eta: 13:40:41 time: 0.4687 data_time: 0.0060 memory: 14901 loss: 3.2222 loss_prob: 2.0541 loss_thr: 0.8136 loss_db: 0.3544 2022/11/02 12:43:57 - mmengine - INFO - Epoch(train) [53][45/63] lr: 1.0515e-03 eta: 13:40:41 time: 0.4703 data_time: 0.0078 memory: 14901 loss: 3.2785 loss_prob: 2.0862 loss_thr: 0.8159 loss_db: 0.3765 2022/11/02 12:43:59 - mmengine - INFO - Epoch(train) [53][50/63] lr: 1.0515e-03 eta: 13:39:54 time: 0.4937 data_time: 0.0204 memory: 14901 loss: 3.1340 loss_prob: 1.9737 loss_thr: 0.8091 loss_db: 0.3511 2022/11/02 12:44:02 - mmengine - INFO - Epoch(train) [53][55/63] lr: 1.0515e-03 eta: 13:39:54 time: 0.4962 data_time: 0.0217 memory: 14901 loss: 3.0716 loss_prob: 1.9329 loss_thr: 0.8097 loss_db: 0.3290 2022/11/02 12:44:04 - mmengine - INFO - Epoch(train) [53][60/63] lr: 1.0515e-03 eta: 13:39:04 time: 0.4809 data_time: 0.0092 memory: 14901 loss: 3.0817 loss_prob: 1.9204 loss_thr: 0.8348 loss_db: 0.3264 2022/11/02 12:44:06 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:44:10 - mmengine - INFO - Epoch(train) [54][5/63] lr: 1.0716e-03 eta: 13:39:04 time: 0.7060 data_time: 0.1991 memory: 14901 loss: 3.1016 loss_prob: 1.9401 loss_thr: 0.8334 loss_db: 0.3281 2022/11/02 12:44:13 - mmengine - INFO - Epoch(train) [54][10/63] lr: 1.0716e-03 eta: 13:38:17 time: 0.7086 data_time: 0.2011 memory: 14901 loss: 3.0816 loss_prob: 1.9258 loss_thr: 0.8267 loss_db: 0.3292 2022/11/02 12:44:16 - mmengine - INFO - Epoch(train) [54][15/63] lr: 1.0716e-03 eta: 13:38:17 time: 0.5344 data_time: 0.0132 memory: 14901 loss: 2.9297 loss_prob: 1.8074 loss_thr: 0.8193 loss_db: 0.3031 2022/11/02 12:44:19 - mmengine - INFO - Epoch(train) [54][20/63] lr: 1.0716e-03 eta: 13:37:53 time: 0.5988 data_time: 0.0115 memory: 14901 loss: 2.8364 loss_prob: 1.7279 loss_thr: 0.8220 loss_db: 0.2865 2022/11/02 12:44:23 - mmengine - INFO - Epoch(train) [54][25/63] lr: 1.0716e-03 eta: 13:37:53 time: 0.7148 data_time: 0.0330 memory: 14901 loss: 2.8896 loss_prob: 1.7746 loss_thr: 0.8191 loss_db: 0.2959 2022/11/02 12:44:26 - mmengine - INFO - Epoch(train) [54][30/63] lr: 1.0716e-03 eta: 13:37:55 time: 0.7219 data_time: 0.0330 memory: 14901 loss: 2.9558 loss_prob: 1.8356 loss_thr: 0.8098 loss_db: 0.3104 2022/11/02 12:44:29 - mmengine - INFO - Epoch(train) [54][35/63] lr: 1.0716e-03 eta: 13:37:55 time: 0.5721 data_time: 0.0053 memory: 14901 loss: 2.8186 loss_prob: 1.7256 loss_thr: 0.8010 loss_db: 0.2920 2022/11/02 12:44:31 - mmengine - INFO - Epoch(train) [54][40/63] lr: 1.0716e-03 eta: 13:37:11 time: 0.5036 data_time: 0.0081 memory: 14901 loss: 2.8886 loss_prob: 1.7811 loss_thr: 0.8073 loss_db: 0.3003 2022/11/02 12:44:34 - mmengine - INFO - Epoch(train) [54][45/63] lr: 1.0716e-03 eta: 13:37:11 time: 0.5087 data_time: 0.0077 memory: 14901 loss: 2.9748 loss_prob: 1.8582 loss_thr: 0.8023 loss_db: 0.3143 2022/11/02 12:44:37 - mmengine - INFO - Epoch(train) [54][50/63] lr: 1.0716e-03 eta: 13:36:54 time: 0.6321 data_time: 0.0571 memory: 14901 loss: 2.8819 loss_prob: 1.7986 loss_thr: 0.7809 loss_db: 0.3024 2022/11/02 12:44:40 - mmengine - INFO - Epoch(train) [54][55/63] lr: 1.0716e-03 eta: 13:36:54 time: 0.6385 data_time: 0.0573 memory: 14901 loss: 2.9601 loss_prob: 1.8507 loss_thr: 0.7978 loss_db: 0.3117 2022/11/02 12:44:43 - mmengine - INFO - Epoch(train) [54][60/63] lr: 1.0716e-03 eta: 13:36:15 time: 0.5264 data_time: 0.0048 memory: 14901 loss: 3.0358 loss_prob: 1.9008 loss_thr: 0.8140 loss_db: 0.3210 2022/11/02 12:44:44 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:44:50 - mmengine - INFO - Epoch(train) [55][5/63] lr: 1.0918e-03 eta: 13:36:15 time: 0.8501 data_time: 0.2278 memory: 14901 loss: 3.3001 loss_prob: 2.0954 loss_thr: 0.8140 loss_db: 0.3907 2022/11/02 12:44:53 - mmengine - INFO - Epoch(train) [55][10/63] lr: 1.0918e-03 eta: 13:36:10 time: 0.9022 data_time: 0.2289 memory: 14901 loss: 3.3289 loss_prob: 2.1140 loss_thr: 0.8232 loss_db: 0.3917 2022/11/02 12:44:57 - mmengine - INFO - Epoch(train) [55][15/63] lr: 1.0918e-03 eta: 13:36:10 time: 0.7242 data_time: 0.0090 memory: 14901 loss: 3.0003 loss_prob: 1.8919 loss_thr: 0.7938 loss_db: 0.3146 2022/11/02 12:45:00 - mmengine - INFO - Epoch(train) [55][20/63] lr: 1.0918e-03 eta: 13:36:08 time: 0.7005 data_time: 0.0082 memory: 14901 loss: 2.9184 loss_prob: 1.8260 loss_thr: 0.7870 loss_db: 0.3053 2022/11/02 12:45:04 - mmengine - INFO - Epoch(train) [55][25/63] lr: 1.0918e-03 eta: 13:36:08 time: 0.6530 data_time: 0.0355 memory: 14901 loss: 3.2904 loss_prob: 2.0816 loss_thr: 0.8402 loss_db: 0.3686 2022/11/02 12:45:07 - mmengine - INFO - Epoch(train) [55][30/63] lr: 1.0918e-03 eta: 13:36:09 time: 0.7142 data_time: 0.0379 memory: 14901 loss: 3.1731 loss_prob: 1.9949 loss_thr: 0.8310 loss_db: 0.3472 2022/11/02 12:45:10 - mmengine - INFO - Epoch(train) [55][35/63] lr: 1.0918e-03 eta: 13:36:09 time: 0.6090 data_time: 0.0081 memory: 14901 loss: 2.9633 loss_prob: 1.8324 loss_thr: 0.8214 loss_db: 0.3094 2022/11/02 12:45:13 - mmengine - INFO - Epoch(train) [55][40/63] lr: 1.0918e-03 eta: 13:35:56 time: 0.6499 data_time: 0.0046 memory: 14901 loss: 3.0185 loss_prob: 1.8656 loss_thr: 0.8372 loss_db: 0.3156 2022/11/02 12:45:16 - mmengine - INFO - Epoch(train) [55][45/63] lr: 1.0918e-03 eta: 13:35:56 time: 0.6027 data_time: 0.0153 memory: 14901 loss: 2.8120 loss_prob: 1.7327 loss_thr: 0.7938 loss_db: 0.2855 2022/11/02 12:45:19 - mmengine - INFO - Epoch(train) [55][50/63] lr: 1.0918e-03 eta: 13:35:16 time: 0.5182 data_time: 0.0222 memory: 14901 loss: 2.8344 loss_prob: 1.7627 loss_thr: 0.7760 loss_db: 0.2958 2022/11/02 12:45:21 - mmengine - INFO - Epoch(train) [55][55/63] lr: 1.0918e-03 eta: 13:35:16 time: 0.5606 data_time: 0.0116 memory: 14901 loss: 2.9871 loss_prob: 1.8659 loss_thr: 0.8017 loss_db: 0.3195 2022/11/02 12:45:24 - mmengine - INFO - Epoch(train) [55][60/63] lr: 1.0918e-03 eta: 13:34:35 time: 0.5160 data_time: 0.0048 memory: 14901 loss: 2.9568 loss_prob: 1.8380 loss_thr: 0.8102 loss_db: 0.3086 2022/11/02 12:45:25 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:45:29 - mmengine - INFO - Epoch(train) [56][5/63] lr: 1.1120e-03 eta: 13:34:35 time: 0.6747 data_time: 0.2043 memory: 14901 loss: 2.7822 loss_prob: 1.7181 loss_thr: 0.7715 loss_db: 0.2926 2022/11/02 12:45:32 - mmengine - INFO - Epoch(train) [56][10/63] lr: 1.1120e-03 eta: 13:33:47 time: 0.6906 data_time: 0.2060 memory: 14901 loss: 2.7907 loss_prob: 1.7214 loss_thr: 0.7785 loss_db: 0.2908 2022/11/02 12:45:35 - mmengine - INFO - Epoch(train) [56][15/63] lr: 1.1120e-03 eta: 13:33:47 time: 0.5044 data_time: 0.0074 memory: 14901 loss: 2.7665 loss_prob: 1.7045 loss_thr: 0.7822 loss_db: 0.2798 2022/11/02 12:45:37 - mmengine - INFO - Epoch(train) [56][20/63] lr: 1.1120e-03 eta: 13:33:05 time: 0.5051 data_time: 0.0055 memory: 14901 loss: 2.8595 loss_prob: 1.7786 loss_thr: 0.7803 loss_db: 0.3006 2022/11/02 12:45:40 - mmengine - INFO - Epoch(train) [56][25/63] lr: 1.1120e-03 eta: 13:33:05 time: 0.5714 data_time: 0.0310 memory: 14901 loss: 2.7994 loss_prob: 1.7372 loss_thr: 0.7700 loss_db: 0.2922 2022/11/02 12:45:43 - mmengine - INFO - Epoch(train) [56][30/63] lr: 1.1120e-03 eta: 13:32:46 time: 0.6194 data_time: 0.0403 memory: 14901 loss: 2.7122 loss_prob: 1.6607 loss_thr: 0.7743 loss_db: 0.2772 2022/11/02 12:45:46 - mmengine - INFO - Epoch(train) [56][35/63] lr: 1.1120e-03 eta: 13:32:46 time: 0.5607 data_time: 0.0193 memory: 14901 loss: 2.8025 loss_prob: 1.7149 loss_thr: 0.7951 loss_db: 0.2924 2022/11/02 12:45:48 - mmengine - INFO - Epoch(train) [56][40/63] lr: 1.1120e-03 eta: 13:32:07 time: 0.5178 data_time: 0.0093 memory: 14901 loss: 2.9233 loss_prob: 1.8192 loss_thr: 0.7948 loss_db: 0.3092 2022/11/02 12:45:51 - mmengine - INFO - Epoch(train) [56][45/63] lr: 1.1120e-03 eta: 13:32:07 time: 0.5019 data_time: 0.0062 memory: 14901 loss: 2.9426 loss_prob: 1.8388 loss_thr: 0.7935 loss_db: 0.3103 2022/11/02 12:45:53 - mmengine - INFO - Epoch(train) [56][50/63] lr: 1.1120e-03 eta: 13:31:19 time: 0.4785 data_time: 0.0173 memory: 14901 loss: 2.9461 loss_prob: 1.8287 loss_thr: 0.8093 loss_db: 0.3080 2022/11/02 12:45:55 - mmengine - INFO - Epoch(train) [56][55/63] lr: 1.1120e-03 eta: 13:31:19 time: 0.4640 data_time: 0.0202 memory: 14901 loss: 2.9912 loss_prob: 1.8647 loss_thr: 0.8098 loss_db: 0.3167 2022/11/02 12:45:58 - mmengine - INFO - Epoch(train) [56][60/63] lr: 1.1120e-03 eta: 13:30:30 time: 0.4669 data_time: 0.0090 memory: 14901 loss: 2.8958 loss_prob: 1.7904 loss_thr: 0.8036 loss_db: 0.3018 2022/11/02 12:45:59 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:46:04 - mmengine - INFO - Epoch(train) [57][5/63] lr: 1.1322e-03 eta: 13:30:30 time: 0.6950 data_time: 0.1879 memory: 14901 loss: 2.8150 loss_prob: 1.7244 loss_thr: 0.7976 loss_db: 0.2931 2022/11/02 12:46:06 - mmengine - INFO - Epoch(train) [57][10/63] lr: 1.1322e-03 eta: 13:29:50 time: 0.7213 data_time: 0.1895 memory: 14901 loss: 2.7663 loss_prob: 1.7002 loss_thr: 0.7845 loss_db: 0.2815 2022/11/02 12:46:09 - mmengine - INFO - Epoch(train) [57][15/63] lr: 1.1322e-03 eta: 13:29:50 time: 0.4987 data_time: 0.0069 memory: 14901 loss: 2.8371 loss_prob: 1.7613 loss_thr: 0.7858 loss_db: 0.2901 2022/11/02 12:46:11 - mmengine - INFO - Epoch(train) [57][20/63] lr: 1.1322e-03 eta: 13:29:06 time: 0.4954 data_time: 0.0063 memory: 14901 loss: 2.8975 loss_prob: 1.7964 loss_thr: 0.8021 loss_db: 0.2990 2022/11/02 12:46:14 - mmengine - INFO - Epoch(train) [57][25/63] lr: 1.1322e-03 eta: 13:29:06 time: 0.5180 data_time: 0.0078 memory: 14901 loss: 3.3469 loss_prob: 2.1367 loss_thr: 0.8393 loss_db: 0.3710 2022/11/02 12:46:17 - mmengine - INFO - Epoch(train) [57][30/63] lr: 1.1322e-03 eta: 13:28:38 time: 0.5650 data_time: 0.0309 memory: 14901 loss: 3.7353 loss_prob: 2.4336 loss_thr: 0.8713 loss_db: 0.4304 2022/11/02 12:46:19 - mmengine - INFO - Epoch(train) [57][35/63] lr: 1.1322e-03 eta: 13:28:38 time: 0.5069 data_time: 0.0312 memory: 14901 loss: 3.4630 loss_prob: 2.2276 loss_thr: 0.8415 loss_db: 0.3939 2022/11/02 12:46:22 - mmengine - INFO - Epoch(train) [57][40/63] lr: 1.1322e-03 eta: 13:27:52 time: 0.4799 data_time: 0.0077 memory: 14901 loss: 3.4175 loss_prob: 2.1708 loss_thr: 0.8384 loss_db: 0.4084 2022/11/02 12:46:24 - mmengine - INFO - Epoch(train) [57][45/63] lr: 1.1322e-03 eta: 13:27:52 time: 0.4927 data_time: 0.0055 memory: 14901 loss: 3.3904 loss_prob: 2.1641 loss_thr: 0.8216 loss_db: 0.4047 2022/11/02 12:46:26 - mmengine - INFO - Epoch(train) [57][50/63] lr: 1.1322e-03 eta: 13:27:06 time: 0.4778 data_time: 0.0070 memory: 14901 loss: 3.1555 loss_prob: 2.0064 loss_thr: 0.7979 loss_db: 0.3512 2022/11/02 12:46:29 - mmengine - INFO - Epoch(train) [57][55/63] lr: 1.1322e-03 eta: 13:27:06 time: 0.4874 data_time: 0.0216 memory: 14901 loss: 3.1870 loss_prob: 2.0135 loss_thr: 0.8295 loss_db: 0.3441 2022/11/02 12:46:31 - mmengine - INFO - Epoch(train) [57][60/63] lr: 1.1322e-03 eta: 13:26:19 time: 0.4732 data_time: 0.0201 memory: 14901 loss: 3.0164 loss_prob: 1.8918 loss_thr: 0.8155 loss_db: 0.3092 2022/11/02 12:46:32 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:46:38 - mmengine - INFO - Epoch(train) [58][5/63] lr: 1.1524e-03 eta: 13:26:19 time: 0.7241 data_time: 0.2508 memory: 14901 loss: 3.3699 loss_prob: 2.1879 loss_thr: 0.7886 loss_db: 0.3934 2022/11/02 12:46:40 - mmengine - INFO - Epoch(train) [58][10/63] lr: 1.1524e-03 eta: 13:25:46 time: 0.7508 data_time: 0.2488 memory: 14901 loss: 4.0614 loss_prob: 2.6926 loss_thr: 0.8503 loss_db: 0.5185 2022/11/02 12:46:42 - mmengine - INFO - Epoch(train) [58][15/63] lr: 1.1524e-03 eta: 13:25:46 time: 0.4968 data_time: 0.0044 memory: 14901 loss: 4.0673 loss_prob: 2.6590 loss_thr: 0.8757 loss_db: 0.5326 2022/11/02 12:46:45 - mmengine - INFO - Epoch(train) [58][20/63] lr: 1.1524e-03 eta: 13:25:02 time: 0.4847 data_time: 0.0043 memory: 14901 loss: 3.7227 loss_prob: 2.4075 loss_thr: 0.8527 loss_db: 0.4625 2022/11/02 12:46:48 - mmengine - INFO - Epoch(train) [58][25/63] lr: 1.1524e-03 eta: 13:25:02 time: 0.5069 data_time: 0.0322 memory: 14901 loss: 3.5488 loss_prob: 2.2967 loss_thr: 0.8488 loss_db: 0.4033 2022/11/02 12:46:50 - mmengine - INFO - Epoch(train) [58][30/63] lr: 1.1524e-03 eta: 13:24:30 time: 0.5416 data_time: 0.0429 memory: 14901 loss: 3.4253 loss_prob: 2.2024 loss_thr: 0.8432 loss_db: 0.3796 2022/11/02 12:46:53 - mmengine - INFO - Epoch(train) [58][35/63] lr: 1.1524e-03 eta: 13:24:30 time: 0.4970 data_time: 0.0158 memory: 14901 loss: 3.3122 loss_prob: 2.1087 loss_thr: 0.8403 loss_db: 0.3631 2022/11/02 12:46:55 - mmengine - INFO - Epoch(train) [58][40/63] lr: 1.1524e-03 eta: 13:23:48 time: 0.4941 data_time: 0.0050 memory: 14901 loss: 3.1532 loss_prob: 1.9970 loss_thr: 0.8167 loss_db: 0.3395 2022/11/02 12:46:58 - mmengine - INFO - Epoch(train) [58][45/63] lr: 1.1524e-03 eta: 13:23:48 time: 0.5878 data_time: 0.0052 memory: 14901 loss: 3.1479 loss_prob: 1.9881 loss_thr: 0.8122 loss_db: 0.3476 2022/11/02 12:47:01 - mmengine - INFO - Epoch(train) [58][50/63] lr: 1.1524e-03 eta: 13:23:29 time: 0.6108 data_time: 0.0216 memory: 14901 loss: 3.3050 loss_prob: 2.1071 loss_thr: 0.8365 loss_db: 0.3614 2022/11/02 12:47:04 - mmengine - INFO - Epoch(train) [58][55/63] lr: 1.1524e-03 eta: 13:23:29 time: 0.5810 data_time: 0.0230 memory: 14901 loss: 3.3071 loss_prob: 2.1135 loss_thr: 0.8281 loss_db: 0.3654 2022/11/02 12:47:07 - mmengine - INFO - Epoch(train) [58][60/63] lr: 1.1524e-03 eta: 13:22:59 time: 0.5487 data_time: 0.0067 memory: 14901 loss: 3.2692 loss_prob: 2.0752 loss_thr: 0.8271 loss_db: 0.3669 2022/11/02 12:47:08 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:47:14 - mmengine - INFO - Epoch(train) [59][5/63] lr: 1.1725e-03 eta: 13:22:59 time: 0.8208 data_time: 0.2426 memory: 14901 loss: 3.3072 loss_prob: 2.0971 loss_thr: 0.8360 loss_db: 0.3741 2022/11/02 12:47:17 - mmengine - INFO - Epoch(train) [59][10/63] lr: 1.1725e-03 eta: 13:22:55 time: 0.8965 data_time: 0.2404 memory: 14901 loss: 3.2032 loss_prob: 2.0230 loss_thr: 0.8262 loss_db: 0.3539 2022/11/02 12:47:20 - mmengine - INFO - Epoch(train) [59][15/63] lr: 1.1725e-03 eta: 13:22:55 time: 0.5627 data_time: 0.0049 memory: 14901 loss: 3.0334 loss_prob: 1.9035 loss_thr: 0.8042 loss_db: 0.3257 2022/11/02 12:47:22 - mmengine - INFO - Epoch(train) [59][20/63] lr: 1.1725e-03 eta: 13:22:25 time: 0.5477 data_time: 0.0047 memory: 14901 loss: 3.1089 loss_prob: 1.9558 loss_thr: 0.8160 loss_db: 0.3371 2022/11/02 12:47:25 - mmengine - INFO - Epoch(train) [59][25/63] lr: 1.1725e-03 eta: 13:22:25 time: 0.5486 data_time: 0.0057 memory: 14901 loss: 2.8442 loss_prob: 1.7699 loss_thr: 0.7779 loss_db: 0.2965 2022/11/02 12:47:28 - mmengine - INFO - Epoch(train) [59][30/63] lr: 1.1725e-03 eta: 13:21:54 time: 0.5464 data_time: 0.0214 memory: 14901 loss: 2.7108 loss_prob: 1.6637 loss_thr: 0.7741 loss_db: 0.2730 2022/11/02 12:47:31 - mmengine - INFO - Epoch(train) [59][35/63] lr: 1.1725e-03 eta: 13:21:54 time: 0.5815 data_time: 0.0217 memory: 14901 loss: 2.7803 loss_prob: 1.7040 loss_thr: 0.7955 loss_db: 0.2808 2022/11/02 12:47:35 - mmengine - INFO - Epoch(train) [59][40/63] lr: 1.1725e-03 eta: 13:21:59 time: 0.7273 data_time: 0.0098 memory: 14901 loss: 2.7361 loss_prob: 1.6592 loss_thr: 0.8040 loss_db: 0.2729 2022/11/02 12:47:39 - mmengine - INFO - Epoch(train) [59][45/63] lr: 1.1725e-03 eta: 13:21:59 time: 0.7636 data_time: 0.0087 memory: 14901 loss: 2.6245 loss_prob: 1.5871 loss_thr: 0.7797 loss_db: 0.2577 2022/11/02 12:47:41 - mmengine - INFO - Epoch(train) [59][50/63] lr: 1.1725e-03 eta: 13:21:33 time: 0.5715 data_time: 0.0061 memory: 14901 loss: 2.7059 loss_prob: 1.6556 loss_thr: 0.7780 loss_db: 0.2723 2022/11/02 12:47:44 - mmengine - INFO - Epoch(train) [59][55/63] lr: 1.1725e-03 eta: 13:21:33 time: 0.5243 data_time: 0.0225 memory: 14901 loss: 2.8048 loss_prob: 1.7141 loss_thr: 0.8037 loss_db: 0.2870 2022/11/02 12:47:47 - mmengine - INFO - Epoch(train) [59][60/63] lr: 1.1725e-03 eta: 13:21:11 time: 0.5895 data_time: 0.0211 memory: 14901 loss: 3.1072 loss_prob: 1.9392 loss_thr: 0.8306 loss_db: 0.3374 2022/11/02 12:47:48 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:47:54 - mmengine - INFO - Epoch(train) [60][5/63] lr: 1.1927e-03 eta: 13:21:11 time: 0.7857 data_time: 0.2265 memory: 14901 loss: 3.0274 loss_prob: 1.8998 loss_thr: 0.8050 loss_db: 0.3225 2022/11/02 12:47:56 - mmengine - INFO - Epoch(train) [60][10/63] lr: 1.1927e-03 eta: 13:20:50 time: 0.8074 data_time: 0.2266 memory: 14901 loss: 2.8817 loss_prob: 1.7731 loss_thr: 0.8114 loss_db: 0.2973 2022/11/02 12:47:59 - mmengine - INFO - Epoch(train) [60][15/63] lr: 1.1927e-03 eta: 13:20:50 time: 0.4939 data_time: 0.0068 memory: 14901 loss: 2.8083 loss_prob: 1.7174 loss_thr: 0.8083 loss_db: 0.2826 2022/11/02 12:48:01 - mmengine - INFO - Epoch(train) [60][20/63] lr: 1.1927e-03 eta: 13:20:09 time: 0.4869 data_time: 0.0066 memory: 14901 loss: 2.7558 loss_prob: 1.6959 loss_thr: 0.7790 loss_db: 0.2809 2022/11/02 12:48:04 - mmengine - INFO - Epoch(train) [60][25/63] lr: 1.1927e-03 eta: 13:20:09 time: 0.5073 data_time: 0.0245 memory: 14901 loss: 2.6334 loss_prob: 1.6086 loss_thr: 0.7619 loss_db: 0.2629 2022/11/02 12:48:06 - mmengine - INFO - Epoch(train) [60][30/63] lr: 1.1927e-03 eta: 13:19:32 time: 0.5094 data_time: 0.0351 memory: 14901 loss: 2.6366 loss_prob: 1.6078 loss_thr: 0.7673 loss_db: 0.2616 2022/11/02 12:48:09 - mmengine - INFO - Epoch(train) [60][35/63] lr: 1.1927e-03 eta: 13:19:32 time: 0.4963 data_time: 0.0152 memory: 14901 loss: 2.7521 loss_prob: 1.6891 loss_thr: 0.7809 loss_db: 0.2821 2022/11/02 12:48:12 - mmengine - INFO - Epoch(train) [60][40/63] lr: 1.1927e-03 eta: 13:19:09 time: 0.5828 data_time: 0.0057 memory: 14901 loss: 2.8542 loss_prob: 1.7666 loss_thr: 0.7907 loss_db: 0.2969 2022/11/02 12:48:14 - mmengine - INFO - Epoch(train) [60][45/63] lr: 1.1927e-03 eta: 13:19:09 time: 0.5867 data_time: 0.0063 memory: 14901 loss: 2.9270 loss_prob: 1.8156 loss_thr: 0.8065 loss_db: 0.3050 2022/11/02 12:48:17 - mmengine - INFO - Epoch(train) [60][50/63] lr: 1.1927e-03 eta: 13:18:30 time: 0.4991 data_time: 0.0192 memory: 14901 loss: 2.7514 loss_prob: 1.6718 loss_thr: 0.8031 loss_db: 0.2765 2022/11/02 12:48:20 - mmengine - INFO - Epoch(train) [60][55/63] lr: 1.1927e-03 eta: 13:18:30 time: 0.5183 data_time: 0.0236 memory: 14901 loss: 2.7866 loss_prob: 1.6995 loss_thr: 0.8019 loss_db: 0.2852 2022/11/02 12:48:22 - mmengine - INFO - Epoch(train) [60][60/63] lr: 1.1927e-03 eta: 13:17:55 time: 0.5167 data_time: 0.0098 memory: 14901 loss: 2.8892 loss_prob: 1.7796 loss_thr: 0.8088 loss_db: 0.3008 2022/11/02 12:48:23 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:48:23 - mmengine - INFO - Saving checkpoint at 60 epochs 2022/11/02 12:48:27 - mmengine - INFO - Epoch(val) [60][5/500] eta: 13:17:55 time: 0.0630 data_time: 0.0052 memory: 14901 2022/11/02 12:48:27 - mmengine - INFO - Epoch(val) [60][10/500] eta: 0:00:21 time: 0.0445 data_time: 0.0046 memory: 1008 2022/11/02 12:48:27 - mmengine - INFO - Epoch(val) [60][15/500] eta: 0:00:21 time: 0.0389 data_time: 0.0024 memory: 1008 2022/11/02 12:48:27 - mmengine - INFO - Epoch(val) [60][20/500] eta: 0:00:17 time: 0.0364 data_time: 0.0021 memory: 1008 2022/11/02 12:48:27 - mmengine - INFO - Epoch(val) [60][25/500] eta: 0:00:17 time: 0.0360 data_time: 0.0022 memory: 1008 2022/11/02 12:48:28 - mmengine - INFO - Epoch(val) [60][30/500] eta: 0:00:18 time: 0.0393 data_time: 0.0023 memory: 1008 2022/11/02 12:48:28 - mmengine - INFO - Epoch(val) [60][35/500] eta: 0:00:18 time: 0.0405 data_time: 0.0023 memory: 1008 2022/11/02 12:48:28 - mmengine - INFO - Epoch(val) [60][40/500] eta: 0:00:19 time: 0.0432 data_time: 0.0024 memory: 1008 2022/11/02 12:48:28 - mmengine - INFO - Epoch(val) [60][45/500] eta: 0:00:19 time: 0.0506 data_time: 0.0025 memory: 1008 2022/11/02 12:48:29 - mmengine - INFO - Epoch(val) [60][50/500] eta: 0:00:21 time: 0.0487 data_time: 0.0025 memory: 1008 2022/11/02 12:48:29 - mmengine - INFO - Epoch(val) [60][55/500] eta: 0:00:21 time: 0.0405 data_time: 0.0023 memory: 1008 2022/11/02 12:48:29 - mmengine - INFO - Epoch(val) [60][60/500] eta: 0:00:16 time: 0.0377 data_time: 0.0024 memory: 1008 2022/11/02 12:48:29 - mmengine - INFO - Epoch(val) [60][65/500] eta: 0:00:16 time: 0.0395 data_time: 0.0024 memory: 1008 2022/11/02 12:48:29 - mmengine - INFO - Epoch(val) [60][70/500] eta: 0:00:17 time: 0.0407 data_time: 0.0023 memory: 1008 2022/11/02 12:48:30 - mmengine - INFO - Epoch(val) [60][75/500] eta: 0:00:17 time: 0.0403 data_time: 0.0025 memory: 1008 2022/11/02 12:48:30 - mmengine - INFO - Epoch(val) [60][80/500] eta: 0:00:15 time: 0.0371 data_time: 0.0026 memory: 1008 2022/11/02 12:48:30 - mmengine - INFO - Epoch(val) [60][85/500] eta: 0:00:15 time: 0.0362 data_time: 0.0024 memory: 1008 2022/11/02 12:48:30 - mmengine - INFO - Epoch(val) [60][90/500] eta: 0:00:16 time: 0.0405 data_time: 0.0026 memory: 1008 2022/11/02 12:48:30 - mmengine - INFO - Epoch(val) [60][95/500] eta: 0:00:16 time: 0.0405 data_time: 0.0025 memory: 1008 2022/11/02 12:48:30 - mmengine - INFO - Epoch(val) [60][100/500] eta: 0:00:15 time: 0.0395 data_time: 0.0028 memory: 1008 2022/11/02 12:48:31 - mmengine - INFO - Epoch(val) [60][105/500] eta: 0:00:15 time: 0.0389 data_time: 0.0030 memory: 1008 2022/11/02 12:48:31 - mmengine - INFO - Epoch(val) [60][110/500] eta: 0:00:15 time: 0.0395 data_time: 0.0027 memory: 1008 2022/11/02 12:48:31 - mmengine - INFO - Epoch(val) [60][115/500] eta: 0:00:15 time: 0.0415 data_time: 0.0026 memory: 1008 2022/11/02 12:48:31 - mmengine - INFO - Epoch(val) [60][120/500] eta: 0:00:15 time: 0.0404 data_time: 0.0024 memory: 1008 2022/11/02 12:48:32 - mmengine - INFO - Epoch(val) [60][125/500] eta: 0:00:15 time: 0.0410 data_time: 0.0025 memory: 1008 2022/11/02 12:48:32 - mmengine - INFO - Epoch(val) [60][130/500] eta: 0:00:14 time: 0.0393 data_time: 0.0025 memory: 1008 2022/11/02 12:48:32 - mmengine - INFO - Epoch(val) [60][135/500] eta: 0:00:14 time: 0.0363 data_time: 0.0022 memory: 1008 2022/11/02 12:48:32 - mmengine - INFO - Epoch(val) [60][140/500] eta: 0:00:13 time: 0.0382 data_time: 0.0023 memory: 1008 2022/11/02 12:48:32 - mmengine - INFO - Epoch(val) [60][145/500] eta: 0:00:13 time: 0.0396 data_time: 0.0023 memory: 1008 2022/11/02 12:48:32 - mmengine - INFO - Epoch(val) [60][150/500] eta: 0:00:14 time: 0.0403 data_time: 0.0024 memory: 1008 2022/11/02 12:48:33 - mmengine - INFO - Epoch(val) [60][155/500] eta: 0:00:14 time: 0.0421 data_time: 0.0025 memory: 1008 2022/11/02 12:48:33 - mmengine - INFO - Epoch(val) [60][160/500] eta: 0:00:14 time: 0.0430 data_time: 0.0024 memory: 1008 2022/11/02 12:48:33 - mmengine - INFO - Epoch(val) [60][165/500] eta: 0:00:14 time: 0.0414 data_time: 0.0023 memory: 1008 2022/11/02 12:48:33 - mmengine - INFO - Epoch(val) [60][170/500] eta: 0:00:13 time: 0.0411 data_time: 0.0023 memory: 1008 2022/11/02 12:48:34 - mmengine - INFO - Epoch(val) [60][175/500] eta: 0:00:13 time: 0.0407 data_time: 0.0023 memory: 1008 2022/11/02 12:48:34 - mmengine - INFO - Epoch(val) [60][180/500] eta: 0:00:12 time: 0.0380 data_time: 0.0023 memory: 1008 2022/11/02 12:48:34 - mmengine - INFO - Epoch(val) [60][185/500] eta: 0:00:12 time: 0.0400 data_time: 0.0024 memory: 1008 2022/11/02 12:48:34 - mmengine - INFO - Epoch(val) [60][190/500] eta: 0:00:13 time: 0.0429 data_time: 0.0024 memory: 1008 2022/11/02 12:48:34 - mmengine - INFO - Epoch(val) [60][195/500] eta: 0:00:13 time: 0.0385 data_time: 0.0023 memory: 1008 2022/11/02 12:48:35 - mmengine - INFO - Epoch(val) [60][200/500] eta: 0:00:12 time: 0.0426 data_time: 0.0024 memory: 1008 2022/11/02 12:48:35 - mmengine - INFO - Epoch(val) [60][205/500] eta: 0:00:12 time: 0.0430 data_time: 0.0024 memory: 1008 2022/11/02 12:48:35 - mmengine - INFO - Epoch(val) [60][210/500] eta: 0:00:10 time: 0.0363 data_time: 0.0024 memory: 1008 2022/11/02 12:48:35 - mmengine - INFO - Epoch(val) [60][215/500] eta: 0:00:10 time: 0.0381 data_time: 0.0024 memory: 1008 2022/11/02 12:48:35 - mmengine - INFO - Epoch(val) [60][220/500] eta: 0:00:10 time: 0.0377 data_time: 0.0022 memory: 1008 2022/11/02 12:48:36 - mmengine - INFO - Epoch(val) [60][225/500] eta: 0:00:10 time: 0.0412 data_time: 0.0023 memory: 1008 2022/11/02 12:48:36 - mmengine - INFO - Epoch(val) [60][230/500] eta: 0:00:10 time: 0.0395 data_time: 0.0024 memory: 1008 2022/11/02 12:48:36 - mmengine - INFO - Epoch(val) [60][235/500] eta: 0:00:10 time: 0.0355 data_time: 0.0022 memory: 1008 2022/11/02 12:48:36 - mmengine - INFO - Epoch(val) [60][240/500] eta: 0:00:10 time: 0.0394 data_time: 0.0022 memory: 1008 2022/11/02 12:48:36 - mmengine - INFO - Epoch(val) [60][245/500] eta: 0:00:10 time: 0.0372 data_time: 0.0022 memory: 1008 2022/11/02 12:48:36 - mmengine - INFO - Epoch(val) [60][250/500] eta: 0:00:09 time: 0.0362 data_time: 0.0020 memory: 1008 2022/11/02 12:48:37 - mmengine - INFO - Epoch(val) [60][255/500] eta: 0:00:09 time: 0.0388 data_time: 0.0022 memory: 1008 2022/11/02 12:48:37 - mmengine - INFO - Epoch(val) [60][260/500] eta: 0:00:08 time: 0.0368 data_time: 0.0023 memory: 1008 2022/11/02 12:48:37 - mmengine - INFO - Epoch(val) [60][265/500] eta: 0:00:08 time: 0.0357 data_time: 0.0023 memory: 1008 2022/11/02 12:48:37 - mmengine - INFO - Epoch(val) [60][270/500] eta: 0:00:10 time: 0.0448 data_time: 0.0070 memory: 1008 2022/11/02 12:48:37 - mmengine - INFO - Epoch(val) [60][275/500] eta: 0:00:10 time: 0.0432 data_time: 0.0066 memory: 1008 2022/11/02 12:48:38 - mmengine - INFO - Epoch(val) [60][280/500] eta: 0:00:09 time: 0.0420 data_time: 0.0024 memory: 1008 2022/11/02 12:48:38 - mmengine - INFO - Epoch(val) [60][285/500] eta: 0:00:09 time: 0.0433 data_time: 0.0026 memory: 1008 2022/11/02 12:48:38 - mmengine - INFO - Epoch(val) [60][290/500] eta: 0:00:08 time: 0.0411 data_time: 0.0026 memory: 1008 2022/11/02 12:48:38 - mmengine - INFO - Epoch(val) [60][295/500] eta: 0:00:08 time: 0.0428 data_time: 0.0027 memory: 1008 2022/11/02 12:48:38 - mmengine - INFO - Epoch(val) [60][300/500] eta: 0:00:07 time: 0.0392 data_time: 0.0025 memory: 1008 2022/11/02 12:48:39 - mmengine - INFO - Epoch(val) [60][305/500] eta: 0:00:07 time: 0.0381 data_time: 0.0025 memory: 1008 2022/11/02 12:48:39 - mmengine - INFO - Epoch(val) [60][310/500] eta: 0:00:07 time: 0.0390 data_time: 0.0024 memory: 1008 2022/11/02 12:48:39 - mmengine - INFO - Epoch(val) [60][315/500] eta: 0:00:07 time: 0.0416 data_time: 0.0023 memory: 1008 2022/11/02 12:48:39 - mmengine - INFO - Epoch(val) [60][320/500] eta: 0:00:07 time: 0.0414 data_time: 0.0024 memory: 1008 2022/11/02 12:48:40 - mmengine - INFO - Epoch(val) [60][325/500] eta: 0:00:07 time: 0.0511 data_time: 0.0025 memory: 1008 2022/11/02 12:48:40 - mmengine - INFO - Epoch(val) [60][330/500] eta: 0:00:08 time: 0.0492 data_time: 0.0023 memory: 1008 2022/11/02 12:48:40 - mmengine - INFO - Epoch(val) [60][335/500] eta: 0:00:08 time: 0.0362 data_time: 0.0023 memory: 1008 2022/11/02 12:48:40 - mmengine - INFO - Epoch(val) [60][340/500] eta: 0:00:08 time: 0.0518 data_time: 0.0025 memory: 1008 2022/11/02 12:48:40 - mmengine - INFO - Epoch(val) [60][345/500] eta: 0:00:08 time: 0.0522 data_time: 0.0024 memory: 1008 2022/11/02 12:48:41 - mmengine - INFO - Epoch(val) [60][350/500] eta: 0:00:06 time: 0.0432 data_time: 0.0023 memory: 1008 2022/11/02 12:48:41 - mmengine - INFO - Epoch(val) [60][355/500] eta: 0:00:06 time: 0.0425 data_time: 0.0023 memory: 1008 2022/11/02 12:48:41 - mmengine - INFO - Epoch(val) [60][360/500] eta: 0:00:05 time: 0.0396 data_time: 0.0027 memory: 1008 2022/11/02 12:48:41 - mmengine - INFO - Epoch(val) [60][365/500] eta: 0:00:05 time: 0.0446 data_time: 0.0029 memory: 1008 2022/11/02 12:48:42 - mmengine - INFO - Epoch(val) [60][370/500] eta: 0:00:05 time: 0.0398 data_time: 0.0025 memory: 1008 2022/11/02 12:48:42 - mmengine - INFO - Epoch(val) [60][375/500] eta: 0:00:05 time: 0.0356 data_time: 0.0023 memory: 1008 2022/11/02 12:48:42 - mmengine - INFO - Epoch(val) [60][380/500] eta: 0:00:04 time: 0.0401 data_time: 0.0025 memory: 1008 2022/11/02 12:48:42 - mmengine - INFO - Epoch(val) [60][385/500] eta: 0:00:04 time: 0.0398 data_time: 0.0025 memory: 1008 2022/11/02 12:48:42 - mmengine - INFO - Epoch(val) [60][390/500] eta: 0:00:04 time: 0.0393 data_time: 0.0024 memory: 1008 2022/11/02 12:48:43 - mmengine - INFO - Epoch(val) [60][395/500] eta: 0:00:04 time: 0.0395 data_time: 0.0024 memory: 1008 2022/11/02 12:48:43 - mmengine - INFO - Epoch(val) [60][400/500] eta: 0:00:03 time: 0.0378 data_time: 0.0023 memory: 1008 2022/11/02 12:48:43 - mmengine - INFO - Epoch(val) [60][405/500] eta: 0:00:03 time: 0.0394 data_time: 0.0025 memory: 1008 2022/11/02 12:48:43 - mmengine - INFO - Epoch(val) [60][410/500] eta: 0:00:03 time: 0.0414 data_time: 0.0025 memory: 1008 2022/11/02 12:48:43 - mmengine - INFO - Epoch(val) [60][415/500] eta: 0:00:03 time: 0.0405 data_time: 0.0025 memory: 1008 2022/11/02 12:48:44 - mmengine - INFO - Epoch(val) [60][420/500] eta: 0:00:07 time: 0.0955 data_time: 0.0608 memory: 1008 2022/11/02 12:48:44 - mmengine - INFO - Epoch(val) [60][425/500] eta: 0:00:07 time: 0.0974 data_time: 0.0605 memory: 1008 2022/11/02 12:48:44 - mmengine - INFO - Epoch(val) [60][430/500] eta: 0:00:02 time: 0.0403 data_time: 0.0023 memory: 1008 2022/11/02 12:48:45 - mmengine - INFO - Epoch(val) [60][435/500] eta: 0:00:02 time: 0.0388 data_time: 0.0026 memory: 1008 2022/11/02 12:48:45 - mmengine - INFO - Epoch(val) [60][440/500] eta: 0:00:02 time: 0.0419 data_time: 0.0028 memory: 1008 2022/11/02 12:48:45 - mmengine - INFO - Epoch(val) [60][445/500] eta: 0:00:02 time: 0.0452 data_time: 0.0029 memory: 1008 2022/11/02 12:48:45 - mmengine - INFO - Epoch(val) [60][450/500] eta: 0:00:02 time: 0.0477 data_time: 0.0030 memory: 1008 2022/11/02 12:48:46 - mmengine - INFO - Epoch(val) [60][455/500] eta: 0:00:02 time: 0.0452 data_time: 0.0028 memory: 1008 2022/11/02 12:48:46 - mmengine - INFO - Epoch(val) [60][460/500] eta: 0:00:01 time: 0.0410 data_time: 0.0026 memory: 1008 2022/11/02 12:48:46 - mmengine - INFO - Epoch(val) [60][465/500] eta: 0:00:01 time: 0.0400 data_time: 0.0028 memory: 1008 2022/11/02 12:48:46 - mmengine - INFO - Epoch(val) [60][470/500] eta: 0:00:01 time: 0.0387 data_time: 0.0027 memory: 1008 2022/11/02 12:48:46 - mmengine - INFO - Epoch(val) [60][475/500] eta: 0:00:01 time: 0.0375 data_time: 0.0029 memory: 1008 2022/11/02 12:48:47 - mmengine - INFO - Epoch(val) [60][480/500] eta: 0:00:00 time: 0.0374 data_time: 0.0031 memory: 1008 2022/11/02 12:48:47 - mmengine - INFO - Epoch(val) [60][485/500] eta: 0:00:00 time: 0.0388 data_time: 0.0028 memory: 1008 2022/11/02 12:48:47 - mmengine - INFO - Epoch(val) [60][490/500] eta: 0:00:00 time: 0.0400 data_time: 0.0026 memory: 1008 2022/11/02 12:48:47 - mmengine - INFO - Epoch(val) [60][495/500] eta: 0:00:00 time: 0.0421 data_time: 0.0024 memory: 1008 2022/11/02 12:48:47 - mmengine - INFO - Epoch(val) [60][500/500] eta: 0:00:00 time: 0.0421 data_time: 0.0027 memory: 1008 2022/11/02 12:48:47 - mmengine - INFO - Evaluating hmean-iou... 2022/11/02 12:48:47 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.6601, precision: 0.5316, hmean: 0.5889 2022/11/02 12:48:47 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.6596, precision: 0.6524, hmean: 0.6560 2022/11/02 12:48:47 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.6442, precision: 0.7598, hmean: 0.6972 2022/11/02 12:48:47 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.5494, precision: 0.8483, hmean: 0.6669 2022/11/02 12:48:47 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.2287, precision: 0.9170, hmean: 0.3661 2022/11/02 12:48:47 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.0014, precision: 0.7500, hmean: 0.0029 2022/11/02 12:48:47 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0000, precision: 0.0000, hmean: 0.0000 2022/11/02 12:48:47 - mmengine - INFO - Epoch(val) [60][500/500] icdar/precision: 0.7598 icdar/recall: 0.6442 icdar/hmean: 0.6972 2022/11/02 12:48:52 - mmengine - INFO - Epoch(train) [61][5/63] lr: 1.2129e-03 eta: 0:00:00 time: 0.6782 data_time: 0.2188 memory: 14901 loss: 2.8272 loss_prob: 1.7379 loss_thr: 0.7943 loss_db: 0.2950 2022/11/02 12:48:55 - mmengine - INFO - Epoch(train) [61][10/63] lr: 1.2129e-03 eta: 13:17:20 time: 0.7280 data_time: 0.2191 memory: 14901 loss: 3.1072 loss_prob: 1.9454 loss_thr: 0.8257 loss_db: 0.3360 2022/11/02 12:48:58 - mmengine - INFO - Epoch(train) [61][15/63] lr: 1.2129e-03 eta: 13:17:20 time: 0.5348 data_time: 0.0359 memory: 14901 loss: 3.2231 loss_prob: 2.0395 loss_thr: 0.8233 loss_db: 0.3603 2022/11/02 12:49:00 - mmengine - INFO - Epoch(train) [61][20/63] lr: 1.2129e-03 eta: 13:16:47 time: 0.5257 data_time: 0.0360 memory: 14901 loss: 2.9006 loss_prob: 1.8028 loss_thr: 0.7886 loss_db: 0.3091 2022/11/02 12:49:03 - mmengine - INFO - Epoch(train) [61][25/63] lr: 1.2129e-03 eta: 13:16:47 time: 0.5108 data_time: 0.0049 memory: 14901 loss: 2.8166 loss_prob: 1.7338 loss_thr: 0.7969 loss_db: 0.2860 2022/11/02 12:49:05 - mmengine - INFO - Epoch(train) [61][30/63] lr: 1.2129e-03 eta: 13:16:11 time: 0.5133 data_time: 0.0238 memory: 14901 loss: 2.9106 loss_prob: 1.7934 loss_thr: 0.8116 loss_db: 0.3055 2022/11/02 12:49:08 - mmengine - INFO - Epoch(train) [61][35/63] lr: 1.2129e-03 eta: 13:16:11 time: 0.4926 data_time: 0.0237 memory: 14901 loss: 2.8702 loss_prob: 1.7778 loss_thr: 0.7931 loss_db: 0.2993 2022/11/02 12:49:10 - mmengine - INFO - Epoch(train) [61][40/63] lr: 1.2129e-03 eta: 13:15:38 time: 0.5223 data_time: 0.0232 memory: 14901 loss: 2.9024 loss_prob: 1.8147 loss_thr: 0.7821 loss_db: 0.3055 2022/11/02 12:49:13 - mmengine - INFO - Epoch(train) [61][45/63] lr: 1.2129e-03 eta: 13:15:38 time: 0.5227 data_time: 0.0231 memory: 14901 loss: 2.9737 loss_prob: 1.8721 loss_thr: 0.7855 loss_db: 0.3161 2022/11/02 12:49:15 - mmengine - INFO - Epoch(train) [61][50/63] lr: 1.2129e-03 eta: 13:15:01 time: 0.5028 data_time: 0.0044 memory: 14901 loss: 2.8896 loss_prob: 1.8143 loss_thr: 0.7745 loss_db: 0.3008 2022/11/02 12:49:18 - mmengine - INFO - Epoch(train) [61][55/63] lr: 1.2129e-03 eta: 13:15:01 time: 0.5001 data_time: 0.0073 memory: 14901 loss: 2.7960 loss_prob: 1.7393 loss_thr: 0.7704 loss_db: 0.2863 2022/11/02 12:49:20 - mmengine - INFO - Epoch(train) [61][60/63] lr: 1.2129e-03 eta: 13:14:20 time: 0.4832 data_time: 0.0072 memory: 14901 loss: 2.9508 loss_prob: 1.8467 loss_thr: 0.7863 loss_db: 0.3179 2022/11/02 12:49:22 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:49:27 - mmengine - INFO - Epoch(train) [62][5/63] lr: 1.2331e-03 eta: 13:14:20 time: 0.7476 data_time: 0.2383 memory: 14901 loss: 2.8626 loss_prob: 1.7727 loss_thr: 0.7853 loss_db: 0.3045 2022/11/02 12:49:30 - mmengine - INFO - Epoch(train) [62][10/63] lr: 1.2331e-03 eta: 13:14:03 time: 0.8203 data_time: 0.2379 memory: 14901 loss: 2.6601 loss_prob: 1.6249 loss_thr: 0.7685 loss_db: 0.2667 2022/11/02 12:49:32 - mmengine - INFO - Epoch(train) [62][15/63] lr: 1.2331e-03 eta: 13:14:03 time: 0.5346 data_time: 0.0095 memory: 14901 loss: 2.7955 loss_prob: 1.7307 loss_thr: 0.7714 loss_db: 0.2934 2022/11/02 12:49:34 - mmengine - INFO - Epoch(train) [62][20/63] lr: 1.2331e-03 eta: 13:13:21 time: 0.4693 data_time: 0.0068 memory: 14901 loss: 2.8120 loss_prob: 1.7278 loss_thr: 0.7927 loss_db: 0.2915 2022/11/02 12:49:37 - mmengine - INFO - Epoch(train) [62][25/63] lr: 1.2331e-03 eta: 13:13:21 time: 0.5291 data_time: 0.0120 memory: 14901 loss: 2.7707 loss_prob: 1.7061 loss_thr: 0.7843 loss_db: 0.2803 2022/11/02 12:49:40 - mmengine - INFO - Epoch(train) [62][30/63] lr: 1.2331e-03 eta: 13:12:58 time: 0.5771 data_time: 0.0353 memory: 14901 loss: 2.7477 loss_prob: 1.7001 loss_thr: 0.7658 loss_db: 0.2818 2022/11/02 12:49:44 - mmengine - INFO - Epoch(train) [62][35/63] lr: 1.2331e-03 eta: 13:12:58 time: 0.6335 data_time: 0.0285 memory: 14901 loss: 2.7006 loss_prob: 1.6572 loss_thr: 0.7670 loss_db: 0.2764 2022/11/02 12:49:47 - mmengine - INFO - Epoch(train) [62][40/63] lr: 1.2331e-03 eta: 13:12:48 time: 0.6445 data_time: 0.0098 memory: 14901 loss: 2.8112 loss_prob: 1.7367 loss_thr: 0.7830 loss_db: 0.2915 2022/11/02 12:49:49 - mmengine - INFO - Epoch(train) [62][45/63] lr: 1.2331e-03 eta: 13:12:48 time: 0.5601 data_time: 0.0091 memory: 14901 loss: 2.9143 loss_prob: 1.8139 loss_thr: 0.7936 loss_db: 0.3069 2022/11/02 12:49:52 - mmengine - INFO - Epoch(train) [62][50/63] lr: 1.2331e-03 eta: 13:12:16 time: 0.5272 data_time: 0.0156 memory: 14901 loss: 2.9711 loss_prob: 1.8522 loss_thr: 0.8050 loss_db: 0.3140 2022/11/02 12:49:55 - mmengine - INFO - Epoch(train) [62][55/63] lr: 1.2331e-03 eta: 13:12:16 time: 0.5428 data_time: 0.0235 memory: 14901 loss: 2.8856 loss_prob: 1.7890 loss_thr: 0.7939 loss_db: 0.3027 2022/11/02 12:49:58 - mmengine - INFO - Epoch(train) [62][60/63] lr: 1.2331e-03 eta: 13:11:55 time: 0.5862 data_time: 0.0130 memory: 14901 loss: 2.9942 loss_prob: 1.8961 loss_thr: 0.7747 loss_db: 0.3233 2022/11/02 12:49:59 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:50:06 - mmengine - INFO - Epoch(train) [63][5/63] lr: 1.2533e-03 eta: 13:11:55 time: 0.9228 data_time: 0.2180 memory: 14901 loss: 3.3712 loss_prob: 2.1828 loss_thr: 0.8080 loss_db: 0.3804 2022/11/02 12:50:09 - mmengine - INFO - Epoch(train) [63][10/63] lr: 1.2533e-03 eta: 13:12:07 time: 0.9709 data_time: 0.2175 memory: 14901 loss: 3.1882 loss_prob: 2.0296 loss_thr: 0.8023 loss_db: 0.3563 2022/11/02 12:50:12 - mmengine - INFO - Epoch(train) [63][15/63] lr: 1.2533e-03 eta: 13:12:07 time: 0.6166 data_time: 0.0060 memory: 14901 loss: 2.9453 loss_prob: 1.8502 loss_thr: 0.7810 loss_db: 0.3141 2022/11/02 12:50:14 - mmengine - INFO - Epoch(train) [63][20/63] lr: 1.2533e-03 eta: 13:11:37 time: 0.5345 data_time: 0.0055 memory: 14901 loss: 2.9539 loss_prob: 1.8513 loss_thr: 0.7939 loss_db: 0.3087 2022/11/02 12:50:19 - mmengine - INFO - Epoch(train) [63][25/63] lr: 1.2533e-03 eta: 13:11:37 time: 0.6945 data_time: 0.0328 memory: 14901 loss: 3.1915 loss_prob: 2.0117 loss_thr: 0.8228 loss_db: 0.3569 2022/11/02 12:50:23 - mmengine - INFO - Epoch(train) [63][30/63] lr: 1.2533e-03 eta: 13:12:00 time: 0.8260 data_time: 0.0399 memory: 14901 loss: 3.1786 loss_prob: 1.9992 loss_thr: 0.8205 loss_db: 0.3589 2022/11/02 12:50:26 - mmengine - INFO - Epoch(train) [63][35/63] lr: 1.2533e-03 eta: 13:12:00 time: 0.7051 data_time: 0.0117 memory: 14901 loss: 2.8580 loss_prob: 1.7614 loss_thr: 0.8017 loss_db: 0.2949 2022/11/02 12:50:28 - mmengine - INFO - Epoch(train) [63][40/63] lr: 1.2533e-03 eta: 13:11:40 time: 0.5885 data_time: 0.0081 memory: 14901 loss: 3.2246 loss_prob: 2.0092 loss_thr: 0.8577 loss_db: 0.3577 2022/11/02 12:50:31 - mmengine - INFO - Epoch(train) [63][45/63] lr: 1.2533e-03 eta: 13:11:40 time: 0.5024 data_time: 0.0080 memory: 14901 loss: 3.2135 loss_prob: 2.0130 loss_thr: 0.8393 loss_db: 0.3613 2022/11/02 12:50:34 - mmengine - INFO - Epoch(train) [63][50/63] lr: 1.2533e-03 eta: 13:11:18 time: 0.5831 data_time: 0.0195 memory: 14901 loss: 2.9621 loss_prob: 1.8488 loss_thr: 0.7954 loss_db: 0.3179 2022/11/02 12:50:37 - mmengine - INFO - Epoch(train) [63][55/63] lr: 1.2533e-03 eta: 13:11:18 time: 0.6292 data_time: 0.0245 memory: 14901 loss: 3.0890 loss_prob: 1.9547 loss_thr: 0.8012 loss_db: 0.3331 2022/11/02 12:50:40 - mmengine - INFO - Epoch(train) [63][60/63] lr: 1.2533e-03 eta: 13:10:51 time: 0.5459 data_time: 0.0093 memory: 14901 loss: 2.9113 loss_prob: 1.8278 loss_thr: 0.7855 loss_db: 0.2980 2022/11/02 12:50:41 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:50:46 - mmengine - INFO - Epoch(train) [64][5/63] lr: 1.2735e-03 eta: 13:10:51 time: 0.6945 data_time: 0.1889 memory: 14901 loss: 2.7394 loss_prob: 1.6563 loss_thr: 0.8103 loss_db: 0.2728 2022/11/02 12:50:48 - mmengine - INFO - Epoch(train) [64][10/63] lr: 1.2735e-03 eta: 13:10:18 time: 0.7261 data_time: 0.1986 memory: 14901 loss: 2.6335 loss_prob: 1.5811 loss_thr: 0.7901 loss_db: 0.2623 2022/11/02 12:50:51 - mmengine - INFO - Epoch(train) [64][15/63] lr: 1.2735e-03 eta: 13:10:18 time: 0.4737 data_time: 0.0162 memory: 14901 loss: 2.7832 loss_prob: 1.6957 loss_thr: 0.7989 loss_db: 0.2886 2022/11/02 12:50:53 - mmengine - INFO - Epoch(train) [64][20/63] lr: 1.2735e-03 eta: 13:09:36 time: 0.4671 data_time: 0.0064 memory: 14901 loss: 2.7350 loss_prob: 1.6752 loss_thr: 0.7781 loss_db: 0.2817 2022/11/02 12:50:55 - mmengine - INFO - Epoch(train) [64][25/63] lr: 1.2735e-03 eta: 13:09:36 time: 0.4863 data_time: 0.0209 memory: 14901 loss: 2.8626 loss_prob: 1.7987 loss_thr: 0.7631 loss_db: 0.3008 2022/11/02 12:50:58 - mmengine - INFO - Epoch(train) [64][30/63] lr: 1.2735e-03 eta: 13:08:59 time: 0.4923 data_time: 0.0246 memory: 14901 loss: 3.0983 loss_prob: 1.9607 loss_thr: 0.8034 loss_db: 0.3342 2022/11/02 12:50:58 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:51:00 - mmengine - INFO - Epoch(train) [64][35/63] lr: 1.2735e-03 eta: 13:08:59 time: 0.4701 data_time: 0.0172 memory: 14901 loss: 2.9367 loss_prob: 1.8221 loss_thr: 0.8020 loss_db: 0.3125 2022/11/02 12:51:02 - mmengine - INFO - Epoch(train) [64][40/63] lr: 1.2735e-03 eta: 13:08:19 time: 0.4723 data_time: 0.0145 memory: 14901 loss: 2.8852 loss_prob: 1.7878 loss_thr: 0.7942 loss_db: 0.3032 2022/11/02 12:51:05 - mmengine - INFO - Epoch(train) [64][45/63] lr: 1.2735e-03 eta: 13:08:19 time: 0.4748 data_time: 0.0063 memory: 14901 loss: 2.8346 loss_prob: 1.7634 loss_thr: 0.7801 loss_db: 0.2911 2022/11/02 12:51:07 - mmengine - INFO - Epoch(train) [64][50/63] lr: 1.2735e-03 eta: 13:07:41 time: 0.4857 data_time: 0.0162 memory: 14901 loss: 2.6417 loss_prob: 1.6332 loss_thr: 0.7441 loss_db: 0.2644 2022/11/02 12:51:10 - mmengine - INFO - Epoch(train) [64][55/63] lr: 1.2735e-03 eta: 13:07:41 time: 0.5028 data_time: 0.0200 memory: 14901 loss: 2.6763 loss_prob: 1.6501 loss_thr: 0.7550 loss_db: 0.2712 2022/11/02 12:51:12 - mmengine - INFO - Epoch(train) [64][60/63] lr: 1.2735e-03 eta: 13:07:06 time: 0.5000 data_time: 0.0114 memory: 14901 loss: 3.0340 loss_prob: 1.9002 loss_thr: 0.8087 loss_db: 0.3250 2022/11/02 12:51:14 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:51:18 - mmengine - INFO - Epoch(train) [65][5/63] lr: 1.2936e-03 eta: 13:07:06 time: 0.6834 data_time: 0.2156 memory: 14901 loss: 2.9008 loss_prob: 1.7780 loss_thr: 0.8197 loss_db: 0.3030 2022/11/02 12:51:21 - mmengine - INFO - Epoch(train) [65][10/63] lr: 1.2936e-03 eta: 13:06:31 time: 0.7103 data_time: 0.2242 memory: 14901 loss: 2.7855 loss_prob: 1.6979 loss_thr: 0.8113 loss_db: 0.2763 2022/11/02 12:51:24 - mmengine - INFO - Epoch(train) [65][15/63] lr: 1.2936e-03 eta: 13:06:31 time: 0.5325 data_time: 0.0154 memory: 14901 loss: 2.9230 loss_prob: 1.8223 loss_thr: 0.7998 loss_db: 0.3010 2022/11/02 12:51:26 - mmengine - INFO - Epoch(train) [65][20/63] lr: 1.2936e-03 eta: 13:05:59 time: 0.5158 data_time: 0.0054 memory: 14901 loss: 2.9888 loss_prob: 1.8672 loss_thr: 0.8048 loss_db: 0.3168 2022/11/02 12:51:28 - mmengine - INFO - Epoch(train) [65][25/63] lr: 1.2936e-03 eta: 13:05:59 time: 0.4724 data_time: 0.0179 memory: 14901 loss: 2.8396 loss_prob: 1.7675 loss_thr: 0.7775 loss_db: 0.2946 2022/11/02 12:51:31 - mmengine - INFO - Epoch(train) [65][30/63] lr: 1.2936e-03 eta: 13:05:23 time: 0.4916 data_time: 0.0269 memory: 14901 loss: 2.8425 loss_prob: 1.7773 loss_thr: 0.7703 loss_db: 0.2949 2022/11/02 12:51:33 - mmengine - INFO - Epoch(train) [65][35/63] lr: 1.2936e-03 eta: 13:05:23 time: 0.4869 data_time: 0.0221 memory: 14901 loss: 2.7381 loss_prob: 1.6937 loss_thr: 0.7673 loss_db: 0.2770 2022/11/02 12:51:36 - mmengine - INFO - Epoch(train) [65][40/63] lr: 1.2936e-03 eta: 13:04:44 time: 0.4720 data_time: 0.0142 memory: 14901 loss: 2.7681 loss_prob: 1.7088 loss_thr: 0.7780 loss_db: 0.2813 2022/11/02 12:51:38 - mmengine - INFO - Epoch(train) [65][45/63] lr: 1.2936e-03 eta: 13:04:44 time: 0.4854 data_time: 0.0056 memory: 14901 loss: 2.8378 loss_prob: 1.7699 loss_thr: 0.7728 loss_db: 0.2952 2022/11/02 12:51:41 - mmengine - INFO - Epoch(train) [65][50/63] lr: 1.2936e-03 eta: 13:04:12 time: 0.5122 data_time: 0.0135 memory: 14901 loss: 2.7032 loss_prob: 1.6757 loss_thr: 0.7544 loss_db: 0.2731 2022/11/02 12:51:43 - mmengine - INFO - Epoch(train) [65][55/63] lr: 1.2936e-03 eta: 13:04:12 time: 0.5150 data_time: 0.0199 memory: 14901 loss: 2.7182 loss_prob: 1.6825 loss_thr: 0.7606 loss_db: 0.2751 2022/11/02 12:51:46 - mmengine - INFO - Epoch(train) [65][60/63] lr: 1.2936e-03 eta: 13:03:42 time: 0.5257 data_time: 0.0128 memory: 14901 loss: 2.9911 loss_prob: 1.8667 loss_thr: 0.7986 loss_db: 0.3258 2022/11/02 12:51:47 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:51:53 - mmengine - INFO - Epoch(train) [66][5/63] lr: 1.3138e-03 eta: 13:03:42 time: 0.8189 data_time: 0.2167 memory: 14901 loss: 2.7758 loss_prob: 1.7218 loss_thr: 0.7634 loss_db: 0.2906 2022/11/02 12:51:56 - mmengine - INFO - Epoch(train) [66][10/63] lr: 1.3138e-03 eta: 13:03:32 time: 0.8454 data_time: 0.2231 memory: 14901 loss: 2.7628 loss_prob: 1.6954 loss_thr: 0.7834 loss_db: 0.2840 2022/11/02 12:51:58 - mmengine - INFO - Epoch(train) [66][15/63] lr: 1.3138e-03 eta: 13:03:32 time: 0.5146 data_time: 0.0119 memory: 14901 loss: 2.8800 loss_prob: 1.7813 loss_thr: 0.8008 loss_db: 0.2979 2022/11/02 12:52:01 - mmengine - INFO - Epoch(train) [66][20/63] lr: 1.3138e-03 eta: 13:02:58 time: 0.4980 data_time: 0.0050 memory: 14901 loss: 3.0248 loss_prob: 1.9065 loss_thr: 0.7921 loss_db: 0.3262 2022/11/02 12:52:03 - mmengine - INFO - Epoch(train) [66][25/63] lr: 1.3138e-03 eta: 13:02:58 time: 0.4914 data_time: 0.0182 memory: 14901 loss: 2.9477 loss_prob: 1.8526 loss_thr: 0.7752 loss_db: 0.3199 2022/11/02 12:52:06 - mmengine - INFO - Epoch(train) [66][30/63] lr: 1.3138e-03 eta: 13:02:23 time: 0.4966 data_time: 0.0319 memory: 14901 loss: 2.8816 loss_prob: 1.7965 loss_thr: 0.7804 loss_db: 0.3046 2022/11/02 12:52:08 - mmengine - INFO - Epoch(train) [66][35/63] lr: 1.3138e-03 eta: 13:02:23 time: 0.5077 data_time: 0.0231 memory: 14901 loss: 2.7228 loss_prob: 1.6836 loss_thr: 0.7628 loss_db: 0.2764 2022/11/02 12:52:11 - mmengine - INFO - Epoch(train) [66][40/63] lr: 1.3138e-03 eta: 13:02:05 time: 0.5911 data_time: 0.0097 memory: 14901 loss: 2.6882 loss_prob: 1.6570 loss_thr: 0.7606 loss_db: 0.2705 2022/11/02 12:52:15 - mmengine - INFO - Epoch(train) [66][45/63] lr: 1.3138e-03 eta: 13:02:05 time: 0.6662 data_time: 0.0051 memory: 14901 loss: 2.7573 loss_prob: 1.7150 loss_thr: 0.7619 loss_db: 0.2804 2022/11/02 12:52:18 - mmengine - INFO - Epoch(train) [66][50/63] lr: 1.3138e-03 eta: 13:01:58 time: 0.6505 data_time: 0.0184 memory: 14901 loss: 2.5882 loss_prob: 1.5896 loss_thr: 0.7418 loss_db: 0.2568 2022/11/02 12:52:21 - mmengine - INFO - Epoch(train) [66][55/63] lr: 1.3138e-03 eta: 13:01:58 time: 0.5738 data_time: 0.0217 memory: 14901 loss: 2.5589 loss_prob: 1.5443 loss_thr: 0.7614 loss_db: 0.2532 2022/11/02 12:52:23 - mmengine - INFO - Epoch(train) [66][60/63] lr: 1.3138e-03 eta: 13:01:30 time: 0.5310 data_time: 0.0094 memory: 14901 loss: 2.6759 loss_prob: 1.6317 loss_thr: 0.7744 loss_db: 0.2698 2022/11/02 12:52:25 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:52:32 - mmengine - INFO - Epoch(train) [67][5/63] lr: 1.3340e-03 eta: 13:01:30 time: 0.9652 data_time: 0.2282 memory: 14901 loss: 2.7962 loss_prob: 1.7194 loss_thr: 0.7850 loss_db: 0.2917 2022/11/02 12:52:35 - mmengine - INFO - Epoch(train) [67][10/63] lr: 1.3340e-03 eta: 13:01:46 time: 1.0012 data_time: 0.2308 memory: 14901 loss: 2.7671 loss_prob: 1.7169 loss_thr: 0.7612 loss_db: 0.2891 2022/11/02 12:52:38 - mmengine - INFO - Epoch(train) [67][15/63] lr: 1.3340e-03 eta: 13:01:46 time: 0.6178 data_time: 0.0072 memory: 14901 loss: 2.8273 loss_prob: 1.7503 loss_thr: 0.7863 loss_db: 0.2907 2022/11/02 12:52:40 - mmengine - INFO - Epoch(train) [67][20/63] lr: 1.3340e-03 eta: 13:01:22 time: 0.5522 data_time: 0.0050 memory: 14901 loss: 2.8450 loss_prob: 1.7434 loss_thr: 0.8134 loss_db: 0.2883 2022/11/02 12:52:43 - mmengine - INFO - Epoch(train) [67][25/63] lr: 1.3340e-03 eta: 13:01:22 time: 0.5227 data_time: 0.0322 memory: 14901 loss: 2.7866 loss_prob: 1.7191 loss_thr: 0.7791 loss_db: 0.2884 2022/11/02 12:52:46 - mmengine - INFO - Epoch(train) [67][30/63] lr: 1.3340e-03 eta: 13:00:52 time: 0.5200 data_time: 0.0328 memory: 14901 loss: 2.8757 loss_prob: 1.7900 loss_thr: 0.7788 loss_db: 0.3068 2022/11/02 12:52:48 - mmengine - INFO - Epoch(train) [67][35/63] lr: 1.3340e-03 eta: 13:00:52 time: 0.5267 data_time: 0.0083 memory: 14901 loss: 2.8250 loss_prob: 1.7505 loss_thr: 0.7744 loss_db: 0.3000 2022/11/02 12:52:51 - mmengine - INFO - Epoch(train) [67][40/63] lr: 1.3340e-03 eta: 13:00:26 time: 0.5437 data_time: 0.0087 memory: 14901 loss: 2.7413 loss_prob: 1.6929 loss_thr: 0.7609 loss_db: 0.2875 2022/11/02 12:52:54 - mmengine - INFO - Epoch(train) [67][45/63] lr: 1.3340e-03 eta: 13:00:26 time: 0.5599 data_time: 0.0074 memory: 14901 loss: 2.8476 loss_prob: 1.7707 loss_thr: 0.7751 loss_db: 0.3018 2022/11/02 12:52:57 - mmengine - INFO - Epoch(train) [67][50/63] lr: 1.3340e-03 eta: 13:00:09 time: 0.5899 data_time: 0.0309 memory: 14901 loss: 2.9941 loss_prob: 1.8677 loss_thr: 0.8021 loss_db: 0.3243 2022/11/02 12:52:59 - mmengine - INFO - Epoch(train) [67][55/63] lr: 1.3340e-03 eta: 13:00:09 time: 0.5389 data_time: 0.0291 memory: 14901 loss: 3.2952 loss_prob: 2.0766 loss_thr: 0.8457 loss_db: 0.3728 2022/11/02 12:53:02 - mmengine - INFO - Epoch(train) [67][60/63] lr: 1.3340e-03 eta: 12:59:42 time: 0.5364 data_time: 0.0080 memory: 14901 loss: 3.1245 loss_prob: 1.9797 loss_thr: 0.7915 loss_db: 0.3533 2022/11/02 12:53:04 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:53:09 - mmengine - INFO - Epoch(train) [68][5/63] lr: 1.3542e-03 eta: 12:59:42 time: 0.8346 data_time: 0.2178 memory: 14901 loss: 3.0324 loss_prob: 1.9370 loss_thr: 0.7548 loss_db: 0.3406 2022/11/02 12:53:12 - mmengine - INFO - Epoch(train) [68][10/63] lr: 1.3542e-03 eta: 12:59:23 time: 0.7892 data_time: 0.2155 memory: 14901 loss: 3.1199 loss_prob: 1.9920 loss_thr: 0.7790 loss_db: 0.3489 2022/11/02 12:53:15 - mmengine - INFO - Epoch(train) [68][15/63] lr: 1.3542e-03 eta: 12:59:23 time: 0.5292 data_time: 0.0056 memory: 14901 loss: 2.8887 loss_prob: 1.8071 loss_thr: 0.7731 loss_db: 0.3085 2022/11/02 12:53:17 - mmengine - INFO - Epoch(train) [68][20/63] lr: 1.3542e-03 eta: 12:58:55 time: 0.5309 data_time: 0.0057 memory: 14901 loss: 3.0287 loss_prob: 1.9095 loss_thr: 0.7861 loss_db: 0.3330 2022/11/02 12:53:20 - mmengine - INFO - Epoch(train) [68][25/63] lr: 1.3542e-03 eta: 12:58:55 time: 0.5215 data_time: 0.0261 memory: 14901 loss: 3.2563 loss_prob: 2.0693 loss_thr: 0.8243 loss_db: 0.3627 2022/11/02 12:53:22 - mmengine - INFO - Epoch(train) [68][30/63] lr: 1.3542e-03 eta: 12:58:28 time: 0.5333 data_time: 0.0326 memory: 14901 loss: 2.9389 loss_prob: 1.8374 loss_thr: 0.7918 loss_db: 0.3097 2022/11/02 12:53:25 - mmengine - INFO - Epoch(train) [68][35/63] lr: 1.3542e-03 eta: 12:58:28 time: 0.4995 data_time: 0.0166 memory: 14901 loss: 2.6609 loss_prob: 1.6367 loss_thr: 0.7571 loss_db: 0.2671 2022/11/02 12:53:27 - mmengine - INFO - Epoch(train) [68][40/63] lr: 1.3542e-03 eta: 12:57:53 time: 0.4798 data_time: 0.0095 memory: 14901 loss: 2.5431 loss_prob: 1.5454 loss_thr: 0.7516 loss_db: 0.2461 2022/11/02 12:53:30 - mmengine - INFO - Epoch(train) [68][45/63] lr: 1.3542e-03 eta: 12:57:53 time: 0.4775 data_time: 0.0041 memory: 14901 loss: 2.4529 loss_prob: 1.4595 loss_thr: 0.7567 loss_db: 0.2368 2022/11/02 12:53:32 - mmengine - INFO - Epoch(train) [68][50/63] lr: 1.3542e-03 eta: 12:57:24 time: 0.5208 data_time: 0.0223 memory: 14901 loss: 2.6238 loss_prob: 1.5981 loss_thr: 0.7583 loss_db: 0.2675 2022/11/02 12:53:35 - mmengine - INFO - Epoch(train) [68][55/63] lr: 1.3542e-03 eta: 12:57:24 time: 0.5316 data_time: 0.0271 memory: 14901 loss: 2.6257 loss_prob: 1.6226 loss_thr: 0.7325 loss_db: 0.2706 2022/11/02 12:53:37 - mmengine - INFO - Epoch(train) [68][60/63] lr: 1.3542e-03 eta: 12:56:48 time: 0.4813 data_time: 0.0101 memory: 14901 loss: 2.5461 loss_prob: 1.5663 loss_thr: 0.7220 loss_db: 0.2578 2022/11/02 12:53:39 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:53:44 - mmengine - INFO - Epoch(train) [69][5/63] lr: 1.3744e-03 eta: 12:56:48 time: 0.7609 data_time: 0.1679 memory: 14901 loss: 2.9272 loss_prob: 1.8257 loss_thr: 0.7899 loss_db: 0.3116 2022/11/02 12:53:46 - mmengine - INFO - Epoch(train) [69][10/63] lr: 1.3744e-03 eta: 12:56:30 time: 0.7876 data_time: 0.1832 memory: 14901 loss: 2.7999 loss_prob: 1.7236 loss_thr: 0.7822 loss_db: 0.2941 2022/11/02 12:53:49 - mmengine - INFO - Epoch(train) [69][15/63] lr: 1.3744e-03 eta: 12:56:30 time: 0.5052 data_time: 0.0233 memory: 14901 loss: 2.5803 loss_prob: 1.5634 loss_thr: 0.7575 loss_db: 0.2594 2022/11/02 12:53:51 - mmengine - INFO - Epoch(train) [69][20/63] lr: 1.3744e-03 eta: 12:55:55 time: 0.4845 data_time: 0.0075 memory: 14901 loss: 2.6101 loss_prob: 1.5973 loss_thr: 0.7496 loss_db: 0.2633 2022/11/02 12:53:54 - mmengine - INFO - Epoch(train) [69][25/63] lr: 1.3744e-03 eta: 12:55:55 time: 0.4761 data_time: 0.0109 memory: 14901 loss: 2.6706 loss_prob: 1.6445 loss_thr: 0.7473 loss_db: 0.2788 2022/11/02 12:53:56 - mmengine - INFO - Epoch(train) [69][30/63] lr: 1.3744e-03 eta: 12:55:23 time: 0.4986 data_time: 0.0378 memory: 14901 loss: 2.5978 loss_prob: 1.5735 loss_thr: 0.7629 loss_db: 0.2614 2022/11/02 12:53:59 - mmengine - INFO - Epoch(train) [69][35/63] lr: 1.3744e-03 eta: 12:55:23 time: 0.5124 data_time: 0.0331 memory: 14901 loss: 2.5305 loss_prob: 1.5335 loss_thr: 0.7498 loss_db: 0.2472 2022/11/02 12:54:01 - mmengine - INFO - Epoch(train) [69][40/63] lr: 1.3744e-03 eta: 12:54:49 time: 0.4884 data_time: 0.0071 memory: 14901 loss: 2.6722 loss_prob: 1.6433 loss_thr: 0.7555 loss_db: 0.2733 2022/11/02 12:54:03 - mmengine - INFO - Epoch(train) [69][45/63] lr: 1.3744e-03 eta: 12:54:49 time: 0.4751 data_time: 0.0057 memory: 14901 loss: 2.9424 loss_prob: 1.8379 loss_thr: 0.7883 loss_db: 0.3162 2022/11/02 12:54:06 - mmengine - INFO - Epoch(train) [69][50/63] lr: 1.3744e-03 eta: 12:54:15 time: 0.4839 data_time: 0.0088 memory: 14901 loss: 2.8180 loss_prob: 1.7555 loss_thr: 0.7645 loss_db: 0.2980 2022/11/02 12:54:08 - mmengine - INFO - Epoch(train) [69][55/63] lr: 1.3744e-03 eta: 12:54:15 time: 0.4912 data_time: 0.0187 memory: 14901 loss: 2.5777 loss_prob: 1.5721 loss_thr: 0.7482 loss_db: 0.2574 2022/11/02 12:54:11 - mmengine - INFO - Epoch(train) [69][60/63] lr: 1.3744e-03 eta: 12:53:40 time: 0.4752 data_time: 0.0158 memory: 14901 loss: 2.6306 loss_prob: 1.6212 loss_thr: 0.7445 loss_db: 0.2649 2022/11/02 12:54:12 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:54:17 - mmengine - INFO - Epoch(train) [70][5/63] lr: 1.3945e-03 eta: 12:53:40 time: 0.7041 data_time: 0.2048 memory: 14901 loss: 2.6999 loss_prob: 1.6571 loss_thr: 0.7678 loss_db: 0.2750 2022/11/02 12:54:20 - mmengine - INFO - Epoch(train) [70][10/63] lr: 1.3945e-03 eta: 12:53:18 time: 0.7642 data_time: 0.2084 memory: 14901 loss: 2.4924 loss_prob: 1.5089 loss_thr: 0.7389 loss_db: 0.2445 2022/11/02 12:54:22 - mmengine - INFO - Epoch(train) [70][15/63] lr: 1.3945e-03 eta: 12:53:18 time: 0.5077 data_time: 0.0103 memory: 14901 loss: 2.5358 loss_prob: 1.5399 loss_thr: 0.7442 loss_db: 0.2518 2022/11/02 12:54:24 - mmengine - INFO - Epoch(train) [70][20/63] lr: 1.3945e-03 eta: 12:52:45 time: 0.4932 data_time: 0.0049 memory: 14901 loss: 2.6072 loss_prob: 1.5991 loss_thr: 0.7481 loss_db: 0.2599 2022/11/02 12:54:27 - mmengine - INFO - Epoch(train) [70][25/63] lr: 1.3945e-03 eta: 12:52:45 time: 0.5190 data_time: 0.0249 memory: 14901 loss: 2.6032 loss_prob: 1.5883 loss_thr: 0.7534 loss_db: 0.2615 2022/11/02 12:54:29 - mmengine - INFO - Epoch(train) [70][30/63] lr: 1.3945e-03 eta: 12:52:14 time: 0.4999 data_time: 0.0354 memory: 14901 loss: 2.6367 loss_prob: 1.6099 loss_thr: 0.7614 loss_db: 0.2655 2022/11/02 12:54:32 - mmengine - INFO - Epoch(train) [70][35/63] lr: 1.3945e-03 eta: 12:52:14 time: 0.4749 data_time: 0.0182 memory: 14901 loss: 2.7782 loss_prob: 1.7327 loss_thr: 0.7596 loss_db: 0.2860 2022/11/02 12:54:34 - mmengine - INFO - Epoch(train) [70][40/63] lr: 1.3945e-03 eta: 12:51:38 time: 0.4691 data_time: 0.0075 memory: 14901 loss: 2.6511 loss_prob: 1.6468 loss_thr: 0.7327 loss_db: 0.2717 2022/11/02 12:54:37 - mmengine - INFO - Epoch(train) [70][45/63] lr: 1.3945e-03 eta: 12:51:38 time: 0.4868 data_time: 0.0044 memory: 14901 loss: 2.5510 loss_prob: 1.5574 loss_thr: 0.7382 loss_db: 0.2554 2022/11/02 12:54:39 - mmengine - INFO - Epoch(train) [70][50/63] lr: 1.3945e-03 eta: 12:51:08 time: 0.5033 data_time: 0.0176 memory: 14901 loss: 2.6350 loss_prob: 1.6228 loss_thr: 0.7406 loss_db: 0.2716 2022/11/02 12:54:42 - mmengine - INFO - Epoch(train) [70][55/63] lr: 1.3945e-03 eta: 12:51:08 time: 0.4838 data_time: 0.0200 memory: 14901 loss: 2.7017 loss_prob: 1.6799 loss_thr: 0.7369 loss_db: 0.2849 2022/11/02 12:54:44 - mmengine - INFO - Epoch(train) [70][60/63] lr: 1.3945e-03 eta: 12:50:35 time: 0.4815 data_time: 0.0080 memory: 14901 loss: 2.8004 loss_prob: 1.7344 loss_thr: 0.7722 loss_db: 0.2938 2022/11/02 12:54:45 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:54:51 - mmengine - INFO - Epoch(train) [71][5/63] lr: 1.4147e-03 eta: 12:50:35 time: 0.8380 data_time: 0.2075 memory: 14901 loss: 2.5773 loss_prob: 1.5576 loss_thr: 0.7641 loss_db: 0.2556 2022/11/02 12:54:55 - mmengine - INFO - Epoch(train) [71][10/63] lr: 1.4147e-03 eta: 12:50:42 time: 0.9448 data_time: 0.2135 memory: 14901 loss: 2.6191 loss_prob: 1.5891 loss_thr: 0.7677 loss_db: 0.2623 2022/11/02 12:54:57 - mmengine - INFO - Epoch(train) [71][15/63] lr: 1.4147e-03 eta: 12:50:42 time: 0.5934 data_time: 0.0161 memory: 14901 loss: 2.5638 loss_prob: 1.5667 loss_thr: 0.7377 loss_db: 0.2595 2022/11/02 12:55:00 - mmengine - INFO - Epoch(train) [71][20/63] lr: 1.4147e-03 eta: 12:50:13 time: 0.5075 data_time: 0.0111 memory: 14901 loss: 2.5050 loss_prob: 1.5380 loss_thr: 0.7146 loss_db: 0.2524 2022/11/02 12:55:03 - mmengine - INFO - Epoch(train) [71][25/63] lr: 1.4147e-03 eta: 12:50:13 time: 0.5310 data_time: 0.0056 memory: 14901 loss: 2.5602 loss_prob: 1.5710 loss_thr: 0.7340 loss_db: 0.2553 2022/11/02 12:55:05 - mmengine - INFO - Epoch(train) [71][30/63] lr: 1.4147e-03 eta: 12:49:53 time: 0.5625 data_time: 0.0285 memory: 14901 loss: 2.6281 loss_prob: 1.6223 loss_thr: 0.7429 loss_db: 0.2629 2022/11/02 12:55:09 - mmengine - INFO - Epoch(train) [71][35/63] lr: 1.4147e-03 eta: 12:49:53 time: 0.6527 data_time: 0.0327 memory: 14901 loss: 2.6382 loss_prob: 1.6385 loss_thr: 0.7334 loss_db: 0.2664 2022/11/02 12:55:12 - mmengine - INFO - Epoch(train) [71][40/63] lr: 1.4147e-03 eta: 12:49:39 time: 0.6077 data_time: 0.0154 memory: 14901 loss: 2.7163 loss_prob: 1.6906 loss_thr: 0.7432 loss_db: 0.2825 2022/11/02 12:55:16 - mmengine - INFO - Epoch(train) [71][45/63] lr: 1.4147e-03 eta: 12:49:39 time: 0.6592 data_time: 0.0131 memory: 14901 loss: 2.8405 loss_prob: 1.7854 loss_thr: 0.7456 loss_db: 0.3095 2022/11/02 12:55:18 - mmengine - INFO - Epoch(train) [71][50/63] lr: 1.4147e-03 eta: 12:49:37 time: 0.6723 data_time: 0.0197 memory: 14901 loss: 2.9489 loss_prob: 1.8617 loss_thr: 0.7626 loss_db: 0.3245 2022/11/02 12:55:21 - mmengine - INFO - Epoch(train) [71][55/63] lr: 1.4147e-03 eta: 12:49:37 time: 0.5142 data_time: 0.0193 memory: 14901 loss: 2.9338 loss_prob: 1.8413 loss_thr: 0.7780 loss_db: 0.3144 2022/11/02 12:55:24 - mmengine - INFO - Epoch(train) [71][60/63] lr: 1.4147e-03 eta: 12:49:11 time: 0.5308 data_time: 0.0063 memory: 14901 loss: 2.7112 loss_prob: 1.6659 loss_thr: 0.7658 loss_db: 0.2794 2022/11/02 12:55:25 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:55:31 - mmengine - INFO - Epoch(train) [72][5/63] lr: 1.4349e-03 eta: 12:49:11 time: 0.7899 data_time: 0.2043 memory: 14901 loss: 2.7789 loss_prob: 1.7285 loss_thr: 0.7665 loss_db: 0.2840 2022/11/02 12:55:33 - mmengine - INFO - Epoch(train) [72][10/63] lr: 1.4349e-03 eta: 12:49:07 time: 0.8704 data_time: 0.2059 memory: 14901 loss: 2.9226 loss_prob: 1.8394 loss_thr: 0.7752 loss_db: 0.3080 2022/11/02 12:55:36 - mmengine - INFO - Epoch(train) [72][15/63] lr: 1.4349e-03 eta: 12:49:07 time: 0.5281 data_time: 0.0129 memory: 14901 loss: 2.9722 loss_prob: 1.8675 loss_thr: 0.7843 loss_db: 0.3204 2022/11/02 12:55:39 - mmengine - INFO - Epoch(train) [72][20/63] lr: 1.4349e-03 eta: 12:48:43 time: 0.5361 data_time: 0.0128 memory: 14901 loss: 2.9405 loss_prob: 1.8430 loss_thr: 0.7846 loss_db: 0.3129 2022/11/02 12:55:42 - mmengine - INFO - Epoch(train) [72][25/63] lr: 1.4349e-03 eta: 12:48:43 time: 0.6098 data_time: 0.0244 memory: 14901 loss: 2.7980 loss_prob: 1.7601 loss_thr: 0.7483 loss_db: 0.2896 2022/11/02 12:55:45 - mmengine - INFO - Epoch(train) [72][30/63] lr: 1.4349e-03 eta: 12:48:37 time: 0.6555 data_time: 0.0325 memory: 14901 loss: 2.6799 loss_prob: 1.6677 loss_thr: 0.7377 loss_db: 0.2745 2022/11/02 12:55:48 - mmengine - INFO - Epoch(train) [72][35/63] lr: 1.4349e-03 eta: 12:48:37 time: 0.5815 data_time: 0.0168 memory: 14901 loss: 2.6212 loss_prob: 1.6099 loss_thr: 0.7515 loss_db: 0.2599 2022/11/02 12:55:50 - mmengine - INFO - Epoch(train) [72][40/63] lr: 1.4349e-03 eta: 12:48:04 time: 0.4789 data_time: 0.0108 memory: 14901 loss: 2.5513 loss_prob: 1.5555 loss_thr: 0.7477 loss_db: 0.2481 2022/11/02 12:55:53 - mmengine - INFO - Epoch(train) [72][45/63] lr: 1.4349e-03 eta: 12:48:04 time: 0.4794 data_time: 0.0107 memory: 14901 loss: 2.6017 loss_prob: 1.5921 loss_thr: 0.7493 loss_db: 0.2603 2022/11/02 12:55:55 - mmengine - INFO - Epoch(train) [72][50/63] lr: 1.4349e-03 eta: 12:47:37 time: 0.5192 data_time: 0.0110 memory: 14901 loss: 2.8350 loss_prob: 1.7722 loss_thr: 0.7656 loss_db: 0.2972 2022/11/02 12:55:58 - mmengine - INFO - Epoch(train) [72][55/63] lr: 1.4349e-03 eta: 12:47:37 time: 0.5433 data_time: 0.0193 memory: 14901 loss: 2.7174 loss_prob: 1.6973 loss_thr: 0.7400 loss_db: 0.2801 2022/11/02 12:56:01 - mmengine - INFO - Epoch(train) [72][60/63] lr: 1.4349e-03 eta: 12:47:12 time: 0.5289 data_time: 0.0130 memory: 14901 loss: 2.4078 loss_prob: 1.4648 loss_thr: 0.7098 loss_db: 0.2332 2022/11/02 12:56:02 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:56:07 - mmengine - INFO - Epoch(train) [73][5/63] lr: 1.4551e-03 eta: 12:47:12 time: 0.7120 data_time: 0.2226 memory: 14901 loss: 2.3909 loss_prob: 1.4371 loss_thr: 0.7203 loss_db: 0.2335 2022/11/02 12:56:09 - mmengine - INFO - Epoch(train) [73][10/63] lr: 1.4551e-03 eta: 12:46:44 time: 0.7156 data_time: 0.2219 memory: 14901 loss: 2.5304 loss_prob: 1.5235 loss_thr: 0.7535 loss_db: 0.2534 2022/11/02 12:56:12 - mmengine - INFO - Epoch(train) [73][15/63] lr: 1.4551e-03 eta: 12:46:44 time: 0.4954 data_time: 0.0110 memory: 14901 loss: 2.6947 loss_prob: 1.6460 loss_thr: 0.7757 loss_db: 0.2731 2022/11/02 12:56:14 - mmengine - INFO - Epoch(train) [73][20/63] lr: 1.4551e-03 eta: 12:46:15 time: 0.5003 data_time: 0.0094 memory: 14901 loss: 2.6821 loss_prob: 1.6718 loss_thr: 0.7423 loss_db: 0.2680 2022/11/02 12:56:17 - mmengine - INFO - Epoch(train) [73][25/63] lr: 1.4551e-03 eta: 12:46:15 time: 0.5127 data_time: 0.0116 memory: 14901 loss: 2.5554 loss_prob: 1.5894 loss_thr: 0.7095 loss_db: 0.2565 2022/11/02 12:56:19 - mmengine - INFO - Epoch(train) [73][30/63] lr: 1.4551e-03 eta: 12:45:50 time: 0.5289 data_time: 0.0276 memory: 14901 loss: 2.4108 loss_prob: 1.4533 loss_thr: 0.7171 loss_db: 0.2404 2022/11/02 12:56:22 - mmengine - INFO - Epoch(train) [73][35/63] lr: 1.4551e-03 eta: 12:45:50 time: 0.4967 data_time: 0.0216 memory: 14901 loss: 2.7969 loss_prob: 1.7199 loss_thr: 0.7875 loss_db: 0.2895 2022/11/02 12:56:25 - mmengine - INFO - Epoch(train) [73][40/63] lr: 1.4551e-03 eta: 12:45:22 time: 0.5122 data_time: 0.0132 memory: 14901 loss: 2.9348 loss_prob: 1.8326 loss_thr: 0.7917 loss_db: 0.3105 2022/11/02 12:56:27 - mmengine - INFO - Epoch(train) [73][45/63] lr: 1.4551e-03 eta: 12:45:22 time: 0.5637 data_time: 0.0122 memory: 14901 loss: 2.7110 loss_prob: 1.6759 loss_thr: 0.7570 loss_db: 0.2780 2022/11/02 12:56:30 - mmengine - INFO - Epoch(train) [73][50/63] lr: 1.4551e-03 eta: 12:44:57 time: 0.5226 data_time: 0.0182 memory: 14901 loss: 2.6231 loss_prob: 1.6080 loss_thr: 0.7518 loss_db: 0.2632 2022/11/02 12:56:33 - mmengine - INFO - Epoch(train) [73][55/63] lr: 1.4551e-03 eta: 12:44:57 time: 0.5110 data_time: 0.0256 memory: 14901 loss: 2.5216 loss_prob: 1.5335 loss_thr: 0.7407 loss_db: 0.2474 2022/11/02 12:56:35 - mmengine - INFO - Epoch(train) [73][60/63] lr: 1.4551e-03 eta: 12:44:33 time: 0.5369 data_time: 0.0147 memory: 14901 loss: 2.5976 loss_prob: 1.5825 loss_thr: 0.7590 loss_db: 0.2561 2022/11/02 12:56:36 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:56:41 - mmengine - INFO - Epoch(train) [74][5/63] lr: 1.4753e-03 eta: 12:44:33 time: 0.6741 data_time: 0.1929 memory: 14901 loss: 2.4547 loss_prob: 1.5026 loss_thr: 0.7147 loss_db: 0.2374 2022/11/02 12:56:43 - mmengine - INFO - Epoch(train) [74][10/63] lr: 1.4753e-03 eta: 12:44:02 time: 0.6932 data_time: 0.1942 memory: 14901 loss: 2.4889 loss_prob: 1.5241 loss_thr: 0.7172 loss_db: 0.2476 2022/11/02 12:56:46 - mmengine - INFO - Epoch(train) [74][15/63] lr: 1.4753e-03 eta: 12:44:02 time: 0.4961 data_time: 0.0079 memory: 14901 loss: 2.5217 loss_prob: 1.5432 loss_thr: 0.7205 loss_db: 0.2580 2022/11/02 12:56:48 - mmengine - INFO - Epoch(train) [74][20/63] lr: 1.4753e-03 eta: 12:43:29 time: 0.4712 data_time: 0.0062 memory: 14901 loss: 2.7122 loss_prob: 1.6815 loss_thr: 0.7366 loss_db: 0.2941 2022/11/02 12:56:51 - mmengine - INFO - Epoch(train) [74][25/63] lr: 1.4753e-03 eta: 12:43:29 time: 0.4895 data_time: 0.0291 memory: 14901 loss: 2.9194 loss_prob: 1.8436 loss_thr: 0.7526 loss_db: 0.3232 2022/11/02 12:56:53 - mmengine - INFO - Epoch(train) [74][30/63] lr: 1.4753e-03 eta: 12:42:58 time: 0.4860 data_time: 0.0271 memory: 14901 loss: 3.0590 loss_prob: 1.9442 loss_thr: 0.7805 loss_db: 0.3343 2022/11/02 12:56:55 - mmengine - INFO - Epoch(train) [74][35/63] lr: 1.4753e-03 eta: 12:42:58 time: 0.4723 data_time: 0.0082 memory: 14901 loss: 3.1509 loss_prob: 1.9731 loss_thr: 0.8305 loss_db: 0.3472 2022/11/02 12:56:58 - mmengine - INFO - Epoch(train) [74][40/63] lr: 1.4753e-03 eta: 12:42:27 time: 0.4810 data_time: 0.0083 memory: 14901 loss: 3.2397 loss_prob: 2.0545 loss_thr: 0.8188 loss_db: 0.3664 2022/11/02 12:57:00 - mmengine - INFO - Epoch(train) [74][45/63] lr: 1.4753e-03 eta: 12:42:27 time: 0.4805 data_time: 0.0061 memory: 14901 loss: 3.2449 loss_prob: 2.0761 loss_thr: 0.8045 loss_db: 0.3642 2022/11/02 12:57:03 - mmengine - INFO - Epoch(train) [74][50/63] lr: 1.4753e-03 eta: 12:42:05 time: 0.5452 data_time: 0.0215 memory: 14901 loss: 3.1657 loss_prob: 2.0055 loss_thr: 0.8048 loss_db: 0.3554 2022/11/02 12:57:06 - mmengine - INFO - Epoch(train) [74][55/63] lr: 1.4753e-03 eta: 12:42:05 time: 0.5490 data_time: 0.0200 memory: 14901 loss: 3.0384 loss_prob: 1.9132 loss_thr: 0.7897 loss_db: 0.3355 2022/11/02 12:57:08 - mmengine - INFO - Epoch(train) [74][60/63] lr: 1.4753e-03 eta: 12:41:40 time: 0.5213 data_time: 0.0071 memory: 14901 loss: 2.9817 loss_prob: 1.8706 loss_thr: 0.7944 loss_db: 0.3167 2022/11/02 12:57:10 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:57:14 - mmengine - INFO - Epoch(train) [75][5/63] lr: 1.4955e-03 eta: 12:41:40 time: 0.6828 data_time: 0.2067 memory: 14901 loss: 3.0031 loss_prob: 1.8885 loss_thr: 0.7929 loss_db: 0.3218 2022/11/02 12:57:17 - mmengine - INFO - Epoch(train) [75][10/63] lr: 1.4955e-03 eta: 12:41:12 time: 0.7103 data_time: 0.2092 memory: 14901 loss: 2.9852 loss_prob: 1.8853 loss_thr: 0.7838 loss_db: 0.3161 2022/11/02 12:57:19 - mmengine - INFO - Epoch(train) [75][15/63] lr: 1.4955e-03 eta: 12:41:12 time: 0.4782 data_time: 0.0081 memory: 14901 loss: 2.7157 loss_prob: 1.6976 loss_thr: 0.7438 loss_db: 0.2742 2022/11/02 12:57:22 - mmengine - INFO - Epoch(train) [75][20/63] lr: 1.4955e-03 eta: 12:40:42 time: 0.4888 data_time: 0.0058 memory: 14901 loss: 2.6802 loss_prob: 1.6622 loss_thr: 0.7469 loss_db: 0.2711 2022/11/02 12:57:25 - mmengine - INFO - Epoch(train) [75][25/63] lr: 1.4955e-03 eta: 12:40:42 time: 0.5942 data_time: 0.0328 memory: 14901 loss: 2.7968 loss_prob: 1.7328 loss_thr: 0.7732 loss_db: 0.2908 2022/11/02 12:57:28 - mmengine - INFO - Epoch(train) [75][30/63] lr: 1.4955e-03 eta: 12:40:32 time: 0.6176 data_time: 0.0332 memory: 14901 loss: 2.7350 loss_prob: 1.6893 loss_thr: 0.7594 loss_db: 0.2863 2022/11/02 12:57:30 - mmengine - INFO - Epoch(train) [75][35/63] lr: 1.4955e-03 eta: 12:40:32 time: 0.5328 data_time: 0.0095 memory: 14901 loss: 2.7224 loss_prob: 1.6901 loss_thr: 0.7422 loss_db: 0.2901 2022/11/02 12:57:33 - mmengine - INFO - Epoch(train) [75][40/63] lr: 1.4955e-03 eta: 12:40:08 time: 0.5264 data_time: 0.0083 memory: 14901 loss: 2.9209 loss_prob: 1.8297 loss_thr: 0.7772 loss_db: 0.3140 2022/11/02 12:57:36 - mmengine - INFO - Epoch(train) [75][45/63] lr: 1.4955e-03 eta: 12:40:08 time: 0.5635 data_time: 0.0045 memory: 14901 loss: 2.9574 loss_prob: 1.8641 loss_thr: 0.7823 loss_db: 0.3110 2022/11/02 12:57:39 - mmengine - INFO - Epoch(train) [75][50/63] lr: 1.4955e-03 eta: 12:39:49 time: 0.5625 data_time: 0.0193 memory: 14901 loss: 2.7368 loss_prob: 1.7061 loss_thr: 0.7503 loss_db: 0.2804 2022/11/02 12:57:42 - mmengine - INFO - Epoch(train) [75][55/63] lr: 1.4955e-03 eta: 12:39:49 time: 0.6071 data_time: 0.0205 memory: 14901 loss: 2.5970 loss_prob: 1.5908 loss_thr: 0.7450 loss_db: 0.2612 2022/11/02 12:57:45 - mmengine - INFO - Epoch(train) [75][60/63] lr: 1.4955e-03 eta: 12:39:37 time: 0.6042 data_time: 0.0100 memory: 14901 loss: 2.5011 loss_prob: 1.5117 loss_thr: 0.7423 loss_db: 0.2471 2022/11/02 12:57:46 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:57:51 - mmengine - INFO - Epoch(train) [76][5/63] lr: 1.5156e-03 eta: 12:39:37 time: 0.7204 data_time: 0.2366 memory: 14901 loss: 2.6103 loss_prob: 1.6168 loss_thr: 0.7216 loss_db: 0.2719 2022/11/02 12:57:54 - mmengine - INFO - Epoch(train) [76][10/63] lr: 1.5156e-03 eta: 12:39:24 time: 0.8092 data_time: 0.2396 memory: 14901 loss: 2.5721 loss_prob: 1.5830 loss_thr: 0.7266 loss_db: 0.2625 2022/11/02 12:57:58 - mmengine - INFO - Epoch(train) [76][15/63] lr: 1.5156e-03 eta: 12:39:24 time: 0.6706 data_time: 0.0117 memory: 14901 loss: 2.7466 loss_prob: 1.7042 loss_thr: 0.7590 loss_db: 0.2834 2022/11/02 12:58:01 - mmengine - INFO - Epoch(train) [76][20/63] lr: 1.5156e-03 eta: 12:39:29 time: 0.7195 data_time: 0.0091 memory: 14901 loss: 2.7328 loss_prob: 1.7003 loss_thr: 0.7473 loss_db: 0.2853 2022/11/02 12:58:04 - mmengine - INFO - Epoch(train) [76][25/63] lr: 1.5156e-03 eta: 12:39:29 time: 0.6349 data_time: 0.0202 memory: 14901 loss: 2.4620 loss_prob: 1.4996 loss_thr: 0.7149 loss_db: 0.2475 2022/11/02 12:58:07 - mmengine - INFO - Epoch(train) [76][30/63] lr: 1.5156e-03 eta: 12:39:09 time: 0.5507 data_time: 0.0302 memory: 14901 loss: 2.5380 loss_prob: 1.5570 loss_thr: 0.7235 loss_db: 0.2575 2022/11/02 12:58:10 - mmengine - INFO - Epoch(train) [76][35/63] lr: 1.5156e-03 eta: 12:39:09 time: 0.5709 data_time: 0.0161 memory: 14901 loss: 2.6194 loss_prob: 1.6191 loss_thr: 0.7306 loss_db: 0.2697 2022/11/02 12:58:13 - mmengine - INFO - Epoch(train) [76][40/63] lr: 1.5156e-03 eta: 12:38:55 time: 0.5870 data_time: 0.0111 memory: 14901 loss: 2.6421 loss_prob: 1.6485 loss_thr: 0.7199 loss_db: 0.2737 2022/11/02 12:58:16 - mmengine - INFO - Epoch(train) [76][45/63] lr: 1.5156e-03 eta: 12:38:55 time: 0.6201 data_time: 0.0105 memory: 14901 loss: 2.8210 loss_prob: 1.7740 loss_thr: 0.7505 loss_db: 0.2965 2022/11/02 12:58:20 - mmengine - INFO - Epoch(train) [76][50/63] lr: 1.5156e-03 eta: 12:38:56 time: 0.6936 data_time: 0.0196 memory: 14901 loss: 2.8656 loss_prob: 1.7884 loss_thr: 0.7791 loss_db: 0.2981 2022/11/02 12:58:22 - mmengine - INFO - Epoch(train) [76][55/63] lr: 1.5156e-03 eta: 12:38:56 time: 0.6374 data_time: 0.0195 memory: 14901 loss: 2.6253 loss_prob: 1.6115 loss_thr: 0.7494 loss_db: 0.2644 2022/11/02 12:58:25 - mmengine - INFO - Epoch(train) [76][60/63] lr: 1.5156e-03 eta: 12:38:31 time: 0.5216 data_time: 0.0070 memory: 14901 loss: 2.5242 loss_prob: 1.5347 loss_thr: 0.7348 loss_db: 0.2547 2022/11/02 12:58:26 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:58:31 - mmengine - INFO - Epoch(train) [77][5/63] lr: 1.5358e-03 eta: 12:38:31 time: 0.7218 data_time: 0.2290 memory: 14901 loss: 2.3763 loss_prob: 1.4304 loss_thr: 0.7130 loss_db: 0.2329 2022/11/02 12:58:33 - mmengine - INFO - Epoch(train) [77][10/63] lr: 1.5358e-03 eta: 12:38:11 time: 0.7522 data_time: 0.2316 memory: 14901 loss: 2.4639 loss_prob: 1.4937 loss_thr: 0.7255 loss_db: 0.2447 2022/11/02 12:58:36 - mmengine - INFO - Epoch(train) [77][15/63] lr: 1.5358e-03 eta: 12:38:11 time: 0.5312 data_time: 0.0109 memory: 14901 loss: 2.6598 loss_prob: 1.6232 loss_thr: 0.7659 loss_db: 0.2707 2022/11/02 12:58:38 - mmengine - INFO - Epoch(train) [77][20/63] lr: 1.5358e-03 eta: 12:37:44 time: 0.5022 data_time: 0.0105 memory: 14901 loss: 2.6003 loss_prob: 1.5778 loss_thr: 0.7578 loss_db: 0.2647 2022/11/02 12:58:41 - mmengine - INFO - Epoch(train) [77][25/63] lr: 1.5358e-03 eta: 12:37:44 time: 0.5043 data_time: 0.0340 memory: 14901 loss: 2.5698 loss_prob: 1.5659 loss_thr: 0.7420 loss_db: 0.2620 2022/11/02 12:58:44 - mmengine - INFO - Epoch(train) [77][30/63] lr: 1.5358e-03 eta: 12:37:21 time: 0.5317 data_time: 0.0300 memory: 14901 loss: 2.5894 loss_prob: 1.5905 loss_thr: 0.7333 loss_db: 0.2656 2022/11/02 12:58:46 - mmengine - INFO - Epoch(train) [77][35/63] lr: 1.5358e-03 eta: 12:37:21 time: 0.4956 data_time: 0.0043 memory: 14901 loss: 2.5211 loss_prob: 1.5283 loss_thr: 0.7418 loss_db: 0.2510 2022/11/02 12:58:48 - mmengine - INFO - Epoch(train) [77][40/63] lr: 1.5358e-03 eta: 12:36:48 time: 0.4608 data_time: 0.0042 memory: 14901 loss: 2.4255 loss_prob: 1.4578 loss_thr: 0.7281 loss_db: 0.2396 2022/11/02 12:58:51 - mmengine - INFO - Epoch(train) [77][45/63] lr: 1.5358e-03 eta: 12:36:48 time: 0.4668 data_time: 0.0097 memory: 14901 loss: 2.4885 loss_prob: 1.5046 loss_thr: 0.7302 loss_db: 0.2537 2022/11/02 12:58:53 - mmengine - INFO - Epoch(train) [77][50/63] lr: 1.5358e-03 eta: 12:36:18 time: 0.4842 data_time: 0.0242 memory: 14901 loss: 2.6133 loss_prob: 1.5942 loss_thr: 0.7539 loss_db: 0.2651 2022/11/02 12:58:56 - mmengine - INFO - Epoch(train) [77][55/63] lr: 1.5358e-03 eta: 12:36:18 time: 0.4817 data_time: 0.0186 memory: 14901 loss: 2.4556 loss_prob: 1.5075 loss_thr: 0.7014 loss_db: 0.2467 2022/11/02 12:58:58 - mmengine - INFO - Epoch(train) [77][60/63] lr: 1.5358e-03 eta: 12:35:49 time: 0.4831 data_time: 0.0050 memory: 14901 loss: 2.3907 loss_prob: 1.4495 loss_thr: 0.7018 loss_db: 0.2394 2022/11/02 12:59:00 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:59:05 - mmengine - INFO - Epoch(train) [78][5/63] lr: 1.5560e-03 eta: 12:35:49 time: 0.7466 data_time: 0.2203 memory: 14901 loss: 2.6002 loss_prob: 1.5870 loss_thr: 0.7495 loss_db: 0.2637 2022/11/02 12:59:07 - mmengine - INFO - Epoch(train) [78][10/63] lr: 1.5560e-03 eta: 12:35:22 time: 0.7049 data_time: 0.2183 memory: 14901 loss: 2.5909 loss_prob: 1.6111 loss_thr: 0.7148 loss_db: 0.2650 2022/11/02 12:59:09 - mmengine - INFO - Epoch(train) [78][15/63] lr: 1.5560e-03 eta: 12:35:22 time: 0.4778 data_time: 0.0080 memory: 14901 loss: 2.6180 loss_prob: 1.6323 loss_thr: 0.7166 loss_db: 0.2691 2022/11/02 12:59:12 - mmengine - INFO - Epoch(train) [78][20/63] lr: 1.5560e-03 eta: 12:34:55 time: 0.5022 data_time: 0.0083 memory: 14901 loss: 2.6831 loss_prob: 1.6595 loss_thr: 0.7432 loss_db: 0.2803 2022/11/02 12:59:15 - mmengine - INFO - Epoch(train) [78][25/63] lr: 1.5560e-03 eta: 12:34:55 time: 0.5346 data_time: 0.0302 memory: 14901 loss: 2.6273 loss_prob: 1.5895 loss_thr: 0.7692 loss_db: 0.2685 2022/11/02 12:59:17 - mmengine - INFO - Epoch(train) [78][30/63] lr: 1.5560e-03 eta: 12:34:34 time: 0.5374 data_time: 0.0366 memory: 14901 loss: 2.4782 loss_prob: 1.4879 loss_thr: 0.7417 loss_db: 0.2485 2022/11/02 12:59:20 - mmengine - INFO - Epoch(train) [78][35/63] lr: 1.5560e-03 eta: 12:34:34 time: 0.5202 data_time: 0.0153 memory: 14901 loss: 2.6458 loss_prob: 1.6356 loss_thr: 0.7278 loss_db: 0.2824 2022/11/02 12:59:22 - mmengine - INFO - Epoch(train) [78][40/63] lr: 1.5560e-03 eta: 12:34:06 time: 0.4926 data_time: 0.0086 memory: 14901 loss: 3.1911 loss_prob: 2.0318 loss_thr: 0.7959 loss_db: 0.3635 2022/11/02 12:59:24 - mmengine - INFO - Epoch(train) [78][45/63] lr: 1.5560e-03 eta: 12:34:06 time: 0.4641 data_time: 0.0043 memory: 14901 loss: 3.3606 loss_prob: 2.1662 loss_thr: 0.8065 loss_db: 0.3878 2022/11/02 12:59:27 - mmengine - INFO - Epoch(train) [78][50/63] lr: 1.5560e-03 eta: 12:33:35 time: 0.4640 data_time: 0.0160 memory: 14901 loss: 3.0592 loss_prob: 1.9360 loss_thr: 0.7862 loss_db: 0.3371 2022/11/02 12:59:29 - mmengine - INFO - Epoch(train) [78][55/63] lr: 1.5560e-03 eta: 12:33:35 time: 0.4808 data_time: 0.0224 memory: 14901 loss: 3.0664 loss_prob: 1.9264 loss_thr: 0.8087 loss_db: 0.3312 2022/11/02 12:59:32 - mmengine - INFO - Epoch(train) [78][60/63] lr: 1.5560e-03 eta: 12:33:06 time: 0.4833 data_time: 0.0139 memory: 14901 loss: 3.1127 loss_prob: 1.9709 loss_thr: 0.7976 loss_db: 0.3443 2022/11/02 12:59:33 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 12:59:39 - mmengine - INFO - Epoch(train) [79][5/63] lr: 1.5762e-03 eta: 12:33:06 time: 0.8007 data_time: 0.2322 memory: 14901 loss: 2.6988 loss_prob: 1.6545 loss_thr: 0.7673 loss_db: 0.2770 2022/11/02 12:59:41 - mmengine - INFO - Epoch(train) [79][10/63] lr: 1.5762e-03 eta: 12:32:57 time: 0.8311 data_time: 0.2323 memory: 14901 loss: 2.8411 loss_prob: 1.7587 loss_thr: 0.7838 loss_db: 0.2987 2022/11/02 12:59:44 - mmengine - INFO - Epoch(train) [79][15/63] lr: 1.5762e-03 eta: 12:32:57 time: 0.4792 data_time: 0.0134 memory: 14901 loss: 2.7733 loss_prob: 1.7138 loss_thr: 0.7746 loss_db: 0.2849 2022/11/02 12:59:46 - mmengine - INFO - Epoch(train) [79][20/63] lr: 1.5762e-03 eta: 12:32:30 time: 0.4939 data_time: 0.0100 memory: 14901 loss: 2.5829 loss_prob: 1.5648 loss_thr: 0.7629 loss_db: 0.2552 2022/11/02 12:59:49 - mmengine - INFO - Epoch(train) [79][25/63] lr: 1.5762e-03 eta: 12:32:30 time: 0.5311 data_time: 0.0123 memory: 14901 loss: 2.6672 loss_prob: 1.6416 loss_thr: 0.7543 loss_db: 0.2712 2022/11/02 12:59:51 - mmengine - INFO - Epoch(train) [79][30/63] lr: 1.5762e-03 eta: 12:32:05 time: 0.5081 data_time: 0.0256 memory: 14901 loss: 3.0425 loss_prob: 1.9196 loss_thr: 0.7929 loss_db: 0.3300 2022/11/02 12:59:54 - mmengine - INFO - Epoch(train) [79][35/63] lr: 1.5762e-03 eta: 12:32:05 time: 0.4928 data_time: 0.0207 memory: 14901 loss: 3.0756 loss_prob: 1.9457 loss_thr: 0.7912 loss_db: 0.3386 2022/11/02 12:59:56 - mmengine - INFO - Epoch(train) [79][40/63] lr: 1.5762e-03 eta: 12:31:41 time: 0.5170 data_time: 0.0109 memory: 14901 loss: 2.8330 loss_prob: 1.7726 loss_thr: 0.7631 loss_db: 0.2973 2022/11/02 12:59:59 - mmengine - INFO - Epoch(train) [79][45/63] lr: 1.5762e-03 eta: 12:31:41 time: 0.5609 data_time: 0.0095 memory: 14901 loss: 2.9924 loss_prob: 1.8865 loss_thr: 0.7805 loss_db: 0.3254 2022/11/02 13:00:03 - mmengine - INFO - Epoch(train) [79][50/63] lr: 1.5762e-03 eta: 12:31:40 time: 0.6753 data_time: 0.0136 memory: 14901 loss: 2.8794 loss_prob: 1.8140 loss_thr: 0.7544 loss_db: 0.3111 2022/11/02 13:00:06 - mmengine - INFO - Epoch(train) [79][55/63] lr: 1.5762e-03 eta: 12:31:40 time: 0.6728 data_time: 0.0201 memory: 14901 loss: 2.7714 loss_prob: 1.7353 loss_thr: 0.7432 loss_db: 0.2929 2022/11/02 13:00:09 - mmengine - INFO - Epoch(train) [79][60/63] lr: 1.5762e-03 eta: 12:31:22 time: 0.5544 data_time: 0.0128 memory: 14901 loss: 2.7942 loss_prob: 1.7393 loss_thr: 0.7623 loss_db: 0.2926 2022/11/02 13:00:10 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:00:16 - mmengine - INFO - Epoch(train) [80][5/63] lr: 1.5964e-03 eta: 12:31:22 time: 0.8394 data_time: 0.2492 memory: 14901 loss: 2.9380 loss_prob: 1.8317 loss_thr: 0.7801 loss_db: 0.3261 2022/11/02 13:00:19 - mmengine - INFO - Epoch(train) [80][10/63] lr: 1.5964e-03 eta: 12:31:21 time: 0.8826 data_time: 0.2508 memory: 14901 loss: 2.9534 loss_prob: 1.8629 loss_thr: 0.7714 loss_db: 0.3190 2022/11/02 13:00:23 - mmengine - INFO - Epoch(train) [80][15/63] lr: 1.5964e-03 eta: 12:31:21 time: 0.6675 data_time: 0.0090 memory: 14901 loss: 2.9909 loss_prob: 1.9058 loss_thr: 0.7575 loss_db: 0.3277 2022/11/02 13:00:27 - mmengine - INFO - Epoch(train) [80][20/63] lr: 1.5964e-03 eta: 12:31:38 time: 0.8029 data_time: 0.0058 memory: 14901 loss: 2.8422 loss_prob: 1.8013 loss_thr: 0.7379 loss_db: 0.3030 2022/11/02 13:00:29 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:00:31 - mmengine - INFO - Epoch(train) [80][25/63] lr: 1.5964e-03 eta: 12:31:38 time: 0.7916 data_time: 0.0373 memory: 14901 loss: 2.7582 loss_prob: 1.7331 loss_thr: 0.7314 loss_db: 0.2938 2022/11/02 13:00:33 - mmengine - INFO - Epoch(train) [80][30/63] lr: 1.5964e-03 eta: 12:31:29 time: 0.6192 data_time: 0.0365 memory: 14901 loss: 2.7215 loss_prob: 1.6993 loss_thr: 0.7338 loss_db: 0.2885 2022/11/02 13:00:36 - mmengine - INFO - Epoch(train) [80][35/63] lr: 1.5964e-03 eta: 12:31:29 time: 0.5232 data_time: 0.0046 memory: 14901 loss: 2.7479 loss_prob: 1.7155 loss_thr: 0.7451 loss_db: 0.2873 2022/11/02 13:00:38 - mmengine - INFO - Epoch(train) [80][40/63] lr: 1.5964e-03 eta: 12:31:05 time: 0.5137 data_time: 0.0047 memory: 14901 loss: 2.7415 loss_prob: 1.7161 loss_thr: 0.7430 loss_db: 0.2824 2022/11/02 13:00:41 - mmengine - INFO - Epoch(train) [80][45/63] lr: 1.5964e-03 eta: 12:31:05 time: 0.5212 data_time: 0.0045 memory: 14901 loss: 2.5689 loss_prob: 1.5840 loss_thr: 0.7315 loss_db: 0.2533 2022/11/02 13:00:43 - mmengine - INFO - Epoch(train) [80][50/63] lr: 1.5964e-03 eta: 12:30:41 time: 0.5151 data_time: 0.0230 memory: 14901 loss: 2.6176 loss_prob: 1.6098 loss_thr: 0.7464 loss_db: 0.2614 2022/11/02 13:00:46 - mmengine - INFO - Epoch(train) [80][55/63] lr: 1.5964e-03 eta: 12:30:41 time: 0.5331 data_time: 0.0236 memory: 14901 loss: 2.7161 loss_prob: 1.6753 loss_thr: 0.7662 loss_db: 0.2745 2022/11/02 13:00:49 - mmengine - INFO - Epoch(train) [80][60/63] lr: 1.5964e-03 eta: 12:30:29 time: 0.5973 data_time: 0.0051 memory: 14901 loss: 2.5813 loss_prob: 1.5742 loss_thr: 0.7489 loss_db: 0.2582 2022/11/02 13:00:51 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:00:51 - mmengine - INFO - Saving checkpoint at 80 epochs 2022/11/02 13:00:54 - mmengine - INFO - Epoch(val) [80][5/500] eta: 12:30:29 time: 0.0455 data_time: 0.0047 memory: 14901 2022/11/02 13:00:55 - mmengine - INFO - Epoch(val) [80][10/500] eta: 0:00:22 time: 0.0452 data_time: 0.0043 memory: 1008 2022/11/02 13:00:55 - mmengine - INFO - Epoch(val) [80][15/500] eta: 0:00:22 time: 0.0400 data_time: 0.0023 memory: 1008 2022/11/02 13:00:55 - mmengine - INFO - Epoch(val) [80][20/500] eta: 0:00:19 time: 0.0399 data_time: 0.0023 memory: 1008 2022/11/02 13:00:55 - mmengine - INFO - Epoch(val) [80][25/500] eta: 0:00:19 time: 0.0392 data_time: 0.0027 memory: 1008 2022/11/02 13:00:55 - mmengine - INFO - Epoch(val) [80][30/500] eta: 0:00:20 time: 0.0439 data_time: 0.0031 memory: 1008 2022/11/02 13:00:56 - mmengine - INFO - Epoch(val) [80][35/500] eta: 0:00:20 time: 0.0431 data_time: 0.0027 memory: 1008 2022/11/02 13:00:56 - mmengine - INFO - Epoch(val) [80][40/500] eta: 0:00:20 time: 0.0456 data_time: 0.0026 memory: 1008 2022/11/02 13:00:56 - mmengine - INFO - Epoch(val) [80][45/500] eta: 0:00:20 time: 0.0501 data_time: 0.0027 memory: 1008 2022/11/02 13:00:56 - mmengine - INFO - Epoch(val) [80][50/500] eta: 0:00:19 time: 0.0435 data_time: 0.0025 memory: 1008 2022/11/02 13:00:57 - mmengine - INFO - Epoch(val) [80][55/500] eta: 0:00:19 time: 0.0412 data_time: 0.0029 memory: 1008 2022/11/02 13:00:57 - mmengine - INFO - Epoch(val) [80][60/500] eta: 0:00:16 time: 0.0382 data_time: 0.0028 memory: 1008 2022/11/02 13:00:57 - mmengine - INFO - Epoch(val) [80][65/500] eta: 0:00:16 time: 0.0407 data_time: 0.0025 memory: 1008 2022/11/02 13:00:57 - mmengine - INFO - Epoch(val) [80][70/500] eta: 0:00:18 time: 0.0434 data_time: 0.0026 memory: 1008 2022/11/02 13:00:57 - mmengine - INFO - Epoch(val) [80][75/500] eta: 0:00:18 time: 0.0400 data_time: 0.0030 memory: 1008 2022/11/02 13:00:58 - mmengine - INFO - Epoch(val) [80][80/500] eta: 0:00:17 time: 0.0417 data_time: 0.0035 memory: 1008 2022/11/02 13:00:58 - mmengine - INFO - Epoch(val) [80][85/500] eta: 0:00:17 time: 0.0417 data_time: 0.0032 memory: 1008 2022/11/02 13:00:58 - mmengine - INFO - Epoch(val) [80][90/500] eta: 0:00:16 time: 0.0408 data_time: 0.0026 memory: 1008 2022/11/02 13:00:58 - mmengine - INFO - Epoch(val) [80][95/500] eta: 0:00:16 time: 0.0450 data_time: 0.0027 memory: 1008 2022/11/02 13:00:58 - mmengine - INFO - Epoch(val) [80][100/500] eta: 0:00:16 time: 0.0420 data_time: 0.0029 memory: 1008 2022/11/02 13:00:59 - mmengine - INFO - Epoch(val) [80][105/500] eta: 0:00:16 time: 0.0375 data_time: 0.0025 memory: 1008 2022/11/02 13:00:59 - mmengine - INFO - Epoch(val) [80][110/500] eta: 0:00:15 time: 0.0388 data_time: 0.0024 memory: 1008 2022/11/02 13:00:59 - mmengine - INFO - Epoch(val) [80][115/500] eta: 0:00:15 time: 0.0413 data_time: 0.0027 memory: 1008 2022/11/02 13:00:59 - mmengine - INFO - Epoch(val) [80][120/500] eta: 0:00:15 time: 0.0400 data_time: 0.0026 memory: 1008 2022/11/02 13:00:59 - mmengine - INFO - Epoch(val) [80][125/500] eta: 0:00:15 time: 0.0375 data_time: 0.0023 memory: 1008 2022/11/02 13:01:00 - mmengine - INFO - Epoch(val) [80][130/500] eta: 0:00:14 time: 0.0400 data_time: 0.0033 memory: 1008 2022/11/02 13:01:00 - mmengine - INFO - Epoch(val) [80][135/500] eta: 0:00:14 time: 0.0397 data_time: 0.0035 memory: 1008 2022/11/02 13:01:00 - mmengine - INFO - Epoch(val) [80][140/500] eta: 0:00:14 time: 0.0394 data_time: 0.0033 memory: 1008 2022/11/02 13:01:00 - mmengine - INFO - Epoch(val) [80][145/500] eta: 0:00:14 time: 0.0426 data_time: 0.0033 memory: 1008 2022/11/02 13:01:00 - mmengine - INFO - Epoch(val) [80][150/500] eta: 0:00:15 time: 0.0433 data_time: 0.0025 memory: 1008 2022/11/02 13:01:01 - mmengine - INFO - Epoch(val) [80][155/500] eta: 0:00:15 time: 0.0438 data_time: 0.0025 memory: 1008 2022/11/02 13:01:01 - mmengine - INFO - Epoch(val) [80][160/500] eta: 0:00:14 time: 0.0440 data_time: 0.0025 memory: 1008 2022/11/02 13:01:01 - mmengine - INFO - Epoch(val) [80][165/500] eta: 0:00:14 time: 0.0436 data_time: 0.0027 memory: 1008 2022/11/02 13:01:01 - mmengine - INFO - Epoch(val) [80][170/500] eta: 0:00:14 time: 0.0444 data_time: 0.0028 memory: 1008 2022/11/02 13:01:02 - mmengine - INFO - Epoch(val) [80][175/500] eta: 0:00:14 time: 0.0416 data_time: 0.0027 memory: 1008 2022/11/02 13:01:02 - mmengine - INFO - Epoch(val) [80][180/500] eta: 0:00:12 time: 0.0391 data_time: 0.0027 memory: 1008 2022/11/02 13:01:02 - mmengine - INFO - Epoch(val) [80][185/500] eta: 0:00:12 time: 0.0425 data_time: 0.0027 memory: 1008 2022/11/02 13:01:02 - mmengine - INFO - Epoch(val) [80][190/500] eta: 0:00:13 time: 0.0432 data_time: 0.0025 memory: 1008 2022/11/02 13:01:02 - mmengine - INFO - Epoch(val) [80][195/500] eta: 0:00:13 time: 0.0401 data_time: 0.0031 memory: 1008 2022/11/02 13:01:03 - mmengine - INFO - Epoch(val) [80][200/500] eta: 0:00:13 time: 0.0462 data_time: 0.0032 memory: 1008 2022/11/02 13:01:03 - mmengine - INFO - Epoch(val) [80][205/500] eta: 0:00:13 time: 0.0477 data_time: 0.0027 memory: 1008 2022/11/02 13:01:03 - mmengine - INFO - Epoch(val) [80][210/500] eta: 0:00:11 time: 0.0404 data_time: 0.0028 memory: 1008 2022/11/02 13:01:03 - mmengine - INFO - Epoch(val) [80][215/500] eta: 0:00:11 time: 0.0390 data_time: 0.0027 memory: 1008 2022/11/02 13:01:03 - mmengine - INFO - Epoch(val) [80][220/500] eta: 0:00:10 time: 0.0391 data_time: 0.0024 memory: 1008 2022/11/02 13:01:04 - mmengine - INFO - Epoch(val) [80][225/500] eta: 0:00:10 time: 0.0412 data_time: 0.0025 memory: 1008 2022/11/02 13:01:04 - mmengine - INFO - Epoch(val) [80][230/500] eta: 0:00:11 time: 0.0410 data_time: 0.0028 memory: 1008 2022/11/02 13:01:04 - mmengine - INFO - Epoch(val) [80][235/500] eta: 0:00:11 time: 0.0397 data_time: 0.0027 memory: 1008 2022/11/02 13:01:04 - mmengine - INFO - Epoch(val) [80][240/500] eta: 0:00:10 time: 0.0403 data_time: 0.0023 memory: 1008 2022/11/02 13:01:04 - mmengine - INFO - Epoch(val) [80][245/500] eta: 0:00:10 time: 0.0382 data_time: 0.0024 memory: 1008 2022/11/02 13:01:05 - mmengine - INFO - Epoch(val) [80][250/500] eta: 0:00:09 time: 0.0386 data_time: 0.0022 memory: 1008 2022/11/02 13:01:05 - mmengine - INFO - Epoch(val) [80][255/500] eta: 0:00:09 time: 0.0377 data_time: 0.0023 memory: 1008 2022/11/02 13:01:05 - mmengine - INFO - Epoch(val) [80][260/500] eta: 0:00:08 time: 0.0359 data_time: 0.0023 memory: 1008 2022/11/02 13:01:05 - mmengine - INFO - Epoch(val) [80][265/500] eta: 0:00:08 time: 0.0367 data_time: 0.0022 memory: 1008 2022/11/02 13:01:05 - mmengine - INFO - Epoch(val) [80][270/500] eta: 0:00:08 time: 0.0381 data_time: 0.0022 memory: 1008 2022/11/02 13:01:06 - mmengine - INFO - Epoch(val) [80][275/500] eta: 0:00:08 time: 0.0369 data_time: 0.0023 memory: 1008 2022/11/02 13:01:06 - mmengine - INFO - Epoch(val) [80][280/500] eta: 0:00:08 time: 0.0369 data_time: 0.0025 memory: 1008 2022/11/02 13:01:06 - mmengine - INFO - Epoch(val) [80][285/500] eta: 0:00:08 time: 0.0377 data_time: 0.0023 memory: 1008 2022/11/02 13:01:06 - mmengine - INFO - Epoch(val) [80][290/500] eta: 0:00:07 time: 0.0372 data_time: 0.0022 memory: 1008 2022/11/02 13:01:06 - mmengine - INFO - Epoch(val) [80][295/500] eta: 0:00:07 time: 0.0393 data_time: 0.0025 memory: 1008 2022/11/02 13:01:06 - mmengine - INFO - Epoch(val) [80][300/500] eta: 0:00:08 time: 0.0403 data_time: 0.0028 memory: 1008 2022/11/02 13:01:07 - mmengine - INFO - Epoch(val) [80][305/500] eta: 0:00:08 time: 0.0411 data_time: 0.0027 memory: 1008 2022/11/02 13:01:07 - mmengine - INFO - Epoch(val) [80][310/500] eta: 0:00:07 time: 0.0409 data_time: 0.0026 memory: 1008 2022/11/02 13:01:07 - mmengine - INFO - Epoch(val) [80][315/500] eta: 0:00:07 time: 0.0418 data_time: 0.0024 memory: 1008 2022/11/02 13:01:07 - mmengine - INFO - Epoch(val) [80][320/500] eta: 0:00:07 time: 0.0397 data_time: 0.0023 memory: 1008 2022/11/02 13:01:08 - mmengine - INFO - Epoch(val) [80][325/500] eta: 0:00:07 time: 0.0487 data_time: 0.0025 memory: 1008 2022/11/02 13:01:08 - mmengine - INFO - Epoch(val) [80][330/500] eta: 0:00:08 time: 0.0491 data_time: 0.0025 memory: 1008 2022/11/02 13:01:08 - mmengine - INFO - Epoch(val) [80][335/500] eta: 0:00:08 time: 0.0387 data_time: 0.0030 memory: 1008 2022/11/02 13:01:08 - mmengine - INFO - Epoch(val) [80][340/500] eta: 0:00:08 time: 0.0512 data_time: 0.0032 memory: 1008 2022/11/02 13:01:08 - mmengine - INFO - Epoch(val) [80][345/500] eta: 0:00:08 time: 0.0512 data_time: 0.0026 memory: 1008 2022/11/02 13:01:09 - mmengine - INFO - Epoch(val) [80][350/500] eta: 0:00:06 time: 0.0458 data_time: 0.0025 memory: 1008 2022/11/02 13:01:09 - mmengine - INFO - Epoch(val) [80][355/500] eta: 0:00:06 time: 0.0441 data_time: 0.0025 memory: 1008 2022/11/02 13:01:09 - mmengine - INFO - Epoch(val) [80][360/500] eta: 0:00:05 time: 0.0404 data_time: 0.0029 memory: 1008 2022/11/02 13:01:09 - mmengine - INFO - Epoch(val) [80][365/500] eta: 0:00:05 time: 0.0421 data_time: 0.0028 memory: 1008 2022/11/02 13:01:10 - mmengine - INFO - Epoch(val) [80][370/500] eta: 0:00:05 time: 0.0386 data_time: 0.0024 memory: 1008 2022/11/02 13:01:10 - mmengine - INFO - Epoch(val) [80][375/500] eta: 0:00:05 time: 0.0356 data_time: 0.0024 memory: 1008 2022/11/02 13:01:10 - mmengine - INFO - Epoch(val) [80][380/500] eta: 0:00:04 time: 0.0375 data_time: 0.0022 memory: 1008 2022/11/02 13:01:10 - mmengine - INFO - Epoch(val) [80][385/500] eta: 0:00:04 time: 0.0378 data_time: 0.0021 memory: 1008 2022/11/02 13:01:10 - mmengine - INFO - Epoch(val) [80][390/500] eta: 0:00:04 time: 0.0369 data_time: 0.0022 memory: 1008 2022/11/02 13:01:10 - mmengine - INFO - Epoch(val) [80][395/500] eta: 0:00:04 time: 0.0392 data_time: 0.0023 memory: 1008 2022/11/02 13:01:11 - mmengine - INFO - Epoch(val) [80][400/500] eta: 0:00:04 time: 0.0402 data_time: 0.0025 memory: 1008 2022/11/02 13:01:11 - mmengine - INFO - Epoch(val) [80][405/500] eta: 0:00:04 time: 0.0428 data_time: 0.0026 memory: 1008 2022/11/02 13:01:11 - mmengine - INFO - Epoch(val) [80][410/500] eta: 0:00:03 time: 0.0434 data_time: 0.0025 memory: 1008 2022/11/02 13:01:11 - mmengine - INFO - Epoch(val) [80][415/500] eta: 0:00:03 time: 0.0399 data_time: 0.0024 memory: 1008 2022/11/02 13:01:11 - mmengine - INFO - Epoch(val) [80][420/500] eta: 0:00:02 time: 0.0358 data_time: 0.0025 memory: 1008 2022/11/02 13:01:12 - mmengine - INFO - Epoch(val) [80][425/500] eta: 0:00:02 time: 0.0374 data_time: 0.0025 memory: 1008 2022/11/02 13:01:12 - mmengine - INFO - Epoch(val) [80][430/500] eta: 0:00:02 time: 0.0414 data_time: 0.0028 memory: 1008 2022/11/02 13:01:12 - mmengine - INFO - Epoch(val) [80][435/500] eta: 0:00:02 time: 0.0414 data_time: 0.0027 memory: 1008 2022/11/02 13:01:12 - mmengine - INFO - Epoch(val) [80][440/500] eta: 0:00:02 time: 0.0416 data_time: 0.0024 memory: 1008 2022/11/02 13:01:13 - mmengine - INFO - Epoch(val) [80][445/500] eta: 0:00:02 time: 0.0402 data_time: 0.0024 memory: 1008 2022/11/02 13:01:13 - mmengine - INFO - Epoch(val) [80][450/500] eta: 0:00:02 time: 0.0407 data_time: 0.0023 memory: 1008 2022/11/02 13:01:13 - mmengine - INFO - Epoch(val) [80][455/500] eta: 0:00:02 time: 0.0408 data_time: 0.0024 memory: 1008 2022/11/02 13:01:13 - mmengine - INFO - Epoch(val) [80][460/500] eta: 0:00:01 time: 0.0362 data_time: 0.0024 memory: 1008 2022/11/02 13:01:13 - mmengine - INFO - Epoch(val) [80][465/500] eta: 0:00:01 time: 0.0360 data_time: 0.0023 memory: 1008 2022/11/02 13:01:13 - mmengine - INFO - Epoch(val) [80][470/500] eta: 0:00:01 time: 0.0403 data_time: 0.0025 memory: 1008 2022/11/02 13:01:14 - mmengine - INFO - Epoch(val) [80][475/500] eta: 0:00:01 time: 0.0389 data_time: 0.0027 memory: 1008 2022/11/02 13:01:14 - mmengine - INFO - Epoch(val) [80][480/500] eta: 0:00:00 time: 0.0366 data_time: 0.0026 memory: 1008 2022/11/02 13:01:14 - mmengine - INFO - Epoch(val) [80][485/500] eta: 0:00:00 time: 0.0383 data_time: 0.0032 memory: 1008 2022/11/02 13:01:14 - mmengine - INFO - Epoch(val) [80][490/500] eta: 0:00:00 time: 0.0406 data_time: 0.0032 memory: 1008 2022/11/02 13:01:14 - mmengine - INFO - Epoch(val) [80][495/500] eta: 0:00:00 time: 0.0423 data_time: 0.0024 memory: 1008 2022/11/02 13:01:15 - mmengine - INFO - Epoch(val) [80][500/500] eta: 0:00:00 time: 0.0390 data_time: 0.0024 memory: 1008 2022/11/02 13:01:15 - mmengine - INFO - Evaluating hmean-iou... 2022/11/02 13:01:15 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.7410, precision: 0.5901, hmean: 0.6570 2022/11/02 13:01:15 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.7405, precision: 0.7010, hmean: 0.7202 2022/11/02 13:01:15 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.7265, precision: 0.7851, hmean: 0.7547 2022/11/02 13:01:15 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.6673, precision: 0.8545, hmean: 0.7494 2022/11/02 13:01:15 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.4535, precision: 0.9235, hmean: 0.6083 2022/11/02 13:01:15 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.0294, precision: 0.9531, hmean: 0.0570 2022/11/02 13:01:15 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0000, precision: 0.0000, hmean: 0.0000 2022/11/02 13:01:15 - mmengine - INFO - Epoch(val) [80][500/500] icdar/precision: 0.7851 icdar/recall: 0.7265 icdar/hmean: 0.7547 2022/11/02 13:01:20 - mmengine - INFO - Epoch(train) [81][5/63] lr: 1.6165e-03 eta: 0:00:00 time: 0.7645 data_time: 0.2190 memory: 14901 loss: 2.5941 loss_prob: 1.5841 loss_thr: 0.7470 loss_db: 0.2630 2022/11/02 13:01:23 - mmengine - INFO - Epoch(train) [81][10/63] lr: 1.6165e-03 eta: 12:30:15 time: 0.7883 data_time: 0.2185 memory: 14901 loss: 2.5399 loss_prob: 1.5530 loss_thr: 0.7317 loss_db: 0.2551 2022/11/02 13:01:25 - mmengine - INFO - Epoch(train) [81][15/63] lr: 1.6165e-03 eta: 12:30:15 time: 0.4825 data_time: 0.0046 memory: 14901 loss: 2.5147 loss_prob: 1.5399 loss_thr: 0.7199 loss_db: 0.2550 2022/11/02 13:01:27 - mmengine - INFO - Epoch(train) [81][20/63] lr: 1.6165e-03 eta: 12:29:47 time: 0.4848 data_time: 0.0045 memory: 14901 loss: 2.5502 loss_prob: 1.5618 loss_thr: 0.7302 loss_db: 0.2581 2022/11/02 13:01:30 - mmengine - INFO - Epoch(train) [81][25/63] lr: 1.6165e-03 eta: 12:29:47 time: 0.5005 data_time: 0.0189 memory: 14901 loss: 2.5624 loss_prob: 1.5795 loss_thr: 0.7196 loss_db: 0.2633 2022/11/02 13:01:33 - mmengine - INFO - Epoch(train) [81][30/63] lr: 1.6165e-03 eta: 12:29:24 time: 0.5167 data_time: 0.0327 memory: 14901 loss: 2.5626 loss_prob: 1.5756 loss_thr: 0.7205 loss_db: 0.2665 2022/11/02 13:01:35 - mmengine - INFO - Epoch(train) [81][35/63] lr: 1.6165e-03 eta: 12:29:24 time: 0.5284 data_time: 0.0183 memory: 14901 loss: 2.5705 loss_prob: 1.5713 loss_thr: 0.7340 loss_db: 0.2652 2022/11/02 13:01:38 - mmengine - INFO - Epoch(train) [81][40/63] lr: 1.6165e-03 eta: 12:28:59 time: 0.5058 data_time: 0.0051 memory: 14901 loss: 2.5114 loss_prob: 1.5437 loss_thr: 0.7131 loss_db: 0.2546 2022/11/02 13:01:40 - mmengine - INFO - Epoch(train) [81][45/63] lr: 1.6165e-03 eta: 12:28:59 time: 0.4950 data_time: 0.0060 memory: 14901 loss: 2.4527 loss_prob: 1.5105 loss_thr: 0.6970 loss_db: 0.2452 2022/11/02 13:01:43 - mmengine - INFO - Epoch(train) [81][50/63] lr: 1.6165e-03 eta: 12:28:34 time: 0.5019 data_time: 0.0243 memory: 14901 loss: 2.5566 loss_prob: 1.5775 loss_thr: 0.7206 loss_db: 0.2585 2022/11/02 13:01:45 - mmengine - INFO - Epoch(train) [81][55/63] lr: 1.6165e-03 eta: 12:28:34 time: 0.4939 data_time: 0.0233 memory: 14901 loss: 2.6616 loss_prob: 1.6585 loss_thr: 0.7309 loss_db: 0.2722 2022/11/02 13:01:48 - mmengine - INFO - Epoch(train) [81][60/63] lr: 1.6165e-03 eta: 12:28:08 time: 0.4909 data_time: 0.0046 memory: 14901 loss: 2.5882 loss_prob: 1.5842 loss_thr: 0.7447 loss_db: 0.2593 2022/11/02 13:01:49 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:01:53 - mmengine - INFO - Epoch(train) [82][5/63] lr: 1.6367e-03 eta: 12:28:08 time: 0.6765 data_time: 0.1927 memory: 14901 loss: 2.4237 loss_prob: 1.4511 loss_thr: 0.7363 loss_db: 0.2362 2022/11/02 13:01:55 - mmengine - INFO - Epoch(train) [82][10/63] lr: 1.6367e-03 eta: 12:27:37 time: 0.6637 data_time: 0.1960 memory: 14901 loss: 2.3859 loss_prob: 1.4451 loss_thr: 0.7092 loss_db: 0.2316 2022/11/02 13:01:58 - mmengine - INFO - Epoch(train) [82][15/63] lr: 1.6367e-03 eta: 12:27:37 time: 0.4580 data_time: 0.0078 memory: 14901 loss: 2.4396 loss_prob: 1.4720 loss_thr: 0.7276 loss_db: 0.2400 2022/11/02 13:02:00 - mmengine - INFO - Epoch(train) [82][20/63] lr: 1.6367e-03 eta: 12:27:11 time: 0.4992 data_time: 0.0050 memory: 14901 loss: 2.4055 loss_prob: 1.4484 loss_thr: 0.7187 loss_db: 0.2384 2022/11/02 13:02:03 - mmengine - INFO - Epoch(train) [82][25/63] lr: 1.6367e-03 eta: 12:27:11 time: 0.5206 data_time: 0.0257 memory: 14901 loss: 2.5495 loss_prob: 1.5790 loss_thr: 0.7118 loss_db: 0.2586 2022/11/02 13:02:05 - mmengine - INFO - Epoch(train) [82][30/63] lr: 1.6367e-03 eta: 12:26:46 time: 0.4978 data_time: 0.0300 memory: 14901 loss: 2.6408 loss_prob: 1.6453 loss_thr: 0.7274 loss_db: 0.2681 2022/11/02 13:02:08 - mmengine - INFO - Epoch(train) [82][35/63] lr: 1.6367e-03 eta: 12:26:46 time: 0.4751 data_time: 0.0124 memory: 14901 loss: 2.3609 loss_prob: 1.4251 loss_thr: 0.7038 loss_db: 0.2320 2022/11/02 13:02:10 - mmengine - INFO - Epoch(train) [82][40/63] lr: 1.6367e-03 eta: 12:26:19 time: 0.4853 data_time: 0.0080 memory: 14901 loss: 2.3588 loss_prob: 1.4360 loss_thr: 0.6865 loss_db: 0.2363 2022/11/02 13:02:13 - mmengine - INFO - Epoch(train) [82][45/63] lr: 1.6367e-03 eta: 12:26:19 time: 0.4854 data_time: 0.0047 memory: 14901 loss: 2.4466 loss_prob: 1.4917 loss_thr: 0.7076 loss_db: 0.2473 2022/11/02 13:02:15 - mmengine - INFO - Epoch(train) [82][50/63] lr: 1.6367e-03 eta: 12:25:55 time: 0.5026 data_time: 0.0228 memory: 14901 loss: 2.4039 loss_prob: 1.4468 loss_thr: 0.7199 loss_db: 0.2372 2022/11/02 13:02:18 - mmengine - INFO - Epoch(train) [82][55/63] lr: 1.6367e-03 eta: 12:25:55 time: 0.4937 data_time: 0.0255 memory: 14901 loss: 2.4650 loss_prob: 1.4899 loss_thr: 0.7325 loss_db: 0.2426 2022/11/02 13:02:20 - mmengine - INFO - Epoch(train) [82][60/63] lr: 1.6367e-03 eta: 12:25:27 time: 0.4784 data_time: 0.0106 memory: 14901 loss: 2.7761 loss_prob: 1.7379 loss_thr: 0.7508 loss_db: 0.2873 2022/11/02 13:02:21 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:02:26 - mmengine - INFO - Epoch(train) [83][5/63] lr: 1.6569e-03 eta: 12:25:27 time: 0.7060 data_time: 0.2243 memory: 14901 loss: 2.5806 loss_prob: 1.6128 loss_thr: 0.6990 loss_db: 0.2688 2022/11/02 13:02:29 - mmengine - INFO - Epoch(train) [83][10/63] lr: 1.6569e-03 eta: 12:25:04 time: 0.7211 data_time: 0.2243 memory: 14901 loss: 2.7664 loss_prob: 1.7500 loss_thr: 0.7191 loss_db: 0.2973 2022/11/02 13:02:32 - mmengine - INFO - Epoch(train) [83][15/63] lr: 1.6569e-03 eta: 12:25:04 time: 0.5519 data_time: 0.0049 memory: 14901 loss: 2.7910 loss_prob: 1.7382 loss_thr: 0.7578 loss_db: 0.2950 2022/11/02 13:02:35 - mmengine - INFO - Epoch(train) [83][20/63] lr: 1.6569e-03 eta: 12:24:54 time: 0.6021 data_time: 0.0053 memory: 14901 loss: 2.8661 loss_prob: 1.7976 loss_thr: 0.7628 loss_db: 0.3057 2022/11/02 13:02:37 - mmengine - INFO - Epoch(train) [83][25/63] lr: 1.6569e-03 eta: 12:24:54 time: 0.5648 data_time: 0.0358 memory: 14901 loss: 2.9866 loss_prob: 1.8915 loss_thr: 0.7705 loss_db: 0.3246 2022/11/02 13:02:40 - mmengine - INFO - Epoch(train) [83][30/63] lr: 1.6569e-03 eta: 12:24:38 time: 0.5650 data_time: 0.0356 memory: 14901 loss: 2.6714 loss_prob: 1.6457 loss_thr: 0.7504 loss_db: 0.2754 2022/11/02 13:02:43 - mmengine - INFO - Epoch(train) [83][35/63] lr: 1.6569e-03 eta: 12:24:38 time: 0.5630 data_time: 0.0065 memory: 14901 loss: 2.4789 loss_prob: 1.5143 loss_thr: 0.7156 loss_db: 0.2490 2022/11/02 13:02:45 - mmengine - INFO - Epoch(train) [83][40/63] lr: 1.6569e-03 eta: 12:24:16 time: 0.5198 data_time: 0.0063 memory: 14901 loss: 2.5035 loss_prob: 1.5272 loss_thr: 0.7256 loss_db: 0.2507 2022/11/02 13:02:49 - mmengine - INFO - Epoch(train) [83][45/63] lr: 1.6569e-03 eta: 12:24:16 time: 0.6062 data_time: 0.0050 memory: 14901 loss: 2.8528 loss_prob: 1.7874 loss_thr: 0.7729 loss_db: 0.2926 2022/11/02 13:02:52 - mmengine - INFO - Epoch(train) [83][50/63] lr: 1.6569e-03 eta: 12:24:12 time: 0.6515 data_time: 0.0241 memory: 14901 loss: 2.8848 loss_prob: 1.8158 loss_thr: 0.7734 loss_db: 0.2957 2022/11/02 13:02:55 - mmengine - INFO - Epoch(train) [83][55/63] lr: 1.6569e-03 eta: 12:24:12 time: 0.5598 data_time: 0.0291 memory: 14901 loss: 2.6871 loss_prob: 1.6712 loss_thr: 0.7352 loss_db: 0.2807 2022/11/02 13:02:58 - mmengine - INFO - Epoch(train) [83][60/63] lr: 1.6569e-03 eta: 12:23:56 time: 0.5635 data_time: 0.0107 memory: 14901 loss: 2.6170 loss_prob: 1.6277 loss_thr: 0.7154 loss_db: 0.2739 2022/11/02 13:02:59 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:03:06 - mmengine - INFO - Epoch(train) [84][5/63] lr: 1.6771e-03 eta: 12:23:56 time: 0.9172 data_time: 0.2185 memory: 14901 loss: 2.7209 loss_prob: 1.6878 loss_thr: 0.7556 loss_db: 0.2775 2022/11/02 13:03:08 - mmengine - INFO - Epoch(train) [84][10/63] lr: 1.6771e-03 eta: 12:23:53 time: 0.8628 data_time: 0.2274 memory: 14901 loss: 2.7388 loss_prob: 1.7034 loss_thr: 0.7530 loss_db: 0.2824 2022/11/02 13:03:11 - mmengine - INFO - Epoch(train) [84][15/63] lr: 1.6771e-03 eta: 12:23:53 time: 0.5182 data_time: 0.0154 memory: 14901 loss: 2.5810 loss_prob: 1.5944 loss_thr: 0.7217 loss_db: 0.2650 2022/11/02 13:03:13 - mmengine - INFO - Epoch(train) [84][20/63] lr: 1.6771e-03 eta: 12:23:29 time: 0.5021 data_time: 0.0072 memory: 14901 loss: 2.5050 loss_prob: 1.5067 loss_thr: 0.7484 loss_db: 0.2500 2022/11/02 13:03:16 - mmengine - INFO - Epoch(train) [84][25/63] lr: 1.6771e-03 eta: 12:23:29 time: 0.5476 data_time: 0.0212 memory: 14901 loss: 2.6673 loss_prob: 1.6173 loss_thr: 0.7727 loss_db: 0.2773 2022/11/02 13:03:19 - mmengine - INFO - Epoch(train) [84][30/63] lr: 1.6771e-03 eta: 12:23:14 time: 0.5684 data_time: 0.0229 memory: 14901 loss: 2.7023 loss_prob: 1.6675 loss_thr: 0.7527 loss_db: 0.2821 2022/11/02 13:03:21 - mmengine - INFO - Epoch(train) [84][35/63] lr: 1.6771e-03 eta: 12:23:14 time: 0.5275 data_time: 0.0164 memory: 14901 loss: 2.5882 loss_prob: 1.5993 loss_thr: 0.7244 loss_db: 0.2644 2022/11/02 13:03:24 - mmengine - INFO - Epoch(train) [84][40/63] lr: 1.6771e-03 eta: 12:22:50 time: 0.5065 data_time: 0.0135 memory: 14901 loss: 2.6047 loss_prob: 1.6051 loss_thr: 0.7276 loss_db: 0.2720 2022/11/02 13:03:27 - mmengine - INFO - Epoch(train) [84][45/63] lr: 1.6771e-03 eta: 12:22:50 time: 0.5714 data_time: 0.0056 memory: 14901 loss: 2.6434 loss_prob: 1.6308 loss_thr: 0.7356 loss_db: 0.2770 2022/11/02 13:03:30 - mmengine - INFO - Epoch(train) [84][50/63] lr: 1.6771e-03 eta: 12:22:47 time: 0.6552 data_time: 0.0219 memory: 14901 loss: 2.6622 loss_prob: 1.6519 loss_thr: 0.7341 loss_db: 0.2763 2022/11/02 13:03:33 - mmengine - INFO - Epoch(train) [84][55/63] lr: 1.6771e-03 eta: 12:22:47 time: 0.5952 data_time: 0.0240 memory: 14901 loss: 2.6088 loss_prob: 1.6038 loss_thr: 0.7362 loss_db: 0.2688 2022/11/02 13:03:36 - mmengine - INFO - Epoch(train) [84][60/63] lr: 1.6771e-03 eta: 12:22:25 time: 0.5185 data_time: 0.0116 memory: 14901 loss: 2.4614 loss_prob: 1.4952 loss_thr: 0.7144 loss_db: 0.2519 2022/11/02 13:03:37 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:03:42 - mmengine - INFO - Epoch(train) [85][5/63] lr: 1.6973e-03 eta: 12:22:25 time: 0.6850 data_time: 0.2141 memory: 14901 loss: 2.5375 loss_prob: 1.5562 loss_thr: 0.7250 loss_db: 0.2563 2022/11/02 13:03:44 - mmengine - INFO - Epoch(train) [85][10/63] lr: 1.6973e-03 eta: 12:22:00 time: 0.6992 data_time: 0.2209 memory: 14901 loss: 2.7261 loss_prob: 1.6931 loss_thr: 0.7512 loss_db: 0.2819 2022/11/02 13:03:47 - mmengine - INFO - Epoch(train) [85][15/63] lr: 1.6973e-03 eta: 12:22:00 time: 0.5166 data_time: 0.0126 memory: 14901 loss: 2.6698 loss_prob: 1.6504 loss_thr: 0.7429 loss_db: 0.2766 2022/11/02 13:03:49 - mmengine - INFO - Epoch(train) [85][20/63] lr: 1.6973e-03 eta: 12:21:38 time: 0.5086 data_time: 0.0057 memory: 14901 loss: 2.4745 loss_prob: 1.5076 loss_thr: 0.7217 loss_db: 0.2453 2022/11/02 13:03:52 - mmengine - INFO - Epoch(train) [85][25/63] lr: 1.6973e-03 eta: 12:21:38 time: 0.4963 data_time: 0.0336 memory: 14901 loss: 2.4797 loss_prob: 1.5169 loss_thr: 0.7126 loss_db: 0.2502 2022/11/02 13:03:54 - mmengine - INFO - Epoch(train) [85][30/63] lr: 1.6973e-03 eta: 12:21:11 time: 0.4806 data_time: 0.0366 memory: 14901 loss: 2.4895 loss_prob: 1.5205 loss_thr: 0.7144 loss_db: 0.2546 2022/11/02 13:03:56 - mmengine - INFO - Epoch(train) [85][35/63] lr: 1.6973e-03 eta: 12:21:11 time: 0.4632 data_time: 0.0121 memory: 14901 loss: 2.3491 loss_prob: 1.4159 loss_thr: 0.7019 loss_db: 0.2312 2022/11/02 13:03:59 - mmengine - INFO - Epoch(train) [85][40/63] lr: 1.6973e-03 eta: 12:20:44 time: 0.4763 data_time: 0.0100 memory: 14901 loss: 2.3491 loss_prob: 1.4284 loss_thr: 0.6912 loss_db: 0.2295 2022/11/02 13:04:01 - mmengine - INFO - Epoch(train) [85][45/63] lr: 1.6973e-03 eta: 12:20:44 time: 0.4789 data_time: 0.0051 memory: 14901 loss: 2.6975 loss_prob: 1.6704 loss_thr: 0.7527 loss_db: 0.2745 2022/11/02 13:04:04 - mmengine - INFO - Epoch(train) [85][50/63] lr: 1.6973e-03 eta: 12:20:20 time: 0.4931 data_time: 0.0192 memory: 14901 loss: 2.6892 loss_prob: 1.6453 loss_thr: 0.7708 loss_db: 0.2730 2022/11/02 13:04:06 - mmengine - INFO - Epoch(train) [85][55/63] lr: 1.6973e-03 eta: 12:20:20 time: 0.4795 data_time: 0.0195 memory: 14901 loss: 2.4851 loss_prob: 1.5035 loss_thr: 0.7314 loss_db: 0.2502 2022/11/02 13:04:08 - mmengine - INFO - Epoch(train) [85][60/63] lr: 1.6973e-03 eta: 12:19:52 time: 0.4736 data_time: 0.0063 memory: 14901 loss: 2.5965 loss_prob: 1.6081 loss_thr: 0.7256 loss_db: 0.2628 2022/11/02 13:04:09 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:04:14 - mmengine - INFO - Epoch(train) [86][5/63] lr: 1.7175e-03 eta: 12:19:52 time: 0.6916 data_time: 0.2398 memory: 14901 loss: 2.7018 loss_prob: 1.6965 loss_thr: 0.7248 loss_db: 0.2805 2022/11/02 13:04:17 - mmengine - INFO - Epoch(train) [86][10/63] lr: 1.7175e-03 eta: 12:19:31 time: 0.7218 data_time: 0.2384 memory: 14901 loss: 2.8263 loss_prob: 1.7667 loss_thr: 0.7459 loss_db: 0.3136 2022/11/02 13:04:19 - mmengine - INFO - Epoch(train) [86][15/63] lr: 1.7175e-03 eta: 12:19:31 time: 0.4840 data_time: 0.0047 memory: 14901 loss: 2.6909 loss_prob: 1.6809 loss_thr: 0.7140 loss_db: 0.2959 2022/11/02 13:04:21 - mmengine - INFO - Epoch(train) [86][20/63] lr: 1.7175e-03 eta: 12:19:05 time: 0.4830 data_time: 0.0060 memory: 14901 loss: 2.5789 loss_prob: 1.5980 loss_thr: 0.7105 loss_db: 0.2703 2022/11/02 13:04:24 - mmengine - INFO - Epoch(train) [86][25/63] lr: 1.7175e-03 eta: 12:19:05 time: 0.4930 data_time: 0.0325 memory: 14901 loss: 2.6371 loss_prob: 1.6319 loss_thr: 0.7286 loss_db: 0.2766 2022/11/02 13:04:27 - mmengine - INFO - Epoch(train) [86][30/63] lr: 1.7175e-03 eta: 12:18:45 time: 0.5214 data_time: 0.0345 memory: 14901 loss: 2.6310 loss_prob: 1.6336 loss_thr: 0.7269 loss_db: 0.2705 2022/11/02 13:04:29 - mmengine - INFO - Epoch(train) [86][35/63] lr: 1.7175e-03 eta: 12:18:45 time: 0.5198 data_time: 0.0142 memory: 14901 loss: 2.5838 loss_prob: 1.5884 loss_thr: 0.7324 loss_db: 0.2629 2022/11/02 13:04:32 - mmengine - INFO - Epoch(train) [86][40/63] lr: 1.7175e-03 eta: 12:18:23 time: 0.5166 data_time: 0.0112 memory: 14901 loss: 2.6518 loss_prob: 1.6424 loss_thr: 0.7378 loss_db: 0.2716 2022/11/02 13:04:34 - mmengine - INFO - Epoch(train) [86][45/63] lr: 1.7175e-03 eta: 12:18:23 time: 0.5178 data_time: 0.0078 memory: 14901 loss: 2.7528 loss_prob: 1.7295 loss_thr: 0.7362 loss_db: 0.2872 2022/11/02 13:04:37 - mmengine - INFO - Epoch(train) [86][50/63] lr: 1.7175e-03 eta: 12:18:06 time: 0.5473 data_time: 0.0235 memory: 14901 loss: 2.8617 loss_prob: 1.8190 loss_thr: 0.7372 loss_db: 0.3054 2022/11/02 13:04:40 - mmengine - INFO - Epoch(train) [86][55/63] lr: 1.7175e-03 eta: 12:18:06 time: 0.5579 data_time: 0.0231 memory: 14901 loss: 2.8344 loss_prob: 1.7945 loss_thr: 0.7366 loss_db: 0.3033 2022/11/02 13:04:42 - mmengine - INFO - Epoch(train) [86][60/63] lr: 1.7175e-03 eta: 12:17:43 time: 0.4984 data_time: 0.0070 memory: 14901 loss: 2.7241 loss_prob: 1.6934 loss_thr: 0.7433 loss_db: 0.2874 2022/11/02 13:04:44 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:04:49 - mmengine - INFO - Epoch(train) [87][5/63] lr: 1.7376e-03 eta: 12:17:43 time: 0.7113 data_time: 0.2018 memory: 14901 loss: 2.8757 loss_prob: 1.8155 loss_thr: 0.7552 loss_db: 0.3050 2022/11/02 13:04:51 - mmengine - INFO - Epoch(train) [87][10/63] lr: 1.7376e-03 eta: 12:17:24 time: 0.7367 data_time: 0.2018 memory: 14901 loss: 2.7945 loss_prob: 1.7490 loss_thr: 0.7533 loss_db: 0.2921 2022/11/02 13:04:54 - mmengine - INFO - Epoch(train) [87][15/63] lr: 1.7376e-03 eta: 12:17:24 time: 0.5037 data_time: 0.0054 memory: 14901 loss: 2.5974 loss_prob: 1.6087 loss_thr: 0.7267 loss_db: 0.2620 2022/11/02 13:04:56 - mmengine - INFO - Epoch(train) [87][20/63] lr: 1.7376e-03 eta: 12:17:01 time: 0.5018 data_time: 0.0057 memory: 14901 loss: 2.6689 loss_prob: 1.6680 loss_thr: 0.7278 loss_db: 0.2732 2022/11/02 13:04:58 - mmengine - INFO - Epoch(train) [87][25/63] lr: 1.7376e-03 eta: 12:17:01 time: 0.4814 data_time: 0.0158 memory: 14901 loss: 2.6789 loss_prob: 1.6678 loss_thr: 0.7339 loss_db: 0.2772 2022/11/02 13:05:01 - mmengine - INFO - Epoch(train) [87][30/63] lr: 1.7376e-03 eta: 12:16:35 time: 0.4808 data_time: 0.0318 memory: 14901 loss: 3.0227 loss_prob: 1.9247 loss_thr: 0.7605 loss_db: 0.3375 2022/11/02 13:05:03 - mmengine - INFO - Epoch(train) [87][35/63] lr: 1.7376e-03 eta: 12:16:35 time: 0.4854 data_time: 0.0210 memory: 14901 loss: 3.0585 loss_prob: 1.9639 loss_thr: 0.7527 loss_db: 0.3419 2022/11/02 13:05:06 - mmengine - INFO - Epoch(train) [87][40/63] lr: 1.7376e-03 eta: 12:16:12 time: 0.4954 data_time: 0.0048 memory: 14901 loss: 2.6513 loss_prob: 1.6603 loss_thr: 0.7203 loss_db: 0.2707 2022/11/02 13:05:09 - mmengine - INFO - Epoch(train) [87][45/63] lr: 1.7376e-03 eta: 12:16:12 time: 0.5473 data_time: 0.0064 memory: 14901 loss: 2.6424 loss_prob: 1.6326 loss_thr: 0.7387 loss_db: 0.2711 2022/11/02 13:05:11 - mmengine - INFO - Epoch(train) [87][50/63] lr: 1.7376e-03 eta: 12:15:56 time: 0.5533 data_time: 0.0193 memory: 14901 loss: 2.6242 loss_prob: 1.5945 loss_thr: 0.7552 loss_db: 0.2745 2022/11/02 13:05:14 - mmengine - INFO - Epoch(train) [87][55/63] lr: 1.7376e-03 eta: 12:15:56 time: 0.5081 data_time: 0.0256 memory: 14901 loss: 2.5956 loss_prob: 1.5717 loss_thr: 0.7606 loss_db: 0.2632 2022/11/02 13:05:17 - mmengine - INFO - Epoch(train) [87][60/63] lr: 1.7376e-03 eta: 12:15:38 time: 0.5401 data_time: 0.0127 memory: 14901 loss: 2.4274 loss_prob: 1.4680 loss_thr: 0.7236 loss_db: 0.2358 2022/11/02 13:05:18 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:05:23 - mmengine - INFO - Epoch(train) [88][5/63] lr: 1.7578e-03 eta: 12:15:38 time: 0.7687 data_time: 0.2395 memory: 14901 loss: 2.3475 loss_prob: 1.4041 loss_thr: 0.7149 loss_db: 0.2284 2022/11/02 13:05:26 - mmengine - INFO - Epoch(train) [88][10/63] lr: 1.7578e-03 eta: 12:15:31 time: 0.8260 data_time: 0.2406 memory: 14901 loss: 2.3657 loss_prob: 1.4262 loss_thr: 0.7061 loss_db: 0.2334 2022/11/02 13:05:30 - mmengine - INFO - Epoch(train) [88][15/63] lr: 1.7578e-03 eta: 12:15:31 time: 0.6787 data_time: 0.0065 memory: 14901 loss: 2.3311 loss_prob: 1.4085 loss_thr: 0.6917 loss_db: 0.2308 2022/11/02 13:05:33 - mmengine - INFO - Epoch(train) [88][20/63] lr: 1.7578e-03 eta: 12:15:28 time: 0.6579 data_time: 0.0048 memory: 14901 loss: 2.4404 loss_prob: 1.4848 loss_thr: 0.7124 loss_db: 0.2431 2022/11/02 13:05:36 - mmengine - INFO - Epoch(train) [88][25/63] lr: 1.7578e-03 eta: 12:15:28 time: 0.6340 data_time: 0.0272 memory: 14901 loss: 2.6023 loss_prob: 1.5807 loss_thr: 0.7618 loss_db: 0.2598 2022/11/02 13:05:39 - mmengine - INFO - Epoch(train) [88][30/63] lr: 1.7578e-03 eta: 12:15:19 time: 0.6053 data_time: 0.0357 memory: 14901 loss: 2.6235 loss_prob: 1.5912 loss_thr: 0.7742 loss_db: 0.2580 2022/11/02 13:05:42 - mmengine - INFO - Epoch(train) [88][35/63] lr: 1.7578e-03 eta: 12:15:19 time: 0.5242 data_time: 0.0177 memory: 14901 loss: 2.6133 loss_prob: 1.5997 loss_thr: 0.7514 loss_db: 0.2621 2022/11/02 13:05:44 - mmengine - INFO - Epoch(train) [88][40/63] lr: 1.7578e-03 eta: 12:14:59 time: 0.5261 data_time: 0.0092 memory: 14901 loss: 2.6795 loss_prob: 1.6432 loss_thr: 0.7614 loss_db: 0.2749 2022/11/02 13:05:47 - mmengine - INFO - Epoch(train) [88][45/63] lr: 1.7578e-03 eta: 12:14:59 time: 0.5390 data_time: 0.0047 memory: 14901 loss: 2.7031 loss_prob: 1.6537 loss_thr: 0.7742 loss_db: 0.2751 2022/11/02 13:05:50 - mmengine - INFO - Epoch(train) [88][50/63] lr: 1.7578e-03 eta: 12:14:49 time: 0.5986 data_time: 0.0173 memory: 14901 loss: 2.5914 loss_prob: 1.5786 loss_thr: 0.7517 loss_db: 0.2610 2022/11/02 13:05:55 - mmengine - INFO - Epoch(train) [88][55/63] lr: 1.7578e-03 eta: 12:14:49 time: 0.7785 data_time: 0.0365 memory: 14901 loss: 2.5725 loss_prob: 1.5756 loss_thr: 0.7324 loss_db: 0.2645 2022/11/02 13:05:58 - mmengine - INFO - Epoch(train) [88][60/63] lr: 1.7578e-03 eta: 12:15:03 time: 0.7853 data_time: 0.0244 memory: 14901 loss: 2.5988 loss_prob: 1.5971 loss_thr: 0.7354 loss_db: 0.2663 2022/11/02 13:05:59 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:06:05 - mmengine - INFO - Epoch(train) [89][5/63] lr: 1.7780e-03 eta: 12:15:03 time: 0.8029 data_time: 0.2179 memory: 14901 loss: 2.5000 loss_prob: 1.5588 loss_thr: 0.6884 loss_db: 0.2528 2022/11/02 13:06:07 - mmengine - INFO - Epoch(train) [89][10/63] lr: 1.7780e-03 eta: 12:14:58 time: 0.8480 data_time: 0.2176 memory: 14901 loss: 2.6643 loss_prob: 1.6714 loss_thr: 0.7223 loss_db: 0.2706 2022/11/02 13:06:10 - mmengine - INFO - Epoch(train) [89][15/63] lr: 1.7780e-03 eta: 12:14:58 time: 0.5336 data_time: 0.0053 memory: 14901 loss: 2.7865 loss_prob: 1.7659 loss_thr: 0.7268 loss_db: 0.2938 2022/11/02 13:06:12 - mmengine - INFO - Epoch(train) [89][20/63] lr: 1.7780e-03 eta: 12:14:36 time: 0.5005 data_time: 0.0044 memory: 14901 loss: 2.8106 loss_prob: 1.7790 loss_thr: 0.7310 loss_db: 0.3006 2022/11/02 13:06:15 - mmengine - INFO - Epoch(train) [89][25/63] lr: 1.7780e-03 eta: 12:14:36 time: 0.4897 data_time: 0.0176 memory: 14901 loss: 2.7177 loss_prob: 1.6946 loss_thr: 0.7419 loss_db: 0.2812 2022/11/02 13:06:17 - mmengine - INFO - Epoch(train) [89][30/63] lr: 1.7780e-03 eta: 12:14:10 time: 0.4768 data_time: 0.0309 memory: 14901 loss: 2.5523 loss_prob: 1.5865 loss_thr: 0.7067 loss_db: 0.2591 2022/11/02 13:06:20 - mmengine - INFO - Epoch(train) [89][35/63] lr: 1.7780e-03 eta: 12:14:10 time: 0.5078 data_time: 0.0181 memory: 14901 loss: 2.6324 loss_prob: 1.6384 loss_thr: 0.7177 loss_db: 0.2764 2022/11/02 13:06:22 - mmengine - INFO - Epoch(train) [89][40/63] lr: 1.7780e-03 eta: 12:13:49 time: 0.5123 data_time: 0.0049 memory: 14901 loss: 2.7679 loss_prob: 1.7194 loss_thr: 0.7513 loss_db: 0.2972 2022/11/02 13:06:25 - mmengine - INFO - Epoch(train) [89][45/63] lr: 1.7780e-03 eta: 12:13:49 time: 0.4993 data_time: 0.0060 memory: 14901 loss: 2.7343 loss_prob: 1.6764 loss_thr: 0.7724 loss_db: 0.2855 2022/11/02 13:06:28 - mmengine - INFO - Epoch(train) [89][50/63] lr: 1.7780e-03 eta: 12:13:31 time: 0.5317 data_time: 0.0134 memory: 14901 loss: 2.6689 loss_prob: 1.6371 loss_thr: 0.7595 loss_db: 0.2723 2022/11/02 13:06:30 - mmengine - INFO - Epoch(train) [89][55/63] lr: 1.7780e-03 eta: 12:13:31 time: 0.5275 data_time: 0.0268 memory: 14901 loss: 2.5603 loss_prob: 1.5751 loss_thr: 0.7301 loss_db: 0.2550 2022/11/02 13:06:33 - mmengine - INFO - Epoch(train) [89][60/63] lr: 1.7780e-03 eta: 12:13:08 time: 0.4977 data_time: 0.0202 memory: 14901 loss: 2.5005 loss_prob: 1.5213 loss_thr: 0.7323 loss_db: 0.2469 2022/11/02 13:06:34 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:06:39 - mmengine - INFO - Epoch(train) [90][5/63] lr: 1.7982e-03 eta: 12:13:08 time: 0.7400 data_time: 0.2119 memory: 14901 loss: 2.5863 loss_prob: 1.5927 loss_thr: 0.7284 loss_db: 0.2653 2022/11/02 13:06:42 - mmengine - INFO - Epoch(train) [90][10/63] lr: 1.7982e-03 eta: 12:12:54 time: 0.7718 data_time: 0.2107 memory: 14901 loss: 2.7174 loss_prob: 1.6781 loss_thr: 0.7606 loss_db: 0.2787 2022/11/02 13:06:44 - mmengine - INFO - Epoch(train) [90][15/63] lr: 1.7982e-03 eta: 12:12:54 time: 0.5001 data_time: 0.0122 memory: 14901 loss: 2.6088 loss_prob: 1.6129 loss_thr: 0.7296 loss_db: 0.2663 2022/11/02 13:06:46 - mmengine - INFO - Epoch(train) [90][20/63] lr: 1.7982e-03 eta: 12:12:29 time: 0.4748 data_time: 0.0122 memory: 14901 loss: 2.5135 loss_prob: 1.5489 loss_thr: 0.7094 loss_db: 0.2552 2022/11/02 13:06:49 - mmengine - INFO - Epoch(train) [90][25/63] lr: 1.7982e-03 eta: 12:12:29 time: 0.5013 data_time: 0.0204 memory: 14901 loss: 2.5498 loss_prob: 1.5746 loss_thr: 0.7171 loss_db: 0.2580 2022/11/02 13:06:51 - mmengine - INFO - Epoch(train) [90][30/63] lr: 1.7982e-03 eta: 12:12:07 time: 0.5028 data_time: 0.0275 memory: 14901 loss: 2.5626 loss_prob: 1.5814 loss_thr: 0.7227 loss_db: 0.2586 2022/11/02 13:06:54 - mmengine - INFO - Epoch(train) [90][35/63] lr: 1.7982e-03 eta: 12:12:07 time: 0.4728 data_time: 0.0133 memory: 14901 loss: 2.6067 loss_prob: 1.6041 loss_thr: 0.7434 loss_db: 0.2592 2022/11/02 13:06:56 - mmengine - INFO - Epoch(train) [90][40/63] lr: 1.7982e-03 eta: 12:11:46 time: 0.5075 data_time: 0.0265 memory: 14901 loss: 2.6445 loss_prob: 1.6342 loss_thr: 0.7451 loss_db: 0.2652 2022/11/02 13:06:59 - mmengine - INFO - Epoch(train) [90][45/63] lr: 1.7982e-03 eta: 12:11:46 time: 0.5096 data_time: 0.0259 memory: 14901 loss: 2.5746 loss_prob: 1.5904 loss_thr: 0.7216 loss_db: 0.2625 2022/11/02 13:07:01 - mmengine - INFO - Epoch(train) [90][50/63] lr: 1.7982e-03 eta: 12:11:21 time: 0.4807 data_time: 0.0164 memory: 14901 loss: 2.5517 loss_prob: 1.5731 loss_thr: 0.7190 loss_db: 0.2596 2022/11/02 13:07:04 - mmengine - INFO - Epoch(train) [90][55/63] lr: 1.7982e-03 eta: 12:11:21 time: 0.4779 data_time: 0.0189 memory: 14901 loss: 2.6046 loss_prob: 1.6224 loss_thr: 0.7137 loss_db: 0.2685 2022/11/02 13:07:06 - mmengine - INFO - Epoch(train) [90][60/63] lr: 1.7982e-03 eta: 12:10:57 time: 0.4764 data_time: 0.0107 memory: 14901 loss: 2.5012 loss_prob: 1.5410 loss_thr: 0.7080 loss_db: 0.2522 2022/11/02 13:07:08 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:07:13 - mmengine - INFO - Epoch(train) [91][5/63] lr: 1.8184e-03 eta: 12:10:57 time: 0.8173 data_time: 0.3313 memory: 14901 loss: 2.3552 loss_prob: 1.4107 loss_thr: 0.7177 loss_db: 0.2267 2022/11/02 13:07:16 - mmengine - INFO - Epoch(train) [91][10/63] lr: 1.8184e-03 eta: 12:10:47 time: 0.8046 data_time: 0.3278 memory: 14901 loss: 2.2908 loss_prob: 1.3636 loss_thr: 0.7080 loss_db: 0.2192 2022/11/02 13:07:18 - mmengine - INFO - Epoch(train) [91][15/63] lr: 1.8184e-03 eta: 12:10:47 time: 0.4857 data_time: 0.0048 memory: 14901 loss: 2.3060 loss_prob: 1.3944 loss_thr: 0.6866 loss_db: 0.2250 2022/11/02 13:07:21 - mmengine - INFO - Epoch(train) [91][20/63] lr: 1.8184e-03 eta: 12:10:25 time: 0.4955 data_time: 0.0056 memory: 14901 loss: 2.4536 loss_prob: 1.5095 loss_thr: 0.6985 loss_db: 0.2456 2022/11/02 13:07:23 - mmengine - INFO - Epoch(train) [91][25/63] lr: 1.8184e-03 eta: 12:10:25 time: 0.5069 data_time: 0.0266 memory: 14901 loss: 2.3580 loss_prob: 1.4376 loss_thr: 0.6852 loss_db: 0.2352 2022/11/02 13:07:26 - mmengine - INFO - Epoch(train) [91][30/63] lr: 1.8184e-03 eta: 12:10:05 time: 0.5191 data_time: 0.0261 memory: 14901 loss: 2.4565 loss_prob: 1.5049 loss_thr: 0.7062 loss_db: 0.2454 2022/11/02 13:07:28 - mmengine - INFO - Epoch(train) [91][35/63] lr: 1.8184e-03 eta: 12:10:05 time: 0.5238 data_time: 0.0098 memory: 14901 loss: 2.5172 loss_prob: 1.5463 loss_thr: 0.7211 loss_db: 0.2497 2022/11/02 13:07:31 - mmengine - INFO - Epoch(train) [91][40/63] lr: 1.8184e-03 eta: 12:09:42 time: 0.4897 data_time: 0.0099 memory: 14901 loss: 2.4102 loss_prob: 1.4432 loss_thr: 0.7318 loss_db: 0.2352 2022/11/02 13:07:33 - mmengine - INFO - Epoch(train) [91][45/63] lr: 1.8184e-03 eta: 12:09:42 time: 0.4833 data_time: 0.0109 memory: 14901 loss: 2.3824 loss_prob: 1.4231 loss_thr: 0.7251 loss_db: 0.2342 2022/11/02 13:07:36 - mmengine - INFO - Epoch(train) [91][50/63] lr: 1.8184e-03 eta: 12:09:20 time: 0.4964 data_time: 0.0269 memory: 14901 loss: 2.4724 loss_prob: 1.5102 loss_thr: 0.7126 loss_db: 0.2496 2022/11/02 13:07:38 - mmengine - INFO - Epoch(train) [91][55/63] lr: 1.8184e-03 eta: 12:09:20 time: 0.4710 data_time: 0.0210 memory: 14901 loss: 2.4659 loss_prob: 1.5044 loss_thr: 0.7153 loss_db: 0.2462 2022/11/02 13:07:40 - mmengine - INFO - Epoch(train) [91][60/63] lr: 1.8184e-03 eta: 12:08:53 time: 0.4591 data_time: 0.0063 memory: 14901 loss: 2.5264 loss_prob: 1.5485 loss_thr: 0.7213 loss_db: 0.2565 2022/11/02 13:07:41 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:07:47 - mmengine - INFO - Epoch(train) [92][5/63] lr: 1.8385e-03 eta: 12:08:53 time: 0.7885 data_time: 0.2937 memory: 14901 loss: 2.7824 loss_prob: 1.7497 loss_thr: 0.7388 loss_db: 0.2939 2022/11/02 13:07:52 - mmengine - INFO - Epoch(train) [92][10/63] lr: 1.8385e-03 eta: 12:09:09 time: 1.0109 data_time: 0.2964 memory: 14901 loss: 2.7339 loss_prob: 1.7153 loss_thr: 0.7335 loss_db: 0.2851 2022/11/02 13:07:55 - mmengine - INFO - Epoch(train) [92][15/63] lr: 1.8385e-03 eta: 12:09:09 time: 0.7471 data_time: 0.0075 memory: 14901 loss: 2.6433 loss_prob: 1.6362 loss_thr: 0.7343 loss_db: 0.2728 2022/11/02 13:07:58 - mmengine - INFO - Epoch(train) [92][20/63] lr: 1.8385e-03 eta: 12:09:03 time: 0.6295 data_time: 0.0054 memory: 14901 loss: 2.7112 loss_prob: 1.6672 loss_thr: 0.7588 loss_db: 0.2853 2022/11/02 13:08:01 - mmengine - INFO - Epoch(train) [92][25/63] lr: 1.8385e-03 eta: 12:09:03 time: 0.6300 data_time: 0.0310 memory: 14901 loss: 2.6483 loss_prob: 1.6218 loss_thr: 0.7467 loss_db: 0.2797 2022/11/02 13:08:04 - mmengine - INFO - Epoch(train) [92][30/63] lr: 1.8385e-03 eta: 12:08:55 time: 0.6099 data_time: 0.0317 memory: 14901 loss: 2.5859 loss_prob: 1.6025 loss_thr: 0.7148 loss_db: 0.2686 2022/11/02 13:08:07 - mmengine - INFO - Epoch(train) [92][35/63] lr: 1.8385e-03 eta: 12:08:55 time: 0.5491 data_time: 0.0077 memory: 14901 loss: 2.6866 loss_prob: 1.6877 loss_thr: 0.7202 loss_db: 0.2788 2022/11/02 13:08:10 - mmengine - INFO - Epoch(train) [92][40/63] lr: 1.8385e-03 eta: 12:08:44 time: 0.5905 data_time: 0.0067 memory: 14901 loss: 2.6143 loss_prob: 1.6219 loss_thr: 0.7239 loss_db: 0.2684 2022/11/02 13:08:13 - mmengine - INFO - Epoch(train) [92][45/63] lr: 1.8385e-03 eta: 12:08:44 time: 0.6159 data_time: 0.0074 memory: 14901 loss: 2.6275 loss_prob: 1.6255 loss_thr: 0.7313 loss_db: 0.2707 2022/11/02 13:08:15 - mmengine - INFO - Epoch(train) [92][50/63] lr: 1.8385e-03 eta: 12:08:29 time: 0.5476 data_time: 0.0227 memory: 14901 loss: 2.6902 loss_prob: 1.6739 loss_thr: 0.7376 loss_db: 0.2787 2022/11/02 13:08:19 - mmengine - INFO - Epoch(train) [92][55/63] lr: 1.8385e-03 eta: 12:08:29 time: 0.5811 data_time: 0.0223 memory: 14901 loss: 2.5672 loss_prob: 1.5863 loss_thr: 0.7256 loss_db: 0.2554 2022/11/02 13:08:22 - mmengine - INFO - Epoch(train) [92][60/63] lr: 1.8385e-03 eta: 12:08:24 time: 0.6360 data_time: 0.0078 memory: 14901 loss: 2.5613 loss_prob: 1.5798 loss_thr: 0.7270 loss_db: 0.2545 2022/11/02 13:08:23 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:08:31 - mmengine - INFO - Epoch(train) [93][5/63] lr: 1.8587e-03 eta: 12:08:24 time: 0.9887 data_time: 0.2632 memory: 14901 loss: 2.5019 loss_prob: 1.5315 loss_thr: 0.7147 loss_db: 0.2557 2022/11/02 13:08:34 - mmengine - INFO - Epoch(train) [93][10/63] lr: 1.8587e-03 eta: 12:08:40 time: 1.0161 data_time: 0.2629 memory: 14901 loss: 2.3364 loss_prob: 1.4064 loss_thr: 0.6960 loss_db: 0.2340 2022/11/02 13:08:37 - mmengine - INFO - Epoch(train) [93][15/63] lr: 1.8587e-03 eta: 12:08:40 time: 0.6368 data_time: 0.0078 memory: 14901 loss: 2.2230 loss_prob: 1.3100 loss_thr: 0.6973 loss_db: 0.2157 2022/11/02 13:08:39 - mmengine - INFO - Epoch(train) [93][20/63] lr: 1.8587e-03 eta: 12:08:27 time: 0.5744 data_time: 0.0088 memory: 14901 loss: 2.4929 loss_prob: 1.5083 loss_thr: 0.7326 loss_db: 0.2520 2022/11/02 13:08:42 - mmengine - INFO - Epoch(train) [93][25/63] lr: 1.8587e-03 eta: 12:08:27 time: 0.5178 data_time: 0.0101 memory: 14901 loss: 2.5283 loss_prob: 1.5447 loss_thr: 0.7278 loss_db: 0.2558 2022/11/02 13:08:45 - mmengine - INFO - Epoch(train) [93][30/63] lr: 1.8587e-03 eta: 12:08:12 time: 0.5540 data_time: 0.0364 memory: 14901 loss: 2.4871 loss_prob: 1.5235 loss_thr: 0.7101 loss_db: 0.2535 2022/11/02 13:08:48 - mmengine - INFO - Epoch(train) [93][35/63] lr: 1.8587e-03 eta: 12:08:12 time: 0.5440 data_time: 0.0329 memory: 14901 loss: 2.5873 loss_prob: 1.5937 loss_thr: 0.7270 loss_db: 0.2666 2022/11/02 13:08:50 - mmengine - INFO - Epoch(train) [93][40/63] lr: 1.8587e-03 eta: 12:07:51 time: 0.5018 data_time: 0.0047 memory: 14901 loss: 2.6496 loss_prob: 1.6339 loss_thr: 0.7449 loss_db: 0.2708 2022/11/02 13:08:52 - mmengine - INFO - Epoch(train) [93][45/63] lr: 1.8587e-03 eta: 12:07:51 time: 0.4657 data_time: 0.0075 memory: 14901 loss: 2.6505 loss_prob: 1.6432 loss_thr: 0.7350 loss_db: 0.2723 2022/11/02 13:08:55 - mmengine - INFO - Epoch(train) [93][50/63] lr: 1.8587e-03 eta: 12:07:33 time: 0.5246 data_time: 0.0244 memory: 14901 loss: 2.4871 loss_prob: 1.5313 loss_thr: 0.7003 loss_db: 0.2555 2022/11/02 13:08:58 - mmengine - INFO - Epoch(train) [93][55/63] lr: 1.8587e-03 eta: 12:07:33 time: 0.5347 data_time: 0.0218 memory: 14901 loss: 2.5251 loss_prob: 1.5709 loss_thr: 0.6908 loss_db: 0.2634 2022/11/02 13:09:00 - mmengine - INFO - Epoch(train) [93][60/63] lr: 1.8587e-03 eta: 12:07:08 time: 0.4726 data_time: 0.0049 memory: 14901 loss: 2.6057 loss_prob: 1.6188 loss_thr: 0.7167 loss_db: 0.2702 2022/11/02 13:09:01 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:09:06 - mmengine - INFO - Epoch(train) [94][5/63] lr: 1.8789e-03 eta: 12:07:08 time: 0.6957 data_time: 0.1964 memory: 14901 loss: 2.4105 loss_prob: 1.4464 loss_thr: 0.7236 loss_db: 0.2405 2022/11/02 13:09:09 - mmengine - INFO - Epoch(train) [94][10/63] lr: 1.8789e-03 eta: 12:06:59 time: 0.8019 data_time: 0.2080 memory: 14901 loss: 2.3791 loss_prob: 1.4324 loss_thr: 0.7145 loss_db: 0.2322 2022/11/02 13:09:11 - mmengine - INFO - Epoch(train) [94][15/63] lr: 1.8789e-03 eta: 12:06:59 time: 0.5489 data_time: 0.0165 memory: 14901 loss: 2.4514 loss_prob: 1.4850 loss_thr: 0.7269 loss_db: 0.2394 2022/11/02 13:09:14 - mmengine - INFO - Epoch(train) [94][20/63] lr: 1.8789e-03 eta: 12:06:36 time: 0.4867 data_time: 0.0051 memory: 14901 loss: 2.4382 loss_prob: 1.4773 loss_thr: 0.7180 loss_db: 0.2429 2022/11/02 13:09:17 - mmengine - INFO - Epoch(train) [94][25/63] lr: 1.8789e-03 eta: 12:06:36 time: 0.5414 data_time: 0.0198 memory: 14901 loss: 2.4987 loss_prob: 1.5087 loss_thr: 0.7369 loss_db: 0.2531 2022/11/02 13:09:19 - mmengine - INFO - Epoch(train) [94][30/63] lr: 1.8789e-03 eta: 12:06:18 time: 0.5220 data_time: 0.0203 memory: 14901 loss: 2.5334 loss_prob: 1.5396 loss_thr: 0.7365 loss_db: 0.2573 2022/11/02 13:09:22 - mmengine - INFO - Epoch(train) [94][35/63] lr: 1.8789e-03 eta: 12:06:18 time: 0.4972 data_time: 0.0189 memory: 14901 loss: 2.8395 loss_prob: 1.7837 loss_thr: 0.7600 loss_db: 0.2958 2022/11/02 13:09:24 - mmengine - INFO - Epoch(train) [94][40/63] lr: 1.8789e-03 eta: 12:05:55 time: 0.4872 data_time: 0.0179 memory: 14901 loss: 3.1340 loss_prob: 1.9945 loss_thr: 0.8107 loss_db: 0.3288 2022/11/02 13:09:26 - mmengine - INFO - Epoch(train) [94][45/63] lr: 1.8789e-03 eta: 12:05:55 time: 0.4713 data_time: 0.0044 memory: 14901 loss: 2.9372 loss_prob: 1.8504 loss_thr: 0.7875 loss_db: 0.2993 2022/11/02 13:09:29 - mmengine - INFO - Epoch(train) [94][50/63] lr: 1.8789e-03 eta: 12:05:32 time: 0.4796 data_time: 0.0145 memory: 14901 loss: 2.7100 loss_prob: 1.6971 loss_thr: 0.7322 loss_db: 0.2807 2022/11/02 13:09:31 - mmengine - INFO - Epoch(train) [94][55/63] lr: 1.8789e-03 eta: 12:05:32 time: 0.4988 data_time: 0.0191 memory: 14901 loss: 2.7712 loss_prob: 1.7223 loss_thr: 0.7462 loss_db: 0.3028 2022/11/02 13:09:34 - mmengine - INFO - Epoch(train) [94][60/63] lr: 1.8789e-03 eta: 12:05:09 time: 0.4853 data_time: 0.0132 memory: 14901 loss: 3.0663 loss_prob: 1.9575 loss_thr: 0.7608 loss_db: 0.3480 2022/11/02 13:09:35 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:09:39 - mmengine - INFO - Epoch(train) [95][5/63] lr: 1.8991e-03 eta: 12:05:09 time: 0.6509 data_time: 0.1926 memory: 14901 loss: 3.2111 loss_prob: 2.0301 loss_thr: 0.8307 loss_db: 0.3503 2022/11/02 13:09:42 - mmengine - INFO - Epoch(train) [95][10/63] lr: 1.8991e-03 eta: 12:04:53 time: 0.7463 data_time: 0.1924 memory: 14901 loss: 3.2160 loss_prob: 2.0317 loss_thr: 0.8214 loss_db: 0.3629 2022/11/02 13:09:45 - mmengine - INFO - Epoch(train) [95][15/63] lr: 1.8991e-03 eta: 12:04:53 time: 0.5488 data_time: 0.0070 memory: 14901 loss: 3.0939 loss_prob: 1.9807 loss_thr: 0.7609 loss_db: 0.3522 2022/11/02 13:09:47 - mmengine - INFO - Epoch(train) [95][20/63] lr: 1.8991e-03 eta: 12:04:31 time: 0.4854 data_time: 0.0068 memory: 14901 loss: 2.8518 loss_prob: 1.8059 loss_thr: 0.7322 loss_db: 0.3137 2022/11/02 13:09:50 - mmengine - INFO - Epoch(train) [95][25/63] lr: 1.8991e-03 eta: 12:04:31 time: 0.5042 data_time: 0.0080 memory: 14901 loss: 2.9305 loss_prob: 1.8512 loss_thr: 0.7632 loss_db: 0.3160 2022/11/02 13:09:52 - mmengine - INFO - Epoch(train) [95][30/63] lr: 1.8991e-03 eta: 12:04:13 time: 0.5226 data_time: 0.0366 memory: 14901 loss: 2.8911 loss_prob: 1.8244 loss_thr: 0.7607 loss_db: 0.3060 2022/11/02 13:09:55 - mmengine - INFO - Epoch(train) [95][35/63] lr: 1.8991e-03 eta: 12:04:13 time: 0.4984 data_time: 0.0343 memory: 14901 loss: 2.5966 loss_prob: 1.6104 loss_thr: 0.7257 loss_db: 0.2604 2022/11/02 13:09:57 - mmengine - INFO - Epoch(train) [95][40/63] lr: 1.8991e-03 eta: 12:03:50 time: 0.4816 data_time: 0.0046 memory: 14901 loss: 2.7627 loss_prob: 1.7335 loss_thr: 0.7445 loss_db: 0.2847 2022/11/02 13:10:00 - mmengine - INFO - Epoch(train) [95][45/63] lr: 1.8991e-03 eta: 12:03:50 time: 0.4865 data_time: 0.0070 memory: 14901 loss: 2.6894 loss_prob: 1.6567 loss_thr: 0.7617 loss_db: 0.2711 2022/11/02 13:10:02 - mmengine - INFO - Epoch(train) [95][50/63] lr: 1.8991e-03 eta: 12:03:28 time: 0.4907 data_time: 0.0214 memory: 14901 loss: 2.3878 loss_prob: 1.4325 loss_thr: 0.7237 loss_db: 0.2316 2022/11/02 13:10:04 - mmengine - INFO - Epoch(train) [95][55/63] lr: 1.8991e-03 eta: 12:03:28 time: 0.4838 data_time: 0.0300 memory: 14901 loss: 2.3895 loss_prob: 1.4623 loss_thr: 0.6881 loss_db: 0.2390 2022/11/02 13:10:07 - mmengine - INFO - Epoch(train) [95][60/63] lr: 1.8991e-03 eta: 12:03:08 time: 0.5070 data_time: 0.0158 memory: 14901 loss: 2.3897 loss_prob: 1.4635 loss_thr: 0.6847 loss_db: 0.2416 2022/11/02 13:10:09 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:10:13 - mmengine - INFO - Epoch(train) [96][5/63] lr: 1.9193e-03 eta: 12:03:08 time: 0.7126 data_time: 0.2207 memory: 14901 loss: 2.3866 loss_prob: 1.4752 loss_thr: 0.6721 loss_db: 0.2392 2022/11/02 13:10:15 - mmengine - INFO - Epoch(train) [96][10/63] lr: 1.9193e-03 eta: 12:02:46 time: 0.6877 data_time: 0.2210 memory: 14901 loss: 2.5783 loss_prob: 1.6130 loss_thr: 0.6949 loss_db: 0.2704 2022/11/02 13:10:18 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:10:18 - mmengine - INFO - Epoch(train) [96][15/63] lr: 1.9193e-03 eta: 12:02:46 time: 0.5059 data_time: 0.0049 memory: 14901 loss: 2.5858 loss_prob: 1.5884 loss_thr: 0.7300 loss_db: 0.2674 2022/11/02 13:10:21 - mmengine - INFO - Epoch(train) [96][20/63] lr: 1.9193e-03 eta: 12:02:30 time: 0.5427 data_time: 0.0076 memory: 14901 loss: 2.6342 loss_prob: 1.6267 loss_thr: 0.7391 loss_db: 0.2684 2022/11/02 13:10:24 - mmengine - INFO - Epoch(train) [96][25/63] lr: 1.9193e-03 eta: 12:02:30 time: 0.5967 data_time: 0.0428 memory: 14901 loss: 2.5940 loss_prob: 1.6090 loss_thr: 0.7225 loss_db: 0.2625 2022/11/02 13:10:28 - mmengine - INFO - Epoch(train) [96][30/63] lr: 1.9193e-03 eta: 12:02:30 time: 0.6741 data_time: 0.0398 memory: 14901 loss: 2.4620 loss_prob: 1.5065 loss_thr: 0.7078 loss_db: 0.2478 2022/11/02 13:10:30 - mmengine - INFO - Epoch(train) [96][35/63] lr: 1.9193e-03 eta: 12:02:30 time: 0.5827 data_time: 0.0045 memory: 14901 loss: 2.5307 loss_prob: 1.5448 loss_thr: 0.7289 loss_db: 0.2570 2022/11/02 13:10:33 - mmengine - INFO - Epoch(train) [96][40/63] lr: 1.9193e-03 eta: 12:02:16 time: 0.5553 data_time: 0.0050 memory: 14901 loss: 2.6976 loss_prob: 1.6774 loss_thr: 0.7417 loss_db: 0.2785 2022/11/02 13:10:36 - mmengine - INFO - Epoch(train) [96][45/63] lr: 1.9193e-03 eta: 12:02:16 time: 0.5643 data_time: 0.0053 memory: 14901 loss: 2.6433 loss_prob: 1.6543 loss_thr: 0.7153 loss_db: 0.2737 2022/11/02 13:10:38 - mmengine - INFO - Epoch(train) [96][50/63] lr: 1.9193e-03 eta: 12:01:59 time: 0.5328 data_time: 0.0241 memory: 14901 loss: 2.4294 loss_prob: 1.4756 loss_thr: 0.7129 loss_db: 0.2409 2022/11/02 13:10:41 - mmengine - INFO - Epoch(train) [96][55/63] lr: 1.9193e-03 eta: 12:01:59 time: 0.5539 data_time: 0.0238 memory: 14901 loss: 2.5000 loss_prob: 1.5074 loss_thr: 0.7396 loss_db: 0.2530 2022/11/02 13:10:44 - mmengine - INFO - Epoch(train) [96][60/63] lr: 1.9193e-03 eta: 12:01:42 time: 0.5233 data_time: 0.0054 memory: 14901 loss: 2.5250 loss_prob: 1.5424 loss_thr: 0.7251 loss_db: 0.2575 2022/11/02 13:10:45 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:10:51 - mmengine - INFO - Epoch(train) [97][5/63] lr: 1.9395e-03 eta: 12:01:42 time: 0.8038 data_time: 0.2235 memory: 14901 loss: 2.6313 loss_prob: 1.6362 loss_thr: 0.7230 loss_db: 0.2721 2022/11/02 13:10:53 - mmengine - INFO - Epoch(train) [97][10/63] lr: 1.9395e-03 eta: 12:01:32 time: 0.7964 data_time: 0.2321 memory: 14901 loss: 2.5684 loss_prob: 1.5916 loss_thr: 0.7141 loss_db: 0.2626 2022/11/02 13:10:56 - mmengine - INFO - Epoch(train) [97][15/63] lr: 1.9395e-03 eta: 12:01:32 time: 0.5394 data_time: 0.0151 memory: 14901 loss: 2.3870 loss_prob: 1.4481 loss_thr: 0.7013 loss_db: 0.2375 2022/11/02 13:11:00 - mmengine - INFO - Epoch(train) [97][20/63] lr: 1.9395e-03 eta: 12:01:30 time: 0.6538 data_time: 0.0068 memory: 14901 loss: 2.5229 loss_prob: 1.5485 loss_thr: 0.7174 loss_db: 0.2570 2022/11/02 13:11:03 - mmengine - INFO - Epoch(train) [97][25/63] lr: 1.9395e-03 eta: 12:01:30 time: 0.7299 data_time: 0.0207 memory: 14901 loss: 2.6312 loss_prob: 1.6323 loss_thr: 0.7260 loss_db: 0.2729 2022/11/02 13:11:07 - mmengine - INFO - Epoch(train) [97][30/63] lr: 1.9395e-03 eta: 12:01:33 time: 0.7052 data_time: 0.0275 memory: 14901 loss: 2.5905 loss_prob: 1.6069 loss_thr: 0.7165 loss_db: 0.2671 2022/11/02 13:11:10 - mmengine - INFO - Epoch(train) [97][35/63] lr: 1.9395e-03 eta: 12:01:33 time: 0.6153 data_time: 0.0199 memory: 14901 loss: 2.4291 loss_prob: 1.4860 loss_thr: 0.6965 loss_db: 0.2467 2022/11/02 13:11:12 - mmengine - INFO - Epoch(train) [97][40/63] lr: 1.9395e-03 eta: 12:01:15 time: 0.5224 data_time: 0.0121 memory: 14901 loss: 2.4245 loss_prob: 1.4863 loss_thr: 0.6917 loss_db: 0.2464 2022/11/02 13:11:15 - mmengine - INFO - Epoch(train) [97][45/63] lr: 1.9395e-03 eta: 12:01:15 time: 0.5053 data_time: 0.0047 memory: 14901 loss: 2.5858 loss_prob: 1.6097 loss_thr: 0.7085 loss_db: 0.2676 2022/11/02 13:11:17 - mmengine - INFO - Epoch(train) [97][50/63] lr: 1.9395e-03 eta: 12:00:58 time: 0.5307 data_time: 0.0142 memory: 14901 loss: 2.5573 loss_prob: 1.5669 loss_thr: 0.7268 loss_db: 0.2636 2022/11/02 13:11:20 - mmengine - INFO - Epoch(train) [97][55/63] lr: 1.9395e-03 eta: 12:00:58 time: 0.5108 data_time: 0.0188 memory: 14901 loss: 2.4004 loss_prob: 1.4619 loss_thr: 0.7014 loss_db: 0.2370 2022/11/02 13:11:22 - mmengine - INFO - Epoch(train) [97][60/63] lr: 1.9395e-03 eta: 12:00:37 time: 0.4858 data_time: 0.0136 memory: 14901 loss: 2.3927 loss_prob: 1.4636 loss_thr: 0.6946 loss_db: 0.2345 2022/11/02 13:11:23 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:11:28 - mmengine - INFO - Epoch(train) [98][5/63] lr: 1.9596e-03 eta: 12:00:37 time: 0.7008 data_time: 0.2318 memory: 14901 loss: 2.3822 loss_prob: 1.4438 loss_thr: 0.6977 loss_db: 0.2407 2022/11/02 13:11:31 - mmengine - INFO - Epoch(train) [98][10/63] lr: 1.9596e-03 eta: 12:00:20 time: 0.7293 data_time: 0.2298 memory: 14901 loss: 2.4352 loss_prob: 1.4769 loss_thr: 0.7123 loss_db: 0.2460 2022/11/02 13:11:33 - mmengine - INFO - Epoch(train) [98][15/63] lr: 1.9596e-03 eta: 12:00:20 time: 0.5127 data_time: 0.0045 memory: 14901 loss: 2.5364 loss_prob: 1.5787 loss_thr: 0.6976 loss_db: 0.2600 2022/11/02 13:11:36 - mmengine - INFO - Epoch(train) [98][20/63] lr: 1.9596e-03 eta: 12:00:01 time: 0.5164 data_time: 0.0068 memory: 14901 loss: 2.5211 loss_prob: 1.5801 loss_thr: 0.6863 loss_db: 0.2547 2022/11/02 13:11:39 - mmengine - INFO - Epoch(train) [98][25/63] lr: 1.9596e-03 eta: 12:00:01 time: 0.5195 data_time: 0.0327 memory: 14901 loss: 2.4701 loss_prob: 1.5219 loss_thr: 0.7027 loss_db: 0.2455 2022/11/02 13:11:41 - mmengine - INFO - Epoch(train) [98][30/63] lr: 1.9596e-03 eta: 11:59:45 time: 0.5296 data_time: 0.0379 memory: 14901 loss: 2.4317 loss_prob: 1.4769 loss_thr: 0.7099 loss_db: 0.2448 2022/11/02 13:11:44 - mmengine - INFO - Epoch(train) [98][35/63] lr: 1.9596e-03 eta: 11:59:45 time: 0.4955 data_time: 0.0121 memory: 14901 loss: 2.6054 loss_prob: 1.6162 loss_thr: 0.7209 loss_db: 0.2683 2022/11/02 13:11:46 - mmengine - INFO - Epoch(train) [98][40/63] lr: 1.9596e-03 eta: 11:59:24 time: 0.4936 data_time: 0.0042 memory: 14901 loss: 2.6818 loss_prob: 1.6646 loss_thr: 0.7410 loss_db: 0.2763 2022/11/02 13:11:48 - mmengine - INFO - Epoch(train) [98][45/63] lr: 1.9596e-03 eta: 11:59:24 time: 0.4775 data_time: 0.0067 memory: 14901 loss: 2.5025 loss_prob: 1.5157 loss_thr: 0.7371 loss_db: 0.2497 2022/11/02 13:11:51 - mmengine - INFO - Epoch(train) [98][50/63] lr: 1.9596e-03 eta: 11:59:03 time: 0.4912 data_time: 0.0236 memory: 14901 loss: 2.3590 loss_prob: 1.4329 loss_thr: 0.6937 loss_db: 0.2324 2022/11/02 13:11:53 - mmengine - INFO - Epoch(train) [98][55/63] lr: 1.9596e-03 eta: 11:59:03 time: 0.5162 data_time: 0.0268 memory: 14901 loss: 2.3327 loss_prob: 1.4116 loss_thr: 0.6925 loss_db: 0.2286 2022/11/02 13:11:56 - mmengine - INFO - Epoch(train) [98][60/63] lr: 1.9596e-03 eta: 11:58:44 time: 0.5045 data_time: 0.0103 memory: 14901 loss: 2.4848 loss_prob: 1.5179 loss_thr: 0.7182 loss_db: 0.2487 2022/11/02 13:11:57 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:12:02 - mmengine - INFO - Epoch(train) [99][5/63] lr: 1.9798e-03 eta: 11:58:44 time: 0.7071 data_time: 0.1995 memory: 14901 loss: 2.4599 loss_prob: 1.5016 loss_thr: 0.7099 loss_db: 0.2483 2022/11/02 13:12:04 - mmengine - INFO - Epoch(train) [99][10/63] lr: 1.9798e-03 eta: 11:58:27 time: 0.7332 data_time: 0.1988 memory: 14901 loss: 2.2635 loss_prob: 1.3651 loss_thr: 0.6764 loss_db: 0.2220 2022/11/02 13:12:07 - mmengine - INFO - Epoch(train) [99][15/63] lr: 1.9798e-03 eta: 11:58:27 time: 0.4776 data_time: 0.0078 memory: 14901 loss: 2.1877 loss_prob: 1.3109 loss_thr: 0.6621 loss_db: 0.2147 2022/11/02 13:12:09 - mmengine - INFO - Epoch(train) [99][20/63] lr: 1.9798e-03 eta: 11:58:05 time: 0.4745 data_time: 0.0091 memory: 14901 loss: 2.3053 loss_prob: 1.3941 loss_thr: 0.6848 loss_db: 0.2264 2022/11/02 13:12:12 - mmengine - INFO - Epoch(train) [99][25/63] lr: 1.9798e-03 eta: 11:58:05 time: 0.4783 data_time: 0.0208 memory: 14901 loss: 2.4254 loss_prob: 1.4694 loss_thr: 0.7171 loss_db: 0.2390 2022/11/02 13:12:14 - mmengine - INFO - Epoch(train) [99][30/63] lr: 1.9798e-03 eta: 11:57:44 time: 0.4892 data_time: 0.0306 memory: 14901 loss: 2.3229 loss_prob: 1.3968 loss_thr: 0.6985 loss_db: 0.2277 2022/11/02 13:12:17 - mmengine - INFO - Epoch(train) [99][35/63] lr: 1.9798e-03 eta: 11:57:44 time: 0.5392 data_time: 0.0183 memory: 14901 loss: 2.2579 loss_prob: 1.3538 loss_thr: 0.6814 loss_db: 0.2228 2022/11/02 13:12:19 - mmengine - INFO - Epoch(train) [99][40/63] lr: 1.9798e-03 eta: 11:57:28 time: 0.5332 data_time: 0.0080 memory: 14901 loss: 2.3174 loss_prob: 1.3917 loss_thr: 0.6930 loss_db: 0.2328 2022/11/02 13:12:22 - mmengine - INFO - Epoch(train) [99][45/63] lr: 1.9798e-03 eta: 11:57:28 time: 0.5099 data_time: 0.0064 memory: 14901 loss: 2.4030 loss_prob: 1.4594 loss_thr: 0.7023 loss_db: 0.2414 2022/11/02 13:12:25 - mmengine - INFO - Epoch(train) [99][50/63] lr: 1.9798e-03 eta: 11:57:15 time: 0.5650 data_time: 0.0238 memory: 14901 loss: 2.5208 loss_prob: 1.5544 loss_thr: 0.7151 loss_db: 0.2513 2022/11/02 13:12:28 - mmengine - INFO - Epoch(train) [99][55/63] lr: 1.9798e-03 eta: 11:57:15 time: 0.5848 data_time: 0.0234 memory: 14901 loss: 2.4893 loss_prob: 1.5264 loss_thr: 0.7125 loss_db: 0.2504 2022/11/02 13:12:31 - mmengine - INFO - Epoch(train) [99][60/63] lr: 1.9798e-03 eta: 11:57:01 time: 0.5456 data_time: 0.0061 memory: 14901 loss: 2.5746 loss_prob: 1.5720 loss_thr: 0.7337 loss_db: 0.2689 2022/11/02 13:12:32 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:12:37 - mmengine - INFO - Epoch(train) [100][5/63] lr: 2.0000e-03 eta: 11:57:01 time: 0.7654 data_time: 0.2374 memory: 14901 loss: 2.5772 loss_prob: 1.5808 loss_thr: 0.7413 loss_db: 0.2552 2022/11/02 13:12:39 - mmengine - INFO - Epoch(train) [100][10/63] lr: 2.0000e-03 eta: 11:56:46 time: 0.7458 data_time: 0.2357 memory: 14901 loss: 2.6708 loss_prob: 1.6663 loss_thr: 0.7365 loss_db: 0.2680 2022/11/02 13:12:42 - mmengine - INFO - Epoch(train) [100][15/63] lr: 2.0000e-03 eta: 11:56:46 time: 0.4999 data_time: 0.0064 memory: 14901 loss: 2.5861 loss_prob: 1.6080 loss_thr: 0.7127 loss_db: 0.2654 2022/11/02 13:12:44 - mmengine - INFO - Epoch(train) [100][20/63] lr: 2.0000e-03 eta: 11:56:28 time: 0.5124 data_time: 0.0062 memory: 14901 loss: 2.4709 loss_prob: 1.5279 loss_thr: 0.6888 loss_db: 0.2542 2022/11/02 13:12:47 - mmengine - INFO - Epoch(train) [100][25/63] lr: 2.0000e-03 eta: 11:56:28 time: 0.4927 data_time: 0.0276 memory: 14901 loss: 2.7793 loss_prob: 1.7670 loss_thr: 0.7262 loss_db: 0.2860 2022/11/02 13:12:49 - mmengine - INFO - Epoch(train) [100][30/63] lr: 2.0000e-03 eta: 11:56:06 time: 0.4806 data_time: 0.0297 memory: 14901 loss: 3.0767 loss_prob: 2.0095 loss_thr: 0.7376 loss_db: 0.3295 2022/11/02 13:12:52 - mmengine - INFO - Epoch(train) [100][35/63] lr: 2.0000e-03 eta: 11:56:06 time: 0.4665 data_time: 0.0074 memory: 14901 loss: 2.8187 loss_prob: 1.8227 loss_thr: 0.6936 loss_db: 0.3023 2022/11/02 13:12:54 - mmengine - INFO - Epoch(train) [100][40/63] lr: 2.0000e-03 eta: 11:55:45 time: 0.4865 data_time: 0.0078 memory: 14901 loss: 2.6690 loss_prob: 1.6677 loss_thr: 0.7272 loss_db: 0.2741 2022/11/02 13:12:57 - mmengine - INFO - Epoch(train) [100][45/63] lr: 2.0000e-03 eta: 11:55:45 time: 0.4964 data_time: 0.0080 memory: 14901 loss: 2.4726 loss_prob: 1.5066 loss_thr: 0.7218 loss_db: 0.2442 2022/11/02 13:13:01 - mmengine - INFO - Epoch(train) [100][50/63] lr: 2.0000e-03 eta: 11:55:45 time: 0.6682 data_time: 0.0229 memory: 14901 loss: 2.5254 loss_prob: 1.5544 loss_thr: 0.7134 loss_db: 0.2576 2022/11/02 13:13:04 - mmengine - INFO - Epoch(train) [100][55/63] lr: 2.0000e-03 eta: 11:55:45 time: 0.7403 data_time: 0.0266 memory: 14901 loss: 2.6892 loss_prob: 1.6560 loss_thr: 0.7576 loss_db: 0.2756 2022/11/02 13:13:07 - mmengine - INFO - Epoch(train) [100][60/63] lr: 2.0000e-03 eta: 11:55:43 time: 0.6572 data_time: 0.0092 memory: 14901 loss: 2.4227 loss_prob: 1.4659 loss_thr: 0.7154 loss_db: 0.2414 2022/11/02 13:13:09 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:13:09 - mmengine - INFO - Saving checkpoint at 100 epochs 2022/11/02 13:13:12 - mmengine - INFO - Epoch(val) [100][5/500] eta: 11:55:43 time: 0.0460 data_time: 0.0048 memory: 14901 2022/11/02 13:13:13 - mmengine - INFO - Epoch(val) [100][10/500] eta: 0:00:24 time: 0.0491 data_time: 0.0050 memory: 1008 2022/11/02 13:13:13 - mmengine - INFO - Epoch(val) [100][15/500] eta: 0:00:24 time: 0.0447 data_time: 0.0031 memory: 1008 2022/11/02 13:13:13 - mmengine - INFO - Epoch(val) [100][20/500] eta: 0:00:20 time: 0.0421 data_time: 0.0030 memory: 1008 2022/11/02 13:13:13 - mmengine - INFO - Epoch(val) [100][25/500] eta: 0:00:20 time: 0.0371 data_time: 0.0027 memory: 1008 2022/11/02 13:13:13 - mmengine - INFO - Epoch(val) [100][30/500] eta: 0:00:18 time: 0.0396 data_time: 0.0025 memory: 1008 2022/11/02 13:13:14 - mmengine - INFO - Epoch(val) [100][35/500] eta: 0:00:18 time: 0.0443 data_time: 0.0046 memory: 1008 2022/11/02 13:13:14 - mmengine - INFO - Epoch(val) [100][40/500] eta: 0:00:21 time: 0.0457 data_time: 0.0047 memory: 1008 2022/11/02 13:13:14 - mmengine - INFO - Epoch(val) [100][45/500] eta: 0:00:21 time: 0.0414 data_time: 0.0024 memory: 1008 2022/11/02 13:13:14 - mmengine - INFO - Epoch(val) [100][50/500] eta: 0:00:18 time: 0.0419 data_time: 0.0026 memory: 1008 2022/11/02 13:13:15 - mmengine - INFO - Epoch(val) [100][55/500] eta: 0:00:18 time: 0.0433 data_time: 0.0028 memory: 1008 2022/11/02 13:13:15 - mmengine - INFO - Epoch(val) [100][60/500] eta: 0:00:17 time: 0.0392 data_time: 0.0025 memory: 1008 2022/11/02 13:13:15 - mmengine - INFO - Epoch(val) [100][65/500] eta: 0:00:17 time: 0.0404 data_time: 0.0033 memory: 1008 2022/11/02 13:13:15 - mmengine - INFO - Epoch(val) [100][70/500] eta: 0:00:17 time: 0.0414 data_time: 0.0034 memory: 1008 2022/11/02 13:13:15 - mmengine - INFO - Epoch(val) [100][75/500] eta: 0:00:17 time: 0.0431 data_time: 0.0039 memory: 1008 2022/11/02 13:13:16 - mmengine - INFO - Epoch(val) [100][80/500] eta: 0:00:45 time: 0.1085 data_time: 0.0713 memory: 1008 2022/11/02 13:13:16 - mmengine - INFO - Epoch(val) [100][85/500] eta: 0:00:45 time: 0.1025 data_time: 0.0695 memory: 1008 2022/11/02 13:13:17 - mmengine - INFO - Epoch(val) [100][90/500] eta: 0:00:15 time: 0.0372 data_time: 0.0020 memory: 1008 2022/11/02 13:13:17 - mmengine - INFO - Epoch(val) [100][95/500] eta: 0:00:15 time: 0.0423 data_time: 0.0027 memory: 1008 2022/11/02 13:13:17 - mmengine - INFO - Epoch(val) [100][100/500] eta: 0:00:18 time: 0.0452 data_time: 0.0034 memory: 1008 2022/11/02 13:13:17 - mmengine - INFO - Epoch(val) [100][105/500] eta: 0:00:18 time: 0.0413 data_time: 0.0032 memory: 1008 2022/11/02 13:13:17 - mmengine - INFO - Epoch(val) [100][110/500] eta: 0:00:15 time: 0.0386 data_time: 0.0026 memory: 1008 2022/11/02 13:13:18 - mmengine - INFO - Epoch(val) [100][115/500] eta: 0:00:15 time: 0.0410 data_time: 0.0024 memory: 1008 2022/11/02 13:13:18 - mmengine - INFO - Epoch(val) [100][120/500] eta: 0:00:16 time: 0.0429 data_time: 0.0024 memory: 1008 2022/11/02 13:13:18 - mmengine - INFO - Epoch(val) [100][125/500] eta: 0:00:16 time: 0.0403 data_time: 0.0026 memory: 1008 2022/11/02 13:13:18 - mmengine - INFO - Epoch(val) [100][130/500] eta: 0:00:13 time: 0.0364 data_time: 0.0023 memory: 1008 2022/11/02 13:13:18 - mmengine - INFO - Epoch(val) [100][135/500] eta: 0:00:13 time: 0.0402 data_time: 0.0025 memory: 1008 2022/11/02 13:13:19 - mmengine - INFO - Epoch(val) [100][140/500] eta: 0:00:15 time: 0.0421 data_time: 0.0031 memory: 1008 2022/11/02 13:13:19 - mmengine - INFO - Epoch(val) [100][145/500] eta: 0:00:15 time: 0.0401 data_time: 0.0029 memory: 1008 2022/11/02 13:13:19 - mmengine - INFO - Epoch(val) [100][150/500] eta: 0:00:13 time: 0.0397 data_time: 0.0023 memory: 1008 2022/11/02 13:13:20 - mmengine - INFO - Epoch(val) [100][155/500] eta: 0:00:13 time: 0.0698 data_time: 0.0286 memory: 1008 2022/11/02 13:13:20 - mmengine - INFO - Epoch(val) [100][160/500] eta: 0:00:23 time: 0.0684 data_time: 0.0283 memory: 1008 2022/11/02 13:13:20 - mmengine - INFO - Epoch(val) [100][165/500] eta: 0:00:23 time: 0.0388 data_time: 0.0020 memory: 1008 2022/11/02 13:13:20 - mmengine - INFO - Epoch(val) [100][170/500] eta: 0:00:14 time: 0.0437 data_time: 0.0028 memory: 1008 2022/11/02 13:13:20 - mmengine - INFO - Epoch(val) [100][175/500] eta: 0:00:14 time: 0.0450 data_time: 0.0040 memory: 1008 2022/11/02 13:13:21 - mmengine - INFO - Epoch(val) [100][180/500] eta: 0:00:13 time: 0.0426 data_time: 0.0037 memory: 1008 2022/11/02 13:13:21 - mmengine - INFO - Epoch(val) [100][185/500] eta: 0:00:13 time: 0.0433 data_time: 0.0028 memory: 1008 2022/11/02 13:13:21 - mmengine - INFO - Epoch(val) [100][190/500] eta: 0:00:13 time: 0.0451 data_time: 0.0028 memory: 1008 2022/11/02 13:13:21 - mmengine - INFO - Epoch(val) [100][195/500] eta: 0:00:13 time: 0.0438 data_time: 0.0028 memory: 1008 2022/11/02 13:13:22 - mmengine - INFO - Epoch(val) [100][200/500] eta: 0:00:13 time: 0.0456 data_time: 0.0027 memory: 1008 2022/11/02 13:13:22 - mmengine - INFO - Epoch(val) [100][205/500] eta: 0:00:13 time: 0.0429 data_time: 0.0024 memory: 1008 2022/11/02 13:13:22 - mmengine - INFO - Epoch(val) [100][210/500] eta: 0:00:10 time: 0.0379 data_time: 0.0025 memory: 1008 2022/11/02 13:13:22 - mmengine - INFO - Epoch(val) [100][215/500] eta: 0:00:10 time: 0.0438 data_time: 0.0032 memory: 1008 2022/11/02 13:13:22 - mmengine - INFO - Epoch(val) [100][220/500] eta: 0:00:12 time: 0.0436 data_time: 0.0031 memory: 1008 2022/11/02 13:13:23 - mmengine - INFO - Epoch(val) [100][225/500] eta: 0:00:12 time: 0.0400 data_time: 0.0026 memory: 1008 2022/11/02 13:13:23 - mmengine - INFO - Epoch(val) [100][230/500] eta: 0:00:10 time: 0.0380 data_time: 0.0026 memory: 1008 2022/11/02 13:13:23 - mmengine - INFO - Epoch(val) [100][235/500] eta: 0:00:10 time: 0.0394 data_time: 0.0029 memory: 1008 2022/11/02 13:13:23 - mmengine - INFO - Epoch(val) [100][240/500] eta: 0:00:11 time: 0.0448 data_time: 0.0029 memory: 1008 2022/11/02 13:13:23 - mmengine - INFO - Epoch(val) [100][245/500] eta: 0:00:11 time: 0.0399 data_time: 0.0024 memory: 1008 2022/11/02 13:13:24 - mmengine - INFO - Epoch(val) [100][250/500] eta: 0:00:10 time: 0.0400 data_time: 0.0024 memory: 1008 2022/11/02 13:13:24 - mmengine - INFO - Epoch(val) [100][255/500] eta: 0:00:10 time: 0.0442 data_time: 0.0031 memory: 1008 2022/11/02 13:13:24 - mmengine - INFO - Epoch(val) [100][260/500] eta: 0:00:09 time: 0.0405 data_time: 0.0031 memory: 1008 2022/11/02 13:13:24 - mmengine - INFO - Epoch(val) [100][265/500] eta: 0:00:09 time: 0.0380 data_time: 0.0024 memory: 1008 2022/11/02 13:13:24 - mmengine - INFO - Epoch(val) [100][270/500] eta: 0:00:09 time: 0.0393 data_time: 0.0024 memory: 1008 2022/11/02 13:13:25 - mmengine - INFO - Epoch(val) [100][275/500] eta: 0:00:09 time: 0.0417 data_time: 0.0025 memory: 1008 2022/11/02 13:13:25 - mmengine - INFO - Epoch(val) [100][280/500] eta: 0:00:10 time: 0.0461 data_time: 0.0026 memory: 1008 2022/11/02 13:13:25 - mmengine - INFO - Epoch(val) [100][285/500] eta: 0:00:10 time: 0.0431 data_time: 0.0024 memory: 1008 2022/11/02 13:13:25 - mmengine - INFO - Epoch(val) [100][290/500] eta: 0:00:08 time: 0.0392 data_time: 0.0024 memory: 1008 2022/11/02 13:13:25 - mmengine - INFO - Epoch(val) [100][295/500] eta: 0:00:08 time: 0.0394 data_time: 0.0024 memory: 1008 2022/11/02 13:13:26 - mmengine - INFO - Epoch(val) [100][300/500] eta: 0:00:07 time: 0.0376 data_time: 0.0023 memory: 1008 2022/11/02 13:13:26 - mmengine - INFO - Epoch(val) [100][305/500] eta: 0:00:07 time: 0.0384 data_time: 0.0033 memory: 1008 2022/11/02 13:13:26 - mmengine - INFO - Epoch(val) [100][310/500] eta: 0:00:07 time: 0.0382 data_time: 0.0032 memory: 1008 2022/11/02 13:13:26 - mmengine - INFO - Epoch(val) [100][315/500] eta: 0:00:07 time: 0.0441 data_time: 0.0026 memory: 1008 2022/11/02 13:13:26 - mmengine - INFO - Epoch(val) [100][320/500] eta: 0:00:08 time: 0.0466 data_time: 0.0035 memory: 1008 2022/11/02 13:13:27 - mmengine - INFO - Epoch(val) [100][325/500] eta: 0:00:08 time: 0.0577 data_time: 0.0034 memory: 1008 2022/11/02 13:13:27 - mmengine - INFO - Epoch(val) [100][330/500] eta: 0:00:09 time: 0.0543 data_time: 0.0032 memory: 1008 2022/11/02 13:13:27 - mmengine - INFO - Epoch(val) [100][335/500] eta: 0:00:09 time: 0.0346 data_time: 0.0031 memory: 1008 2022/11/02 13:13:27 - mmengine - INFO - Epoch(val) [100][340/500] eta: 0:00:07 time: 0.0483 data_time: 0.0023 memory: 1008 2022/11/02 13:13:28 - mmengine - INFO - Epoch(val) [100][345/500] eta: 0:00:07 time: 0.0494 data_time: 0.0022 memory: 1008 2022/11/02 13:13:28 - mmengine - INFO - Epoch(val) [100][350/500] eta: 0:00:06 time: 0.0416 data_time: 0.0025 memory: 1008 2022/11/02 13:13:28 - mmengine - INFO - Epoch(val) [100][355/500] eta: 0:00:06 time: 0.0464 data_time: 0.0027 memory: 1008 2022/11/02 13:13:28 - mmengine - INFO - Epoch(val) [100][360/500] eta: 0:00:05 time: 0.0415 data_time: 0.0026 memory: 1008 2022/11/02 13:13:29 - mmengine - INFO - Epoch(val) [100][365/500] eta: 0:00:05 time: 0.0416 data_time: 0.0030 memory: 1008 2022/11/02 13:13:29 - mmengine - INFO - Epoch(val) [100][370/500] eta: 0:00:05 time: 0.0394 data_time: 0.0030 memory: 1008 2022/11/02 13:13:29 - mmengine - INFO - Epoch(val) [100][375/500] eta: 0:00:05 time: 0.0363 data_time: 0.0023 memory: 1008 2022/11/02 13:13:29 - mmengine - INFO - Epoch(val) [100][380/500] eta: 0:00:05 time: 0.0436 data_time: 0.0034 memory: 1008 2022/11/02 13:13:29 - mmengine - INFO - Epoch(val) [100][385/500] eta: 0:00:05 time: 0.0457 data_time: 0.0037 memory: 1008 2022/11/02 13:13:30 - mmengine - INFO - Epoch(val) [100][390/500] eta: 0:00:04 time: 0.0454 data_time: 0.0030 memory: 1008 2022/11/02 13:13:30 - mmengine - INFO - Epoch(val) [100][395/500] eta: 0:00:04 time: 0.0486 data_time: 0.0033 memory: 1008 2022/11/02 13:13:30 - mmengine - INFO - Epoch(val) [100][400/500] eta: 0:00:04 time: 0.0464 data_time: 0.0034 memory: 1008 2022/11/02 13:13:30 - mmengine - INFO - Epoch(val) [100][405/500] eta: 0:00:04 time: 0.0426 data_time: 0.0028 memory: 1008 2022/11/02 13:13:31 - mmengine - INFO - Epoch(val) [100][410/500] eta: 0:00:04 time: 0.0521 data_time: 0.0037 memory: 1008 2022/11/02 13:13:31 - mmengine - INFO - Epoch(val) [100][415/500] eta: 0:00:04 time: 0.0628 data_time: 0.0087 memory: 1008 2022/11/02 13:13:31 - mmengine - INFO - Epoch(val) [100][420/500] eta: 0:00:04 time: 0.0559 data_time: 0.0108 memory: 1008 2022/11/02 13:13:31 - mmengine - INFO - Epoch(val) [100][425/500] eta: 0:00:04 time: 0.0450 data_time: 0.0060 memory: 1008 2022/11/02 13:13:32 - mmengine - INFO - Epoch(val) [100][430/500] eta: 0:00:02 time: 0.0403 data_time: 0.0028 memory: 1008 2022/11/02 13:13:32 - mmengine - INFO - Epoch(val) [100][435/500] eta: 0:00:02 time: 0.0406 data_time: 0.0040 memory: 1008 2022/11/02 13:13:32 - mmengine - INFO - Epoch(val) [100][440/500] eta: 0:00:02 time: 0.0461 data_time: 0.0044 memory: 1008 2022/11/02 13:13:32 - mmengine - INFO - Epoch(val) [100][445/500] eta: 0:00:02 time: 0.0517 data_time: 0.0046 memory: 1008 2022/11/02 13:13:32 - mmengine - INFO - Epoch(val) [100][450/500] eta: 0:00:02 time: 0.0496 data_time: 0.0049 memory: 1008 2022/11/02 13:13:33 - mmengine - INFO - Epoch(val) [100][455/500] eta: 0:00:02 time: 0.0474 data_time: 0.0038 memory: 1008 2022/11/02 13:13:33 - mmengine - INFO - Epoch(val) [100][460/500] eta: 0:00:01 time: 0.0430 data_time: 0.0031 memory: 1008 2022/11/02 13:13:33 - mmengine - INFO - Epoch(val) [100][465/500] eta: 0:00:01 time: 0.0362 data_time: 0.0026 memory: 1008 2022/11/02 13:13:33 - mmengine - INFO - Epoch(val) [100][470/500] eta: 0:00:01 time: 0.0367 data_time: 0.0024 memory: 1008 2022/11/02 13:13:33 - mmengine - INFO - Epoch(val) [100][475/500] eta: 0:00:01 time: 0.0366 data_time: 0.0024 memory: 1008 2022/11/02 13:13:34 - mmengine - INFO - Epoch(val) [100][480/500] eta: 0:00:00 time: 0.0383 data_time: 0.0038 memory: 1008 2022/11/02 13:13:34 - mmengine - INFO - Epoch(val) [100][485/500] eta: 0:00:00 time: 0.0398 data_time: 0.0035 memory: 1008 2022/11/02 13:13:34 - mmengine - INFO - Epoch(val) [100][490/500] eta: 0:00:00 time: 0.0386 data_time: 0.0020 memory: 1008 2022/11/02 13:13:34 - mmengine - INFO - Epoch(val) [100][495/500] eta: 0:00:00 time: 0.0423 data_time: 0.0024 memory: 1008 2022/11/02 13:13:34 - mmengine - INFO - Epoch(val) [100][500/500] eta: 0:00:00 time: 0.0422 data_time: 0.0031 memory: 1008 2022/11/02 13:13:34 - mmengine - INFO - Evaluating hmean-iou... 2022/11/02 13:13:35 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.7188, precision: 0.6768, hmean: 0.6972 2022/11/02 13:13:35 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.7174, precision: 0.7888, hmean: 0.7514 2022/11/02 13:13:35 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.7044, precision: 0.8452, hmean: 0.7684 2022/11/02 13:13:35 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.6394, precision: 0.8895, hmean: 0.7440 2022/11/02 13:13:35 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.4338, precision: 0.9366, hmean: 0.5930 2022/11/02 13:13:35 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.0255, precision: 0.9464, hmean: 0.0497 2022/11/02 13:13:35 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0000, precision: 0.0000, hmean: 0.0000 2022/11/02 13:13:35 - mmengine - INFO - Epoch(val) [100][500/500] icdar/precision: 0.8452 icdar/recall: 0.7044 icdar/hmean: 0.7684 2022/11/02 13:13:41 - mmengine - INFO - Epoch(train) [101][5/63] lr: 2.0000e-03 eta: 0:00:00 time: 0.8934 data_time: 0.2492 memory: 14901 loss: 2.3203 loss_prob: 1.3946 loss_thr: 0.6986 loss_db: 0.2271 2022/11/02 13:13:44 - mmengine - INFO - Epoch(train) [101][10/63] lr: 2.0000e-03 eta: 11:55:53 time: 0.9734 data_time: 0.2505 memory: 14901 loss: 2.5264 loss_prob: 1.5569 loss_thr: 0.7164 loss_db: 0.2531 2022/11/02 13:13:48 - mmengine - INFO - Epoch(train) [101][15/63] lr: 2.0000e-03 eta: 11:55:53 time: 0.6411 data_time: 0.0150 memory: 14901 loss: 2.5360 loss_prob: 1.5662 loss_thr: 0.7198 loss_db: 0.2500 2022/11/02 13:13:50 - mmengine - INFO - Epoch(train) [101][20/63] lr: 2.0000e-03 eta: 11:55:47 time: 0.6215 data_time: 0.0133 memory: 14901 loss: 2.4899 loss_prob: 1.5267 loss_thr: 0.7179 loss_db: 0.2454 2022/11/02 13:13:53 - mmengine - INFO - Epoch(train) [101][25/63] lr: 2.0000e-03 eta: 11:55:47 time: 0.5416 data_time: 0.0047 memory: 14901 loss: 2.4769 loss_prob: 1.5080 loss_thr: 0.7210 loss_db: 0.2480 2022/11/02 13:13:56 - mmengine - INFO - Epoch(train) [101][30/63] lr: 2.0000e-03 eta: 11:55:30 time: 0.5204 data_time: 0.0253 memory: 14901 loss: 2.3911 loss_prob: 1.4428 loss_thr: 0.7150 loss_db: 0.2332 2022/11/02 13:13:58 - mmengine - INFO - Epoch(train) [101][35/63] lr: 2.0000e-03 eta: 11:55:30 time: 0.4881 data_time: 0.0267 memory: 14901 loss: 2.2739 loss_prob: 1.3581 loss_thr: 0.6940 loss_db: 0.2218 2022/11/02 13:14:01 - mmengine - INFO - Epoch(train) [101][40/63] lr: 2.0000e-03 eta: 11:55:11 time: 0.4995 data_time: 0.0136 memory: 14901 loss: 2.1834 loss_prob: 1.2995 loss_thr: 0.6659 loss_db: 0.2180 2022/11/02 13:14:03 - mmengine - INFO - Epoch(train) [101][45/63] lr: 2.0000e-03 eta: 11:55:11 time: 0.5035 data_time: 0.0121 memory: 14901 loss: 2.3654 loss_prob: 1.4436 loss_thr: 0.6816 loss_db: 0.2402 2022/11/02 13:14:05 - mmengine - INFO - Epoch(train) [101][50/63] lr: 2.0000e-03 eta: 11:54:48 time: 0.4695 data_time: 0.0144 memory: 14901 loss: 2.4333 loss_prob: 1.4984 loss_thr: 0.6890 loss_db: 0.2458 2022/11/02 13:14:08 - mmengine - INFO - Epoch(train) [101][55/63] lr: 2.0000e-03 eta: 11:54:48 time: 0.4713 data_time: 0.0147 memory: 14901 loss: 2.6820 loss_prob: 1.6826 loss_thr: 0.7173 loss_db: 0.2821 2022/11/02 13:14:10 - mmengine - INFO - Epoch(train) [101][60/63] lr: 2.0000e-03 eta: 11:54:28 time: 0.4915 data_time: 0.0059 memory: 14901 loss: 2.7857 loss_prob: 1.7358 loss_thr: 0.7576 loss_db: 0.2923 2022/11/02 13:14:12 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:14:17 - mmengine - INFO - Epoch(train) [102][5/63] lr: 1.9984e-03 eta: 11:54:28 time: 0.7541 data_time: 0.2565 memory: 14901 loss: 2.9031 loss_prob: 1.8359 loss_thr: 0.7516 loss_db: 0.3156 2022/11/02 13:14:19 - mmengine - INFO - Epoch(train) [102][10/63] lr: 1.9984e-03 eta: 11:54:16 time: 0.7707 data_time: 0.2616 memory: 14901 loss: 2.7169 loss_prob: 1.6976 loss_thr: 0.7313 loss_db: 0.2879 2022/11/02 13:14:22 - mmengine - INFO - Epoch(train) [102][15/63] lr: 1.9984e-03 eta: 11:54:16 time: 0.5296 data_time: 0.0121 memory: 14901 loss: 2.5126 loss_prob: 1.5562 loss_thr: 0.6979 loss_db: 0.2585 2022/11/02 13:14:25 - mmengine - INFO - Epoch(train) [102][20/63] lr: 1.9984e-03 eta: 11:53:58 time: 0.5120 data_time: 0.0051 memory: 14901 loss: 2.3412 loss_prob: 1.4102 loss_thr: 0.7031 loss_db: 0.2279 2022/11/02 13:14:27 - mmengine - INFO - Epoch(train) [102][25/63] lr: 1.9984e-03 eta: 11:53:58 time: 0.5354 data_time: 0.0282 memory: 14901 loss: 2.2673 loss_prob: 1.3658 loss_thr: 0.6838 loss_db: 0.2177 2022/11/02 13:14:30 - mmengine - INFO - Epoch(train) [102][30/63] lr: 1.9984e-03 eta: 11:53:44 time: 0.5421 data_time: 0.0430 memory: 14901 loss: 2.3479 loss_prob: 1.4412 loss_thr: 0.6769 loss_db: 0.2298 2022/11/02 13:14:32 - mmengine - INFO - Epoch(train) [102][35/63] lr: 1.9984e-03 eta: 11:53:44 time: 0.4882 data_time: 0.0195 memory: 14901 loss: 2.3001 loss_prob: 1.3913 loss_thr: 0.6839 loss_db: 0.2249 2022/11/02 13:14:35 - mmengine - INFO - Epoch(train) [102][40/63] lr: 1.9984e-03 eta: 11:53:21 time: 0.4610 data_time: 0.0063 memory: 14901 loss: 2.1910 loss_prob: 1.3143 loss_thr: 0.6628 loss_db: 0.2139 2022/11/02 13:14:37 - mmengine - INFO - Epoch(train) [102][45/63] lr: 1.9984e-03 eta: 11:53:21 time: 0.4854 data_time: 0.0061 memory: 14901 loss: 2.2713 loss_prob: 1.3733 loss_thr: 0.6775 loss_db: 0.2205 2022/11/02 13:14:40 - mmengine - INFO - Epoch(train) [102][50/63] lr: 1.9984e-03 eta: 11:53:03 time: 0.5100 data_time: 0.0198 memory: 14901 loss: 2.2226 loss_prob: 1.3248 loss_thr: 0.6870 loss_db: 0.2108 2022/11/02 13:14:42 - mmengine - INFO - Epoch(train) [102][55/63] lr: 1.9984e-03 eta: 11:53:03 time: 0.4831 data_time: 0.0231 memory: 14901 loss: 2.1938 loss_prob: 1.3012 loss_thr: 0.6794 loss_db: 0.2132 2022/11/02 13:14:45 - mmengine - INFO - Epoch(train) [102][60/63] lr: 1.9984e-03 eta: 11:52:42 time: 0.4810 data_time: 0.0082 memory: 14901 loss: 2.5730 loss_prob: 1.5968 loss_thr: 0.7171 loss_db: 0.2592 2022/11/02 13:14:46 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:14:51 - mmengine - INFO - Epoch(train) [103][5/63] lr: 1.9967e-03 eta: 11:52:42 time: 0.7986 data_time: 0.2400 memory: 14901 loss: 2.5000 loss_prob: 1.5373 loss_thr: 0.7109 loss_db: 0.2519 2022/11/02 13:14:54 - mmengine - INFO - Epoch(train) [103][10/63] lr: 1.9967e-03 eta: 11:52:34 time: 0.8049 data_time: 0.2419 memory: 14901 loss: 2.2510 loss_prob: 1.3474 loss_thr: 0.6806 loss_db: 0.2230 2022/11/02 13:14:56 - mmengine - INFO - Epoch(train) [103][15/63] lr: 1.9967e-03 eta: 11:52:34 time: 0.4778 data_time: 0.0072 memory: 14901 loss: 2.2981 loss_prob: 1.3815 loss_thr: 0.6931 loss_db: 0.2236 2022/11/02 13:14:59 - mmengine - INFO - Epoch(train) [103][20/63] lr: 1.9967e-03 eta: 11:52:12 time: 0.4748 data_time: 0.0050 memory: 14901 loss: 2.3587 loss_prob: 1.4146 loss_thr: 0.7108 loss_db: 0.2334 2022/11/02 13:15:01 - mmengine - INFO - Epoch(train) [103][25/63] lr: 1.9967e-03 eta: 11:52:12 time: 0.4934 data_time: 0.0246 memory: 14901 loss: 2.2657 loss_prob: 1.3505 loss_thr: 0.6938 loss_db: 0.2214 2022/11/02 13:15:04 - mmengine - INFO - Epoch(train) [103][30/63] lr: 1.9967e-03 eta: 11:51:55 time: 0.5112 data_time: 0.0310 memory: 14901 loss: 2.3952 loss_prob: 1.4637 loss_thr: 0.6920 loss_db: 0.2395 2022/11/02 13:15:06 - mmengine - INFO - Epoch(train) [103][35/63] lr: 1.9967e-03 eta: 11:51:55 time: 0.4928 data_time: 0.0149 memory: 14901 loss: 2.5035 loss_prob: 1.5427 loss_thr: 0.7108 loss_db: 0.2500 2022/11/02 13:15:09 - mmengine - INFO - Epoch(train) [103][40/63] lr: 1.9967e-03 eta: 11:51:33 time: 0.4774 data_time: 0.0079 memory: 14901 loss: 2.3714 loss_prob: 1.4156 loss_thr: 0.7295 loss_db: 0.2262 2022/11/02 13:15:11 - mmengine - INFO - Epoch(train) [103][45/63] lr: 1.9967e-03 eta: 11:51:33 time: 0.4730 data_time: 0.0054 memory: 14901 loss: 2.5489 loss_prob: 1.5477 loss_thr: 0.7472 loss_db: 0.2540 2022/11/02 13:15:14 - mmengine - INFO - Epoch(train) [103][50/63] lr: 1.9967e-03 eta: 11:51:15 time: 0.5022 data_time: 0.0210 memory: 14901 loss: 2.6936 loss_prob: 1.6743 loss_thr: 0.7409 loss_db: 0.2785 2022/11/02 13:15:16 - mmengine - INFO - Epoch(train) [103][55/63] lr: 1.9967e-03 eta: 11:51:15 time: 0.4969 data_time: 0.0209 memory: 14901 loss: 2.6107 loss_prob: 1.6190 loss_thr: 0.7228 loss_db: 0.2690 2022/11/02 13:15:18 - mmengine - INFO - Epoch(train) [103][60/63] lr: 1.9967e-03 eta: 11:50:52 time: 0.4569 data_time: 0.0055 memory: 14901 loss: 2.6309 loss_prob: 1.6336 loss_thr: 0.7257 loss_db: 0.2716 2022/11/02 13:15:19 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:15:24 - mmengine - INFO - Epoch(train) [104][5/63] lr: 1.9951e-03 eta: 11:50:52 time: 0.6576 data_time: 0.2166 memory: 14901 loss: 2.2904 loss_prob: 1.3781 loss_thr: 0.6802 loss_db: 0.2321 2022/11/02 13:15:27 - mmengine - INFO - Epoch(train) [104][10/63] lr: 1.9951e-03 eta: 11:50:37 time: 0.7352 data_time: 0.2174 memory: 14901 loss: 2.6400 loss_prob: 1.6375 loss_thr: 0.7222 loss_db: 0.2803 2022/11/02 13:15:29 - mmengine - INFO - Epoch(train) [104][15/63] lr: 1.9951e-03 eta: 11:50:37 time: 0.5248 data_time: 0.0082 memory: 14901 loss: 2.7491 loss_prob: 1.7113 loss_thr: 0.7551 loss_db: 0.2827 2022/11/02 13:15:32 - mmengine - INFO - Epoch(train) [104][20/63] lr: 1.9951e-03 eta: 11:50:17 time: 0.4885 data_time: 0.0076 memory: 14901 loss: 2.4258 loss_prob: 1.4778 loss_thr: 0.7067 loss_db: 0.2413 2022/11/02 13:15:35 - mmengine - INFO - Epoch(train) [104][25/63] lr: 1.9951e-03 eta: 11:50:17 time: 0.5828 data_time: 0.0284 memory: 14901 loss: 2.2654 loss_prob: 1.3754 loss_thr: 0.6674 loss_db: 0.2226 2022/11/02 13:15:38 - mmengine - INFO - Epoch(train) [104][30/63] lr: 1.9951e-03 eta: 11:50:13 time: 0.6381 data_time: 0.0334 memory: 14901 loss: 2.1978 loss_prob: 1.3176 loss_thr: 0.6730 loss_db: 0.2072 2022/11/02 13:15:41 - mmengine - INFO - Epoch(train) [104][35/63] lr: 1.9951e-03 eta: 11:50:13 time: 0.5769 data_time: 0.0106 memory: 14901 loss: 2.2481 loss_prob: 1.3446 loss_thr: 0.6862 loss_db: 0.2173 2022/11/02 13:15:44 - mmengine - INFO - Epoch(train) [104][40/63] lr: 1.9951e-03 eta: 11:50:02 time: 0.5732 data_time: 0.0059 memory: 14901 loss: 2.3371 loss_prob: 1.3885 loss_thr: 0.7181 loss_db: 0.2305 2022/11/02 13:15:47 - mmengine - INFO - Epoch(train) [104][45/63] lr: 1.9951e-03 eta: 11:50:02 time: 0.5801 data_time: 0.0054 memory: 14901 loss: 2.2768 loss_prob: 1.3403 loss_thr: 0.7155 loss_db: 0.2210 2022/11/02 13:15:49 - mmengine - INFO - Epoch(train) [104][50/63] lr: 1.9951e-03 eta: 11:49:47 time: 0.5330 data_time: 0.0167 memory: 14901 loss: 2.2001 loss_prob: 1.3261 loss_thr: 0.6593 loss_db: 0.2147 2022/11/02 13:15:52 - mmengine - INFO - Epoch(train) [104][55/63] lr: 1.9951e-03 eta: 11:49:47 time: 0.5029 data_time: 0.0207 memory: 14901 loss: 2.2387 loss_prob: 1.3574 loss_thr: 0.6613 loss_db: 0.2200 2022/11/02 13:15:54 - mmengine - INFO - Epoch(train) [104][60/63] lr: 1.9951e-03 eta: 11:49:28 time: 0.4923 data_time: 0.0088 memory: 14901 loss: 2.3610 loss_prob: 1.4358 loss_thr: 0.6919 loss_db: 0.2333 2022/11/02 13:15:55 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:16:00 - mmengine - INFO - Epoch(train) [105][5/63] lr: 1.9934e-03 eta: 11:49:28 time: 0.7203 data_time: 0.2083 memory: 14901 loss: 2.3204 loss_prob: 1.4219 loss_thr: 0.6704 loss_db: 0.2280 2022/11/02 13:16:03 - mmengine - INFO - Epoch(train) [105][10/63] lr: 1.9934e-03 eta: 11:49:17 time: 0.7787 data_time: 0.2172 memory: 14901 loss: 2.4406 loss_prob: 1.5056 loss_thr: 0.6924 loss_db: 0.2426 2022/11/02 13:16:06 - mmengine - INFO - Epoch(train) [105][15/63] lr: 1.9934e-03 eta: 11:49:17 time: 0.6049 data_time: 0.0143 memory: 14901 loss: 2.4369 loss_prob: 1.4959 loss_thr: 0.6981 loss_db: 0.2430 2022/11/02 13:16:09 - mmengine - INFO - Epoch(train) [105][20/63] lr: 1.9934e-03 eta: 11:49:08 time: 0.5904 data_time: 0.0063 memory: 14901 loss: 2.5126 loss_prob: 1.5482 loss_thr: 0.7122 loss_db: 0.2521 2022/11/02 13:16:13 - mmengine - INFO - Epoch(train) [105][25/63] lr: 1.9934e-03 eta: 11:49:08 time: 0.6452 data_time: 0.0314 memory: 14901 loss: 2.6492 loss_prob: 1.6397 loss_thr: 0.7445 loss_db: 0.2649 2022/11/02 13:16:16 - mmengine - INFO - Epoch(train) [105][30/63] lr: 1.9934e-03 eta: 11:49:12 time: 0.7109 data_time: 0.0304 memory: 14901 loss: 2.5634 loss_prob: 1.5782 loss_thr: 0.7252 loss_db: 0.2600 2022/11/02 13:16:19 - mmengine - INFO - Epoch(train) [105][35/63] lr: 1.9934e-03 eta: 11:49:12 time: 0.6225 data_time: 0.0082 memory: 14901 loss: 2.5418 loss_prob: 1.5637 loss_thr: 0.7129 loss_db: 0.2652 2022/11/02 13:16:22 - mmengine - INFO - Epoch(train) [105][40/63] lr: 1.9934e-03 eta: 11:49:02 time: 0.5744 data_time: 0.0079 memory: 14901 loss: 2.5180 loss_prob: 1.5538 loss_thr: 0.7046 loss_db: 0.2595 2022/11/02 13:16:24 - mmengine - INFO - Epoch(train) [105][45/63] lr: 1.9934e-03 eta: 11:49:02 time: 0.5044 data_time: 0.0042 memory: 14901 loss: 2.5078 loss_prob: 1.5415 loss_thr: 0.7096 loss_db: 0.2567 2022/11/02 13:16:26 - mmengine - INFO - Epoch(train) [105][50/63] lr: 1.9934e-03 eta: 11:48:40 time: 0.4677 data_time: 0.0176 memory: 14901 loss: 2.4354 loss_prob: 1.4848 loss_thr: 0.7037 loss_db: 0.2469 2022/11/02 13:16:29 - mmengine - INFO - Epoch(train) [105][55/63] lr: 1.9934e-03 eta: 11:48:40 time: 0.5152 data_time: 0.0176 memory: 14901 loss: 2.4525 loss_prob: 1.5067 loss_thr: 0.6882 loss_db: 0.2576 2022/11/02 13:16:32 - mmengine - INFO - Epoch(train) [105][60/63] lr: 1.9934e-03 eta: 11:48:25 time: 0.5350 data_time: 0.0070 memory: 14901 loss: 2.4735 loss_prob: 1.5124 loss_thr: 0.7011 loss_db: 0.2600 2022/11/02 13:16:33 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:16:38 - mmengine - INFO - Epoch(train) [106][5/63] lr: 1.9918e-03 eta: 11:48:25 time: 0.7162 data_time: 0.2124 memory: 14901 loss: 2.3691 loss_prob: 1.4353 loss_thr: 0.7005 loss_db: 0.2334 2022/11/02 13:16:40 - mmengine - INFO - Epoch(train) [106][10/63] lr: 1.9918e-03 eta: 11:48:14 time: 0.7700 data_time: 0.2138 memory: 14901 loss: 2.4110 loss_prob: 1.4501 loss_thr: 0.7270 loss_db: 0.2339 2022/11/02 13:16:43 - mmengine - INFO - Epoch(train) [106][15/63] lr: 1.9918e-03 eta: 11:48:14 time: 0.5246 data_time: 0.0117 memory: 14901 loss: 2.3242 loss_prob: 1.4000 loss_thr: 0.6952 loss_db: 0.2290 2022/11/02 13:16:46 - mmengine - INFO - Epoch(train) [106][20/63] lr: 1.9918e-03 eta: 11:48:01 time: 0.5467 data_time: 0.0134 memory: 14901 loss: 2.3149 loss_prob: 1.4038 loss_thr: 0.6791 loss_db: 0.2319 2022/11/02 13:16:49 - mmengine - INFO - Epoch(train) [106][25/63] lr: 1.9918e-03 eta: 11:48:01 time: 0.5439 data_time: 0.0303 memory: 14901 loss: 2.3456 loss_prob: 1.4273 loss_thr: 0.6845 loss_db: 0.2338 2022/11/02 13:16:51 - mmengine - INFO - Epoch(train) [106][30/63] lr: 1.9918e-03 eta: 11:47:42 time: 0.4965 data_time: 0.0302 memory: 14901 loss: 2.4816 loss_prob: 1.5284 loss_thr: 0.7005 loss_db: 0.2527 2022/11/02 13:16:53 - mmengine - INFO - Epoch(train) [106][35/63] lr: 1.9918e-03 eta: 11:47:42 time: 0.4690 data_time: 0.0095 memory: 14901 loss: 2.5966 loss_prob: 1.6139 loss_thr: 0.7150 loss_db: 0.2678 2022/11/02 13:16:56 - mmengine - INFO - Epoch(train) [106][40/63] lr: 1.9918e-03 eta: 11:47:23 time: 0.4899 data_time: 0.0063 memory: 14901 loss: 2.5419 loss_prob: 1.5789 loss_thr: 0.7006 loss_db: 0.2623 2022/11/02 13:16:58 - mmengine - INFO - Epoch(train) [106][45/63] lr: 1.9918e-03 eta: 11:47:23 time: 0.5124 data_time: 0.0042 memory: 14901 loss: 2.3853 loss_prob: 1.4605 loss_thr: 0.6805 loss_db: 0.2443 2022/11/02 13:17:01 - mmengine - INFO - Epoch(train) [106][50/63] lr: 1.9918e-03 eta: 11:47:06 time: 0.5122 data_time: 0.0180 memory: 14901 loss: 2.3809 loss_prob: 1.4462 loss_thr: 0.6953 loss_db: 0.2395 2022/11/02 13:17:03 - mmengine - INFO - Epoch(train) [106][55/63] lr: 1.9918e-03 eta: 11:47:06 time: 0.4702 data_time: 0.0179 memory: 14901 loss: 2.4963 loss_prob: 1.5130 loss_thr: 0.7333 loss_db: 0.2501 2022/11/02 13:17:06 - mmengine - INFO - Epoch(train) [106][60/63] lr: 1.9918e-03 eta: 11:46:44 time: 0.4630 data_time: 0.0048 memory: 14901 loss: 2.4527 loss_prob: 1.4867 loss_thr: 0.7227 loss_db: 0.2434 2022/11/02 13:17:07 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:17:12 - mmengine - INFO - Epoch(train) [107][5/63] lr: 1.9902e-03 eta: 11:46:44 time: 0.7038 data_time: 0.2012 memory: 14901 loss: 2.3227 loss_prob: 1.3864 loss_thr: 0.7141 loss_db: 0.2222 2022/11/02 13:17:14 - mmengine - INFO - Epoch(train) [107][10/63] lr: 1.9902e-03 eta: 11:46:29 time: 0.7301 data_time: 0.2039 memory: 14901 loss: 2.2322 loss_prob: 1.3158 loss_thr: 0.7080 loss_db: 0.2084 2022/11/02 13:17:17 - mmengine - INFO - Epoch(train) [107][15/63] lr: 1.9902e-03 eta: 11:46:29 time: 0.4885 data_time: 0.0092 memory: 14901 loss: 2.2335 loss_prob: 1.3320 loss_thr: 0.6864 loss_db: 0.2151 2022/11/02 13:17:19 - mmengine - INFO - Epoch(train) [107][20/63] lr: 1.9902e-03 eta: 11:46:09 time: 0.4814 data_time: 0.0092 memory: 14901 loss: 2.2032 loss_prob: 1.3148 loss_thr: 0.6760 loss_db: 0.2124 2022/11/02 13:17:21 - mmengine - INFO - Epoch(train) [107][25/63] lr: 1.9902e-03 eta: 11:46:09 time: 0.4843 data_time: 0.0242 memory: 14901 loss: 2.4223 loss_prob: 1.4875 loss_thr: 0.6952 loss_db: 0.2396 2022/11/02 13:17:24 - mmengine - INFO - Epoch(train) [107][30/63] lr: 1.9902e-03 eta: 11:45:50 time: 0.4885 data_time: 0.0300 memory: 14901 loss: 2.4352 loss_prob: 1.4951 loss_thr: 0.6980 loss_db: 0.2421 2022/11/02 13:17:26 - mmengine - INFO - Epoch(train) [107][35/63] lr: 1.9902e-03 eta: 11:45:50 time: 0.4880 data_time: 0.0149 memory: 14901 loss: 2.1969 loss_prob: 1.3130 loss_thr: 0.6682 loss_db: 0.2157 2022/11/02 13:17:29 - mmengine - INFO - Epoch(train) [107][40/63] lr: 1.9902e-03 eta: 11:45:29 time: 0.4725 data_time: 0.0065 memory: 14901 loss: 2.1681 loss_prob: 1.2948 loss_thr: 0.6594 loss_db: 0.2139 2022/11/02 13:17:31 - mmengine - INFO - Epoch(train) [107][45/63] lr: 1.9902e-03 eta: 11:45:29 time: 0.4558 data_time: 0.0054 memory: 14901 loss: 2.4360 loss_prob: 1.4787 loss_thr: 0.7155 loss_db: 0.2418 2022/11/02 13:17:34 - mmengine - INFO - Epoch(train) [107][50/63] lr: 1.9902e-03 eta: 11:45:12 time: 0.5067 data_time: 0.0291 memory: 14901 loss: 2.4985 loss_prob: 1.5297 loss_thr: 0.7226 loss_db: 0.2462 2022/11/02 13:17:36 - mmengine - INFO - Epoch(train) [107][55/63] lr: 1.9902e-03 eta: 11:45:12 time: 0.5216 data_time: 0.0350 memory: 14901 loss: 2.3380 loss_prob: 1.4231 loss_thr: 0.6840 loss_db: 0.2309 2022/11/02 13:17:39 - mmengine - INFO - Epoch(train) [107][60/63] lr: 1.9902e-03 eta: 11:44:53 time: 0.4894 data_time: 0.0149 memory: 14901 loss: 2.3676 loss_prob: 1.4525 loss_thr: 0.6752 loss_db: 0.2399 2022/11/02 13:17:40 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:17:44 - mmengine - INFO - Epoch(train) [108][5/63] lr: 1.9885e-03 eta: 11:44:53 time: 0.6668 data_time: 0.1955 memory: 14901 loss: 2.4456 loss_prob: 1.4815 loss_thr: 0.7225 loss_db: 0.2416 2022/11/02 13:17:47 - mmengine - INFO - Epoch(train) [108][10/63] lr: 1.9885e-03 eta: 11:44:36 time: 0.7080 data_time: 0.2000 memory: 14901 loss: 2.5499 loss_prob: 1.5545 loss_thr: 0.7342 loss_db: 0.2612 2022/11/02 13:17:50 - mmengine - INFO - Epoch(train) [108][15/63] lr: 1.9885e-03 eta: 11:44:36 time: 0.5471 data_time: 0.0178 memory: 14901 loss: 2.8256 loss_prob: 1.7801 loss_thr: 0.7384 loss_db: 0.3071 2022/11/02 13:17:52 - mmengine - INFO - Epoch(train) [108][20/63] lr: 1.9885e-03 eta: 11:44:20 time: 0.5195 data_time: 0.0116 memory: 14901 loss: 2.7950 loss_prob: 1.7602 loss_thr: 0.7380 loss_db: 0.2968 2022/11/02 13:17:54 - mmengine - INFO - Epoch(train) [108][25/63] lr: 1.9885e-03 eta: 11:44:20 time: 0.4664 data_time: 0.0077 memory: 14901 loss: 2.7431 loss_prob: 1.7041 loss_thr: 0.7501 loss_db: 0.2888 2022/11/02 13:17:57 - mmengine - INFO - Epoch(train) [108][30/63] lr: 1.9885e-03 eta: 11:44:01 time: 0.4847 data_time: 0.0298 memory: 14901 loss: 2.7729 loss_prob: 1.7297 loss_thr: 0.7449 loss_db: 0.2984 2022/11/02 13:17:59 - mmengine - INFO - Epoch(train) [108][35/63] lr: 1.9885e-03 eta: 11:44:01 time: 0.4971 data_time: 0.0322 memory: 14901 loss: 2.6886 loss_prob: 1.6849 loss_thr: 0.7137 loss_db: 0.2900 2022/11/02 13:18:02 - mmengine - INFO - Epoch(train) [108][40/63] lr: 1.9885e-03 eta: 11:43:44 time: 0.5064 data_time: 0.0146 memory: 14901 loss: 2.5315 loss_prob: 1.5607 loss_thr: 0.7088 loss_db: 0.2620 2022/11/02 13:18:04 - mmengine - INFO - Epoch(train) [108][45/63] lr: 1.9885e-03 eta: 11:43:44 time: 0.4803 data_time: 0.0089 memory: 14901 loss: 2.3014 loss_prob: 1.3880 loss_thr: 0.6859 loss_db: 0.2274 2022/11/02 13:18:07 - mmengine - INFO - Epoch(train) [108][50/63] lr: 1.9885e-03 eta: 11:43:28 time: 0.5191 data_time: 0.0100 memory: 14901 loss: 2.2132 loss_prob: 1.3425 loss_thr: 0.6535 loss_db: 0.2173 2022/11/02 13:18:10 - mmengine - INFO - Epoch(train) [108][55/63] lr: 1.9885e-03 eta: 11:43:28 time: 0.5744 data_time: 0.0157 memory: 14901 loss: 2.2251 loss_prob: 1.3474 loss_thr: 0.6600 loss_db: 0.2177 2022/11/02 13:18:14 - mmengine - INFO - Epoch(train) [108][60/63] lr: 1.9885e-03 eta: 11:43:26 time: 0.6523 data_time: 0.0138 memory: 14901 loss: 2.4323 loss_prob: 1.4885 loss_thr: 0.7030 loss_db: 0.2408 2022/11/02 13:18:16 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:18:23 - mmengine - INFO - Epoch(train) [109][5/63] lr: 1.9869e-03 eta: 11:43:26 time: 1.1239 data_time: 0.2476 memory: 14901 loss: 2.4425 loss_prob: 1.4735 loss_thr: 0.7235 loss_db: 0.2455 2022/11/02 13:18:26 - mmengine - INFO - Epoch(train) [109][10/63] lr: 1.9869e-03 eta: 11:43:40 time: 1.0195 data_time: 0.2469 memory: 14901 loss: 2.5662 loss_prob: 1.5753 loss_thr: 0.7340 loss_db: 0.2569 2022/11/02 13:18:30 - mmengine - INFO - Epoch(train) [109][15/63] lr: 1.9869e-03 eta: 11:43:40 time: 0.6770 data_time: 0.0289 memory: 14901 loss: 2.5831 loss_prob: 1.5997 loss_thr: 0.7229 loss_db: 0.2606 2022/11/02 13:18:33 - mmengine - INFO - Epoch(train) [109][20/63] lr: 1.9869e-03 eta: 11:43:43 time: 0.7048 data_time: 0.0286 memory: 14901 loss: 2.4580 loss_prob: 1.5094 loss_thr: 0.7023 loss_db: 0.2462 2022/11/02 13:18:36 - mmengine - INFO - Epoch(train) [109][25/63] lr: 1.9869e-03 eta: 11:43:43 time: 0.5769 data_time: 0.0069 memory: 14901 loss: 2.4388 loss_prob: 1.4902 loss_thr: 0.7068 loss_db: 0.2418 2022/11/02 13:18:38 - mmengine - INFO - Epoch(train) [109][30/63] lr: 1.9869e-03 eta: 11:43:29 time: 0.5311 data_time: 0.0104 memory: 14901 loss: 2.2902 loss_prob: 1.3873 loss_thr: 0.6815 loss_db: 0.2214 2022/11/02 13:18:41 - mmengine - INFO - Epoch(train) [109][35/63] lr: 1.9869e-03 eta: 11:43:29 time: 0.5258 data_time: 0.0097 memory: 14901 loss: 2.2397 loss_prob: 1.3529 loss_thr: 0.6702 loss_db: 0.2165 2022/11/02 13:18:45 - mmengine - INFO - Epoch(train) [109][40/63] lr: 1.9869e-03 eta: 11:43:28 time: 0.6673 data_time: 0.0177 memory: 14901 loss: 2.2158 loss_prob: 1.3242 loss_thr: 0.6768 loss_db: 0.2149 2022/11/02 13:18:47 - mmengine - INFO - Epoch(train) [109][45/63] lr: 1.9869e-03 eta: 11:43:28 time: 0.6524 data_time: 0.0162 memory: 14901 loss: 2.1318 loss_prob: 1.2664 loss_thr: 0.6586 loss_db: 0.2067 2022/11/02 13:18:51 - mmengine - INFO - Epoch(train) [109][50/63] lr: 1.9869e-03 eta: 11:43:19 time: 0.5857 data_time: 0.0105 memory: 14901 loss: 2.2693 loss_prob: 1.3808 loss_thr: 0.6640 loss_db: 0.2245 2022/11/02 13:18:53 - mmengine - INFO - Epoch(train) [109][55/63] lr: 1.9869e-03 eta: 11:43:19 time: 0.5712 data_time: 0.0105 memory: 14901 loss: 2.3564 loss_prob: 1.4353 loss_thr: 0.6930 loss_db: 0.2281 2022/11/02 13:18:56 - mmengine - INFO - Epoch(train) [109][60/63] lr: 1.9869e-03 eta: 11:43:10 time: 0.5798 data_time: 0.0044 memory: 14901 loss: 2.4111 loss_prob: 1.4685 loss_thr: 0.7050 loss_db: 0.2375 2022/11/02 13:18:58 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:19:04 - mmengine - INFO - Epoch(train) [110][5/63] lr: 1.9853e-03 eta: 11:43:10 time: 0.9076 data_time: 0.1909 memory: 14901 loss: 2.2616 loss_prob: 1.3589 loss_thr: 0.6820 loss_db: 0.2206 2022/11/02 13:19:07 - mmengine - INFO - Epoch(train) [110][10/63] lr: 1.9853e-03 eta: 11:43:13 time: 0.9079 data_time: 0.1980 memory: 14901 loss: 2.4201 loss_prob: 1.4706 loss_thr: 0.7101 loss_db: 0.2394 2022/11/02 13:19:09 - mmengine - INFO - Epoch(train) [110][15/63] lr: 1.9853e-03 eta: 11:43:13 time: 0.4840 data_time: 0.0132 memory: 14901 loss: 2.5311 loss_prob: 1.5323 loss_thr: 0.7454 loss_db: 0.2534 2022/11/02 13:19:12 - mmengine - INFO - Epoch(train) [110][20/63] lr: 1.9853e-03 eta: 11:42:53 time: 0.4838 data_time: 0.0052 memory: 14901 loss: 2.3395 loss_prob: 1.3837 loss_thr: 0.7260 loss_db: 0.2298 2022/11/02 13:19:15 - mmengine - INFO - Epoch(train) [110][25/63] lr: 1.9853e-03 eta: 11:42:53 time: 0.5604 data_time: 0.0239 memory: 14901 loss: 2.1511 loss_prob: 1.2787 loss_thr: 0.6650 loss_db: 0.2073 2022/11/02 13:19:17 - mmengine - INFO - Epoch(train) [110][30/63] lr: 1.9853e-03 eta: 11:42:42 time: 0.5609 data_time: 0.0336 memory: 14901 loss: 2.1842 loss_prob: 1.2991 loss_thr: 0.6746 loss_db: 0.2105 2022/11/02 13:19:20 - mmengine - INFO - Epoch(train) [110][35/63] lr: 1.9853e-03 eta: 11:42:42 time: 0.4778 data_time: 0.0159 memory: 14901 loss: 2.1812 loss_prob: 1.2899 loss_thr: 0.6838 loss_db: 0.2075 2022/11/02 13:19:22 - mmengine - INFO - Epoch(train) [110][40/63] lr: 1.9853e-03 eta: 11:42:23 time: 0.4818 data_time: 0.0067 memory: 14901 loss: 2.2497 loss_prob: 1.3563 loss_thr: 0.6749 loss_db: 0.2184 2022/11/02 13:19:25 - mmengine - INFO - Epoch(train) [110][45/63] lr: 1.9853e-03 eta: 11:42:23 time: 0.5202 data_time: 0.0069 memory: 14901 loss: 2.4179 loss_prob: 1.4745 loss_thr: 0.6971 loss_db: 0.2463 2022/11/02 13:19:27 - mmengine - INFO - Epoch(train) [110][50/63] lr: 1.9853e-03 eta: 11:42:08 time: 0.5307 data_time: 0.0168 memory: 14901 loss: 2.5221 loss_prob: 1.5482 loss_thr: 0.7133 loss_db: 0.2606 2022/11/02 13:19:30 - mmengine - INFO - Epoch(train) [110][55/63] lr: 1.9853e-03 eta: 11:42:08 time: 0.5085 data_time: 0.0248 memory: 14901 loss: 2.4844 loss_prob: 1.5243 loss_thr: 0.7075 loss_db: 0.2526 2022/11/02 13:19:32 - mmengine - INFO - Epoch(train) [110][60/63] lr: 1.9853e-03 eta: 11:41:51 time: 0.4992 data_time: 0.0152 memory: 14901 loss: 2.4112 loss_prob: 1.4699 loss_thr: 0.6957 loss_db: 0.2456 2022/11/02 13:19:34 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:19:38 - mmengine - INFO - Epoch(train) [111][5/63] lr: 1.9836e-03 eta: 11:41:51 time: 0.7075 data_time: 0.1933 memory: 14901 loss: 2.3723 loss_prob: 1.4380 loss_thr: 0.6931 loss_db: 0.2412 2022/11/02 13:19:41 - mmengine - INFO - Epoch(train) [111][10/63] lr: 1.9836e-03 eta: 11:41:35 time: 0.7117 data_time: 0.2037 memory: 14901 loss: 2.1784 loss_prob: 1.2941 loss_thr: 0.6658 loss_db: 0.2185 2022/11/02 13:19:44 - mmengine - INFO - Epoch(train) [111][15/63] lr: 1.9836e-03 eta: 11:41:35 time: 0.5162 data_time: 0.0194 memory: 14901 loss: 2.3630 loss_prob: 1.4404 loss_thr: 0.6889 loss_db: 0.2337 2022/11/02 13:19:46 - mmengine - INFO - Epoch(train) [111][20/63] lr: 1.9836e-03 eta: 11:41:18 time: 0.5083 data_time: 0.0096 memory: 14901 loss: 2.4161 loss_prob: 1.4972 loss_thr: 0.6752 loss_db: 0.2437 2022/11/02 13:19:49 - mmengine - INFO - Epoch(train) [111][25/63] lr: 1.9836e-03 eta: 11:41:18 time: 0.4976 data_time: 0.0169 memory: 14901 loss: 2.2420 loss_prob: 1.3739 loss_thr: 0.6368 loss_db: 0.2313 2022/11/02 13:19:51 - mmengine - INFO - Epoch(train) [111][30/63] lr: 1.9836e-03 eta: 11:41:04 time: 0.5361 data_time: 0.0314 memory: 14901 loss: 2.2558 loss_prob: 1.3686 loss_thr: 0.6595 loss_db: 0.2277 2022/11/02 13:19:54 - mmengine - INFO - Epoch(train) [111][35/63] lr: 1.9836e-03 eta: 11:41:04 time: 0.5550 data_time: 0.0222 memory: 14901 loss: 2.3569 loss_prob: 1.4137 loss_thr: 0.7122 loss_db: 0.2310 2022/11/02 13:19:57 - mmengine - INFO - Epoch(train) [111][40/63] lr: 1.9836e-03 eta: 11:40:49 time: 0.5192 data_time: 0.0092 memory: 14901 loss: 2.2384 loss_prob: 1.3365 loss_thr: 0.6844 loss_db: 0.2175 2022/11/02 13:19:59 - mmengine - INFO - Epoch(train) [111][45/63] lr: 1.9836e-03 eta: 11:40:49 time: 0.4812 data_time: 0.0071 memory: 14901 loss: 2.1708 loss_prob: 1.2898 loss_thr: 0.6740 loss_db: 0.2070 2022/11/02 13:20:01 - mmengine - INFO - Epoch(train) [111][50/63] lr: 1.9836e-03 eta: 11:40:31 time: 0.4938 data_time: 0.0186 memory: 14901 loss: 2.3269 loss_prob: 1.3966 loss_thr: 0.7025 loss_db: 0.2278 2022/11/02 13:20:04 - mmengine - INFO - Epoch(train) [111][55/63] lr: 1.9836e-03 eta: 11:40:31 time: 0.5022 data_time: 0.0230 memory: 14901 loss: 2.4145 loss_prob: 1.4766 loss_thr: 0.6951 loss_db: 0.2428 2022/11/02 13:20:07 - mmengine - INFO - Epoch(train) [111][60/63] lr: 1.9836e-03 eta: 11:40:15 time: 0.5115 data_time: 0.0116 memory: 14901 loss: 2.4406 loss_prob: 1.4929 loss_thr: 0.6998 loss_db: 0.2479 2022/11/02 13:20:08 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:20:12 - mmengine - INFO - Epoch(train) [112][5/63] lr: 1.9820e-03 eta: 11:40:15 time: 0.6922 data_time: 0.2127 memory: 14901 loss: 2.3366 loss_prob: 1.4108 loss_thr: 0.6948 loss_db: 0.2311 2022/11/02 13:20:14 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:20:15 - mmengine - INFO - Epoch(train) [112][10/63] lr: 1.9820e-03 eta: 11:40:00 time: 0.7186 data_time: 0.2110 memory: 14901 loss: 2.3400 loss_prob: 1.4082 loss_thr: 0.7034 loss_db: 0.2284 2022/11/02 13:20:17 - mmengine - INFO - Epoch(train) [112][15/63] lr: 1.9820e-03 eta: 11:40:00 time: 0.4922 data_time: 0.0073 memory: 14901 loss: 2.3322 loss_prob: 1.4131 loss_thr: 0.6846 loss_db: 0.2345 2022/11/02 13:20:20 - mmengine - INFO - Epoch(train) [112][20/63] lr: 1.9820e-03 eta: 11:39:41 time: 0.4875 data_time: 0.0083 memory: 14901 loss: 2.3763 loss_prob: 1.4703 loss_thr: 0.6618 loss_db: 0.2442 2022/11/02 13:20:22 - mmengine - INFO - Epoch(train) [112][25/63] lr: 1.9820e-03 eta: 11:39:41 time: 0.4776 data_time: 0.0145 memory: 14901 loss: 2.2822 loss_prob: 1.3776 loss_thr: 0.6799 loss_db: 0.2247 2022/11/02 13:20:25 - mmengine - INFO - Epoch(train) [112][30/63] lr: 1.9820e-03 eta: 11:39:26 time: 0.5143 data_time: 0.0306 memory: 14901 loss: 2.2491 loss_prob: 1.3480 loss_thr: 0.6792 loss_db: 0.2219 2022/11/02 13:20:28 - mmengine - INFO - Epoch(train) [112][35/63] lr: 1.9820e-03 eta: 11:39:26 time: 0.5744 data_time: 0.0256 memory: 14901 loss: 2.3028 loss_prob: 1.4036 loss_thr: 0.6706 loss_db: 0.2286 2022/11/02 13:20:30 - mmengine - INFO - Epoch(train) [112][40/63] lr: 1.9820e-03 eta: 11:39:13 time: 0.5455 data_time: 0.0094 memory: 14901 loss: 2.4420 loss_prob: 1.4875 loss_thr: 0.7102 loss_db: 0.2443 2022/11/02 13:20:33 - mmengine - INFO - Epoch(train) [112][45/63] lr: 1.9820e-03 eta: 11:39:13 time: 0.4838 data_time: 0.0054 memory: 14901 loss: 2.4072 loss_prob: 1.4380 loss_thr: 0.7345 loss_db: 0.2347 2022/11/02 13:20:35 - mmengine - INFO - Epoch(train) [112][50/63] lr: 1.9820e-03 eta: 11:38:54 time: 0.4787 data_time: 0.0107 memory: 14901 loss: 2.4278 loss_prob: 1.4684 loss_thr: 0.7168 loss_db: 0.2426 2022/11/02 13:20:38 - mmengine - INFO - Epoch(train) [112][55/63] lr: 1.9820e-03 eta: 11:38:54 time: 0.4862 data_time: 0.0208 memory: 14901 loss: 2.3832 loss_prob: 1.4363 loss_thr: 0.7054 loss_db: 0.2415 2022/11/02 13:20:40 - mmengine - INFO - Epoch(train) [112][60/63] lr: 1.9820e-03 eta: 11:38:34 time: 0.4661 data_time: 0.0156 memory: 14901 loss: 2.4286 loss_prob: 1.4686 loss_thr: 0.7190 loss_db: 0.2410 2022/11/02 13:20:41 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:20:46 - mmengine - INFO - Epoch(train) [113][5/63] lr: 1.9803e-03 eta: 11:38:34 time: 0.7125 data_time: 0.2097 memory: 14901 loss: 2.3678 loss_prob: 1.4339 loss_thr: 0.7075 loss_db: 0.2264 2022/11/02 13:20:49 - mmengine - INFO - Epoch(train) [113][10/63] lr: 1.9803e-03 eta: 11:38:23 time: 0.7702 data_time: 0.2089 memory: 14901 loss: 2.7038 loss_prob: 1.7005 loss_thr: 0.7339 loss_db: 0.2694 2022/11/02 13:20:51 - mmengine - INFO - Epoch(train) [113][15/63] lr: 1.9803e-03 eta: 11:38:23 time: 0.5232 data_time: 0.0076 memory: 14901 loss: 2.5624 loss_prob: 1.6005 loss_thr: 0.7005 loss_db: 0.2614 2022/11/02 13:20:54 - mmengine - INFO - Epoch(train) [113][20/63] lr: 1.9803e-03 eta: 11:38:07 time: 0.5062 data_time: 0.0076 memory: 14901 loss: 2.3149 loss_prob: 1.4028 loss_thr: 0.6844 loss_db: 0.2277 2022/11/02 13:20:57 - mmengine - INFO - Epoch(train) [113][25/63] lr: 1.9803e-03 eta: 11:38:07 time: 0.5654 data_time: 0.0588 memory: 14901 loss: 2.3144 loss_prob: 1.4062 loss_thr: 0.6809 loss_db: 0.2274 2022/11/02 13:21:00 - mmengine - INFO - Epoch(train) [113][30/63] lr: 1.9803e-03 eta: 11:37:57 time: 0.5742 data_time: 0.0588 memory: 14901 loss: 2.2953 loss_prob: 1.3986 loss_thr: 0.6704 loss_db: 0.2263 2022/11/02 13:21:02 - mmengine - INFO - Epoch(train) [113][35/63] lr: 1.9803e-03 eta: 11:37:57 time: 0.5117 data_time: 0.0071 memory: 14901 loss: 2.4115 loss_prob: 1.4796 loss_thr: 0.6937 loss_db: 0.2382 2022/11/02 13:21:05 - mmengine - INFO - Epoch(train) [113][40/63] lr: 1.9803e-03 eta: 11:37:49 time: 0.5877 data_time: 0.0098 memory: 14901 loss: 2.6182 loss_prob: 1.6173 loss_thr: 0.7294 loss_db: 0.2715 2022/11/02 13:21:09 - mmengine - INFO - Epoch(train) [113][45/63] lr: 1.9803e-03 eta: 11:37:49 time: 0.6611 data_time: 0.0089 memory: 14901 loss: 2.6886 loss_prob: 1.6802 loss_thr: 0.7232 loss_db: 0.2852 2022/11/02 13:21:12 - mmengine - INFO - Epoch(train) [113][50/63] lr: 1.9803e-03 eta: 11:37:51 time: 0.6945 data_time: 0.0264 memory: 14901 loss: 2.4787 loss_prob: 1.5463 loss_thr: 0.6841 loss_db: 0.2483 2022/11/02 13:21:16 - mmengine - INFO - Epoch(train) [113][55/63] lr: 1.9803e-03 eta: 11:37:51 time: 0.6780 data_time: 0.0266 memory: 14901 loss: 2.4993 loss_prob: 1.5601 loss_thr: 0.6888 loss_db: 0.2504 2022/11/02 13:21:19 - mmengine - INFO - Epoch(train) [113][60/63] lr: 1.9803e-03 eta: 11:37:47 time: 0.6367 data_time: 0.0064 memory: 14901 loss: 2.5300 loss_prob: 1.5696 loss_thr: 0.7008 loss_db: 0.2596 2022/11/02 13:21:20 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:21:26 - mmengine - INFO - Epoch(train) [114][5/63] lr: 1.9787e-03 eta: 11:37:47 time: 0.7882 data_time: 0.2484 memory: 14901 loss: 2.5605 loss_prob: 1.5763 loss_thr: 0.7223 loss_db: 0.2620 2022/11/02 13:21:29 - mmengine - INFO - Epoch(train) [114][10/63] lr: 1.9787e-03 eta: 11:37:43 time: 0.8340 data_time: 0.2486 memory: 14901 loss: 2.8673 loss_prob: 1.8073 loss_thr: 0.7639 loss_db: 0.2961 2022/11/02 13:21:31 - mmengine - INFO - Epoch(train) [114][15/63] lr: 1.9787e-03 eta: 11:37:43 time: 0.5845 data_time: 0.0057 memory: 14901 loss: 2.9140 loss_prob: 1.8697 loss_thr: 0.7398 loss_db: 0.3045 2022/11/02 13:21:34 - mmengine - INFO - Epoch(train) [114][20/63] lr: 1.9787e-03 eta: 11:37:32 time: 0.5559 data_time: 0.0065 memory: 14901 loss: 2.5200 loss_prob: 1.5644 loss_thr: 0.7031 loss_db: 0.2524 2022/11/02 13:21:37 - mmengine - INFO - Epoch(train) [114][25/63] lr: 1.9787e-03 eta: 11:37:32 time: 0.5659 data_time: 0.0063 memory: 14901 loss: 2.3318 loss_prob: 1.4136 loss_thr: 0.6925 loss_db: 0.2257 2022/11/02 13:21:40 - mmengine - INFO - Epoch(train) [114][30/63] lr: 1.9787e-03 eta: 11:37:24 time: 0.5917 data_time: 0.0313 memory: 14901 loss: 2.5235 loss_prob: 1.5710 loss_thr: 0.6983 loss_db: 0.2542 2022/11/02 13:21:43 - mmengine - INFO - Epoch(train) [114][35/63] lr: 1.9787e-03 eta: 11:37:24 time: 0.6290 data_time: 0.0354 memory: 14901 loss: 2.5875 loss_prob: 1.5896 loss_thr: 0.7328 loss_db: 0.2651 2022/11/02 13:21:46 - mmengine - INFO - Epoch(train) [114][40/63] lr: 1.9787e-03 eta: 11:37:17 time: 0.6091 data_time: 0.0086 memory: 14901 loss: 2.5793 loss_prob: 1.5848 loss_thr: 0.7295 loss_db: 0.2650 2022/11/02 13:21:49 - mmengine - INFO - Epoch(train) [114][45/63] lr: 1.9787e-03 eta: 11:37:17 time: 0.6000 data_time: 0.0046 memory: 14901 loss: 2.4857 loss_prob: 1.5400 loss_thr: 0.6914 loss_db: 0.2543 2022/11/02 13:21:52 - mmengine - INFO - Epoch(train) [114][50/63] lr: 1.9787e-03 eta: 11:37:06 time: 0.5566 data_time: 0.0149 memory: 14901 loss: 2.3147 loss_prob: 1.4073 loss_thr: 0.6744 loss_db: 0.2330 2022/11/02 13:21:54 - mmengine - INFO - Epoch(train) [114][55/63] lr: 1.9787e-03 eta: 11:37:06 time: 0.4706 data_time: 0.0210 memory: 14901 loss: 2.4140 loss_prob: 1.4810 loss_thr: 0.6873 loss_db: 0.2456 2022/11/02 13:21:57 - mmengine - INFO - Epoch(train) [114][60/63] lr: 1.9787e-03 eta: 11:36:47 time: 0.4819 data_time: 0.0112 memory: 14901 loss: 2.4283 loss_prob: 1.4815 loss_thr: 0.7012 loss_db: 0.2456 2022/11/02 13:21:58 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:22:03 - mmengine - INFO - Epoch(train) [115][5/63] lr: 1.9771e-03 eta: 11:36:47 time: 0.7366 data_time: 0.1930 memory: 14901 loss: 2.5100 loss_prob: 1.5320 loss_thr: 0.7228 loss_db: 0.2552 2022/11/02 13:22:05 - mmengine - INFO - Epoch(train) [115][10/63] lr: 1.9771e-03 eta: 11:36:34 time: 0.7355 data_time: 0.1930 memory: 14901 loss: 2.3823 loss_prob: 1.4320 loss_thr: 0.7146 loss_db: 0.2357 2022/11/02 13:22:08 - mmengine - INFO - Epoch(train) [115][15/63] lr: 1.9771e-03 eta: 11:36:34 time: 0.5190 data_time: 0.0051 memory: 14901 loss: 2.4300 loss_prob: 1.4682 loss_thr: 0.7213 loss_db: 0.2405 2022/11/02 13:22:11 - mmengine - INFO - Epoch(train) [115][20/63] lr: 1.9771e-03 eta: 11:36:21 time: 0.5436 data_time: 0.0051 memory: 14901 loss: 2.7053 loss_prob: 1.6918 loss_thr: 0.7298 loss_db: 0.2837 2022/11/02 13:22:13 - mmengine - INFO - Epoch(train) [115][25/63] lr: 1.9771e-03 eta: 11:36:21 time: 0.4742 data_time: 0.0083 memory: 14901 loss: 2.5448 loss_prob: 1.5823 loss_thr: 0.7005 loss_db: 0.2620 2022/11/02 13:22:16 - mmengine - INFO - Epoch(train) [115][30/63] lr: 1.9771e-03 eta: 11:36:04 time: 0.4870 data_time: 0.0340 memory: 14901 loss: 2.4630 loss_prob: 1.5087 loss_thr: 0.7029 loss_db: 0.2513 2022/11/02 13:22:18 - mmengine - INFO - Epoch(train) [115][35/63] lr: 1.9771e-03 eta: 11:36:04 time: 0.5000 data_time: 0.0306 memory: 14901 loss: 2.4585 loss_prob: 1.5205 loss_thr: 0.6854 loss_db: 0.2526 2022/11/02 13:22:20 - mmengine - INFO - Epoch(train) [115][40/63] lr: 1.9771e-03 eta: 11:35:45 time: 0.4795 data_time: 0.0047 memory: 14901 loss: 2.3094 loss_prob: 1.4184 loss_thr: 0.6654 loss_db: 0.2256 2022/11/02 13:22:23 - mmengine - INFO - Epoch(train) [115][45/63] lr: 1.9771e-03 eta: 11:35:45 time: 0.4786 data_time: 0.0044 memory: 14901 loss: 2.3366 loss_prob: 1.4255 loss_thr: 0.6781 loss_db: 0.2331 2022/11/02 13:22:25 - mmengine - INFO - Epoch(train) [115][50/63] lr: 1.9771e-03 eta: 11:35:29 time: 0.5070 data_time: 0.0114 memory: 14901 loss: 2.3790 loss_prob: 1.4565 loss_thr: 0.6798 loss_db: 0.2427 2022/11/02 13:22:28 - mmengine - INFO - Epoch(train) [115][55/63] lr: 1.9771e-03 eta: 11:35:29 time: 0.5371 data_time: 0.0228 memory: 14901 loss: 2.3888 loss_prob: 1.4565 loss_thr: 0.6881 loss_db: 0.2443 2022/11/02 13:22:30 - mmengine - INFO - Epoch(train) [115][60/63] lr: 1.9771e-03 eta: 11:35:11 time: 0.4857 data_time: 0.0157 memory: 14901 loss: 2.4082 loss_prob: 1.4664 loss_thr: 0.6952 loss_db: 0.2467 2022/11/02 13:22:31 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:22:36 - mmengine - INFO - Epoch(train) [116][5/63] lr: 1.9754e-03 eta: 11:35:11 time: 0.6711 data_time: 0.2130 memory: 14901 loss: 2.3866 loss_prob: 1.4591 loss_thr: 0.6885 loss_db: 0.2391 2022/11/02 13:22:38 - mmengine - INFO - Epoch(train) [116][10/63] lr: 1.9754e-03 eta: 11:34:53 time: 0.6858 data_time: 0.2140 memory: 14901 loss: 2.2025 loss_prob: 1.3324 loss_thr: 0.6522 loss_db: 0.2180 2022/11/02 13:22:41 - mmengine - INFO - Epoch(train) [116][15/63] lr: 1.9754e-03 eta: 11:34:53 time: 0.4600 data_time: 0.0069 memory: 14901 loss: 2.2776 loss_prob: 1.3898 loss_thr: 0.6594 loss_db: 0.2283 2022/11/02 13:22:43 - mmengine - INFO - Epoch(train) [116][20/63] lr: 1.9754e-03 eta: 11:34:35 time: 0.4809 data_time: 0.0059 memory: 14901 loss: 2.5066 loss_prob: 1.5589 loss_thr: 0.6885 loss_db: 0.2591 2022/11/02 13:22:46 - mmengine - INFO - Epoch(train) [116][25/63] lr: 1.9754e-03 eta: 11:34:35 time: 0.4918 data_time: 0.0217 memory: 14901 loss: 2.5276 loss_prob: 1.5682 loss_thr: 0.7024 loss_db: 0.2570 2022/11/02 13:22:48 - mmengine - INFO - Epoch(train) [116][30/63] lr: 1.9754e-03 eta: 11:34:16 time: 0.4746 data_time: 0.0351 memory: 14901 loss: 2.3959 loss_prob: 1.4659 loss_thr: 0.6889 loss_db: 0.2410 2022/11/02 13:22:50 - mmengine - INFO - Epoch(train) [116][35/63] lr: 1.9754e-03 eta: 11:34:16 time: 0.4777 data_time: 0.0182 memory: 14901 loss: 2.4282 loss_prob: 1.4993 loss_thr: 0.6810 loss_db: 0.2479 2022/11/02 13:22:53 - mmengine - INFO - Epoch(train) [116][40/63] lr: 1.9754e-03 eta: 11:33:57 time: 0.4748 data_time: 0.0057 memory: 14901 loss: 2.4659 loss_prob: 1.5141 loss_thr: 0.6996 loss_db: 0.2522 2022/11/02 13:22:55 - mmengine - INFO - Epoch(train) [116][45/63] lr: 1.9754e-03 eta: 11:33:57 time: 0.4947 data_time: 0.0054 memory: 14901 loss: 2.6072 loss_prob: 1.6177 loss_thr: 0.7177 loss_db: 0.2718 2022/11/02 13:22:58 - mmengine - INFO - Epoch(train) [116][50/63] lr: 1.9754e-03 eta: 11:33:45 time: 0.5402 data_time: 0.0123 memory: 14901 loss: 2.5915 loss_prob: 1.6045 loss_thr: 0.7216 loss_db: 0.2654 2022/11/02 13:23:01 - mmengine - INFO - Epoch(train) [116][55/63] lr: 1.9754e-03 eta: 11:33:45 time: 0.5405 data_time: 0.0217 memory: 14901 loss: 2.5563 loss_prob: 1.5649 loss_thr: 0.7231 loss_db: 0.2683 2022/11/02 13:23:03 - mmengine - INFO - Epoch(train) [116][60/63] lr: 1.9754e-03 eta: 11:33:32 time: 0.5354 data_time: 0.0144 memory: 14901 loss: 2.6658 loss_prob: 1.6580 loss_thr: 0.7194 loss_db: 0.2884 2022/11/02 13:23:05 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:23:09 - mmengine - INFO - Epoch(train) [117][5/63] lr: 1.9738e-03 eta: 11:33:32 time: 0.6703 data_time: 0.1914 memory: 14901 loss: 2.3741 loss_prob: 1.4420 loss_thr: 0.6877 loss_db: 0.2444 2022/11/02 13:23:12 - mmengine - INFO - Epoch(train) [117][10/63] lr: 1.9738e-03 eta: 11:33:14 time: 0.6898 data_time: 0.1914 memory: 14901 loss: 2.4325 loss_prob: 1.4970 loss_thr: 0.6827 loss_db: 0.2527 2022/11/02 13:23:14 - mmengine - INFO - Epoch(train) [117][15/63] lr: 1.9738e-03 eta: 11:33:14 time: 0.4986 data_time: 0.0060 memory: 14901 loss: 2.6618 loss_prob: 1.6394 loss_thr: 0.7431 loss_db: 0.2793 2022/11/02 13:23:17 - mmengine - INFO - Epoch(train) [117][20/63] lr: 1.9738e-03 eta: 11:33:00 time: 0.5182 data_time: 0.0047 memory: 14901 loss: 2.5187 loss_prob: 1.5206 loss_thr: 0.7422 loss_db: 0.2559 2022/11/02 13:23:20 - mmengine - INFO - Epoch(train) [117][25/63] lr: 1.9738e-03 eta: 11:33:00 time: 0.5467 data_time: 0.0210 memory: 14901 loss: 2.4142 loss_prob: 1.4599 loss_thr: 0.7141 loss_db: 0.2402 2022/11/02 13:23:22 - mmengine - INFO - Epoch(train) [117][30/63] lr: 1.9738e-03 eta: 11:32:48 time: 0.5474 data_time: 0.0307 memory: 14901 loss: 2.3756 loss_prob: 1.4323 loss_thr: 0.7074 loss_db: 0.2359 2022/11/02 13:23:25 - mmengine - INFO - Epoch(train) [117][35/63] lr: 1.9738e-03 eta: 11:32:48 time: 0.5899 data_time: 0.0156 memory: 14901 loss: 2.4016 loss_prob: 1.4486 loss_thr: 0.7133 loss_db: 0.2398 2022/11/02 13:23:29 - mmengine - INFO - Epoch(train) [117][40/63] lr: 1.9738e-03 eta: 11:32:44 time: 0.6322 data_time: 0.0062 memory: 14901 loss: 2.2439 loss_prob: 1.3309 loss_thr: 0.6962 loss_db: 0.2168 2022/11/02 13:23:31 - mmengine - INFO - Epoch(train) [117][45/63] lr: 1.9738e-03 eta: 11:32:44 time: 0.5793 data_time: 0.0048 memory: 14901 loss: 2.1974 loss_prob: 1.2955 loss_thr: 0.6939 loss_db: 0.2080 2022/11/02 13:23:34 - mmengine - INFO - Epoch(train) [117][50/63] lr: 1.9738e-03 eta: 11:32:31 time: 0.5292 data_time: 0.0190 memory: 14901 loss: 2.2281 loss_prob: 1.3253 loss_thr: 0.6908 loss_db: 0.2120 2022/11/02 13:23:37 - mmengine - INFO - Epoch(train) [117][55/63] lr: 1.9738e-03 eta: 11:32:31 time: 0.5487 data_time: 0.0222 memory: 14901 loss: 2.2754 loss_prob: 1.3614 loss_thr: 0.6939 loss_db: 0.2201 2022/11/02 13:23:40 - mmengine - INFO - Epoch(train) [117][60/63] lr: 1.9738e-03 eta: 11:32:26 time: 0.6226 data_time: 0.0083 memory: 14901 loss: 2.3210 loss_prob: 1.3971 loss_thr: 0.6913 loss_db: 0.2327 2022/11/02 13:23:42 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:23:47 - mmengine - INFO - Epoch(train) [118][5/63] lr: 1.9721e-03 eta: 11:32:26 time: 0.8332 data_time: 0.2319 memory: 14901 loss: 2.1986 loss_prob: 1.3012 loss_thr: 0.6816 loss_db: 0.2157 2022/11/02 13:23:50 - mmengine - INFO - Epoch(train) [118][10/63] lr: 1.9721e-03 eta: 11:32:20 time: 0.8112 data_time: 0.2440 memory: 14901 loss: 2.1903 loss_prob: 1.3086 loss_thr: 0.6623 loss_db: 0.2194 2022/11/02 13:23:53 - mmengine - INFO - Epoch(train) [118][15/63] lr: 1.9721e-03 eta: 11:32:20 time: 0.6076 data_time: 0.0215 memory: 14901 loss: 2.1625 loss_prob: 1.2807 loss_thr: 0.6683 loss_db: 0.2136 2022/11/02 13:23:55 - mmengine - INFO - Epoch(train) [118][20/63] lr: 1.9721e-03 eta: 11:32:09 time: 0.5563 data_time: 0.0093 memory: 14901 loss: 2.1173 loss_prob: 1.2382 loss_thr: 0.6773 loss_db: 0.2018 2022/11/02 13:23:58 - mmengine - INFO - Epoch(train) [118][25/63] lr: 1.9721e-03 eta: 11:32:09 time: 0.4986 data_time: 0.0113 memory: 14901 loss: 2.0701 loss_prob: 1.2176 loss_thr: 0.6562 loss_db: 0.1962 2022/11/02 13:24:01 - mmengine - INFO - Epoch(train) [118][30/63] lr: 1.9721e-03 eta: 11:32:00 time: 0.5803 data_time: 0.0267 memory: 14901 loss: 2.0383 loss_prob: 1.1982 loss_thr: 0.6449 loss_db: 0.1952 2022/11/02 13:24:04 - mmengine - INFO - Epoch(train) [118][35/63] lr: 1.9721e-03 eta: 11:32:00 time: 0.6091 data_time: 0.0292 memory: 14901 loss: 2.0815 loss_prob: 1.2362 loss_thr: 0.6405 loss_db: 0.2048 2022/11/02 13:24:06 - mmengine - INFO - Epoch(train) [118][40/63] lr: 1.9721e-03 eta: 11:31:48 time: 0.5407 data_time: 0.0137 memory: 14901 loss: 2.0761 loss_prob: 1.2396 loss_thr: 0.6337 loss_db: 0.2029 2022/11/02 13:24:09 - mmengine - INFO - Epoch(train) [118][45/63] lr: 1.9721e-03 eta: 11:31:48 time: 0.5272 data_time: 0.0066 memory: 14901 loss: 2.2138 loss_prob: 1.3409 loss_thr: 0.6563 loss_db: 0.2166 2022/11/02 13:24:12 - mmengine - INFO - Epoch(train) [118][50/63] lr: 1.9721e-03 eta: 11:31:35 time: 0.5337 data_time: 0.0166 memory: 14901 loss: 2.2842 loss_prob: 1.3779 loss_thr: 0.6792 loss_db: 0.2271 2022/11/02 13:24:15 - mmengine - INFO - Epoch(train) [118][55/63] lr: 1.9721e-03 eta: 11:31:35 time: 0.5616 data_time: 0.0180 memory: 14901 loss: 2.2075 loss_prob: 1.3045 loss_thr: 0.6848 loss_db: 0.2182 2022/11/02 13:24:17 - mmengine - INFO - Epoch(train) [118][60/63] lr: 1.9721e-03 eta: 11:31:24 time: 0.5633 data_time: 0.0121 memory: 14901 loss: 2.3136 loss_prob: 1.3958 loss_thr: 0.6851 loss_db: 0.2327 2022/11/02 13:24:19 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:24:25 - mmengine - INFO - Epoch(train) [119][5/63] lr: 1.9705e-03 eta: 11:31:24 time: 0.8429 data_time: 0.2689 memory: 14901 loss: 2.2566 loss_prob: 1.3659 loss_thr: 0.6679 loss_db: 0.2228 2022/11/02 13:24:27 - mmengine - INFO - Epoch(train) [119][10/63] lr: 1.9705e-03 eta: 11:31:18 time: 0.8056 data_time: 0.2703 memory: 14901 loss: 2.3244 loss_prob: 1.4027 loss_thr: 0.6915 loss_db: 0.2302 2022/11/02 13:24:29 - mmengine - INFO - Epoch(train) [119][15/63] lr: 1.9705e-03 eta: 11:31:18 time: 0.4721 data_time: 0.0123 memory: 14901 loss: 2.3608 loss_prob: 1.4453 loss_thr: 0.6794 loss_db: 0.2361 2022/11/02 13:24:32 - mmengine - INFO - Epoch(train) [119][20/63] lr: 1.9705e-03 eta: 11:30:59 time: 0.4732 data_time: 0.0107 memory: 14901 loss: 2.4128 loss_prob: 1.4901 loss_thr: 0.6763 loss_db: 0.2463 2022/11/02 13:24:34 - mmengine - INFO - Epoch(train) [119][25/63] lr: 1.9705e-03 eta: 11:30:59 time: 0.4866 data_time: 0.0268 memory: 14901 loss: 2.4113 loss_prob: 1.4927 loss_thr: 0.6710 loss_db: 0.2476 2022/11/02 13:24:37 - mmengine - INFO - Epoch(train) [119][30/63] lr: 1.9705e-03 eta: 11:30:44 time: 0.5114 data_time: 0.0266 memory: 14901 loss: 2.2729 loss_prob: 1.3821 loss_thr: 0.6636 loss_db: 0.2272 2022/11/02 13:24:39 - mmengine - INFO - Epoch(train) [119][35/63] lr: 1.9705e-03 eta: 11:30:44 time: 0.4960 data_time: 0.0056 memory: 14901 loss: 2.2092 loss_prob: 1.3200 loss_thr: 0.6703 loss_db: 0.2190 2022/11/02 13:24:42 - mmengine - INFO - Epoch(train) [119][40/63] lr: 1.9705e-03 eta: 11:30:26 time: 0.4765 data_time: 0.0107 memory: 14901 loss: 2.3928 loss_prob: 1.4728 loss_thr: 0.6795 loss_db: 0.2405 2022/11/02 13:24:44 - mmengine - INFO - Epoch(train) [119][45/63] lr: 1.9705e-03 eta: 11:30:26 time: 0.4884 data_time: 0.0099 memory: 14901 loss: 2.3951 loss_prob: 1.4820 loss_thr: 0.6734 loss_db: 0.2397 2022/11/02 13:24:47 - mmengine - INFO - Epoch(train) [119][50/63] lr: 1.9705e-03 eta: 11:30:14 time: 0.5356 data_time: 0.0198 memory: 14901 loss: 2.2221 loss_prob: 1.3404 loss_thr: 0.6612 loss_db: 0.2206 2022/11/02 13:24:49 - mmengine - INFO - Epoch(train) [119][55/63] lr: 1.9705e-03 eta: 11:30:14 time: 0.5095 data_time: 0.0197 memory: 14901 loss: 2.2906 loss_prob: 1.3541 loss_thr: 0.7115 loss_db: 0.2249 2022/11/02 13:24:52 - mmengine - INFO - Epoch(train) [119][60/63] lr: 1.9705e-03 eta: 11:29:56 time: 0.4835 data_time: 0.0061 memory: 14901 loss: 2.3809 loss_prob: 1.4240 loss_thr: 0.7201 loss_db: 0.2368 2022/11/02 13:24:53 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:24:59 - mmengine - INFO - Epoch(train) [120][5/63] lr: 1.9689e-03 eta: 11:29:56 time: 0.7619 data_time: 0.2225 memory: 14901 loss: 2.4716 loss_prob: 1.5105 loss_thr: 0.7067 loss_db: 0.2544 2022/11/02 13:25:01 - mmengine - INFO - Epoch(train) [120][10/63] lr: 1.9689e-03 eta: 11:29:50 time: 0.8111 data_time: 0.2159 memory: 14901 loss: 2.2421 loss_prob: 1.3473 loss_thr: 0.6706 loss_db: 0.2242 2022/11/02 13:25:04 - mmengine - INFO - Epoch(train) [120][15/63] lr: 1.9689e-03 eta: 11:29:50 time: 0.5451 data_time: 0.0060 memory: 14901 loss: 2.3376 loss_prob: 1.4097 loss_thr: 0.6932 loss_db: 0.2347 2022/11/02 13:25:07 - mmengine - INFO - Epoch(train) [120][20/63] lr: 1.9689e-03 eta: 11:29:38 time: 0.5357 data_time: 0.0051 memory: 14901 loss: 2.2790 loss_prob: 1.3558 loss_thr: 0.7007 loss_db: 0.2224 2022/11/02 13:25:09 - mmengine - INFO - Epoch(train) [120][25/63] lr: 1.9689e-03 eta: 11:29:38 time: 0.5172 data_time: 0.0235 memory: 14901 loss: 2.3592 loss_prob: 1.4197 loss_thr: 0.7068 loss_db: 0.2327 2022/11/02 13:25:12 - mmengine - INFO - Epoch(train) [120][30/63] lr: 1.9689e-03 eta: 11:29:23 time: 0.5130 data_time: 0.0315 memory: 14901 loss: 2.3930 loss_prob: 1.4724 loss_thr: 0.6764 loss_db: 0.2441 2022/11/02 13:25:14 - mmengine - INFO - Epoch(train) [120][35/63] lr: 1.9689e-03 eta: 11:29:23 time: 0.4819 data_time: 0.0127 memory: 14901 loss: 2.2956 loss_prob: 1.4015 loss_thr: 0.6580 loss_db: 0.2362 2022/11/02 13:25:16 - mmengine - INFO - Epoch(train) [120][40/63] lr: 1.9689e-03 eta: 11:29:03 time: 0.4574 data_time: 0.0045 memory: 14901 loss: 2.3041 loss_prob: 1.4138 loss_thr: 0.6568 loss_db: 0.2335 2022/11/02 13:25:19 - mmengine - INFO - Epoch(train) [120][45/63] lr: 1.9689e-03 eta: 11:29:03 time: 0.4807 data_time: 0.0044 memory: 14901 loss: 2.5028 loss_prob: 1.5618 loss_thr: 0.6829 loss_db: 0.2580 2022/11/02 13:25:22 - mmengine - INFO - Epoch(train) [120][50/63] lr: 1.9689e-03 eta: 11:28:49 time: 0.5212 data_time: 0.0218 memory: 14901 loss: 2.4371 loss_prob: 1.5036 loss_thr: 0.6899 loss_db: 0.2436 2022/11/02 13:25:24 - mmengine - INFO - Epoch(train) [120][55/63] lr: 1.9689e-03 eta: 11:28:49 time: 0.5080 data_time: 0.0217 memory: 14901 loss: 2.2898 loss_prob: 1.3860 loss_thr: 0.6810 loss_db: 0.2228 2022/11/02 13:25:26 - mmengine - INFO - Epoch(train) [120][60/63] lr: 1.9689e-03 eta: 11:28:32 time: 0.4810 data_time: 0.0045 memory: 14901 loss: 2.1935 loss_prob: 1.3048 loss_thr: 0.6729 loss_db: 0.2158 2022/11/02 13:25:28 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:25:28 - mmengine - INFO - Saving checkpoint at 120 epochs 2022/11/02 13:25:31 - mmengine - INFO - Epoch(val) [120][5/500] eta: 11:28:32 time: 0.0455 data_time: 0.0050 memory: 14901 2022/11/02 13:25:31 - mmengine - INFO - Epoch(val) [120][10/500] eta: 0:00:21 time: 0.0444 data_time: 0.0042 memory: 1008 2022/11/02 13:25:32 - mmengine - INFO - Epoch(val) [120][15/500] eta: 0:00:21 time: 0.0361 data_time: 0.0023 memory: 1008 2022/11/02 13:25:32 - mmengine - INFO - Epoch(val) [120][20/500] eta: 0:00:17 time: 0.0360 data_time: 0.0023 memory: 1008 2022/11/02 13:25:32 - mmengine - INFO - Epoch(val) [120][25/500] eta: 0:00:17 time: 0.0373 data_time: 0.0025 memory: 1008 2022/11/02 13:25:32 - mmengine - INFO - Epoch(val) [120][30/500] eta: 0:00:19 time: 0.0422 data_time: 0.0032 memory: 1008 2022/11/02 13:25:32 - mmengine - INFO - Epoch(val) [120][35/500] eta: 0:00:19 time: 0.0449 data_time: 0.0032 memory: 1008 2022/11/02 13:25:33 - mmengine - INFO - Epoch(val) [120][40/500] eta: 0:00:22 time: 0.0484 data_time: 0.0030 memory: 1008 2022/11/02 13:25:33 - mmengine - INFO - Epoch(val) [120][45/500] eta: 0:00:22 time: 0.0508 data_time: 0.0034 memory: 1008 2022/11/02 13:25:33 - mmengine - INFO - Epoch(val) [120][50/500] eta: 0:00:22 time: 0.0490 data_time: 0.0033 memory: 1008 2022/11/02 13:25:33 - mmengine - INFO - Epoch(val) [120][55/500] eta: 0:00:22 time: 0.0483 data_time: 0.0030 memory: 1008 2022/11/02 13:25:34 - mmengine - INFO - Epoch(val) [120][60/500] eta: 0:00:20 time: 0.0458 data_time: 0.0034 memory: 1008 2022/11/02 13:25:34 - mmengine - INFO - Epoch(val) [120][65/500] eta: 0:00:20 time: 0.0501 data_time: 0.0040 memory: 1008 2022/11/02 13:25:34 - mmengine - INFO - Epoch(val) [120][70/500] eta: 0:00:21 time: 0.0498 data_time: 0.0037 memory: 1008 2022/11/02 13:25:34 - mmengine - INFO - Epoch(val) [120][75/500] eta: 0:00:21 time: 0.0433 data_time: 0.0032 memory: 1008 2022/11/02 13:25:35 - mmengine - INFO - Epoch(val) [120][80/500] eta: 0:00:16 time: 0.0400 data_time: 0.0029 memory: 1008 2022/11/02 13:25:35 - mmengine - INFO - Epoch(val) [120][85/500] eta: 0:00:16 time: 0.0387 data_time: 0.0026 memory: 1008 2022/11/02 13:25:35 - mmengine - INFO - Epoch(val) [120][90/500] eta: 0:00:17 time: 0.0435 data_time: 0.0026 memory: 1008 2022/11/02 13:25:35 - mmengine - INFO - Epoch(val) [120][95/500] eta: 0:00:17 time: 0.0451 data_time: 0.0028 memory: 1008 2022/11/02 13:25:35 - mmengine - INFO - Epoch(val) [120][100/500] eta: 0:00:15 time: 0.0388 data_time: 0.0027 memory: 1008 2022/11/02 13:25:36 - mmengine - INFO - Epoch(val) [120][105/500] eta: 0:00:15 time: 0.0375 data_time: 0.0028 memory: 1008 2022/11/02 13:25:36 - mmengine - INFO - Epoch(val) [120][110/500] eta: 0:00:15 time: 0.0396 data_time: 0.0028 memory: 1008 2022/11/02 13:25:36 - mmengine - INFO - Epoch(val) [120][115/500] eta: 0:00:15 time: 0.0381 data_time: 0.0025 memory: 1008 2022/11/02 13:25:36 - mmengine - INFO - Epoch(val) [120][120/500] eta: 0:00:16 time: 0.0430 data_time: 0.0025 memory: 1008 2022/11/02 13:25:36 - mmengine - INFO - Epoch(val) [120][125/500] eta: 0:00:16 time: 0.0418 data_time: 0.0025 memory: 1008 2022/11/02 13:25:37 - mmengine - INFO - Epoch(val) [120][130/500] eta: 0:00:15 time: 0.0416 data_time: 0.0025 memory: 1008 2022/11/02 13:25:37 - mmengine - INFO - Epoch(val) [120][135/500] eta: 0:00:15 time: 0.0461 data_time: 0.0029 memory: 1008 2022/11/02 13:25:37 - mmengine - INFO - Epoch(val) [120][140/500] eta: 0:00:14 time: 0.0412 data_time: 0.0026 memory: 1008 2022/11/02 13:25:37 - mmengine - INFO - Epoch(val) [120][145/500] eta: 0:00:14 time: 0.0402 data_time: 0.0023 memory: 1008 2022/11/02 13:25:37 - mmengine - INFO - Epoch(val) [120][150/500] eta: 0:00:14 time: 0.0405 data_time: 0.0023 memory: 1008 2022/11/02 13:25:38 - mmengine - INFO - Epoch(val) [120][155/500] eta: 0:00:14 time: 0.0422 data_time: 0.0025 memory: 1008 2022/11/02 13:25:38 - mmengine - INFO - Epoch(val) [120][160/500] eta: 0:00:14 time: 0.0418 data_time: 0.0025 memory: 1008 2022/11/02 13:25:38 - mmengine - INFO - Epoch(val) [120][165/500] eta: 0:00:14 time: 0.0392 data_time: 0.0023 memory: 1008 2022/11/02 13:25:38 - mmengine - INFO - Epoch(val) [120][170/500] eta: 0:00:13 time: 0.0411 data_time: 0.0024 memory: 1008 2022/11/02 13:25:38 - mmengine - INFO - Epoch(val) [120][175/500] eta: 0:00:13 time: 0.0424 data_time: 0.0027 memory: 1008 2022/11/02 13:25:39 - mmengine - INFO - Epoch(val) [120][180/500] eta: 0:00:12 time: 0.0404 data_time: 0.0025 memory: 1008 2022/11/02 13:25:39 - mmengine - INFO - Epoch(val) [120][185/500] eta: 0:00:12 time: 0.0409 data_time: 0.0023 memory: 1008 2022/11/02 13:25:39 - mmengine - INFO - Epoch(val) [120][190/500] eta: 0:00:12 time: 0.0411 data_time: 0.0024 memory: 1008 2022/11/02 13:25:39 - mmengine - INFO - Epoch(val) [120][195/500] eta: 0:00:12 time: 0.0376 data_time: 0.0024 memory: 1008 2022/11/02 13:25:40 - mmengine - INFO - Epoch(val) [120][200/500] eta: 0:00:13 time: 0.0448 data_time: 0.0024 memory: 1008 2022/11/02 13:25:40 - mmengine - INFO - Epoch(val) [120][205/500] eta: 0:00:13 time: 0.0444 data_time: 0.0023 memory: 1008 2022/11/02 13:25:40 - mmengine - INFO - Epoch(val) [120][210/500] eta: 0:00:10 time: 0.0355 data_time: 0.0023 memory: 1008 2022/11/02 13:25:40 - mmengine - INFO - Epoch(val) [120][215/500] eta: 0:00:10 time: 0.0366 data_time: 0.0023 memory: 1008 2022/11/02 13:25:40 - mmengine - INFO - Epoch(val) [120][220/500] eta: 0:00:10 time: 0.0387 data_time: 0.0025 memory: 1008 2022/11/02 13:25:40 - mmengine - INFO - Epoch(val) [120][225/500] eta: 0:00:10 time: 0.0407 data_time: 0.0023 memory: 1008 2022/11/02 13:25:41 - mmengine - INFO - Epoch(val) [120][230/500] eta: 0:00:10 time: 0.0375 data_time: 0.0023 memory: 1008 2022/11/02 13:25:41 - mmengine - INFO - Epoch(val) [120][235/500] eta: 0:00:10 time: 0.0370 data_time: 0.0024 memory: 1008 2022/11/02 13:25:41 - mmengine - INFO - Epoch(val) [120][240/500] eta: 0:00:10 time: 0.0409 data_time: 0.0025 memory: 1008 2022/11/02 13:25:41 - mmengine - INFO - Epoch(val) [120][245/500] eta: 0:00:10 time: 0.0405 data_time: 0.0028 memory: 1008 2022/11/02 13:25:41 - mmengine - INFO - Epoch(val) [120][250/500] eta: 0:00:10 time: 0.0423 data_time: 0.0029 memory: 1008 2022/11/02 13:25:42 - mmengine - INFO - Epoch(val) [120][255/500] eta: 0:00:10 time: 0.0397 data_time: 0.0025 memory: 1008 2022/11/02 13:25:42 - mmengine - INFO - Epoch(val) [120][260/500] eta: 0:00:08 time: 0.0365 data_time: 0.0022 memory: 1008 2022/11/02 13:25:42 - mmengine - INFO - Epoch(val) [120][265/500] eta: 0:00:08 time: 0.0373 data_time: 0.0023 memory: 1008 2022/11/02 13:25:42 - mmengine - INFO - Epoch(val) [120][270/500] eta: 0:00:08 time: 0.0372 data_time: 0.0024 memory: 1008 2022/11/02 13:25:42 - mmengine - INFO - Epoch(val) [120][275/500] eta: 0:00:08 time: 0.0377 data_time: 0.0024 memory: 1008 2022/11/02 13:25:43 - mmengine - INFO - Epoch(val) [120][280/500] eta: 0:00:08 time: 0.0395 data_time: 0.0024 memory: 1008 2022/11/02 13:25:43 - mmengine - INFO - Epoch(val) [120][285/500] eta: 0:00:08 time: 0.0381 data_time: 0.0024 memory: 1008 2022/11/02 13:25:43 - mmengine - INFO - Epoch(val) [120][290/500] eta: 0:00:08 time: 0.0392 data_time: 0.0023 memory: 1008 2022/11/02 13:25:43 - mmengine - INFO - Epoch(val) [120][295/500] eta: 0:00:08 time: 0.0396 data_time: 0.0023 memory: 1008 2022/11/02 13:25:43 - mmengine - INFO - Epoch(val) [120][300/500] eta: 0:00:07 time: 0.0361 data_time: 0.0022 memory: 1008 2022/11/02 13:25:44 - mmengine - INFO - Epoch(val) [120][305/500] eta: 0:00:07 time: 0.0388 data_time: 0.0023 memory: 1008 2022/11/02 13:25:44 - mmengine - INFO - Epoch(val) [120][310/500] eta: 0:00:07 time: 0.0386 data_time: 0.0024 memory: 1008 2022/11/02 13:25:44 - mmengine - INFO - Epoch(val) [120][315/500] eta: 0:00:07 time: 0.0373 data_time: 0.0023 memory: 1008 2022/11/02 13:25:44 - mmengine - INFO - Epoch(val) [120][320/500] eta: 0:00:07 time: 0.0391 data_time: 0.0025 memory: 1008 2022/11/02 13:25:44 - mmengine - INFO - Epoch(val) [120][325/500] eta: 0:00:07 time: 0.0534 data_time: 0.0027 memory: 1008 2022/11/02 13:25:45 - mmengine - INFO - Epoch(val) [120][330/500] eta: 0:00:08 time: 0.0515 data_time: 0.0025 memory: 1008 2022/11/02 13:25:45 - mmengine - INFO - Epoch(val) [120][335/500] eta: 0:00:08 time: 0.0344 data_time: 0.0023 memory: 1008 2022/11/02 13:25:45 - mmengine - INFO - Epoch(val) [120][340/500] eta: 0:00:07 time: 0.0473 data_time: 0.0024 memory: 1008 2022/11/02 13:25:45 - mmengine - INFO - Epoch(val) [120][345/500] eta: 0:00:07 time: 0.0499 data_time: 0.0025 memory: 1008 2022/11/02 13:25:46 - mmengine - INFO - Epoch(val) [120][350/500] eta: 0:00:06 time: 0.0429 data_time: 0.0026 memory: 1008 2022/11/02 13:25:46 - mmengine - INFO - Epoch(val) [120][355/500] eta: 0:00:06 time: 0.0427 data_time: 0.0026 memory: 1008 2022/11/02 13:25:46 - mmengine - INFO - Epoch(val) [120][360/500] eta: 0:00:05 time: 0.0388 data_time: 0.0026 memory: 1008 2022/11/02 13:25:46 - mmengine - INFO - Epoch(val) [120][365/500] eta: 0:00:05 time: 0.0403 data_time: 0.0026 memory: 1008 2022/11/02 13:25:46 - mmengine - INFO - Epoch(val) [120][370/500] eta: 0:00:04 time: 0.0377 data_time: 0.0027 memory: 1008 2022/11/02 13:25:46 - mmengine - INFO - Epoch(val) [120][375/500] eta: 0:00:04 time: 0.0350 data_time: 0.0026 memory: 1008 2022/11/02 13:25:47 - mmengine - INFO - Epoch(val) [120][380/500] eta: 0:00:04 time: 0.0383 data_time: 0.0023 memory: 1008 2022/11/02 13:25:47 - mmengine - INFO - Epoch(val) [120][385/500] eta: 0:00:04 time: 0.0404 data_time: 0.0024 memory: 1008 2022/11/02 13:25:47 - mmengine - INFO - Epoch(val) [120][390/500] eta: 0:00:04 time: 0.0388 data_time: 0.0023 memory: 1008 2022/11/02 13:25:47 - mmengine - INFO - Epoch(val) [120][395/500] eta: 0:00:04 time: 0.0380 data_time: 0.0024 memory: 1008 2022/11/02 13:25:47 - mmengine - INFO - Epoch(val) [120][400/500] eta: 0:00:03 time: 0.0388 data_time: 0.0024 memory: 1008 2022/11/02 13:25:48 - mmengine - INFO - Epoch(val) [120][405/500] eta: 0:00:03 time: 0.0391 data_time: 0.0024 memory: 1008 2022/11/02 13:25:48 - mmengine - INFO - Epoch(val) [120][410/500] eta: 0:00:03 time: 0.0411 data_time: 0.0024 memory: 1008 2022/11/02 13:25:48 - mmengine - INFO - Epoch(val) [120][415/500] eta: 0:00:03 time: 0.0395 data_time: 0.0023 memory: 1008 2022/11/02 13:25:48 - mmengine - INFO - Epoch(val) [120][420/500] eta: 0:00:02 time: 0.0353 data_time: 0.0026 memory: 1008 2022/11/02 13:25:48 - mmengine - INFO - Epoch(val) [120][425/500] eta: 0:00:02 time: 0.0397 data_time: 0.0027 memory: 1008 2022/11/02 13:25:49 - mmengine - INFO - Epoch(val) [120][430/500] eta: 0:00:02 time: 0.0404 data_time: 0.0024 memory: 1008 2022/11/02 13:25:49 - mmengine - INFO - Epoch(val) [120][435/500] eta: 0:00:02 time: 0.0366 data_time: 0.0023 memory: 1008 2022/11/02 13:25:49 - mmengine - INFO - Epoch(val) [120][440/500] eta: 0:00:02 time: 0.0382 data_time: 0.0022 memory: 1008 2022/11/02 13:25:49 - mmengine - INFO - Epoch(val) [120][445/500] eta: 0:00:02 time: 0.0391 data_time: 0.0022 memory: 1008 2022/11/02 13:25:49 - mmengine - INFO - Epoch(val) [120][450/500] eta: 0:00:01 time: 0.0396 data_time: 0.0022 memory: 1008 2022/11/02 13:25:50 - mmengine - INFO - Epoch(val) [120][455/500] eta: 0:00:01 time: 0.0391 data_time: 0.0022 memory: 1008 2022/11/02 13:25:50 - mmengine - INFO - Epoch(val) [120][460/500] eta: 0:00:01 time: 0.0353 data_time: 0.0021 memory: 1008 2022/11/02 13:25:50 - mmengine - INFO - Epoch(val) [120][465/500] eta: 0:00:01 time: 0.0365 data_time: 0.0023 memory: 1008 2022/11/02 13:25:50 - mmengine - INFO - Epoch(val) [120][470/500] eta: 0:00:01 time: 0.0369 data_time: 0.0024 memory: 1008 2022/11/02 13:25:50 - mmengine - INFO - Epoch(val) [120][475/500] eta: 0:00:01 time: 0.0340 data_time: 0.0022 memory: 1008 2022/11/02 13:25:51 - mmengine - INFO - Epoch(val) [120][480/500] eta: 0:00:00 time: 0.0354 data_time: 0.0022 memory: 1008 2022/11/02 13:25:51 - mmengine - INFO - Epoch(val) [120][485/500] eta: 0:00:00 time: 0.0368 data_time: 0.0022 memory: 1008 2022/11/02 13:25:51 - mmengine - INFO - Epoch(val) [120][490/500] eta: 0:00:00 time: 0.0425 data_time: 0.0023 memory: 1008 2022/11/02 13:25:51 - mmengine - INFO - Epoch(val) [120][495/500] eta: 0:00:00 time: 0.0477 data_time: 0.0026 memory: 1008 2022/11/02 13:25:51 - mmengine - INFO - Epoch(val) [120][500/500] eta: 0:00:00 time: 0.0401 data_time: 0.0024 memory: 1008 2022/11/02 13:25:51 - mmengine - INFO - Evaluating hmean-iou... 2022/11/02 13:25:51 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.7737, precision: 0.6741, hmean: 0.7205 2022/11/02 13:25:51 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.7737, precision: 0.7634, hmean: 0.7685 2022/11/02 13:25:51 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.7675, precision: 0.8242, hmean: 0.7948 2022/11/02 13:25:51 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.7386, precision: 0.8771, hmean: 0.8019 2022/11/02 13:25:51 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.6057, precision: 0.9182, hmean: 0.7299 2022/11/02 13:25:51 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.1526, precision: 0.9694, hmean: 0.2637 2022/11/02 13:25:51 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0000, precision: 0.0000, hmean: 0.0000 2022/11/02 13:25:51 - mmengine - INFO - Epoch(val) [120][500/500] icdar/precision: 0.8771 icdar/recall: 0.7386 icdar/hmean: 0.8019 2022/11/02 13:25:56 - mmengine - INFO - Epoch(train) [121][5/63] lr: 1.9672e-03 eta: 0:00:00 time: 0.7006 data_time: 0.1929 memory: 14901 loss: 2.0111 loss_prob: 1.1805 loss_thr: 0.6363 loss_db: 0.1942 2022/11/02 13:25:59 - mmengine - INFO - Epoch(train) [121][10/63] lr: 1.9672e-03 eta: 11:28:21 time: 0.7599 data_time: 0.1931 memory: 14901 loss: 2.1155 loss_prob: 1.2615 loss_thr: 0.6447 loss_db: 0.2093 2022/11/02 13:26:02 - mmengine - INFO - Epoch(train) [121][15/63] lr: 1.9672e-03 eta: 11:28:21 time: 0.5741 data_time: 0.0050 memory: 14901 loss: 2.1614 loss_prob: 1.2864 loss_thr: 0.6652 loss_db: 0.2098 2022/11/02 13:26:05 - mmengine - INFO - Epoch(train) [121][20/63] lr: 1.9672e-03 eta: 11:28:13 time: 0.5823 data_time: 0.0051 memory: 14901 loss: 2.3351 loss_prob: 1.4131 loss_thr: 0.6873 loss_db: 0.2347 2022/11/02 13:26:08 - mmengine - INFO - Epoch(train) [121][25/63] lr: 1.9672e-03 eta: 11:28:13 time: 0.5907 data_time: 0.0202 memory: 14901 loss: 2.4241 loss_prob: 1.4738 loss_thr: 0.7013 loss_db: 0.2490 2022/11/02 13:26:11 - mmengine - INFO - Epoch(train) [121][30/63] lr: 1.9672e-03 eta: 11:28:08 time: 0.6138 data_time: 0.0347 memory: 14901 loss: 2.5640 loss_prob: 1.5766 loss_thr: 0.7197 loss_db: 0.2677 2022/11/02 13:26:15 - mmengine - INFO - Epoch(train) [121][35/63] lr: 1.9672e-03 eta: 11:28:08 time: 0.7176 data_time: 0.0299 memory: 14901 loss: 2.6353 loss_prob: 1.6339 loss_thr: 0.7266 loss_db: 0.2749 2022/11/02 13:26:18 - mmengine - INFO - Epoch(train) [121][40/63] lr: 1.9672e-03 eta: 11:28:07 time: 0.6653 data_time: 0.0152 memory: 14901 loss: 2.5065 loss_prob: 1.5588 loss_thr: 0.6899 loss_db: 0.2578 2022/11/02 13:26:20 - mmengine - INFO - Epoch(train) [121][45/63] lr: 1.9672e-03 eta: 11:28:07 time: 0.5080 data_time: 0.0047 memory: 14901 loss: 2.5655 loss_prob: 1.5979 loss_thr: 0.7096 loss_db: 0.2581 2022/11/02 13:26:24 - mmengine - INFO - Epoch(train) [121][50/63] lr: 1.9672e-03 eta: 11:28:00 time: 0.5998 data_time: 0.0112 memory: 14901 loss: 2.3935 loss_prob: 1.4501 loss_thr: 0.7116 loss_db: 0.2318 2022/11/02 13:26:26 - mmengine - INFO - Epoch(train) [121][55/63] lr: 1.9672e-03 eta: 11:28:00 time: 0.5997 data_time: 0.0174 memory: 14901 loss: 2.3313 loss_prob: 1.3961 loss_thr: 0.7042 loss_db: 0.2310 2022/11/02 13:26:29 - mmengine - INFO - Epoch(train) [121][60/63] lr: 1.9672e-03 eta: 11:27:48 time: 0.5425 data_time: 0.0163 memory: 14901 loss: 2.3084 loss_prob: 1.3732 loss_thr: 0.7090 loss_db: 0.2263 2022/11/02 13:26:31 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:26:37 - mmengine - INFO - Epoch(train) [122][5/63] lr: 1.9656e-03 eta: 11:27:48 time: 0.8836 data_time: 0.2381 memory: 14901 loss: 2.3818 loss_prob: 1.4766 loss_thr: 0.6613 loss_db: 0.2439 2022/11/02 13:26:40 - mmengine - INFO - Epoch(train) [122][10/63] lr: 1.9656e-03 eta: 11:27:55 time: 0.9506 data_time: 0.2369 memory: 14901 loss: 2.3854 loss_prob: 1.4611 loss_thr: 0.6824 loss_db: 0.2419 2022/11/02 13:26:45 - mmengine - INFO - Epoch(train) [122][15/63] lr: 1.9656e-03 eta: 11:27:55 time: 0.7957 data_time: 0.0044 memory: 14901 loss: 2.3653 loss_prob: 1.4377 loss_thr: 0.6913 loss_db: 0.2363 2022/11/02 13:26:47 - mmengine - INFO - Epoch(train) [122][20/63] lr: 1.9656e-03 eta: 11:27:53 time: 0.6598 data_time: 0.0043 memory: 14901 loss: 2.3970 loss_prob: 1.4648 loss_thr: 0.6887 loss_db: 0.2436 2022/11/02 13:26:51 - mmengine - INFO - Epoch(train) [122][25/63] lr: 1.9656e-03 eta: 11:27:53 time: 0.6108 data_time: 0.0262 memory: 14901 loss: 2.5219 loss_prob: 1.5516 loss_thr: 0.7118 loss_db: 0.2585 2022/11/02 13:26:53 - mmengine - INFO - Epoch(train) [122][30/63] lr: 1.9656e-03 eta: 11:27:52 time: 0.6568 data_time: 0.0337 memory: 14901 loss: 2.4792 loss_prob: 1.5320 loss_thr: 0.6996 loss_db: 0.2476 2022/11/02 13:26:57 - mmengine - INFO - Epoch(train) [122][35/63] lr: 1.9656e-03 eta: 11:27:52 time: 0.5968 data_time: 0.0125 memory: 14901 loss: 2.2753 loss_prob: 1.3836 loss_thr: 0.6699 loss_db: 0.2219 2022/11/02 13:27:00 - mmengine - INFO - Epoch(train) [122][40/63] lr: 1.9656e-03 eta: 11:27:45 time: 0.6038 data_time: 0.0056 memory: 14901 loss: 2.1979 loss_prob: 1.3270 loss_thr: 0.6549 loss_db: 0.2160 2022/11/02 13:27:02 - mmengine - INFO - Epoch(train) [122][45/63] lr: 1.9656e-03 eta: 11:27:45 time: 0.5226 data_time: 0.0069 memory: 14901 loss: 2.1933 loss_prob: 1.3321 loss_thr: 0.6403 loss_db: 0.2210 2022/11/02 13:27:04 - mmengine - INFO - Epoch(train) [122][50/63] lr: 1.9656e-03 eta: 11:27:28 time: 0.4863 data_time: 0.0236 memory: 14901 loss: 2.2638 loss_prob: 1.3656 loss_thr: 0.6723 loss_db: 0.2259 2022/11/02 13:27:07 - mmengine - INFO - Epoch(train) [122][55/63] lr: 1.9656e-03 eta: 11:27:28 time: 0.5099 data_time: 0.0242 memory: 14901 loss: 2.3154 loss_prob: 1.3821 loss_thr: 0.7103 loss_db: 0.2229 2022/11/02 13:27:09 - mmengine - INFO - Epoch(train) [122][60/63] lr: 1.9656e-03 eta: 11:27:13 time: 0.5020 data_time: 0.0071 memory: 14901 loss: 2.2358 loss_prob: 1.3147 loss_thr: 0.7055 loss_db: 0.2156 2022/11/02 13:27:11 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:27:15 - mmengine - INFO - Epoch(train) [123][5/63] lr: 1.9639e-03 eta: 11:27:13 time: 0.6842 data_time: 0.1976 memory: 14901 loss: 2.1682 loss_prob: 1.2896 loss_thr: 0.6713 loss_db: 0.2073 2022/11/02 13:27:18 - mmengine - INFO - Epoch(train) [123][10/63] lr: 1.9639e-03 eta: 11:27:02 time: 0.7512 data_time: 0.2058 memory: 14901 loss: 2.2052 loss_prob: 1.3247 loss_thr: 0.6665 loss_db: 0.2141 2022/11/02 13:27:21 - mmengine - INFO - Epoch(train) [123][15/63] lr: 1.9639e-03 eta: 11:27:02 time: 0.5564 data_time: 0.0153 memory: 14901 loss: 2.3729 loss_prob: 1.4570 loss_thr: 0.6751 loss_db: 0.2408 2022/11/02 13:27:23 - mmengine - INFO - Epoch(train) [123][20/63] lr: 1.9639e-03 eta: 11:26:48 time: 0.5178 data_time: 0.0073 memory: 14901 loss: 2.3506 loss_prob: 1.4317 loss_thr: 0.6814 loss_db: 0.2374 2022/11/02 13:27:26 - mmengine - INFO - Epoch(train) [123][25/63] lr: 1.9639e-03 eta: 11:26:48 time: 0.4942 data_time: 0.0066 memory: 14901 loss: 2.4160 loss_prob: 1.4464 loss_thr: 0.7306 loss_db: 0.2389 2022/11/02 13:27:28 - mmengine - INFO - Epoch(train) [123][30/63] lr: 1.9639e-03 eta: 11:26:34 time: 0.5165 data_time: 0.0290 memory: 14901 loss: 2.3736 loss_prob: 1.4297 loss_thr: 0.7102 loss_db: 0.2338 2022/11/02 13:27:31 - mmengine - INFO - Epoch(train) [123][35/63] lr: 1.9639e-03 eta: 11:26:34 time: 0.5304 data_time: 0.0295 memory: 14901 loss: 2.2594 loss_prob: 1.3799 loss_thr: 0.6541 loss_db: 0.2254 2022/11/02 13:27:34 - mmengine - INFO - Epoch(train) [123][40/63] lr: 1.9639e-03 eta: 11:26:20 time: 0.5161 data_time: 0.0097 memory: 14901 loss: 2.4484 loss_prob: 1.5176 loss_thr: 0.6762 loss_db: 0.2546 2022/11/02 13:27:36 - mmengine - INFO - Epoch(train) [123][45/63] lr: 1.9639e-03 eta: 11:26:20 time: 0.5125 data_time: 0.0101 memory: 14901 loss: 2.3414 loss_prob: 1.4233 loss_thr: 0.6784 loss_db: 0.2397 2022/11/02 13:27:39 - mmengine - INFO - Epoch(train) [123][50/63] lr: 1.9639e-03 eta: 11:26:05 time: 0.5048 data_time: 0.0192 memory: 14901 loss: 2.0835 loss_prob: 1.2273 loss_thr: 0.6563 loss_db: 0.1999 2022/11/02 13:27:41 - mmengine - INFO - Epoch(train) [123][55/63] lr: 1.9639e-03 eta: 11:26:05 time: 0.4823 data_time: 0.0176 memory: 14901 loss: 2.1788 loss_prob: 1.3006 loss_thr: 0.6644 loss_db: 0.2138 2022/11/02 13:27:43 - mmengine - INFO - Epoch(train) [123][60/63] lr: 1.9639e-03 eta: 11:25:48 time: 0.4803 data_time: 0.0116 memory: 14901 loss: 2.1667 loss_prob: 1.3055 loss_thr: 0.6488 loss_db: 0.2124 2022/11/02 13:27:45 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:27:50 - mmengine - INFO - Epoch(train) [124][5/63] lr: 1.9623e-03 eta: 11:25:48 time: 0.7316 data_time: 0.2130 memory: 14901 loss: 2.2661 loss_prob: 1.3649 loss_thr: 0.6806 loss_db: 0.2206 2022/11/02 13:27:52 - mmengine - INFO - Epoch(train) [124][10/63] lr: 1.9623e-03 eta: 11:25:37 time: 0.7521 data_time: 0.2099 memory: 14901 loss: 2.1045 loss_prob: 1.2255 loss_thr: 0.6741 loss_db: 0.2049 2022/11/02 13:27:55 - mmengine - INFO - Epoch(train) [124][15/63] lr: 1.9623e-03 eta: 11:25:37 time: 0.4847 data_time: 0.0088 memory: 14901 loss: 2.2657 loss_prob: 1.3549 loss_thr: 0.6890 loss_db: 0.2219 2022/11/02 13:27:57 - mmengine - INFO - Epoch(train) [124][20/63] lr: 1.9623e-03 eta: 11:25:19 time: 0.4706 data_time: 0.0081 memory: 14901 loss: 2.3459 loss_prob: 1.4333 loss_thr: 0.6797 loss_db: 0.2329 2022/11/02 13:28:00 - mmengine - INFO - Epoch(train) [124][25/63] lr: 1.9623e-03 eta: 11:25:19 time: 0.5082 data_time: 0.0197 memory: 14901 loss: 2.2074 loss_prob: 1.3383 loss_thr: 0.6572 loss_db: 0.2118 2022/11/02 13:28:02 - mmengine - INFO - Epoch(train) [124][30/63] lr: 1.9623e-03 eta: 11:25:06 time: 0.5313 data_time: 0.0451 memory: 14901 loss: 2.1846 loss_prob: 1.3163 loss_thr: 0.6588 loss_db: 0.2095 2022/11/02 13:28:05 - mmengine - INFO - Epoch(train) [124][35/63] lr: 1.9623e-03 eta: 11:25:06 time: 0.5016 data_time: 0.0298 memory: 14901 loss: 2.2426 loss_prob: 1.3625 loss_thr: 0.6603 loss_db: 0.2197 2022/11/02 13:28:07 - mmengine - INFO - Epoch(train) [124][40/63] lr: 1.9623e-03 eta: 11:24:50 time: 0.4927 data_time: 0.0053 memory: 14901 loss: 2.3413 loss_prob: 1.4256 loss_thr: 0.6821 loss_db: 0.2337 2022/11/02 13:28:10 - mmengine - INFO - Epoch(train) [124][45/63] lr: 1.9623e-03 eta: 11:24:50 time: 0.5732 data_time: 0.0058 memory: 14901 loss: 2.3582 loss_prob: 1.4376 loss_thr: 0.6790 loss_db: 0.2417 2022/11/02 13:28:13 - mmengine - INFO - Epoch(train) [124][50/63] lr: 1.9623e-03 eta: 11:24:42 time: 0.5818 data_time: 0.0145 memory: 14901 loss: 2.5207 loss_prob: 1.5534 loss_thr: 0.7033 loss_db: 0.2640 2022/11/02 13:28:16 - mmengine - INFO - Epoch(train) [124][55/63] lr: 1.9623e-03 eta: 11:24:42 time: 0.5075 data_time: 0.0209 memory: 14901 loss: 2.9650 loss_prob: 1.8995 loss_thr: 0.7460 loss_db: 0.3195 2022/11/02 13:28:18 - mmengine - INFO - Epoch(train) [124][60/63] lr: 1.9623e-03 eta: 11:24:25 time: 0.4826 data_time: 0.0122 memory: 14901 loss: 3.1216 loss_prob: 2.0265 loss_thr: 0.7559 loss_db: 0.3392 2022/11/02 13:28:19 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:28:24 - mmengine - INFO - Epoch(train) [125][5/63] lr: 1.9607e-03 eta: 11:24:25 time: 0.6778 data_time: 0.2250 memory: 14901 loss: 2.6234 loss_prob: 1.6272 loss_thr: 0.7215 loss_db: 0.2747 2022/11/02 13:28:26 - mmengine - INFO - Epoch(train) [125][10/63] lr: 1.9607e-03 eta: 11:24:11 time: 0.7151 data_time: 0.2232 memory: 14901 loss: 2.4449 loss_prob: 1.5144 loss_thr: 0.6773 loss_db: 0.2532 2022/11/02 13:28:29 - mmengine - INFO - Epoch(train) [125][15/63] lr: 1.9607e-03 eta: 11:24:11 time: 0.4898 data_time: 0.0061 memory: 14901 loss: 2.3412 loss_prob: 1.4407 loss_thr: 0.6650 loss_db: 0.2356 2022/11/02 13:28:31 - mmengine - INFO - Epoch(train) [125][20/63] lr: 1.9607e-03 eta: 11:23:54 time: 0.4745 data_time: 0.0056 memory: 14901 loss: 2.3384 loss_prob: 1.4207 loss_thr: 0.6852 loss_db: 0.2325 2022/11/02 13:28:34 - mmengine - INFO - Epoch(train) [125][25/63] lr: 1.9607e-03 eta: 11:23:54 time: 0.5098 data_time: 0.0347 memory: 14901 loss: 2.4138 loss_prob: 1.4620 loss_thr: 0.7099 loss_db: 0.2420 2022/11/02 13:28:37 - mmengine - INFO - Epoch(train) [125][30/63] lr: 1.9607e-03 eta: 11:23:44 time: 0.5661 data_time: 0.0360 memory: 14901 loss: 2.2773 loss_prob: 1.3743 loss_thr: 0.6798 loss_db: 0.2232 2022/11/02 13:28:40 - mmengine - INFO - Epoch(train) [125][35/63] lr: 1.9607e-03 eta: 11:23:44 time: 0.6550 data_time: 0.0061 memory: 14901 loss: 2.1099 loss_prob: 1.2546 loss_thr: 0.6523 loss_db: 0.2030 2022/11/02 13:28:43 - mmengine - INFO - Epoch(train) [125][40/63] lr: 1.9607e-03 eta: 11:23:40 time: 0.6235 data_time: 0.0046 memory: 14901 loss: 2.2164 loss_prob: 1.3118 loss_thr: 0.6893 loss_db: 0.2154 2022/11/02 13:28:46 - mmengine - INFO - Epoch(train) [125][45/63] lr: 1.9607e-03 eta: 11:23:40 time: 0.5790 data_time: 0.0063 memory: 14901 loss: 2.2454 loss_prob: 1.3431 loss_thr: 0.6827 loss_db: 0.2196 2022/11/02 13:28:50 - mmengine - INFO - Epoch(train) [125][50/63] lr: 1.9607e-03 eta: 11:23:47 time: 0.7586 data_time: 0.0244 memory: 14901 loss: 2.3350 loss_prob: 1.4124 loss_thr: 0.6891 loss_db: 0.2335 2022/11/02 13:28:54 - mmengine - INFO - Epoch(train) [125][55/63] lr: 1.9607e-03 eta: 11:23:47 time: 0.7675 data_time: 0.0242 memory: 14901 loss: 2.5523 loss_prob: 1.5731 loss_thr: 0.7154 loss_db: 0.2638 2022/11/02 13:28:56 - mmengine - INFO - Epoch(train) [125][60/63] lr: 1.9607e-03 eta: 11:23:39 time: 0.5817 data_time: 0.0066 memory: 14901 loss: 2.3889 loss_prob: 1.4714 loss_thr: 0.6737 loss_db: 0.2438 2022/11/02 13:28:58 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:29:04 - mmengine - INFO - Epoch(train) [126][5/63] lr: 1.9590e-03 eta: 11:23:39 time: 0.8428 data_time: 0.1998 memory: 14901 loss: 2.4313 loss_prob: 1.4913 loss_thr: 0.6947 loss_db: 0.2453 2022/11/02 13:29:06 - mmengine - INFO - Epoch(train) [126][10/63] lr: 1.9590e-03 eta: 11:23:33 time: 0.8106 data_time: 0.2011 memory: 14901 loss: 2.4451 loss_prob: 1.5070 loss_thr: 0.6916 loss_db: 0.2466 2022/11/02 13:29:09 - mmengine - INFO - Epoch(train) [126][15/63] lr: 1.9590e-03 eta: 11:23:33 time: 0.5085 data_time: 0.0075 memory: 14901 loss: 2.3047 loss_prob: 1.3988 loss_thr: 0.6811 loss_db: 0.2248 2022/11/02 13:29:12 - mmengine - INFO - Epoch(train) [126][20/63] lr: 1.9590e-03 eta: 11:23:29 time: 0.6305 data_time: 0.0053 memory: 14901 loss: 2.1917 loss_prob: 1.3209 loss_thr: 0.6581 loss_db: 0.2126 2022/11/02 13:29:16 - mmengine - INFO - Epoch(train) [126][25/63] lr: 1.9590e-03 eta: 11:23:29 time: 0.6821 data_time: 0.0156 memory: 14901 loss: 2.2369 loss_prob: 1.3523 loss_thr: 0.6669 loss_db: 0.2177 2022/11/02 13:29:19 - mmengine - INFO - Epoch(train) [126][30/63] lr: 1.9590e-03 eta: 11:23:25 time: 0.6341 data_time: 0.0331 memory: 14901 loss: 2.3451 loss_prob: 1.4187 loss_thr: 0.6942 loss_db: 0.2322 2022/11/02 13:29:22 - mmengine - INFO - Epoch(train) [126][35/63] lr: 1.9590e-03 eta: 11:23:25 time: 0.6387 data_time: 0.0222 memory: 14901 loss: 2.3163 loss_prob: 1.3992 loss_thr: 0.6882 loss_db: 0.2290 2022/11/02 13:29:25 - mmengine - INFO - Epoch(train) [126][40/63] lr: 1.9590e-03 eta: 11:23:17 time: 0.5778 data_time: 0.0068 memory: 14901 loss: 2.2898 loss_prob: 1.3783 loss_thr: 0.6846 loss_db: 0.2270 2022/11/02 13:29:27 - mmengine - INFO - Epoch(train) [126][45/63] lr: 1.9590e-03 eta: 11:23:17 time: 0.5138 data_time: 0.0068 memory: 14901 loss: 2.2363 loss_prob: 1.3361 loss_thr: 0.6786 loss_db: 0.2217 2022/11/02 13:29:31 - mmengine - INFO - Epoch(train) [126][50/63] lr: 1.9590e-03 eta: 11:23:13 time: 0.6363 data_time: 0.0173 memory: 14901 loss: 2.2743 loss_prob: 1.3767 loss_thr: 0.6755 loss_db: 0.2222 2022/11/02 13:29:34 - mmengine - INFO - Epoch(train) [126][55/63] lr: 1.9590e-03 eta: 11:23:13 time: 0.7106 data_time: 0.0246 memory: 14901 loss: 2.2111 loss_prob: 1.3158 loss_thr: 0.6823 loss_db: 0.2129 2022/11/02 13:29:37 - mmengine - INFO - Epoch(train) [126][60/63] lr: 1.9590e-03 eta: 11:23:05 time: 0.5811 data_time: 0.0124 memory: 14901 loss: 2.1759 loss_prob: 1.2867 loss_thr: 0.6794 loss_db: 0.2097 2022/11/02 13:29:38 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:29:43 - mmengine - INFO - Epoch(train) [127][5/63] lr: 1.9574e-03 eta: 11:23:05 time: 0.7661 data_time: 0.2637 memory: 14901 loss: 2.3790 loss_prob: 1.4566 loss_thr: 0.6910 loss_db: 0.2314 2022/11/02 13:29:46 - mmengine - INFO - Epoch(train) [127][10/63] lr: 1.9574e-03 eta: 11:23:00 time: 0.8196 data_time: 0.2632 memory: 14901 loss: 2.3356 loss_prob: 1.4092 loss_thr: 0.7007 loss_db: 0.2257 2022/11/02 13:29:49 - mmengine - INFO - Epoch(train) [127][15/63] lr: 1.9574e-03 eta: 11:23:00 time: 0.5227 data_time: 0.0054 memory: 14901 loss: 2.4047 loss_prob: 1.4424 loss_thr: 0.7201 loss_db: 0.2422 2022/11/02 13:29:51 - mmengine - INFO - Epoch(train) [127][20/63] lr: 1.9574e-03 eta: 11:22:43 time: 0.4782 data_time: 0.0061 memory: 14901 loss: 2.3198 loss_prob: 1.3961 loss_thr: 0.6885 loss_db: 0.2351 2022/11/02 13:29:54 - mmengine - INFO - Epoch(train) [127][25/63] lr: 1.9574e-03 eta: 11:22:43 time: 0.4864 data_time: 0.0118 memory: 14901 loss: 2.3565 loss_prob: 1.4499 loss_thr: 0.6695 loss_db: 0.2371 2022/11/02 13:29:56 - mmengine - INFO - Epoch(train) [127][30/63] lr: 1.9574e-03 eta: 11:22:32 time: 0.5490 data_time: 0.0272 memory: 14901 loss: 2.3957 loss_prob: 1.4695 loss_thr: 0.6847 loss_db: 0.2415 2022/11/02 13:29:59 - mmengine - INFO - Epoch(train) [127][35/63] lr: 1.9574e-03 eta: 11:22:32 time: 0.5276 data_time: 0.0268 memory: 14901 loss: 2.1710 loss_prob: 1.2933 loss_thr: 0.6709 loss_db: 0.2069 2022/11/02 13:30:01 - mmengine - INFO - Epoch(train) [127][40/63] lr: 1.9574e-03 eta: 11:22:18 time: 0.5019 data_time: 0.0118 memory: 14901 loss: 2.3383 loss_prob: 1.4231 loss_thr: 0.6858 loss_db: 0.2294 2022/11/02 13:30:04 - mmengine - INFO - Epoch(train) [127][45/63] lr: 1.9574e-03 eta: 11:22:18 time: 0.4922 data_time: 0.0056 memory: 14901 loss: 2.3624 loss_prob: 1.4392 loss_thr: 0.6847 loss_db: 0.2385 2022/11/02 13:30:06 - mmengine - INFO - Epoch(train) [127][50/63] lr: 1.9574e-03 eta: 11:22:03 time: 0.5079 data_time: 0.0229 memory: 14901 loss: 2.0855 loss_prob: 1.2426 loss_thr: 0.6414 loss_db: 0.2015 2022/11/02 13:30:09 - mmengine - INFO - Epoch(train) [127][55/63] lr: 1.9574e-03 eta: 11:22:03 time: 0.5587 data_time: 0.0243 memory: 14901 loss: 2.0721 loss_prob: 1.2220 loss_thr: 0.6540 loss_db: 0.1961 2022/11/02 13:30:12 - mmengine - INFO - Epoch(train) [127][60/63] lr: 1.9574e-03 eta: 11:21:54 time: 0.5647 data_time: 0.0076 memory: 14901 loss: 2.3381 loss_prob: 1.4252 loss_thr: 0.6765 loss_db: 0.2364 2022/11/02 13:30:13 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:30:13 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:30:18 - mmengine - INFO - Epoch(train) [128][5/63] lr: 1.9557e-03 eta: 11:21:54 time: 0.7331 data_time: 0.2150 memory: 14901 loss: 2.1568 loss_prob: 1.2976 loss_thr: 0.6459 loss_db: 0.2133 2022/11/02 13:30:21 - mmengine - INFO - Epoch(train) [128][10/63] lr: 1.9557e-03 eta: 11:21:46 time: 0.7899 data_time: 0.2150 memory: 14901 loss: 2.3149 loss_prob: 1.4094 loss_thr: 0.6752 loss_db: 0.2302 2022/11/02 13:30:24 - mmengine - INFO - Epoch(train) [128][15/63] lr: 1.9557e-03 eta: 11:21:46 time: 0.5281 data_time: 0.0049 memory: 14901 loss: 2.2982 loss_prob: 1.3949 loss_thr: 0.6780 loss_db: 0.2253 2022/11/02 13:30:26 - mmengine - INFO - Epoch(train) [128][20/63] lr: 1.9557e-03 eta: 11:21:30 time: 0.4899 data_time: 0.0051 memory: 14901 loss: 2.1454 loss_prob: 1.2764 loss_thr: 0.6653 loss_db: 0.2037 2022/11/02 13:30:28 - mmengine - INFO - Epoch(train) [128][25/63] lr: 1.9557e-03 eta: 11:21:30 time: 0.4766 data_time: 0.0160 memory: 14901 loss: 2.2290 loss_prob: 1.3310 loss_thr: 0.6853 loss_db: 0.2128 2022/11/02 13:30:31 - mmengine - INFO - Epoch(train) [128][30/63] lr: 1.9557e-03 eta: 11:21:14 time: 0.4834 data_time: 0.0367 memory: 14901 loss: 2.3074 loss_prob: 1.4068 loss_thr: 0.6760 loss_db: 0.2246 2022/11/02 13:30:33 - mmengine - INFO - Epoch(train) [128][35/63] lr: 1.9557e-03 eta: 11:21:14 time: 0.5039 data_time: 0.0278 memory: 14901 loss: 2.4137 loss_prob: 1.4956 loss_thr: 0.6803 loss_db: 0.2378 2022/11/02 13:30:36 - mmengine - INFO - Epoch(train) [128][40/63] lr: 1.9557e-03 eta: 11:21:01 time: 0.5218 data_time: 0.0071 memory: 14901 loss: 2.5015 loss_prob: 1.5257 loss_thr: 0.7273 loss_db: 0.2484 2022/11/02 13:30:38 - mmengine - INFO - Epoch(train) [128][45/63] lr: 1.9557e-03 eta: 11:21:01 time: 0.5106 data_time: 0.0049 memory: 14901 loss: 2.2760 loss_prob: 1.3540 loss_thr: 0.6991 loss_db: 0.2229 2022/11/02 13:30:41 - mmengine - INFO - Epoch(train) [128][50/63] lr: 1.9557e-03 eta: 11:20:47 time: 0.5127 data_time: 0.0283 memory: 14901 loss: 2.1371 loss_prob: 1.2755 loss_thr: 0.6583 loss_db: 0.2034 2022/11/02 13:30:44 - mmengine - INFO - Epoch(train) [128][55/63] lr: 1.9557e-03 eta: 11:20:47 time: 0.5368 data_time: 0.0336 memory: 14901 loss: 2.2015 loss_prob: 1.3163 loss_thr: 0.6736 loss_db: 0.2116 2022/11/02 13:30:46 - mmengine - INFO - Epoch(train) [128][60/63] lr: 1.9557e-03 eta: 11:20:32 time: 0.4929 data_time: 0.0097 memory: 14901 loss: 2.4065 loss_prob: 1.4660 loss_thr: 0.7004 loss_db: 0.2402 2022/11/02 13:30:47 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:30:52 - mmengine - INFO - Epoch(train) [129][5/63] lr: 1.9541e-03 eta: 11:20:32 time: 0.6942 data_time: 0.1993 memory: 14901 loss: 2.3648 loss_prob: 1.4533 loss_thr: 0.6716 loss_db: 0.2399 2022/11/02 13:30:55 - mmengine - INFO - Epoch(train) [129][10/63] lr: 1.9541e-03 eta: 11:20:18 time: 0.7176 data_time: 0.2007 memory: 14901 loss: 2.2218 loss_prob: 1.3315 loss_thr: 0.6682 loss_db: 0.2221 2022/11/02 13:30:57 - mmengine - INFO - Epoch(train) [129][15/63] lr: 1.9541e-03 eta: 11:20:18 time: 0.4708 data_time: 0.0091 memory: 14901 loss: 2.2256 loss_prob: 1.3250 loss_thr: 0.6846 loss_db: 0.2160 2022/11/02 13:30:59 - mmengine - INFO - Epoch(train) [129][20/63] lr: 1.9541e-03 eta: 11:20:01 time: 0.4657 data_time: 0.0101 memory: 14901 loss: 2.2561 loss_prob: 1.3541 loss_thr: 0.6836 loss_db: 0.2183 2022/11/02 13:31:02 - mmengine - INFO - Epoch(train) [129][25/63] lr: 1.9541e-03 eta: 11:20:01 time: 0.4849 data_time: 0.0229 memory: 14901 loss: 2.2992 loss_prob: 1.3903 loss_thr: 0.6860 loss_db: 0.2229 2022/11/02 13:31:04 - mmengine - INFO - Epoch(train) [129][30/63] lr: 1.9541e-03 eta: 11:19:45 time: 0.4919 data_time: 0.0281 memory: 14901 loss: 2.2288 loss_prob: 1.3413 loss_thr: 0.6715 loss_db: 0.2160 2022/11/02 13:31:06 - mmengine - INFO - Epoch(train) [129][35/63] lr: 1.9541e-03 eta: 11:19:45 time: 0.4757 data_time: 0.0124 memory: 14901 loss: 2.2245 loss_prob: 1.3304 loss_thr: 0.6759 loss_db: 0.2182 2022/11/02 13:31:09 - mmengine - INFO - Epoch(train) [129][40/63] lr: 1.9541e-03 eta: 11:19:29 time: 0.4787 data_time: 0.0087 memory: 14901 loss: 2.3465 loss_prob: 1.4128 loss_thr: 0.7019 loss_db: 0.2317 2022/11/02 13:31:11 - mmengine - INFO - Epoch(train) [129][45/63] lr: 1.9541e-03 eta: 11:19:29 time: 0.4834 data_time: 0.0082 memory: 14901 loss: 2.2971 loss_prob: 1.3630 loss_thr: 0.7092 loss_db: 0.2249 2022/11/02 13:31:16 - mmengine - INFO - Epoch(train) [129][50/63] lr: 1.9541e-03 eta: 11:19:31 time: 0.7015 data_time: 0.0197 memory: 14901 loss: 2.1768 loss_prob: 1.2917 loss_thr: 0.6757 loss_db: 0.2093 2022/11/02 13:31:19 - mmengine - INFO - Epoch(train) [129][55/63] lr: 1.9541e-03 eta: 11:19:31 time: 0.7830 data_time: 0.0261 memory: 14901 loss: 2.2376 loss_prob: 1.3528 loss_thr: 0.6647 loss_db: 0.2202 2022/11/02 13:31:22 - mmengine - INFO - Epoch(train) [129][60/63] lr: 1.9541e-03 eta: 11:19:26 time: 0.6219 data_time: 0.0108 memory: 14901 loss: 2.2955 loss_prob: 1.3806 loss_thr: 0.6874 loss_db: 0.2274 2022/11/02 13:31:24 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:31:31 - mmengine - INFO - Epoch(train) [130][5/63] lr: 1.9524e-03 eta: 11:19:26 time: 0.9506 data_time: 0.2483 memory: 14901 loss: 2.2965 loss_prob: 1.3618 loss_thr: 0.7130 loss_db: 0.2217 2022/11/02 13:31:34 - mmengine - INFO - Epoch(train) [130][10/63] lr: 1.9524e-03 eta: 11:19:31 time: 0.9334 data_time: 0.2434 memory: 14901 loss: 2.2479 loss_prob: 1.3333 loss_thr: 0.6967 loss_db: 0.2179 2022/11/02 13:31:36 - mmengine - INFO - Epoch(train) [130][15/63] lr: 1.9524e-03 eta: 11:19:31 time: 0.5605 data_time: 0.0051 memory: 14901 loss: 2.2992 loss_prob: 1.3896 loss_thr: 0.6841 loss_db: 0.2255 2022/11/02 13:31:39 - mmengine - INFO - Epoch(train) [130][20/63] lr: 1.9524e-03 eta: 11:19:18 time: 0.5286 data_time: 0.0048 memory: 14901 loss: 2.2394 loss_prob: 1.3316 loss_thr: 0.6933 loss_db: 0.2145 2022/11/02 13:31:41 - mmengine - INFO - Epoch(train) [130][25/63] lr: 1.9524e-03 eta: 11:19:18 time: 0.5255 data_time: 0.0141 memory: 14901 loss: 2.1578 loss_prob: 1.2686 loss_thr: 0.6786 loss_db: 0.2106 2022/11/02 13:31:45 - mmengine - INFO - Epoch(train) [130][30/63] lr: 1.9524e-03 eta: 11:19:17 time: 0.6607 data_time: 0.0348 memory: 14901 loss: 2.1121 loss_prob: 1.2529 loss_thr: 0.6517 loss_db: 0.2076 2022/11/02 13:31:48 - mmengine - INFO - Epoch(train) [130][35/63] lr: 1.9524e-03 eta: 11:19:17 time: 0.6498 data_time: 0.0253 memory: 14901 loss: 2.1842 loss_prob: 1.2940 loss_thr: 0.6827 loss_db: 0.2076 2022/11/02 13:31:50 - mmengine - INFO - Epoch(train) [130][40/63] lr: 1.9524e-03 eta: 11:19:00 time: 0.4754 data_time: 0.0043 memory: 14901 loss: 2.2674 loss_prob: 1.3597 loss_thr: 0.6922 loss_db: 0.2155 2022/11/02 13:31:53 - mmengine - INFO - Epoch(train) [130][45/63] lr: 1.9524e-03 eta: 11:19:00 time: 0.5313 data_time: 0.0072 memory: 14901 loss: 2.3965 loss_prob: 1.4621 loss_thr: 0.7045 loss_db: 0.2300 2022/11/02 13:31:57 - mmengine - INFO - Epoch(train) [130][50/63] lr: 1.9524e-03 eta: 11:18:59 time: 0.6595 data_time: 0.0267 memory: 14901 loss: 2.3782 loss_prob: 1.4402 loss_thr: 0.7033 loss_db: 0.2348 2022/11/02 13:32:00 - mmengine - INFO - Epoch(train) [130][55/63] lr: 1.9524e-03 eta: 11:18:59 time: 0.7128 data_time: 0.0243 memory: 14901 loss: 2.1381 loss_prob: 1.2609 loss_thr: 0.6693 loss_db: 0.2079 2022/11/02 13:32:04 - mmengine - INFO - Epoch(train) [130][60/63] lr: 1.9524e-03 eta: 11:19:03 time: 0.7339 data_time: 0.0056 memory: 14901 loss: 2.2583 loss_prob: 1.3660 loss_thr: 0.6682 loss_db: 0.2242 2022/11/02 13:32:05 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:32:11 - mmengine - INFO - Epoch(train) [131][5/63] lr: 1.9508e-03 eta: 11:19:03 time: 0.8337 data_time: 0.2293 memory: 14901 loss: 2.2031 loss_prob: 1.3395 loss_thr: 0.6436 loss_db: 0.2199 2022/11/02 13:32:14 - mmengine - INFO - Epoch(train) [131][10/63] lr: 1.9508e-03 eta: 11:19:00 time: 0.8413 data_time: 0.2295 memory: 14901 loss: 2.2059 loss_prob: 1.3155 loss_thr: 0.6754 loss_db: 0.2150 2022/11/02 13:32:17 - mmengine - INFO - Epoch(train) [131][15/63] lr: 1.9508e-03 eta: 11:19:00 time: 0.5395 data_time: 0.0063 memory: 14901 loss: 2.1983 loss_prob: 1.3140 loss_thr: 0.6696 loss_db: 0.2148 2022/11/02 13:32:19 - mmengine - INFO - Epoch(train) [131][20/63] lr: 1.9508e-03 eta: 11:18:49 time: 0.5388 data_time: 0.0068 memory: 14901 loss: 2.2292 loss_prob: 1.3277 loss_thr: 0.6837 loss_db: 0.2178 2022/11/02 13:32:22 - mmengine - INFO - Epoch(train) [131][25/63] lr: 1.9508e-03 eta: 11:18:49 time: 0.5037 data_time: 0.0286 memory: 14901 loss: 2.3849 loss_prob: 1.4476 loss_thr: 0.6998 loss_db: 0.2375 2022/11/02 13:32:24 - mmengine - INFO - Epoch(train) [131][30/63] lr: 1.9508e-03 eta: 11:18:33 time: 0.4855 data_time: 0.0324 memory: 14901 loss: 2.2996 loss_prob: 1.4020 loss_thr: 0.6721 loss_db: 0.2256 2022/11/02 13:32:26 - mmengine - INFO - Epoch(train) [131][35/63] lr: 1.9508e-03 eta: 11:18:33 time: 0.4677 data_time: 0.0092 memory: 14901 loss: 2.1439 loss_prob: 1.2679 loss_thr: 0.6718 loss_db: 0.2042 2022/11/02 13:32:29 - mmengine - INFO - Epoch(train) [131][40/63] lr: 1.9508e-03 eta: 11:18:17 time: 0.4772 data_time: 0.0057 memory: 14901 loss: 2.2691 loss_prob: 1.3665 loss_thr: 0.6785 loss_db: 0.2241 2022/11/02 13:32:31 - mmengine - INFO - Epoch(train) [131][45/63] lr: 1.9508e-03 eta: 11:18:17 time: 0.5059 data_time: 0.0055 memory: 14901 loss: 2.2870 loss_prob: 1.3805 loss_thr: 0.6797 loss_db: 0.2268 2022/11/02 13:32:34 - mmengine - INFO - Epoch(train) [131][50/63] lr: 1.9508e-03 eta: 11:18:08 time: 0.5743 data_time: 0.0164 memory: 14901 loss: 2.2382 loss_prob: 1.3407 loss_thr: 0.6761 loss_db: 0.2214 2022/11/02 13:32:38 - mmengine - INFO - Epoch(train) [131][55/63] lr: 1.9508e-03 eta: 11:18:08 time: 0.6188 data_time: 0.0205 memory: 14901 loss: 2.1399 loss_prob: 1.2783 loss_thr: 0.6543 loss_db: 0.2073 2022/11/02 13:32:40 - mmengine - INFO - Epoch(train) [131][60/63] lr: 1.9508e-03 eta: 11:18:02 time: 0.5988 data_time: 0.0089 memory: 14901 loss: 2.2688 loss_prob: 1.4041 loss_thr: 0.6426 loss_db: 0.2221 2022/11/02 13:32:42 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:32:47 - mmengine - INFO - Epoch(train) [132][5/63] lr: 1.9492e-03 eta: 11:18:02 time: 0.7696 data_time: 0.2345 memory: 14901 loss: 2.2711 loss_prob: 1.4033 loss_thr: 0.6467 loss_db: 0.2212 2022/11/02 13:32:50 - mmengine - INFO - Epoch(train) [132][10/63] lr: 1.9492e-03 eta: 11:17:51 time: 0.7459 data_time: 0.2367 memory: 14901 loss: 2.2123 loss_prob: 1.3392 loss_thr: 0.6571 loss_db: 0.2159 2022/11/02 13:32:52 - mmengine - INFO - Epoch(train) [132][15/63] lr: 1.9492e-03 eta: 11:17:51 time: 0.4813 data_time: 0.0079 memory: 14901 loss: 2.3495 loss_prob: 1.4328 loss_thr: 0.6877 loss_db: 0.2289 2022/11/02 13:32:54 - mmengine - INFO - Epoch(train) [132][20/63] lr: 1.9492e-03 eta: 11:17:35 time: 0.4881 data_time: 0.0054 memory: 14901 loss: 2.3187 loss_prob: 1.4063 loss_thr: 0.6892 loss_db: 0.2233 2022/11/02 13:32:57 - mmengine - INFO - Epoch(train) [132][25/63] lr: 1.9492e-03 eta: 11:17:35 time: 0.5014 data_time: 0.0167 memory: 14901 loss: 2.1858 loss_prob: 1.3058 loss_thr: 0.6640 loss_db: 0.2160 2022/11/02 13:33:00 - mmengine - INFO - Epoch(train) [132][30/63] lr: 1.9492e-03 eta: 11:17:22 time: 0.5211 data_time: 0.0273 memory: 14901 loss: 2.1734 loss_prob: 1.2988 loss_thr: 0.6614 loss_db: 0.2132 2022/11/02 13:33:02 - mmengine - INFO - Epoch(train) [132][35/63] lr: 1.9492e-03 eta: 11:17:22 time: 0.5569 data_time: 0.0204 memory: 14901 loss: 2.1552 loss_prob: 1.2928 loss_thr: 0.6522 loss_db: 0.2101 2022/11/02 13:33:05 - mmengine - INFO - Epoch(train) [132][40/63] lr: 1.9492e-03 eta: 11:17:10 time: 0.5280 data_time: 0.0105 memory: 14901 loss: 2.1003 loss_prob: 1.2480 loss_thr: 0.6477 loss_db: 0.2045 2022/11/02 13:33:07 - mmengine - INFO - Epoch(train) [132][45/63] lr: 1.9492e-03 eta: 11:17:10 time: 0.4803 data_time: 0.0090 memory: 14901 loss: 2.0844 loss_prob: 1.2226 loss_thr: 0.6597 loss_db: 0.2021 2022/11/02 13:33:10 - mmengine - INFO - Epoch(train) [132][50/63] lr: 1.9492e-03 eta: 11:16:55 time: 0.4912 data_time: 0.0194 memory: 14901 loss: 2.0616 loss_prob: 1.2091 loss_thr: 0.6552 loss_db: 0.1973 2022/11/02 13:33:13 - mmengine - INFO - Epoch(train) [132][55/63] lr: 1.9492e-03 eta: 11:16:55 time: 0.5230 data_time: 0.0189 memory: 14901 loss: 2.1003 loss_prob: 1.2273 loss_thr: 0.6738 loss_db: 0.1993 2022/11/02 13:33:15 - mmengine - INFO - Epoch(train) [132][60/63] lr: 1.9492e-03 eta: 11:16:43 time: 0.5289 data_time: 0.0106 memory: 14901 loss: 2.1697 loss_prob: 1.2764 loss_thr: 0.6792 loss_db: 0.2141 2022/11/02 13:33:16 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:33:21 - mmengine - INFO - Epoch(train) [133][5/63] lr: 1.9475e-03 eta: 11:16:43 time: 0.7189 data_time: 0.2352 memory: 14901 loss: 2.2361 loss_prob: 1.3299 loss_thr: 0.6788 loss_db: 0.2274 2022/11/02 13:33:24 - mmengine - INFO - Epoch(train) [133][10/63] lr: 1.9475e-03 eta: 11:16:31 time: 0.7373 data_time: 0.2447 memory: 14901 loss: 2.4229 loss_prob: 1.4678 loss_thr: 0.7079 loss_db: 0.2472 2022/11/02 13:33:26 - mmengine - INFO - Epoch(train) [133][15/63] lr: 1.9475e-03 eta: 11:16:31 time: 0.4998 data_time: 0.0200 memory: 14901 loss: 2.5062 loss_prob: 1.5263 loss_thr: 0.7290 loss_db: 0.2509 2022/11/02 13:33:29 - mmengine - INFO - Epoch(train) [133][20/63] lr: 1.9475e-03 eta: 11:16:18 time: 0.5124 data_time: 0.0135 memory: 14901 loss: 2.5565 loss_prob: 1.5531 loss_thr: 0.7466 loss_db: 0.2568 2022/11/02 13:33:32 - mmengine - INFO - Epoch(train) [133][25/63] lr: 1.9475e-03 eta: 11:16:18 time: 0.5582 data_time: 0.0392 memory: 14901 loss: 2.8499 loss_prob: 1.8026 loss_thr: 0.7555 loss_db: 0.2918 2022/11/02 13:33:34 - mmengine - INFO - Epoch(train) [133][30/63] lr: 1.9475e-03 eta: 11:16:08 time: 0.5620 data_time: 0.0506 memory: 14901 loss: 2.8141 loss_prob: 1.7889 loss_thr: 0.7381 loss_db: 0.2872 2022/11/02 13:33:37 - mmengine - INFO - Epoch(train) [133][35/63] lr: 1.9475e-03 eta: 11:16:08 time: 0.5013 data_time: 0.0221 memory: 14901 loss: 2.4377 loss_prob: 1.4903 loss_thr: 0.7029 loss_db: 0.2445 2022/11/02 13:33:39 - mmengine - INFO - Epoch(train) [133][40/63] lr: 1.9475e-03 eta: 11:15:52 time: 0.4778 data_time: 0.0046 memory: 14901 loss: 2.2320 loss_prob: 1.3394 loss_thr: 0.6761 loss_db: 0.2164 2022/11/02 13:33:42 - mmengine - INFO - Epoch(train) [133][45/63] lr: 1.9475e-03 eta: 11:15:52 time: 0.4795 data_time: 0.0045 memory: 14901 loss: 2.2238 loss_prob: 1.3341 loss_thr: 0.6702 loss_db: 0.2196 2022/11/02 13:33:44 - mmengine - INFO - Epoch(train) [133][50/63] lr: 1.9475e-03 eta: 11:15:40 time: 0.5182 data_time: 0.0155 memory: 14901 loss: 2.2708 loss_prob: 1.3615 loss_thr: 0.6817 loss_db: 0.2275 2022/11/02 13:33:47 - mmengine - INFO - Epoch(train) [133][55/63] lr: 1.9475e-03 eta: 11:15:40 time: 0.5252 data_time: 0.0243 memory: 14901 loss: 2.2205 loss_prob: 1.3458 loss_thr: 0.6565 loss_db: 0.2182 2022/11/02 13:33:49 - mmengine - INFO - Epoch(train) [133][60/63] lr: 1.9475e-03 eta: 11:15:25 time: 0.5030 data_time: 0.0140 memory: 14901 loss: 2.3105 loss_prob: 1.4083 loss_thr: 0.6758 loss_db: 0.2264 2022/11/02 13:33:51 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:33:58 - mmengine - INFO - Epoch(train) [134][5/63] lr: 1.9459e-03 eta: 11:15:25 time: 0.9886 data_time: 0.2509 memory: 14901 loss: 2.1130 loss_prob: 1.2655 loss_thr: 0.6426 loss_db: 0.2049 2022/11/02 13:34:02 - mmengine - INFO - Epoch(train) [134][10/63] lr: 1.9459e-03 eta: 11:15:39 time: 1.0463 data_time: 0.2515 memory: 14901 loss: 2.1722 loss_prob: 1.3093 loss_thr: 0.6507 loss_db: 0.2123 2022/11/02 13:34:05 - mmengine - INFO - Epoch(train) [134][15/63] lr: 1.9459e-03 eta: 11:15:39 time: 0.6522 data_time: 0.0092 memory: 14901 loss: 2.1830 loss_prob: 1.2949 loss_thr: 0.6747 loss_db: 0.2134 2022/11/02 13:34:08 - mmengine - INFO - Epoch(train) [134][20/63] lr: 1.9459e-03 eta: 11:15:35 time: 0.6334 data_time: 0.0079 memory: 14901 loss: 2.1469 loss_prob: 1.2577 loss_thr: 0.6792 loss_db: 0.2100 2022/11/02 13:34:10 - mmengine - INFO - Epoch(train) [134][25/63] lr: 1.9459e-03 eta: 11:15:35 time: 0.5670 data_time: 0.0254 memory: 14901 loss: 2.1160 loss_prob: 1.2392 loss_thr: 0.6686 loss_db: 0.2082 2022/11/02 13:34:13 - mmengine - INFO - Epoch(train) [134][30/63] lr: 1.9459e-03 eta: 11:15:23 time: 0.5260 data_time: 0.0280 memory: 14901 loss: 2.1437 loss_prob: 1.2710 loss_thr: 0.6633 loss_db: 0.2094 2022/11/02 13:34:16 - mmengine - INFO - Epoch(train) [134][35/63] lr: 1.9459e-03 eta: 11:15:23 time: 0.5203 data_time: 0.0203 memory: 14901 loss: 2.1606 loss_prob: 1.3120 loss_thr: 0.6357 loss_db: 0.2130 2022/11/02 13:34:19 - mmengine - INFO - Epoch(train) [134][40/63] lr: 1.9459e-03 eta: 11:15:12 time: 0.5406 data_time: 0.0196 memory: 14901 loss: 2.1336 loss_prob: 1.2927 loss_thr: 0.6310 loss_db: 0.2099 2022/11/02 13:34:21 - mmengine - INFO - Epoch(train) [134][45/63] lr: 1.9459e-03 eta: 11:15:12 time: 0.5555 data_time: 0.0068 memory: 14901 loss: 2.2131 loss_prob: 1.3455 loss_thr: 0.6533 loss_db: 0.2143 2022/11/02 13:34:24 - mmengine - INFO - Epoch(train) [134][50/63] lr: 1.9459e-03 eta: 11:15:05 time: 0.5878 data_time: 0.0175 memory: 14901 loss: 2.3468 loss_prob: 1.4269 loss_thr: 0.6925 loss_db: 0.2274 2022/11/02 13:34:27 - mmengine - INFO - Epoch(train) [134][55/63] lr: 1.9459e-03 eta: 11:15:05 time: 0.6045 data_time: 0.0196 memory: 14901 loss: 2.3580 loss_prob: 1.4192 loss_thr: 0.7079 loss_db: 0.2309 2022/11/02 13:34:30 - mmengine - INFO - Epoch(train) [134][60/63] lr: 1.9459e-03 eta: 11:14:55 time: 0.5630 data_time: 0.0085 memory: 14901 loss: 2.2040 loss_prob: 1.3183 loss_thr: 0.6754 loss_db: 0.2102 2022/11/02 13:34:31 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:34:36 - mmengine - INFO - Epoch(train) [135][5/63] lr: 1.9442e-03 eta: 11:14:55 time: 0.7564 data_time: 0.1978 memory: 14901 loss: 2.0174 loss_prob: 1.1890 loss_thr: 0.6371 loss_db: 0.1914 2022/11/02 13:34:39 - mmengine - INFO - Epoch(train) [135][10/63] lr: 1.9442e-03 eta: 11:14:47 time: 0.7779 data_time: 0.2042 memory: 14901 loss: 2.1285 loss_prob: 1.2657 loss_thr: 0.6570 loss_db: 0.2058 2022/11/02 13:34:42 - mmengine - INFO - Epoch(train) [135][15/63] lr: 1.9442e-03 eta: 11:14:47 time: 0.5685 data_time: 0.0171 memory: 14901 loss: 2.2452 loss_prob: 1.3486 loss_thr: 0.6799 loss_db: 0.2167 2022/11/02 13:34:44 - mmengine - INFO - Epoch(train) [135][20/63] lr: 1.9442e-03 eta: 11:14:36 time: 0.5344 data_time: 0.0079 memory: 14901 loss: 2.2940 loss_prob: 1.3782 loss_thr: 0.6910 loss_db: 0.2249 2022/11/02 13:34:48 - mmengine - INFO - Epoch(train) [135][25/63] lr: 1.9442e-03 eta: 11:14:36 time: 0.6082 data_time: 0.0145 memory: 14901 loss: 2.2552 loss_prob: 1.3451 loss_thr: 0.6910 loss_db: 0.2190 2022/11/02 13:34:51 - mmengine - INFO - Epoch(train) [135][30/63] lr: 1.9442e-03 eta: 11:14:34 time: 0.6546 data_time: 0.0254 memory: 14901 loss: 2.3337 loss_prob: 1.4144 loss_thr: 0.6907 loss_db: 0.2286 2022/11/02 13:34:54 - mmengine - INFO - Epoch(train) [135][35/63] lr: 1.9442e-03 eta: 11:14:34 time: 0.5640 data_time: 0.0221 memory: 14901 loss: 2.2595 loss_prob: 1.3707 loss_thr: 0.6665 loss_db: 0.2223 2022/11/02 13:34:56 - mmengine - INFO - Epoch(train) [135][40/63] lr: 1.9442e-03 eta: 11:14:21 time: 0.5198 data_time: 0.0127 memory: 14901 loss: 2.1070 loss_prob: 1.2484 loss_thr: 0.6549 loss_db: 0.2037 2022/11/02 13:34:59 - mmengine - INFO - Epoch(train) [135][45/63] lr: 1.9442e-03 eta: 11:14:21 time: 0.4918 data_time: 0.0064 memory: 14901 loss: 2.3327 loss_prob: 1.4197 loss_thr: 0.6859 loss_db: 0.2271 2022/11/02 13:35:01 - mmengine - INFO - Epoch(train) [135][50/63] lr: 1.9442e-03 eta: 11:14:06 time: 0.4920 data_time: 0.0106 memory: 14901 loss: 2.3826 loss_prob: 1.4775 loss_thr: 0.6704 loss_db: 0.2347 2022/11/02 13:35:04 - mmengine - INFO - Epoch(train) [135][55/63] lr: 1.9442e-03 eta: 11:14:06 time: 0.5019 data_time: 0.0158 memory: 14901 loss: 2.2181 loss_prob: 1.3294 loss_thr: 0.6747 loss_db: 0.2141 2022/11/02 13:35:06 - mmengine - INFO - Epoch(train) [135][60/63] lr: 1.9442e-03 eta: 11:13:50 time: 0.4782 data_time: 0.0123 memory: 14901 loss: 2.1362 loss_prob: 1.2530 loss_thr: 0.6798 loss_db: 0.2034 2022/11/02 13:35:07 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:35:12 - mmengine - INFO - Epoch(train) [136][5/63] lr: 1.9426e-03 eta: 11:13:50 time: 0.7231 data_time: 0.2257 memory: 14901 loss: 2.0040 loss_prob: 1.1646 loss_thr: 0.6468 loss_db: 0.1925 2022/11/02 13:35:15 - mmengine - INFO - Epoch(train) [136][10/63] lr: 1.9426e-03 eta: 11:13:41 time: 0.7616 data_time: 0.2222 memory: 14901 loss: 2.1612 loss_prob: 1.2957 loss_thr: 0.6518 loss_db: 0.2137 2022/11/02 13:35:17 - mmengine - INFO - Epoch(train) [136][15/63] lr: 1.9426e-03 eta: 11:13:41 time: 0.5196 data_time: 0.0070 memory: 14901 loss: 2.2027 loss_prob: 1.3341 loss_thr: 0.6497 loss_db: 0.2190 2022/11/02 13:35:20 - mmengine - INFO - Epoch(train) [136][20/63] lr: 1.9426e-03 eta: 11:13:29 time: 0.5348 data_time: 0.0060 memory: 14901 loss: 2.0608 loss_prob: 1.2082 loss_thr: 0.6522 loss_db: 0.2003 2022/11/02 13:35:23 - mmengine - INFO - Epoch(train) [136][25/63] lr: 1.9426e-03 eta: 11:13:29 time: 0.5190 data_time: 0.0289 memory: 14901 loss: 2.1217 loss_prob: 1.2682 loss_thr: 0.6453 loss_db: 0.2082 2022/11/02 13:35:25 - mmengine - INFO - Epoch(train) [136][30/63] lr: 1.9426e-03 eta: 11:13:15 time: 0.4976 data_time: 0.0332 memory: 14901 loss: 2.1063 loss_prob: 1.2579 loss_thr: 0.6423 loss_db: 0.2061 2022/11/02 13:35:27 - mmengine - INFO - Epoch(train) [136][35/63] lr: 1.9426e-03 eta: 11:13:15 time: 0.4742 data_time: 0.0125 memory: 14901 loss: 1.9606 loss_prob: 1.1373 loss_thr: 0.6337 loss_db: 0.1897 2022/11/02 13:35:30 - mmengine - INFO - Epoch(train) [136][40/63] lr: 1.9426e-03 eta: 11:13:01 time: 0.4923 data_time: 0.0083 memory: 14901 loss: 2.1321 loss_prob: 1.2719 loss_thr: 0.6482 loss_db: 0.2120 2022/11/02 13:35:32 - mmengine - INFO - Epoch(train) [136][45/63] lr: 1.9426e-03 eta: 11:13:01 time: 0.4836 data_time: 0.0052 memory: 14901 loss: 2.3291 loss_prob: 1.4193 loss_thr: 0.6809 loss_db: 0.2289 2022/11/02 13:35:35 - mmengine - INFO - Epoch(train) [136][50/63] lr: 1.9426e-03 eta: 11:12:44 time: 0.4724 data_time: 0.0155 memory: 14901 loss: 2.2230 loss_prob: 1.3284 loss_thr: 0.6789 loss_db: 0.2157 2022/11/02 13:35:37 - mmengine - INFO - Epoch(train) [136][55/63] lr: 1.9426e-03 eta: 11:12:44 time: 0.4808 data_time: 0.0200 memory: 14901 loss: 2.3333 loss_prob: 1.4102 loss_thr: 0.6871 loss_db: 0.2360 2022/11/02 13:35:40 - mmengine - INFO - Epoch(train) [136][60/63] lr: 1.9426e-03 eta: 11:12:32 time: 0.5154 data_time: 0.0097 memory: 14901 loss: 2.2626 loss_prob: 1.3728 loss_thr: 0.6648 loss_db: 0.2250 2022/11/02 13:35:41 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:35:46 - mmengine - INFO - Epoch(train) [137][5/63] lr: 1.9409e-03 eta: 11:12:32 time: 0.7241 data_time: 0.2210 memory: 14901 loss: 2.0501 loss_prob: 1.2233 loss_thr: 0.6259 loss_db: 0.2009 2022/11/02 13:35:48 - mmengine - INFO - Epoch(train) [137][10/63] lr: 1.9409e-03 eta: 11:12:20 time: 0.7315 data_time: 0.2211 memory: 14901 loss: 1.9719 loss_prob: 1.1621 loss_thr: 0.6212 loss_db: 0.1886 2022/11/02 13:35:51 - mmengine - INFO - Epoch(train) [137][15/63] lr: 1.9409e-03 eta: 11:12:20 time: 0.4936 data_time: 0.0077 memory: 14901 loss: 1.9787 loss_prob: 1.1533 loss_thr: 0.6329 loss_db: 0.1925 2022/11/02 13:35:54 - mmengine - INFO - Epoch(train) [137][20/63] lr: 1.9409e-03 eta: 11:12:08 time: 0.5315 data_time: 0.0097 memory: 14901 loss: 1.8813 loss_prob: 1.0850 loss_thr: 0.6171 loss_db: 0.1793 2022/11/02 13:35:56 - mmengine - INFO - Epoch(train) [137][25/63] lr: 1.9409e-03 eta: 11:12:08 time: 0.5288 data_time: 0.0168 memory: 14901 loss: 1.9412 loss_prob: 1.1337 loss_thr: 0.6226 loss_db: 0.1848 2022/11/02 13:35:59 - mmengine - INFO - Epoch(train) [137][30/63] lr: 1.9409e-03 eta: 11:11:54 time: 0.4925 data_time: 0.0278 memory: 14901 loss: 2.0871 loss_prob: 1.2371 loss_thr: 0.6492 loss_db: 0.2008 2022/11/02 13:36:01 - mmengine - INFO - Epoch(train) [137][35/63] lr: 1.9409e-03 eta: 11:11:54 time: 0.4821 data_time: 0.0236 memory: 14901 loss: 2.0692 loss_prob: 1.2111 loss_thr: 0.6618 loss_db: 0.1964 2022/11/02 13:36:03 - mmengine - INFO - Epoch(train) [137][40/63] lr: 1.9409e-03 eta: 11:11:38 time: 0.4771 data_time: 0.0134 memory: 14901 loss: 2.0661 loss_prob: 1.2067 loss_thr: 0.6631 loss_db: 0.1964 2022/11/02 13:36:06 - mmengine - INFO - Epoch(train) [137][45/63] lr: 1.9409e-03 eta: 11:11:38 time: 0.4785 data_time: 0.0075 memory: 14901 loss: 2.0519 loss_prob: 1.2118 loss_thr: 0.6444 loss_db: 0.1957 2022/11/02 13:36:08 - mmengine - INFO - Epoch(train) [137][50/63] lr: 1.9409e-03 eta: 11:11:23 time: 0.4865 data_time: 0.0156 memory: 14901 loss: 2.0066 loss_prob: 1.1752 loss_thr: 0.6371 loss_db: 0.1943 2022/11/02 13:36:11 - mmengine - INFO - Epoch(train) [137][55/63] lr: 1.9409e-03 eta: 11:11:23 time: 0.4900 data_time: 0.0203 memory: 14901 loss: 2.0842 loss_prob: 1.2080 loss_thr: 0.6718 loss_db: 0.2043 2022/11/02 13:36:13 - mmengine - INFO - Epoch(train) [137][60/63] lr: 1.9409e-03 eta: 11:11:08 time: 0.4789 data_time: 0.0114 memory: 14901 loss: 2.2044 loss_prob: 1.3067 loss_thr: 0.6829 loss_db: 0.2149 2022/11/02 13:36:14 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:36:19 - mmengine - INFO - Epoch(train) [138][5/63] lr: 1.9393e-03 eta: 11:11:08 time: 0.6839 data_time: 0.2155 memory: 14901 loss: 2.1913 loss_prob: 1.3094 loss_thr: 0.6709 loss_db: 0.2111 2022/11/02 13:36:21 - mmengine - INFO - Epoch(train) [138][10/63] lr: 1.9393e-03 eta: 11:10:55 time: 0.7189 data_time: 0.2129 memory: 14901 loss: 2.0425 loss_prob: 1.1916 loss_thr: 0.6582 loss_db: 0.1927 2022/11/02 13:36:24 - mmengine - INFO - Epoch(train) [138][15/63] lr: 1.9393e-03 eta: 11:10:55 time: 0.4868 data_time: 0.0077 memory: 14901 loss: 1.9860 loss_prob: 1.1581 loss_thr: 0.6389 loss_db: 0.1890 2022/11/02 13:36:26 - mmengine - INFO - Epoch(train) [138][20/63] lr: 1.9393e-03 eta: 11:10:42 time: 0.5131 data_time: 0.0077 memory: 14901 loss: 2.0007 loss_prob: 1.1713 loss_thr: 0.6374 loss_db: 0.1920 2022/11/02 13:36:31 - mmengine - INFO - Epoch(train) [138][25/63] lr: 1.9393e-03 eta: 11:10:42 time: 0.7407 data_time: 0.0547 memory: 14901 loss: 1.9432 loss_prob: 1.1331 loss_thr: 0.6264 loss_db: 0.1837 2022/11/02 13:36:34 - mmengine - INFO - Epoch(train) [138][30/63] lr: 1.9393e-03 eta: 11:10:51 time: 0.7943 data_time: 0.0604 memory: 14901 loss: 2.1576 loss_prob: 1.2815 loss_thr: 0.6653 loss_db: 0.2108 2022/11/02 13:36:37 - mmengine - INFO - Epoch(train) [138][35/63] lr: 1.9393e-03 eta: 11:10:51 time: 0.6023 data_time: 0.0107 memory: 14901 loss: 2.1629 loss_prob: 1.2868 loss_thr: 0.6601 loss_db: 0.2160 2022/11/02 13:36:40 - mmengine - INFO - Epoch(train) [138][40/63] lr: 1.9393e-03 eta: 11:10:44 time: 0.5940 data_time: 0.0064 memory: 14901 loss: 2.0832 loss_prob: 1.2208 loss_thr: 0.6614 loss_db: 0.2010 2022/11/02 13:36:44 - mmengine - INFO - Epoch(train) [138][45/63] lr: 1.9393e-03 eta: 11:10:44 time: 0.6623 data_time: 0.0062 memory: 14901 loss: 2.1978 loss_prob: 1.3053 loss_thr: 0.6836 loss_db: 0.2089 2022/11/02 13:36:47 - mmengine - INFO - Epoch(train) [138][50/63] lr: 1.9393e-03 eta: 11:10:42 time: 0.6509 data_time: 0.0207 memory: 14901 loss: 2.1939 loss_prob: 1.3075 loss_thr: 0.6766 loss_db: 0.2098 2022/11/02 13:36:50 - mmengine - INFO - Epoch(train) [138][55/63] lr: 1.9393e-03 eta: 11:10:42 time: 0.6158 data_time: 0.0271 memory: 14901 loss: 2.4324 loss_prob: 1.4897 loss_thr: 0.6985 loss_db: 0.2442 2022/11/02 13:36:54 - mmengine - INFO - Epoch(train) [138][60/63] lr: 1.9393e-03 eta: 11:10:41 time: 0.6694 data_time: 0.0108 memory: 14901 loss: 2.5797 loss_prob: 1.6235 loss_thr: 0.6823 loss_db: 0.2739 2022/11/02 13:36:55 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:37:01 - mmengine - INFO - Epoch(train) [139][5/63] lr: 1.9377e-03 eta: 11:10:41 time: 0.8643 data_time: 0.2445 memory: 14901 loss: 2.4245 loss_prob: 1.4976 loss_thr: 0.6902 loss_db: 0.2366 2022/11/02 13:37:04 - mmengine - INFO - Epoch(train) [139][10/63] lr: 1.9377e-03 eta: 11:10:41 time: 0.8779 data_time: 0.2444 memory: 14901 loss: 2.2880 loss_prob: 1.3850 loss_thr: 0.6826 loss_db: 0.2204 2022/11/02 13:37:07 - mmengine - INFO - Epoch(train) [139][15/63] lr: 1.9377e-03 eta: 11:10:41 time: 0.5969 data_time: 0.0047 memory: 14901 loss: 2.3451 loss_prob: 1.4205 loss_thr: 0.6953 loss_db: 0.2293 2022/11/02 13:37:10 - mmengine - INFO - Epoch(train) [139][20/63] lr: 1.9377e-03 eta: 11:10:37 time: 0.6228 data_time: 0.0049 memory: 14901 loss: 2.3967 loss_prob: 1.4661 loss_thr: 0.6930 loss_db: 0.2376 2022/11/02 13:37:13 - mmengine - INFO - Epoch(train) [139][25/63] lr: 1.9377e-03 eta: 11:10:37 time: 0.5864 data_time: 0.0220 memory: 14901 loss: 2.2370 loss_prob: 1.3588 loss_thr: 0.6521 loss_db: 0.2261 2022/11/02 13:37:16 - mmengine - INFO - Epoch(train) [139][30/63] lr: 1.9377e-03 eta: 11:10:35 time: 0.6616 data_time: 0.0451 memory: 14901 loss: 2.6440 loss_prob: 1.6683 loss_thr: 0.6963 loss_db: 0.2794 2022/11/02 13:37:20 - mmengine - INFO - Epoch(train) [139][35/63] lr: 1.9377e-03 eta: 11:10:35 time: 0.6696 data_time: 0.0284 memory: 14901 loss: 2.7323 loss_prob: 1.7232 loss_thr: 0.7206 loss_db: 0.2886 2022/11/02 13:37:23 - mmengine - INFO - Epoch(train) [139][40/63] lr: 1.9377e-03 eta: 11:10:30 time: 0.6140 data_time: 0.0051 memory: 14901 loss: 2.3215 loss_prob: 1.4099 loss_thr: 0.6788 loss_db: 0.2328 2022/11/02 13:37:25 - mmengine - INFO - Epoch(train) [139][45/63] lr: 1.9377e-03 eta: 11:10:30 time: 0.5607 data_time: 0.0045 memory: 14901 loss: 2.3058 loss_prob: 1.4096 loss_thr: 0.6718 loss_db: 0.2245 2022/11/02 13:37:28 - mmengine - INFO - Epoch(train) [139][50/63] lr: 1.9377e-03 eta: 11:10:19 time: 0.5396 data_time: 0.0193 memory: 14901 loss: 2.2723 loss_prob: 1.3867 loss_thr: 0.6618 loss_db: 0.2238 2022/11/02 13:37:30 - mmengine - INFO - Epoch(train) [139][55/63] lr: 1.9377e-03 eta: 11:10:19 time: 0.5195 data_time: 0.0207 memory: 14901 loss: 2.1352 loss_prob: 1.2612 loss_thr: 0.6671 loss_db: 0.2068 2022/11/02 13:37:33 - mmengine - INFO - Epoch(train) [139][60/63] lr: 1.9377e-03 eta: 11:10:03 time: 0.4720 data_time: 0.0056 memory: 14901 loss: 2.1815 loss_prob: 1.3009 loss_thr: 0.6725 loss_db: 0.2081 2022/11/02 13:37:34 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:37:39 - mmengine - INFO - Epoch(train) [140][5/63] lr: 1.9360e-03 eta: 11:10:03 time: 0.7071 data_time: 0.1969 memory: 14901 loss: 2.2107 loss_prob: 1.3262 loss_thr: 0.6666 loss_db: 0.2179 2022/11/02 13:37:41 - mmengine - INFO - Epoch(train) [140][10/63] lr: 1.9360e-03 eta: 11:09:52 time: 0.7300 data_time: 0.2015 memory: 14901 loss: 2.1478 loss_prob: 1.2749 loss_thr: 0.6693 loss_db: 0.2035 2022/11/02 13:37:44 - mmengine - INFO - Epoch(train) [140][15/63] lr: 1.9360e-03 eta: 11:09:52 time: 0.4920 data_time: 0.0094 memory: 14901 loss: 2.1519 loss_prob: 1.2791 loss_thr: 0.6668 loss_db: 0.2060 2022/11/02 13:37:46 - mmengine - INFO - Epoch(train) [140][20/63] lr: 1.9360e-03 eta: 11:09:39 time: 0.5109 data_time: 0.0067 memory: 14901 loss: 2.2235 loss_prob: 1.3231 loss_thr: 0.6855 loss_db: 0.2149 2022/11/02 13:37:49 - mmengine - INFO - Epoch(train) [140][25/63] lr: 1.9360e-03 eta: 11:09:39 time: 0.5062 data_time: 0.0114 memory: 14901 loss: 2.0259 loss_prob: 1.1892 loss_thr: 0.6449 loss_db: 0.1918 2022/11/02 13:37:51 - mmengine - INFO - Epoch(train) [140][30/63] lr: 1.9360e-03 eta: 11:09:26 time: 0.5085 data_time: 0.0297 memory: 14901 loss: 1.9237 loss_prob: 1.1225 loss_thr: 0.6203 loss_db: 0.1808 2022/11/02 13:37:54 - mmengine - INFO - Epoch(train) [140][35/63] lr: 1.9360e-03 eta: 11:09:26 time: 0.5152 data_time: 0.0289 memory: 14901 loss: 2.2093 loss_prob: 1.3272 loss_thr: 0.6676 loss_db: 0.2145 2022/11/02 13:37:56 - mmengine - INFO - Epoch(train) [140][40/63] lr: 1.9360e-03 eta: 11:09:12 time: 0.5028 data_time: 0.0085 memory: 14901 loss: 2.1991 loss_prob: 1.3327 loss_thr: 0.6543 loss_db: 0.2121 2022/11/02 13:37:59 - mmengine - INFO - Epoch(train) [140][45/63] lr: 1.9360e-03 eta: 11:09:12 time: 0.4974 data_time: 0.0100 memory: 14901 loss: 2.0239 loss_prob: 1.1972 loss_thr: 0.6360 loss_db: 0.1908 2022/11/02 13:38:01 - mmengine - INFO - Epoch(train) [140][50/63] lr: 1.9360e-03 eta: 11:08:57 time: 0.4825 data_time: 0.0170 memory: 14901 loss: 2.3265 loss_prob: 1.4209 loss_thr: 0.6747 loss_db: 0.2308 2022/11/02 13:38:04 - mmengine - INFO - Epoch(train) [140][55/63] lr: 1.9360e-03 eta: 11:08:57 time: 0.4686 data_time: 0.0179 memory: 14901 loss: 2.3583 loss_prob: 1.4389 loss_thr: 0.6855 loss_db: 0.2339 2022/11/02 13:38:06 - mmengine - INFO - Epoch(train) [140][60/63] lr: 1.9360e-03 eta: 11:08:41 time: 0.4643 data_time: 0.0129 memory: 14901 loss: 2.1343 loss_prob: 1.2594 loss_thr: 0.6681 loss_db: 0.2068 2022/11/02 13:38:07 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:38:07 - mmengine - INFO - Saving checkpoint at 140 epochs 2022/11/02 13:38:11 - mmengine - INFO - Epoch(val) [140][5/500] eta: 11:08:41 time: 0.0413 data_time: 0.0042 memory: 14901 2022/11/02 13:38:11 - mmengine - INFO - Epoch(val) [140][10/500] eta: 0:00:23 time: 0.0472 data_time: 0.0043 memory: 1008 2022/11/02 13:38:11 - mmengine - INFO - Epoch(val) [140][15/500] eta: 0:00:23 time: 0.0414 data_time: 0.0023 memory: 1008 2022/11/02 13:38:11 - mmengine - INFO - Epoch(val) [140][20/500] eta: 0:00:19 time: 0.0397 data_time: 0.0024 memory: 1008 2022/11/02 13:38:12 - mmengine - INFO - Epoch(val) [140][25/500] eta: 0:00:19 time: 0.0414 data_time: 0.0024 memory: 1008 2022/11/02 13:38:12 - mmengine - INFO - Epoch(val) [140][30/500] eta: 0:00:19 time: 0.0412 data_time: 0.0024 memory: 1008 2022/11/02 13:38:12 - mmengine - INFO - Epoch(val) [140][35/500] eta: 0:00:19 time: 0.0390 data_time: 0.0023 memory: 1008 2022/11/02 13:38:12 - mmengine - INFO - Epoch(val) [140][40/500] eta: 0:00:20 time: 0.0436 data_time: 0.0038 memory: 1008 2022/11/02 13:38:12 - mmengine - INFO - Epoch(val) [140][45/500] eta: 0:00:20 time: 0.0446 data_time: 0.0040 memory: 1008 2022/11/02 13:38:13 - mmengine - INFO - Epoch(val) [140][50/500] eta: 0:00:18 time: 0.0411 data_time: 0.0028 memory: 1008 2022/11/02 13:38:13 - mmengine - INFO - Epoch(val) [140][55/500] eta: 0:00:18 time: 0.0417 data_time: 0.0025 memory: 1008 2022/11/02 13:38:13 - mmengine - INFO - Epoch(val) [140][60/500] eta: 0:00:16 time: 0.0371 data_time: 0.0021 memory: 1008 2022/11/02 13:38:13 - mmengine - INFO - Epoch(val) [140][65/500] eta: 0:00:16 time: 0.0395 data_time: 0.0023 memory: 1008 2022/11/02 13:38:13 - mmengine - INFO - Epoch(val) [140][70/500] eta: 0:00:18 time: 0.0422 data_time: 0.0024 memory: 1008 2022/11/02 13:38:14 - mmengine - INFO - Epoch(val) [140][75/500] eta: 0:00:18 time: 0.0386 data_time: 0.0024 memory: 1008 2022/11/02 13:38:14 - mmengine - INFO - Epoch(val) [140][80/500] eta: 0:00:32 time: 0.0773 data_time: 0.0424 memory: 1008 2022/11/02 13:38:14 - mmengine - INFO - Epoch(val) [140][85/500] eta: 0:00:32 time: 0.0758 data_time: 0.0422 memory: 1008 2022/11/02 13:38:15 - mmengine - INFO - Epoch(val) [140][90/500] eta: 0:00:15 time: 0.0376 data_time: 0.0021 memory: 1008 2022/11/02 13:38:15 - mmengine - INFO - Epoch(val) [140][95/500] eta: 0:00:15 time: 0.0425 data_time: 0.0022 memory: 1008 2022/11/02 13:38:15 - mmengine - INFO - Epoch(val) [140][100/500] eta: 0:00:15 time: 0.0394 data_time: 0.0022 memory: 1008 2022/11/02 13:38:15 - mmengine - INFO - Epoch(val) [140][105/500] eta: 0:00:15 time: 0.0359 data_time: 0.0023 memory: 1008 2022/11/02 13:38:15 - mmengine - INFO - Epoch(val) [140][110/500] eta: 0:00:14 time: 0.0363 data_time: 0.0023 memory: 1008 2022/11/02 13:38:15 - mmengine - INFO - Epoch(val) [140][115/500] eta: 0:00:14 time: 0.0390 data_time: 0.0024 memory: 1008 2022/11/02 13:38:16 - mmengine - INFO - Epoch(val) [140][120/500] eta: 0:00:15 time: 0.0420 data_time: 0.0025 memory: 1008 2022/11/02 13:38:16 - mmengine - INFO - Epoch(val) [140][125/500] eta: 0:00:15 time: 0.0397 data_time: 0.0024 memory: 1008 2022/11/02 13:38:16 - mmengine - INFO - Epoch(val) [140][130/500] eta: 0:00:14 time: 0.0381 data_time: 0.0023 memory: 1008 2022/11/02 13:38:16 - mmengine - INFO - Epoch(val) [140][135/500] eta: 0:00:14 time: 0.0389 data_time: 0.0024 memory: 1008 2022/11/02 13:38:16 - mmengine - INFO - Epoch(val) [140][140/500] eta: 0:00:13 time: 0.0387 data_time: 0.0023 memory: 1008 2022/11/02 13:38:17 - mmengine - INFO - Epoch(val) [140][145/500] eta: 0:00:13 time: 0.0414 data_time: 0.0023 memory: 1008 2022/11/02 13:38:17 - mmengine - INFO - Epoch(val) [140][150/500] eta: 0:00:14 time: 0.0416 data_time: 0.0023 memory: 1008 2022/11/02 13:38:17 - mmengine - INFO - Epoch(val) [140][155/500] eta: 0:00:14 time: 0.0708 data_time: 0.0306 memory: 1008 2022/11/02 13:38:18 - mmengine - INFO - Epoch(val) [140][160/500] eta: 0:00:24 time: 0.0723 data_time: 0.0302 memory: 1008 2022/11/02 13:38:18 - mmengine - INFO - Epoch(val) [140][165/500] eta: 0:00:24 time: 0.0432 data_time: 0.0020 memory: 1008 2022/11/02 13:38:18 - mmengine - INFO - Epoch(val) [140][170/500] eta: 0:00:14 time: 0.0433 data_time: 0.0024 memory: 1008 2022/11/02 13:38:18 - mmengine - INFO - Epoch(val) [140][175/500] eta: 0:00:14 time: 0.0388 data_time: 0.0023 memory: 1008 2022/11/02 13:38:18 - mmengine - INFO - Epoch(val) [140][180/500] eta: 0:00:12 time: 0.0375 data_time: 0.0023 memory: 1008 2022/11/02 13:38:19 - mmengine - INFO - Epoch(val) [140][185/500] eta: 0:00:12 time: 0.0400 data_time: 0.0024 memory: 1008 2022/11/02 13:38:19 - mmengine - INFO - Epoch(val) [140][190/500] eta: 0:00:12 time: 0.0407 data_time: 0.0023 memory: 1008 2022/11/02 13:38:19 - mmengine - INFO - Epoch(val) [140][195/500] eta: 0:00:12 time: 0.0396 data_time: 0.0023 memory: 1008 2022/11/02 13:38:19 - mmengine - INFO - Epoch(val) [140][200/500] eta: 0:00:13 time: 0.0440 data_time: 0.0022 memory: 1008 2022/11/02 13:38:19 - mmengine - INFO - Epoch(val) [140][205/500] eta: 0:00:13 time: 0.0482 data_time: 0.0037 memory: 1008 2022/11/02 13:38:20 - mmengine - INFO - Epoch(val) [140][210/500] eta: 0:00:11 time: 0.0409 data_time: 0.0037 memory: 1008 2022/11/02 13:38:20 - mmengine - INFO - Epoch(val) [140][215/500] eta: 0:00:11 time: 0.0374 data_time: 0.0022 memory: 1008 2022/11/02 13:38:20 - mmengine - INFO - Epoch(val) [140][220/500] eta: 0:00:23 time: 0.0830 data_time: 0.0451 memory: 1008 2022/11/02 13:38:21 - mmengine - INFO - Epoch(val) [140][225/500] eta: 0:00:23 time: 0.0823 data_time: 0.0447 memory: 1008 2022/11/02 13:38:21 - mmengine - INFO - Epoch(val) [140][230/500] eta: 0:00:09 time: 0.0353 data_time: 0.0016 memory: 1008 2022/11/02 13:38:21 - mmengine - INFO - Epoch(val) [140][235/500] eta: 0:00:09 time: 0.0380 data_time: 0.0020 memory: 1008 2022/11/02 13:38:21 - mmengine - INFO - Epoch(val) [140][240/500] eta: 0:00:10 time: 0.0418 data_time: 0.0023 memory: 1008 2022/11/02 13:38:21 - mmengine - INFO - Epoch(val) [140][245/500] eta: 0:00:10 time: 0.0382 data_time: 0.0024 memory: 1008 2022/11/02 13:38:22 - mmengine - INFO - Epoch(val) [140][250/500] eta: 0:00:10 time: 0.0411 data_time: 0.0032 memory: 1008 2022/11/02 13:38:22 - mmengine - INFO - Epoch(val) [140][255/500] eta: 0:00:10 time: 0.0407 data_time: 0.0031 memory: 1008 2022/11/02 13:38:22 - mmengine - INFO - Epoch(val) [140][260/500] eta: 0:00:08 time: 0.0372 data_time: 0.0024 memory: 1008 2022/11/02 13:38:22 - mmengine - INFO - Epoch(val) [140][265/500] eta: 0:00:08 time: 0.0376 data_time: 0.0025 memory: 1008 2022/11/02 13:38:22 - mmengine - INFO - Epoch(val) [140][270/500] eta: 0:00:08 time: 0.0379 data_time: 0.0024 memory: 1008 2022/11/02 13:38:23 - mmengine - INFO - Epoch(val) [140][275/500] eta: 0:00:08 time: 0.0400 data_time: 0.0032 memory: 1008 2022/11/02 13:38:23 - mmengine - INFO - Epoch(val) [140][280/500] eta: 0:00:09 time: 0.0422 data_time: 0.0032 memory: 1008 2022/11/02 13:38:23 - mmengine - INFO - Epoch(val) [140][285/500] eta: 0:00:09 time: 0.0400 data_time: 0.0024 memory: 1008 2022/11/02 13:38:23 - mmengine - INFO - Epoch(val) [140][290/500] eta: 0:00:08 time: 0.0397 data_time: 0.0025 memory: 1008 2022/11/02 13:38:23 - mmengine - INFO - Epoch(val) [140][295/500] eta: 0:00:08 time: 0.0413 data_time: 0.0026 memory: 1008 2022/11/02 13:38:24 - mmengine - INFO - Epoch(val) [140][300/500] eta: 0:00:07 time: 0.0395 data_time: 0.0025 memory: 1008 2022/11/02 13:38:24 - mmengine - INFO - Epoch(val) [140][305/500] eta: 0:00:07 time: 0.0375 data_time: 0.0024 memory: 1008 2022/11/02 13:38:24 - mmengine - INFO - Epoch(val) [140][310/500] eta: 0:00:07 time: 0.0370 data_time: 0.0023 memory: 1008 2022/11/02 13:38:24 - mmengine - INFO - Epoch(val) [140][315/500] eta: 0:00:07 time: 0.0411 data_time: 0.0023 memory: 1008 2022/11/02 13:38:24 - mmengine - INFO - Epoch(val) [140][320/500] eta: 0:00:07 time: 0.0411 data_time: 0.0024 memory: 1008 2022/11/02 13:38:25 - mmengine - INFO - Epoch(val) [140][325/500] eta: 0:00:07 time: 0.0531 data_time: 0.0026 memory: 1008 2022/11/02 13:38:25 - mmengine - INFO - Epoch(val) [140][330/500] eta: 0:00:09 time: 0.0541 data_time: 0.0028 memory: 1008 2022/11/02 13:38:25 - mmengine - INFO - Epoch(val) [140][335/500] eta: 0:00:09 time: 0.0419 data_time: 0.0031 memory: 1008 2022/11/02 13:38:26 - mmengine - INFO - Epoch(val) [140][340/500] eta: 0:00:08 time: 0.0539 data_time: 0.0031 memory: 1008 2022/11/02 13:38:26 - mmengine - INFO - Epoch(val) [140][345/500] eta: 0:00:08 time: 0.0517 data_time: 0.0028 memory: 1008 2022/11/02 13:38:26 - mmengine - INFO - Epoch(val) [140][350/500] eta: 0:00:07 time: 0.0478 data_time: 0.0029 memory: 1008 2022/11/02 13:38:26 - mmengine - INFO - Epoch(val) [140][355/500] eta: 0:00:07 time: 0.0484 data_time: 0.0031 memory: 1008 2022/11/02 13:38:26 - mmengine - INFO - Epoch(val) [140][360/500] eta: 0:00:06 time: 0.0436 data_time: 0.0031 memory: 1008 2022/11/02 13:38:27 - mmengine - INFO - Epoch(val) [140][365/500] eta: 0:00:06 time: 0.0448 data_time: 0.0031 memory: 1008 2022/11/02 13:38:27 - mmengine - INFO - Epoch(val) [140][370/500] eta: 0:00:05 time: 0.0397 data_time: 0.0029 memory: 1008 2022/11/02 13:38:27 - mmengine - INFO - Epoch(val) [140][375/500] eta: 0:00:05 time: 0.0368 data_time: 0.0029 memory: 1008 2022/11/02 13:38:27 - mmengine - INFO - Epoch(val) [140][380/500] eta: 0:00:05 time: 0.0455 data_time: 0.0031 memory: 1008 2022/11/02 13:38:27 - mmengine - INFO - Epoch(val) [140][385/500] eta: 0:00:05 time: 0.0471 data_time: 0.0030 memory: 1008 2022/11/02 13:38:28 - mmengine - INFO - Epoch(val) [140][390/500] eta: 0:00:04 time: 0.0403 data_time: 0.0027 memory: 1008 2022/11/02 13:38:28 - mmengine - INFO - Epoch(val) [140][395/500] eta: 0:00:04 time: 0.0412 data_time: 0.0028 memory: 1008 2022/11/02 13:38:28 - mmengine - INFO - Epoch(val) [140][400/500] eta: 0:00:04 time: 0.0403 data_time: 0.0028 memory: 1008 2022/11/02 13:38:28 - mmengine - INFO - Epoch(val) [140][405/500] eta: 0:00:04 time: 0.0393 data_time: 0.0024 memory: 1008 2022/11/02 13:38:29 - mmengine - INFO - Epoch(val) [140][410/500] eta: 0:00:03 time: 0.0420 data_time: 0.0023 memory: 1008 2022/11/02 13:38:29 - mmengine - INFO - Epoch(val) [140][415/500] eta: 0:00:03 time: 0.0392 data_time: 0.0024 memory: 1008 2022/11/02 13:38:29 - mmengine - INFO - Epoch(val) [140][420/500] eta: 0:00:02 time: 0.0348 data_time: 0.0023 memory: 1008 2022/11/02 13:38:29 - mmengine - INFO - Epoch(val) [140][425/500] eta: 0:00:02 time: 0.0381 data_time: 0.0024 memory: 1008 2022/11/02 13:38:29 - mmengine - INFO - Epoch(val) [140][430/500] eta: 0:00:02 time: 0.0389 data_time: 0.0023 memory: 1008 2022/11/02 13:38:29 - mmengine - INFO - Epoch(val) [140][435/500] eta: 0:00:02 time: 0.0374 data_time: 0.0023 memory: 1008 2022/11/02 13:38:30 - mmengine - INFO - Epoch(val) [140][440/500] eta: 0:00:02 time: 0.0391 data_time: 0.0024 memory: 1008 2022/11/02 13:38:30 - mmengine - INFO - Epoch(val) [140][445/500] eta: 0:00:02 time: 0.0395 data_time: 0.0024 memory: 1008 2022/11/02 13:38:30 - mmengine - INFO - Epoch(val) [140][450/500] eta: 0:00:02 time: 0.0437 data_time: 0.0028 memory: 1008 2022/11/02 13:38:30 - mmengine - INFO - Epoch(val) [140][455/500] eta: 0:00:02 time: 0.0427 data_time: 0.0027 memory: 1008 2022/11/02 13:38:30 - mmengine - INFO - Epoch(val) [140][460/500] eta: 0:00:01 time: 0.0366 data_time: 0.0027 memory: 1008 2022/11/02 13:38:31 - mmengine - INFO - Epoch(val) [140][465/500] eta: 0:00:01 time: 0.0369 data_time: 0.0029 memory: 1008 2022/11/02 13:38:31 - mmengine - INFO - Epoch(val) [140][470/500] eta: 0:00:01 time: 0.0380 data_time: 0.0025 memory: 1008 2022/11/02 13:38:31 - mmengine - INFO - Epoch(val) [140][475/500] eta: 0:00:01 time: 0.0377 data_time: 0.0025 memory: 1008 2022/11/02 13:38:31 - mmengine - INFO - Epoch(val) [140][480/500] eta: 0:00:00 time: 0.0395 data_time: 0.0025 memory: 1008 2022/11/02 13:38:31 - mmengine - INFO - Epoch(val) [140][485/500] eta: 0:00:00 time: 0.0384 data_time: 0.0025 memory: 1008 2022/11/02 13:38:32 - mmengine - INFO - Epoch(val) [140][490/500] eta: 0:00:00 time: 0.0408 data_time: 0.0024 memory: 1008 2022/11/02 13:38:32 - mmengine - INFO - Epoch(val) [140][495/500] eta: 0:00:00 time: 0.0450 data_time: 0.0026 memory: 1008 2022/11/02 13:38:32 - mmengine - INFO - Epoch(val) [140][500/500] eta: 0:00:00 time: 0.0400 data_time: 0.0025 memory: 1008 2022/11/02 13:38:32 - mmengine - INFO - Evaluating hmean-iou... 2022/11/02 13:38:32 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.7896, precision: 0.6600, hmean: 0.7190 2022/11/02 13:38:32 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.7896, precision: 0.7441, hmean: 0.7662 2022/11/02 13:38:32 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.7843, precision: 0.7985, hmean: 0.7914 2022/11/02 13:38:32 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.7617, precision: 0.8428, hmean: 0.8002 2022/11/02 13:38:32 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.6740, precision: 0.8946, hmean: 0.7688 2022/11/02 13:38:32 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.2374, precision: 0.9667, hmean: 0.3811 2022/11/02 13:38:32 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0000, precision: 0.0000, hmean: 0.0000 2022/11/02 13:38:32 - mmengine - INFO - Epoch(val) [140][500/500] icdar/precision: 0.8428 icdar/recall: 0.7617 icdar/hmean: 0.8002 2022/11/02 13:38:38 - mmengine - INFO - Epoch(train) [141][5/63] lr: 1.9344e-03 eta: 0:00:00 time: 0.7598 data_time: 0.2255 memory: 14901 loss: 2.2836 loss_prob: 1.3721 loss_thr: 0.6866 loss_db: 0.2249 2022/11/02 13:38:40 - mmengine - INFO - Epoch(train) [141][10/63] lr: 1.9344e-03 eta: 11:08:33 time: 0.7853 data_time: 0.2236 memory: 14901 loss: 2.2399 loss_prob: 1.3411 loss_thr: 0.6786 loss_db: 0.2202 2022/11/02 13:38:42 - mmengine - INFO - Epoch(train) [141][15/63] lr: 1.9344e-03 eta: 11:08:33 time: 0.4790 data_time: 0.0044 memory: 14901 loss: 2.1202 loss_prob: 1.2573 loss_thr: 0.6528 loss_db: 0.2101 2022/11/02 13:38:45 - mmengine - INFO - Epoch(train) [141][20/63] lr: 1.9344e-03 eta: 11:08:18 time: 0.4727 data_time: 0.0048 memory: 14901 loss: 2.1425 loss_prob: 1.2694 loss_thr: 0.6638 loss_db: 0.2093 2022/11/02 13:38:47 - mmengine - INFO - Epoch(train) [141][25/63] lr: 1.9344e-03 eta: 11:08:18 time: 0.5023 data_time: 0.0365 memory: 14901 loss: 2.0957 loss_prob: 1.2439 loss_thr: 0.6508 loss_db: 0.2010 2022/11/02 13:38:50 - mmengine - INFO - Epoch(train) [141][30/63] lr: 1.9344e-03 eta: 11:08:06 time: 0.5241 data_time: 0.0360 memory: 14901 loss: 2.1403 loss_prob: 1.3013 loss_thr: 0.6259 loss_db: 0.2131 2022/11/02 13:38:52 - mmengine - INFO - Epoch(train) [141][35/63] lr: 1.9344e-03 eta: 11:08:06 time: 0.4797 data_time: 0.0040 memory: 14901 loss: 2.2026 loss_prob: 1.3342 loss_thr: 0.6491 loss_db: 0.2194 2022/11/02 13:38:54 - mmengine - INFO - Epoch(train) [141][40/63] lr: 1.9344e-03 eta: 11:07:49 time: 0.4505 data_time: 0.0041 memory: 14901 loss: 2.2130 loss_prob: 1.3297 loss_thr: 0.6670 loss_db: 0.2164 2022/11/02 13:38:57 - mmengine - INFO - Epoch(train) [141][45/63] lr: 1.9344e-03 eta: 11:07:49 time: 0.4689 data_time: 0.0041 memory: 14901 loss: 2.1937 loss_prob: 1.3346 loss_thr: 0.6402 loss_db: 0.2189 2022/11/02 13:38:59 - mmengine - INFO - Epoch(train) [141][50/63] lr: 1.9344e-03 eta: 11:07:35 time: 0.4959 data_time: 0.0199 memory: 14901 loss: 2.0912 loss_prob: 1.2614 loss_thr: 0.6241 loss_db: 0.2057 2022/11/02 13:39:02 - mmengine - INFO - Epoch(train) [141][55/63] lr: 1.9344e-03 eta: 11:07:35 time: 0.5201 data_time: 0.0205 memory: 14901 loss: 2.1774 loss_prob: 1.3111 loss_thr: 0.6546 loss_db: 0.2117 2022/11/02 13:39:06 - mmengine - INFO - Epoch(train) [141][60/63] lr: 1.9344e-03 eta: 11:07:32 time: 0.6501 data_time: 0.0058 memory: 14901 loss: 2.1592 loss_prob: 1.2795 loss_thr: 0.6720 loss_db: 0.2077 2022/11/02 13:39:08 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:39:13 - mmengine - INFO - Epoch(train) [142][5/63] lr: 1.9327e-03 eta: 11:07:32 time: 0.8321 data_time: 0.2652 memory: 14901 loss: 2.1629 loss_prob: 1.2913 loss_thr: 0.6668 loss_db: 0.2047 2022/11/02 13:39:16 - mmengine - INFO - Epoch(train) [142][10/63] lr: 1.9327e-03 eta: 11:07:30 time: 0.8575 data_time: 0.2651 memory: 14901 loss: 2.2548 loss_prob: 1.3669 loss_thr: 0.6709 loss_db: 0.2170 2022/11/02 13:39:19 - mmengine - INFO - Epoch(train) [142][15/63] lr: 1.9327e-03 eta: 11:07:30 time: 0.5947 data_time: 0.0057 memory: 14901 loss: 2.0879 loss_prob: 1.2426 loss_thr: 0.6414 loss_db: 0.2039 2022/11/02 13:39:21 - mmengine - INFO - Epoch(train) [142][20/63] lr: 1.9327e-03 eta: 11:07:20 time: 0.5349 data_time: 0.0056 memory: 14901 loss: 2.1574 loss_prob: 1.2971 loss_thr: 0.6486 loss_db: 0.2117 2022/11/02 13:39:26 - mmengine - INFO - Epoch(train) [142][25/63] lr: 1.9327e-03 eta: 11:07:20 time: 0.6544 data_time: 0.0315 memory: 14901 loss: 2.1751 loss_prob: 1.3004 loss_thr: 0.6665 loss_db: 0.2082 2022/11/02 13:39:28 - mmengine - INFO - Epoch(train) [142][30/63] lr: 1.9327e-03 eta: 11:07:20 time: 0.6926 data_time: 0.0352 memory: 14901 loss: 2.1092 loss_prob: 1.2372 loss_thr: 0.6720 loss_db: 0.2000 2022/11/02 13:39:32 - mmengine - INFO - Epoch(train) [142][35/63] lr: 1.9327e-03 eta: 11:07:20 time: 0.6131 data_time: 0.0093 memory: 14901 loss: 2.0976 loss_prob: 1.2467 loss_thr: 0.6453 loss_db: 0.2056 2022/11/02 13:39:35 - mmengine - INFO - Epoch(train) [142][40/63] lr: 1.9327e-03 eta: 11:07:15 time: 0.6136 data_time: 0.0044 memory: 14901 loss: 2.2200 loss_prob: 1.3362 loss_thr: 0.6657 loss_db: 0.2181 2022/11/02 13:39:38 - mmengine - INFO - Epoch(train) [142][45/63] lr: 1.9327e-03 eta: 11:07:15 time: 0.6045 data_time: 0.0044 memory: 14901 loss: 2.2395 loss_prob: 1.3452 loss_thr: 0.6773 loss_db: 0.2170 2022/11/02 13:39:41 - mmengine - INFO - Epoch(train) [142][50/63] lr: 1.9327e-03 eta: 11:07:13 time: 0.6494 data_time: 0.0199 memory: 14901 loss: 2.2045 loss_prob: 1.3238 loss_thr: 0.6634 loss_db: 0.2174 2022/11/02 13:39:44 - mmengine - INFO - Epoch(train) [142][55/63] lr: 1.9327e-03 eta: 11:07:13 time: 0.6408 data_time: 0.0208 memory: 14901 loss: 2.3647 loss_prob: 1.4232 loss_thr: 0.6988 loss_db: 0.2427 2022/11/02 13:39:48 - mmengine - INFO - Epoch(train) [142][60/63] lr: 1.9327e-03 eta: 11:07:16 time: 0.7181 data_time: 0.0068 memory: 14901 loss: 2.3818 loss_prob: 1.4415 loss_thr: 0.6977 loss_db: 0.2426 2022/11/02 13:39:50 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:39:55 - mmengine - INFO - Epoch(train) [143][5/63] lr: 1.9311e-03 eta: 11:07:16 time: 0.9306 data_time: 0.2200 memory: 14901 loss: 2.0335 loss_prob: 1.1992 loss_thr: 0.6370 loss_db: 0.1973 2022/11/02 13:39:58 - mmengine - INFO - Epoch(train) [143][10/63] lr: 1.9311e-03 eta: 11:07:14 time: 0.8645 data_time: 0.2193 memory: 14901 loss: 2.1034 loss_prob: 1.2417 loss_thr: 0.6580 loss_db: 0.2036 2022/11/02 13:40:01 - mmengine - INFO - Epoch(train) [143][15/63] lr: 1.9311e-03 eta: 11:07:14 time: 0.5977 data_time: 0.0057 memory: 14901 loss: 2.2096 loss_prob: 1.3142 loss_thr: 0.6782 loss_db: 0.2171 2022/11/02 13:40:04 - mmengine - INFO - Epoch(train) [143][20/63] lr: 1.9311e-03 eta: 11:07:05 time: 0.5541 data_time: 0.0061 memory: 14901 loss: 2.1580 loss_prob: 1.2793 loss_thr: 0.6685 loss_db: 0.2101 2022/11/02 13:40:06 - mmengine - INFO - Epoch(train) [143][25/63] lr: 1.9311e-03 eta: 11:07:05 time: 0.5216 data_time: 0.0201 memory: 14901 loss: 1.9897 loss_prob: 1.1797 loss_thr: 0.6150 loss_db: 0.1949 2022/11/02 13:40:09 - mmengine - INFO - Epoch(train) [143][30/63] lr: 1.9311e-03 eta: 11:06:52 time: 0.5105 data_time: 0.0326 memory: 14901 loss: 2.0369 loss_prob: 1.2190 loss_thr: 0.6145 loss_db: 0.2033 2022/11/02 13:40:11 - mmengine - INFO - Epoch(train) [143][35/63] lr: 1.9311e-03 eta: 11:06:52 time: 0.4968 data_time: 0.0173 memory: 14901 loss: 2.1994 loss_prob: 1.3071 loss_thr: 0.6782 loss_db: 0.2140 2022/11/02 13:40:14 - mmengine - INFO - Epoch(train) [143][40/63] lr: 1.9311e-03 eta: 11:06:36 time: 0.4636 data_time: 0.0079 memory: 14901 loss: 2.3259 loss_prob: 1.3993 loss_thr: 0.6991 loss_db: 0.2275 2022/11/02 13:40:16 - mmengine - INFO - Epoch(train) [143][45/63] lr: 1.9311e-03 eta: 11:06:36 time: 0.4782 data_time: 0.0084 memory: 14901 loss: 2.3093 loss_prob: 1.4297 loss_thr: 0.6484 loss_db: 0.2312 2022/11/02 13:40:19 - mmengine - INFO - Epoch(train) [143][50/63] lr: 1.9311e-03 eta: 11:06:22 time: 0.4889 data_time: 0.0159 memory: 14901 loss: 2.2265 loss_prob: 1.3650 loss_thr: 0.6426 loss_db: 0.2189 2022/11/02 13:40:21 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:40:21 - mmengine - INFO - Epoch(train) [143][55/63] lr: 1.9311e-03 eta: 11:06:22 time: 0.4846 data_time: 0.0230 memory: 14901 loss: 2.6483 loss_prob: 1.6677 loss_thr: 0.7199 loss_db: 0.2607 2022/11/02 13:40:24 - mmengine - INFO - Epoch(train) [143][60/63] lr: 1.9311e-03 eta: 11:06:11 time: 0.5320 data_time: 0.0117 memory: 14901 loss: 2.7513 loss_prob: 1.7294 loss_thr: 0.7503 loss_db: 0.2716 2022/11/02 13:40:25 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:40:31 - mmengine - INFO - Epoch(train) [144][5/63] lr: 1.9294e-03 eta: 11:06:11 time: 0.8021 data_time: 0.2283 memory: 14901 loss: 2.7211 loss_prob: 1.6903 loss_thr: 0.7333 loss_db: 0.2975 2022/11/02 13:40:34 - mmengine - INFO - Epoch(train) [144][10/63] lr: 1.9294e-03 eta: 11:06:09 time: 0.8697 data_time: 0.2324 memory: 14901 loss: 2.6096 loss_prob: 1.6105 loss_thr: 0.7181 loss_db: 0.2810 2022/11/02 13:40:37 - mmengine - INFO - Epoch(train) [144][15/63] lr: 1.9294e-03 eta: 11:06:09 time: 0.5824 data_time: 0.0138 memory: 14901 loss: 2.5940 loss_prob: 1.6051 loss_thr: 0.7190 loss_db: 0.2698 2022/11/02 13:40:39 - mmengine - INFO - Epoch(train) [144][20/63] lr: 1.9294e-03 eta: 11:05:56 time: 0.4983 data_time: 0.0096 memory: 14901 loss: 2.4981 loss_prob: 1.5403 loss_thr: 0.6989 loss_db: 0.2589 2022/11/02 13:40:41 - mmengine - INFO - Epoch(train) [144][25/63] lr: 1.9294e-03 eta: 11:05:56 time: 0.4835 data_time: 0.0117 memory: 14901 loss: 2.3862 loss_prob: 1.4659 loss_thr: 0.6837 loss_db: 0.2366 2022/11/02 13:40:44 - mmengine - INFO - Epoch(train) [144][30/63] lr: 1.9294e-03 eta: 11:05:48 time: 0.5703 data_time: 0.0277 memory: 14901 loss: 2.3587 loss_prob: 1.4510 loss_thr: 0.6755 loss_db: 0.2322 2022/11/02 13:40:47 - mmengine - INFO - Epoch(train) [144][35/63] lr: 1.9294e-03 eta: 11:05:48 time: 0.5655 data_time: 0.0253 memory: 14901 loss: 2.1905 loss_prob: 1.3374 loss_thr: 0.6368 loss_db: 0.2163 2022/11/02 13:40:50 - mmengine - INFO - Epoch(train) [144][40/63] lr: 1.9294e-03 eta: 11:05:35 time: 0.5071 data_time: 0.0248 memory: 14901 loss: 2.1784 loss_prob: 1.3109 loss_thr: 0.6540 loss_db: 0.2136 2022/11/02 13:40:52 - mmengine - INFO - Epoch(train) [144][45/63] lr: 1.9294e-03 eta: 11:05:35 time: 0.4977 data_time: 0.0243 memory: 14901 loss: 2.1385 loss_prob: 1.2864 loss_thr: 0.6435 loss_db: 0.2086 2022/11/02 13:40:54 - mmengine - INFO - Epoch(train) [144][50/63] lr: 1.9294e-03 eta: 11:05:21 time: 0.4968 data_time: 0.0173 memory: 14901 loss: 2.0807 loss_prob: 1.2534 loss_thr: 0.6215 loss_db: 0.2057 2022/11/02 13:40:57 - mmengine - INFO - Epoch(train) [144][55/63] lr: 1.9294e-03 eta: 11:05:21 time: 0.4836 data_time: 0.0206 memory: 14901 loss: 2.1290 loss_prob: 1.2619 loss_thr: 0.6597 loss_db: 0.2074 2022/11/02 13:40:59 - mmengine - INFO - Epoch(train) [144][60/63] lr: 1.9294e-03 eta: 11:05:06 time: 0.4717 data_time: 0.0126 memory: 14901 loss: 2.1781 loss_prob: 1.2934 loss_thr: 0.6697 loss_db: 0.2151 2022/11/02 13:41:00 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:41:06 - mmengine - INFO - Epoch(train) [145][5/63] lr: 1.9278e-03 eta: 11:05:06 time: 0.7635 data_time: 0.2225 memory: 14901 loss: 2.2760 loss_prob: 1.3717 loss_thr: 0.6685 loss_db: 0.2358 2022/11/02 13:41:08 - mmengine - INFO - Epoch(train) [145][10/63] lr: 1.9278e-03 eta: 11:04:59 time: 0.7958 data_time: 0.2185 memory: 14901 loss: 2.2577 loss_prob: 1.3628 loss_thr: 0.6648 loss_db: 0.2301 2022/11/02 13:41:11 - mmengine - INFO - Epoch(train) [145][15/63] lr: 1.9278e-03 eta: 11:04:59 time: 0.4836 data_time: 0.0051 memory: 14901 loss: 2.3072 loss_prob: 1.3927 loss_thr: 0.6865 loss_db: 0.2280 2022/11/02 13:41:14 - mmengine - INFO - Epoch(train) [145][20/63] lr: 1.9278e-03 eta: 11:04:50 time: 0.5506 data_time: 0.0076 memory: 14901 loss: 2.1989 loss_prob: 1.3030 loss_thr: 0.6860 loss_db: 0.2099 2022/11/02 13:41:16 - mmengine - INFO - Epoch(train) [145][25/63] lr: 1.9278e-03 eta: 11:04:50 time: 0.5649 data_time: 0.0314 memory: 14901 loss: 2.1650 loss_prob: 1.2838 loss_thr: 0.6741 loss_db: 0.2071 2022/11/02 13:41:19 - mmengine - INFO - Epoch(train) [145][30/63] lr: 1.9278e-03 eta: 11:04:37 time: 0.5035 data_time: 0.0347 memory: 14901 loss: 2.1523 loss_prob: 1.2870 loss_thr: 0.6526 loss_db: 0.2127 2022/11/02 13:41:21 - mmengine - INFO - Epoch(train) [145][35/63] lr: 1.9278e-03 eta: 11:04:37 time: 0.4802 data_time: 0.0175 memory: 14901 loss: 2.0748 loss_prob: 1.2298 loss_thr: 0.6430 loss_db: 0.2020 2022/11/02 13:41:23 - mmengine - INFO - Epoch(train) [145][40/63] lr: 1.9278e-03 eta: 11:04:21 time: 0.4644 data_time: 0.0117 memory: 14901 loss: 2.2105 loss_prob: 1.3280 loss_thr: 0.6665 loss_db: 0.2160 2022/11/02 13:41:26 - mmengine - INFO - Epoch(train) [145][45/63] lr: 1.9278e-03 eta: 11:04:21 time: 0.4895 data_time: 0.0045 memory: 14901 loss: 2.2637 loss_prob: 1.3537 loss_thr: 0.6887 loss_db: 0.2212 2022/11/02 13:41:29 - mmengine - INFO - Epoch(train) [145][50/63] lr: 1.9278e-03 eta: 11:04:11 time: 0.5469 data_time: 0.0127 memory: 14901 loss: 2.2318 loss_prob: 1.3332 loss_thr: 0.6784 loss_db: 0.2202 2022/11/02 13:41:31 - mmengine - INFO - Epoch(train) [145][55/63] lr: 1.9278e-03 eta: 11:04:11 time: 0.5039 data_time: 0.0146 memory: 14901 loss: 2.2004 loss_prob: 1.3302 loss_thr: 0.6517 loss_db: 0.2184 2022/11/02 13:41:33 - mmengine - INFO - Epoch(train) [145][60/63] lr: 1.9278e-03 eta: 11:03:54 time: 0.4473 data_time: 0.0076 memory: 14901 loss: 2.0249 loss_prob: 1.1914 loss_thr: 0.6410 loss_db: 0.1925 2022/11/02 13:41:35 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:41:40 - mmengine - INFO - Epoch(train) [146][5/63] lr: 1.9261e-03 eta: 11:03:54 time: 0.7335 data_time: 0.2326 memory: 14901 loss: 2.2729 loss_prob: 1.3729 loss_thr: 0.6632 loss_db: 0.2368 2022/11/02 13:41:44 - mmengine - INFO - Epoch(train) [146][10/63] lr: 1.9261e-03 eta: 11:03:53 time: 0.8816 data_time: 0.2325 memory: 14901 loss: 2.1696 loss_prob: 1.3006 loss_thr: 0.6497 loss_db: 0.2193 2022/11/02 13:41:47 - mmengine - INFO - Epoch(train) [146][15/63] lr: 1.9261e-03 eta: 11:03:53 time: 0.6994 data_time: 0.0055 memory: 14901 loss: 2.1669 loss_prob: 1.2960 loss_thr: 0.6570 loss_db: 0.2139 2022/11/02 13:41:50 - mmengine - INFO - Epoch(train) [146][20/63] lr: 1.9261e-03 eta: 11:03:51 time: 0.6419 data_time: 0.0172 memory: 14901 loss: 2.2071 loss_prob: 1.3072 loss_thr: 0.6823 loss_db: 0.2176 2022/11/02 13:41:53 - mmengine - INFO - Epoch(train) [146][25/63] lr: 1.9261e-03 eta: 11:03:51 time: 0.6186 data_time: 0.0379 memory: 14901 loss: 2.2320 loss_prob: 1.3199 loss_thr: 0.6958 loss_db: 0.2163 2022/11/02 13:41:56 - mmengine - INFO - Epoch(train) [146][30/63] lr: 1.9261e-03 eta: 11:03:43 time: 0.5769 data_time: 0.0251 memory: 14901 loss: 2.0999 loss_prob: 1.2407 loss_thr: 0.6591 loss_db: 0.2001 2022/11/02 13:41:59 - mmengine - INFO - Epoch(train) [146][35/63] lr: 1.9261e-03 eta: 11:03:43 time: 0.5514 data_time: 0.0043 memory: 14901 loss: 2.0333 loss_prob: 1.1931 loss_thr: 0.6432 loss_db: 0.1971 2022/11/02 13:42:02 - mmengine - INFO - Epoch(train) [146][40/63] lr: 1.9261e-03 eta: 11:03:39 time: 0.6250 data_time: 0.0046 memory: 14901 loss: 2.0029 loss_prob: 1.1735 loss_thr: 0.6356 loss_db: 0.1938 2022/11/02 13:42:05 - mmengine - INFO - Epoch(train) [146][45/63] lr: 1.9261e-03 eta: 11:03:39 time: 0.6335 data_time: 0.0102 memory: 14901 loss: 2.0739 loss_prob: 1.2353 loss_thr: 0.6387 loss_db: 0.1999 2022/11/02 13:42:08 - mmengine - INFO - Epoch(train) [146][50/63] lr: 1.9261e-03 eta: 11:03:35 time: 0.6330 data_time: 0.0232 memory: 14901 loss: 2.1400 loss_prob: 1.2857 loss_thr: 0.6477 loss_db: 0.2067 2022/11/02 13:42:12 - mmengine - INFO - Epoch(train) [146][55/63] lr: 1.9261e-03 eta: 11:03:35 time: 0.6864 data_time: 0.0175 memory: 14901 loss: 2.1388 loss_prob: 1.2784 loss_thr: 0.6581 loss_db: 0.2022 2022/11/02 13:42:14 - mmengine - INFO - Epoch(train) [146][60/63] lr: 1.9261e-03 eta: 11:03:29 time: 0.5985 data_time: 0.0042 memory: 14901 loss: 2.2330 loss_prob: 1.3402 loss_thr: 0.6783 loss_db: 0.2145 2022/11/02 13:42:16 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:42:22 - mmengine - INFO - Epoch(train) [147][5/63] lr: 1.9245e-03 eta: 11:03:29 time: 0.8281 data_time: 0.2622 memory: 14901 loss: 2.0387 loss_prob: 1.2209 loss_thr: 0.6226 loss_db: 0.1952 2022/11/02 13:42:25 - mmengine - INFO - Epoch(train) [147][10/63] lr: 1.9245e-03 eta: 11:03:30 time: 0.8969 data_time: 0.2634 memory: 14901 loss: 2.0211 loss_prob: 1.1874 loss_thr: 0.6436 loss_db: 0.1901 2022/11/02 13:42:29 - mmengine - INFO - Epoch(train) [147][15/63] lr: 1.9245e-03 eta: 11:03:30 time: 0.6912 data_time: 0.0062 memory: 14901 loss: 1.9316 loss_prob: 1.1114 loss_thr: 0.6387 loss_db: 0.1815 2022/11/02 13:42:31 - mmengine - INFO - Epoch(train) [147][20/63] lr: 1.9245e-03 eta: 11:03:26 time: 0.6367 data_time: 0.0054 memory: 14901 loss: 2.0090 loss_prob: 1.1863 loss_thr: 0.6259 loss_db: 0.1967 2022/11/02 13:42:34 - mmengine - INFO - Epoch(train) [147][25/63] lr: 1.9245e-03 eta: 11:03:26 time: 0.5530 data_time: 0.0053 memory: 14901 loss: 2.1058 loss_prob: 1.2712 loss_thr: 0.6324 loss_db: 0.2022 2022/11/02 13:42:38 - mmengine - INFO - Epoch(train) [147][30/63] lr: 1.9245e-03 eta: 11:03:24 time: 0.6526 data_time: 0.0190 memory: 14901 loss: 2.2598 loss_prob: 1.3916 loss_thr: 0.6532 loss_db: 0.2149 2022/11/02 13:42:40 - mmengine - INFO - Epoch(train) [147][35/63] lr: 1.9245e-03 eta: 11:03:24 time: 0.6039 data_time: 0.0184 memory: 14901 loss: 2.3893 loss_prob: 1.4800 loss_thr: 0.6712 loss_db: 0.2381 2022/11/02 13:42:43 - mmengine - INFO - Epoch(train) [147][40/63] lr: 1.9245e-03 eta: 11:03:09 time: 0.4735 data_time: 0.0049 memory: 14901 loss: 2.3380 loss_prob: 1.4322 loss_thr: 0.6694 loss_db: 0.2364 2022/11/02 13:42:45 - mmengine - INFO - Epoch(train) [147][45/63] lr: 1.9245e-03 eta: 11:03:09 time: 0.4888 data_time: 0.0049 memory: 14901 loss: 2.2418 loss_prob: 1.3611 loss_thr: 0.6579 loss_db: 0.2227 2022/11/02 13:42:48 - mmengine - INFO - Epoch(train) [147][50/63] lr: 1.9245e-03 eta: 11:02:57 time: 0.5167 data_time: 0.0042 memory: 14901 loss: 2.1847 loss_prob: 1.3069 loss_thr: 0.6610 loss_db: 0.2168 2022/11/02 13:42:50 - mmengine - INFO - Epoch(train) [147][55/63] lr: 1.9245e-03 eta: 11:02:57 time: 0.5121 data_time: 0.0182 memory: 14901 loss: 2.1982 loss_prob: 1.3188 loss_thr: 0.6618 loss_db: 0.2175 2022/11/02 13:42:52 - mmengine - INFO - Epoch(train) [147][60/63] lr: 1.9245e-03 eta: 11:02:43 time: 0.4786 data_time: 0.0202 memory: 14901 loss: 2.0902 loss_prob: 1.2525 loss_thr: 0.6328 loss_db: 0.2050 2022/11/02 13:42:54 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:42:58 - mmengine - INFO - Epoch(train) [148][5/63] lr: 1.9229e-03 eta: 11:02:43 time: 0.6646 data_time: 0.1864 memory: 14901 loss: 1.9948 loss_prob: 1.1781 loss_thr: 0.6196 loss_db: 0.1970 2022/11/02 13:43:01 - mmengine - INFO - Epoch(train) [148][10/63] lr: 1.9229e-03 eta: 11:02:29 time: 0.6912 data_time: 0.1869 memory: 14901 loss: 2.0959 loss_prob: 1.2451 loss_thr: 0.6430 loss_db: 0.2079 2022/11/02 13:43:03 - mmengine - INFO - Epoch(train) [148][15/63] lr: 1.9229e-03 eta: 11:02:29 time: 0.4646 data_time: 0.0055 memory: 14901 loss: 2.1294 loss_prob: 1.2743 loss_thr: 0.6410 loss_db: 0.2140 2022/11/02 13:43:06 - mmengine - INFO - Epoch(train) [148][20/63] lr: 1.9229e-03 eta: 11:02:17 time: 0.5259 data_time: 0.0072 memory: 14901 loss: 2.0478 loss_prob: 1.2095 loss_thr: 0.6334 loss_db: 0.2049 2022/11/02 13:43:09 - mmengine - INFO - Epoch(train) [148][25/63] lr: 1.9229e-03 eta: 11:02:17 time: 0.5844 data_time: 0.0165 memory: 14901 loss: 2.1094 loss_prob: 1.2447 loss_thr: 0.6597 loss_db: 0.2050 2022/11/02 13:43:11 - mmengine - INFO - Epoch(train) [148][30/63] lr: 1.9229e-03 eta: 11:02:09 time: 0.5662 data_time: 0.0315 memory: 14901 loss: 2.0854 loss_prob: 1.2196 loss_thr: 0.6684 loss_db: 0.1974 2022/11/02 13:43:14 - mmengine - INFO - Epoch(train) [148][35/63] lr: 1.9229e-03 eta: 11:02:09 time: 0.5085 data_time: 0.0215 memory: 14901 loss: 1.9314 loss_prob: 1.1043 loss_thr: 0.6452 loss_db: 0.1819 2022/11/02 13:43:16 - mmengine - INFO - Epoch(train) [148][40/63] lr: 1.9229e-03 eta: 11:01:54 time: 0.4666 data_time: 0.0058 memory: 14901 loss: 1.9613 loss_prob: 1.1417 loss_thr: 0.6340 loss_db: 0.1856 2022/11/02 13:43:19 - mmengine - INFO - Epoch(train) [148][45/63] lr: 1.9229e-03 eta: 11:01:54 time: 0.4715 data_time: 0.0061 memory: 14901 loss: 2.0861 loss_prob: 1.2500 loss_thr: 0.6331 loss_db: 0.2030 2022/11/02 13:43:21 - mmengine - INFO - Epoch(train) [148][50/63] lr: 1.9229e-03 eta: 11:01:39 time: 0.4783 data_time: 0.0108 memory: 14901 loss: 2.1850 loss_prob: 1.2999 loss_thr: 0.6752 loss_db: 0.2100 2022/11/02 13:43:24 - mmengine - INFO - Epoch(train) [148][55/63] lr: 1.9229e-03 eta: 11:01:39 time: 0.5076 data_time: 0.0331 memory: 14901 loss: 2.2092 loss_prob: 1.2948 loss_thr: 0.7046 loss_db: 0.2098 2022/11/02 13:43:26 - mmengine - INFO - Epoch(train) [148][60/63] lr: 1.9229e-03 eta: 11:01:27 time: 0.5168 data_time: 0.0267 memory: 14901 loss: 2.2761 loss_prob: 1.3553 loss_thr: 0.6974 loss_db: 0.2235 2022/11/02 13:43:27 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:43:32 - mmengine - INFO - Epoch(train) [149][5/63] lr: 1.9212e-03 eta: 11:01:27 time: 0.6941 data_time: 0.2075 memory: 14901 loss: 2.1652 loss_prob: 1.3043 loss_thr: 0.6524 loss_db: 0.2085 2022/11/02 13:43:35 - mmengine - INFO - Epoch(train) [149][10/63] lr: 1.9212e-03 eta: 11:01:16 time: 0.7308 data_time: 0.2044 memory: 14901 loss: 2.1399 loss_prob: 1.2672 loss_thr: 0.6654 loss_db: 0.2073 2022/11/02 13:43:37 - mmengine - INFO - Epoch(train) [149][15/63] lr: 1.9212e-03 eta: 11:01:16 time: 0.4998 data_time: 0.0048 memory: 14901 loss: 2.1244 loss_prob: 1.2483 loss_thr: 0.6722 loss_db: 0.2039 2022/11/02 13:43:39 - mmengine - INFO - Epoch(train) [149][20/63] lr: 1.9212e-03 eta: 11:01:02 time: 0.4869 data_time: 0.0053 memory: 14901 loss: 2.1403 loss_prob: 1.2558 loss_thr: 0.6815 loss_db: 0.2029 2022/11/02 13:43:42 - mmengine - INFO - Epoch(train) [149][25/63] lr: 1.9212e-03 eta: 11:01:02 time: 0.5248 data_time: 0.0189 memory: 14901 loss: 2.1451 loss_prob: 1.2653 loss_thr: 0.6767 loss_db: 0.2031 2022/11/02 13:43:45 - mmengine - INFO - Epoch(train) [149][30/63] lr: 1.9212e-03 eta: 11:00:52 time: 0.5346 data_time: 0.0375 memory: 14901 loss: 2.1713 loss_prob: 1.2933 loss_thr: 0.6660 loss_db: 0.2120 2022/11/02 13:43:47 - mmengine - INFO - Epoch(train) [149][35/63] lr: 1.9212e-03 eta: 11:00:52 time: 0.5119 data_time: 0.0234 memory: 14901 loss: 2.1362 loss_prob: 1.2750 loss_thr: 0.6521 loss_db: 0.2091 2022/11/02 13:43:50 - mmengine - INFO - Epoch(train) [149][40/63] lr: 1.9212e-03 eta: 11:00:40 time: 0.5257 data_time: 0.0044 memory: 14901 loss: 2.0412 loss_prob: 1.2121 loss_thr: 0.6302 loss_db: 0.1989 2022/11/02 13:43:52 - mmengine - INFO - Epoch(train) [149][45/63] lr: 1.9212e-03 eta: 11:00:40 time: 0.4981 data_time: 0.0042 memory: 14901 loss: 2.0792 loss_prob: 1.2386 loss_thr: 0.6279 loss_db: 0.2126 2022/11/02 13:43:55 - mmengine - INFO - Epoch(train) [149][50/63] lr: 1.9212e-03 eta: 11:00:27 time: 0.4971 data_time: 0.0161 memory: 14901 loss: 2.1876 loss_prob: 1.3113 loss_thr: 0.6540 loss_db: 0.2223 2022/11/02 13:43:57 - mmengine - INFO - Epoch(train) [149][55/63] lr: 1.9212e-03 eta: 11:00:27 time: 0.4926 data_time: 0.0216 memory: 14901 loss: 2.2010 loss_prob: 1.3184 loss_thr: 0.6639 loss_db: 0.2187 2022/11/02 13:44:00 - mmengine - INFO - Epoch(train) [149][60/63] lr: 1.9212e-03 eta: 11:00:12 time: 0.4732 data_time: 0.0099 memory: 14901 loss: 2.2333 loss_prob: 1.3394 loss_thr: 0.6703 loss_db: 0.2236 2022/11/02 13:44:01 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:44:05 - mmengine - INFO - Epoch(train) [150][5/63] lr: 1.9196e-03 eta: 11:00:12 time: 0.6512 data_time: 0.2010 memory: 14901 loss: 2.1187 loss_prob: 1.2771 loss_thr: 0.6313 loss_db: 0.2104 2022/11/02 13:44:08 - mmengine - INFO - Epoch(train) [150][10/63] lr: 1.9196e-03 eta: 11:00:00 time: 0.7070 data_time: 0.2026 memory: 14901 loss: 2.2827 loss_prob: 1.3981 loss_thr: 0.6619 loss_db: 0.2227 2022/11/02 13:44:11 - mmengine - INFO - Epoch(train) [150][15/63] lr: 1.9196e-03 eta: 11:00:00 time: 0.5437 data_time: 0.0063 memory: 14901 loss: 2.4804 loss_prob: 1.5561 loss_thr: 0.6738 loss_db: 0.2504 2022/11/02 13:44:13 - mmengine - INFO - Epoch(train) [150][20/63] lr: 1.9196e-03 eta: 10:59:48 time: 0.5179 data_time: 0.0071 memory: 14901 loss: 2.4007 loss_prob: 1.5001 loss_thr: 0.6621 loss_db: 0.2386 2022/11/02 13:44:16 - mmengine - INFO - Epoch(train) [150][25/63] lr: 1.9196e-03 eta: 10:59:48 time: 0.5012 data_time: 0.0335 memory: 14901 loss: 2.2654 loss_prob: 1.3841 loss_thr: 0.6625 loss_db: 0.2188 2022/11/02 13:44:18 - mmengine - INFO - Epoch(train) [150][30/63] lr: 1.9196e-03 eta: 10:59:36 time: 0.5162 data_time: 0.0312 memory: 14901 loss: 2.0922 loss_prob: 1.2411 loss_thr: 0.6529 loss_db: 0.1982 2022/11/02 13:44:21 - mmengine - INFO - Epoch(train) [150][35/63] lr: 1.9196e-03 eta: 10:59:36 time: 0.5110 data_time: 0.0062 memory: 14901 loss: 2.3470 loss_prob: 1.4068 loss_thr: 0.7072 loss_db: 0.2330 2022/11/02 13:44:23 - mmengine - INFO - Epoch(train) [150][40/63] lr: 1.9196e-03 eta: 10:59:24 time: 0.5091 data_time: 0.0064 memory: 14901 loss: 2.4049 loss_prob: 1.4507 loss_thr: 0.7118 loss_db: 0.2425 2022/11/02 13:44:26 - mmengine - INFO - Epoch(train) [150][45/63] lr: 1.9196e-03 eta: 10:59:24 time: 0.5136 data_time: 0.0059 memory: 14901 loss: 2.1166 loss_prob: 1.2561 loss_thr: 0.6505 loss_db: 0.2100 2022/11/02 13:44:29 - mmengine - INFO - Epoch(train) [150][50/63] lr: 1.9196e-03 eta: 10:59:14 time: 0.5346 data_time: 0.0279 memory: 14901 loss: 2.2269 loss_prob: 1.3343 loss_thr: 0.6683 loss_db: 0.2244 2022/11/02 13:44:31 - mmengine - INFO - Epoch(train) [150][55/63] lr: 1.9196e-03 eta: 10:59:14 time: 0.5330 data_time: 0.0266 memory: 14901 loss: 2.2772 loss_prob: 1.3754 loss_thr: 0.6738 loss_db: 0.2280 2022/11/02 13:44:34 - mmengine - INFO - Epoch(train) [150][60/63] lr: 1.9196e-03 eta: 10:59:02 time: 0.5204 data_time: 0.0045 memory: 14901 loss: 2.1579 loss_prob: 1.3029 loss_thr: 0.6429 loss_db: 0.2121 2022/11/02 13:44:35 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:44:41 - mmengine - INFO - Epoch(train) [151][5/63] lr: 1.9179e-03 eta: 10:59:02 time: 0.7675 data_time: 0.2545 memory: 14901 loss: 2.3056 loss_prob: 1.4067 loss_thr: 0.6702 loss_db: 0.2286 2022/11/02 13:44:43 - mmengine - INFO - Epoch(train) [151][10/63] lr: 1.9179e-03 eta: 10:58:58 time: 0.8228 data_time: 0.2552 memory: 14901 loss: 2.2110 loss_prob: 1.3329 loss_thr: 0.6606 loss_db: 0.2175 2022/11/02 13:44:48 - mmengine - INFO - Epoch(train) [151][15/63] lr: 1.9179e-03 eta: 10:58:58 time: 0.7126 data_time: 0.0071 memory: 14901 loss: 2.0958 loss_prob: 1.2396 loss_thr: 0.6521 loss_db: 0.2040 2022/11/02 13:44:51 - mmengine - INFO - Epoch(train) [151][20/63] lr: 1.9179e-03 eta: 10:59:02 time: 0.7400 data_time: 0.0065 memory: 14901 loss: 2.0228 loss_prob: 1.1672 loss_thr: 0.6651 loss_db: 0.1906 2022/11/02 13:44:54 - mmengine - INFO - Epoch(train) [151][25/63] lr: 1.9179e-03 eta: 10:59:02 time: 0.5977 data_time: 0.0366 memory: 14901 loss: 1.9623 loss_prob: 1.1377 loss_thr: 0.6416 loss_db: 0.1830 2022/11/02 13:44:57 - mmengine - INFO - Epoch(train) [151][30/63] lr: 1.9179e-03 eta: 10:58:54 time: 0.5694 data_time: 0.0383 memory: 14901 loss: 1.9628 loss_prob: 1.1444 loss_thr: 0.6298 loss_db: 0.1885 2022/11/02 13:45:00 - mmengine - INFO - Epoch(train) [151][35/63] lr: 1.9179e-03 eta: 10:58:54 time: 0.6250 data_time: 0.0063 memory: 14901 loss: 2.0322 loss_prob: 1.1978 loss_thr: 0.6343 loss_db: 0.2001 2022/11/02 13:45:03 - mmengine - INFO - Epoch(train) [151][40/63] lr: 1.9179e-03 eta: 10:58:49 time: 0.6122 data_time: 0.0044 memory: 14901 loss: 1.9779 loss_prob: 1.1594 loss_thr: 0.6263 loss_db: 0.1922 2022/11/02 13:45:06 - mmengine - INFO - Epoch(train) [151][45/63] lr: 1.9179e-03 eta: 10:58:49 time: 0.5973 data_time: 0.0045 memory: 14901 loss: 1.9935 loss_prob: 1.1517 loss_thr: 0.6495 loss_db: 0.1924 2022/11/02 13:45:09 - mmengine - INFO - Epoch(train) [151][50/63] lr: 1.9179e-03 eta: 10:58:44 time: 0.6236 data_time: 0.0241 memory: 14901 loss: 2.1670 loss_prob: 1.2887 loss_thr: 0.6620 loss_db: 0.2163 2022/11/02 13:45:11 - mmengine - INFO - Epoch(train) [151][55/63] lr: 1.9179e-03 eta: 10:58:44 time: 0.5597 data_time: 0.0251 memory: 14901 loss: 2.2978 loss_prob: 1.4023 loss_thr: 0.6659 loss_db: 0.2296 2022/11/02 13:45:14 - mmengine - INFO - Epoch(train) [151][60/63] lr: 1.9179e-03 eta: 10:58:31 time: 0.4844 data_time: 0.0051 memory: 14901 loss: 2.1841 loss_prob: 1.3103 loss_thr: 0.6646 loss_db: 0.2092 2022/11/02 13:45:15 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:45:20 - mmengine - INFO - Epoch(train) [152][5/63] lr: 1.9163e-03 eta: 10:58:31 time: 0.7513 data_time: 0.2338 memory: 14901 loss: 2.3397 loss_prob: 1.4410 loss_thr: 0.6637 loss_db: 0.2350 2022/11/02 13:45:23 - mmengine - INFO - Epoch(train) [152][10/63] lr: 1.9163e-03 eta: 10:58:23 time: 0.7862 data_time: 0.2385 memory: 14901 loss: 2.2872 loss_prob: 1.3845 loss_thr: 0.6744 loss_db: 0.2283 2022/11/02 13:45:26 - mmengine - INFO - Epoch(train) [152][15/63] lr: 1.9163e-03 eta: 10:58:23 time: 0.5627 data_time: 0.0100 memory: 14901 loss: 2.2156 loss_prob: 1.3235 loss_thr: 0.6789 loss_db: 0.2132 2022/11/02 13:45:29 - mmengine - INFO - Epoch(train) [152][20/63] lr: 1.9163e-03 eta: 10:58:16 time: 0.5710 data_time: 0.0094 memory: 14901 loss: 2.2661 loss_prob: 1.3545 loss_thr: 0.6888 loss_db: 0.2228 2022/11/02 13:45:31 - mmengine - INFO - Epoch(train) [152][25/63] lr: 1.9163e-03 eta: 10:58:16 time: 0.5130 data_time: 0.0295 memory: 14901 loss: 2.3718 loss_prob: 1.4465 loss_thr: 0.6830 loss_db: 0.2423 2022/11/02 13:45:34 - mmengine - INFO - Epoch(train) [152][30/63] lr: 1.9163e-03 eta: 10:58:03 time: 0.5020 data_time: 0.0343 memory: 14901 loss: 2.3213 loss_prob: 1.4227 loss_thr: 0.6667 loss_db: 0.2320 2022/11/02 13:45:36 - mmengine - INFO - Epoch(train) [152][35/63] lr: 1.9163e-03 eta: 10:58:03 time: 0.4928 data_time: 0.0143 memory: 14901 loss: 2.2723 loss_prob: 1.3833 loss_thr: 0.6693 loss_db: 0.2197 2022/11/02 13:45:39 - mmengine - INFO - Epoch(train) [152][40/63] lr: 1.9163e-03 eta: 10:57:50 time: 0.4948 data_time: 0.0055 memory: 14901 loss: 2.1642 loss_prob: 1.3011 loss_thr: 0.6540 loss_db: 0.2091 2022/11/02 13:45:41 - mmengine - INFO - Epoch(train) [152][45/63] lr: 1.9163e-03 eta: 10:57:50 time: 0.4978 data_time: 0.0045 memory: 14901 loss: 2.1066 loss_prob: 1.2527 loss_thr: 0.6523 loss_db: 0.2015 2022/11/02 13:45:44 - mmengine - INFO - Epoch(train) [152][50/63] lr: 1.9163e-03 eta: 10:57:37 time: 0.5009 data_time: 0.0175 memory: 14901 loss: 1.9882 loss_prob: 1.1698 loss_thr: 0.6320 loss_db: 0.1864 2022/11/02 13:45:46 - mmengine - INFO - Epoch(train) [152][55/63] lr: 1.9163e-03 eta: 10:57:37 time: 0.4924 data_time: 0.0222 memory: 14901 loss: 1.9574 loss_prob: 1.1274 loss_thr: 0.6418 loss_db: 0.1882 2022/11/02 13:45:48 - mmengine - INFO - Epoch(train) [152][60/63] lr: 1.9163e-03 eta: 10:57:24 time: 0.4961 data_time: 0.0106 memory: 14901 loss: 1.9795 loss_prob: 1.1346 loss_thr: 0.6529 loss_db: 0.1920 2022/11/02 13:45:50 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:45:55 - mmengine - INFO - Epoch(train) [153][5/63] lr: 1.9146e-03 eta: 10:57:24 time: 0.7117 data_time: 0.1874 memory: 14901 loss: 2.2464 loss_prob: 1.3694 loss_thr: 0.6487 loss_db: 0.2282 2022/11/02 13:45:57 - mmengine - INFO - Epoch(train) [153][10/63] lr: 1.9146e-03 eta: 10:57:11 time: 0.6970 data_time: 0.1926 memory: 14901 loss: 2.3536 loss_prob: 1.4426 loss_thr: 0.6658 loss_db: 0.2453 2022/11/02 13:45:59 - mmengine - INFO - Epoch(train) [153][15/63] lr: 1.9146e-03 eta: 10:57:11 time: 0.4667 data_time: 0.0129 memory: 14901 loss: 2.5258 loss_prob: 1.5622 loss_thr: 0.7044 loss_db: 0.2592 2022/11/02 13:46:02 - mmengine - INFO - Epoch(train) [153][20/63] lr: 1.9146e-03 eta: 10:57:00 time: 0.5289 data_time: 0.0083 memory: 14901 loss: 2.2549 loss_prob: 1.3557 loss_thr: 0.6784 loss_db: 0.2209 2022/11/02 13:46:05 - mmengine - INFO - Epoch(train) [153][25/63] lr: 1.9146e-03 eta: 10:57:00 time: 0.5467 data_time: 0.0249 memory: 14901 loss: 2.1324 loss_prob: 1.2672 loss_thr: 0.6594 loss_db: 0.2058 2022/11/02 13:46:08 - mmengine - INFO - Epoch(train) [153][30/63] lr: 1.9146e-03 eta: 10:56:51 time: 0.5451 data_time: 0.0297 memory: 14901 loss: 2.2577 loss_prob: 1.3736 loss_thr: 0.6625 loss_db: 0.2216 2022/11/02 13:46:10 - mmengine - INFO - Epoch(train) [153][35/63] lr: 1.9146e-03 eta: 10:56:51 time: 0.5408 data_time: 0.0159 memory: 14901 loss: 2.2869 loss_prob: 1.3987 loss_thr: 0.6629 loss_db: 0.2253 2022/11/02 13:46:12 - mmengine - INFO - Epoch(train) [153][40/63] lr: 1.9146e-03 eta: 10:56:36 time: 0.4748 data_time: 0.0123 memory: 14901 loss: 2.3292 loss_prob: 1.4110 loss_thr: 0.6910 loss_db: 0.2272 2022/11/02 13:46:15 - mmengine - INFO - Epoch(train) [153][45/63] lr: 1.9146e-03 eta: 10:56:36 time: 0.4610 data_time: 0.0063 memory: 14901 loss: 2.1374 loss_prob: 1.2619 loss_thr: 0.6694 loss_db: 0.2060 2022/11/02 13:46:17 - mmengine - INFO - Epoch(train) [153][50/63] lr: 1.9146e-03 eta: 10:56:24 time: 0.5064 data_time: 0.0127 memory: 14901 loss: 2.0012 loss_prob: 1.1688 loss_thr: 0.6373 loss_db: 0.1951 2022/11/02 13:46:20 - mmengine - INFO - Epoch(train) [153][55/63] lr: 1.9146e-03 eta: 10:56:24 time: 0.5327 data_time: 0.0288 memory: 14901 loss: 2.0059 loss_prob: 1.1798 loss_thr: 0.6306 loss_db: 0.1955 2022/11/02 13:46:22 - mmengine - INFO - Epoch(train) [153][60/63] lr: 1.9146e-03 eta: 10:56:12 time: 0.5071 data_time: 0.0217 memory: 14901 loss: 2.0839 loss_prob: 1.2384 loss_thr: 0.6442 loss_db: 0.2013 2022/11/02 13:46:24 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:46:29 - mmengine - INFO - Epoch(train) [154][5/63] lr: 1.9130e-03 eta: 10:56:12 time: 0.7230 data_time: 0.1968 memory: 14901 loss: 2.2539 loss_prob: 1.3769 loss_thr: 0.6524 loss_db: 0.2245 2022/11/02 13:46:31 - mmengine - INFO - Epoch(train) [154][10/63] lr: 1.9130e-03 eta: 10:56:02 time: 0.7363 data_time: 0.1951 memory: 14901 loss: 2.1424 loss_prob: 1.2941 loss_thr: 0.6360 loss_db: 0.2123 2022/11/02 13:46:34 - mmengine - INFO - Epoch(train) [154][15/63] lr: 1.9130e-03 eta: 10:56:02 time: 0.5171 data_time: 0.0082 memory: 14901 loss: 2.1430 loss_prob: 1.2856 loss_thr: 0.6483 loss_db: 0.2092 2022/11/02 13:46:36 - mmengine - INFO - Epoch(train) [154][20/63] lr: 1.9130e-03 eta: 10:55:51 time: 0.5306 data_time: 0.0107 memory: 14901 loss: 2.1902 loss_prob: 1.3225 loss_thr: 0.6542 loss_db: 0.2135 2022/11/02 13:46:39 - mmengine - INFO - Epoch(train) [154][25/63] lr: 1.9130e-03 eta: 10:55:51 time: 0.5184 data_time: 0.0175 memory: 14901 loss: 2.0595 loss_prob: 1.2076 loss_thr: 0.6539 loss_db: 0.1979 2022/11/02 13:46:42 - mmengine - INFO - Epoch(train) [154][30/63] lr: 1.9130e-03 eta: 10:55:43 time: 0.5605 data_time: 0.0301 memory: 14901 loss: 2.0397 loss_prob: 1.1843 loss_thr: 0.6612 loss_db: 0.1941 2022/11/02 13:46:44 - mmengine - INFO - Epoch(train) [154][35/63] lr: 1.9130e-03 eta: 10:55:43 time: 0.5290 data_time: 0.0242 memory: 14901 loss: 1.9942 loss_prob: 1.1656 loss_thr: 0.6377 loss_db: 0.1908 2022/11/02 13:46:47 - mmengine - INFO - Epoch(train) [154][40/63] lr: 1.9130e-03 eta: 10:55:29 time: 0.4796 data_time: 0.0091 memory: 14901 loss: 2.0269 loss_prob: 1.1921 loss_thr: 0.6379 loss_db: 0.1968 2022/11/02 13:46:49 - mmengine - INFO - Epoch(train) [154][45/63] lr: 1.9130e-03 eta: 10:55:29 time: 0.5021 data_time: 0.0063 memory: 14901 loss: 2.0265 loss_prob: 1.1940 loss_thr: 0.6353 loss_db: 0.1972 2022/11/02 13:46:52 - mmengine - INFO - Epoch(train) [154][50/63] lr: 1.9130e-03 eta: 10:55:20 time: 0.5535 data_time: 0.0139 memory: 14901 loss: 2.1415 loss_prob: 1.2865 loss_thr: 0.6451 loss_db: 0.2099 2022/11/02 13:46:55 - mmengine - INFO - Epoch(train) [154][55/63] lr: 1.9130e-03 eta: 10:55:20 time: 0.5889 data_time: 0.0229 memory: 14901 loss: 2.1527 loss_prob: 1.2839 loss_thr: 0.6598 loss_db: 0.2090 2022/11/02 13:46:58 - mmengine - INFO - Epoch(train) [154][60/63] lr: 1.9130e-03 eta: 10:55:13 time: 0.5876 data_time: 0.0163 memory: 14901 loss: 2.0116 loss_prob: 1.1825 loss_thr: 0.6373 loss_db: 0.1918 2022/11/02 13:47:00 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:47:07 - mmengine - INFO - Epoch(train) [155][5/63] lr: 1.9113e-03 eta: 10:55:13 time: 0.9843 data_time: 0.2498 memory: 14901 loss: 2.5393 loss_prob: 1.5811 loss_thr: 0.6972 loss_db: 0.2610 2022/11/02 13:47:09 - mmengine - INFO - Epoch(train) [155][10/63] lr: 1.9113e-03 eta: 10:55:17 time: 0.9500 data_time: 0.2478 memory: 14901 loss: 2.1454 loss_prob: 1.2830 loss_thr: 0.6458 loss_db: 0.2166 2022/11/02 13:47:12 - mmengine - INFO - Epoch(train) [155][15/63] lr: 1.9113e-03 eta: 10:55:17 time: 0.5261 data_time: 0.0044 memory: 14901 loss: 2.0686 loss_prob: 1.2309 loss_thr: 0.6380 loss_db: 0.1997 2022/11/02 13:47:14 - mmengine - INFO - Epoch(train) [155][20/63] lr: 1.9113e-03 eta: 10:55:06 time: 0.5207 data_time: 0.0048 memory: 14901 loss: 2.0419 loss_prob: 1.2118 loss_thr: 0.6371 loss_db: 0.1930 2022/11/02 13:47:17 - mmengine - INFO - Epoch(train) [155][25/63] lr: 1.9113e-03 eta: 10:55:06 time: 0.5544 data_time: 0.0289 memory: 14901 loss: 2.2008 loss_prob: 1.3147 loss_thr: 0.6724 loss_db: 0.2138 2022/11/02 13:47:21 - mmengine - INFO - Epoch(train) [155][30/63] lr: 1.9113e-03 eta: 10:55:01 time: 0.6046 data_time: 0.0432 memory: 14901 loss: 2.1998 loss_prob: 1.3049 loss_thr: 0.6801 loss_db: 0.2148 2022/11/02 13:47:24 - mmengine - INFO - Epoch(train) [155][35/63] lr: 1.9113e-03 eta: 10:55:01 time: 0.6808 data_time: 0.0192 memory: 14901 loss: 2.1364 loss_prob: 1.2631 loss_thr: 0.6646 loss_db: 0.2087 2022/11/02 13:47:27 - mmengine - INFO - Epoch(train) [155][40/63] lr: 1.9113e-03 eta: 10:54:58 time: 0.6438 data_time: 0.0044 memory: 14901 loss: 2.0697 loss_prob: 1.2346 loss_thr: 0.6309 loss_db: 0.2042 2022/11/02 13:47:30 - mmengine - INFO - Epoch(train) [155][45/63] lr: 1.9113e-03 eta: 10:54:58 time: 0.6101 data_time: 0.0050 memory: 14901 loss: 2.0600 loss_prob: 1.2314 loss_thr: 0.6302 loss_db: 0.1984 2022/11/02 13:47:34 - mmengine - INFO - Epoch(train) [155][50/63] lr: 1.9113e-03 eta: 10:54:56 time: 0.6598 data_time: 0.0152 memory: 14901 loss: 2.1499 loss_prob: 1.2908 loss_thr: 0.6507 loss_db: 0.2084 2022/11/02 13:47:37 - mmengine - INFO - Epoch(train) [155][55/63] lr: 1.9113e-03 eta: 10:54:56 time: 0.6709 data_time: 0.0220 memory: 14901 loss: 2.0770 loss_prob: 1.2347 loss_thr: 0.6403 loss_db: 0.2020 2022/11/02 13:47:40 - mmengine - INFO - Epoch(train) [155][60/63] lr: 1.9113e-03 eta: 10:54:53 time: 0.6347 data_time: 0.0117 memory: 14901 loss: 1.9611 loss_prob: 1.1403 loss_thr: 0.6355 loss_db: 0.1852 2022/11/02 13:47:41 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:47:46 - mmengine - INFO - Epoch(train) [156][5/63] lr: 1.9097e-03 eta: 10:54:53 time: 0.7359 data_time: 0.2048 memory: 14901 loss: 2.3773 loss_prob: 1.4397 loss_thr: 0.7019 loss_db: 0.2357 2022/11/02 13:47:49 - mmengine - INFO - Epoch(train) [156][10/63] lr: 1.9097e-03 eta: 10:54:45 time: 0.7728 data_time: 0.2072 memory: 14901 loss: 2.1244 loss_prob: 1.2437 loss_thr: 0.6769 loss_db: 0.2038 2022/11/02 13:47:52 - mmengine - INFO - Epoch(train) [156][15/63] lr: 1.9097e-03 eta: 10:54:45 time: 0.5557 data_time: 0.0078 memory: 14901 loss: 2.0407 loss_prob: 1.2093 loss_thr: 0.6338 loss_db: 0.1977 2022/11/02 13:47:54 - mmengine - INFO - Epoch(train) [156][20/63] lr: 1.9097e-03 eta: 10:54:35 time: 0.5410 data_time: 0.0083 memory: 14901 loss: 1.9655 loss_prob: 1.1661 loss_thr: 0.6128 loss_db: 0.1866 2022/11/02 13:47:57 - mmengine - INFO - Epoch(train) [156][25/63] lr: 1.9097e-03 eta: 10:54:35 time: 0.4878 data_time: 0.0173 memory: 14901 loss: 2.0530 loss_prob: 1.2049 loss_thr: 0.6544 loss_db: 0.1937 2022/11/02 13:47:59 - mmengine - INFO - Epoch(train) [156][30/63] lr: 1.9097e-03 eta: 10:54:22 time: 0.4908 data_time: 0.0293 memory: 14901 loss: 2.1411 loss_prob: 1.2548 loss_thr: 0.6763 loss_db: 0.2099 2022/11/02 13:48:02 - mmengine - INFO - Epoch(train) [156][35/63] lr: 1.9097e-03 eta: 10:54:22 time: 0.5068 data_time: 0.0225 memory: 14901 loss: 2.0969 loss_prob: 1.2307 loss_thr: 0.6643 loss_db: 0.2019 2022/11/02 13:48:05 - mmengine - INFO - Epoch(train) [156][40/63] lr: 1.9097e-03 eta: 10:54:12 time: 0.5382 data_time: 0.0109 memory: 14901 loss: 1.9975 loss_prob: 1.1510 loss_thr: 0.6588 loss_db: 0.1877 2022/11/02 13:48:07 - mmengine - INFO - Epoch(train) [156][45/63] lr: 1.9097e-03 eta: 10:54:12 time: 0.5226 data_time: 0.0077 memory: 14901 loss: 2.0909 loss_prob: 1.2114 loss_thr: 0.6764 loss_db: 0.2031 2022/11/02 13:48:10 - mmengine - INFO - Epoch(train) [156][50/63] lr: 1.9097e-03 eta: 10:54:01 time: 0.5133 data_time: 0.0143 memory: 14901 loss: 2.0978 loss_prob: 1.2308 loss_thr: 0.6621 loss_db: 0.2049 2022/11/02 13:48:12 - mmengine - INFO - Epoch(train) [156][55/63] lr: 1.9097e-03 eta: 10:54:01 time: 0.5185 data_time: 0.0197 memory: 14901 loss: 2.0862 loss_prob: 1.2361 loss_thr: 0.6532 loss_db: 0.1969 2022/11/02 13:48:15 - mmengine - INFO - Epoch(train) [156][60/63] lr: 1.9097e-03 eta: 10:53:48 time: 0.4989 data_time: 0.0123 memory: 14901 loss: 2.3807 loss_prob: 1.4522 loss_thr: 0.6968 loss_db: 0.2317 2022/11/02 13:48:16 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:48:21 - mmengine - INFO - Epoch(train) [157][5/63] lr: 1.9080e-03 eta: 10:53:48 time: 0.7486 data_time: 0.2821 memory: 14901 loss: 2.3360 loss_prob: 1.4276 loss_thr: 0.6712 loss_db: 0.2372 2022/11/02 13:48:24 - mmengine - INFO - Epoch(train) [157][10/63] lr: 1.9080e-03 eta: 10:53:40 time: 0.7613 data_time: 0.2812 memory: 14901 loss: 2.1483 loss_prob: 1.3121 loss_thr: 0.6260 loss_db: 0.2102 2022/11/02 13:48:26 - mmengine - INFO - Epoch(train) [157][15/63] lr: 1.9080e-03 eta: 10:53:40 time: 0.4894 data_time: 0.0052 memory: 14901 loss: 2.1318 loss_prob: 1.2711 loss_thr: 0.6562 loss_db: 0.2045 2022/11/02 13:48:29 - mmengine - INFO - Epoch(train) [157][20/63] lr: 1.9080e-03 eta: 10:53:28 time: 0.5053 data_time: 0.0079 memory: 14901 loss: 2.0586 loss_prob: 1.1901 loss_thr: 0.6724 loss_db: 0.1962 2022/11/02 13:48:31 - mmengine - INFO - Epoch(train) [157][25/63] lr: 1.9080e-03 eta: 10:53:28 time: 0.5268 data_time: 0.0304 memory: 14901 loss: 2.2408 loss_prob: 1.3393 loss_thr: 0.6704 loss_db: 0.2310 2022/11/02 13:48:34 - mmengine - INFO - Epoch(train) [157][30/63] lr: 1.9080e-03 eta: 10:53:17 time: 0.5217 data_time: 0.0270 memory: 14901 loss: 2.4434 loss_prob: 1.4940 loss_thr: 0.6896 loss_db: 0.2598 2022/11/02 13:48:36 - mmengine - INFO - Epoch(train) [157][35/63] lr: 1.9080e-03 eta: 10:53:17 time: 0.5071 data_time: 0.0051 memory: 14901 loss: 2.2434 loss_prob: 1.3506 loss_thr: 0.6674 loss_db: 0.2254 2022/11/02 13:48:39 - mmengine - INFO - Epoch(train) [157][40/63] lr: 1.9080e-03 eta: 10:53:04 time: 0.4955 data_time: 0.0058 memory: 14901 loss: 2.1027 loss_prob: 1.2397 loss_thr: 0.6599 loss_db: 0.2032 2022/11/02 13:48:41 - mmengine - INFO - Epoch(train) [157][45/63] lr: 1.9080e-03 eta: 10:53:04 time: 0.4831 data_time: 0.0046 memory: 14901 loss: 2.2000 loss_prob: 1.3164 loss_thr: 0.6678 loss_db: 0.2158 2022/11/02 13:48:44 - mmengine - INFO - Epoch(train) [157][50/63] lr: 1.9080e-03 eta: 10:52:51 time: 0.4893 data_time: 0.0212 memory: 14901 loss: 2.2611 loss_prob: 1.3715 loss_thr: 0.6618 loss_db: 0.2278 2022/11/02 13:48:46 - mmengine - INFO - Epoch(train) [157][55/63] lr: 1.9080e-03 eta: 10:52:51 time: 0.4897 data_time: 0.0226 memory: 14901 loss: 2.1873 loss_prob: 1.3094 loss_thr: 0.6570 loss_db: 0.2209 2022/11/02 13:48:49 - mmengine - INFO - Epoch(train) [157][60/63] lr: 1.9080e-03 eta: 10:52:38 time: 0.4907 data_time: 0.0056 memory: 14901 loss: 2.0768 loss_prob: 1.2280 loss_thr: 0.6458 loss_db: 0.2030 2022/11/02 13:48:50 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:48:55 - mmengine - INFO - Epoch(train) [158][5/63] lr: 1.9064e-03 eta: 10:52:38 time: 0.6928 data_time: 0.2176 memory: 14901 loss: 2.2341 loss_prob: 1.3465 loss_thr: 0.6684 loss_db: 0.2193 2022/11/02 13:48:57 - mmengine - INFO - Epoch(train) [158][10/63] lr: 1.9064e-03 eta: 10:52:26 time: 0.7098 data_time: 0.2174 memory: 14901 loss: 2.2260 loss_prob: 1.3448 loss_thr: 0.6623 loss_db: 0.2188 2022/11/02 13:48:59 - mmengine - INFO - Epoch(train) [158][15/63] lr: 1.9064e-03 eta: 10:52:26 time: 0.4564 data_time: 0.0042 memory: 14901 loss: 2.0584 loss_prob: 1.2132 loss_thr: 0.6455 loss_db: 0.1997 2022/11/02 13:49:02 - mmengine - INFO - Epoch(train) [158][20/63] lr: 1.9064e-03 eta: 10:52:11 time: 0.4689 data_time: 0.0041 memory: 14901 loss: 2.0524 loss_prob: 1.2054 loss_thr: 0.6461 loss_db: 0.2009 2022/11/02 13:49:04 - mmengine - INFO - Epoch(train) [158][25/63] lr: 1.9064e-03 eta: 10:52:11 time: 0.4977 data_time: 0.0209 memory: 14901 loss: 2.0902 loss_prob: 1.2301 loss_thr: 0.6562 loss_db: 0.2039 2022/11/02 13:49:07 - mmengine - INFO - Epoch(train) [158][30/63] lr: 1.9064e-03 eta: 10:51:59 time: 0.4952 data_time: 0.0310 memory: 14901 loss: 2.0792 loss_prob: 1.2267 loss_thr: 0.6499 loss_db: 0.2026 2022/11/02 13:49:09 - mmengine - INFO - Epoch(train) [158][35/63] lr: 1.9064e-03 eta: 10:51:59 time: 0.4953 data_time: 0.0150 memory: 14901 loss: 2.2790 loss_prob: 1.3816 loss_thr: 0.6711 loss_db: 0.2263 2022/11/02 13:49:12 - mmengine - INFO - Epoch(train) [158][40/63] lr: 1.9064e-03 eta: 10:51:48 time: 0.5197 data_time: 0.0054 memory: 14901 loss: 2.3036 loss_prob: 1.4084 loss_thr: 0.6646 loss_db: 0.2306 2022/11/02 13:49:14 - mmengine - INFO - Epoch(train) [158][45/63] lr: 1.9064e-03 eta: 10:51:48 time: 0.4953 data_time: 0.0074 memory: 14901 loss: 2.0656 loss_prob: 1.2277 loss_thr: 0.6380 loss_db: 0.1999 2022/11/02 13:49:17 - mmengine - INFO - Epoch(train) [158][50/63] lr: 1.9064e-03 eta: 10:51:35 time: 0.4888 data_time: 0.0216 memory: 14901 loss: 2.1950 loss_prob: 1.3049 loss_thr: 0.6752 loss_db: 0.2149 2022/11/02 13:49:19 - mmengine - INFO - Epoch(train) [158][55/63] lr: 1.9064e-03 eta: 10:51:35 time: 0.5294 data_time: 0.0263 memory: 14901 loss: 2.5972 loss_prob: 1.6279 loss_thr: 0.7040 loss_db: 0.2653 2022/11/02 13:49:22 - mmengine - INFO - Epoch(train) [158][60/63] lr: 1.9064e-03 eta: 10:51:22 time: 0.4856 data_time: 0.0119 memory: 14901 loss: 2.4970 loss_prob: 1.5709 loss_thr: 0.6767 loss_db: 0.2494 2022/11/02 13:49:23 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:49:29 - mmengine - INFO - Epoch(train) [159][5/63] lr: 1.9048e-03 eta: 10:51:22 time: 0.8282 data_time: 0.2343 memory: 14901 loss: 2.3623 loss_prob: 1.4372 loss_thr: 0.6942 loss_db: 0.2309 2022/11/02 13:49:33 - mmengine - INFO - Epoch(train) [159][10/63] lr: 1.9048e-03 eta: 10:51:26 time: 0.9589 data_time: 0.2380 memory: 14901 loss: 2.2905 loss_prob: 1.3984 loss_thr: 0.6671 loss_db: 0.2249 2022/11/02 13:49:35 - mmengine - INFO - Epoch(train) [159][15/63] lr: 1.9048e-03 eta: 10:51:26 time: 0.6055 data_time: 0.0079 memory: 14901 loss: 2.1675 loss_prob: 1.3035 loss_thr: 0.6473 loss_db: 0.2167 2022/11/02 13:49:39 - mmengine - INFO - Epoch(train) [159][20/63] lr: 1.9048e-03 eta: 10:51:21 time: 0.6010 data_time: 0.0042 memory: 14901 loss: 2.2648 loss_prob: 1.3634 loss_thr: 0.6737 loss_db: 0.2277 2022/11/02 13:49:41 - mmengine - INFO - Epoch(train) [159][25/63] lr: 1.9048e-03 eta: 10:51:21 time: 0.5831 data_time: 0.0173 memory: 14901 loss: 2.1269 loss_prob: 1.2695 loss_thr: 0.6533 loss_db: 0.2041 2022/11/02 13:49:44 - mmengine - INFO - Epoch(train) [159][30/63] lr: 1.9048e-03 eta: 10:51:08 time: 0.5027 data_time: 0.0334 memory: 14901 loss: 2.0525 loss_prob: 1.2020 loss_thr: 0.6562 loss_db: 0.1944 2022/11/02 13:49:47 - mmengine - INFO - Epoch(train) [159][35/63] lr: 1.9048e-03 eta: 10:51:08 time: 0.6001 data_time: 0.0202 memory: 14901 loss: 1.9669 loss_prob: 1.1267 loss_thr: 0.6531 loss_db: 0.1871 2022/11/02 13:49:49 - mmengine - INFO - Epoch(train) [159][40/63] lr: 1.9048e-03 eta: 10:51:02 time: 0.5858 data_time: 0.0047 memory: 14901 loss: 1.9014 loss_prob: 1.0989 loss_thr: 0.6232 loss_db: 0.1793 2022/11/02 13:49:53 - mmengine - INFO - Epoch(train) [159][45/63] lr: 1.9048e-03 eta: 10:51:02 time: 0.6176 data_time: 0.0055 memory: 14901 loss: 2.0631 loss_prob: 1.2218 loss_thr: 0.6433 loss_db: 0.1981 2022/11/02 13:49:54 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:49:56 - mmengine - INFO - Epoch(train) [159][50/63] lr: 1.9048e-03 eta: 10:51:01 time: 0.6778 data_time: 0.0313 memory: 14901 loss: 2.2949 loss_prob: 1.3887 loss_thr: 0.6774 loss_db: 0.2288 2022/11/02 13:49:59 - mmengine - INFO - Epoch(train) [159][55/63] lr: 1.9048e-03 eta: 10:51:01 time: 0.6059 data_time: 0.0317 memory: 14901 loss: 2.2824 loss_prob: 1.3862 loss_thr: 0.6686 loss_db: 0.2277 2022/11/02 13:50:02 - mmengine - INFO - Epoch(train) [159][60/63] lr: 1.9048e-03 eta: 10:50:52 time: 0.5507 data_time: 0.0062 memory: 14901 loss: 2.0958 loss_prob: 1.2569 loss_thr: 0.6341 loss_db: 0.2048 2022/11/02 13:50:03 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:50:08 - mmengine - INFO - Epoch(train) [160][5/63] lr: 1.9031e-03 eta: 10:50:52 time: 0.7393 data_time: 0.2394 memory: 14901 loss: 2.1852 loss_prob: 1.3238 loss_thr: 0.6454 loss_db: 0.2161 2022/11/02 13:50:11 - mmengine - INFO - Epoch(train) [160][10/63] lr: 1.9031e-03 eta: 10:50:46 time: 0.8016 data_time: 0.2361 memory: 14901 loss: 2.2059 loss_prob: 1.3405 loss_thr: 0.6514 loss_db: 0.2140 2022/11/02 13:50:14 - mmengine - INFO - Epoch(train) [160][15/63] lr: 1.9031e-03 eta: 10:50:46 time: 0.6032 data_time: 0.0053 memory: 14901 loss: 2.1831 loss_prob: 1.3149 loss_thr: 0.6511 loss_db: 0.2171 2022/11/02 13:50:17 - mmengine - INFO - Epoch(train) [160][20/63] lr: 1.9031e-03 eta: 10:50:39 time: 0.5656 data_time: 0.0060 memory: 14901 loss: 2.4485 loss_prob: 1.5213 loss_thr: 0.6843 loss_db: 0.2429 2022/11/02 13:50:19 - mmengine - INFO - Epoch(train) [160][25/63] lr: 1.9031e-03 eta: 10:50:39 time: 0.5207 data_time: 0.0186 memory: 14901 loss: 2.3799 loss_prob: 1.4549 loss_thr: 0.6948 loss_db: 0.2301 2022/11/02 13:50:24 - mmengine - INFO - Epoch(train) [160][30/63] lr: 1.9031e-03 eta: 10:50:42 time: 0.7407 data_time: 0.0321 memory: 14901 loss: 2.1821 loss_prob: 1.2990 loss_thr: 0.6684 loss_db: 0.2147 2022/11/02 13:50:27 - mmengine - INFO - Epoch(train) [160][35/63] lr: 1.9031e-03 eta: 10:50:42 time: 0.7574 data_time: 0.0186 memory: 14901 loss: 2.1191 loss_prob: 1.2630 loss_thr: 0.6473 loss_db: 0.2088 2022/11/02 13:50:29 - mmengine - INFO - Epoch(train) [160][40/63] lr: 1.9031e-03 eta: 10:50:30 time: 0.5035 data_time: 0.0041 memory: 14901 loss: 2.0242 loss_prob: 1.1915 loss_thr: 0.6362 loss_db: 0.1965 2022/11/02 13:50:32 - mmengine - INFO - Epoch(train) [160][45/63] lr: 1.9031e-03 eta: 10:50:30 time: 0.4766 data_time: 0.0041 memory: 14901 loss: 2.1285 loss_prob: 1.2678 loss_thr: 0.6536 loss_db: 0.2071 2022/11/02 13:50:34 - mmengine - INFO - Epoch(train) [160][50/63] lr: 1.9031e-03 eta: 10:50:18 time: 0.5080 data_time: 0.0168 memory: 14901 loss: 2.1882 loss_prob: 1.3228 loss_thr: 0.6534 loss_db: 0.2119 2022/11/02 13:50:37 - mmengine - INFO - Epoch(train) [160][55/63] lr: 1.9031e-03 eta: 10:50:18 time: 0.5024 data_time: 0.0253 memory: 14901 loss: 2.1808 loss_prob: 1.3250 loss_thr: 0.6460 loss_db: 0.2097 2022/11/02 13:50:39 - mmengine - INFO - Epoch(train) [160][60/63] lr: 1.9031e-03 eta: 10:50:05 time: 0.4848 data_time: 0.0130 memory: 14901 loss: 2.0668 loss_prob: 1.2340 loss_thr: 0.6343 loss_db: 0.1985 2022/11/02 13:50:40 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:50:40 - mmengine - INFO - Saving checkpoint at 160 epochs 2022/11/02 13:50:44 - mmengine - INFO - Epoch(val) [160][5/500] eta: 10:50:05 time: 0.0453 data_time: 0.0049 memory: 14901 2022/11/02 13:50:44 - mmengine - INFO - Epoch(val) [160][10/500] eta: 0:00:22 time: 0.0457 data_time: 0.0046 memory: 1008 2022/11/02 13:50:44 - mmengine - INFO - Epoch(val) [160][15/500] eta: 0:00:22 time: 0.0373 data_time: 0.0020 memory: 1008 2022/11/02 13:50:44 - mmengine - INFO - Epoch(val) [160][20/500] eta: 0:00:18 time: 0.0388 data_time: 0.0027 memory: 1008 2022/11/02 13:50:45 - mmengine - INFO - Epoch(val) [160][25/500] eta: 0:00:18 time: 0.0375 data_time: 0.0028 memory: 1008 2022/11/02 13:50:45 - mmengine - INFO - Epoch(val) [160][30/500] eta: 0:00:19 time: 0.0412 data_time: 0.0026 memory: 1008 2022/11/02 13:50:45 - mmengine - INFO - Epoch(val) [160][35/500] eta: 0:00:19 time: 0.0434 data_time: 0.0028 memory: 1008 2022/11/02 13:50:45 - mmengine - INFO - Epoch(val) [160][40/500] eta: 0:00:20 time: 0.0446 data_time: 0.0029 memory: 1008 2022/11/02 13:50:46 - mmengine - INFO - Epoch(val) [160][45/500] eta: 0:00:20 time: 0.0445 data_time: 0.0028 memory: 1008 2022/11/02 13:50:46 - mmengine - INFO - Epoch(val) [160][50/500] eta: 0:00:18 time: 0.0412 data_time: 0.0030 memory: 1008 2022/11/02 13:50:46 - mmengine - INFO - Epoch(val) [160][55/500] eta: 0:00:18 time: 0.0432 data_time: 0.0025 memory: 1008 2022/11/02 13:50:46 - mmengine - INFO - Epoch(val) [160][60/500] eta: 0:00:18 time: 0.0417 data_time: 0.0023 memory: 1008 2022/11/02 13:50:46 - mmengine - INFO - Epoch(val) [160][65/500] eta: 0:00:18 time: 0.0411 data_time: 0.0026 memory: 1008 2022/11/02 13:50:47 - mmengine - INFO - Epoch(val) [160][70/500] eta: 0:00:17 time: 0.0417 data_time: 0.0024 memory: 1008 2022/11/02 13:50:47 - mmengine - INFO - Epoch(val) [160][75/500] eta: 0:00:17 time: 0.0424 data_time: 0.0028 memory: 1008 2022/11/02 13:50:47 - mmengine - INFO - Epoch(val) [160][80/500] eta: 0:00:16 time: 0.0394 data_time: 0.0028 memory: 1008 2022/11/02 13:50:47 - mmengine - INFO - Epoch(val) [160][85/500] eta: 0:00:16 time: 0.0363 data_time: 0.0024 memory: 1008 2022/11/02 13:50:47 - mmengine - INFO - Epoch(val) [160][90/500] eta: 0:00:16 time: 0.0407 data_time: 0.0026 memory: 1008 2022/11/02 13:50:48 - mmengine - INFO - Epoch(val) [160][95/500] eta: 0:00:16 time: 0.0425 data_time: 0.0026 memory: 1008 2022/11/02 13:50:48 - mmengine - INFO - Epoch(val) [160][100/500] eta: 0:00:15 time: 0.0392 data_time: 0.0029 memory: 1008 2022/11/02 13:50:48 - mmengine - INFO - Epoch(val) [160][105/500] eta: 0:00:15 time: 0.0369 data_time: 0.0027 memory: 1008 2022/11/02 13:50:48 - mmengine - INFO - Epoch(val) [160][110/500] eta: 0:00:14 time: 0.0363 data_time: 0.0022 memory: 1008 2022/11/02 13:50:48 - mmengine - INFO - Epoch(val) [160][115/500] eta: 0:00:14 time: 0.0402 data_time: 0.0025 memory: 1008 2022/11/02 13:50:49 - mmengine - INFO - Epoch(val) [160][120/500] eta: 0:00:15 time: 0.0416 data_time: 0.0025 memory: 1008 2022/11/02 13:50:49 - mmengine - INFO - Epoch(val) [160][125/500] eta: 0:00:15 time: 0.0385 data_time: 0.0024 memory: 1008 2022/11/02 13:50:49 - mmengine - INFO - Epoch(val) [160][130/500] eta: 0:00:13 time: 0.0367 data_time: 0.0024 memory: 1008 2022/11/02 13:50:49 - mmengine - INFO - Epoch(val) [160][135/500] eta: 0:00:13 time: 0.0364 data_time: 0.0024 memory: 1008 2022/11/02 13:50:49 - mmengine - INFO - Epoch(val) [160][140/500] eta: 0:00:13 time: 0.0381 data_time: 0.0024 memory: 1008 2022/11/02 13:50:50 - mmengine - INFO - Epoch(val) [160][145/500] eta: 0:00:13 time: 0.0451 data_time: 0.0025 memory: 1008 2022/11/02 13:50:50 - mmengine - INFO - Epoch(val) [160][150/500] eta: 0:00:16 time: 0.0463 data_time: 0.0025 memory: 1008 2022/11/02 13:50:50 - mmengine - INFO - Epoch(val) [160][155/500] eta: 0:00:16 time: 0.0441 data_time: 0.0024 memory: 1008 2022/11/02 13:50:50 - mmengine - INFO - Epoch(val) [160][160/500] eta: 0:00:14 time: 0.0438 data_time: 0.0024 memory: 1008 2022/11/02 13:50:50 - mmengine - INFO - Epoch(val) [160][165/500] eta: 0:00:14 time: 0.0422 data_time: 0.0025 memory: 1008 2022/11/02 13:50:51 - mmengine - INFO - Epoch(val) [160][170/500] eta: 0:00:14 time: 0.0432 data_time: 0.0028 memory: 1008 2022/11/02 13:50:51 - mmengine - INFO - Epoch(val) [160][175/500] eta: 0:00:14 time: 0.0419 data_time: 0.0041 memory: 1008 2022/11/02 13:50:51 - mmengine - INFO - Epoch(val) [160][180/500] eta: 0:00:12 time: 0.0391 data_time: 0.0036 memory: 1008 2022/11/02 13:50:51 - mmengine - INFO - Epoch(val) [160][185/500] eta: 0:00:12 time: 0.0424 data_time: 0.0023 memory: 1008 2022/11/02 13:50:51 - mmengine - INFO - Epoch(val) [160][190/500] eta: 0:00:14 time: 0.0459 data_time: 0.0026 memory: 1008 2022/11/02 13:50:52 - mmengine - INFO - Epoch(val) [160][195/500] eta: 0:00:14 time: 0.0414 data_time: 0.0025 memory: 1008 2022/11/02 13:50:52 - mmengine - INFO - Epoch(val) [160][200/500] eta: 0:00:13 time: 0.0458 data_time: 0.0025 memory: 1008 2022/11/02 13:50:52 - mmengine - INFO - Epoch(val) [160][205/500] eta: 0:00:13 time: 0.0450 data_time: 0.0025 memory: 1008 2022/11/02 13:50:52 - mmengine - INFO - Epoch(val) [160][210/500] eta: 0:00:10 time: 0.0374 data_time: 0.0024 memory: 1008 2022/11/02 13:50:53 - mmengine - INFO - Epoch(val) [160][215/500] eta: 0:00:10 time: 0.0427 data_time: 0.0026 memory: 1008 2022/11/02 13:50:53 - mmengine - INFO - Epoch(val) [160][220/500] eta: 0:00:12 time: 0.0429 data_time: 0.0026 memory: 1008 2022/11/02 13:50:53 - mmengine - INFO - Epoch(val) [160][225/500] eta: 0:00:12 time: 0.0416 data_time: 0.0026 memory: 1008 2022/11/02 13:50:53 - mmengine - INFO - Epoch(val) [160][230/500] eta: 0:00:10 time: 0.0391 data_time: 0.0026 memory: 1008 2022/11/02 13:50:53 - mmengine - INFO - Epoch(val) [160][235/500] eta: 0:00:10 time: 0.0366 data_time: 0.0025 memory: 1008 2022/11/02 13:50:54 - mmengine - INFO - Epoch(val) [160][240/500] eta: 0:00:10 time: 0.0403 data_time: 0.0024 memory: 1008 2022/11/02 13:50:54 - mmengine - INFO - Epoch(val) [160][245/500] eta: 0:00:10 time: 0.0387 data_time: 0.0024 memory: 1008 2022/11/02 13:50:54 - mmengine - INFO - Epoch(val) [160][250/500] eta: 0:00:09 time: 0.0399 data_time: 0.0025 memory: 1008 2022/11/02 13:50:54 - mmengine - INFO - Epoch(val) [160][255/500] eta: 0:00:09 time: 0.0390 data_time: 0.0024 memory: 1008 2022/11/02 13:50:54 - mmengine - INFO - Epoch(val) [160][260/500] eta: 0:00:08 time: 0.0359 data_time: 0.0024 memory: 1008 2022/11/02 13:50:55 - mmengine - INFO - Epoch(val) [160][265/500] eta: 0:00:08 time: 0.0431 data_time: 0.0062 memory: 1008 2022/11/02 13:50:55 - mmengine - INFO - Epoch(val) [160][270/500] eta: 0:00:10 time: 0.0440 data_time: 0.0062 memory: 1008 2022/11/02 13:50:55 - mmengine - INFO - Epoch(val) [160][275/500] eta: 0:00:10 time: 0.0369 data_time: 0.0023 memory: 1008 2022/11/02 13:50:55 - mmengine - INFO - Epoch(val) [160][280/500] eta: 0:00:09 time: 0.0415 data_time: 0.0034 memory: 1008 2022/11/02 13:50:55 - mmengine - INFO - Epoch(val) [160][285/500] eta: 0:00:09 time: 0.0411 data_time: 0.0036 memory: 1008 2022/11/02 13:50:56 - mmengine - INFO - Epoch(val) [160][290/500] eta: 0:00:07 time: 0.0376 data_time: 0.0023 memory: 1008 2022/11/02 13:50:56 - mmengine - INFO - Epoch(val) [160][295/500] eta: 0:00:07 time: 0.0414 data_time: 0.0028 memory: 1008 2022/11/02 13:50:56 - mmengine - INFO - Epoch(val) [160][300/500] eta: 0:00:07 time: 0.0395 data_time: 0.0029 memory: 1008 2022/11/02 13:50:56 - mmengine - INFO - Epoch(val) [160][305/500] eta: 0:00:07 time: 0.0360 data_time: 0.0022 memory: 1008 2022/11/02 13:50:56 - mmengine - INFO - Epoch(val) [160][310/500] eta: 0:00:06 time: 0.0353 data_time: 0.0021 memory: 1008 2022/11/02 13:50:57 - mmengine - INFO - Epoch(val) [160][315/500] eta: 0:00:06 time: 0.0439 data_time: 0.0025 memory: 1008 2022/11/02 13:50:57 - mmengine - INFO - Epoch(val) [160][320/500] eta: 0:00:08 time: 0.0471 data_time: 0.0029 memory: 1008 2022/11/02 13:50:57 - mmengine - INFO - Epoch(val) [160][325/500] eta: 0:00:08 time: 0.0567 data_time: 0.0027 memory: 1008 2022/11/02 13:50:57 - mmengine - INFO - Epoch(val) [160][330/500] eta: 0:00:09 time: 0.0550 data_time: 0.0026 memory: 1008 2022/11/02 13:50:57 - mmengine - INFO - Epoch(val) [160][335/500] eta: 0:00:09 time: 0.0366 data_time: 0.0024 memory: 1008 2022/11/02 13:50:58 - mmengine - INFO - Epoch(val) [160][340/500] eta: 0:00:07 time: 0.0461 data_time: 0.0024 memory: 1008 2022/11/02 13:50:58 - mmengine - INFO - Epoch(val) [160][345/500] eta: 0:00:07 time: 0.0476 data_time: 0.0024 memory: 1008 2022/11/02 13:50:58 - mmengine - INFO - Epoch(val) [160][350/500] eta: 0:00:06 time: 0.0425 data_time: 0.0024 memory: 1008 2022/11/02 13:50:58 - mmengine - INFO - Epoch(val) [160][355/500] eta: 0:00:06 time: 0.0446 data_time: 0.0024 memory: 1008 2022/11/02 13:50:59 - mmengine - INFO - Epoch(val) [160][360/500] eta: 0:00:05 time: 0.0412 data_time: 0.0024 memory: 1008 2022/11/02 13:50:59 - mmengine - INFO - Epoch(val) [160][365/500] eta: 0:00:05 time: 0.0430 data_time: 0.0026 memory: 1008 2022/11/02 13:50:59 - mmengine - INFO - Epoch(val) [160][370/500] eta: 0:00:05 time: 0.0420 data_time: 0.0035 memory: 1008 2022/11/02 13:50:59 - mmengine - INFO - Epoch(val) [160][375/500] eta: 0:00:05 time: 0.0370 data_time: 0.0031 memory: 1008 2022/11/02 13:50:59 - mmengine - INFO - Epoch(val) [160][380/500] eta: 0:00:05 time: 0.0423 data_time: 0.0024 memory: 1008 2022/11/02 13:51:00 - mmengine - INFO - Epoch(val) [160][385/500] eta: 0:00:05 time: 0.0438 data_time: 0.0027 memory: 1008 2022/11/02 13:51:00 - mmengine - INFO - Epoch(val) [160][390/500] eta: 0:00:04 time: 0.0384 data_time: 0.0025 memory: 1008 2022/11/02 13:51:00 - mmengine - INFO - Epoch(val) [160][395/500] eta: 0:00:04 time: 0.0403 data_time: 0.0025 memory: 1008 2022/11/02 13:51:00 - mmengine - INFO - Epoch(val) [160][400/500] eta: 0:00:04 time: 0.0401 data_time: 0.0024 memory: 1008 2022/11/02 13:51:00 - mmengine - INFO - Epoch(val) [160][405/500] eta: 0:00:04 time: 0.0384 data_time: 0.0024 memory: 1008 2022/11/02 13:51:01 - mmengine - INFO - Epoch(val) [160][410/500] eta: 0:00:03 time: 0.0412 data_time: 0.0024 memory: 1008 2022/11/02 13:51:01 - mmengine - INFO - Epoch(val) [160][415/500] eta: 0:00:03 time: 0.0399 data_time: 0.0023 memory: 1008 2022/11/02 13:51:01 - mmengine - INFO - Epoch(val) [160][420/500] eta: 0:00:02 time: 0.0354 data_time: 0.0024 memory: 1008 2022/11/02 13:51:01 - mmengine - INFO - Epoch(val) [160][425/500] eta: 0:00:02 time: 0.0366 data_time: 0.0025 memory: 1008 2022/11/02 13:51:01 - mmengine - INFO - Epoch(val) [160][430/500] eta: 0:00:02 time: 0.0380 data_time: 0.0024 memory: 1008 2022/11/02 13:51:02 - mmengine - INFO - Epoch(val) [160][435/500] eta: 0:00:02 time: 0.0385 data_time: 0.0023 memory: 1008 2022/11/02 13:51:02 - mmengine - INFO - Epoch(val) [160][440/500] eta: 0:00:02 time: 0.0391 data_time: 0.0024 memory: 1008 2022/11/02 13:51:02 - mmengine - INFO - Epoch(val) [160][445/500] eta: 0:00:02 time: 0.0402 data_time: 0.0024 memory: 1008 2022/11/02 13:51:02 - mmengine - INFO - Epoch(val) [160][450/500] eta: 0:00:02 time: 0.0463 data_time: 0.0042 memory: 1008 2022/11/02 13:51:02 - mmengine - INFO - Epoch(val) [160][455/500] eta: 0:00:02 time: 0.0442 data_time: 0.0041 memory: 1008 2022/11/02 13:51:03 - mmengine - INFO - Epoch(val) [160][460/500] eta: 0:00:01 time: 0.0361 data_time: 0.0023 memory: 1008 2022/11/02 13:51:03 - mmengine - INFO - Epoch(val) [160][465/500] eta: 0:00:01 time: 0.0355 data_time: 0.0023 memory: 1008 2022/11/02 13:51:03 - mmengine - INFO - Epoch(val) [160][470/500] eta: 0:00:01 time: 0.0362 data_time: 0.0024 memory: 1008 2022/11/02 13:51:03 - mmengine - INFO - Epoch(val) [160][475/500] eta: 0:00:01 time: 0.0377 data_time: 0.0024 memory: 1008 2022/11/02 13:51:03 - mmengine - INFO - Epoch(val) [160][480/500] eta: 0:00:00 time: 0.0407 data_time: 0.0025 memory: 1008 2022/11/02 13:51:04 - mmengine - INFO - Epoch(val) [160][485/500] eta: 0:00:00 time: 0.0394 data_time: 0.0026 memory: 1008 2022/11/02 13:51:04 - mmengine - INFO - Epoch(val) [160][490/500] eta: 0:00:00 time: 0.0404 data_time: 0.0026 memory: 1008 2022/11/02 13:51:04 - mmengine - INFO - Epoch(val) [160][495/500] eta: 0:00:00 time: 0.0421 data_time: 0.0025 memory: 1008 2022/11/02 13:51:04 - mmengine - INFO - Epoch(val) [160][500/500] eta: 0:00:00 time: 0.0375 data_time: 0.0024 memory: 1008 2022/11/02 13:51:04 - mmengine - INFO - Evaluating hmean-iou... 2022/11/02 13:51:04 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.8098, precision: 0.6474, hmean: 0.7196 2022/11/02 13:51:04 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.8098, precision: 0.7269, hmean: 0.7661 2022/11/02 13:51:04 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.8065, precision: 0.7842, hmean: 0.7952 2022/11/02 13:51:04 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.7853, precision: 0.8429, hmean: 0.8131 2022/11/02 13:51:04 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.6875, precision: 0.9119, hmean: 0.7840 2022/11/02 13:51:04 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.1974, precision: 0.9535, hmean: 0.3271 2022/11/02 13:51:04 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0010, precision: 1.0000, hmean: 0.0019 2022/11/02 13:51:04 - mmengine - INFO - Epoch(val) [160][500/500] icdar/precision: 0.8429 icdar/recall: 0.7853 icdar/hmean: 0.8131 2022/11/02 13:51:09 - mmengine - INFO - Epoch(train) [161][5/63] lr: 1.9015e-03 eta: 0:00:00 time: 0.7435 data_time: 0.2079 memory: 14901 loss: 1.9386 loss_prob: 1.1364 loss_thr: 0.6155 loss_db: 0.1868 2022/11/02 13:51:12 - mmengine - INFO - Epoch(train) [161][10/63] lr: 1.9015e-03 eta: 10:49:56 time: 0.7540 data_time: 0.2086 memory: 14901 loss: 1.8914 loss_prob: 1.0974 loss_thr: 0.6178 loss_db: 0.1762 2022/11/02 13:51:14 - mmengine - INFO - Epoch(train) [161][15/63] lr: 1.9015e-03 eta: 10:49:56 time: 0.4845 data_time: 0.0085 memory: 14901 loss: 1.8821 loss_prob: 1.0929 loss_thr: 0.6103 loss_db: 0.1788 2022/11/02 13:51:17 - mmengine - INFO - Epoch(train) [161][20/63] lr: 1.9015e-03 eta: 10:49:43 time: 0.4813 data_time: 0.0079 memory: 14901 loss: 1.9884 loss_prob: 1.1724 loss_thr: 0.6212 loss_db: 0.1948 2022/11/02 13:51:19 - mmengine - INFO - Epoch(train) [161][25/63] lr: 1.9015e-03 eta: 10:49:43 time: 0.4825 data_time: 0.0148 memory: 14901 loss: 2.0063 loss_prob: 1.1748 loss_thr: 0.6384 loss_db: 0.1931 2022/11/02 13:51:22 - mmengine - INFO - Epoch(train) [161][30/63] lr: 1.9015e-03 eta: 10:49:31 time: 0.5002 data_time: 0.0366 memory: 14901 loss: 1.9663 loss_prob: 1.1467 loss_thr: 0.6295 loss_db: 0.1901 2022/11/02 13:51:24 - mmengine - INFO - Epoch(train) [161][35/63] lr: 1.9015e-03 eta: 10:49:31 time: 0.4980 data_time: 0.0266 memory: 14901 loss: 2.1138 loss_prob: 1.2490 loss_thr: 0.6570 loss_db: 0.2078 2022/11/02 13:51:27 - mmengine - INFO - Epoch(train) [161][40/63] lr: 1.9015e-03 eta: 10:49:18 time: 0.4952 data_time: 0.0042 memory: 14901 loss: 2.2533 loss_prob: 1.3613 loss_thr: 0.6724 loss_db: 0.2195 2022/11/02 13:51:29 - mmengine - INFO - Epoch(train) [161][45/63] lr: 1.9015e-03 eta: 10:49:18 time: 0.4846 data_time: 0.0067 memory: 14901 loss: 2.1098 loss_prob: 1.2687 loss_thr: 0.6355 loss_db: 0.2056 2022/11/02 13:51:31 - mmengine - INFO - Epoch(train) [161][50/63] lr: 1.9015e-03 eta: 10:49:06 time: 0.4884 data_time: 0.0189 memory: 14901 loss: 2.0478 loss_prob: 1.2122 loss_thr: 0.6331 loss_db: 0.2025 2022/11/02 13:51:34 - mmengine - INFO - Epoch(train) [161][55/63] lr: 1.9015e-03 eta: 10:49:06 time: 0.4952 data_time: 0.0262 memory: 14901 loss: 2.2120 loss_prob: 1.3320 loss_thr: 0.6626 loss_db: 0.2174 2022/11/02 13:51:36 - mmengine - INFO - Epoch(train) [161][60/63] lr: 1.9015e-03 eta: 10:48:52 time: 0.4736 data_time: 0.0145 memory: 14901 loss: 2.2532 loss_prob: 1.3528 loss_thr: 0.6847 loss_db: 0.2157 2022/11/02 13:51:38 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:51:42 - mmengine - INFO - Epoch(train) [162][5/63] lr: 1.8998e-03 eta: 10:48:52 time: 0.6803 data_time: 0.2112 memory: 14901 loss: 2.1123 loss_prob: 1.2569 loss_thr: 0.6476 loss_db: 0.2078 2022/11/02 13:51:45 - mmengine - INFO - Epoch(train) [162][10/63] lr: 1.8998e-03 eta: 10:48:40 time: 0.7082 data_time: 0.2138 memory: 14901 loss: 2.1167 loss_prob: 1.2393 loss_thr: 0.6731 loss_db: 0.2042 2022/11/02 13:51:47 - mmengine - INFO - Epoch(train) [162][15/63] lr: 1.8998e-03 eta: 10:48:40 time: 0.5321 data_time: 0.0112 memory: 14901 loss: 1.9923 loss_prob: 1.1618 loss_thr: 0.6412 loss_db: 0.1893 2022/11/02 13:51:50 - mmengine - INFO - Epoch(train) [162][20/63] lr: 1.8998e-03 eta: 10:48:28 time: 0.4974 data_time: 0.0087 memory: 14901 loss: 1.9423 loss_prob: 1.1374 loss_thr: 0.6194 loss_db: 0.1855 2022/11/02 13:51:52 - mmengine - INFO - Epoch(train) [162][25/63] lr: 1.8998e-03 eta: 10:48:28 time: 0.5143 data_time: 0.0370 memory: 14901 loss: 2.0694 loss_prob: 1.2343 loss_thr: 0.6294 loss_db: 0.2056 2022/11/02 13:51:55 - mmengine - INFO - Epoch(train) [162][30/63] lr: 1.8998e-03 eta: 10:48:17 time: 0.5178 data_time: 0.0378 memory: 14901 loss: 2.2766 loss_prob: 1.3917 loss_thr: 0.6533 loss_db: 0.2317 2022/11/02 13:51:57 - mmengine - INFO - Epoch(train) [162][35/63] lr: 1.8998e-03 eta: 10:48:17 time: 0.4594 data_time: 0.0061 memory: 14901 loss: 2.1350 loss_prob: 1.2730 loss_thr: 0.6560 loss_db: 0.2060 2022/11/02 13:51:59 - mmengine - INFO - Epoch(train) [162][40/63] lr: 1.8998e-03 eta: 10:48:03 time: 0.4717 data_time: 0.0072 memory: 14901 loss: 1.9422 loss_prob: 1.1198 loss_thr: 0.6415 loss_db: 0.1809 2022/11/02 13:52:02 - mmengine - INFO - Epoch(train) [162][45/63] lr: 1.8998e-03 eta: 10:48:03 time: 0.4876 data_time: 0.0072 memory: 14901 loss: 1.9701 loss_prob: 1.1504 loss_thr: 0.6300 loss_db: 0.1896 2022/11/02 13:52:05 - mmengine - INFO - Epoch(train) [162][50/63] lr: 1.8998e-03 eta: 10:47:52 time: 0.5182 data_time: 0.0229 memory: 14901 loss: 1.9752 loss_prob: 1.1790 loss_thr: 0.6019 loss_db: 0.1944 2022/11/02 13:52:07 - mmengine - INFO - Epoch(train) [162][55/63] lr: 1.8998e-03 eta: 10:47:52 time: 0.5283 data_time: 0.0245 memory: 14901 loss: 2.1931 loss_prob: 1.3349 loss_thr: 0.6429 loss_db: 0.2154 2022/11/02 13:52:10 - mmengine - INFO - Epoch(train) [162][60/63] lr: 1.8998e-03 eta: 10:47:41 time: 0.5176 data_time: 0.0069 memory: 14901 loss: 2.1472 loss_prob: 1.2831 loss_thr: 0.6572 loss_db: 0.2069 2022/11/02 13:52:11 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:52:17 - mmengine - INFO - Epoch(train) [163][5/63] lr: 1.8982e-03 eta: 10:47:41 time: 0.7873 data_time: 0.2499 memory: 14901 loss: 1.9953 loss_prob: 1.2007 loss_thr: 0.6030 loss_db: 0.1916 2022/11/02 13:52:20 - mmengine - INFO - Epoch(train) [163][10/63] lr: 1.8982e-03 eta: 10:47:39 time: 0.8631 data_time: 0.2506 memory: 14901 loss: 1.8570 loss_prob: 1.0819 loss_thr: 0.6065 loss_db: 0.1686 2022/11/02 13:52:23 - mmengine - INFO - Epoch(train) [163][15/63] lr: 1.8982e-03 eta: 10:47:39 time: 0.6211 data_time: 0.0077 memory: 14901 loss: 1.8359 loss_prob: 1.0558 loss_thr: 0.6098 loss_db: 0.1703 2022/11/02 13:52:26 - mmengine - INFO - Epoch(train) [163][20/63] lr: 1.8982e-03 eta: 10:47:37 time: 0.6508 data_time: 0.0070 memory: 14901 loss: 1.9544 loss_prob: 1.1378 loss_thr: 0.6270 loss_db: 0.1896 2022/11/02 13:52:29 - mmengine - INFO - Epoch(train) [163][25/63] lr: 1.8982e-03 eta: 10:47:37 time: 0.5896 data_time: 0.0153 memory: 14901 loss: 2.1014 loss_prob: 1.2402 loss_thr: 0.6542 loss_db: 0.2069 2022/11/02 13:52:33 - mmengine - INFO - Epoch(train) [163][30/63] lr: 1.8982e-03 eta: 10:47:34 time: 0.6358 data_time: 0.0334 memory: 14901 loss: 2.1207 loss_prob: 1.2738 loss_thr: 0.6405 loss_db: 0.2064 2022/11/02 13:52:35 - mmengine - INFO - Epoch(train) [163][35/63] lr: 1.8982e-03 eta: 10:47:34 time: 0.6569 data_time: 0.0244 memory: 14901 loss: 2.0832 loss_prob: 1.2456 loss_thr: 0.6364 loss_db: 0.2012 2022/11/02 13:52:38 - mmengine - INFO - Epoch(train) [163][40/63] lr: 1.8982e-03 eta: 10:47:26 time: 0.5613 data_time: 0.0059 memory: 14901 loss: 2.1750 loss_prob: 1.2756 loss_thr: 0.6880 loss_db: 0.2113 2022/11/02 13:52:42 - mmengine - INFO - Epoch(train) [163][45/63] lr: 1.8982e-03 eta: 10:47:26 time: 0.6542 data_time: 0.0059 memory: 14901 loss: 2.1518 loss_prob: 1.2626 loss_thr: 0.6822 loss_db: 0.2070 2022/11/02 13:52:45 - mmengine - INFO - Epoch(train) [163][50/63] lr: 1.8982e-03 eta: 10:47:22 time: 0.6296 data_time: 0.0210 memory: 14901 loss: 2.0008 loss_prob: 1.1634 loss_thr: 0.6469 loss_db: 0.1905 2022/11/02 13:52:48 - mmengine - INFO - Epoch(train) [163][55/63] lr: 1.8982e-03 eta: 10:47:22 time: 0.6350 data_time: 0.0209 memory: 14901 loss: 1.9595 loss_prob: 1.1271 loss_thr: 0.6467 loss_db: 0.1857 2022/11/02 13:52:51 - mmengine - INFO - Epoch(train) [163][60/63] lr: 1.8982e-03 eta: 10:47:22 time: 0.6830 data_time: 0.0048 memory: 14901 loss: 1.9706 loss_prob: 1.1478 loss_thr: 0.6331 loss_db: 0.1896 2022/11/02 13:52:53 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:52:58 - mmengine - INFO - Epoch(train) [164][5/63] lr: 1.8965e-03 eta: 10:47:22 time: 0.8463 data_time: 0.2397 memory: 14901 loss: 1.8499 loss_prob: 1.0565 loss_thr: 0.6166 loss_db: 0.1768 2022/11/02 13:53:01 - mmengine - INFO - Epoch(train) [164][10/63] lr: 1.8965e-03 eta: 10:47:15 time: 0.7964 data_time: 0.2401 memory: 14901 loss: 1.8778 loss_prob: 1.0741 loss_thr: 0.6283 loss_db: 0.1753 2022/11/02 13:53:04 - mmengine - INFO - Epoch(train) [164][15/63] lr: 1.8965e-03 eta: 10:47:15 time: 0.5582 data_time: 0.0069 memory: 14901 loss: 1.8605 loss_prob: 1.0819 loss_thr: 0.6033 loss_db: 0.1754 2022/11/02 13:53:07 - mmengine - INFO - Epoch(train) [164][20/63] lr: 1.8965e-03 eta: 10:47:10 time: 0.5953 data_time: 0.0067 memory: 14901 loss: 1.9705 loss_prob: 1.1764 loss_thr: 0.6058 loss_db: 0.1883 2022/11/02 13:53:09 - mmengine - INFO - Epoch(train) [164][25/63] lr: 1.8965e-03 eta: 10:47:10 time: 0.5244 data_time: 0.0119 memory: 14901 loss: 2.1162 loss_prob: 1.2754 loss_thr: 0.6373 loss_db: 0.2035 2022/11/02 13:53:12 - mmengine - INFO - Epoch(train) [164][30/63] lr: 1.8965e-03 eta: 10:46:58 time: 0.5093 data_time: 0.0443 memory: 14901 loss: 1.9389 loss_prob: 1.1394 loss_thr: 0.6127 loss_db: 0.1868 2022/11/02 13:53:14 - mmengine - INFO - Epoch(train) [164][35/63] lr: 1.8965e-03 eta: 10:46:58 time: 0.5143 data_time: 0.0371 memory: 14901 loss: 2.0509 loss_prob: 1.2177 loss_thr: 0.6350 loss_db: 0.1982 2022/11/02 13:53:17 - mmengine - INFO - Epoch(train) [164][40/63] lr: 1.8965e-03 eta: 10:46:45 time: 0.4865 data_time: 0.0045 memory: 14901 loss: 2.1884 loss_prob: 1.3085 loss_thr: 0.6664 loss_db: 0.2136 2022/11/02 13:53:19 - mmengine - INFO - Epoch(train) [164][45/63] lr: 1.8965e-03 eta: 10:46:45 time: 0.4867 data_time: 0.0046 memory: 14901 loss: 2.0725 loss_prob: 1.2219 loss_thr: 0.6470 loss_db: 0.2036 2022/11/02 13:53:21 - mmengine - INFO - Epoch(train) [164][50/63] lr: 1.8965e-03 eta: 10:46:32 time: 0.4836 data_time: 0.0198 memory: 14901 loss: 1.8663 loss_prob: 1.0854 loss_thr: 0.6012 loss_db: 0.1797 2022/11/02 13:53:24 - mmengine - INFO - Epoch(train) [164][55/63] lr: 1.8965e-03 eta: 10:46:32 time: 0.4745 data_time: 0.0210 memory: 14901 loss: 1.7859 loss_prob: 1.0335 loss_thr: 0.5844 loss_db: 0.1680 2022/11/02 13:53:26 - mmengine - INFO - Epoch(train) [164][60/63] lr: 1.8965e-03 eta: 10:46:19 time: 0.4742 data_time: 0.0061 memory: 14901 loss: 2.0426 loss_prob: 1.2047 loss_thr: 0.6451 loss_db: 0.1927 2022/11/02 13:53:28 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:53:32 - mmengine - INFO - Epoch(train) [165][5/63] lr: 1.8949e-03 eta: 10:46:19 time: 0.7071 data_time: 0.2223 memory: 14901 loss: 2.0553 loss_prob: 1.2124 loss_thr: 0.6416 loss_db: 0.2014 2022/11/02 13:53:35 - mmengine - INFO - Epoch(train) [165][10/63] lr: 1.8949e-03 eta: 10:46:07 time: 0.7067 data_time: 0.2270 memory: 14901 loss: 2.1420 loss_prob: 1.2943 loss_thr: 0.6343 loss_db: 0.2134 2022/11/02 13:53:37 - mmengine - INFO - Epoch(train) [165][15/63] lr: 1.8949e-03 eta: 10:46:07 time: 0.4815 data_time: 0.0093 memory: 14901 loss: 2.4237 loss_prob: 1.5017 loss_thr: 0.6825 loss_db: 0.2396 2022/11/02 13:53:40 - mmengine - INFO - Epoch(train) [165][20/63] lr: 1.8949e-03 eta: 10:45:55 time: 0.4936 data_time: 0.0049 memory: 14901 loss: 2.4205 loss_prob: 1.5089 loss_thr: 0.6696 loss_db: 0.2419 2022/11/02 13:53:43 - mmengine - INFO - Epoch(train) [165][25/63] lr: 1.8949e-03 eta: 10:45:55 time: 0.5416 data_time: 0.0304 memory: 14901 loss: 2.2844 loss_prob: 1.3971 loss_thr: 0.6566 loss_db: 0.2307 2022/11/02 13:53:45 - mmengine - INFO - Epoch(train) [165][30/63] lr: 1.8949e-03 eta: 10:45:45 time: 0.5276 data_time: 0.0316 memory: 14901 loss: 2.0823 loss_prob: 1.2457 loss_thr: 0.6336 loss_db: 0.2030 2022/11/02 13:53:47 - mmengine - INFO - Epoch(train) [165][35/63] lr: 1.8949e-03 eta: 10:45:45 time: 0.4735 data_time: 0.0084 memory: 14901 loss: 2.0178 loss_prob: 1.2026 loss_thr: 0.6203 loss_db: 0.1950 2022/11/02 13:53:50 - mmengine - INFO - Epoch(train) [165][40/63] lr: 1.8949e-03 eta: 10:45:32 time: 0.4860 data_time: 0.0072 memory: 14901 loss: 2.1877 loss_prob: 1.3041 loss_thr: 0.6712 loss_db: 0.2123 2022/11/02 13:53:52 - mmengine - INFO - Epoch(train) [165][45/63] lr: 1.8949e-03 eta: 10:45:32 time: 0.4976 data_time: 0.0047 memory: 14901 loss: 2.0820 loss_prob: 1.2102 loss_thr: 0.6736 loss_db: 0.1981 2022/11/02 13:53:55 - mmengine - INFO - Epoch(train) [165][50/63] lr: 1.8949e-03 eta: 10:45:21 time: 0.5114 data_time: 0.0185 memory: 14901 loss: 2.0462 loss_prob: 1.1916 loss_thr: 0.6568 loss_db: 0.1979 2022/11/02 13:53:57 - mmengine - INFO - Epoch(train) [165][55/63] lr: 1.8949e-03 eta: 10:45:21 time: 0.5107 data_time: 0.0227 memory: 14901 loss: 2.4320 loss_prob: 1.5033 loss_thr: 0.6770 loss_db: 0.2517 2022/11/02 13:54:00 - mmengine - INFO - Epoch(train) [165][60/63] lr: 1.8949e-03 eta: 10:45:08 time: 0.4839 data_time: 0.0091 memory: 14901 loss: 2.3696 loss_prob: 1.4741 loss_thr: 0.6515 loss_db: 0.2439 2022/11/02 13:54:01 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:54:06 - mmengine - INFO - Epoch(train) [166][5/63] lr: 1.8932e-03 eta: 10:45:08 time: 0.7288 data_time: 0.1796 memory: 14901 loss: 2.2025 loss_prob: 1.3413 loss_thr: 0.6447 loss_db: 0.2166 2022/11/02 13:54:08 - mmengine - INFO - Epoch(train) [166][10/63] lr: 1.8932e-03 eta: 10:44:58 time: 0.7386 data_time: 0.1842 memory: 14901 loss: 2.1990 loss_prob: 1.3375 loss_thr: 0.6424 loss_db: 0.2192 2022/11/02 13:54:11 - mmengine - INFO - Epoch(train) [166][15/63] lr: 1.8932e-03 eta: 10:44:58 time: 0.4994 data_time: 0.0135 memory: 14901 loss: 2.1497 loss_prob: 1.3122 loss_thr: 0.6231 loss_db: 0.2144 2022/11/02 13:54:14 - mmengine - INFO - Epoch(train) [166][20/63] lr: 1.8932e-03 eta: 10:44:47 time: 0.5115 data_time: 0.0120 memory: 14901 loss: 2.2358 loss_prob: 1.3613 loss_thr: 0.6498 loss_db: 0.2247 2022/11/02 13:54:16 - mmengine - INFO - Epoch(train) [166][25/63] lr: 1.8932e-03 eta: 10:44:47 time: 0.4964 data_time: 0.0210 memory: 14901 loss: 2.2906 loss_prob: 1.3941 loss_thr: 0.6595 loss_db: 0.2370 2022/11/02 13:54:19 - mmengine - INFO - Epoch(train) [166][30/63] lr: 1.8932e-03 eta: 10:44:38 time: 0.5367 data_time: 0.0265 memory: 14901 loss: 2.2495 loss_prob: 1.3719 loss_thr: 0.6486 loss_db: 0.2290 2022/11/02 13:54:22 - mmengine - INFO - Epoch(train) [166][35/63] lr: 1.8932e-03 eta: 10:44:38 time: 0.5554 data_time: 0.0146 memory: 14901 loss: 2.2549 loss_prob: 1.3722 loss_thr: 0.6595 loss_db: 0.2233 2022/11/02 13:54:24 - mmengine - INFO - Epoch(train) [166][40/63] lr: 1.8932e-03 eta: 10:44:28 time: 0.5292 data_time: 0.0124 memory: 14901 loss: 2.3096 loss_prob: 1.3965 loss_thr: 0.6826 loss_db: 0.2306 2022/11/02 13:54:27 - mmengine - INFO - Epoch(train) [166][45/63] lr: 1.8932e-03 eta: 10:44:28 time: 0.5016 data_time: 0.0111 memory: 14901 loss: 2.1292 loss_prob: 1.2592 loss_thr: 0.6643 loss_db: 0.2056 2022/11/02 13:54:29 - mmengine - INFO - Epoch(train) [166][50/63] lr: 1.8932e-03 eta: 10:44:15 time: 0.4902 data_time: 0.0101 memory: 14901 loss: 2.1017 loss_prob: 1.2412 loss_thr: 0.6593 loss_db: 0.2012 2022/11/02 13:54:32 - mmengine - INFO - Epoch(train) [166][55/63] lr: 1.8932e-03 eta: 10:44:15 time: 0.5132 data_time: 0.0189 memory: 14901 loss: 2.2612 loss_prob: 1.3580 loss_thr: 0.6870 loss_db: 0.2162 2022/11/02 13:54:34 - mmengine - INFO - Epoch(train) [166][60/63] lr: 1.8932e-03 eta: 10:44:05 time: 0.5264 data_time: 0.0172 memory: 14901 loss: 2.2019 loss_prob: 1.3142 loss_thr: 0.6709 loss_db: 0.2168 2022/11/02 13:54:36 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:54:42 - mmengine - INFO - Epoch(train) [167][5/63] lr: 1.8916e-03 eta: 10:44:05 time: 0.8426 data_time: 0.2112 memory: 14901 loss: 2.0043 loss_prob: 1.1908 loss_thr: 0.6187 loss_db: 0.1948 2022/11/02 13:54:44 - mmengine - INFO - Epoch(train) [167][10/63] lr: 1.8916e-03 eta: 10:44:05 time: 0.8863 data_time: 0.2077 memory: 14901 loss: 1.9585 loss_prob: 1.1328 loss_thr: 0.6407 loss_db: 0.1850 2022/11/02 13:54:48 - mmengine - INFO - Epoch(train) [167][15/63] lr: 1.8916e-03 eta: 10:44:05 time: 0.5873 data_time: 0.0049 memory: 14901 loss: 1.9892 loss_prob: 1.1723 loss_thr: 0.6251 loss_db: 0.1918 2022/11/02 13:54:50 - mmengine - INFO - Epoch(train) [167][20/63] lr: 1.8916e-03 eta: 10:43:59 time: 0.5918 data_time: 0.0054 memory: 14901 loss: 2.1755 loss_prob: 1.3336 loss_thr: 0.6234 loss_db: 0.2185 2022/11/02 13:54:53 - mmengine - INFO - Epoch(train) [167][25/63] lr: 1.8916e-03 eta: 10:43:59 time: 0.5340 data_time: 0.0148 memory: 14901 loss: 2.2829 loss_prob: 1.3967 loss_thr: 0.6542 loss_db: 0.2320 2022/11/02 13:54:56 - mmengine - INFO - Epoch(train) [167][30/63] lr: 1.8916e-03 eta: 10:43:50 time: 0.5518 data_time: 0.0335 memory: 14901 loss: 2.1082 loss_prob: 1.2585 loss_thr: 0.6440 loss_db: 0.2057 2022/11/02 13:54:59 - mmengine - INFO - Epoch(train) [167][35/63] lr: 1.8916e-03 eta: 10:43:50 time: 0.6045 data_time: 0.0247 memory: 14901 loss: 2.1479 loss_prob: 1.2696 loss_thr: 0.6738 loss_db: 0.2045 2022/11/02 13:55:02 - mmengine - INFO - Epoch(train) [167][40/63] lr: 1.8916e-03 eta: 10:43:42 time: 0.5600 data_time: 0.0054 memory: 14901 loss: 2.0628 loss_prob: 1.2066 loss_thr: 0.6570 loss_db: 0.1992 2022/11/02 13:55:05 - mmengine - INFO - Epoch(train) [167][45/63] lr: 1.8916e-03 eta: 10:43:42 time: 0.5476 data_time: 0.0047 memory: 14901 loss: 2.0263 loss_prob: 1.1958 loss_thr: 0.6305 loss_db: 0.2001 2022/11/02 13:55:08 - mmengine - INFO - Epoch(train) [167][50/63] lr: 1.8916e-03 eta: 10:43:37 time: 0.6045 data_time: 0.0148 memory: 14901 loss: 1.9327 loss_prob: 1.1366 loss_thr: 0.6103 loss_db: 0.1857 2022/11/02 13:55:11 - mmengine - INFO - Epoch(train) [167][55/63] lr: 1.8916e-03 eta: 10:43:37 time: 0.5931 data_time: 0.0258 memory: 14901 loss: 1.7923 loss_prob: 1.0275 loss_thr: 0.5980 loss_db: 0.1668 2022/11/02 13:55:14 - mmengine - INFO - Epoch(train) [167][60/63] lr: 1.8916e-03 eta: 10:43:32 time: 0.6033 data_time: 0.0205 memory: 14901 loss: 1.9025 loss_prob: 1.0787 loss_thr: 0.6454 loss_db: 0.1784 2022/11/02 13:55:15 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:55:20 - mmengine - INFO - Epoch(train) [168][5/63] lr: 1.8899e-03 eta: 10:43:32 time: 0.8295 data_time: 0.2390 memory: 14901 loss: 1.8721 loss_prob: 1.0813 loss_thr: 0.6145 loss_db: 0.1763 2022/11/02 13:55:24 - mmengine - INFO - Epoch(train) [168][10/63] lr: 1.8899e-03 eta: 10:43:32 time: 0.8991 data_time: 0.2421 memory: 14901 loss: 1.9586 loss_prob: 1.1605 loss_thr: 0.6049 loss_db: 0.1932 2022/11/02 13:55:27 - mmengine - INFO - Epoch(train) [168][15/63] lr: 1.8899e-03 eta: 10:43:32 time: 0.6337 data_time: 0.0079 memory: 14901 loss: 2.0099 loss_prob: 1.1903 loss_thr: 0.6237 loss_db: 0.1959 2022/11/02 13:55:29 - mmengine - INFO - Epoch(train) [168][20/63] lr: 1.8899e-03 eta: 10:43:24 time: 0.5631 data_time: 0.0049 memory: 14901 loss: 2.0203 loss_prob: 1.1819 loss_thr: 0.6480 loss_db: 0.1904 2022/11/02 13:55:32 - mmengine - INFO - Epoch(train) [168][25/63] lr: 1.8899e-03 eta: 10:43:24 time: 0.5651 data_time: 0.0283 memory: 14901 loss: 2.0737 loss_prob: 1.2165 loss_thr: 0.6547 loss_db: 0.2025 2022/11/02 13:55:35 - mmengine - INFO - Epoch(train) [168][30/63] lr: 1.8899e-03 eta: 10:43:16 time: 0.5573 data_time: 0.0286 memory: 14901 loss: 1.9544 loss_prob: 1.1452 loss_thr: 0.6203 loss_db: 0.1888 2022/11/02 13:55:38 - mmengine - INFO - Epoch(train) [168][35/63] lr: 1.8899e-03 eta: 10:43:16 time: 0.5337 data_time: 0.0091 memory: 14901 loss: 1.8836 loss_prob: 1.1020 loss_thr: 0.6032 loss_db: 0.1784 2022/11/02 13:55:41 - mmengine - INFO - Epoch(train) [168][40/63] lr: 1.8899e-03 eta: 10:43:07 time: 0.5497 data_time: 0.0091 memory: 14901 loss: 1.9455 loss_prob: 1.1322 loss_thr: 0.6298 loss_db: 0.1835 2022/11/02 13:55:43 - mmengine - INFO - Epoch(train) [168][45/63] lr: 1.8899e-03 eta: 10:43:07 time: 0.5438 data_time: 0.0049 memory: 14901 loss: 2.1859 loss_prob: 1.3033 loss_thr: 0.6765 loss_db: 0.2061 2022/11/02 13:55:46 - mmengine - INFO - Epoch(train) [168][50/63] lr: 1.8899e-03 eta: 10:42:59 time: 0.5625 data_time: 0.0176 memory: 14901 loss: 2.3491 loss_prob: 1.4266 loss_thr: 0.6925 loss_db: 0.2301 2022/11/02 13:55:49 - mmengine - INFO - Epoch(train) [168][55/63] lr: 1.8899e-03 eta: 10:42:59 time: 0.5676 data_time: 0.0186 memory: 14901 loss: 2.3872 loss_prob: 1.4915 loss_thr: 0.6605 loss_db: 0.2352 2022/11/02 13:55:51 - mmengine - INFO - Epoch(train) [168][60/63] lr: 1.8899e-03 eta: 10:42:48 time: 0.5066 data_time: 0.0090 memory: 14901 loss: 2.3807 loss_prob: 1.5082 loss_thr: 0.6400 loss_db: 0.2325 2022/11/02 13:55:52 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:55:58 - mmengine - INFO - Epoch(train) [169][5/63] lr: 1.8883e-03 eta: 10:42:48 time: 0.7218 data_time: 0.2168 memory: 14901 loss: 2.4048 loss_prob: 1.4745 loss_thr: 0.6836 loss_db: 0.2467 2022/11/02 13:56:00 - mmengine - INFO - Epoch(train) [169][10/63] lr: 1.8883e-03 eta: 10:42:38 time: 0.7346 data_time: 0.2169 memory: 14901 loss: 2.2236 loss_prob: 1.3562 loss_thr: 0.6463 loss_db: 0.2210 2022/11/02 13:56:02 - mmengine - INFO - Epoch(train) [169][15/63] lr: 1.8883e-03 eta: 10:42:38 time: 0.4865 data_time: 0.0057 memory: 14901 loss: 2.1512 loss_prob: 1.2981 loss_thr: 0.6457 loss_db: 0.2074 2022/11/02 13:56:05 - mmengine - INFO - Epoch(train) [169][20/63] lr: 1.8883e-03 eta: 10:42:25 time: 0.4795 data_time: 0.0051 memory: 14901 loss: 2.1061 loss_prob: 1.2516 loss_thr: 0.6470 loss_db: 0.2075 2022/11/02 13:56:07 - mmengine - INFO - Epoch(train) [169][25/63] lr: 1.8883e-03 eta: 10:42:25 time: 0.4978 data_time: 0.0083 memory: 14901 loss: 1.9916 loss_prob: 1.1730 loss_thr: 0.6274 loss_db: 0.1912 2022/11/02 13:56:10 - mmengine - INFO - Epoch(train) [169][30/63] lr: 1.8883e-03 eta: 10:42:16 time: 0.5363 data_time: 0.0310 memory: 14901 loss: 2.0322 loss_prob: 1.2111 loss_thr: 0.6276 loss_db: 0.1935 2022/11/02 13:56:12 - mmengine - INFO - Epoch(train) [169][35/63] lr: 1.8883e-03 eta: 10:42:16 time: 0.5084 data_time: 0.0308 memory: 14901 loss: 2.0118 loss_prob: 1.2094 loss_thr: 0.6067 loss_db: 0.1957 2022/11/02 13:56:15 - mmengine - INFO - Epoch(train) [169][40/63] lr: 1.8883e-03 eta: 10:42:03 time: 0.4828 data_time: 0.0078 memory: 14901 loss: 2.1146 loss_prob: 1.2783 loss_thr: 0.6273 loss_db: 0.2090 2022/11/02 13:56:18 - mmengine - INFO - Epoch(train) [169][45/63] lr: 1.8883e-03 eta: 10:42:03 time: 0.5084 data_time: 0.0045 memory: 14901 loss: 2.1568 loss_prob: 1.2858 loss_thr: 0.6575 loss_db: 0.2135 2022/11/02 13:56:20 - mmengine - INFO - Epoch(train) [169][50/63] lr: 1.8883e-03 eta: 10:41:55 time: 0.5603 data_time: 0.0134 memory: 14901 loss: 2.0821 loss_prob: 1.2203 loss_thr: 0.6589 loss_db: 0.2030 2022/11/02 13:56:24 - mmengine - INFO - Epoch(train) [169][55/63] lr: 1.8883e-03 eta: 10:41:55 time: 0.6010 data_time: 0.0210 memory: 14901 loss: 2.1672 loss_prob: 1.2852 loss_thr: 0.6727 loss_db: 0.2093 2022/11/02 13:56:26 - mmengine - INFO - Epoch(train) [169][60/63] lr: 1.8883e-03 eta: 10:41:48 time: 0.5774 data_time: 0.0133 memory: 14901 loss: 2.1081 loss_prob: 1.2534 loss_thr: 0.6523 loss_db: 0.2024 2022/11/02 13:56:27 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:56:32 - mmengine - INFO - Epoch(train) [170][5/63] lr: 1.8866e-03 eta: 10:41:48 time: 0.6639 data_time: 0.1959 memory: 14901 loss: 2.1628 loss_prob: 1.3069 loss_thr: 0.6439 loss_db: 0.2121 2022/11/02 13:56:35 - mmengine - INFO - Epoch(train) [170][10/63] lr: 1.8866e-03 eta: 10:41:38 time: 0.7272 data_time: 0.1969 memory: 14901 loss: 2.2356 loss_prob: 1.3707 loss_thr: 0.6397 loss_db: 0.2253 2022/11/02 13:56:38 - mmengine - INFO - Epoch(train) [170][15/63] lr: 1.8866e-03 eta: 10:41:38 time: 0.5635 data_time: 0.0078 memory: 14901 loss: 2.0194 loss_prob: 1.1961 loss_thr: 0.6252 loss_db: 0.1981 2022/11/02 13:56:40 - mmengine - INFO - Epoch(train) [170][20/63] lr: 1.8866e-03 eta: 10:41:29 time: 0.5358 data_time: 0.0091 memory: 14901 loss: 1.9782 loss_prob: 1.1507 loss_thr: 0.6424 loss_db: 0.1851 2022/11/02 13:56:42 - mmengine - INFO - Epoch(train) [170][25/63] lr: 1.8866e-03 eta: 10:41:29 time: 0.4991 data_time: 0.0107 memory: 14901 loss: 1.8839 loss_prob: 1.0867 loss_thr: 0.6218 loss_db: 0.1754 2022/11/02 13:56:45 - mmengine - INFO - Epoch(train) [170][30/63] lr: 1.8866e-03 eta: 10:41:19 time: 0.5275 data_time: 0.0482 memory: 14901 loss: 2.0015 loss_prob: 1.2027 loss_thr: 0.6059 loss_db: 0.1929 2022/11/02 13:56:48 - mmengine - INFO - Epoch(train) [170][35/63] lr: 1.8866e-03 eta: 10:41:19 time: 0.5062 data_time: 0.0442 memory: 14901 loss: 2.3461 loss_prob: 1.4585 loss_thr: 0.6535 loss_db: 0.2342 2022/11/02 13:56:50 - mmengine - INFO - Epoch(train) [170][40/63] lr: 1.8866e-03 eta: 10:41:07 time: 0.5036 data_time: 0.0060 memory: 14901 loss: 2.3400 loss_prob: 1.4223 loss_thr: 0.6872 loss_db: 0.2305 2022/11/02 13:56:53 - mmengine - INFO - Epoch(train) [170][45/63] lr: 1.8866e-03 eta: 10:41:07 time: 0.5356 data_time: 0.0102 memory: 14901 loss: 2.1347 loss_prob: 1.2625 loss_thr: 0.6646 loss_db: 0.2077 2022/11/02 13:56:55 - mmengine - INFO - Epoch(train) [170][50/63] lr: 1.8866e-03 eta: 10:40:57 time: 0.5244 data_time: 0.0257 memory: 14901 loss: 2.1158 loss_prob: 1.2384 loss_thr: 0.6705 loss_db: 0.2069 2022/11/02 13:56:58 - mmengine - INFO - Epoch(train) [170][55/63] lr: 1.8866e-03 eta: 10:40:57 time: 0.4991 data_time: 0.0223 memory: 14901 loss: 2.0970 loss_prob: 1.2266 loss_thr: 0.6663 loss_db: 0.2041 2022/11/02 13:57:01 - mmengine - INFO - Epoch(train) [170][60/63] lr: 1.8866e-03 eta: 10:40:47 time: 0.5196 data_time: 0.0067 memory: 14901 loss: 1.9720 loss_prob: 1.1703 loss_thr: 0.6108 loss_db: 0.1909 2022/11/02 13:57:02 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:57:07 - mmengine - INFO - Epoch(train) [171][5/63] lr: 1.8850e-03 eta: 10:40:47 time: 0.6879 data_time: 0.2132 memory: 14901 loss: 1.9456 loss_prob: 1.1313 loss_thr: 0.6315 loss_db: 0.1828 2022/11/02 13:57:09 - mmengine - INFO - Epoch(train) [171][10/63] lr: 1.8850e-03 eta: 10:40:35 time: 0.7046 data_time: 0.2122 memory: 14901 loss: 1.9231 loss_prob: 1.1088 loss_thr: 0.6359 loss_db: 0.1783 2022/11/02 13:57:12 - mmengine - INFO - Epoch(train) [171][15/63] lr: 1.8850e-03 eta: 10:40:35 time: 0.5023 data_time: 0.0048 memory: 14901 loss: 1.8376 loss_prob: 1.0674 loss_thr: 0.5953 loss_db: 0.1748 2022/11/02 13:57:14 - mmengine - INFO - Epoch(train) [171][20/63] lr: 1.8850e-03 eta: 10:40:23 time: 0.4939 data_time: 0.0052 memory: 14901 loss: 1.9466 loss_prob: 1.1619 loss_thr: 0.5938 loss_db: 0.1909 2022/11/02 13:57:17 - mmengine - INFO - Epoch(train) [171][25/63] lr: 1.8850e-03 eta: 10:40:23 time: 0.5186 data_time: 0.0151 memory: 14901 loss: 1.9097 loss_prob: 1.1243 loss_thr: 0.6056 loss_db: 0.1798 2022/11/02 13:57:19 - mmengine - INFO - Epoch(train) [171][30/63] lr: 1.8850e-03 eta: 10:40:15 time: 0.5549 data_time: 0.0414 memory: 14901 loss: 1.9150 loss_prob: 1.1116 loss_thr: 0.6254 loss_db: 0.1780 2022/11/02 13:57:22 - mmengine - INFO - Epoch(train) [171][35/63] lr: 1.8850e-03 eta: 10:40:15 time: 0.5740 data_time: 0.0398 memory: 14901 loss: 2.0190 loss_prob: 1.1670 loss_thr: 0.6600 loss_db: 0.1919 2022/11/02 13:57:26 - mmengine - INFO - Epoch(train) [171][40/63] lr: 1.8850e-03 eta: 10:40:11 time: 0.6161 data_time: 0.0152 memory: 14901 loss: 1.9327 loss_prob: 1.1186 loss_thr: 0.6286 loss_db: 0.1854 2022/11/02 13:57:29 - mmengine - INFO - Epoch(train) [171][45/63] lr: 1.8850e-03 eta: 10:40:11 time: 0.6781 data_time: 0.0081 memory: 14901 loss: 1.8782 loss_prob: 1.0943 loss_thr: 0.6038 loss_db: 0.1801 2022/11/02 13:57:32 - mmengine - INFO - Epoch(train) [171][50/63] lr: 1.8850e-03 eta: 10:40:07 time: 0.6341 data_time: 0.0166 memory: 14901 loss: 2.0502 loss_prob: 1.2183 loss_thr: 0.6339 loss_db: 0.1980 2022/11/02 13:57:34 - mmengine - INFO - Epoch(train) [171][55/63] lr: 1.8850e-03 eta: 10:40:07 time: 0.5186 data_time: 0.0202 memory: 14901 loss: 2.1588 loss_prob: 1.2883 loss_thr: 0.6622 loss_db: 0.2084 2022/11/02 13:57:37 - mmengine - INFO - Epoch(train) [171][60/63] lr: 1.8850e-03 eta: 10:39:55 time: 0.4811 data_time: 0.0114 memory: 14901 loss: 2.1122 loss_prob: 1.2524 loss_thr: 0.6558 loss_db: 0.2039 2022/11/02 13:57:38 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:57:43 - mmengine - INFO - Epoch(train) [172][5/63] lr: 1.8833e-03 eta: 10:39:55 time: 0.7428 data_time: 0.2428 memory: 14901 loss: 1.8907 loss_prob: 1.0661 loss_thr: 0.6483 loss_db: 0.1763 2022/11/02 13:57:46 - mmengine - INFO - Epoch(train) [172][10/63] lr: 1.8833e-03 eta: 10:39:48 time: 0.7796 data_time: 0.2424 memory: 14901 loss: 2.0542 loss_prob: 1.2094 loss_thr: 0.6525 loss_db: 0.1923 2022/11/02 13:57:49 - mmengine - INFO - Epoch(train) [172][15/63] lr: 1.8833e-03 eta: 10:39:48 time: 0.5310 data_time: 0.0061 memory: 14901 loss: 2.0034 loss_prob: 1.1787 loss_thr: 0.6361 loss_db: 0.1886 2022/11/02 13:57:51 - mmengine - INFO - Epoch(train) [172][20/63] lr: 1.8833e-03 eta: 10:39:37 time: 0.5074 data_time: 0.0051 memory: 14901 loss: 1.8690 loss_prob: 1.0703 loss_thr: 0.6238 loss_db: 0.1749 2022/11/02 13:57:54 - mmengine - INFO - Epoch(train) [172][25/63] lr: 1.8833e-03 eta: 10:39:37 time: 0.5234 data_time: 0.0346 memory: 14901 loss: 1.8518 loss_prob: 1.0567 loss_thr: 0.6237 loss_db: 0.1714 2022/11/02 13:57:56 - mmengine - INFO - Epoch(train) [172][30/63] lr: 1.8833e-03 eta: 10:39:27 time: 0.5285 data_time: 0.0378 memory: 14901 loss: 1.7704 loss_prob: 1.0037 loss_thr: 0.6004 loss_db: 0.1663 2022/11/02 13:57:59 - mmengine - INFO - Epoch(train) [172][35/63] lr: 1.8833e-03 eta: 10:39:27 time: 0.5059 data_time: 0.0080 memory: 14901 loss: 1.8467 loss_prob: 1.0691 loss_thr: 0.6003 loss_db: 0.1773 2022/11/02 13:58:02 - mmengine - INFO - Epoch(train) [172][40/63] lr: 1.8833e-03 eta: 10:39:21 time: 0.5976 data_time: 0.0056 memory: 14901 loss: 1.9426 loss_prob: 1.1390 loss_thr: 0.6191 loss_db: 0.1845 2022/11/02 13:58:06 - mmengine - INFO - Epoch(train) [172][45/63] lr: 1.8833e-03 eta: 10:39:21 time: 0.6849 data_time: 0.0084 memory: 14901 loss: 2.0374 loss_prob: 1.2170 loss_thr: 0.6240 loss_db: 0.1964 2022/11/02 13:58:09 - mmengine - INFO - Epoch(train) [172][50/63] lr: 1.8833e-03 eta: 10:39:21 time: 0.6794 data_time: 0.0257 memory: 14901 loss: 1.9657 loss_prob: 1.1751 loss_thr: 0.6023 loss_db: 0.1883 2022/11/02 13:58:12 - mmengine - INFO - Epoch(train) [172][55/63] lr: 1.8833e-03 eta: 10:39:21 time: 0.5988 data_time: 0.0245 memory: 14901 loss: 1.9301 loss_prob: 1.1560 loss_thr: 0.5866 loss_db: 0.1874 2022/11/02 13:58:14 - mmengine - INFO - Epoch(train) [172][60/63] lr: 1.8833e-03 eta: 10:39:09 time: 0.4999 data_time: 0.0061 memory: 14901 loss: 2.0891 loss_prob: 1.2643 loss_thr: 0.6175 loss_db: 0.2073 2022/11/02 13:58:15 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:58:20 - mmengine - INFO - Epoch(train) [173][5/63] lr: 1.8817e-03 eta: 10:39:09 time: 0.6624 data_time: 0.1990 memory: 14901 loss: 2.0672 loss_prob: 1.2181 loss_thr: 0.6423 loss_db: 0.2068 2022/11/02 13:58:22 - mmengine - INFO - Epoch(train) [173][10/63] lr: 1.8817e-03 eta: 10:38:58 time: 0.7098 data_time: 0.2030 memory: 14901 loss: 2.2057 loss_prob: 1.3278 loss_thr: 0.6607 loss_db: 0.2172 2022/11/02 13:58:25 - mmengine - INFO - Epoch(train) [173][15/63] lr: 1.8817e-03 eta: 10:38:58 time: 0.4898 data_time: 0.0086 memory: 14901 loss: 2.0761 loss_prob: 1.2215 loss_thr: 0.6545 loss_db: 0.2001 2022/11/02 13:58:27 - mmengine - INFO - Epoch(train) [173][20/63] lr: 1.8817e-03 eta: 10:38:44 time: 0.4621 data_time: 0.0057 memory: 14901 loss: 2.0282 loss_prob: 1.1898 loss_thr: 0.6459 loss_db: 0.1926 2022/11/02 13:58:29 - mmengine - INFO - Epoch(train) [173][25/63] lr: 1.8817e-03 eta: 10:38:44 time: 0.4727 data_time: 0.0072 memory: 14901 loss: 2.1118 loss_prob: 1.2661 loss_thr: 0.6405 loss_db: 0.2052 2022/11/02 13:58:32 - mmengine - INFO - Epoch(train) [173][30/63] lr: 1.8817e-03 eta: 10:38:33 time: 0.4979 data_time: 0.0283 memory: 14901 loss: 2.0781 loss_prob: 1.2460 loss_thr: 0.6336 loss_db: 0.1985 2022/11/02 13:58:34 - mmengine - INFO - Epoch(train) [173][35/63] lr: 1.8817e-03 eta: 10:38:33 time: 0.5128 data_time: 0.0325 memory: 14901 loss: 2.0432 loss_prob: 1.2056 loss_thr: 0.6445 loss_db: 0.1932 2022/11/02 13:58:37 - mmengine - INFO - Epoch(train) [173][40/63] lr: 1.8817e-03 eta: 10:38:21 time: 0.4884 data_time: 0.0104 memory: 14901 loss: 2.1407 loss_prob: 1.2620 loss_thr: 0.6713 loss_db: 0.2073 2022/11/02 13:58:39 - mmengine - INFO - Epoch(train) [173][45/63] lr: 1.8817e-03 eta: 10:38:21 time: 0.4906 data_time: 0.0058 memory: 14901 loss: 2.1851 loss_prob: 1.3103 loss_thr: 0.6617 loss_db: 0.2131 2022/11/02 13:58:42 - mmengine - INFO - Epoch(train) [173][50/63] lr: 1.8817e-03 eta: 10:38:11 time: 0.5246 data_time: 0.0144 memory: 14901 loss: 2.0021 loss_prob: 1.1852 loss_thr: 0.6238 loss_db: 0.1931 2022/11/02 13:58:45 - mmengine - INFO - Epoch(train) [173][55/63] lr: 1.8817e-03 eta: 10:38:11 time: 0.5212 data_time: 0.0190 memory: 14901 loss: 1.9740 loss_prob: 1.1442 loss_thr: 0.6437 loss_db: 0.1860 2022/11/02 13:58:47 - mmengine - INFO - Epoch(train) [173][60/63] lr: 1.8817e-03 eta: 10:37:59 time: 0.4926 data_time: 0.0122 memory: 14901 loss: 1.9568 loss_prob: 1.1291 loss_thr: 0.6399 loss_db: 0.1879 2022/11/02 13:58:48 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:58:53 - mmengine - INFO - Epoch(train) [174][5/63] lr: 1.8800e-03 eta: 10:37:59 time: 0.7376 data_time: 0.1712 memory: 14901 loss: 2.0834 loss_prob: 1.2319 loss_thr: 0.6489 loss_db: 0.2026 2022/11/02 13:58:56 - mmengine - INFO - Epoch(train) [174][10/63] lr: 1.8800e-03 eta: 10:37:50 time: 0.7524 data_time: 0.1714 memory: 14901 loss: 2.0804 loss_prob: 1.2301 loss_thr: 0.6466 loss_db: 0.2037 2022/11/02 13:58:58 - mmengine - INFO - Epoch(train) [174][15/63] lr: 1.8800e-03 eta: 10:37:50 time: 0.4834 data_time: 0.0090 memory: 14901 loss: 1.8989 loss_prob: 1.1006 loss_thr: 0.6178 loss_db: 0.1805 2022/11/02 13:59:01 - mmengine - INFO - Epoch(train) [174][20/63] lr: 1.8800e-03 eta: 10:37:38 time: 0.4893 data_time: 0.0100 memory: 14901 loss: 1.9489 loss_prob: 1.1383 loss_thr: 0.6246 loss_db: 0.1860 2022/11/02 13:59:03 - mmengine - INFO - Epoch(train) [174][25/63] lr: 1.8800e-03 eta: 10:37:38 time: 0.5134 data_time: 0.0080 memory: 14901 loss: 1.9737 loss_prob: 1.1623 loss_thr: 0.6216 loss_db: 0.1899 2022/11/02 13:59:06 - mmengine - INFO - Epoch(train) [174][30/63] lr: 1.8800e-03 eta: 10:37:28 time: 0.5240 data_time: 0.0311 memory: 14901 loss: 1.9236 loss_prob: 1.1298 loss_thr: 0.6107 loss_db: 0.1831 2022/11/02 13:59:08 - mmengine - INFO - Epoch(train) [174][35/63] lr: 1.8800e-03 eta: 10:37:28 time: 0.4910 data_time: 0.0309 memory: 14901 loss: 1.9345 loss_prob: 1.1247 loss_thr: 0.6259 loss_db: 0.1838 2022/11/02 13:59:10 - mmengine - INFO - Epoch(train) [174][40/63] lr: 1.8800e-03 eta: 10:37:14 time: 0.4501 data_time: 0.0065 memory: 14901 loss: 1.9299 loss_prob: 1.1170 loss_thr: 0.6301 loss_db: 0.1827 2022/11/02 13:59:13 - mmengine - INFO - Epoch(train) [174][45/63] lr: 1.8800e-03 eta: 10:37:14 time: 0.4810 data_time: 0.0060 memory: 14901 loss: 2.0201 loss_prob: 1.1902 loss_thr: 0.6377 loss_db: 0.1921 2022/11/02 13:59:15 - mmengine - INFO - Epoch(train) [174][50/63] lr: 1.8800e-03 eta: 10:37:03 time: 0.4983 data_time: 0.0110 memory: 14901 loss: 2.0316 loss_prob: 1.1938 loss_thr: 0.6453 loss_db: 0.1925 2022/11/02 13:59:19 - mmengine - INFO - Epoch(train) [174][55/63] lr: 1.8800e-03 eta: 10:37:03 time: 0.5834 data_time: 0.0217 memory: 14901 loss: 1.9703 loss_prob: 1.1255 loss_thr: 0.6601 loss_db: 0.1846 2022/11/02 13:59:22 - mmengine - INFO - Epoch(train) [174][60/63] lr: 1.8800e-03 eta: 10:36:59 time: 0.6319 data_time: 0.0197 memory: 14901 loss: 1.9940 loss_prob: 1.1488 loss_thr: 0.6565 loss_db: 0.1887 2022/11/02 13:59:23 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:59:28 - mmengine - INFO - Epoch(train) [175][5/63] lr: 1.8784e-03 eta: 10:36:59 time: 0.7369 data_time: 0.2386 memory: 14901 loss: 2.1222 loss_prob: 1.2656 loss_thr: 0.6484 loss_db: 0.2082 2022/11/02 13:59:30 - mmengine - INFO - Epoch(train) [175][10/63] lr: 1.8784e-03 eta: 10:36:50 time: 0.7469 data_time: 0.2358 memory: 14901 loss: 2.0102 loss_prob: 1.1817 loss_thr: 0.6356 loss_db: 0.1929 2022/11/02 13:59:33 - mmengine - INFO - Epoch(train) [175][15/63] lr: 1.8784e-03 eta: 10:36:50 time: 0.4996 data_time: 0.0049 memory: 14901 loss: 1.9657 loss_prob: 1.1244 loss_thr: 0.6569 loss_db: 0.1843 2022/11/02 13:59:35 - mmengine - INFO - Epoch(train) [175][20/63] lr: 1.8784e-03 eta: 10:36:37 time: 0.4741 data_time: 0.0056 memory: 14901 loss: 2.0181 loss_prob: 1.1720 loss_thr: 0.6565 loss_db: 0.1895 2022/11/02 13:59:38 - mmengine - INFO - Epoch(train) [175][25/63] lr: 1.8784e-03 eta: 10:36:37 time: 0.4921 data_time: 0.0094 memory: 14901 loss: 1.9135 loss_prob: 1.1126 loss_thr: 0.6202 loss_db: 0.1807 2022/11/02 13:59:40 - mmengine - INFO - Epoch(train) [175][30/63] lr: 1.8784e-03 eta: 10:36:26 time: 0.5056 data_time: 0.0198 memory: 14901 loss: 1.8168 loss_prob: 1.0310 loss_thr: 0.6165 loss_db: 0.1692 2022/11/02 13:59:43 - mmengine - INFO - Epoch(train) [175][35/63] lr: 1.8784e-03 eta: 10:36:26 time: 0.5113 data_time: 0.0178 memory: 14901 loss: 1.9139 loss_prob: 1.0981 loss_thr: 0.6315 loss_db: 0.1843 2022/11/02 13:59:44 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 13:59:45 - mmengine - INFO - Epoch(train) [175][40/63] lr: 1.8784e-03 eta: 10:36:15 time: 0.4933 data_time: 0.0065 memory: 14901 loss: 1.9473 loss_prob: 1.1182 loss_thr: 0.6391 loss_db: 0.1900 2022/11/02 13:59:48 - mmengine - INFO - Epoch(train) [175][45/63] lr: 1.8784e-03 eta: 10:36:15 time: 0.5109 data_time: 0.0053 memory: 14901 loss: 1.9063 loss_prob: 1.0777 loss_thr: 0.6483 loss_db: 0.1804 2022/11/02 13:59:51 - mmengine - INFO - Epoch(train) [175][50/63] lr: 1.8784e-03 eta: 10:36:11 time: 0.6206 data_time: 0.0170 memory: 14901 loss: 1.9547 loss_prob: 1.1395 loss_thr: 0.6263 loss_db: 0.1889 2022/11/02 13:59:55 - mmengine - INFO - Epoch(train) [175][55/63] lr: 1.8784e-03 eta: 10:36:11 time: 0.7147 data_time: 0.0216 memory: 14901 loss: 2.0369 loss_prob: 1.2318 loss_thr: 0.6006 loss_db: 0.2045 2022/11/02 13:59:58 - mmengine - INFO - Epoch(train) [175][60/63] lr: 1.8784e-03 eta: 10:36:09 time: 0.6656 data_time: 0.0106 memory: 14901 loss: 2.0666 loss_prob: 1.2305 loss_thr: 0.6341 loss_db: 0.2020 2022/11/02 13:59:59 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:00:06 - mmengine - INFO - Epoch(train) [176][5/63] lr: 1.8767e-03 eta: 10:36:09 time: 0.8820 data_time: 0.2264 memory: 14901 loss: 2.0231 loss_prob: 1.2036 loss_thr: 0.6257 loss_db: 0.1938 2022/11/02 14:00:09 - mmengine - INFO - Epoch(train) [176][10/63] lr: 1.8767e-03 eta: 10:36:13 time: 0.9687 data_time: 0.2350 memory: 14901 loss: 2.0662 loss_prob: 1.2222 loss_thr: 0.6425 loss_db: 0.2015 2022/11/02 14:00:11 - mmengine - INFO - Epoch(train) [176][15/63] lr: 1.8767e-03 eta: 10:36:13 time: 0.5316 data_time: 0.0131 memory: 14901 loss: 2.1580 loss_prob: 1.2945 loss_thr: 0.6489 loss_db: 0.2146 2022/11/02 14:00:15 - mmengine - INFO - Epoch(train) [176][20/63] lr: 1.8767e-03 eta: 10:36:08 time: 0.5992 data_time: 0.0056 memory: 14901 loss: 2.2930 loss_prob: 1.4129 loss_thr: 0.6461 loss_db: 0.2339 2022/11/02 14:00:18 - mmengine - INFO - Epoch(train) [176][25/63] lr: 1.8767e-03 eta: 10:36:08 time: 0.6766 data_time: 0.0218 memory: 14901 loss: 2.2478 loss_prob: 1.3798 loss_thr: 0.6411 loss_db: 0.2269 2022/11/02 14:00:22 - mmengine - INFO - Epoch(train) [176][30/63] lr: 1.8767e-03 eta: 10:36:08 time: 0.7057 data_time: 0.0223 memory: 14901 loss: 2.1529 loss_prob: 1.2971 loss_thr: 0.6421 loss_db: 0.2138 2022/11/02 14:00:25 - mmengine - INFO - Epoch(train) [176][35/63] lr: 1.8767e-03 eta: 10:36:08 time: 0.7335 data_time: 0.0156 memory: 14901 loss: 2.2487 loss_prob: 1.3639 loss_thr: 0.6534 loss_db: 0.2314 2022/11/02 14:00:28 - mmengine - INFO - Epoch(train) [176][40/63] lr: 1.8767e-03 eta: 10:36:03 time: 0.6070 data_time: 0.0136 memory: 14901 loss: 2.2937 loss_prob: 1.3938 loss_thr: 0.6661 loss_db: 0.2339 2022/11/02 14:00:31 - mmengine - INFO - Epoch(train) [176][45/63] lr: 1.8767e-03 eta: 10:36:03 time: 0.5426 data_time: 0.0040 memory: 14901 loss: 2.2569 loss_prob: 1.3641 loss_thr: 0.6742 loss_db: 0.2187 2022/11/02 14:00:33 - mmengine - INFO - Epoch(train) [176][50/63] lr: 1.8767e-03 eta: 10:35:54 time: 0.5274 data_time: 0.0177 memory: 14901 loss: 2.1854 loss_prob: 1.3196 loss_thr: 0.6576 loss_db: 0.2082 2022/11/02 14:00:36 - mmengine - INFO - Epoch(train) [176][55/63] lr: 1.8767e-03 eta: 10:35:54 time: 0.5705 data_time: 0.0260 memory: 14901 loss: 1.9956 loss_prob: 1.1679 loss_thr: 0.6386 loss_db: 0.1890 2022/11/02 14:00:40 - mmengine - INFO - Epoch(train) [176][60/63] lr: 1.8767e-03 eta: 10:35:53 time: 0.6766 data_time: 0.0131 memory: 14901 loss: 1.9383 loss_prob: 1.1190 loss_thr: 0.6381 loss_db: 0.1812 2022/11/02 14:00:42 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:00:47 - mmengine - INFO - Epoch(train) [177][5/63] lr: 1.8751e-03 eta: 10:35:53 time: 0.8368 data_time: 0.2440 memory: 14901 loss: 2.2151 loss_prob: 1.3237 loss_thr: 0.6658 loss_db: 0.2256 2022/11/02 14:00:50 - mmengine - INFO - Epoch(train) [177][10/63] lr: 1.8751e-03 eta: 10:35:46 time: 0.7856 data_time: 0.2436 memory: 14901 loss: 2.4683 loss_prob: 1.5179 loss_thr: 0.6867 loss_db: 0.2637 2022/11/02 14:00:52 - mmengine - INFO - Epoch(train) [177][15/63] lr: 1.8751e-03 eta: 10:35:46 time: 0.4906 data_time: 0.0053 memory: 14901 loss: 2.4458 loss_prob: 1.5118 loss_thr: 0.6803 loss_db: 0.2536 2022/11/02 14:00:55 - mmengine - INFO - Epoch(train) [177][20/63] lr: 1.8751e-03 eta: 10:35:33 time: 0.4730 data_time: 0.0057 memory: 14901 loss: 2.2890 loss_prob: 1.3885 loss_thr: 0.6785 loss_db: 0.2220 2022/11/02 14:00:57 - mmengine - INFO - Epoch(train) [177][25/63] lr: 1.8751e-03 eta: 10:35:33 time: 0.4827 data_time: 0.0178 memory: 14901 loss: 2.1912 loss_prob: 1.3052 loss_thr: 0.6764 loss_db: 0.2096 2022/11/02 14:01:00 - mmengine - INFO - Epoch(train) [177][30/63] lr: 1.8751e-03 eta: 10:35:23 time: 0.5105 data_time: 0.0330 memory: 14901 loss: 2.0845 loss_prob: 1.2253 loss_thr: 0.6612 loss_db: 0.1979 2022/11/02 14:01:02 - mmengine - INFO - Epoch(train) [177][35/63] lr: 1.8751e-03 eta: 10:35:23 time: 0.4938 data_time: 0.0202 memory: 14901 loss: 2.1328 loss_prob: 1.2493 loss_thr: 0.6760 loss_db: 0.2075 2022/11/02 14:01:04 - mmengine - INFO - Epoch(train) [177][40/63] lr: 1.8751e-03 eta: 10:35:10 time: 0.4763 data_time: 0.0043 memory: 14901 loss: 2.0121 loss_prob: 1.1527 loss_thr: 0.6666 loss_db: 0.1928 2022/11/02 14:01:07 - mmengine - INFO - Epoch(train) [177][45/63] lr: 1.8751e-03 eta: 10:35:10 time: 0.4817 data_time: 0.0059 memory: 14901 loss: 1.8866 loss_prob: 1.0897 loss_thr: 0.6198 loss_db: 0.1771 2022/11/02 14:01:10 - mmengine - INFO - Epoch(train) [177][50/63] lr: 1.8751e-03 eta: 10:35:00 time: 0.5248 data_time: 0.0186 memory: 14901 loss: 1.9348 loss_prob: 1.1524 loss_thr: 0.5974 loss_db: 0.1850 2022/11/02 14:01:12 - mmengine - INFO - Epoch(train) [177][55/63] lr: 1.8751e-03 eta: 10:35:00 time: 0.5279 data_time: 0.0204 memory: 14901 loss: 1.9803 loss_prob: 1.1814 loss_thr: 0.6061 loss_db: 0.1928 2022/11/02 14:01:14 - mmengine - INFO - Epoch(train) [177][60/63] lr: 1.8751e-03 eta: 10:34:48 time: 0.4783 data_time: 0.0076 memory: 14901 loss: 1.8127 loss_prob: 1.0629 loss_thr: 0.5745 loss_db: 0.1752 2022/11/02 14:01:16 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:01:22 - mmengine - INFO - Epoch(train) [178][5/63] lr: 1.8734e-03 eta: 10:34:48 time: 0.8230 data_time: 0.3589 memory: 14901 loss: 2.1930 loss_prob: 1.3341 loss_thr: 0.6473 loss_db: 0.2116 2022/11/02 14:01:24 - mmengine - INFO - Epoch(train) [178][10/63] lr: 1.8734e-03 eta: 10:34:47 time: 0.8849 data_time: 0.3584 memory: 14901 loss: 2.2898 loss_prob: 1.4079 loss_thr: 0.6588 loss_db: 0.2231 2022/11/02 14:01:27 - mmengine - INFO - Epoch(train) [178][15/63] lr: 1.8734e-03 eta: 10:34:47 time: 0.5102 data_time: 0.0047 memory: 14901 loss: 1.9464 loss_prob: 1.1399 loss_thr: 0.6179 loss_db: 0.1887 2022/11/02 14:01:29 - mmengine - INFO - Epoch(train) [178][20/63] lr: 1.8734e-03 eta: 10:34:35 time: 0.4919 data_time: 0.0051 memory: 14901 loss: 1.9708 loss_prob: 1.1486 loss_thr: 0.6329 loss_db: 0.1893 2022/11/02 14:01:32 - mmengine - INFO - Epoch(train) [178][25/63] lr: 1.8734e-03 eta: 10:34:35 time: 0.5342 data_time: 0.0223 memory: 14901 loss: 2.0849 loss_prob: 1.2390 loss_thr: 0.6453 loss_db: 0.2006 2022/11/02 14:01:35 - mmengine - INFO - Epoch(train) [178][30/63] lr: 1.8734e-03 eta: 10:34:25 time: 0.5139 data_time: 0.0220 memory: 14901 loss: 2.1417 loss_prob: 1.2683 loss_thr: 0.6671 loss_db: 0.2063 2022/11/02 14:01:37 - mmengine - INFO - Epoch(train) [178][35/63] lr: 1.8734e-03 eta: 10:34:25 time: 0.4831 data_time: 0.0043 memory: 14901 loss: 2.0843 loss_prob: 1.2202 loss_thr: 0.6618 loss_db: 0.2023 2022/11/02 14:01:39 - mmengine - INFO - Epoch(train) [178][40/63] lr: 1.8734e-03 eta: 10:34:13 time: 0.4821 data_time: 0.0045 memory: 14901 loss: 1.9786 loss_prob: 1.1669 loss_thr: 0.6197 loss_db: 0.1919 2022/11/02 14:01:42 - mmengine - INFO - Epoch(train) [178][45/63] lr: 1.8734e-03 eta: 10:34:13 time: 0.4988 data_time: 0.0044 memory: 14901 loss: 2.0262 loss_prob: 1.1931 loss_thr: 0.6376 loss_db: 0.1955 2022/11/02 14:01:45 - mmengine - INFO - Epoch(train) [178][50/63] lr: 1.8734e-03 eta: 10:34:03 time: 0.5278 data_time: 0.0232 memory: 14901 loss: 2.0084 loss_prob: 1.1734 loss_thr: 0.6405 loss_db: 0.1945 2022/11/02 14:01:47 - mmengine - INFO - Epoch(train) [178][55/63] lr: 1.8734e-03 eta: 10:34:03 time: 0.5055 data_time: 0.0233 memory: 14901 loss: 1.9554 loss_prob: 1.1349 loss_thr: 0.6350 loss_db: 0.1855 2022/11/02 14:01:49 - mmengine - INFO - Epoch(train) [178][60/63] lr: 1.8734e-03 eta: 10:33:51 time: 0.4757 data_time: 0.0065 memory: 14901 loss: 1.9292 loss_prob: 1.1136 loss_thr: 0.6327 loss_db: 0.1829 2022/11/02 14:01:51 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:01:56 - mmengine - INFO - Epoch(train) [179][5/63] lr: 1.8718e-03 eta: 10:33:51 time: 0.7446 data_time: 0.2171 memory: 14901 loss: 2.1118 loss_prob: 1.2608 loss_thr: 0.6448 loss_db: 0.2063 2022/11/02 14:01:58 - mmengine - INFO - Epoch(train) [179][10/63] lr: 1.8718e-03 eta: 10:33:43 time: 0.7631 data_time: 0.2216 memory: 14901 loss: 2.1789 loss_prob: 1.3276 loss_thr: 0.6383 loss_db: 0.2130 2022/11/02 14:02:01 - mmengine - INFO - Epoch(train) [179][15/63] lr: 1.8718e-03 eta: 10:33:43 time: 0.5077 data_time: 0.0106 memory: 14901 loss: 2.0934 loss_prob: 1.2585 loss_thr: 0.6294 loss_db: 0.2055 2022/11/02 14:02:04 - mmengine - INFO - Epoch(train) [179][20/63] lr: 1.8718e-03 eta: 10:33:33 time: 0.5286 data_time: 0.0059 memory: 14901 loss: 2.1727 loss_prob: 1.3175 loss_thr: 0.6414 loss_db: 0.2138 2022/11/02 14:02:06 - mmengine - INFO - Epoch(train) [179][25/63] lr: 1.8718e-03 eta: 10:33:33 time: 0.4936 data_time: 0.0075 memory: 14901 loss: 2.2475 loss_prob: 1.3893 loss_thr: 0.6324 loss_db: 0.2258 2022/11/02 14:02:09 - mmengine - INFO - Epoch(train) [179][30/63] lr: 1.8718e-03 eta: 10:33:23 time: 0.5067 data_time: 0.0499 memory: 14901 loss: 2.2508 loss_prob: 1.3921 loss_thr: 0.6272 loss_db: 0.2314 2022/11/02 14:02:12 - mmengine - INFO - Epoch(train) [179][35/63] lr: 1.8718e-03 eta: 10:33:23 time: 0.5635 data_time: 0.0523 memory: 14901 loss: 2.1996 loss_prob: 1.3356 loss_thr: 0.6423 loss_db: 0.2218 2022/11/02 14:02:14 - mmengine - INFO - Epoch(train) [179][40/63] lr: 1.8718e-03 eta: 10:33:13 time: 0.5269 data_time: 0.0110 memory: 14901 loss: 2.0943 loss_prob: 1.2518 loss_thr: 0.6331 loss_db: 0.2095 2022/11/02 14:02:16 - mmengine - INFO - Epoch(train) [179][45/63] lr: 1.8718e-03 eta: 10:33:13 time: 0.4782 data_time: 0.0057 memory: 14901 loss: 2.1933 loss_prob: 1.3317 loss_thr: 0.6444 loss_db: 0.2172 2022/11/02 14:02:19 - mmengine - INFO - Epoch(train) [179][50/63] lr: 1.8718e-03 eta: 10:33:02 time: 0.5025 data_time: 0.0134 memory: 14901 loss: 2.3058 loss_prob: 1.3926 loss_thr: 0.6849 loss_db: 0.2283 2022/11/02 14:02:22 - mmengine - INFO - Epoch(train) [179][55/63] lr: 1.8718e-03 eta: 10:33:02 time: 0.5416 data_time: 0.0252 memory: 14901 loss: 2.1795 loss_prob: 1.2899 loss_thr: 0.6747 loss_db: 0.2149 2022/11/02 14:02:24 - mmengine - INFO - Epoch(train) [179][60/63] lr: 1.8718e-03 eta: 10:32:53 time: 0.5281 data_time: 0.0186 memory: 14901 loss: 2.0120 loss_prob: 1.1779 loss_thr: 0.6449 loss_db: 0.1893 2022/11/02 14:02:26 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:02:32 - mmengine - INFO - Epoch(train) [180][5/63] lr: 1.8701e-03 eta: 10:32:53 time: 0.8456 data_time: 0.2099 memory: 14901 loss: 2.1740 loss_prob: 1.3051 loss_thr: 0.6608 loss_db: 0.2080 2022/11/02 14:02:36 - mmengine - INFO - Epoch(train) [180][10/63] lr: 1.8701e-03 eta: 10:32:58 time: 0.9910 data_time: 0.2226 memory: 14901 loss: 2.0051 loss_prob: 1.1861 loss_thr: 0.6270 loss_db: 0.1919 2022/11/02 14:02:38 - mmengine - INFO - Epoch(train) [180][15/63] lr: 1.8701e-03 eta: 10:32:58 time: 0.6404 data_time: 0.0177 memory: 14901 loss: 1.9834 loss_prob: 1.1520 loss_thr: 0.6396 loss_db: 0.1917 2022/11/02 14:02:41 - mmengine - INFO - Epoch(train) [180][20/63] lr: 1.8701e-03 eta: 10:32:48 time: 0.5150 data_time: 0.0062 memory: 14901 loss: 2.0285 loss_prob: 1.1775 loss_thr: 0.6559 loss_db: 0.1951 2022/11/02 14:02:45 - mmengine - INFO - Epoch(train) [180][25/63] lr: 1.8701e-03 eta: 10:32:48 time: 0.7200 data_time: 0.0381 memory: 14901 loss: 2.0956 loss_prob: 1.2444 loss_thr: 0.6481 loss_db: 0.2031 2022/11/02 14:02:48 - mmengine - INFO - Epoch(train) [180][30/63] lr: 1.8701e-03 eta: 10:32:51 time: 0.7538 data_time: 0.0415 memory: 14901 loss: 2.0442 loss_prob: 1.2211 loss_thr: 0.6208 loss_db: 0.2023 2022/11/02 14:02:52 - mmengine - INFO - Epoch(train) [180][35/63] lr: 1.8701e-03 eta: 10:32:51 time: 0.6190 data_time: 0.0114 memory: 14901 loss: 1.9852 loss_prob: 1.1662 loss_thr: 0.6249 loss_db: 0.1942 2022/11/02 14:02:55 - mmengine - INFO - Epoch(train) [180][40/63] lr: 1.8701e-03 eta: 10:32:47 time: 0.6301 data_time: 0.0064 memory: 14901 loss: 1.9510 loss_prob: 1.1291 loss_thr: 0.6376 loss_db: 0.1843 2022/11/02 14:02:58 - mmengine - INFO - Epoch(train) [180][45/63] lr: 1.8701e-03 eta: 10:32:47 time: 0.6112 data_time: 0.0062 memory: 14901 loss: 1.8816 loss_prob: 1.0898 loss_thr: 0.6130 loss_db: 0.1788 2022/11/02 14:03:02 - mmengine - INFO - Epoch(train) [180][50/63] lr: 1.8701e-03 eta: 10:32:49 time: 0.7252 data_time: 0.0272 memory: 14901 loss: 1.7627 loss_prob: 1.0116 loss_thr: 0.5854 loss_db: 0.1657 2022/11/02 14:03:04 - mmengine - INFO - Epoch(train) [180][55/63] lr: 1.8701e-03 eta: 10:32:49 time: 0.6777 data_time: 0.0314 memory: 14901 loss: 1.7588 loss_prob: 1.0100 loss_thr: 0.5835 loss_db: 0.1654 2022/11/02 14:03:08 - mmengine - INFO - Epoch(train) [180][60/63] lr: 1.8701e-03 eta: 10:32:43 time: 0.5792 data_time: 0.0115 memory: 14901 loss: 1.8543 loss_prob: 1.0738 loss_thr: 0.6035 loss_db: 0.1769 2022/11/02 14:03:09 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:03:09 - mmengine - INFO - Saving checkpoint at 180 epochs 2022/11/02 14:03:13 - mmengine - INFO - Epoch(val) [180][5/500] eta: 10:32:43 time: 0.0459 data_time: 0.0050 memory: 14901 2022/11/02 14:03:13 - mmengine - INFO - Epoch(val) [180][10/500] eta: 0:00:25 time: 0.0510 data_time: 0.0058 memory: 1008 2022/11/02 14:03:13 - mmengine - INFO - Epoch(val) [180][15/500] eta: 0:00:25 time: 0.0471 data_time: 0.0038 memory: 1008 2022/11/02 14:03:13 - mmengine - INFO - Epoch(val) [180][20/500] eta: 0:00:23 time: 0.0483 data_time: 0.0038 memory: 1008 2022/11/02 14:03:14 - mmengine - INFO - Epoch(val) [180][25/500] eta: 0:00:23 time: 0.0473 data_time: 0.0041 memory: 1008 2022/11/02 14:03:14 - mmengine - INFO - Epoch(val) [180][30/500] eta: 0:00:23 time: 0.0491 data_time: 0.0038 memory: 1008 2022/11/02 14:03:14 - mmengine - INFO - Epoch(val) [180][35/500] eta: 0:00:23 time: 0.0466 data_time: 0.0033 memory: 1008 2022/11/02 14:03:14 - mmengine - INFO - Epoch(val) [180][40/500] eta: 0:00:23 time: 0.0501 data_time: 0.0036 memory: 1008 2022/11/02 14:03:15 - mmengine - INFO - Epoch(val) [180][45/500] eta: 0:00:23 time: 0.0554 data_time: 0.0034 memory: 1008 2022/11/02 14:03:15 - mmengine - INFO - Epoch(val) [180][50/500] eta: 0:00:27 time: 0.0601 data_time: 0.0062 memory: 1008 2022/11/02 14:03:15 - mmengine - INFO - Epoch(val) [180][55/500] eta: 0:00:27 time: 0.0649 data_time: 0.0066 memory: 1008 2022/11/02 14:03:15 - mmengine - INFO - Epoch(val) [180][60/500] eta: 0:00:22 time: 0.0523 data_time: 0.0033 memory: 1008 2022/11/02 14:03:16 - mmengine - INFO - Epoch(val) [180][65/500] eta: 0:00:22 time: 0.0469 data_time: 0.0029 memory: 1008 2022/11/02 14:03:16 - mmengine - INFO - Epoch(val) [180][70/500] eta: 0:00:21 time: 0.0506 data_time: 0.0028 memory: 1008 2022/11/02 14:03:16 - mmengine - INFO - Epoch(val) [180][75/500] eta: 0:00:21 time: 0.0433 data_time: 0.0025 memory: 1008 2022/11/02 14:03:16 - mmengine - INFO - Epoch(val) [180][80/500] eta: 0:00:17 time: 0.0413 data_time: 0.0025 memory: 1008 2022/11/02 14:03:17 - mmengine - INFO - Epoch(val) [180][85/500] eta: 0:00:17 time: 0.0419 data_time: 0.0024 memory: 1008 2022/11/02 14:03:17 - mmengine - INFO - Epoch(val) [180][90/500] eta: 0:00:16 time: 0.0410 data_time: 0.0022 memory: 1008 2022/11/02 14:03:17 - mmengine - INFO - Epoch(val) [180][95/500] eta: 0:00:16 time: 0.0448 data_time: 0.0025 memory: 1008 2022/11/02 14:03:17 - mmengine - INFO - Epoch(val) [180][100/500] eta: 0:00:16 time: 0.0407 data_time: 0.0025 memory: 1008 2022/11/02 14:03:17 - mmengine - INFO - Epoch(val) [180][105/500] eta: 0:00:16 time: 0.0404 data_time: 0.0024 memory: 1008 2022/11/02 14:03:18 - mmengine - INFO - Epoch(val) [180][110/500] eta: 0:00:17 time: 0.0444 data_time: 0.0030 memory: 1008 2022/11/02 14:03:18 - mmengine - INFO - Epoch(val) [180][115/500] eta: 0:00:17 time: 0.0432 data_time: 0.0031 memory: 1008 2022/11/02 14:03:18 - mmengine - INFO - Epoch(val) [180][120/500] eta: 0:00:15 time: 0.0406 data_time: 0.0025 memory: 1008 2022/11/02 14:03:18 - mmengine - INFO - Epoch(val) [180][125/500] eta: 0:00:15 time: 0.0399 data_time: 0.0025 memory: 1008 2022/11/02 14:03:19 - mmengine - INFO - Epoch(val) [180][130/500] eta: 0:00:16 time: 0.0442 data_time: 0.0042 memory: 1008 2022/11/02 14:03:19 - mmengine - INFO - Epoch(val) [180][135/500] eta: 0:00:16 time: 0.0472 data_time: 0.0056 memory: 1008 2022/11/02 14:03:19 - mmengine - INFO - Epoch(val) [180][140/500] eta: 0:00:16 time: 0.0461 data_time: 0.0039 memory: 1008 2022/11/02 14:03:19 - mmengine - INFO - Epoch(val) [180][145/500] eta: 0:00:16 time: 0.0544 data_time: 0.0040 memory: 1008 2022/11/02 14:03:20 - mmengine - INFO - Epoch(val) [180][150/500] eta: 0:00:18 time: 0.0522 data_time: 0.0041 memory: 1008 2022/11/02 14:03:20 - mmengine - INFO - Epoch(val) [180][155/500] eta: 0:00:18 time: 0.0491 data_time: 0.0027 memory: 1008 2022/11/02 14:03:20 - mmengine - INFO - Epoch(val) [180][160/500] eta: 0:00:17 time: 0.0511 data_time: 0.0026 memory: 1008 2022/11/02 14:03:20 - mmengine - INFO - Epoch(val) [180][165/500] eta: 0:00:17 time: 0.0451 data_time: 0.0026 memory: 1008 2022/11/02 14:03:20 - mmengine - INFO - Epoch(val) [180][170/500] eta: 0:00:14 time: 0.0441 data_time: 0.0026 memory: 1008 2022/11/02 14:03:21 - mmengine - INFO - Epoch(val) [180][175/500] eta: 0:00:14 time: 0.0400 data_time: 0.0023 memory: 1008 2022/11/02 14:03:21 - mmengine - INFO - Epoch(val) [180][180/500] eta: 0:00:14 time: 0.0438 data_time: 0.0031 memory: 1008 2022/11/02 14:03:21 - mmengine - INFO - Epoch(val) [180][185/500] eta: 0:00:14 time: 0.0472 data_time: 0.0031 memory: 1008 2022/11/02 14:03:21 - mmengine - INFO - Epoch(val) [180][190/500] eta: 0:00:14 time: 0.0453 data_time: 0.0024 memory: 1008 2022/11/02 14:03:22 - mmengine - INFO - Epoch(val) [180][195/500] eta: 0:00:14 time: 0.0420 data_time: 0.0024 memory: 1008 2022/11/02 14:03:22 - mmengine - INFO - Epoch(val) [180][200/500] eta: 0:00:14 time: 0.0483 data_time: 0.0028 memory: 1008 2022/11/02 14:03:22 - mmengine - INFO - Epoch(val) [180][205/500] eta: 0:00:14 time: 0.0490 data_time: 0.0029 memory: 1008 2022/11/02 14:03:22 - mmengine - INFO - Epoch(val) [180][210/500] eta: 0:00:11 time: 0.0413 data_time: 0.0026 memory: 1008 2022/11/02 14:03:22 - mmengine - INFO - Epoch(val) [180][215/500] eta: 0:00:11 time: 0.0446 data_time: 0.0026 memory: 1008 2022/11/02 14:03:23 - mmengine - INFO - Epoch(val) [180][220/500] eta: 0:00:12 time: 0.0463 data_time: 0.0029 memory: 1008 2022/11/02 14:03:23 - mmengine - INFO - Epoch(val) [180][225/500] eta: 0:00:12 time: 0.0447 data_time: 0.0025 memory: 1008 2022/11/02 14:03:23 - mmengine - INFO - Epoch(val) [180][230/500] eta: 0:00:10 time: 0.0391 data_time: 0.0020 memory: 1008 2022/11/02 14:03:23 - mmengine - INFO - Epoch(val) [180][235/500] eta: 0:00:10 time: 0.0429 data_time: 0.0034 memory: 1008 2022/11/02 14:03:24 - mmengine - INFO - Epoch(val) [180][240/500] eta: 0:00:11 time: 0.0459 data_time: 0.0035 memory: 1008 2022/11/02 14:03:24 - mmengine - INFO - Epoch(val) [180][245/500] eta: 0:00:11 time: 0.0407 data_time: 0.0025 memory: 1008 2022/11/02 14:03:24 - mmengine - INFO - Epoch(val) [180][250/500] eta: 0:00:10 time: 0.0436 data_time: 0.0025 memory: 1008 2022/11/02 14:03:24 - mmengine - INFO - Epoch(val) [180][255/500] eta: 0:00:10 time: 0.0433 data_time: 0.0025 memory: 1008 2022/11/02 14:03:24 - mmengine - INFO - Epoch(val) [180][260/500] eta: 0:00:09 time: 0.0383 data_time: 0.0025 memory: 1008 2022/11/02 14:03:25 - mmengine - INFO - Epoch(val) [180][265/500] eta: 0:00:09 time: 0.0384 data_time: 0.0024 memory: 1008 2022/11/02 14:03:25 - mmengine - INFO - Epoch(val) [180][270/500] eta: 0:00:09 time: 0.0398 data_time: 0.0025 memory: 1008 2022/11/02 14:03:25 - mmengine - INFO - Epoch(val) [180][275/500] eta: 0:00:09 time: 0.0383 data_time: 0.0027 memory: 1008 2022/11/02 14:03:25 - mmengine - INFO - Epoch(val) [180][280/500] eta: 0:00:09 time: 0.0415 data_time: 0.0023 memory: 1008 2022/11/02 14:03:25 - mmengine - INFO - Epoch(val) [180][285/500] eta: 0:00:09 time: 0.0406 data_time: 0.0020 memory: 1008 2022/11/02 14:03:26 - mmengine - INFO - Epoch(val) [180][290/500] eta: 0:00:08 time: 0.0402 data_time: 0.0024 memory: 1008 2022/11/02 14:03:26 - mmengine - INFO - Epoch(val) [180][295/500] eta: 0:00:08 time: 0.0444 data_time: 0.0026 memory: 1008 2022/11/02 14:03:26 - mmengine - INFO - Epoch(val) [180][300/500] eta: 0:00:08 time: 0.0407 data_time: 0.0025 memory: 1008 2022/11/02 14:03:26 - mmengine - INFO - Epoch(val) [180][305/500] eta: 0:00:08 time: 0.0388 data_time: 0.0024 memory: 1008 2022/11/02 14:03:26 - mmengine - INFO - Epoch(val) [180][310/500] eta: 0:00:07 time: 0.0386 data_time: 0.0025 memory: 1008 2022/11/02 14:03:27 - mmengine - INFO - Epoch(val) [180][315/500] eta: 0:00:07 time: 0.0422 data_time: 0.0025 memory: 1008 2022/11/02 14:03:27 - mmengine - INFO - Epoch(val) [180][320/500] eta: 0:00:07 time: 0.0425 data_time: 0.0025 memory: 1008 2022/11/02 14:03:27 - mmengine - INFO - Epoch(val) [180][325/500] eta: 0:00:07 time: 0.0510 data_time: 0.0025 memory: 1008 2022/11/02 14:03:27 - mmengine - INFO - Epoch(val) [180][330/500] eta: 0:00:08 time: 0.0494 data_time: 0.0022 memory: 1008 2022/11/02 14:03:27 - mmengine - INFO - Epoch(val) [180][335/500] eta: 0:00:08 time: 0.0343 data_time: 0.0021 memory: 1008 2022/11/02 14:03:28 - mmengine - INFO - Epoch(val) [180][340/500] eta: 0:00:07 time: 0.0493 data_time: 0.0022 memory: 1008 2022/11/02 14:03:28 - mmengine - INFO - Epoch(val) [180][345/500] eta: 0:00:07 time: 0.0510 data_time: 0.0023 memory: 1008 2022/11/02 14:03:28 - mmengine - INFO - Epoch(val) [180][350/500] eta: 0:00:06 time: 0.0426 data_time: 0.0025 memory: 1008 2022/11/02 14:03:28 - mmengine - INFO - Epoch(val) [180][355/500] eta: 0:00:06 time: 0.0414 data_time: 0.0024 memory: 1008 2022/11/02 14:03:29 - mmengine - INFO - Epoch(val) [180][360/500] eta: 0:00:04 time: 0.0355 data_time: 0.0022 memory: 1008 2022/11/02 14:03:29 - mmengine - INFO - Epoch(val) [180][365/500] eta: 0:00:04 time: 0.0399 data_time: 0.0022 memory: 1008 2022/11/02 14:03:29 - mmengine - INFO - Epoch(val) [180][370/500] eta: 0:00:04 time: 0.0384 data_time: 0.0024 memory: 1008 2022/11/02 14:03:29 - mmengine - INFO - Epoch(val) [180][375/500] eta: 0:00:04 time: 0.0352 data_time: 0.0023 memory: 1008 2022/11/02 14:03:29 - mmengine - INFO - Epoch(val) [180][380/500] eta: 0:00:04 time: 0.0413 data_time: 0.0022 memory: 1008 2022/11/02 14:03:30 - mmengine - INFO - Epoch(val) [180][385/500] eta: 0:00:04 time: 0.0415 data_time: 0.0023 memory: 1008 2022/11/02 14:03:30 - mmengine - INFO - Epoch(val) [180][390/500] eta: 0:00:04 time: 0.0379 data_time: 0.0023 memory: 1008 2022/11/02 14:03:30 - mmengine - INFO - Epoch(val) [180][395/500] eta: 0:00:04 time: 0.0401 data_time: 0.0024 memory: 1008 2022/11/02 14:03:30 - mmengine - INFO - Epoch(val) [180][400/500] eta: 0:00:04 time: 0.0415 data_time: 0.0025 memory: 1008 2022/11/02 14:03:30 - mmengine - INFO - Epoch(val) [180][405/500] eta: 0:00:04 time: 0.0410 data_time: 0.0023 memory: 1008 2022/11/02 14:03:31 - mmengine - INFO - Epoch(val) [180][410/500] eta: 0:00:03 time: 0.0405 data_time: 0.0022 memory: 1008 2022/11/02 14:03:31 - mmengine - INFO - Epoch(val) [180][415/500] eta: 0:00:03 time: 0.0397 data_time: 0.0023 memory: 1008 2022/11/02 14:03:31 - mmengine - INFO - Epoch(val) [180][420/500] eta: 0:00:02 time: 0.0353 data_time: 0.0022 memory: 1008 2022/11/02 14:03:31 - mmengine - INFO - Epoch(val) [180][425/500] eta: 0:00:02 time: 0.0348 data_time: 0.0022 memory: 1008 2022/11/02 14:03:31 - mmengine - INFO - Epoch(val) [180][430/500] eta: 0:00:02 time: 0.0399 data_time: 0.0024 memory: 1008 2022/11/02 14:03:32 - mmengine - INFO - Epoch(val) [180][435/500] eta: 0:00:02 time: 0.0410 data_time: 0.0027 memory: 1008 2022/11/02 14:03:32 - mmengine - INFO - Epoch(val) [180][440/500] eta: 0:00:02 time: 0.0398 data_time: 0.0027 memory: 1008 2022/11/02 14:03:32 - mmengine - INFO - Epoch(val) [180][445/500] eta: 0:00:02 time: 0.0411 data_time: 0.0025 memory: 1008 2022/11/02 14:03:32 - mmengine - INFO - Epoch(val) [180][450/500] eta: 0:00:02 time: 0.0409 data_time: 0.0022 memory: 1008 2022/11/02 14:03:32 - mmengine - INFO - Epoch(val) [180][455/500] eta: 0:00:02 time: 0.0406 data_time: 0.0022 memory: 1008 2022/11/02 14:03:33 - mmengine - INFO - Epoch(val) [180][460/500] eta: 0:00:01 time: 0.0378 data_time: 0.0022 memory: 1008 2022/11/02 14:03:33 - mmengine - INFO - Epoch(val) [180][465/500] eta: 0:00:01 time: 0.0359 data_time: 0.0023 memory: 1008 2022/11/02 14:03:33 - mmengine - INFO - Epoch(val) [180][470/500] eta: 0:00:01 time: 0.0394 data_time: 0.0025 memory: 1008 2022/11/02 14:03:33 - mmengine - INFO - Epoch(val) [180][475/500] eta: 0:00:01 time: 0.0369 data_time: 0.0024 memory: 1008 2022/11/02 14:03:33 - mmengine - INFO - Epoch(val) [180][480/500] eta: 0:00:00 time: 0.0355 data_time: 0.0021 memory: 1008 2022/11/02 14:03:33 - mmengine - INFO - Epoch(val) [180][485/500] eta: 0:00:00 time: 0.0378 data_time: 0.0022 memory: 1008 2022/11/02 14:03:34 - mmengine - INFO - Epoch(val) [180][490/500] eta: 0:00:00 time: 0.0398 data_time: 0.0022 memory: 1008 2022/11/02 14:03:34 - mmengine - INFO - Epoch(val) [180][495/500] eta: 0:00:00 time: 0.0441 data_time: 0.0023 memory: 1008 2022/11/02 14:03:34 - mmengine - INFO - Epoch(val) [180][500/500] eta: 0:00:00 time: 0.0424 data_time: 0.0024 memory: 1008 2022/11/02 14:03:34 - mmengine - INFO - Evaluating hmean-iou... 2022/11/02 14:03:34 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.8228, precision: 0.5748, hmean: 0.6768 2022/11/02 14:03:34 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.8228, precision: 0.6803, hmean: 0.7448 2022/11/02 14:03:34 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.8199, precision: 0.7414, hmean: 0.7787 2022/11/02 14:03:34 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.8050, precision: 0.8019, hmean: 0.8035 2022/11/02 14:03:34 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.7357, precision: 0.8827, hmean: 0.8025 2022/11/02 14:03:34 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.3028, precision: 0.9632, hmean: 0.4608 2022/11/02 14:03:34 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0010, precision: 1.0000, hmean: 0.0019 2022/11/02 14:03:34 - mmengine - INFO - Epoch(val) [180][500/500] icdar/precision: 0.8019 icdar/recall: 0.8050 icdar/hmean: 0.8035 2022/11/02 14:03:39 - mmengine - INFO - Epoch(train) [181][5/63] lr: 1.8685e-03 eta: 0:00:00 time: 0.7865 data_time: 0.1780 memory: 14901 loss: 2.0057 loss_prob: 1.1636 loss_thr: 0.6523 loss_db: 0.1898 2022/11/02 14:03:42 - mmengine - INFO - Epoch(train) [181][10/63] lr: 1.8685e-03 eta: 10:32:38 time: 0.8159 data_time: 0.1837 memory: 14901 loss: 2.0035 loss_prob: 1.1666 loss_thr: 0.6453 loss_db: 0.1915 2022/11/02 14:03:45 - mmengine - INFO - Epoch(train) [181][15/63] lr: 1.8685e-03 eta: 10:32:38 time: 0.5388 data_time: 0.0105 memory: 14901 loss: 1.8820 loss_prob: 1.1041 loss_thr: 0.5967 loss_db: 0.1812 2022/11/02 14:03:47 - mmengine - INFO - Epoch(train) [181][20/63] lr: 1.8685e-03 eta: 10:32:26 time: 0.4895 data_time: 0.0052 memory: 14901 loss: 1.9479 loss_prob: 1.1443 loss_thr: 0.6199 loss_db: 0.1837 2022/11/02 14:03:50 - mmengine - INFO - Epoch(train) [181][25/63] lr: 1.8685e-03 eta: 10:32:26 time: 0.4784 data_time: 0.0065 memory: 14901 loss: 2.1978 loss_prob: 1.3223 loss_thr: 0.6629 loss_db: 0.2125 2022/11/02 14:03:53 - mmengine - INFO - Epoch(train) [181][30/63] lr: 1.8685e-03 eta: 10:32:17 time: 0.5426 data_time: 0.0613 memory: 14901 loss: 2.1929 loss_prob: 1.3147 loss_thr: 0.6625 loss_db: 0.2156 2022/11/02 14:03:55 - mmengine - INFO - Epoch(train) [181][35/63] lr: 1.8685e-03 eta: 10:32:17 time: 0.5488 data_time: 0.0649 memory: 14901 loss: 2.0395 loss_prob: 1.1860 loss_thr: 0.6583 loss_db: 0.1952 2022/11/02 14:03:58 - mmengine - INFO - Epoch(train) [181][40/63] lr: 1.8685e-03 eta: 10:32:07 time: 0.5096 data_time: 0.0097 memory: 14901 loss: 1.9963 loss_prob: 1.1486 loss_thr: 0.6581 loss_db: 0.1895 2022/11/02 14:04:00 - mmengine - INFO - Epoch(train) [181][45/63] lr: 1.8685e-03 eta: 10:32:07 time: 0.4775 data_time: 0.0046 memory: 14901 loss: 2.0353 loss_prob: 1.2006 loss_thr: 0.6353 loss_db: 0.1994 2022/11/02 14:04:02 - mmengine - INFO - Epoch(train) [181][50/63] lr: 1.8685e-03 eta: 10:31:53 time: 0.4566 data_time: 0.0081 memory: 14901 loss: 2.0280 loss_prob: 1.2179 loss_thr: 0.6134 loss_db: 0.1966 2022/11/02 14:04:05 - mmengine - INFO - Epoch(train) [181][55/63] lr: 1.8685e-03 eta: 10:31:53 time: 0.4991 data_time: 0.0203 memory: 14901 loss: 1.9632 loss_prob: 1.1518 loss_thr: 0.6292 loss_db: 0.1822 2022/11/02 14:04:07 - mmengine - INFO - Epoch(train) [181][60/63] lr: 1.8685e-03 eta: 10:31:41 time: 0.4842 data_time: 0.0189 memory: 14901 loss: 1.9907 loss_prob: 1.1623 loss_thr: 0.6365 loss_db: 0.1918 2022/11/02 14:04:09 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:04:14 - mmengine - INFO - Epoch(train) [182][5/63] lr: 1.8668e-03 eta: 10:31:41 time: 0.7294 data_time: 0.2241 memory: 14901 loss: 2.0421 loss_prob: 1.2011 loss_thr: 0.6453 loss_db: 0.1956 2022/11/02 14:04:16 - mmengine - INFO - Epoch(train) [182][10/63] lr: 1.8668e-03 eta: 10:31:31 time: 0.7265 data_time: 0.2241 memory: 14901 loss: 2.1049 loss_prob: 1.2405 loss_thr: 0.6641 loss_db: 0.2003 2022/11/02 14:04:18 - mmengine - INFO - Epoch(train) [182][15/63] lr: 1.8668e-03 eta: 10:31:31 time: 0.4804 data_time: 0.0050 memory: 14901 loss: 1.9144 loss_prob: 1.1141 loss_thr: 0.6171 loss_db: 0.1832 2022/11/02 14:04:21 - mmengine - INFO - Epoch(train) [182][20/63] lr: 1.8668e-03 eta: 10:31:23 time: 0.5426 data_time: 0.0355 memory: 14901 loss: 1.8327 loss_prob: 1.0589 loss_thr: 0.5983 loss_db: 0.1755 2022/11/02 14:04:24 - mmengine - INFO - Epoch(train) [182][25/63] lr: 1.8668e-03 eta: 10:31:23 time: 0.5701 data_time: 0.0353 memory: 14901 loss: 1.9129 loss_prob: 1.1154 loss_thr: 0.6165 loss_db: 0.1810 2022/11/02 14:04:27 - mmengine - INFO - Epoch(train) [182][30/63] lr: 1.8668e-03 eta: 10:31:14 time: 0.5421 data_time: 0.0052 memory: 14901 loss: 2.0098 loss_prob: 1.1789 loss_thr: 0.6341 loss_db: 0.1968 2022/11/02 14:04:30 - mmengine - INFO - Epoch(train) [182][35/63] lr: 1.8668e-03 eta: 10:31:14 time: 0.5742 data_time: 0.0054 memory: 14901 loss: 2.0655 loss_prob: 1.2263 loss_thr: 0.6387 loss_db: 0.2005 2022/11/02 14:04:32 - mmengine - INFO - Epoch(train) [182][40/63] lr: 1.8668e-03 eta: 10:31:06 time: 0.5483 data_time: 0.0052 memory: 14901 loss: 1.9331 loss_prob: 1.1392 loss_thr: 0.6101 loss_db: 0.1837 2022/11/02 14:04:35 - mmengine - INFO - Epoch(train) [182][45/63] lr: 1.8668e-03 eta: 10:31:06 time: 0.5496 data_time: 0.0259 memory: 14901 loss: 1.7582 loss_prob: 1.0066 loss_thr: 0.5825 loss_db: 0.1690 2022/11/02 14:04:38 - mmengine - INFO - Epoch(train) [182][50/63] lr: 1.8668e-03 eta: 10:30:58 time: 0.5463 data_time: 0.0281 memory: 14901 loss: 1.8361 loss_prob: 1.0540 loss_thr: 0.6092 loss_db: 0.1729 2022/11/02 14:04:40 - mmengine - INFO - Epoch(train) [182][55/63] lr: 1.8668e-03 eta: 10:30:58 time: 0.4799 data_time: 0.0082 memory: 14901 loss: 1.8515 loss_prob: 1.0623 loss_thr: 0.6181 loss_db: 0.1711 2022/11/02 14:04:43 - mmengine - INFO - Epoch(train) [182][60/63] lr: 1.8668e-03 eta: 10:30:47 time: 0.5118 data_time: 0.0058 memory: 14901 loss: 1.9188 loss_prob: 1.1170 loss_thr: 0.6222 loss_db: 0.1797 2022/11/02 14:04:44 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:04:49 - mmengine - INFO - Epoch(train) [183][5/63] lr: 1.8652e-03 eta: 10:30:47 time: 0.7193 data_time: 0.2095 memory: 14901 loss: 2.0045 loss_prob: 1.1718 loss_thr: 0.6396 loss_db: 0.1931 2022/11/02 14:04:51 - mmengine - INFO - Epoch(train) [183][10/63] lr: 1.8652e-03 eta: 10:30:37 time: 0.7195 data_time: 0.2118 memory: 14901 loss: 1.9118 loss_prob: 1.1327 loss_thr: 0.5955 loss_db: 0.1836 2022/11/02 14:04:54 - mmengine - INFO - Epoch(train) [183][15/63] lr: 1.8652e-03 eta: 10:30:37 time: 0.4905 data_time: 0.0070 memory: 14901 loss: 1.9365 loss_prob: 1.1337 loss_thr: 0.6197 loss_db: 0.1830 2022/11/02 14:04:56 - mmengine - INFO - Epoch(train) [183][20/63] lr: 1.8652e-03 eta: 10:30:26 time: 0.4972 data_time: 0.0047 memory: 14901 loss: 2.0374 loss_prob: 1.1755 loss_thr: 0.6685 loss_db: 0.1934 2022/11/02 14:04:59 - mmengine - INFO - Epoch(train) [183][25/63] lr: 1.8652e-03 eta: 10:30:26 time: 0.4820 data_time: 0.0077 memory: 14901 loss: 1.9713 loss_prob: 1.1267 loss_thr: 0.6585 loss_db: 0.1861 2022/11/02 14:05:02 - mmengine - INFO - Epoch(train) [183][30/63] lr: 1.8652e-03 eta: 10:30:19 time: 0.5620 data_time: 0.0308 memory: 14901 loss: 1.8150 loss_prob: 1.0303 loss_thr: 0.6180 loss_db: 0.1667 2022/11/02 14:05:05 - mmengine - INFO - Epoch(train) [183][35/63] lr: 1.8652e-03 eta: 10:30:19 time: 0.6574 data_time: 0.0291 memory: 14901 loss: 1.8068 loss_prob: 1.0240 loss_thr: 0.6128 loss_db: 0.1700 2022/11/02 14:05:08 - mmengine - INFO - Epoch(train) [183][40/63] lr: 1.8652e-03 eta: 10:30:14 time: 0.6229 data_time: 0.0070 memory: 14901 loss: 1.8113 loss_prob: 1.0214 loss_thr: 0.6157 loss_db: 0.1742 2022/11/02 14:05:11 - mmengine - INFO - Epoch(train) [183][45/63] lr: 1.8652e-03 eta: 10:30:14 time: 0.5902 data_time: 0.0056 memory: 14901 loss: 1.8551 loss_prob: 1.0714 loss_thr: 0.6075 loss_db: 0.1761 2022/11/02 14:05:14 - mmengine - INFO - Epoch(train) [183][50/63] lr: 1.8652e-03 eta: 10:30:08 time: 0.5836 data_time: 0.0115 memory: 14901 loss: 1.9797 loss_prob: 1.1570 loss_thr: 0.6363 loss_db: 0.1865 2022/11/02 14:05:17 - mmengine - INFO - Epoch(train) [183][55/63] lr: 1.8652e-03 eta: 10:30:08 time: 0.6379 data_time: 0.0233 memory: 14901 loss: 1.9838 loss_prob: 1.1456 loss_thr: 0.6517 loss_db: 0.1865 2022/11/02 14:05:21 - mmengine - INFO - Epoch(train) [183][60/63] lr: 1.8652e-03 eta: 10:30:07 time: 0.6781 data_time: 0.0170 memory: 14901 loss: 1.9922 loss_prob: 1.1575 loss_thr: 0.6424 loss_db: 0.1924 2022/11/02 14:05:22 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:05:28 - mmengine - INFO - Epoch(train) [184][5/63] lr: 1.8635e-03 eta: 10:30:07 time: 0.8169 data_time: 0.2302 memory: 14901 loss: 1.8084 loss_prob: 1.0308 loss_thr: 0.6115 loss_db: 0.1661 2022/11/02 14:05:31 - mmengine - INFO - Epoch(train) [184][10/63] lr: 1.8635e-03 eta: 10:30:06 time: 0.8871 data_time: 0.2295 memory: 14901 loss: 1.8555 loss_prob: 1.0704 loss_thr: 0.6098 loss_db: 0.1753 2022/11/02 14:05:34 - mmengine - INFO - Epoch(train) [184][15/63] lr: 1.8635e-03 eta: 10:30:06 time: 0.5743 data_time: 0.0127 memory: 14901 loss: 1.9063 loss_prob: 1.0998 loss_thr: 0.6243 loss_db: 0.1822 2022/11/02 14:05:36 - mmengine - INFO - Epoch(train) [184][20/63] lr: 1.8635e-03 eta: 10:29:58 time: 0.5525 data_time: 0.0134 memory: 14901 loss: 1.8955 loss_prob: 1.0899 loss_thr: 0.6273 loss_db: 0.1783 2022/11/02 14:05:39 - mmengine - INFO - Epoch(train) [184][25/63] lr: 1.8635e-03 eta: 10:29:58 time: 0.5303 data_time: 0.0155 memory: 14901 loss: 1.9058 loss_prob: 1.1116 loss_thr: 0.6162 loss_db: 0.1780 2022/11/02 14:05:42 - mmengine - INFO - Epoch(train) [184][30/63] lr: 1.8635e-03 eta: 10:29:51 time: 0.5611 data_time: 0.0316 memory: 14901 loss: 1.8487 loss_prob: 1.0677 loss_thr: 0.6087 loss_db: 0.1723 2022/11/02 14:05:45 - mmengine - INFO - Epoch(train) [184][35/63] lr: 1.8635e-03 eta: 10:29:51 time: 0.5690 data_time: 0.0221 memory: 14901 loss: 2.0229 loss_prob: 1.1861 loss_thr: 0.6415 loss_db: 0.1953 2022/11/02 14:05:48 - mmengine - INFO - Epoch(train) [184][40/63] lr: 1.8635e-03 eta: 10:29:46 time: 0.6155 data_time: 0.0107 memory: 14901 loss: 1.9744 loss_prob: 1.1613 loss_thr: 0.6227 loss_db: 0.1904 2022/11/02 14:05:51 - mmengine - INFO - Epoch(train) [184][45/63] lr: 1.8635e-03 eta: 10:29:46 time: 0.6353 data_time: 0.0100 memory: 14901 loss: 1.8828 loss_prob: 1.1018 loss_thr: 0.6027 loss_db: 0.1784 2022/11/02 14:05:54 - mmengine - INFO - Epoch(train) [184][50/63] lr: 1.8635e-03 eta: 10:29:38 time: 0.5526 data_time: 0.0110 memory: 14901 loss: 2.0471 loss_prob: 1.2322 loss_thr: 0.6220 loss_db: 0.1929 2022/11/02 14:05:57 - mmengine - INFO - Epoch(train) [184][55/63] lr: 1.8635e-03 eta: 10:29:38 time: 0.6362 data_time: 0.0185 memory: 14901 loss: 1.9180 loss_prob: 1.1418 loss_thr: 0.5942 loss_db: 0.1820 2022/11/02 14:06:02 - mmengine - INFO - Epoch(train) [184][60/63] lr: 1.8635e-03 eta: 10:29:44 time: 0.7934 data_time: 0.0151 memory: 14901 loss: 1.8473 loss_prob: 1.0847 loss_thr: 0.5798 loss_db: 0.1828 2022/11/02 14:06:03 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:06:08 - mmengine - INFO - Epoch(train) [185][5/63] lr: 1.8619e-03 eta: 10:29:44 time: 0.7168 data_time: 0.1785 memory: 14901 loss: 1.9098 loss_prob: 1.1224 loss_thr: 0.6034 loss_db: 0.1840 2022/11/02 14:06:10 - mmengine - INFO - Epoch(train) [185][10/63] lr: 1.8619e-03 eta: 10:29:34 time: 0.7255 data_time: 0.1758 memory: 14901 loss: 1.8436 loss_prob: 1.0764 loss_thr: 0.5899 loss_db: 0.1773 2022/11/02 14:06:12 - mmengine - INFO - Epoch(train) [185][15/63] lr: 1.8619e-03 eta: 10:29:34 time: 0.4863 data_time: 0.0047 memory: 14901 loss: 1.7912 loss_prob: 1.0344 loss_thr: 0.5846 loss_db: 0.1722 2022/11/02 14:06:15 - mmengine - INFO - Epoch(train) [185][20/63] lr: 1.8619e-03 eta: 10:29:21 time: 0.4734 data_time: 0.0051 memory: 14901 loss: 1.9096 loss_prob: 1.1125 loss_thr: 0.6147 loss_db: 0.1824 2022/11/02 14:06:18 - mmengine - INFO - Epoch(train) [185][25/63] lr: 1.8619e-03 eta: 10:29:21 time: 0.5294 data_time: 0.0113 memory: 14901 loss: 1.9404 loss_prob: 1.1303 loss_thr: 0.6289 loss_db: 0.1811 2022/11/02 14:06:20 - mmengine - INFO - Epoch(train) [185][30/63] lr: 1.8619e-03 eta: 10:29:12 time: 0.5331 data_time: 0.0311 memory: 14901 loss: 1.8769 loss_prob: 1.0774 loss_thr: 0.6222 loss_db: 0.1772 2022/11/02 14:06:23 - mmengine - INFO - Epoch(train) [185][35/63] lr: 1.8619e-03 eta: 10:29:12 time: 0.4881 data_time: 0.0251 memory: 14901 loss: 1.9355 loss_prob: 1.1151 loss_thr: 0.6354 loss_db: 0.1850 2022/11/02 14:06:25 - mmengine - INFO - Epoch(train) [185][40/63] lr: 1.8619e-03 eta: 10:29:01 time: 0.4948 data_time: 0.0049 memory: 14901 loss: 1.9398 loss_prob: 1.1194 loss_thr: 0.6356 loss_db: 0.1848 2022/11/02 14:06:28 - mmengine - INFO - Epoch(train) [185][45/63] lr: 1.8619e-03 eta: 10:29:01 time: 0.5001 data_time: 0.0048 memory: 14901 loss: 2.0148 loss_prob: 1.1760 loss_thr: 0.6476 loss_db: 0.1912 2022/11/02 14:06:31 - mmengine - INFO - Epoch(train) [185][50/63] lr: 1.8619e-03 eta: 10:28:52 time: 0.5366 data_time: 0.0221 memory: 14901 loss: 2.0672 loss_prob: 1.2185 loss_thr: 0.6514 loss_db: 0.1973 2022/11/02 14:06:33 - mmengine - INFO - Epoch(train) [185][55/63] lr: 1.8619e-03 eta: 10:28:52 time: 0.5272 data_time: 0.0231 memory: 14901 loss: 1.9852 loss_prob: 1.1615 loss_thr: 0.6319 loss_db: 0.1917 2022/11/02 14:06:35 - mmengine - INFO - Epoch(train) [185][60/63] lr: 1.8619e-03 eta: 10:28:40 time: 0.4714 data_time: 0.0061 memory: 14901 loss: 1.9146 loss_prob: 1.1048 loss_thr: 0.6297 loss_db: 0.1800 2022/11/02 14:06:37 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:06:41 - mmengine - INFO - Epoch(train) [186][5/63] lr: 1.8602e-03 eta: 10:28:40 time: 0.6986 data_time: 0.2084 memory: 14901 loss: 1.8405 loss_prob: 1.0587 loss_thr: 0.6053 loss_db: 0.1765 2022/11/02 14:06:44 - mmengine - INFO - Epoch(train) [186][10/63] lr: 1.8602e-03 eta: 10:28:29 time: 0.7097 data_time: 0.2085 memory: 14901 loss: 1.8260 loss_prob: 1.0419 loss_thr: 0.6129 loss_db: 0.1711 2022/11/02 14:06:46 - mmengine - INFO - Epoch(train) [186][15/63] lr: 1.8602e-03 eta: 10:28:29 time: 0.4731 data_time: 0.0054 memory: 14901 loss: 1.8274 loss_prob: 1.0484 loss_thr: 0.6106 loss_db: 0.1684 2022/11/02 14:06:48 - mmengine - INFO - Epoch(train) [186][20/63] lr: 1.8602e-03 eta: 10:28:17 time: 0.4802 data_time: 0.0049 memory: 14901 loss: 1.8552 loss_prob: 1.0681 loss_thr: 0.6157 loss_db: 0.1715 2022/11/02 14:06:51 - mmengine - INFO - Epoch(train) [186][25/63] lr: 1.8602e-03 eta: 10:28:17 time: 0.5023 data_time: 0.0182 memory: 14901 loss: 1.9608 loss_prob: 1.1422 loss_thr: 0.6339 loss_db: 0.1847 2022/11/02 14:06:54 - mmengine - INFO - Epoch(train) [186][30/63] lr: 1.8602e-03 eta: 10:28:07 time: 0.5179 data_time: 0.0456 memory: 14901 loss: 1.9384 loss_prob: 1.1421 loss_thr: 0.6115 loss_db: 0.1849 2022/11/02 14:06:56 - mmengine - INFO - Epoch(train) [186][35/63] lr: 1.8602e-03 eta: 10:28:07 time: 0.5051 data_time: 0.0321 memory: 14901 loss: 1.8304 loss_prob: 1.0647 loss_thr: 0.5919 loss_db: 0.1738 2022/11/02 14:06:59 - mmengine - INFO - Epoch(train) [186][40/63] lr: 1.8602e-03 eta: 10:27:56 time: 0.4985 data_time: 0.0048 memory: 14901 loss: 1.8015 loss_prob: 1.0359 loss_thr: 0.5944 loss_db: 0.1712 2022/11/02 14:07:01 - mmengine - INFO - Epoch(train) [186][45/63] lr: 1.8602e-03 eta: 10:27:56 time: 0.4974 data_time: 0.0049 memory: 14901 loss: 1.9133 loss_prob: 1.1103 loss_thr: 0.6147 loss_db: 0.1883 2022/11/02 14:07:04 - mmengine - INFO - Epoch(train) [186][50/63] lr: 1.8602e-03 eta: 10:27:48 time: 0.5459 data_time: 0.0201 memory: 14901 loss: 1.9973 loss_prob: 1.1452 loss_thr: 0.6590 loss_db: 0.1931 2022/11/02 14:07:06 - mmengine - INFO - Epoch(train) [186][55/63] lr: 1.8602e-03 eta: 10:27:48 time: 0.5318 data_time: 0.0261 memory: 14901 loss: 1.8291 loss_prob: 1.0264 loss_thr: 0.6356 loss_db: 0.1671 2022/11/02 14:07:09 - mmengine - INFO - Epoch(train) [186][60/63] lr: 1.8602e-03 eta: 10:27:37 time: 0.4914 data_time: 0.0124 memory: 14901 loss: 1.8873 loss_prob: 1.1015 loss_thr: 0.6104 loss_db: 0.1754 2022/11/02 14:07:10 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:07:15 - mmengine - INFO - Epoch(train) [187][5/63] lr: 1.8586e-03 eta: 10:27:37 time: 0.7137 data_time: 0.2044 memory: 14901 loss: 1.9247 loss_prob: 1.1080 loss_thr: 0.6351 loss_db: 0.1817 2022/11/02 14:07:17 - mmengine - INFO - Epoch(train) [187][10/63] lr: 1.8586e-03 eta: 10:27:27 time: 0.7329 data_time: 0.2043 memory: 14901 loss: 1.7386 loss_prob: 0.9706 loss_thr: 0.6074 loss_db: 0.1605 2022/11/02 14:07:20 - mmengine - INFO - Epoch(train) [187][15/63] lr: 1.8586e-03 eta: 10:27:27 time: 0.4804 data_time: 0.0049 memory: 14901 loss: 1.7758 loss_prob: 1.0084 loss_thr: 0.6019 loss_db: 0.1655 2022/11/02 14:07:22 - mmengine - INFO - Epoch(train) [187][20/63] lr: 1.8586e-03 eta: 10:27:15 time: 0.4788 data_time: 0.0077 memory: 14901 loss: 1.9321 loss_prob: 1.1230 loss_thr: 0.6261 loss_db: 0.1831 2022/11/02 14:07:25 - mmengine - INFO - Epoch(train) [187][25/63] lr: 1.8586e-03 eta: 10:27:15 time: 0.5047 data_time: 0.0297 memory: 14901 loss: 1.9302 loss_prob: 1.1241 loss_thr: 0.6236 loss_db: 0.1825 2022/11/02 14:07:27 - mmengine - INFO - Epoch(train) [187][30/63] lr: 1.8586e-03 eta: 10:27:05 time: 0.5043 data_time: 0.0322 memory: 14901 loss: 1.9946 loss_prob: 1.1803 loss_thr: 0.6220 loss_db: 0.1923 2022/11/02 14:07:30 - mmengine - INFO - Epoch(train) [187][35/63] lr: 1.8586e-03 eta: 10:27:05 time: 0.4881 data_time: 0.0099 memory: 14901 loss: 1.9153 loss_prob: 1.1109 loss_thr: 0.6221 loss_db: 0.1823 2022/11/02 14:07:32 - mmengine - INFO - Epoch(train) [187][40/63] lr: 1.8586e-03 eta: 10:26:54 time: 0.4983 data_time: 0.0046 memory: 14901 loss: 1.8595 loss_prob: 1.0670 loss_thr: 0.6156 loss_db: 0.1770 2022/11/02 14:07:35 - mmengine - INFO - Epoch(train) [187][45/63] lr: 1.8586e-03 eta: 10:26:54 time: 0.5029 data_time: 0.0045 memory: 14901 loss: 1.9403 loss_prob: 1.1451 loss_thr: 0.6037 loss_db: 0.1915 2022/11/02 14:07:38 - mmengine - INFO - Epoch(train) [187][50/63] lr: 1.8586e-03 eta: 10:26:48 time: 0.5839 data_time: 0.0267 memory: 14901 loss: 1.9105 loss_prob: 1.1221 loss_thr: 0.5992 loss_db: 0.1892 2022/11/02 14:07:42 - mmengine - INFO - Epoch(train) [187][55/63] lr: 1.8586e-03 eta: 10:26:48 time: 0.6921 data_time: 0.0278 memory: 14901 loss: 1.9143 loss_prob: 1.1176 loss_thr: 0.6113 loss_db: 0.1854 2022/11/02 14:07:46 - mmengine - INFO - Epoch(train) [187][60/63] lr: 1.8586e-03 eta: 10:26:51 time: 0.7582 data_time: 0.0060 memory: 14901 loss: 1.9551 loss_prob: 1.1478 loss_thr: 0.6173 loss_db: 0.1900 2022/11/02 14:07:48 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:07:53 - mmengine - INFO - Epoch(train) [188][5/63] lr: 1.8569e-03 eta: 10:26:51 time: 0.8994 data_time: 0.2295 memory: 14901 loss: 1.9221 loss_prob: 1.1224 loss_thr: 0.6136 loss_db: 0.1861 2022/11/02 14:07:56 - mmengine - INFO - Epoch(train) [188][10/63] lr: 1.8569e-03 eta: 10:26:46 time: 0.8194 data_time: 0.2326 memory: 14901 loss: 1.9498 loss_prob: 1.1449 loss_thr: 0.6189 loss_db: 0.1860 2022/11/02 14:07:58 - mmengine - INFO - Epoch(train) [188][15/63] lr: 1.8569e-03 eta: 10:26:46 time: 0.5192 data_time: 0.0080 memory: 14901 loss: 2.1966 loss_prob: 1.3316 loss_thr: 0.6508 loss_db: 0.2142 2022/11/02 14:08:01 - mmengine - INFO - Epoch(train) [188][20/63] lr: 1.8569e-03 eta: 10:26:36 time: 0.5061 data_time: 0.0063 memory: 14901 loss: 2.2928 loss_prob: 1.4082 loss_thr: 0.6588 loss_db: 0.2258 2022/11/02 14:08:05 - mmengine - INFO - Epoch(train) [188][25/63] lr: 1.8569e-03 eta: 10:26:36 time: 0.6386 data_time: 0.0269 memory: 14901 loss: 2.0249 loss_prob: 1.2046 loss_thr: 0.6263 loss_db: 0.1940 2022/11/02 14:08:08 - mmengine - INFO - Epoch(train) [188][30/63] lr: 1.8569e-03 eta: 10:26:37 time: 0.7112 data_time: 0.0308 memory: 14901 loss: 1.7858 loss_prob: 1.0361 loss_thr: 0.5826 loss_db: 0.1671 2022/11/02 14:08:11 - mmengine - INFO - Epoch(train) [188][35/63] lr: 1.8569e-03 eta: 10:26:37 time: 0.6433 data_time: 0.0135 memory: 14901 loss: 1.8921 loss_prob: 1.1125 loss_thr: 0.5983 loss_db: 0.1814 2022/11/02 14:08:14 - mmengine - INFO - Epoch(train) [188][40/63] lr: 1.8569e-03 eta: 10:26:29 time: 0.5628 data_time: 0.0125 memory: 14901 loss: 1.9109 loss_prob: 1.1178 loss_thr: 0.6109 loss_db: 0.1823 2022/11/02 14:08:17 - mmengine - INFO - Epoch(train) [188][45/63] lr: 1.8569e-03 eta: 10:26:29 time: 0.5836 data_time: 0.0109 memory: 14901 loss: 1.8708 loss_prob: 1.0776 loss_thr: 0.6153 loss_db: 0.1779 2022/11/02 14:08:20 - mmengine - INFO - Epoch(train) [188][50/63] lr: 1.8569e-03 eta: 10:26:26 time: 0.6434 data_time: 0.0164 memory: 14901 loss: 1.9874 loss_prob: 1.1697 loss_thr: 0.6229 loss_db: 0.1947 2022/11/02 14:08:23 - mmengine - INFO - Epoch(train) [188][55/63] lr: 1.8569e-03 eta: 10:26:26 time: 0.5811 data_time: 0.0209 memory: 14901 loss: 1.9525 loss_prob: 1.1601 loss_thr: 0.6027 loss_db: 0.1896 2022/11/02 14:08:26 - mmengine - INFO - Epoch(train) [188][60/63] lr: 1.8569e-03 eta: 10:26:22 time: 0.6208 data_time: 0.0149 memory: 14901 loss: 1.8329 loss_prob: 1.0676 loss_thr: 0.5952 loss_db: 0.1701 2022/11/02 14:08:27 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:08:33 - mmengine - INFO - Epoch(train) [189][5/63] lr: 1.8553e-03 eta: 10:26:22 time: 0.8491 data_time: 0.2334 memory: 14901 loss: 1.7927 loss_prob: 1.0226 loss_thr: 0.6012 loss_db: 0.1689 2022/11/02 14:08:36 - mmengine - INFO - Epoch(train) [189][10/63] lr: 1.8553e-03 eta: 10:26:19 time: 0.8561 data_time: 0.2416 memory: 14901 loss: 1.7820 loss_prob: 1.0106 loss_thr: 0.6055 loss_db: 0.1659 2022/11/02 14:08:39 - mmengine - INFO - Epoch(train) [189][15/63] lr: 1.8553e-03 eta: 10:26:19 time: 0.5160 data_time: 0.0144 memory: 14901 loss: 1.7867 loss_prob: 1.0156 loss_thr: 0.6059 loss_db: 0.1652 2022/11/02 14:08:41 - mmengine - INFO - Epoch(train) [189][20/63] lr: 1.8553e-03 eta: 10:26:08 time: 0.4911 data_time: 0.0054 memory: 14901 loss: 1.8474 loss_prob: 1.0774 loss_thr: 0.5941 loss_db: 0.1759 2022/11/02 14:08:44 - mmengine - INFO - Epoch(train) [189][25/63] lr: 1.8553e-03 eta: 10:26:08 time: 0.5222 data_time: 0.0234 memory: 14901 loss: 1.8813 loss_prob: 1.0922 loss_thr: 0.6103 loss_db: 0.1789 2022/11/02 14:08:46 - mmengine - INFO - Epoch(train) [189][30/63] lr: 1.8553e-03 eta: 10:25:59 time: 0.5263 data_time: 0.0323 memory: 14901 loss: 1.7766 loss_prob: 1.0155 loss_thr: 0.5923 loss_db: 0.1688 2022/11/02 14:08:49 - mmengine - INFO - Epoch(train) [189][35/63] lr: 1.8553e-03 eta: 10:25:59 time: 0.4985 data_time: 0.0213 memory: 14901 loss: 1.9143 loss_prob: 1.1185 loss_thr: 0.6143 loss_db: 0.1816 2022/11/02 14:08:51 - mmengine - INFO - Epoch(train) [189][40/63] lr: 1.8553e-03 eta: 10:25:50 time: 0.5272 data_time: 0.0148 memory: 14901 loss: 2.0102 loss_prob: 1.1708 loss_thr: 0.6515 loss_db: 0.1879 2022/11/02 14:08:54 - mmengine - INFO - Epoch(train) [189][45/63] lr: 1.8553e-03 eta: 10:25:50 time: 0.5629 data_time: 0.0077 memory: 14901 loss: 1.9033 loss_prob: 1.0834 loss_thr: 0.6393 loss_db: 0.1806 2022/11/02 14:08:57 - mmengine - INFO - Epoch(train) [189][50/63] lr: 1.8553e-03 eta: 10:25:41 time: 0.5347 data_time: 0.0165 memory: 14901 loss: 1.8745 loss_prob: 1.0638 loss_thr: 0.6345 loss_db: 0.1762 2022/11/02 14:08:59 - mmengine - INFO - Epoch(train) [189][55/63] lr: 1.8553e-03 eta: 10:25:41 time: 0.4772 data_time: 0.0210 memory: 14901 loss: 1.9954 loss_prob: 1.1611 loss_thr: 0.6432 loss_db: 0.1911 2022/11/02 14:09:02 - mmengine - INFO - Epoch(train) [189][60/63] lr: 1.8553e-03 eta: 10:25:29 time: 0.4836 data_time: 0.0110 memory: 14901 loss: 1.9833 loss_prob: 1.1718 loss_thr: 0.6206 loss_db: 0.1909 2022/11/02 14:09:03 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:09:07 - mmengine - INFO - Epoch(train) [190][5/63] lr: 1.8536e-03 eta: 10:25:29 time: 0.6781 data_time: 0.2116 memory: 14901 loss: 2.1089 loss_prob: 1.2808 loss_thr: 0.6255 loss_db: 0.2026 2022/11/02 14:09:10 - mmengine - INFO - Epoch(train) [190][10/63] lr: 1.8536e-03 eta: 10:25:20 time: 0.7261 data_time: 0.2173 memory: 14901 loss: 2.2561 loss_prob: 1.3870 loss_thr: 0.6491 loss_db: 0.2199 2022/11/02 14:09:13 - mmengine - INFO - Epoch(train) [190][15/63] lr: 1.8536e-03 eta: 10:25:20 time: 0.5183 data_time: 0.0138 memory: 14901 loss: 2.3900 loss_prob: 1.4922 loss_thr: 0.6567 loss_db: 0.2412 2022/11/02 14:09:15 - mmengine - INFO - Epoch(train) [190][20/63] lr: 1.8536e-03 eta: 10:25:09 time: 0.5052 data_time: 0.0101 memory: 14901 loss: 2.3418 loss_prob: 1.4703 loss_thr: 0.6380 loss_db: 0.2336 2022/11/02 14:09:18 - mmengine - INFO - Epoch(train) [190][25/63] lr: 1.8536e-03 eta: 10:25:09 time: 0.5038 data_time: 0.0300 memory: 14901 loss: 2.0434 loss_prob: 1.2271 loss_thr: 0.6132 loss_db: 0.2031 2022/11/02 14:09:20 - mmengine - INFO - Epoch(train) [190][30/63] lr: 1.8536e-03 eta: 10:24:59 time: 0.5104 data_time: 0.0270 memory: 14901 loss: 1.8872 loss_prob: 1.0879 loss_thr: 0.6182 loss_db: 0.1811 2022/11/02 14:09:23 - mmengine - INFO - Epoch(train) [190][35/63] lr: 1.8536e-03 eta: 10:24:59 time: 0.5005 data_time: 0.0109 memory: 14901 loss: 1.9007 loss_prob: 1.0929 loss_thr: 0.6346 loss_db: 0.1732 2022/11/02 14:09:25 - mmengine - INFO - Epoch(train) [190][40/63] lr: 1.8536e-03 eta: 10:24:48 time: 0.4939 data_time: 0.0156 memory: 14901 loss: 1.8722 loss_prob: 1.0951 loss_thr: 0.6008 loss_db: 0.1764 2022/11/02 14:09:27 - mmengine - INFO - Epoch(train) [190][45/63] lr: 1.8536e-03 eta: 10:24:48 time: 0.4704 data_time: 0.0089 memory: 14901 loss: 1.8459 loss_prob: 1.0782 loss_thr: 0.5900 loss_db: 0.1778 2022/11/02 14:09:30 - mmengine - INFO - Epoch(train) [190][50/63] lr: 1.8536e-03 eta: 10:24:37 time: 0.4825 data_time: 0.0152 memory: 14901 loss: 1.7598 loss_prob: 1.0105 loss_thr: 0.5859 loss_db: 0.1634 2022/11/02 14:09:32 - mmengine - INFO - Epoch(train) [190][55/63] lr: 1.8536e-03 eta: 10:24:37 time: 0.4985 data_time: 0.0150 memory: 14901 loss: 1.7747 loss_prob: 1.0110 loss_thr: 0.5969 loss_db: 0.1668 2022/11/02 14:09:35 - mmengine - INFO - Epoch(train) [190][60/63] lr: 1.8536e-03 eta: 10:24:24 time: 0.4682 data_time: 0.0088 memory: 14901 loss: 1.8467 loss_prob: 1.0581 loss_thr: 0.6123 loss_db: 0.1763 2022/11/02 14:09:36 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:09:42 - mmengine - INFO - Epoch(train) [191][5/63] lr: 1.8520e-03 eta: 10:24:24 time: 0.8393 data_time: 0.2423 memory: 14901 loss: 1.8700 loss_prob: 1.1009 loss_thr: 0.5882 loss_db: 0.1809 2022/11/02 14:09:45 - mmengine - INFO - Epoch(train) [191][10/63] lr: 1.8520e-03 eta: 10:24:23 time: 0.8881 data_time: 0.2410 memory: 14901 loss: 1.6841 loss_prob: 0.9560 loss_thr: 0.5700 loss_db: 0.1581 2022/11/02 14:09:47 - mmengine - INFO - Epoch(train) [191][15/63] lr: 1.8520e-03 eta: 10:24:23 time: 0.5213 data_time: 0.0044 memory: 14901 loss: 1.7553 loss_prob: 0.9996 loss_thr: 0.5866 loss_db: 0.1692 2022/11/02 14:09:50 - mmengine - INFO - Epoch(train) [191][20/63] lr: 1.8520e-03 eta: 10:24:12 time: 0.4912 data_time: 0.0056 memory: 14901 loss: 1.9029 loss_prob: 1.1193 loss_thr: 0.6007 loss_db: 0.1829 2022/11/02 14:09:53 - mmengine - INFO - Epoch(train) [191][25/63] lr: 1.8520e-03 eta: 10:24:12 time: 0.5242 data_time: 0.0269 memory: 14901 loss: 1.9514 loss_prob: 1.1592 loss_thr: 0.6084 loss_db: 0.1838 2022/11/02 14:09:55 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:09:55 - mmengine - INFO - Epoch(train) [191][30/63] lr: 1.8520e-03 eta: 10:24:04 time: 0.5551 data_time: 0.0464 memory: 14901 loss: 1.8445 loss_prob: 1.0729 loss_thr: 0.5967 loss_db: 0.1750 2022/11/02 14:09:58 - mmengine - INFO - Epoch(train) [191][35/63] lr: 1.8520e-03 eta: 10:24:04 time: 0.5100 data_time: 0.0251 memory: 14901 loss: 1.8574 loss_prob: 1.0821 loss_thr: 0.5975 loss_db: 0.1778 2022/11/02 14:10:00 - mmengine - INFO - Epoch(train) [191][40/63] lr: 1.8520e-03 eta: 10:23:54 time: 0.4971 data_time: 0.0047 memory: 14901 loss: 1.8729 loss_prob: 1.0866 loss_thr: 0.6108 loss_db: 0.1755 2022/11/02 14:10:03 - mmengine - INFO - Epoch(train) [191][45/63] lr: 1.8520e-03 eta: 10:23:54 time: 0.4912 data_time: 0.0053 memory: 14901 loss: 1.7825 loss_prob: 1.0178 loss_thr: 0.5970 loss_db: 0.1677 2022/11/02 14:10:05 - mmengine - INFO - Epoch(train) [191][50/63] lr: 1.8520e-03 eta: 10:23:41 time: 0.4641 data_time: 0.0163 memory: 14901 loss: 1.8983 loss_prob: 1.1019 loss_thr: 0.6163 loss_db: 0.1801 2022/11/02 14:10:07 - mmengine - INFO - Epoch(train) [191][55/63] lr: 1.8520e-03 eta: 10:23:41 time: 0.4798 data_time: 0.0213 memory: 14901 loss: 2.0518 loss_prob: 1.2027 loss_thr: 0.6528 loss_db: 0.1963 2022/11/02 14:10:10 - mmengine - INFO - Epoch(train) [191][60/63] lr: 1.8520e-03 eta: 10:23:29 time: 0.4660 data_time: 0.0102 memory: 14901 loss: 2.1239 loss_prob: 1.2491 loss_thr: 0.6705 loss_db: 0.2043 2022/11/02 14:10:11 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:10:16 - mmengine - INFO - Epoch(train) [192][5/63] lr: 1.8503e-03 eta: 10:23:29 time: 0.7209 data_time: 0.2330 memory: 14901 loss: 1.8656 loss_prob: 1.0682 loss_thr: 0.6183 loss_db: 0.1791 2022/11/02 14:10:19 - mmengine - INFO - Epoch(train) [192][10/63] lr: 1.8503e-03 eta: 10:23:25 time: 0.8365 data_time: 0.2344 memory: 14901 loss: 1.9897 loss_prob: 1.1837 loss_thr: 0.6042 loss_db: 0.2019 2022/11/02 14:10:22 - mmengine - INFO - Epoch(train) [192][15/63] lr: 1.8503e-03 eta: 10:23:25 time: 0.6016 data_time: 0.0064 memory: 14901 loss: 2.0647 loss_prob: 1.2555 loss_thr: 0.6014 loss_db: 0.2078 2022/11/02 14:10:24 - mmengine - INFO - Epoch(train) [192][20/63] lr: 1.8503e-03 eta: 10:23:16 time: 0.5283 data_time: 0.0054 memory: 14901 loss: 2.0298 loss_prob: 1.2128 loss_thr: 0.6188 loss_db: 0.1983 2022/11/02 14:10:27 - mmengine - INFO - Epoch(train) [192][25/63] lr: 1.8503e-03 eta: 10:23:16 time: 0.5113 data_time: 0.0158 memory: 14901 loss: 2.0395 loss_prob: 1.1945 loss_thr: 0.6460 loss_db: 0.1989 2022/11/02 14:10:30 - mmengine - INFO - Epoch(train) [192][30/63] lr: 1.8503e-03 eta: 10:23:06 time: 0.5121 data_time: 0.0376 memory: 14901 loss: 2.1677 loss_prob: 1.2977 loss_thr: 0.6583 loss_db: 0.2118 2022/11/02 14:10:32 - mmengine - INFO - Epoch(train) [192][35/63] lr: 1.8503e-03 eta: 10:23:06 time: 0.5387 data_time: 0.0275 memory: 14901 loss: 2.1811 loss_prob: 1.3254 loss_thr: 0.6443 loss_db: 0.2114 2022/11/02 14:10:35 - mmengine - INFO - Epoch(train) [192][40/63] lr: 1.8503e-03 eta: 10:22:57 time: 0.5347 data_time: 0.0058 memory: 14901 loss: 1.9203 loss_prob: 1.1290 loss_thr: 0.6087 loss_db: 0.1826 2022/11/02 14:10:38 - mmengine - INFO - Epoch(train) [192][45/63] lr: 1.8503e-03 eta: 10:22:57 time: 0.5496 data_time: 0.0057 memory: 14901 loss: 1.8049 loss_prob: 1.0324 loss_thr: 0.6027 loss_db: 0.1698 2022/11/02 14:10:41 - mmengine - INFO - Epoch(train) [192][50/63] lr: 1.8503e-03 eta: 10:22:51 time: 0.5821 data_time: 0.0215 memory: 14901 loss: 2.0550 loss_prob: 1.2147 loss_thr: 0.6422 loss_db: 0.1981 2022/11/02 14:10:43 - mmengine - INFO - Epoch(train) [192][55/63] lr: 1.8503e-03 eta: 10:22:51 time: 0.5130 data_time: 0.0232 memory: 14901 loss: 2.1723 loss_prob: 1.2998 loss_thr: 0.6626 loss_db: 0.2099 2022/11/02 14:10:46 - mmengine - INFO - Epoch(train) [192][60/63] lr: 1.8503e-03 eta: 10:22:41 time: 0.5166 data_time: 0.0071 memory: 14901 loss: 2.0369 loss_prob: 1.1902 loss_thr: 0.6547 loss_db: 0.1920 2022/11/02 14:10:48 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:10:57 - mmengine - INFO - Epoch(train) [193][5/63] lr: 1.8487e-03 eta: 10:22:41 time: 1.2482 data_time: 0.2453 memory: 14901 loss: 1.8608 loss_prob: 1.0783 loss_thr: 0.6046 loss_db: 0.1779 2022/11/02 14:11:00 - mmengine - INFO - Epoch(train) [193][10/63] lr: 1.8487e-03 eta: 10:23:00 time: 1.2685 data_time: 0.2515 memory: 14901 loss: 1.8634 loss_prob: 1.0794 loss_thr: 0.6047 loss_db: 0.1792 2022/11/02 14:11:04 - mmengine - INFO - Epoch(train) [193][15/63] lr: 1.8487e-03 eta: 10:23:00 time: 0.7035 data_time: 0.0159 memory: 14901 loss: 1.8004 loss_prob: 1.0263 loss_thr: 0.6027 loss_db: 0.1714 2022/11/02 14:11:07 - mmengine - INFO - Epoch(train) [193][20/63] lr: 1.8487e-03 eta: 10:22:58 time: 0.6553 data_time: 0.0130 memory: 14901 loss: 1.9369 loss_prob: 1.1218 loss_thr: 0.6299 loss_db: 0.1852 2022/11/02 14:11:10 - mmengine - INFO - Epoch(train) [193][25/63] lr: 1.8487e-03 eta: 10:22:58 time: 0.5828 data_time: 0.0206 memory: 14901 loss: 1.9794 loss_prob: 1.1377 loss_thr: 0.6531 loss_db: 0.1885 2022/11/02 14:11:13 - mmengine - INFO - Epoch(train) [193][30/63] lr: 1.8487e-03 eta: 10:22:51 time: 0.5723 data_time: 0.0410 memory: 14901 loss: 1.8511 loss_prob: 1.0459 loss_thr: 0.6307 loss_db: 0.1745 2022/11/02 14:11:16 - mmengine - INFO - Epoch(train) [193][35/63] lr: 1.8487e-03 eta: 10:22:51 time: 0.5791 data_time: 0.0300 memory: 14901 loss: 1.9165 loss_prob: 1.1052 loss_thr: 0.6290 loss_db: 0.1824 2022/11/02 14:11:18 - mmengine - INFO - Epoch(train) [193][40/63] lr: 1.8487e-03 eta: 10:22:45 time: 0.5853 data_time: 0.0096 memory: 14901 loss: 1.9982 loss_prob: 1.1636 loss_thr: 0.6428 loss_db: 0.1918 2022/11/02 14:11:22 - mmengine - INFO - Epoch(train) [193][45/63] lr: 1.8487e-03 eta: 10:22:45 time: 0.5866 data_time: 0.0157 memory: 14901 loss: 1.9394 loss_prob: 1.1335 loss_thr: 0.6187 loss_db: 0.1871 2022/11/02 14:11:25 - mmengine - INFO - Epoch(train) [193][50/63] lr: 1.8487e-03 eta: 10:22:41 time: 0.6224 data_time: 0.0399 memory: 14901 loss: 1.7841 loss_prob: 1.0173 loss_thr: 0.6007 loss_db: 0.1661 2022/11/02 14:11:28 - mmengine - INFO - Epoch(train) [193][55/63] lr: 1.8487e-03 eta: 10:22:41 time: 0.6029 data_time: 0.0399 memory: 14901 loss: 1.7531 loss_prob: 0.9834 loss_thr: 0.6083 loss_db: 0.1615 2022/11/02 14:11:30 - mmengine - INFO - Epoch(train) [193][60/63] lr: 1.8487e-03 eta: 10:22:34 time: 0.5675 data_time: 0.0169 memory: 14901 loss: 1.7981 loss_prob: 1.0242 loss_thr: 0.6041 loss_db: 0.1698 2022/11/02 14:11:32 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:11:41 - mmengine - INFO - Epoch(train) [194][5/63] lr: 1.8470e-03 eta: 10:22:34 time: 1.1638 data_time: 0.2466 memory: 14901 loss: 1.9119 loss_prob: 1.0985 loss_thr: 0.6317 loss_db: 0.1816 2022/11/02 14:11:45 - mmengine - INFO - Epoch(train) [194][10/63] lr: 1.8470e-03 eta: 10:22:53 time: 1.2897 data_time: 0.2596 memory: 14901 loss: 1.9580 loss_prob: 1.1348 loss_thr: 0.6378 loss_db: 0.1854 2022/11/02 14:11:48 - mmengine - INFO - Epoch(train) [194][15/63] lr: 1.8470e-03 eta: 10:22:53 time: 0.7341 data_time: 0.0361 memory: 14901 loss: 1.8393 loss_prob: 1.0585 loss_thr: 0.6096 loss_db: 0.1712 2022/11/02 14:11:51 - mmengine - INFO - Epoch(train) [194][20/63] lr: 1.8470e-03 eta: 10:22:49 time: 0.6250 data_time: 0.0200 memory: 14901 loss: 1.8020 loss_prob: 1.0384 loss_thr: 0.5982 loss_db: 0.1654 2022/11/02 14:11:55 - mmengine - INFO - Epoch(train) [194][25/63] lr: 1.8470e-03 eta: 10:22:49 time: 0.6258 data_time: 0.0135 memory: 14901 loss: 1.7985 loss_prob: 1.0277 loss_thr: 0.6029 loss_db: 0.1680 2022/11/02 14:11:59 - mmengine - INFO - Epoch(train) [194][30/63] lr: 1.8470e-03 eta: 10:22:54 time: 0.7839 data_time: 0.0312 memory: 14901 loss: 1.8353 loss_prob: 1.0539 loss_thr: 0.6044 loss_db: 0.1770 2022/11/02 14:12:02 - mmengine - INFO - Epoch(train) [194][35/63] lr: 1.8470e-03 eta: 10:22:54 time: 0.7768 data_time: 0.0495 memory: 14901 loss: 2.1056 loss_prob: 1.2578 loss_thr: 0.6439 loss_db: 0.2039 2022/11/02 14:12:06 - mmengine - INFO - Epoch(train) [194][40/63] lr: 1.8470e-03 eta: 10:22:55 time: 0.7281 data_time: 0.0336 memory: 14901 loss: 2.1860 loss_prob: 1.3034 loss_thr: 0.6696 loss_db: 0.2129 2022/11/02 14:12:10 - mmengine - INFO - Epoch(train) [194][45/63] lr: 1.8470e-03 eta: 10:22:55 time: 0.7829 data_time: 0.0122 memory: 14901 loss: 2.0273 loss_prob: 1.1862 loss_thr: 0.6393 loss_db: 0.2018 2022/11/02 14:12:13 - mmengine - INFO - Epoch(train) [194][50/63] lr: 1.8470e-03 eta: 10:22:55 time: 0.7023 data_time: 0.0202 memory: 14901 loss: 2.0663 loss_prob: 1.2294 loss_thr: 0.6337 loss_db: 0.2032 2022/11/02 14:12:17 - mmengine - INFO - Epoch(train) [194][55/63] lr: 1.8470e-03 eta: 10:22:55 time: 0.7085 data_time: 0.0324 memory: 14901 loss: 2.2286 loss_prob: 1.3535 loss_thr: 0.6597 loss_db: 0.2154 2022/11/02 14:12:21 - mmengine - INFO - Epoch(train) [194][60/63] lr: 1.8470e-03 eta: 10:22:57 time: 0.7420 data_time: 0.0234 memory: 14901 loss: 2.1945 loss_prob: 1.3261 loss_thr: 0.6530 loss_db: 0.2154 2022/11/02 14:12:22 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:12:30 - mmengine - INFO - Epoch(train) [195][5/63] lr: 1.8454e-03 eta: 10:22:57 time: 1.0785 data_time: 0.2399 memory: 14901 loss: 2.1522 loss_prob: 1.2981 loss_thr: 0.6455 loss_db: 0.2087 2022/11/02 14:12:33 - mmengine - INFO - Epoch(train) [195][10/63] lr: 1.8454e-03 eta: 10:23:05 time: 1.0635 data_time: 0.2416 memory: 14901 loss: 2.2603 loss_prob: 1.3854 loss_thr: 0.6517 loss_db: 0.2232 2022/11/02 14:12:36 - mmengine - INFO - Epoch(train) [195][15/63] lr: 1.8454e-03 eta: 10:23:05 time: 0.6453 data_time: 0.0175 memory: 14901 loss: 2.1226 loss_prob: 1.2793 loss_thr: 0.6374 loss_db: 0.2059 2022/11/02 14:12:39 - mmengine - INFO - Epoch(train) [195][20/63] lr: 1.8454e-03 eta: 10:23:00 time: 0.6226 data_time: 0.0154 memory: 14901 loss: 2.0038 loss_prob: 1.1802 loss_thr: 0.6346 loss_db: 0.1890 2022/11/02 14:12:42 - mmengine - INFO - Epoch(train) [195][25/63] lr: 1.8454e-03 eta: 10:23:00 time: 0.5483 data_time: 0.0138 memory: 14901 loss: 1.9917 loss_prob: 1.1753 loss_thr: 0.6259 loss_db: 0.1904 2022/11/02 14:12:45 - mmengine - INFO - Epoch(train) [195][30/63] lr: 1.8454e-03 eta: 10:22:55 time: 0.5952 data_time: 0.0417 memory: 14901 loss: 1.9354 loss_prob: 1.1155 loss_thr: 0.6402 loss_db: 0.1797 2022/11/02 14:12:48 - mmengine - INFO - Epoch(train) [195][35/63] lr: 1.8454e-03 eta: 10:22:55 time: 0.6000 data_time: 0.0370 memory: 14901 loss: 1.8312 loss_prob: 1.0367 loss_thr: 0.6270 loss_db: 0.1675 2022/11/02 14:12:51 - mmengine - INFO - Epoch(train) [195][40/63] lr: 1.8454e-03 eta: 10:22:48 time: 0.5752 data_time: 0.0107 memory: 14901 loss: 1.7692 loss_prob: 1.0017 loss_thr: 0.6034 loss_db: 0.1641 2022/11/02 14:12:54 - mmengine - INFO - Epoch(train) [195][45/63] lr: 1.8454e-03 eta: 10:22:48 time: 0.6009 data_time: 0.0125 memory: 14901 loss: 1.7661 loss_prob: 0.9921 loss_thr: 0.6085 loss_db: 0.1655 2022/11/02 14:12:57 - mmengine - INFO - Epoch(train) [195][50/63] lr: 1.8454e-03 eta: 10:22:44 time: 0.6166 data_time: 0.0146 memory: 14901 loss: 1.8070 loss_prob: 1.0201 loss_thr: 0.6161 loss_db: 0.1709 2022/11/02 14:13:00 - mmengine - INFO - Epoch(train) [195][55/63] lr: 1.8454e-03 eta: 10:22:44 time: 0.6393 data_time: 0.0233 memory: 14901 loss: 1.8945 loss_prob: 1.1049 loss_thr: 0.6055 loss_db: 0.1841 2022/11/02 14:13:03 - mmengine - INFO - Epoch(train) [195][60/63] lr: 1.8454e-03 eta: 10:22:36 time: 0.5656 data_time: 0.0166 memory: 14901 loss: 2.1639 loss_prob: 1.3209 loss_thr: 0.6356 loss_db: 0.2074 2022/11/02 14:13:04 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:13:10 - mmengine - INFO - Epoch(train) [196][5/63] lr: 1.8437e-03 eta: 10:22:36 time: 0.8379 data_time: 0.2489 memory: 14901 loss: 1.9033 loss_prob: 1.1246 loss_thr: 0.5926 loss_db: 0.1861 2022/11/02 14:13:13 - mmengine - INFO - Epoch(train) [196][10/63] lr: 1.8437e-03 eta: 10:22:36 time: 0.9057 data_time: 0.2466 memory: 14901 loss: 1.8997 loss_prob: 1.0917 loss_thr: 0.6250 loss_db: 0.1829 2022/11/02 14:13:16 - mmengine - INFO - Epoch(train) [196][15/63] lr: 1.8437e-03 eta: 10:22:36 time: 0.5746 data_time: 0.0066 memory: 14901 loss: 1.9975 loss_prob: 1.1638 loss_thr: 0.6477 loss_db: 0.1860 2022/11/02 14:13:19 - mmengine - INFO - Epoch(train) [196][20/63] lr: 1.8437e-03 eta: 10:22:27 time: 0.5352 data_time: 0.0114 memory: 14901 loss: 1.9387 loss_prob: 1.1438 loss_thr: 0.6124 loss_db: 0.1825 2022/11/02 14:13:21 - mmengine - INFO - Epoch(train) [196][25/63] lr: 1.8437e-03 eta: 10:22:27 time: 0.5414 data_time: 0.0402 memory: 14901 loss: 1.8597 loss_prob: 1.0829 loss_thr: 0.5999 loss_db: 0.1769 2022/11/02 14:13:24 - mmengine - INFO - Epoch(train) [196][30/63] lr: 1.8437e-03 eta: 10:22:20 time: 0.5662 data_time: 0.0652 memory: 14901 loss: 1.8142 loss_prob: 1.0379 loss_thr: 0.6049 loss_db: 0.1715 2022/11/02 14:13:27 - mmengine - INFO - Epoch(train) [196][35/63] lr: 1.8437e-03 eta: 10:22:20 time: 0.5479 data_time: 0.0368 memory: 14901 loss: 1.9300 loss_prob: 1.1113 loss_thr: 0.6323 loss_db: 0.1864 2022/11/02 14:13:30 - mmengine - INFO - Epoch(train) [196][40/63] lr: 1.8437e-03 eta: 10:22:12 time: 0.5400 data_time: 0.0100 memory: 14901 loss: 1.9229 loss_prob: 1.1129 loss_thr: 0.6248 loss_db: 0.1851 2022/11/02 14:13:32 - mmengine - INFO - Epoch(train) [196][45/63] lr: 1.8437e-03 eta: 10:22:12 time: 0.5197 data_time: 0.0135 memory: 14901 loss: 1.8069 loss_prob: 1.0378 loss_thr: 0.5984 loss_db: 0.1707 2022/11/02 14:13:35 - mmengine - INFO - Epoch(train) [196][50/63] lr: 1.8437e-03 eta: 10:22:01 time: 0.4984 data_time: 0.0229 memory: 14901 loss: 1.8706 loss_prob: 1.0780 loss_thr: 0.6138 loss_db: 0.1788 2022/11/02 14:13:38 - mmengine - INFO - Epoch(train) [196][55/63] lr: 1.8437e-03 eta: 10:22:01 time: 0.5521 data_time: 0.0306 memory: 14901 loss: 1.7886 loss_prob: 1.0196 loss_thr: 0.5985 loss_db: 0.1706 2022/11/02 14:13:40 - mmengine - INFO - Epoch(train) [196][60/63] lr: 1.8437e-03 eta: 10:21:54 time: 0.5614 data_time: 0.0176 memory: 14901 loss: 1.7612 loss_prob: 1.0202 loss_thr: 0.5718 loss_db: 0.1692 2022/11/02 14:13:42 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:13:47 - mmengine - INFO - Epoch(train) [197][5/63] lr: 1.8421e-03 eta: 10:21:54 time: 0.7888 data_time: 0.2292 memory: 14901 loss: 1.7938 loss_prob: 1.0165 loss_thr: 0.6118 loss_db: 0.1655 2022/11/02 14:13:51 - mmengine - INFO - Epoch(train) [197][10/63] lr: 1.8421e-03 eta: 10:21:53 time: 0.9045 data_time: 0.2438 memory: 14901 loss: 1.8316 loss_prob: 1.0459 loss_thr: 0.6175 loss_db: 0.1682 2022/11/02 14:13:53 - mmengine - INFO - Epoch(train) [197][15/63] lr: 1.8421e-03 eta: 10:21:53 time: 0.6064 data_time: 0.0237 memory: 14901 loss: 1.8491 loss_prob: 1.0805 loss_thr: 0.5978 loss_db: 0.1708 2022/11/02 14:13:56 - mmengine - INFO - Epoch(train) [197][20/63] lr: 1.8421e-03 eta: 10:21:46 time: 0.5636 data_time: 0.0101 memory: 14901 loss: 1.8055 loss_prob: 1.0560 loss_thr: 0.5801 loss_db: 0.1694 2022/11/02 14:13:59 - mmengine - INFO - Epoch(train) [197][25/63] lr: 1.8421e-03 eta: 10:21:46 time: 0.5971 data_time: 0.0159 memory: 14901 loss: 1.7786 loss_prob: 1.0104 loss_thr: 0.6002 loss_db: 0.1679 2022/11/02 14:14:02 - mmengine - INFO - Epoch(train) [197][30/63] lr: 1.8421e-03 eta: 10:21:39 time: 0.5740 data_time: 0.0337 memory: 14901 loss: 2.1337 loss_prob: 1.2911 loss_thr: 0.6318 loss_db: 0.2108 2022/11/02 14:14:05 - mmengine - INFO - Epoch(train) [197][35/63] lr: 1.8421e-03 eta: 10:21:39 time: 0.5556 data_time: 0.0345 memory: 14901 loss: 2.2015 loss_prob: 1.3627 loss_thr: 0.6209 loss_db: 0.2179 2022/11/02 14:14:08 - mmengine - INFO - Epoch(train) [197][40/63] lr: 1.8421e-03 eta: 10:21:31 time: 0.5538 data_time: 0.0154 memory: 14901 loss: 1.9683 loss_prob: 1.1694 loss_thr: 0.6163 loss_db: 0.1826 2022/11/02 14:14:10 - mmengine - INFO - Epoch(train) [197][45/63] lr: 1.8421e-03 eta: 10:21:31 time: 0.5266 data_time: 0.0079 memory: 14901 loss: 1.9365 loss_prob: 1.1261 loss_thr: 0.6290 loss_db: 0.1814 2022/11/02 14:14:13 - mmengine - INFO - Epoch(train) [197][50/63] lr: 1.8421e-03 eta: 10:21:22 time: 0.5268 data_time: 0.0239 memory: 14901 loss: 1.8645 loss_prob: 1.0713 loss_thr: 0.6151 loss_db: 0.1782 2022/11/02 14:14:15 - mmengine - INFO - Epoch(train) [197][55/63] lr: 1.8421e-03 eta: 10:21:22 time: 0.5362 data_time: 0.0277 memory: 14901 loss: 1.8935 loss_prob: 1.0983 loss_thr: 0.6143 loss_db: 0.1809 2022/11/02 14:14:18 - mmengine - INFO - Epoch(train) [197][60/63] lr: 1.8421e-03 eta: 10:21:14 time: 0.5453 data_time: 0.0143 memory: 14901 loss: 1.8618 loss_prob: 1.0740 loss_thr: 0.6105 loss_db: 0.1773 2022/11/02 14:14:20 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:14:25 - mmengine - INFO - Epoch(train) [198][5/63] lr: 1.8404e-03 eta: 10:21:14 time: 0.8283 data_time: 0.3011 memory: 14901 loss: 1.9192 loss_prob: 1.0910 loss_thr: 0.6481 loss_db: 0.1800 2022/11/02 14:14:28 - mmengine - INFO - Epoch(train) [198][10/63] lr: 1.8404e-03 eta: 10:21:11 time: 0.8643 data_time: 0.2983 memory: 14901 loss: 1.8972 loss_prob: 1.0673 loss_thr: 0.6558 loss_db: 0.1741 2022/11/02 14:14:31 - mmengine - INFO - Epoch(train) [198][15/63] lr: 1.8404e-03 eta: 10:21:11 time: 0.5845 data_time: 0.0104 memory: 14901 loss: 1.8036 loss_prob: 1.0351 loss_thr: 0.5954 loss_db: 0.1731 2022/11/02 14:14:34 - mmengine - INFO - Epoch(train) [198][20/63] lr: 1.8404e-03 eta: 10:21:07 time: 0.6244 data_time: 0.0190 memory: 14901 loss: 1.8900 loss_prob: 1.1094 loss_thr: 0.5975 loss_db: 0.1831 2022/11/02 14:14:38 - mmengine - INFO - Epoch(train) [198][25/63] lr: 1.8404e-03 eta: 10:21:07 time: 0.6553 data_time: 0.0458 memory: 14901 loss: 1.8278 loss_prob: 1.0683 loss_thr: 0.5874 loss_db: 0.1721 2022/11/02 14:14:41 - mmengine - INFO - Epoch(train) [198][30/63] lr: 1.8404e-03 eta: 10:21:02 time: 0.6053 data_time: 0.0355 memory: 14901 loss: 1.8008 loss_prob: 1.0379 loss_thr: 0.5919 loss_db: 0.1710 2022/11/02 14:14:43 - mmengine - INFO - Epoch(train) [198][35/63] lr: 1.8404e-03 eta: 10:21:02 time: 0.5366 data_time: 0.0075 memory: 14901 loss: 1.9093 loss_prob: 1.0954 loss_thr: 0.6334 loss_db: 0.1806 2022/11/02 14:14:46 - mmengine - INFO - Epoch(train) [198][40/63] lr: 1.8404e-03 eta: 10:20:53 time: 0.5258 data_time: 0.0105 memory: 14901 loss: 1.9268 loss_prob: 1.1110 loss_thr: 0.6321 loss_db: 0.1837 2022/11/02 14:14:48 - mmengine - INFO - Epoch(train) [198][45/63] lr: 1.8404e-03 eta: 10:20:53 time: 0.5029 data_time: 0.0184 memory: 14901 loss: 1.8610 loss_prob: 1.0654 loss_thr: 0.6201 loss_db: 0.1755 2022/11/02 14:14:51 - mmengine - INFO - Epoch(train) [198][50/63] lr: 1.8404e-03 eta: 10:20:45 time: 0.5503 data_time: 0.0292 memory: 14901 loss: 1.8218 loss_prob: 1.0234 loss_thr: 0.6281 loss_db: 0.1704 2022/11/02 14:14:54 - mmengine - INFO - Epoch(train) [198][55/63] lr: 1.8404e-03 eta: 10:20:45 time: 0.5810 data_time: 0.0222 memory: 14901 loss: 1.9328 loss_prob: 1.1207 loss_thr: 0.6240 loss_db: 0.1882 2022/11/02 14:14:57 - mmengine - INFO - Epoch(train) [198][60/63] lr: 1.8404e-03 eta: 10:20:38 time: 0.5617 data_time: 0.0104 memory: 14901 loss: 2.0287 loss_prob: 1.2145 loss_thr: 0.6123 loss_db: 0.2019 2022/11/02 14:14:58 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:15:03 - mmengine - INFO - Epoch(train) [199][5/63] lr: 1.8388e-03 eta: 10:20:38 time: 0.7242 data_time: 0.2456 memory: 14901 loss: 1.9457 loss_prob: 1.1494 loss_thr: 0.6176 loss_db: 0.1787 2022/11/02 14:15:06 - mmengine - INFO - Epoch(train) [199][10/63] lr: 1.8388e-03 eta: 10:20:30 time: 0.7575 data_time: 0.2475 memory: 14901 loss: 2.0479 loss_prob: 1.1894 loss_thr: 0.6682 loss_db: 0.1903 2022/11/02 14:15:09 - mmengine - INFO - Epoch(train) [199][15/63] lr: 1.8388e-03 eta: 10:20:30 time: 0.6409 data_time: 0.0117 memory: 14901 loss: 2.0675 loss_prob: 1.2121 loss_thr: 0.6546 loss_db: 0.2008 2022/11/02 14:15:12 - mmengine - INFO - Epoch(train) [199][20/63] lr: 1.8388e-03 eta: 10:20:25 time: 0.6241 data_time: 0.0083 memory: 14901 loss: 2.0680 loss_prob: 1.2229 loss_thr: 0.6454 loss_db: 0.1998 2022/11/02 14:15:15 - mmengine - INFO - Epoch(train) [199][25/63] lr: 1.8388e-03 eta: 10:20:25 time: 0.5377 data_time: 0.0109 memory: 14901 loss: 1.9758 loss_prob: 1.1508 loss_thr: 0.6388 loss_db: 0.1862 2022/11/02 14:15:18 - mmengine - INFO - Epoch(train) [199][30/63] lr: 1.8388e-03 eta: 10:20:19 time: 0.5730 data_time: 0.0349 memory: 14901 loss: 1.9437 loss_prob: 1.1311 loss_thr: 0.6264 loss_db: 0.1863 2022/11/02 14:15:20 - mmengine - INFO - Epoch(train) [199][35/63] lr: 1.8388e-03 eta: 10:20:19 time: 0.5344 data_time: 0.0306 memory: 14901 loss: 1.9190 loss_prob: 1.1151 loss_thr: 0.6200 loss_db: 0.1839 2022/11/02 14:15:23 - mmengine - INFO - Epoch(train) [199][40/63] lr: 1.8388e-03 eta: 10:20:09 time: 0.5139 data_time: 0.0098 memory: 14901 loss: 2.0555 loss_prob: 1.2286 loss_thr: 0.6307 loss_db: 0.1962 2022/11/02 14:15:25 - mmengine - INFO - Epoch(train) [199][45/63] lr: 1.8388e-03 eta: 10:20:09 time: 0.5252 data_time: 0.0103 memory: 14901 loss: 2.1755 loss_prob: 1.3197 loss_thr: 0.6437 loss_db: 0.2120 2022/11/02 14:15:28 - mmengine - INFO - Epoch(train) [199][50/63] lr: 1.8388e-03 eta: 10:20:01 time: 0.5599 data_time: 0.0264 memory: 14901 loss: 1.9153 loss_prob: 1.1255 loss_thr: 0.6018 loss_db: 0.1880 2022/11/02 14:15:31 - mmengine - INFO - Epoch(train) [199][55/63] lr: 1.8388e-03 eta: 10:20:01 time: 0.5707 data_time: 0.0330 memory: 14901 loss: 1.7758 loss_prob: 1.0273 loss_thr: 0.5810 loss_db: 0.1675 2022/11/02 14:15:34 - mmengine - INFO - Epoch(train) [199][60/63] lr: 1.8388e-03 eta: 10:19:55 time: 0.5705 data_time: 0.0152 memory: 14901 loss: 1.8258 loss_prob: 1.0722 loss_thr: 0.5795 loss_db: 0.1740 2022/11/02 14:15:36 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:15:41 - mmengine - INFO - Epoch(train) [200][5/63] lr: 1.8371e-03 eta: 10:19:55 time: 0.7682 data_time: 0.2284 memory: 14901 loss: 1.7906 loss_prob: 1.0224 loss_thr: 0.5981 loss_db: 0.1701 2022/11/02 14:15:43 - mmengine - INFO - Epoch(train) [200][10/63] lr: 1.8371e-03 eta: 10:19:46 time: 0.7560 data_time: 0.2365 memory: 14901 loss: 1.9343 loss_prob: 1.1201 loss_thr: 0.6329 loss_db: 0.1813 2022/11/02 14:15:46 - mmengine - INFO - Epoch(train) [200][15/63] lr: 1.8371e-03 eta: 10:19:46 time: 0.5600 data_time: 0.0205 memory: 14901 loss: 1.9286 loss_prob: 1.1203 loss_thr: 0.6238 loss_db: 0.1845 2022/11/02 14:15:50 - mmengine - INFO - Epoch(train) [200][20/63] lr: 1.8371e-03 eta: 10:19:43 time: 0.6414 data_time: 0.0106 memory: 14901 loss: 1.9027 loss_prob: 1.0934 loss_thr: 0.6315 loss_db: 0.1778 2022/11/02 14:15:52 - mmengine - INFO - Epoch(train) [200][25/63] lr: 1.8371e-03 eta: 10:19:43 time: 0.5944 data_time: 0.0163 memory: 14901 loss: 1.9391 loss_prob: 1.1271 loss_thr: 0.6315 loss_db: 0.1805 2022/11/02 14:15:55 - mmengine - INFO - Epoch(train) [200][30/63] lr: 1.8371e-03 eta: 10:19:35 time: 0.5375 data_time: 0.0441 memory: 14901 loss: 1.8822 loss_prob: 1.1022 loss_thr: 0.6015 loss_db: 0.1786 2022/11/02 14:15:58 - mmengine - INFO - Epoch(train) [200][35/63] lr: 1.8371e-03 eta: 10:19:35 time: 0.5439 data_time: 0.0363 memory: 14901 loss: 1.8226 loss_prob: 1.0604 loss_thr: 0.5898 loss_db: 0.1724 2022/11/02 14:16:00 - mmengine - INFO - Epoch(train) [200][40/63] lr: 1.8371e-03 eta: 10:19:26 time: 0.5297 data_time: 0.0104 memory: 14901 loss: 1.8623 loss_prob: 1.0731 loss_thr: 0.6124 loss_db: 0.1768 2022/11/02 14:16:03 - mmengine - INFO - Epoch(train) [200][45/63] lr: 1.8371e-03 eta: 10:19:26 time: 0.5441 data_time: 0.0132 memory: 14901 loss: 1.9072 loss_prob: 1.1036 loss_thr: 0.6203 loss_db: 0.1833 2022/11/02 14:16:05 - mmengine - INFO - Epoch(train) [200][50/63] lr: 1.8371e-03 eta: 10:19:16 time: 0.5179 data_time: 0.0173 memory: 14901 loss: 1.8296 loss_prob: 1.0559 loss_thr: 0.6007 loss_db: 0.1729 2022/11/02 14:16:08 - mmengine - INFO - Epoch(train) [200][55/63] lr: 1.8371e-03 eta: 10:19:16 time: 0.5061 data_time: 0.0236 memory: 14901 loss: 1.7577 loss_prob: 1.0111 loss_thr: 0.5799 loss_db: 0.1668 2022/11/02 14:16:11 - mmengine - INFO - Epoch(train) [200][60/63] lr: 1.8371e-03 eta: 10:19:07 time: 0.5269 data_time: 0.0205 memory: 14901 loss: 1.8048 loss_prob: 1.0345 loss_thr: 0.5991 loss_db: 0.1712 2022/11/02 14:16:12 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:16:12 - mmengine - INFO - Saving checkpoint at 200 epochs 2022/11/02 14:16:16 - mmengine - INFO - Epoch(val) [200][5/500] eta: 10:19:07 time: 0.0467 data_time: 0.0053 memory: 14901 2022/11/02 14:16:16 - mmengine - INFO - Epoch(val) [200][10/500] eta: 0:00:22 time: 0.0459 data_time: 0.0051 memory: 1008 2022/11/02 14:16:16 - mmengine - INFO - Epoch(val) [200][15/500] eta: 0:00:22 time: 0.0388 data_time: 0.0022 memory: 1008 2022/11/02 14:16:16 - mmengine - INFO - Epoch(val) [200][20/500] eta: 0:00:18 time: 0.0388 data_time: 0.0025 memory: 1008 2022/11/02 14:16:17 - mmengine - INFO - Epoch(val) [200][25/500] eta: 0:00:18 time: 0.0362 data_time: 0.0025 memory: 1008 2022/11/02 14:16:17 - mmengine - INFO - Epoch(val) [200][30/500] eta: 0:00:18 time: 0.0398 data_time: 0.0025 memory: 1008 2022/11/02 14:16:17 - mmengine - INFO - Epoch(val) [200][35/500] eta: 0:00:18 time: 0.0410 data_time: 0.0024 memory: 1008 2022/11/02 14:16:17 - mmengine - INFO - Epoch(val) [200][40/500] eta: 0:00:18 time: 0.0412 data_time: 0.0023 memory: 1008 2022/11/02 14:16:17 - mmengine - INFO - Epoch(val) [200][45/500] eta: 0:00:18 time: 0.0429 data_time: 0.0024 memory: 1008 2022/11/02 14:16:18 - mmengine - INFO - Epoch(val) [200][50/500] eta: 0:00:18 time: 0.0410 data_time: 0.0025 memory: 1008 2022/11/02 14:16:18 - mmengine - INFO - Epoch(val) [200][55/500] eta: 0:00:18 time: 0.0458 data_time: 0.0027 memory: 1008 2022/11/02 14:16:18 - mmengine - INFO - Epoch(val) [200][60/500] eta: 0:00:19 time: 0.0446 data_time: 0.0030 memory: 1008 2022/11/02 14:16:18 - mmengine - INFO - Epoch(val) [200][65/500] eta: 0:00:19 time: 0.0416 data_time: 0.0029 memory: 1008 2022/11/02 14:16:18 - mmengine - INFO - Epoch(val) [200][70/500] eta: 0:00:18 time: 0.0429 data_time: 0.0026 memory: 1008 2022/11/02 14:16:19 - mmengine - INFO - Epoch(val) [200][75/500] eta: 0:00:18 time: 0.0419 data_time: 0.0028 memory: 1008 2022/11/02 14:16:19 - mmengine - INFO - Epoch(val) [200][80/500] eta: 0:00:33 time: 0.0792 data_time: 0.0408 memory: 1008 2022/11/02 14:16:19 - mmengine - INFO - Epoch(val) [200][85/500] eta: 0:00:33 time: 0.0759 data_time: 0.0403 memory: 1008 2022/11/02 14:16:20 - mmengine - INFO - Epoch(val) [200][90/500] eta: 0:00:16 time: 0.0400 data_time: 0.0022 memory: 1008 2022/11/02 14:16:20 - mmengine - INFO - Epoch(val) [200][95/500] eta: 0:00:16 time: 0.0442 data_time: 0.0027 memory: 1008 2022/11/02 14:16:20 - mmengine - INFO - Epoch(val) [200][100/500] eta: 0:00:16 time: 0.0425 data_time: 0.0030 memory: 1008 2022/11/02 14:16:20 - mmengine - INFO - Epoch(val) [200][105/500] eta: 0:00:16 time: 0.0400 data_time: 0.0029 memory: 1008 2022/11/02 14:16:20 - mmengine - INFO - Epoch(val) [200][110/500] eta: 0:00:15 time: 0.0385 data_time: 0.0026 memory: 1008 2022/11/02 14:16:21 - mmengine - INFO - Epoch(val) [200][115/500] eta: 0:00:15 time: 0.0414 data_time: 0.0028 memory: 1008 2022/11/02 14:16:21 - mmengine - INFO - Epoch(val) [200][120/500] eta: 0:00:16 time: 0.0426 data_time: 0.0031 memory: 1008 2022/11/02 14:16:21 - mmengine - INFO - Epoch(val) [200][125/500] eta: 0:00:16 time: 0.0405 data_time: 0.0030 memory: 1008 2022/11/02 14:16:21 - mmengine - INFO - Epoch(val) [200][130/500] eta: 0:00:14 time: 0.0397 data_time: 0.0028 memory: 1008 2022/11/02 14:16:21 - mmengine - INFO - Epoch(val) [200][135/500] eta: 0:00:14 time: 0.0389 data_time: 0.0028 memory: 1008 2022/11/02 14:16:22 - mmengine - INFO - Epoch(val) [200][140/500] eta: 0:00:14 time: 0.0399 data_time: 0.0029 memory: 1008 2022/11/02 14:16:22 - mmengine - INFO - Epoch(val) [200][145/500] eta: 0:00:14 time: 0.0458 data_time: 0.0029 memory: 1008 2022/11/02 14:16:22 - mmengine - INFO - Epoch(val) [200][150/500] eta: 0:00:15 time: 0.0448 data_time: 0.0026 memory: 1008 2022/11/02 14:16:22 - mmengine - INFO - Epoch(val) [200][155/500] eta: 0:00:15 time: 0.0450 data_time: 0.0026 memory: 1008 2022/11/02 14:16:23 - mmengine - INFO - Epoch(val) [200][160/500] eta: 0:00:15 time: 0.0458 data_time: 0.0027 memory: 1008 2022/11/02 14:16:23 - mmengine - INFO - Epoch(val) [200][165/500] eta: 0:00:15 time: 0.0415 data_time: 0.0026 memory: 1008 2022/11/02 14:16:23 - mmengine - INFO - Epoch(val) [200][170/500] eta: 0:00:14 time: 0.0431 data_time: 0.0028 memory: 1008 2022/11/02 14:16:23 - mmengine - INFO - Epoch(val) [200][175/500] eta: 0:00:14 time: 0.0419 data_time: 0.0030 memory: 1008 2022/11/02 14:16:23 - mmengine - INFO - Epoch(val) [200][180/500] eta: 0:00:12 time: 0.0402 data_time: 0.0031 memory: 1008 2022/11/02 14:16:24 - mmengine - INFO - Epoch(val) [200][185/500] eta: 0:00:12 time: 0.0423 data_time: 0.0030 memory: 1008 2022/11/02 14:16:24 - mmengine - INFO - Epoch(val) [200][190/500] eta: 0:00:13 time: 0.0438 data_time: 0.0030 memory: 1008 2022/11/02 14:16:24 - mmengine - INFO - Epoch(val) [200][195/500] eta: 0:00:13 time: 0.0405 data_time: 0.0030 memory: 1008 2022/11/02 14:16:24 - mmengine - INFO - Epoch(val) [200][200/500] eta: 0:00:12 time: 0.0426 data_time: 0.0030 memory: 1008 2022/11/02 14:16:25 - mmengine - INFO - Epoch(val) [200][205/500] eta: 0:00:12 time: 0.0439 data_time: 0.0030 memory: 1008 2022/11/02 14:16:25 - mmengine - INFO - Epoch(val) [200][210/500] eta: 0:00:11 time: 0.0407 data_time: 0.0030 memory: 1008 2022/11/02 14:16:25 - mmengine - INFO - Epoch(val) [200][215/500] eta: 0:00:11 time: 0.0427 data_time: 0.0033 memory: 1008 2022/11/02 14:16:25 - mmengine - INFO - Epoch(val) [200][220/500] eta: 0:00:12 time: 0.0437 data_time: 0.0031 memory: 1008 2022/11/02 14:16:25 - mmengine - INFO - Epoch(val) [200][225/500] eta: 0:00:12 time: 0.0478 data_time: 0.0030 memory: 1008 2022/11/02 14:16:26 - mmengine - INFO - Epoch(val) [200][230/500] eta: 0:00:12 time: 0.0457 data_time: 0.0032 memory: 1008 2022/11/02 14:16:26 - mmengine - INFO - Epoch(val) [200][235/500] eta: 0:00:12 time: 0.0418 data_time: 0.0029 memory: 1008 2022/11/02 14:16:26 - mmengine - INFO - Epoch(val) [200][240/500] eta: 0:00:12 time: 0.0468 data_time: 0.0034 memory: 1008 2022/11/02 14:16:26 - mmengine - INFO - Epoch(val) [200][245/500] eta: 0:00:12 time: 0.0445 data_time: 0.0035 memory: 1008 2022/11/02 14:16:27 - mmengine - INFO - Epoch(val) [200][250/500] eta: 0:00:10 time: 0.0424 data_time: 0.0029 memory: 1008 2022/11/02 14:16:27 - mmengine - INFO - Epoch(val) [200][255/500] eta: 0:00:10 time: 0.0407 data_time: 0.0026 memory: 1008 2022/11/02 14:16:27 - mmengine - INFO - Epoch(val) [200][260/500] eta: 0:00:08 time: 0.0372 data_time: 0.0024 memory: 1008 2022/11/02 14:16:27 - mmengine - INFO - Epoch(val) [200][265/500] eta: 0:00:08 time: 0.0419 data_time: 0.0031 memory: 1008 2022/11/02 14:16:27 - mmengine - INFO - Epoch(val) [200][270/500] eta: 0:00:10 time: 0.0469 data_time: 0.0037 memory: 1008 2022/11/02 14:16:28 - mmengine - INFO - Epoch(val) [200][275/500] eta: 0:00:10 time: 0.0433 data_time: 0.0032 memory: 1008 2022/11/02 14:16:28 - mmengine - INFO - Epoch(val) [200][280/500] eta: 0:00:09 time: 0.0411 data_time: 0.0029 memory: 1008 2022/11/02 14:16:28 - mmengine - INFO - Epoch(val) [200][285/500] eta: 0:00:09 time: 0.0393 data_time: 0.0026 memory: 1008 2022/11/02 14:16:28 - mmengine - INFO - Epoch(val) [200][290/500] eta: 0:00:08 time: 0.0393 data_time: 0.0024 memory: 1008 2022/11/02 14:16:28 - mmengine - INFO - Epoch(val) [200][295/500] eta: 0:00:08 time: 0.0429 data_time: 0.0025 memory: 1008 2022/11/02 14:16:29 - mmengine - INFO - Epoch(val) [200][300/500] eta: 0:00:07 time: 0.0400 data_time: 0.0025 memory: 1008 2022/11/02 14:16:29 - mmengine - INFO - Epoch(val) [200][305/500] eta: 0:00:07 time: 0.0392 data_time: 0.0028 memory: 1008 2022/11/02 14:16:29 - mmengine - INFO - Epoch(val) [200][310/500] eta: 0:00:08 time: 0.0458 data_time: 0.0035 memory: 1008 2022/11/02 14:16:29 - mmengine - INFO - Epoch(val) [200][315/500] eta: 0:00:08 time: 0.0505 data_time: 0.0034 memory: 1008 2022/11/02 14:16:29 - mmengine - INFO - Epoch(val) [200][320/500] eta: 0:00:08 time: 0.0447 data_time: 0.0027 memory: 1008 2022/11/02 14:16:30 - mmengine - INFO - Epoch(val) [200][325/500] eta: 0:00:08 time: 0.0556 data_time: 0.0024 memory: 1008 2022/11/02 14:16:30 - mmengine - INFO - Epoch(val) [200][330/500] eta: 0:00:09 time: 0.0549 data_time: 0.0024 memory: 1008 2022/11/02 14:16:30 - mmengine - INFO - Epoch(val) [200][335/500] eta: 0:00:09 time: 0.0367 data_time: 0.0026 memory: 1008 2022/11/02 14:16:31 - mmengine - INFO - Epoch(val) [200][340/500] eta: 0:00:09 time: 0.0583 data_time: 0.0030 memory: 1008 2022/11/02 14:16:31 - mmengine - INFO - Epoch(val) [200][345/500] eta: 0:00:09 time: 0.0610 data_time: 0.0033 memory: 1008 2022/11/02 14:16:31 - mmengine - INFO - Epoch(val) [200][350/500] eta: 0:00:07 time: 0.0479 data_time: 0.0034 memory: 1008 2022/11/02 14:16:31 - mmengine - INFO - Epoch(val) [200][355/500] eta: 0:00:07 time: 0.0460 data_time: 0.0031 memory: 1008 2022/11/02 14:16:31 - mmengine - INFO - Epoch(val) [200][360/500] eta: 0:00:05 time: 0.0394 data_time: 0.0031 memory: 1008 2022/11/02 14:16:32 - mmengine - INFO - Epoch(val) [200][365/500] eta: 0:00:05 time: 0.0428 data_time: 0.0037 memory: 1008 2022/11/02 14:16:32 - mmengine - INFO - Epoch(val) [200][370/500] eta: 0:00:05 time: 0.0394 data_time: 0.0033 memory: 1008 2022/11/02 14:16:32 - mmengine - INFO - Epoch(val) [200][375/500] eta: 0:00:05 time: 0.0372 data_time: 0.0027 memory: 1008 2022/11/02 14:16:32 - mmengine - INFO - Epoch(val) [200][380/500] eta: 0:00:05 time: 0.0417 data_time: 0.0025 memory: 1008 2022/11/02 14:16:32 - mmengine - INFO - Epoch(val) [200][385/500] eta: 0:00:05 time: 0.0423 data_time: 0.0026 memory: 1008 2022/11/02 14:16:33 - mmengine - INFO - Epoch(val) [200][390/500] eta: 0:00:04 time: 0.0420 data_time: 0.0028 memory: 1008 2022/11/02 14:16:33 - mmengine - INFO - Epoch(val) [200][395/500] eta: 0:00:04 time: 0.0430 data_time: 0.0031 memory: 1008 2022/11/02 14:16:33 - mmengine - INFO - Epoch(val) [200][400/500] eta: 0:00:04 time: 0.0463 data_time: 0.0039 memory: 1008 2022/11/02 14:16:33 - mmengine - INFO - Epoch(val) [200][405/500] eta: 0:00:04 time: 0.0457 data_time: 0.0036 memory: 1008 2022/11/02 14:16:34 - mmengine - INFO - Epoch(val) [200][410/500] eta: 0:00:04 time: 0.0449 data_time: 0.0029 memory: 1008 2022/11/02 14:16:34 - mmengine - INFO - Epoch(val) [200][415/500] eta: 0:00:04 time: 0.0425 data_time: 0.0028 memory: 1008 2022/11/02 14:16:34 - mmengine - INFO - Epoch(val) [200][420/500] eta: 0:00:06 time: 0.0771 data_time: 0.0446 memory: 1008 2022/11/02 14:16:35 - mmengine - INFO - Epoch(val) [200][425/500] eta: 0:00:06 time: 0.0804 data_time: 0.0443 memory: 1008 2022/11/02 14:16:35 - mmengine - INFO - Epoch(val) [200][430/500] eta: 0:00:02 time: 0.0413 data_time: 0.0024 memory: 1008 2022/11/02 14:16:35 - mmengine - INFO - Epoch(val) [200][435/500] eta: 0:00:02 time: 0.0383 data_time: 0.0025 memory: 1008 2022/11/02 14:16:35 - mmengine - INFO - Epoch(val) [200][440/500] eta: 0:00:02 time: 0.0405 data_time: 0.0024 memory: 1008 2022/11/02 14:16:35 - mmengine - INFO - Epoch(val) [200][445/500] eta: 0:00:02 time: 0.0419 data_time: 0.0028 memory: 1008 2022/11/02 14:16:36 - mmengine - INFO - Epoch(val) [200][450/500] eta: 0:00:02 time: 0.0410 data_time: 0.0028 memory: 1008 2022/11/02 14:16:36 - mmengine - INFO - Epoch(val) [200][455/500] eta: 0:00:02 time: 0.0439 data_time: 0.0026 memory: 1008 2022/11/02 14:16:36 - mmengine - INFO - Epoch(val) [200][460/500] eta: 0:00:01 time: 0.0413 data_time: 0.0028 memory: 1008 2022/11/02 14:16:36 - mmengine - INFO - Epoch(val) [200][465/500] eta: 0:00:01 time: 0.0367 data_time: 0.0028 memory: 1008 2022/11/02 14:16:36 - mmengine - INFO - Epoch(val) [200][470/500] eta: 0:00:01 time: 0.0382 data_time: 0.0029 memory: 1008 2022/11/02 14:16:37 - mmengine - INFO - Epoch(val) [200][475/500] eta: 0:00:01 time: 0.0374 data_time: 0.0029 memory: 1008 2022/11/02 14:16:37 - mmengine - INFO - Epoch(val) [200][480/500] eta: 0:00:00 time: 0.0384 data_time: 0.0027 memory: 1008 2022/11/02 14:16:37 - mmengine - INFO - Epoch(val) [200][485/500] eta: 0:00:00 time: 0.0397 data_time: 0.0028 memory: 1008 2022/11/02 14:16:37 - mmengine - INFO - Epoch(val) [200][490/500] eta: 0:00:00 time: 0.0390 data_time: 0.0027 memory: 1008 2022/11/02 14:16:37 - mmengine - INFO - Epoch(val) [200][495/500] eta: 0:00:00 time: 0.0418 data_time: 0.0024 memory: 1008 2022/11/02 14:16:38 - mmengine - INFO - Epoch(val) [200][500/500] eta: 0:00:00 time: 0.0407 data_time: 0.0027 memory: 1008 2022/11/02 14:16:38 - mmengine - INFO - Evaluating hmean-iou... 2022/11/02 14:16:38 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.8117, precision: 0.6379, hmean: 0.7144 2022/11/02 14:16:38 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.8113, precision: 0.7161, hmean: 0.7607 2022/11/02 14:16:38 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.8103, precision: 0.7650, hmean: 0.7870 2022/11/02 14:16:38 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.7978, precision: 0.8099, hmean: 0.8038 2022/11/02 14:16:38 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.7328, precision: 0.8702, hmean: 0.7956 2022/11/02 14:16:38 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.3447, precision: 0.9446, hmean: 0.5051 2022/11/02 14:16:38 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0010, precision: 1.0000, hmean: 0.0019 2022/11/02 14:16:38 - mmengine - INFO - Epoch(val) [200][500/500] icdar/precision: 0.8099 icdar/recall: 0.7978 icdar/hmean: 0.8038 2022/11/02 14:16:44 - mmengine - INFO - Epoch(train) [201][5/63] lr: 1.8355e-03 eta: 0:00:00 time: 0.8107 data_time: 0.2384 memory: 14901 loss: 1.9507 loss_prob: 1.1391 loss_thr: 0.6239 loss_db: 0.1877 2022/11/02 14:16:47 - mmengine - INFO - Epoch(train) [201][10/63] lr: 1.8355e-03 eta: 10:19:05 time: 0.8850 data_time: 0.2456 memory: 14901 loss: 1.9479 loss_prob: 1.1553 loss_thr: 0.6071 loss_db: 0.1854 2022/11/02 14:16:49 - mmengine - INFO - Epoch(train) [201][15/63] lr: 1.8355e-03 eta: 10:19:05 time: 0.5750 data_time: 0.0144 memory: 14901 loss: 1.8436 loss_prob: 1.0643 loss_thr: 0.6054 loss_db: 0.1739 2022/11/02 14:16:52 - mmengine - INFO - Epoch(train) [201][20/63] lr: 1.8355e-03 eta: 10:18:58 time: 0.5685 data_time: 0.0076 memory: 14901 loss: 1.7739 loss_prob: 1.0011 loss_thr: 0.6045 loss_db: 0.1682 2022/11/02 14:16:55 - mmengine - INFO - Epoch(train) [201][25/63] lr: 1.8355e-03 eta: 10:18:58 time: 0.5594 data_time: 0.0206 memory: 14901 loss: 1.9241 loss_prob: 1.1208 loss_thr: 0.6209 loss_db: 0.1824 2022/11/02 14:16:58 - mmengine - INFO - Epoch(train) [201][30/63] lr: 1.8355e-03 eta: 10:18:50 time: 0.5345 data_time: 0.0282 memory: 14901 loss: 1.8926 loss_prob: 1.1119 loss_thr: 0.6011 loss_db: 0.1796 2022/11/02 14:17:00 - mmengine - INFO - Epoch(train) [201][35/63] lr: 1.8355e-03 eta: 10:18:50 time: 0.5254 data_time: 0.0230 memory: 14901 loss: 1.8252 loss_prob: 1.0615 loss_thr: 0.5876 loss_db: 0.1762 2022/11/02 14:17:03 - mmengine - INFO - Epoch(train) [201][40/63] lr: 1.8355e-03 eta: 10:18:40 time: 0.5095 data_time: 0.0134 memory: 14901 loss: 1.9431 loss_prob: 1.1329 loss_thr: 0.6248 loss_db: 0.1854 2022/11/02 14:17:05 - mmengine - INFO - Epoch(train) [201][45/63] lr: 1.8355e-03 eta: 10:18:40 time: 0.5277 data_time: 0.0081 memory: 14901 loss: 1.8356 loss_prob: 1.0580 loss_thr: 0.6062 loss_db: 0.1713 2022/11/02 14:17:08 - mmengine - INFO - Epoch(train) [201][50/63] lr: 1.8355e-03 eta: 10:18:33 time: 0.5663 data_time: 0.0231 memory: 14901 loss: 1.8958 loss_prob: 1.1142 loss_thr: 0.5985 loss_db: 0.1831 2022/11/02 14:17:11 - mmengine - INFO - Epoch(train) [201][55/63] lr: 1.8355e-03 eta: 10:18:33 time: 0.5924 data_time: 0.0324 memory: 14901 loss: 1.9986 loss_prob: 1.1874 loss_thr: 0.6158 loss_db: 0.1953 2022/11/02 14:17:14 - mmengine - INFO - Epoch(train) [201][60/63] lr: 1.8355e-03 eta: 10:18:25 time: 0.5412 data_time: 0.0211 memory: 14901 loss: 2.1046 loss_prob: 1.2695 loss_thr: 0.6323 loss_db: 0.2028 2022/11/02 14:17:15 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:17:20 - mmengine - INFO - Epoch(train) [202][5/63] lr: 1.8338e-03 eta: 10:18:25 time: 0.7720 data_time: 0.2195 memory: 14901 loss: 2.0198 loss_prob: 1.1914 loss_thr: 0.6355 loss_db: 0.1929 2022/11/02 14:17:24 - mmengine - INFO - Epoch(train) [202][10/63] lr: 1.8338e-03 eta: 10:18:21 time: 0.8553 data_time: 0.2286 memory: 14901 loss: 1.8320 loss_prob: 1.0584 loss_thr: 0.6034 loss_db: 0.1702 2022/11/02 14:17:26 - mmengine - INFO - Epoch(train) [202][15/63] lr: 1.8338e-03 eta: 10:18:21 time: 0.5688 data_time: 0.0199 memory: 14901 loss: 1.7639 loss_prob: 1.0154 loss_thr: 0.5861 loss_db: 0.1624 2022/11/02 14:17:29 - mmengine - INFO - Epoch(train) [202][20/63] lr: 1.8338e-03 eta: 10:18:12 time: 0.5161 data_time: 0.0085 memory: 14901 loss: 1.8317 loss_prob: 1.0582 loss_thr: 0.5990 loss_db: 0.1745 2022/11/02 14:17:31 - mmengine - INFO - Epoch(train) [202][25/63] lr: 1.8338e-03 eta: 10:18:12 time: 0.5317 data_time: 0.0342 memory: 14901 loss: 1.8135 loss_prob: 1.0457 loss_thr: 0.5963 loss_db: 0.1714 2022/11/02 14:17:34 - mmengine - INFO - Epoch(train) [202][30/63] lr: 1.8338e-03 eta: 10:18:02 time: 0.5173 data_time: 0.0324 memory: 14901 loss: 1.9369 loss_prob: 1.1286 loss_thr: 0.6274 loss_db: 0.1809 2022/11/02 14:17:37 - mmengine - INFO - Epoch(train) [202][35/63] lr: 1.8338e-03 eta: 10:18:02 time: 0.5266 data_time: 0.0141 memory: 14901 loss: 1.9983 loss_prob: 1.1794 loss_thr: 0.6273 loss_db: 0.1915 2022/11/02 14:17:39 - mmengine - INFO - Epoch(train) [202][40/63] lr: 1.8338e-03 eta: 10:17:53 time: 0.5196 data_time: 0.0140 memory: 14901 loss: 1.9314 loss_prob: 1.1270 loss_thr: 0.6195 loss_db: 0.1850 2022/11/02 14:17:42 - mmengine - INFO - Epoch(train) [202][45/63] lr: 1.8338e-03 eta: 10:17:53 time: 0.5210 data_time: 0.0052 memory: 14901 loss: 1.8770 loss_prob: 1.0717 loss_thr: 0.6300 loss_db: 0.1753 2022/11/02 14:17:45 - mmengine - INFO - Epoch(train) [202][50/63] lr: 1.8338e-03 eta: 10:17:46 time: 0.5665 data_time: 0.0243 memory: 14901 loss: 1.8380 loss_prob: 1.0510 loss_thr: 0.6118 loss_db: 0.1752 2022/11/02 14:17:47 - mmengine - INFO - Epoch(train) [202][55/63] lr: 1.8338e-03 eta: 10:17:46 time: 0.5375 data_time: 0.0289 memory: 14901 loss: 1.7070 loss_prob: 0.9678 loss_thr: 0.5779 loss_db: 0.1612 2022/11/02 14:17:50 - mmengine - INFO - Epoch(train) [202][60/63] lr: 1.8338e-03 eta: 10:17:37 time: 0.5370 data_time: 0.0114 memory: 14901 loss: 1.7626 loss_prob: 1.0097 loss_thr: 0.5869 loss_db: 0.1660 2022/11/02 14:17:52 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:17:57 - mmengine - INFO - Epoch(train) [203][5/63] lr: 1.8321e-03 eta: 10:17:37 time: 0.7513 data_time: 0.2582 memory: 14901 loss: 1.7445 loss_prob: 0.9817 loss_thr: 0.5946 loss_db: 0.1682 2022/11/02 14:18:00 - mmengine - INFO - Epoch(train) [203][10/63] lr: 1.8321e-03 eta: 10:17:33 time: 0.8220 data_time: 0.2623 memory: 14901 loss: 1.7552 loss_prob: 0.9842 loss_thr: 0.6064 loss_db: 0.1646 2022/11/02 14:18:02 - mmengine - INFO - Epoch(train) [203][15/63] lr: 1.8321e-03 eta: 10:17:33 time: 0.5758 data_time: 0.0151 memory: 14901 loss: 1.7685 loss_prob: 1.0003 loss_thr: 0.6029 loss_db: 0.1653 2022/11/02 14:18:05 - mmengine - INFO - Epoch(train) [203][20/63] lr: 1.8321e-03 eta: 10:17:23 time: 0.5132 data_time: 0.0099 memory: 14901 loss: 1.7223 loss_prob: 0.9772 loss_thr: 0.5830 loss_db: 0.1621 2022/11/02 14:18:08 - mmengine - INFO - Epoch(train) [203][25/63] lr: 1.8321e-03 eta: 10:17:23 time: 0.5285 data_time: 0.0343 memory: 14901 loss: 1.8261 loss_prob: 1.0375 loss_thr: 0.6156 loss_db: 0.1730 2022/11/02 14:18:10 - mmengine - INFO - Epoch(train) [203][30/63] lr: 1.8321e-03 eta: 10:17:14 time: 0.5353 data_time: 0.0409 memory: 14901 loss: 1.9089 loss_prob: 1.0965 loss_thr: 0.6302 loss_db: 0.1822 2022/11/02 14:18:13 - mmengine - INFO - Epoch(train) [203][35/63] lr: 1.8321e-03 eta: 10:17:14 time: 0.5592 data_time: 0.0159 memory: 14901 loss: 1.8253 loss_prob: 1.0495 loss_thr: 0.6057 loss_db: 0.1702 2022/11/02 14:18:16 - mmengine - INFO - Epoch(train) [203][40/63] lr: 1.8321e-03 eta: 10:17:07 time: 0.5607 data_time: 0.0105 memory: 14901 loss: 1.7470 loss_prob: 0.9878 loss_thr: 0.6001 loss_db: 0.1592 2022/11/02 14:18:19 - mmengine - INFO - Epoch(train) [203][45/63] lr: 1.8321e-03 eta: 10:17:07 time: 0.5342 data_time: 0.0137 memory: 14901 loss: 1.7855 loss_prob: 1.0166 loss_thr: 0.6011 loss_db: 0.1679 2022/11/02 14:18:21 - mmengine - INFO - Epoch(train) [203][50/63] lr: 1.8321e-03 eta: 10:16:59 time: 0.5478 data_time: 0.0275 memory: 14901 loss: 1.9125 loss_prob: 1.1246 loss_thr: 0.6052 loss_db: 0.1826 2022/11/02 14:18:24 - mmengine - INFO - Epoch(train) [203][55/63] lr: 1.8321e-03 eta: 10:16:59 time: 0.5219 data_time: 0.0253 memory: 14901 loss: 1.9156 loss_prob: 1.1314 loss_thr: 0.6029 loss_db: 0.1814 2022/11/02 14:18:27 - mmengine - INFO - Epoch(train) [203][60/63] lr: 1.8321e-03 eta: 10:16:54 time: 0.6103 data_time: 0.0153 memory: 14901 loss: 1.8994 loss_prob: 1.0943 loss_thr: 0.6245 loss_db: 0.1806 2022/11/02 14:18:29 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:18:35 - mmengine - INFO - Epoch(train) [204][5/63] lr: 1.8305e-03 eta: 10:16:54 time: 0.9930 data_time: 0.2514 memory: 14901 loss: 1.8432 loss_prob: 1.0490 loss_thr: 0.6236 loss_db: 0.1706 2022/11/02 14:18:38 - mmengine - INFO - Epoch(train) [204][10/63] lr: 1.8305e-03 eta: 10:16:52 time: 0.8829 data_time: 0.2524 memory: 14901 loss: 1.8681 loss_prob: 1.0775 loss_thr: 0.6153 loss_db: 0.1754 2022/11/02 14:18:41 - mmengine - INFO - Epoch(train) [204][15/63] lr: 1.8305e-03 eta: 10:16:52 time: 0.5477 data_time: 0.0137 memory: 14901 loss: 1.8512 loss_prob: 1.0592 loss_thr: 0.6121 loss_db: 0.1799 2022/11/02 14:18:44 - mmengine - INFO - Epoch(train) [204][20/63] lr: 1.8305e-03 eta: 10:16:45 time: 0.5581 data_time: 0.0108 memory: 14901 loss: 1.7854 loss_prob: 1.0201 loss_thr: 0.5960 loss_db: 0.1692 2022/11/02 14:18:47 - mmengine - INFO - Epoch(train) [204][25/63] lr: 1.8305e-03 eta: 10:16:45 time: 0.5782 data_time: 0.0318 memory: 14901 loss: 1.7031 loss_prob: 0.9964 loss_thr: 0.5478 loss_db: 0.1589 2022/11/02 14:18:49 - mmengine - INFO - Epoch(train) [204][30/63] lr: 1.8305e-03 eta: 10:16:39 time: 0.5867 data_time: 0.0387 memory: 14901 loss: 1.6840 loss_prob: 0.9894 loss_thr: 0.5365 loss_db: 0.1581 2022/11/02 14:18:52 - mmengine - INFO - Epoch(train) [204][35/63] lr: 1.8305e-03 eta: 10:16:39 time: 0.5892 data_time: 0.0162 memory: 14901 loss: 1.7677 loss_prob: 1.0138 loss_thr: 0.5879 loss_db: 0.1660 2022/11/02 14:18:55 - mmengine - INFO - Epoch(train) [204][40/63] lr: 1.8305e-03 eta: 10:16:31 time: 0.5432 data_time: 0.0098 memory: 14901 loss: 1.7187 loss_prob: 0.9736 loss_thr: 0.5842 loss_db: 0.1609 2022/11/02 14:18:58 - mmengine - INFO - Epoch(train) [204][45/63] lr: 1.8305e-03 eta: 10:16:31 time: 0.5048 data_time: 0.0066 memory: 14901 loss: 1.8172 loss_prob: 1.0645 loss_thr: 0.5821 loss_db: 0.1705 2022/11/02 14:19:00 - mmengine - INFO - Epoch(train) [204][50/63] lr: 1.8305e-03 eta: 10:16:22 time: 0.5235 data_time: 0.0183 memory: 14901 loss: 2.0241 loss_prob: 1.2054 loss_thr: 0.6268 loss_db: 0.1919 2022/11/02 14:19:03 - mmengine - INFO - Epoch(train) [204][55/63] lr: 1.8305e-03 eta: 10:16:22 time: 0.5334 data_time: 0.0273 memory: 14901 loss: 1.9484 loss_prob: 1.1342 loss_thr: 0.6260 loss_db: 0.1882 2022/11/02 14:19:06 - mmengine - INFO - Epoch(train) [204][60/63] lr: 1.8305e-03 eta: 10:16:14 time: 0.5585 data_time: 0.0183 memory: 14901 loss: 1.9264 loss_prob: 1.1146 loss_thr: 0.6241 loss_db: 0.1877 2022/11/02 14:19:07 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:19:15 - mmengine - INFO - Epoch(train) [205][5/63] lr: 1.8288e-03 eta: 10:16:14 time: 1.0264 data_time: 0.2518 memory: 14901 loss: 1.7878 loss_prob: 1.0324 loss_thr: 0.5909 loss_db: 0.1645 2022/11/02 14:19:17 - mmengine - INFO - Epoch(train) [205][10/63] lr: 1.8288e-03 eta: 10:16:19 time: 1.0217 data_time: 0.2555 memory: 14901 loss: 1.8547 loss_prob: 1.0742 loss_thr: 0.6029 loss_db: 0.1777 2022/11/02 14:19:20 - mmengine - INFO - Epoch(train) [205][15/63] lr: 1.8288e-03 eta: 10:16:19 time: 0.5636 data_time: 0.0164 memory: 14901 loss: 1.9529 loss_prob: 1.1360 loss_thr: 0.6297 loss_db: 0.1871 2022/11/02 14:19:23 - mmengine - INFO - Epoch(train) [205][20/63] lr: 1.8288e-03 eta: 10:16:13 time: 0.5799 data_time: 0.0104 memory: 14901 loss: 1.8907 loss_prob: 1.0939 loss_thr: 0.6214 loss_db: 0.1754 2022/11/02 14:19:26 - mmengine - INFO - Epoch(train) [205][25/63] lr: 1.8288e-03 eta: 10:16:13 time: 0.5944 data_time: 0.0382 memory: 14901 loss: 1.8536 loss_prob: 1.0716 loss_thr: 0.6077 loss_db: 0.1743 2022/11/02 14:19:29 - mmengine - INFO - Epoch(train) [205][30/63] lr: 1.8288e-03 eta: 10:16:04 time: 0.5415 data_time: 0.0397 memory: 14901 loss: 1.7639 loss_prob: 1.0070 loss_thr: 0.5898 loss_db: 0.1671 2022/11/02 14:19:31 - mmengine - INFO - Epoch(train) [205][35/63] lr: 1.8288e-03 eta: 10:16:04 time: 0.5011 data_time: 0.0114 memory: 14901 loss: 1.7835 loss_prob: 1.0221 loss_thr: 0.5936 loss_db: 0.1677 2022/11/02 14:19:34 - mmengine - INFO - Epoch(train) [205][40/63] lr: 1.8288e-03 eta: 10:15:54 time: 0.4951 data_time: 0.0120 memory: 14901 loss: 1.8157 loss_prob: 1.0466 loss_thr: 0.5954 loss_db: 0.1737 2022/11/02 14:19:36 - mmengine - INFO - Epoch(train) [205][45/63] lr: 1.8288e-03 eta: 10:15:54 time: 0.5007 data_time: 0.0086 memory: 14901 loss: 1.7549 loss_prob: 1.0035 loss_thr: 0.5815 loss_db: 0.1699 2022/11/02 14:19:39 - mmengine - INFO - Epoch(train) [205][50/63] lr: 1.8288e-03 eta: 10:15:44 time: 0.5061 data_time: 0.0233 memory: 14901 loss: 1.7392 loss_prob: 0.9940 loss_thr: 0.5766 loss_db: 0.1686 2022/11/02 14:19:41 - mmengine - INFO - Epoch(train) [205][55/63] lr: 1.8288e-03 eta: 10:15:44 time: 0.5055 data_time: 0.0239 memory: 14901 loss: 1.8012 loss_prob: 1.0297 loss_thr: 0.5998 loss_db: 0.1717 2022/11/02 14:19:44 - mmengine - INFO - Epoch(train) [205][60/63] lr: 1.8288e-03 eta: 10:15:34 time: 0.5128 data_time: 0.0073 memory: 14901 loss: 1.8967 loss_prob: 1.0915 loss_thr: 0.6259 loss_db: 0.1793 2022/11/02 14:19:45 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:19:50 - mmengine - INFO - Epoch(train) [206][5/63] lr: 1.8272e-03 eta: 10:15:34 time: 0.7707 data_time: 0.2525 memory: 14901 loss: 1.7669 loss_prob: 0.9874 loss_thr: 0.6139 loss_db: 0.1656 2022/11/02 14:19:53 - mmengine - INFO - Epoch(train) [206][10/63] lr: 1.8272e-03 eta: 10:15:27 time: 0.7792 data_time: 0.2484 memory: 14901 loss: 1.6453 loss_prob: 0.9188 loss_thr: 0.5748 loss_db: 0.1517 2022/11/02 14:19:56 - mmengine - INFO - Epoch(train) [206][15/63] lr: 1.8272e-03 eta: 10:15:27 time: 0.5914 data_time: 0.0097 memory: 14901 loss: 1.6648 loss_prob: 0.9313 loss_thr: 0.5779 loss_db: 0.1556 2022/11/02 14:19:59 - mmengine - INFO - Epoch(train) [206][20/63] lr: 1.8272e-03 eta: 10:15:21 time: 0.5844 data_time: 0.0108 memory: 14901 loss: 1.7127 loss_prob: 0.9668 loss_thr: 0.5847 loss_db: 0.1611 2022/11/02 14:20:02 - mmengine - INFO - Epoch(train) [206][25/63] lr: 1.8272e-03 eta: 10:15:21 time: 0.5363 data_time: 0.0321 memory: 14901 loss: 1.7662 loss_prob: 1.0083 loss_thr: 0.5922 loss_db: 0.1656 2022/11/02 14:20:05 - mmengine - INFO - Epoch(train) [206][30/63] lr: 1.8272e-03 eta: 10:15:15 time: 0.5887 data_time: 0.0439 memory: 14901 loss: 1.7302 loss_prob: 0.9794 loss_thr: 0.5895 loss_db: 0.1613 2022/11/02 14:20:07 - mmengine - INFO - Epoch(train) [206][35/63] lr: 1.8272e-03 eta: 10:15:15 time: 0.5580 data_time: 0.0252 memory: 14901 loss: 1.7140 loss_prob: 0.9546 loss_thr: 0.5998 loss_db: 0.1595 2022/11/02 14:20:10 - mmengine - INFO - Epoch(train) [206][40/63] lr: 1.8272e-03 eta: 10:15:06 time: 0.5179 data_time: 0.0133 memory: 14901 loss: 1.7527 loss_prob: 0.9770 loss_thr: 0.6145 loss_db: 0.1612 2022/11/02 14:20:13 - mmengine - INFO - Epoch(train) [206][45/63] lr: 1.8272e-03 eta: 10:15:06 time: 0.5667 data_time: 0.0123 memory: 14901 loss: 1.7608 loss_prob: 0.9818 loss_thr: 0.6160 loss_db: 0.1630 2022/11/02 14:20:16 - mmengine - INFO - Epoch(train) [206][50/63] lr: 1.8272e-03 eta: 10:15:01 time: 0.6045 data_time: 0.0225 memory: 14901 loss: 1.9278 loss_prob: 1.1156 loss_thr: 0.6228 loss_db: 0.1894 2022/11/02 14:20:18 - mmengine - INFO - Epoch(train) [206][55/63] lr: 1.8272e-03 eta: 10:15:01 time: 0.5440 data_time: 0.0218 memory: 14901 loss: 1.9143 loss_prob: 1.1304 loss_thr: 0.5951 loss_db: 0.1888 2022/11/02 14:20:21 - mmengine - INFO - Epoch(train) [206][60/63] lr: 1.8272e-03 eta: 10:14:52 time: 0.5340 data_time: 0.0131 memory: 14901 loss: 1.7204 loss_prob: 1.0001 loss_thr: 0.5577 loss_db: 0.1627 2022/11/02 14:20:23 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:20:28 - mmengine - INFO - Epoch(train) [207][5/63] lr: 1.8255e-03 eta: 10:14:52 time: 0.8312 data_time: 0.2620 memory: 14901 loss: 1.9396 loss_prob: 1.1500 loss_thr: 0.6080 loss_db: 0.1816 2022/11/02 14:20:31 - mmengine - INFO - Epoch(train) [207][10/63] lr: 1.8255e-03 eta: 10:14:48 time: 0.8330 data_time: 0.2664 memory: 14901 loss: 2.0969 loss_prob: 1.2593 loss_thr: 0.6325 loss_db: 0.2052 2022/11/02 14:20:34 - mmengine - INFO - Epoch(train) [207][15/63] lr: 1.8255e-03 eta: 10:14:48 time: 0.5348 data_time: 0.0178 memory: 14901 loss: 2.1442 loss_prob: 1.2910 loss_thr: 0.6377 loss_db: 0.2155 2022/11/02 14:20:36 - mmengine - INFO - Epoch(train) [207][20/63] lr: 1.8255e-03 eta: 10:14:39 time: 0.5232 data_time: 0.0127 memory: 14901 loss: 2.2447 loss_prob: 1.3761 loss_thr: 0.6414 loss_db: 0.2272 2022/11/02 14:20:37 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:20:39 - mmengine - INFO - Epoch(train) [207][25/63] lr: 1.8255e-03 eta: 10:14:39 time: 0.5409 data_time: 0.0149 memory: 14901 loss: 2.5821 loss_prob: 1.6371 loss_thr: 0.6700 loss_db: 0.2750 2022/11/02 14:20:42 - mmengine - INFO - Epoch(train) [207][30/63] lr: 1.8255e-03 eta: 10:14:35 time: 0.6245 data_time: 0.0385 memory: 14901 loss: 2.7363 loss_prob: 1.7651 loss_thr: 0.6812 loss_db: 0.2901 2022/11/02 14:20:46 - mmengine - INFO - Epoch(train) [207][35/63] lr: 1.8255e-03 eta: 10:14:35 time: 0.6662 data_time: 0.0310 memory: 14901 loss: 2.3276 loss_prob: 1.4419 loss_thr: 0.6494 loss_db: 0.2363 2022/11/02 14:20:48 - mmengine - INFO - Epoch(train) [207][40/63] lr: 1.8255e-03 eta: 10:14:30 time: 0.6085 data_time: 0.0049 memory: 14901 loss: 2.1987 loss_prob: 1.3550 loss_thr: 0.6282 loss_db: 0.2154 2022/11/02 14:20:51 - mmengine - INFO - Epoch(train) [207][45/63] lr: 1.8255e-03 eta: 10:14:30 time: 0.5363 data_time: 0.0054 memory: 14901 loss: 2.2161 loss_prob: 1.3605 loss_thr: 0.6437 loss_db: 0.2119 2022/11/02 14:20:54 - mmengine - INFO - Epoch(train) [207][50/63] lr: 1.8255e-03 eta: 10:14:21 time: 0.5267 data_time: 0.0145 memory: 14901 loss: 2.2376 loss_prob: 1.3387 loss_thr: 0.6807 loss_db: 0.2182 2022/11/02 14:20:56 - mmengine - INFO - Epoch(train) [207][55/63] lr: 1.8255e-03 eta: 10:14:21 time: 0.5226 data_time: 0.0252 memory: 14901 loss: 2.2373 loss_prob: 1.3589 loss_thr: 0.6552 loss_db: 0.2231 2022/11/02 14:20:59 - mmengine - INFO - Epoch(train) [207][60/63] lr: 1.8255e-03 eta: 10:14:12 time: 0.5288 data_time: 0.0160 memory: 14901 loss: 2.1720 loss_prob: 1.3143 loss_thr: 0.6406 loss_db: 0.2171 2022/11/02 14:21:00 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:21:06 - mmengine - INFO - Epoch(train) [208][5/63] lr: 1.8239e-03 eta: 10:14:12 time: 0.8463 data_time: 0.2780 memory: 14901 loss: 2.0363 loss_prob: 1.2138 loss_thr: 0.6232 loss_db: 0.1993 2022/11/02 14:21:09 - mmengine - INFO - Epoch(train) [208][10/63] lr: 1.8239e-03 eta: 10:14:09 time: 0.8668 data_time: 0.2832 memory: 14901 loss: 1.7981 loss_prob: 1.0402 loss_thr: 0.5906 loss_db: 0.1673 2022/11/02 14:21:12 - mmengine - INFO - Epoch(train) [208][15/63] lr: 1.8239e-03 eta: 10:14:09 time: 0.5599 data_time: 0.0145 memory: 14901 loss: 1.8987 loss_prob: 1.0913 loss_thr: 0.6242 loss_db: 0.1831 2022/11/02 14:21:14 - mmengine - INFO - Epoch(train) [208][20/63] lr: 1.8239e-03 eta: 10:14:01 time: 0.5330 data_time: 0.0103 memory: 14901 loss: 2.3789 loss_prob: 1.4873 loss_thr: 0.6513 loss_db: 0.2402 2022/11/02 14:21:17 - mmengine - INFO - Epoch(train) [208][25/63] lr: 1.8239e-03 eta: 10:14:01 time: 0.4997 data_time: 0.0261 memory: 14901 loss: 2.8783 loss_prob: 1.8994 loss_thr: 0.6950 loss_db: 0.2839 2022/11/02 14:21:20 - mmengine - INFO - Epoch(train) [208][30/63] lr: 1.8239e-03 eta: 10:13:54 time: 0.5785 data_time: 0.0393 memory: 14901 loss: 2.3227 loss_prob: 1.4576 loss_thr: 0.6487 loss_db: 0.2164 2022/11/02 14:21:23 - mmengine - INFO - Epoch(train) [208][35/63] lr: 1.8239e-03 eta: 10:13:54 time: 0.6069 data_time: 0.0201 memory: 14901 loss: 1.8719 loss_prob: 1.0907 loss_thr: 0.6039 loss_db: 0.1773 2022/11/02 14:21:26 - mmengine - INFO - Epoch(train) [208][40/63] lr: 1.8239e-03 eta: 10:13:46 time: 0.5512 data_time: 0.0084 memory: 14901 loss: 2.2092 loss_prob: 1.3117 loss_thr: 0.6802 loss_db: 0.2173 2022/11/02 14:21:28 - mmengine - INFO - Epoch(train) [208][45/63] lr: 1.8239e-03 eta: 10:13:46 time: 0.5205 data_time: 0.0110 memory: 14901 loss: 2.2223 loss_prob: 1.3210 loss_thr: 0.6848 loss_db: 0.2165 2022/11/02 14:21:31 - mmengine - INFO - Epoch(train) [208][50/63] lr: 1.8239e-03 eta: 10:13:38 time: 0.5388 data_time: 0.0214 memory: 14901 loss: 2.0135 loss_prob: 1.1926 loss_thr: 0.6258 loss_db: 0.1950 2022/11/02 14:21:34 - mmengine - INFO - Epoch(train) [208][55/63] lr: 1.8239e-03 eta: 10:13:38 time: 0.5736 data_time: 0.0262 memory: 14901 loss: 1.9582 loss_prob: 1.1467 loss_thr: 0.6214 loss_db: 0.1901 2022/11/02 14:21:36 - mmengine - INFO - Epoch(train) [208][60/63] lr: 1.8239e-03 eta: 10:13:30 time: 0.5368 data_time: 0.0129 memory: 14901 loss: 1.9289 loss_prob: 1.1419 loss_thr: 0.6015 loss_db: 0.1855 2022/11/02 14:21:38 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:21:43 - mmengine - INFO - Epoch(train) [209][5/63] lr: 1.8222e-03 eta: 10:13:30 time: 0.7891 data_time: 0.2158 memory: 14901 loss: 1.8382 loss_prob: 1.0667 loss_thr: 0.5968 loss_db: 0.1746 2022/11/02 14:21:46 - mmengine - INFO - Epoch(train) [209][10/63] lr: 1.8222e-03 eta: 10:13:24 time: 0.8002 data_time: 0.2165 memory: 14901 loss: 1.9047 loss_prob: 1.1114 loss_thr: 0.6111 loss_db: 0.1821 2022/11/02 14:21:49 - mmengine - INFO - Epoch(train) [209][15/63] lr: 1.8222e-03 eta: 10:13:24 time: 0.5360 data_time: 0.0163 memory: 14901 loss: 1.9048 loss_prob: 1.1064 loss_thr: 0.6165 loss_db: 0.1819 2022/11/02 14:21:51 - mmengine - INFO - Epoch(train) [209][20/63] lr: 1.8222e-03 eta: 10:13:16 time: 0.5383 data_time: 0.0163 memory: 14901 loss: 1.8447 loss_prob: 1.0907 loss_thr: 0.5761 loss_db: 0.1779 2022/11/02 14:21:54 - mmengine - INFO - Epoch(train) [209][25/63] lr: 1.8222e-03 eta: 10:13:16 time: 0.5486 data_time: 0.0373 memory: 14901 loss: 2.0271 loss_prob: 1.2187 loss_thr: 0.6056 loss_db: 0.2028 2022/11/02 14:21:57 - mmengine - INFO - Epoch(train) [209][30/63] lr: 1.8222e-03 eta: 10:13:08 time: 0.5499 data_time: 0.0378 memory: 14901 loss: 2.1495 loss_prob: 1.2807 loss_thr: 0.6510 loss_db: 0.2179 2022/11/02 14:21:59 - mmengine - INFO - Epoch(train) [209][35/63] lr: 1.8222e-03 eta: 10:13:08 time: 0.4968 data_time: 0.0080 memory: 14901 loss: 2.3161 loss_prob: 1.3979 loss_thr: 0.6832 loss_db: 0.2350 2022/11/02 14:22:02 - mmengine - INFO - Epoch(train) [209][40/63] lr: 1.8222e-03 eta: 10:12:59 time: 0.5341 data_time: 0.0064 memory: 14901 loss: 2.3006 loss_prob: 1.4001 loss_thr: 0.6700 loss_db: 0.2306 2022/11/02 14:22:05 - mmengine - INFO - Epoch(train) [209][45/63] lr: 1.8222e-03 eta: 10:12:59 time: 0.5990 data_time: 0.0159 memory: 14901 loss: 2.2148 loss_prob: 1.3508 loss_thr: 0.6455 loss_db: 0.2185 2022/11/02 14:22:08 - mmengine - INFO - Epoch(train) [209][50/63] lr: 1.8222e-03 eta: 10:12:52 time: 0.5651 data_time: 0.0338 memory: 14901 loss: 2.1947 loss_prob: 1.3183 loss_thr: 0.6616 loss_db: 0.2148 2022/11/02 14:22:10 - mmengine - INFO - Epoch(train) [209][55/63] lr: 1.8222e-03 eta: 10:12:52 time: 0.5222 data_time: 0.0270 memory: 14901 loss: 2.1020 loss_prob: 1.2399 loss_thr: 0.6578 loss_db: 0.2043 2022/11/02 14:22:13 - mmengine - INFO - Epoch(train) [209][60/63] lr: 1.8222e-03 eta: 10:12:44 time: 0.5473 data_time: 0.0098 memory: 14901 loss: 2.1109 loss_prob: 1.2523 loss_thr: 0.6502 loss_db: 0.2084 2022/11/02 14:22:14 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:22:20 - mmengine - INFO - Epoch(train) [210][5/63] lr: 1.8206e-03 eta: 10:12:44 time: 0.8059 data_time: 0.2772 memory: 14901 loss: 2.1514 loss_prob: 1.2852 loss_thr: 0.6583 loss_db: 0.2080 2022/11/02 14:22:23 - mmengine - INFO - Epoch(train) [210][10/63] lr: 1.8206e-03 eta: 10:12:39 time: 0.8150 data_time: 0.2802 memory: 14901 loss: 2.1113 loss_prob: 1.2513 loss_thr: 0.6536 loss_db: 0.2064 2022/11/02 14:22:25 - mmengine - INFO - Epoch(train) [210][15/63] lr: 1.8206e-03 eta: 10:12:39 time: 0.5470 data_time: 0.0088 memory: 14901 loss: 2.0109 loss_prob: 1.1793 loss_thr: 0.6292 loss_db: 0.2024 2022/11/02 14:22:28 - mmengine - INFO - Epoch(train) [210][20/63] lr: 1.8206e-03 eta: 10:12:33 time: 0.5811 data_time: 0.0108 memory: 14901 loss: 1.9505 loss_prob: 1.1619 loss_thr: 0.5976 loss_db: 0.1910 2022/11/02 14:22:31 - mmengine - INFO - Epoch(train) [210][25/63] lr: 1.8206e-03 eta: 10:12:33 time: 0.5918 data_time: 0.0492 memory: 14901 loss: 2.0800 loss_prob: 1.2396 loss_thr: 0.6381 loss_db: 0.2023 2022/11/02 14:22:34 - mmengine - INFO - Epoch(train) [210][30/63] lr: 1.8206e-03 eta: 10:12:25 time: 0.5388 data_time: 0.0463 memory: 14901 loss: 2.1257 loss_prob: 1.2560 loss_thr: 0.6582 loss_db: 0.2115 2022/11/02 14:22:37 - mmengine - INFO - Epoch(train) [210][35/63] lr: 1.8206e-03 eta: 10:12:25 time: 0.5220 data_time: 0.0114 memory: 14901 loss: 1.9564 loss_prob: 1.1398 loss_thr: 0.6248 loss_db: 0.1918 2022/11/02 14:22:39 - mmengine - INFO - Epoch(train) [210][40/63] lr: 1.8206e-03 eta: 10:12:16 time: 0.5239 data_time: 0.0090 memory: 14901 loss: 1.9317 loss_prob: 1.1318 loss_thr: 0.6096 loss_db: 0.1902 2022/11/02 14:22:42 - mmengine - INFO - Epoch(train) [210][45/63] lr: 1.8206e-03 eta: 10:12:16 time: 0.5561 data_time: 0.0077 memory: 14901 loss: 2.0234 loss_prob: 1.2060 loss_thr: 0.6126 loss_db: 0.2048 2022/11/02 14:22:45 - mmengine - INFO - Epoch(train) [210][50/63] lr: 1.8206e-03 eta: 10:12:08 time: 0.5565 data_time: 0.0276 memory: 14901 loss: 2.0813 loss_prob: 1.2444 loss_thr: 0.6269 loss_db: 0.2100 2022/11/02 14:22:47 - mmengine - INFO - Epoch(train) [210][55/63] lr: 1.8206e-03 eta: 10:12:08 time: 0.4891 data_time: 0.0266 memory: 14901 loss: 2.1387 loss_prob: 1.3032 loss_thr: 0.6222 loss_db: 0.2133 2022/11/02 14:22:50 - mmengine - INFO - Epoch(train) [210][60/63] lr: 1.8206e-03 eta: 10:11:59 time: 0.5148 data_time: 0.0092 memory: 14901 loss: 2.1561 loss_prob: 1.3197 loss_thr: 0.6202 loss_db: 0.2162 2022/11/02 14:22:51 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:22:57 - mmengine - INFO - Epoch(train) [211][5/63] lr: 1.8189e-03 eta: 10:11:59 time: 0.8020 data_time: 0.2617 memory: 14901 loss: 1.9300 loss_prob: 1.1420 loss_thr: 0.5999 loss_db: 0.1880 2022/11/02 14:22:59 - mmengine - INFO - Epoch(train) [211][10/63] lr: 1.8189e-03 eta: 10:11:52 time: 0.7788 data_time: 0.2655 memory: 14901 loss: 1.8185 loss_prob: 1.0529 loss_thr: 0.5901 loss_db: 0.1754 2022/11/02 14:23:01 - mmengine - INFO - Epoch(train) [211][15/63] lr: 1.8189e-03 eta: 10:11:52 time: 0.4893 data_time: 0.0132 memory: 14901 loss: 1.9541 loss_prob: 1.1482 loss_thr: 0.6170 loss_db: 0.1890 2022/11/02 14:23:04 - mmengine - INFO - Epoch(train) [211][20/63] lr: 1.8189e-03 eta: 10:11:42 time: 0.5123 data_time: 0.0095 memory: 14901 loss: 2.0896 loss_prob: 1.2371 loss_thr: 0.6507 loss_db: 0.2018 2022/11/02 14:23:07 - mmengine - INFO - Epoch(train) [211][25/63] lr: 1.8189e-03 eta: 10:11:42 time: 0.5541 data_time: 0.0305 memory: 14901 loss: 1.9559 loss_prob: 1.1331 loss_thr: 0.6384 loss_db: 0.1844 2022/11/02 14:23:10 - mmengine - INFO - Epoch(train) [211][30/63] lr: 1.8189e-03 eta: 10:11:35 time: 0.5607 data_time: 0.0405 memory: 14901 loss: 1.7878 loss_prob: 1.0155 loss_thr: 0.6090 loss_db: 0.1633 2022/11/02 14:23:12 - mmengine - INFO - Epoch(train) [211][35/63] lr: 1.8189e-03 eta: 10:11:35 time: 0.5277 data_time: 0.0204 memory: 14901 loss: 1.8277 loss_prob: 1.0482 loss_thr: 0.6101 loss_db: 0.1693 2022/11/02 14:23:15 - mmengine - INFO - Epoch(train) [211][40/63] lr: 1.8189e-03 eta: 10:11:25 time: 0.4940 data_time: 0.0109 memory: 14901 loss: 1.8811 loss_prob: 1.0838 loss_thr: 0.6180 loss_db: 0.1793 2022/11/02 14:23:17 - mmengine - INFO - Epoch(train) [211][45/63] lr: 1.8189e-03 eta: 10:11:25 time: 0.4937 data_time: 0.0065 memory: 14901 loss: 1.9864 loss_prob: 1.1724 loss_thr: 0.6231 loss_db: 0.1909 2022/11/02 14:23:20 - mmengine - INFO - Epoch(train) [211][50/63] lr: 1.8189e-03 eta: 10:11:18 time: 0.5778 data_time: 0.0197 memory: 14901 loss: 2.0396 loss_prob: 1.2025 loss_thr: 0.6450 loss_db: 0.1921 2022/11/02 14:23:24 - mmengine - INFO - Epoch(train) [211][55/63] lr: 1.8189e-03 eta: 10:11:18 time: 0.6512 data_time: 0.0301 memory: 14901 loss: 1.9659 loss_prob: 1.1339 loss_thr: 0.6462 loss_db: 0.1859 2022/11/02 14:23:26 - mmengine - INFO - Epoch(train) [211][60/63] lr: 1.8189e-03 eta: 10:11:12 time: 0.5840 data_time: 0.0203 memory: 14901 loss: 2.0427 loss_prob: 1.2130 loss_thr: 0.6263 loss_db: 0.2034 2022/11/02 14:23:28 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:23:33 - mmengine - INFO - Epoch(train) [212][5/63] lr: 1.8173e-03 eta: 10:11:12 time: 0.7493 data_time: 0.2651 memory: 14901 loss: 2.1872 loss_prob: 1.3265 loss_thr: 0.6351 loss_db: 0.2256 2022/11/02 14:23:35 - mmengine - INFO - Epoch(train) [212][10/63] lr: 1.8173e-03 eta: 10:11:05 time: 0.7800 data_time: 0.2602 memory: 14901 loss: 2.0773 loss_prob: 1.2446 loss_thr: 0.6275 loss_db: 0.2052 2022/11/02 14:23:38 - mmengine - INFO - Epoch(train) [212][15/63] lr: 1.8173e-03 eta: 10:11:05 time: 0.5351 data_time: 0.0086 memory: 14901 loss: 2.1520 loss_prob: 1.2900 loss_thr: 0.6536 loss_db: 0.2084 2022/11/02 14:23:41 - mmengine - INFO - Epoch(train) [212][20/63] lr: 1.8173e-03 eta: 10:10:57 time: 0.5307 data_time: 0.0094 memory: 14901 loss: 2.1034 loss_prob: 1.2525 loss_thr: 0.6454 loss_db: 0.2055 2022/11/02 14:23:44 - mmengine - INFO - Epoch(train) [212][25/63] lr: 1.8173e-03 eta: 10:10:57 time: 0.5708 data_time: 0.0324 memory: 14901 loss: 1.9624 loss_prob: 1.1473 loss_thr: 0.6272 loss_db: 0.1879 2022/11/02 14:23:47 - mmengine - INFO - Epoch(train) [212][30/63] lr: 1.8173e-03 eta: 10:10:51 time: 0.5924 data_time: 0.0475 memory: 14901 loss: 2.0766 loss_prob: 1.2410 loss_thr: 0.6361 loss_db: 0.1995 2022/11/02 14:23:49 - mmengine - INFO - Epoch(train) [212][35/63] lr: 1.8173e-03 eta: 10:10:51 time: 0.5589 data_time: 0.0237 memory: 14901 loss: 2.1312 loss_prob: 1.2798 loss_thr: 0.6403 loss_db: 0.2111 2022/11/02 14:23:52 - mmengine - INFO - Epoch(train) [212][40/63] lr: 1.8173e-03 eta: 10:10:43 time: 0.5362 data_time: 0.0079 memory: 14901 loss: 2.0118 loss_prob: 1.1764 loss_thr: 0.6370 loss_db: 0.1983 2022/11/02 14:23:54 - mmengine - INFO - Epoch(train) [212][45/63] lr: 1.8173e-03 eta: 10:10:43 time: 0.4984 data_time: 0.0058 memory: 14901 loss: 1.8418 loss_prob: 1.0519 loss_thr: 0.6142 loss_db: 0.1757 2022/11/02 14:23:57 - mmengine - INFO - Epoch(train) [212][50/63] lr: 1.8173e-03 eta: 10:10:35 time: 0.5515 data_time: 0.0189 memory: 14901 loss: 1.8336 loss_prob: 1.0634 loss_thr: 0.5965 loss_db: 0.1738 2022/11/02 14:24:00 - mmengine - INFO - Epoch(train) [212][55/63] lr: 1.8173e-03 eta: 10:10:35 time: 0.5779 data_time: 0.0272 memory: 14901 loss: 1.9205 loss_prob: 1.1185 loss_thr: 0.6166 loss_db: 0.1854 2022/11/02 14:24:04 - mmengine - INFO - Epoch(train) [212][60/63] lr: 1.8173e-03 eta: 10:10:31 time: 0.6232 data_time: 0.0145 memory: 14901 loss: 1.8181 loss_prob: 1.0414 loss_thr: 0.6044 loss_db: 0.1723 2022/11/02 14:24:05 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:24:11 - mmengine - INFO - Epoch(train) [213][5/63] lr: 1.8156e-03 eta: 10:10:31 time: 0.9058 data_time: 0.2550 memory: 14901 loss: 1.8380 loss_prob: 1.0435 loss_thr: 0.6190 loss_db: 0.1755 2022/11/02 14:24:13 - mmengine - INFO - Epoch(train) [213][10/63] lr: 1.8156e-03 eta: 10:10:27 time: 0.8489 data_time: 0.2747 memory: 14901 loss: 1.9489 loss_prob: 1.1283 loss_thr: 0.6311 loss_db: 0.1895 2022/11/02 14:24:16 - mmengine - INFO - Epoch(train) [213][15/63] lr: 1.8156e-03 eta: 10:10:27 time: 0.5265 data_time: 0.0301 memory: 14901 loss: 2.0299 loss_prob: 1.1998 loss_thr: 0.6337 loss_db: 0.1963 2022/11/02 14:24:19 - mmengine - INFO - Epoch(train) [213][20/63] lr: 1.8156e-03 eta: 10:10:18 time: 0.5273 data_time: 0.0103 memory: 14901 loss: 2.0035 loss_prob: 1.1754 loss_thr: 0.6368 loss_db: 0.1914 2022/11/02 14:24:22 - mmengine - INFO - Epoch(train) [213][25/63] lr: 1.8156e-03 eta: 10:10:18 time: 0.5726 data_time: 0.0274 memory: 14901 loss: 1.9564 loss_prob: 1.1500 loss_thr: 0.6192 loss_db: 0.1872 2022/11/02 14:24:25 - mmengine - INFO - Epoch(train) [213][30/63] lr: 1.8156e-03 eta: 10:10:15 time: 0.6422 data_time: 0.0346 memory: 14901 loss: 1.9027 loss_prob: 1.1239 loss_thr: 0.5966 loss_db: 0.1822 2022/11/02 14:24:28 - mmengine - INFO - Epoch(train) [213][35/63] lr: 1.8156e-03 eta: 10:10:15 time: 0.5964 data_time: 0.0194 memory: 14901 loss: 1.9692 loss_prob: 1.1659 loss_thr: 0.6120 loss_db: 0.1913 2022/11/02 14:24:30 - mmengine - INFO - Epoch(train) [213][40/63] lr: 1.8156e-03 eta: 10:10:06 time: 0.5252 data_time: 0.0142 memory: 14901 loss: 1.9069 loss_prob: 1.1161 loss_thr: 0.6075 loss_db: 0.1833 2022/11/02 14:24:33 - mmengine - INFO - Epoch(train) [213][45/63] lr: 1.8156e-03 eta: 10:10:06 time: 0.5134 data_time: 0.0113 memory: 14901 loss: 1.8341 loss_prob: 1.0610 loss_thr: 0.5966 loss_db: 0.1765 2022/11/02 14:24:36 - mmengine - INFO - Epoch(train) [213][50/63] lr: 1.8156e-03 eta: 10:09:57 time: 0.5176 data_time: 0.0199 memory: 14901 loss: 1.9420 loss_prob: 1.1342 loss_thr: 0.6226 loss_db: 0.1851 2022/11/02 14:24:38 - mmengine - INFO - Epoch(train) [213][55/63] lr: 1.8156e-03 eta: 10:09:57 time: 0.5421 data_time: 0.0278 memory: 14901 loss: 1.9782 loss_prob: 1.1579 loss_thr: 0.6331 loss_db: 0.1872 2022/11/02 14:24:41 - mmengine - INFO - Epoch(train) [213][60/63] lr: 1.8156e-03 eta: 10:09:49 time: 0.5465 data_time: 0.0193 memory: 14901 loss: 2.2620 loss_prob: 1.3891 loss_thr: 0.6535 loss_db: 0.2194 2022/11/02 14:24:42 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:24:50 - mmengine - INFO - Epoch(train) [214][5/63] lr: 1.8139e-03 eta: 10:09:49 time: 0.9851 data_time: 0.2422 memory: 14901 loss: 1.7648 loss_prob: 1.0152 loss_thr: 0.5822 loss_db: 0.1674 2022/11/02 14:24:52 - mmengine - INFO - Epoch(train) [214][10/63] lr: 1.8139e-03 eta: 10:09:54 time: 1.0253 data_time: 0.2405 memory: 14901 loss: 1.8119 loss_prob: 1.0295 loss_thr: 0.6142 loss_db: 0.1683 2022/11/02 14:24:55 - mmengine - INFO - Epoch(train) [214][15/63] lr: 1.8139e-03 eta: 10:09:54 time: 0.5497 data_time: 0.0081 memory: 14901 loss: 1.9558 loss_prob: 1.1292 loss_thr: 0.6385 loss_db: 0.1881 2022/11/02 14:24:58 - mmengine - INFO - Epoch(train) [214][20/63] lr: 1.8139e-03 eta: 10:09:45 time: 0.5389 data_time: 0.0107 memory: 14901 loss: 2.0145 loss_prob: 1.1989 loss_thr: 0.6205 loss_db: 0.1951 2022/11/02 14:25:01 - mmengine - INFO - Epoch(train) [214][25/63] lr: 1.8139e-03 eta: 10:09:45 time: 0.5502 data_time: 0.0185 memory: 14901 loss: 1.8944 loss_prob: 1.1109 loss_thr: 0.6038 loss_db: 0.1798 2022/11/02 14:25:04 - mmengine - INFO - Epoch(train) [214][30/63] lr: 1.8139e-03 eta: 10:09:40 time: 0.5899 data_time: 0.0410 memory: 14901 loss: 1.8237 loss_prob: 1.0326 loss_thr: 0.6172 loss_db: 0.1739 2022/11/02 14:25:06 - mmengine - INFO - Epoch(train) [214][35/63] lr: 1.8139e-03 eta: 10:09:40 time: 0.5597 data_time: 0.0341 memory: 14901 loss: 1.7922 loss_prob: 1.0199 loss_thr: 0.5990 loss_db: 0.1734 2022/11/02 14:25:09 - mmengine - INFO - Epoch(train) [214][40/63] lr: 1.8139e-03 eta: 10:09:33 time: 0.5733 data_time: 0.0102 memory: 14901 loss: 1.7140 loss_prob: 0.9831 loss_thr: 0.5675 loss_db: 0.1633 2022/11/02 14:25:12 - mmengine - INFO - Epoch(train) [214][45/63] lr: 1.8139e-03 eta: 10:09:33 time: 0.5893 data_time: 0.0108 memory: 14901 loss: 1.7994 loss_prob: 1.0277 loss_thr: 0.6025 loss_db: 0.1692 2022/11/02 14:25:15 - mmengine - INFO - Epoch(train) [214][50/63] lr: 1.8139e-03 eta: 10:09:25 time: 0.5337 data_time: 0.0196 memory: 14901 loss: 1.8400 loss_prob: 1.0417 loss_thr: 0.6238 loss_db: 0.1744 2022/11/02 14:25:17 - mmengine - INFO - Epoch(train) [214][55/63] lr: 1.8139e-03 eta: 10:09:25 time: 0.5293 data_time: 0.0266 memory: 14901 loss: 1.9350 loss_prob: 1.1288 loss_thr: 0.6166 loss_db: 0.1896 2022/11/02 14:25:20 - mmengine - INFO - Epoch(train) [214][60/63] lr: 1.8139e-03 eta: 10:09:15 time: 0.5106 data_time: 0.0162 memory: 14901 loss: 2.0589 loss_prob: 1.2228 loss_thr: 0.6340 loss_db: 0.2021 2022/11/02 14:25:21 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:25:26 - mmengine - INFO - Epoch(train) [215][5/63] lr: 1.8123e-03 eta: 10:09:15 time: 0.7436 data_time: 0.2425 memory: 14901 loss: 1.9524 loss_prob: 1.1371 loss_thr: 0.6268 loss_db: 0.1886 2022/11/02 14:25:29 - mmengine - INFO - Epoch(train) [215][10/63] lr: 1.8123e-03 eta: 10:09:08 time: 0.7762 data_time: 0.2475 memory: 14901 loss: 1.9502 loss_prob: 1.1443 loss_thr: 0.6174 loss_db: 0.1885 2022/11/02 14:25:32 - mmengine - INFO - Epoch(train) [215][15/63] lr: 1.8123e-03 eta: 10:09:08 time: 0.5170 data_time: 0.0155 memory: 14901 loss: 1.9040 loss_prob: 1.1045 loss_thr: 0.6186 loss_db: 0.1809 2022/11/02 14:25:34 - mmengine - INFO - Epoch(train) [215][20/63] lr: 1.8123e-03 eta: 10:08:59 time: 0.5134 data_time: 0.0103 memory: 14901 loss: 1.8426 loss_prob: 1.0532 loss_thr: 0.6163 loss_db: 0.1731 2022/11/02 14:25:37 - mmengine - INFO - Epoch(train) [215][25/63] lr: 1.8123e-03 eta: 10:08:59 time: 0.5340 data_time: 0.0355 memory: 14901 loss: 1.8494 loss_prob: 1.0666 loss_thr: 0.6098 loss_db: 0.1729 2022/11/02 14:25:40 - mmengine - INFO - Epoch(train) [215][30/63] lr: 1.8123e-03 eta: 10:08:51 time: 0.5601 data_time: 0.0384 memory: 14901 loss: 1.8550 loss_prob: 1.0804 loss_thr: 0.6040 loss_db: 0.1705 2022/11/02 14:25:42 - mmengine - INFO - Epoch(train) [215][35/63] lr: 1.8123e-03 eta: 10:08:51 time: 0.5488 data_time: 0.0213 memory: 14901 loss: 1.8926 loss_prob: 1.0952 loss_thr: 0.6224 loss_db: 0.1750 2022/11/02 14:25:45 - mmengine - INFO - Epoch(train) [215][40/63] lr: 1.8123e-03 eta: 10:08:43 time: 0.5339 data_time: 0.0194 memory: 14901 loss: 1.8441 loss_prob: 1.0497 loss_thr: 0.6209 loss_db: 0.1734 2022/11/02 14:25:47 - mmengine - INFO - Epoch(train) [215][45/63] lr: 1.8123e-03 eta: 10:08:43 time: 0.5075 data_time: 0.0123 memory: 14901 loss: 1.8088 loss_prob: 1.0341 loss_thr: 0.6037 loss_db: 0.1710 2022/11/02 14:25:50 - mmengine - INFO - Epoch(train) [215][50/63] lr: 1.8123e-03 eta: 10:08:33 time: 0.4949 data_time: 0.0235 memory: 14901 loss: 1.9635 loss_prob: 1.1591 loss_thr: 0.6094 loss_db: 0.1951 2022/11/02 14:25:53 - mmengine - INFO - Epoch(train) [215][55/63] lr: 1.8123e-03 eta: 10:08:33 time: 0.5137 data_time: 0.0234 memory: 14901 loss: 2.1719 loss_prob: 1.3277 loss_thr: 0.6201 loss_db: 0.2241 2022/11/02 14:25:55 - mmengine - INFO - Epoch(train) [215][60/63] lr: 1.8123e-03 eta: 10:08:25 time: 0.5353 data_time: 0.0125 memory: 14901 loss: 2.4007 loss_prob: 1.5039 loss_thr: 0.6451 loss_db: 0.2517 2022/11/02 14:25:57 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:26:03 - mmengine - INFO - Epoch(train) [216][5/63] lr: 1.8106e-03 eta: 10:08:25 time: 0.8664 data_time: 0.2431 memory: 14901 loss: 2.4323 loss_prob: 1.5147 loss_thr: 0.6629 loss_db: 0.2548 2022/11/02 14:26:06 - mmengine - INFO - Epoch(train) [216][10/63] lr: 1.8106e-03 eta: 10:08:23 time: 0.9084 data_time: 0.2443 memory: 14901 loss: 2.1684 loss_prob: 1.3161 loss_thr: 0.6358 loss_db: 0.2166 2022/11/02 14:26:09 - mmengine - INFO - Epoch(train) [216][15/63] lr: 1.8106e-03 eta: 10:08:23 time: 0.6193 data_time: 0.0192 memory: 14901 loss: 2.0272 loss_prob: 1.2056 loss_thr: 0.6223 loss_db: 0.1993 2022/11/02 14:26:12 - mmengine - INFO - Epoch(train) [216][20/63] lr: 1.8106e-03 eta: 10:08:17 time: 0.5815 data_time: 0.0167 memory: 14901 loss: 2.1593 loss_prob: 1.3148 loss_thr: 0.6325 loss_db: 0.2121 2022/11/02 14:26:15 - mmengine - INFO - Epoch(train) [216][25/63] lr: 1.8106e-03 eta: 10:08:17 time: 0.5209 data_time: 0.0174 memory: 14901 loss: 2.2474 loss_prob: 1.3913 loss_thr: 0.6352 loss_db: 0.2209 2022/11/02 14:26:17 - mmengine - INFO - Epoch(train) [216][30/63] lr: 1.8106e-03 eta: 10:08:09 time: 0.5380 data_time: 0.0315 memory: 14901 loss: 2.0598 loss_prob: 1.2297 loss_thr: 0.6313 loss_db: 0.1988 2022/11/02 14:26:20 - mmengine - INFO - Epoch(train) [216][35/63] lr: 1.8106e-03 eta: 10:08:09 time: 0.5262 data_time: 0.0287 memory: 14901 loss: 2.0476 loss_prob: 1.2169 loss_thr: 0.6323 loss_db: 0.1984 2022/11/02 14:26:23 - mmengine - INFO - Epoch(train) [216][40/63] lr: 1.8106e-03 eta: 10:08:02 time: 0.5518 data_time: 0.0175 memory: 14901 loss: 2.0970 loss_prob: 1.2400 loss_thr: 0.6544 loss_db: 0.2026 2022/11/02 14:26:25 - mmengine - INFO - Epoch(train) [216][45/63] lr: 1.8106e-03 eta: 10:08:02 time: 0.5491 data_time: 0.0126 memory: 14901 loss: 1.9119 loss_prob: 1.0927 loss_thr: 0.6397 loss_db: 0.1795 2022/11/02 14:26:28 - mmengine - INFO - Epoch(train) [216][50/63] lr: 1.8106e-03 eta: 10:07:53 time: 0.5240 data_time: 0.0213 memory: 14901 loss: 1.9712 loss_prob: 1.1501 loss_thr: 0.6317 loss_db: 0.1895 2022/11/02 14:26:31 - mmengine - INFO - Epoch(train) [216][55/63] lr: 1.8106e-03 eta: 10:07:53 time: 0.5441 data_time: 0.0198 memory: 14901 loss: 2.1273 loss_prob: 1.2625 loss_thr: 0.6574 loss_db: 0.2074 2022/11/02 14:26:33 - mmengine - INFO - Epoch(train) [216][60/63] lr: 1.8106e-03 eta: 10:07:45 time: 0.5494 data_time: 0.0118 memory: 14901 loss: 2.0055 loss_prob: 1.1760 loss_thr: 0.6365 loss_db: 0.1931 2022/11/02 14:26:35 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:26:41 - mmengine - INFO - Epoch(train) [217][5/63] lr: 1.8090e-03 eta: 10:07:45 time: 0.9006 data_time: 0.2113 memory: 14901 loss: 2.0785 loss_prob: 1.2555 loss_thr: 0.6216 loss_db: 0.2013 2022/11/02 14:26:44 - mmengine - INFO - Epoch(train) [217][10/63] lr: 1.8090e-03 eta: 10:07:46 time: 0.9537 data_time: 0.2097 memory: 14901 loss: 2.0434 loss_prob: 1.2226 loss_thr: 0.6236 loss_db: 0.1971 2022/11/02 14:26:47 - mmengine - INFO - Epoch(train) [217][15/63] lr: 1.8090e-03 eta: 10:07:46 time: 0.5530 data_time: 0.0089 memory: 14901 loss: 1.9822 loss_prob: 1.1680 loss_thr: 0.6218 loss_db: 0.1924 2022/11/02 14:26:49 - mmengine - INFO - Epoch(train) [217][20/63] lr: 1.8090e-03 eta: 10:07:37 time: 0.5278 data_time: 0.0121 memory: 14901 loss: 1.8796 loss_prob: 1.0863 loss_thr: 0.6114 loss_db: 0.1819 2022/11/02 14:26:52 - mmengine - INFO - Epoch(train) [217][25/63] lr: 1.8090e-03 eta: 10:07:37 time: 0.5133 data_time: 0.0173 memory: 14901 loss: 1.9615 loss_prob: 1.1227 loss_thr: 0.6524 loss_db: 0.1864 2022/11/02 14:26:55 - mmengine - INFO - Epoch(train) [217][30/63] lr: 1.8090e-03 eta: 10:07:29 time: 0.5379 data_time: 0.0401 memory: 14901 loss: 2.0602 loss_prob: 1.1939 loss_thr: 0.6607 loss_db: 0.2055 2022/11/02 14:26:58 - mmengine - INFO - Epoch(train) [217][35/63] lr: 1.8090e-03 eta: 10:07:29 time: 0.5905 data_time: 0.0366 memory: 14901 loss: 2.0916 loss_prob: 1.2516 loss_thr: 0.6313 loss_db: 0.2087 2022/11/02 14:27:00 - mmengine - INFO - Epoch(train) [217][40/63] lr: 1.8090e-03 eta: 10:07:23 time: 0.5703 data_time: 0.0122 memory: 14901 loss: 2.0220 loss_prob: 1.1932 loss_thr: 0.6402 loss_db: 0.1886 2022/11/02 14:27:03 - mmengine - INFO - Epoch(train) [217][45/63] lr: 1.8090e-03 eta: 10:07:23 time: 0.5516 data_time: 0.0094 memory: 14901 loss: 2.0373 loss_prob: 1.1988 loss_thr: 0.6420 loss_db: 0.1965 2022/11/02 14:27:06 - mmengine - INFO - Epoch(train) [217][50/63] lr: 1.8090e-03 eta: 10:07:15 time: 0.5512 data_time: 0.0163 memory: 14901 loss: 2.0951 loss_prob: 1.2671 loss_thr: 0.6234 loss_db: 0.2045 2022/11/02 14:27:09 - mmengine - INFO - Epoch(train) [217][55/63] lr: 1.8090e-03 eta: 10:07:15 time: 0.5676 data_time: 0.0295 memory: 14901 loss: 2.0905 loss_prob: 1.2565 loss_thr: 0.6276 loss_db: 0.2063 2022/11/02 14:27:12 - mmengine - INFO - Epoch(train) [217][60/63] lr: 1.8090e-03 eta: 10:07:09 time: 0.5840 data_time: 0.0246 memory: 14901 loss: 2.0076 loss_prob: 1.2058 loss_thr: 0.5929 loss_db: 0.2089 2022/11/02 14:27:13 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:27:19 - mmengine - INFO - Epoch(train) [218][5/63] lr: 1.8073e-03 eta: 10:07:09 time: 0.8013 data_time: 0.2260 memory: 14901 loss: 2.0937 loss_prob: 1.2485 loss_thr: 0.6425 loss_db: 0.2027 2022/11/02 14:27:21 - mmengine - INFO - Epoch(train) [218][10/63] lr: 1.8073e-03 eta: 10:07:03 time: 0.8086 data_time: 0.2278 memory: 14901 loss: 2.1128 loss_prob: 1.2635 loss_thr: 0.6368 loss_db: 0.2126 2022/11/02 14:27:25 - mmengine - INFO - Epoch(train) [218][15/63] lr: 1.8073e-03 eta: 10:07:03 time: 0.5962 data_time: 0.0121 memory: 14901 loss: 2.1307 loss_prob: 1.2786 loss_thr: 0.6368 loss_db: 0.2153 2022/11/02 14:27:27 - mmengine - INFO - Epoch(train) [218][20/63] lr: 1.8073e-03 eta: 10:06:56 time: 0.5653 data_time: 0.0109 memory: 14901 loss: 2.0619 loss_prob: 1.2148 loss_thr: 0.6473 loss_db: 0.1997 2022/11/02 14:27:30 - mmengine - INFO - Epoch(train) [218][25/63] lr: 1.8073e-03 eta: 10:06:56 time: 0.5309 data_time: 0.0278 memory: 14901 loss: 1.9303 loss_prob: 1.1232 loss_thr: 0.6219 loss_db: 0.1852 2022/11/02 14:27:33 - mmengine - INFO - Epoch(train) [218][30/63] lr: 1.8073e-03 eta: 10:06:50 time: 0.5887 data_time: 0.0522 memory: 14901 loss: 1.9572 loss_prob: 1.1537 loss_thr: 0.6176 loss_db: 0.1859 2022/11/02 14:27:36 - mmengine - INFO - Epoch(train) [218][35/63] lr: 1.8073e-03 eta: 10:06:50 time: 0.5631 data_time: 0.0390 memory: 14901 loss: 1.9419 loss_prob: 1.1360 loss_thr: 0.6190 loss_db: 0.1870 2022/11/02 14:27:38 - mmengine - INFO - Epoch(train) [218][40/63] lr: 1.8073e-03 eta: 10:06:42 time: 0.5407 data_time: 0.0137 memory: 14901 loss: 1.8960 loss_prob: 1.0888 loss_thr: 0.6254 loss_db: 0.1818 2022/11/02 14:27:41 - mmengine - INFO - Epoch(train) [218][45/63] lr: 1.8073e-03 eta: 10:06:42 time: 0.5508 data_time: 0.0168 memory: 14901 loss: 1.9122 loss_prob: 1.1210 loss_thr: 0.6080 loss_db: 0.1832 2022/11/02 14:27:44 - mmengine - INFO - Epoch(train) [218][50/63] lr: 1.8073e-03 eta: 10:06:36 time: 0.5821 data_time: 0.0353 memory: 14901 loss: 1.8730 loss_prob: 1.1008 loss_thr: 0.5923 loss_db: 0.1799 2022/11/02 14:27:47 - mmengine - INFO - Epoch(train) [218][55/63] lr: 1.8073e-03 eta: 10:06:36 time: 0.6000 data_time: 0.0268 memory: 14901 loss: 2.0086 loss_prob: 1.1876 loss_thr: 0.6257 loss_db: 0.1953 2022/11/02 14:27:50 - mmengine - INFO - Epoch(train) [218][60/63] lr: 1.8073e-03 eta: 10:06:29 time: 0.5664 data_time: 0.0078 memory: 14901 loss: 2.0599 loss_prob: 1.2362 loss_thr: 0.6205 loss_db: 0.2031 2022/11/02 14:27:51 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:27:57 - mmengine - INFO - Epoch(train) [219][5/63] lr: 1.8057e-03 eta: 10:06:29 time: 0.8190 data_time: 0.2233 memory: 14901 loss: 1.9005 loss_prob: 1.1260 loss_thr: 0.5964 loss_db: 0.1782 2022/11/02 14:28:00 - mmengine - INFO - Epoch(train) [219][10/63] lr: 1.8057e-03 eta: 10:06:26 time: 0.8524 data_time: 0.2286 memory: 14901 loss: 1.7891 loss_prob: 1.0248 loss_thr: 0.5966 loss_db: 0.1677 2022/11/02 14:28:03 - mmengine - INFO - Epoch(train) [219][15/63] lr: 1.8057e-03 eta: 10:06:26 time: 0.5869 data_time: 0.0136 memory: 14901 loss: 1.9321 loss_prob: 1.1264 loss_thr: 0.6199 loss_db: 0.1859 2022/11/02 14:28:05 - mmengine - INFO - Epoch(train) [219][20/63] lr: 1.8057e-03 eta: 10:06:17 time: 0.5323 data_time: 0.0111 memory: 14901 loss: 1.9588 loss_prob: 1.1467 loss_thr: 0.6261 loss_db: 0.1859 2022/11/02 14:28:09 - mmengine - INFO - Epoch(train) [219][25/63] lr: 1.8057e-03 eta: 10:06:17 time: 0.6218 data_time: 0.0227 memory: 14901 loss: 1.9750 loss_prob: 1.1515 loss_thr: 0.6359 loss_db: 0.1876 2022/11/02 14:28:13 - mmengine - INFO - Epoch(train) [219][30/63] lr: 1.8057e-03 eta: 10:06:18 time: 0.7325 data_time: 0.0440 memory: 14901 loss: 1.9021 loss_prob: 1.1079 loss_thr: 0.6167 loss_db: 0.1775 2022/11/02 14:28:15 - mmengine - INFO - Epoch(train) [219][35/63] lr: 1.8057e-03 eta: 10:06:18 time: 0.6200 data_time: 0.0334 memory: 14901 loss: 1.6940 loss_prob: 0.9648 loss_thr: 0.5737 loss_db: 0.1555 2022/11/02 14:28:18 - mmengine - INFO - Epoch(train) [219][40/63] lr: 1.8057e-03 eta: 10:06:10 time: 0.5414 data_time: 0.0137 memory: 14901 loss: 1.7788 loss_prob: 1.0298 loss_thr: 0.5783 loss_db: 0.1707 2022/11/02 14:28:21 - mmengine - INFO - Epoch(train) [219][45/63] lr: 1.8057e-03 eta: 10:06:10 time: 0.5690 data_time: 0.0140 memory: 14901 loss: 1.8923 loss_prob: 1.1227 loss_thr: 0.5845 loss_db: 0.1850 2022/11/02 14:28:24 - mmengine - INFO - Epoch(train) [219][50/63] lr: 1.8057e-03 eta: 10:06:03 time: 0.5690 data_time: 0.0158 memory: 14901 loss: 1.8347 loss_prob: 1.0683 loss_thr: 0.5923 loss_db: 0.1740 2022/11/02 14:28:27 - mmengine - INFO - Epoch(train) [219][55/63] lr: 1.8057e-03 eta: 10:06:03 time: 0.6109 data_time: 0.0270 memory: 14901 loss: 1.8054 loss_prob: 1.0224 loss_thr: 0.6143 loss_db: 0.1687 2022/11/02 14:28:29 - mmengine - INFO - Epoch(train) [219][60/63] lr: 1.8057e-03 eta: 10:05:56 time: 0.5602 data_time: 0.0210 memory: 14901 loss: 1.8887 loss_prob: 1.0766 loss_thr: 0.6308 loss_db: 0.1814 2022/11/02 14:28:31 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:28:36 - mmengine - INFO - Epoch(train) [220][5/63] lr: 1.8040e-03 eta: 10:05:56 time: 0.7788 data_time: 0.2283 memory: 14901 loss: 1.9529 loss_prob: 1.1372 loss_thr: 0.6280 loss_db: 0.1877 2022/11/02 14:28:39 - mmengine - INFO - Epoch(train) [220][10/63] lr: 1.8040e-03 eta: 10:05:50 time: 0.8002 data_time: 0.2288 memory: 14901 loss: 1.8994 loss_prob: 1.0999 loss_thr: 0.6168 loss_db: 0.1827 2022/11/02 14:28:41 - mmengine - INFO - Epoch(train) [220][15/63] lr: 1.8040e-03 eta: 10:05:50 time: 0.4913 data_time: 0.0063 memory: 14901 loss: 1.8446 loss_prob: 1.0516 loss_thr: 0.6186 loss_db: 0.1744 2022/11/02 14:28:44 - mmengine - INFO - Epoch(train) [220][20/63] lr: 1.8040e-03 eta: 10:05:40 time: 0.5066 data_time: 0.0103 memory: 14901 loss: 1.7816 loss_prob: 1.0042 loss_thr: 0.6103 loss_db: 0.1670 2022/11/02 14:28:47 - mmengine - INFO - Epoch(train) [220][25/63] lr: 1.8040e-03 eta: 10:05:40 time: 0.5906 data_time: 0.0493 memory: 14901 loss: 2.1321 loss_prob: 1.3012 loss_thr: 0.6268 loss_db: 0.2041 2022/11/02 14:28:50 - mmengine - INFO - Epoch(train) [220][30/63] lr: 1.8040e-03 eta: 10:05:35 time: 0.5908 data_time: 0.0460 memory: 14901 loss: 2.2965 loss_prob: 1.4326 loss_thr: 0.6425 loss_db: 0.2214 2022/11/02 14:28:52 - mmengine - INFO - Epoch(train) [220][35/63] lr: 1.8040e-03 eta: 10:05:35 time: 0.5593 data_time: 0.0083 memory: 14901 loss: 1.9392 loss_prob: 1.1458 loss_thr: 0.6088 loss_db: 0.1845 2022/11/02 14:28:55 - mmengine - INFO - Epoch(train) [220][40/63] lr: 1.8040e-03 eta: 10:05:27 time: 0.5378 data_time: 0.0073 memory: 14901 loss: 1.8066 loss_prob: 1.0524 loss_thr: 0.5891 loss_db: 0.1651 2022/11/02 14:28:58 - mmengine - INFO - Epoch(train) [220][45/63] lr: 1.8040e-03 eta: 10:05:27 time: 0.5290 data_time: 0.0067 memory: 14901 loss: 1.8298 loss_prob: 1.0604 loss_thr: 0.6006 loss_db: 0.1689 2022/11/02 14:29:01 - mmengine - INFO - Epoch(train) [220][50/63] lr: 1.8040e-03 eta: 10:05:21 time: 0.5915 data_time: 0.0270 memory: 14901 loss: 1.7916 loss_prob: 1.0245 loss_thr: 0.5966 loss_db: 0.1706 2022/11/02 14:29:04 - mmengine - INFO - Epoch(train) [220][55/63] lr: 1.8040e-03 eta: 10:05:21 time: 0.6172 data_time: 0.0264 memory: 14901 loss: 1.8362 loss_prob: 1.0565 loss_thr: 0.6070 loss_db: 0.1727 2022/11/02 14:29:06 - mmengine - INFO - Epoch(train) [220][60/63] lr: 1.8040e-03 eta: 10:05:13 time: 0.5531 data_time: 0.0114 memory: 14901 loss: 1.8344 loss_prob: 1.0525 loss_thr: 0.6120 loss_db: 0.1699 2022/11/02 14:29:08 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:29:08 - mmengine - INFO - Saving checkpoint at 220 epochs 2022/11/02 14:29:12 - mmengine - INFO - Epoch(val) [220][5/500] eta: 10:05:13 time: 0.0460 data_time: 0.0069 memory: 14901 2022/11/02 14:29:12 - mmengine - INFO - Epoch(val) [220][10/500] eta: 0:00:23 time: 0.0486 data_time: 0.0065 memory: 1008 2022/11/02 14:29:12 - mmengine - INFO - Epoch(val) [220][15/500] eta: 0:00:23 time: 0.0414 data_time: 0.0027 memory: 1008 2022/11/02 14:29:12 - mmengine - INFO - Epoch(val) [220][20/500] eta: 0:00:18 time: 0.0391 data_time: 0.0031 memory: 1008 2022/11/02 14:29:12 - mmengine - INFO - Epoch(val) [220][25/500] eta: 0:00:18 time: 0.0408 data_time: 0.0036 memory: 1008 2022/11/02 14:29:13 - mmengine - INFO - Epoch(val) [220][30/500] eta: 0:00:20 time: 0.0447 data_time: 0.0039 memory: 1008 2022/11/02 14:29:13 - mmengine - INFO - Epoch(val) [220][35/500] eta: 0:00:20 time: 0.0420 data_time: 0.0032 memory: 1008 2022/11/02 14:29:13 - mmengine - INFO - Epoch(val) [220][40/500] eta: 0:00:20 time: 0.0441 data_time: 0.0028 memory: 1008 2022/11/02 14:29:13 - mmengine - INFO - Epoch(val) [220][45/500] eta: 0:00:20 time: 0.0438 data_time: 0.0028 memory: 1008 2022/11/02 14:29:14 - mmengine - INFO - Epoch(val) [220][50/500] eta: 0:00:18 time: 0.0418 data_time: 0.0025 memory: 1008 2022/11/02 14:29:14 - mmengine - INFO - Epoch(val) [220][55/500] eta: 0:00:18 time: 0.0451 data_time: 0.0032 memory: 1008 2022/11/02 14:29:14 - mmengine - INFO - Epoch(val) [220][60/500] eta: 0:00:17 time: 0.0403 data_time: 0.0033 memory: 1008 2022/11/02 14:29:14 - mmengine - INFO - Epoch(val) [220][65/500] eta: 0:00:17 time: 0.0408 data_time: 0.0024 memory: 1008 2022/11/02 14:29:14 - mmengine - INFO - Epoch(val) [220][70/500] eta: 0:00:18 time: 0.0431 data_time: 0.0024 memory: 1008 2022/11/02 14:29:15 - mmengine - INFO - Epoch(val) [220][75/500] eta: 0:00:18 time: 0.0401 data_time: 0.0030 memory: 1008 2022/11/02 14:29:15 - mmengine - INFO - Epoch(val) [220][80/500] eta: 0:00:16 time: 0.0391 data_time: 0.0033 memory: 1008 2022/11/02 14:29:15 - mmengine - INFO - Epoch(val) [220][85/500] eta: 0:00:16 time: 0.0364 data_time: 0.0027 memory: 1008 2022/11/02 14:29:15 - mmengine - INFO - Epoch(val) [220][90/500] eta: 0:00:16 time: 0.0392 data_time: 0.0025 memory: 1008 2022/11/02 14:29:15 - mmengine - INFO - Epoch(val) [220][95/500] eta: 0:00:16 time: 0.0452 data_time: 0.0027 memory: 1008 2022/11/02 14:29:16 - mmengine - INFO - Epoch(val) [220][100/500] eta: 0:00:17 time: 0.0433 data_time: 0.0028 memory: 1008 2022/11/02 14:29:16 - mmengine - INFO - Epoch(val) [220][105/500] eta: 0:00:17 time: 0.0416 data_time: 0.0039 memory: 1008 2022/11/02 14:29:16 - mmengine - INFO - Epoch(val) [220][110/500] eta: 0:00:15 time: 0.0396 data_time: 0.0037 memory: 1008 2022/11/02 14:29:16 - mmengine - INFO - Epoch(val) [220][115/500] eta: 0:00:15 time: 0.0365 data_time: 0.0023 memory: 1008 2022/11/02 14:29:16 - mmengine - INFO - Epoch(val) [220][120/500] eta: 0:00:15 time: 0.0404 data_time: 0.0032 memory: 1008 2022/11/02 14:29:17 - mmengine - INFO - Epoch(val) [220][125/500] eta: 0:00:15 time: 0.0396 data_time: 0.0034 memory: 1008 2022/11/02 14:29:17 - mmengine - INFO - Epoch(val) [220][130/500] eta: 0:00:12 time: 0.0348 data_time: 0.0023 memory: 1008 2022/11/02 14:29:17 - mmengine - INFO - Epoch(val) [220][135/500] eta: 0:00:12 time: 0.0413 data_time: 0.0024 memory: 1008 2022/11/02 14:29:17 - mmengine - INFO - Epoch(val) [220][140/500] eta: 0:00:15 time: 0.0421 data_time: 0.0024 memory: 1008 2022/11/02 14:29:17 - mmengine - INFO - Epoch(val) [220][145/500] eta: 0:00:15 time: 0.0407 data_time: 0.0023 memory: 1008 2022/11/02 14:29:18 - mmengine - INFO - Epoch(val) [220][150/500] eta: 0:00:15 time: 0.0449 data_time: 0.0026 memory: 1008 2022/11/02 14:29:18 - mmengine - INFO - Epoch(val) [220][155/500] eta: 0:00:15 time: 0.0476 data_time: 0.0028 memory: 1008 2022/11/02 14:29:18 - mmengine - INFO - Epoch(val) [220][160/500] eta: 0:00:15 time: 0.0465 data_time: 0.0028 memory: 1008 2022/11/02 14:29:18 - mmengine - INFO - Epoch(val) [220][165/500] eta: 0:00:15 time: 0.0427 data_time: 0.0030 memory: 1008 2022/11/02 14:29:18 - mmengine - INFO - Epoch(val) [220][170/500] eta: 0:00:13 time: 0.0408 data_time: 0.0028 memory: 1008 2022/11/02 14:29:19 - mmengine - INFO - Epoch(val) [220][175/500] eta: 0:00:13 time: 0.0376 data_time: 0.0025 memory: 1008 2022/11/02 14:29:19 - mmengine - INFO - Epoch(val) [220][180/500] eta: 0:00:13 time: 0.0435 data_time: 0.0041 memory: 1008 2022/11/02 14:29:19 - mmengine - INFO - Epoch(val) [220][185/500] eta: 0:00:13 time: 0.0466 data_time: 0.0042 memory: 1008 2022/11/02 14:29:19 - mmengine - INFO - Epoch(val) [220][190/500] eta: 0:00:12 time: 0.0413 data_time: 0.0025 memory: 1008 2022/11/02 14:29:19 - mmengine - INFO - Epoch(val) [220][195/500] eta: 0:00:12 time: 0.0378 data_time: 0.0025 memory: 1008 2022/11/02 14:29:20 - mmengine - INFO - Epoch(val) [220][200/500] eta: 0:00:12 time: 0.0420 data_time: 0.0027 memory: 1008 2022/11/02 14:29:20 - mmengine - INFO - Epoch(val) [220][205/500] eta: 0:00:12 time: 0.0430 data_time: 0.0027 memory: 1008 2022/11/02 14:29:20 - mmengine - INFO - Epoch(val) [220][210/500] eta: 0:00:11 time: 0.0394 data_time: 0.0028 memory: 1008 2022/11/02 14:29:20 - mmengine - INFO - Epoch(val) [220][215/500] eta: 0:00:11 time: 0.0433 data_time: 0.0033 memory: 1008 2022/11/02 14:29:21 - mmengine - INFO - Epoch(val) [220][220/500] eta: 0:00:12 time: 0.0459 data_time: 0.0033 memory: 1008 2022/11/02 14:29:21 - mmengine - INFO - Epoch(val) [220][225/500] eta: 0:00:12 time: 0.0434 data_time: 0.0027 memory: 1008 2022/11/02 14:29:21 - mmengine - INFO - Epoch(val) [220][230/500] eta: 0:00:10 time: 0.0384 data_time: 0.0025 memory: 1008 2022/11/02 14:29:21 - mmengine - INFO - Epoch(val) [220][235/500] eta: 0:00:10 time: 0.0398 data_time: 0.0027 memory: 1008 2022/11/02 14:29:21 - mmengine - INFO - Epoch(val) [220][240/500] eta: 0:00:11 time: 0.0429 data_time: 0.0029 memory: 1008 2022/11/02 14:29:22 - mmengine - INFO - Epoch(val) [220][245/500] eta: 0:00:11 time: 0.0440 data_time: 0.0041 memory: 1008 2022/11/02 14:29:22 - mmengine - INFO - Epoch(val) [220][250/500] eta: 0:00:11 time: 0.0442 data_time: 0.0040 memory: 1008 2022/11/02 14:29:22 - mmengine - INFO - Epoch(val) [220][255/500] eta: 0:00:11 time: 0.0389 data_time: 0.0025 memory: 1008 2022/11/02 14:29:22 - mmengine - INFO - Epoch(val) [220][260/500] eta: 0:00:08 time: 0.0348 data_time: 0.0023 memory: 1008 2022/11/02 14:29:22 - mmengine - INFO - Epoch(val) [220][265/500] eta: 0:00:08 time: 0.0368 data_time: 0.0024 memory: 1008 2022/11/02 14:29:23 - mmengine - INFO - Epoch(val) [220][270/500] eta: 0:00:09 time: 0.0397 data_time: 0.0028 memory: 1008 2022/11/02 14:29:23 - mmengine - INFO - Epoch(val) [220][275/500] eta: 0:00:09 time: 0.0381 data_time: 0.0028 memory: 1008 2022/11/02 14:29:23 - mmengine - INFO - Epoch(val) [220][280/500] eta: 0:00:08 time: 0.0399 data_time: 0.0025 memory: 1008 2022/11/02 14:29:23 - mmengine - INFO - Epoch(val) [220][285/500] eta: 0:00:08 time: 0.0391 data_time: 0.0024 memory: 1008 2022/11/02 14:29:23 - mmengine - INFO - Epoch(val) [220][290/500] eta: 0:00:08 time: 0.0388 data_time: 0.0024 memory: 1008 2022/11/02 14:29:24 - mmengine - INFO - Epoch(val) [220][295/500] eta: 0:00:08 time: 0.0446 data_time: 0.0028 memory: 1008 2022/11/02 14:29:24 - mmengine - INFO - Epoch(val) [220][300/500] eta: 0:00:08 time: 0.0433 data_time: 0.0030 memory: 1008 2022/11/02 14:29:24 - mmengine - INFO - Epoch(val) [220][305/500] eta: 0:00:08 time: 0.0416 data_time: 0.0031 memory: 1008 2022/11/02 14:29:24 - mmengine - INFO - Epoch(val) [220][310/500] eta: 0:00:08 time: 0.0433 data_time: 0.0030 memory: 1008 2022/11/02 14:29:24 - mmengine - INFO - Epoch(val) [220][315/500] eta: 0:00:08 time: 0.0477 data_time: 0.0034 memory: 1008 2022/11/02 14:29:25 - mmengine - INFO - Epoch(val) [220][320/500] eta: 0:00:08 time: 0.0483 data_time: 0.0035 memory: 1008 2022/11/02 14:29:25 - mmengine - INFO - Epoch(val) [220][325/500] eta: 0:00:08 time: 0.0539 data_time: 0.0028 memory: 1008 2022/11/02 14:29:25 - mmengine - INFO - Epoch(val) [220][330/500] eta: 0:00:09 time: 0.0536 data_time: 0.0028 memory: 1008 2022/11/02 14:29:25 - mmengine - INFO - Epoch(val) [220][335/500] eta: 0:00:09 time: 0.0404 data_time: 0.0031 memory: 1008 2022/11/02 14:29:26 - mmengine - INFO - Epoch(val) [220][340/500] eta: 0:00:07 time: 0.0498 data_time: 0.0030 memory: 1008 2022/11/02 14:29:26 - mmengine - INFO - Epoch(val) [220][345/500] eta: 0:00:07 time: 0.0528 data_time: 0.0030 memory: 1008 2022/11/02 14:29:26 - mmengine - INFO - Epoch(val) [220][350/500] eta: 0:00:07 time: 0.0479 data_time: 0.0031 memory: 1008 2022/11/02 14:29:26 - mmengine - INFO - Epoch(val) [220][355/500] eta: 0:00:07 time: 0.0472 data_time: 0.0030 memory: 1008 2022/11/02 14:29:27 - mmengine - INFO - Epoch(val) [220][360/500] eta: 0:00:05 time: 0.0416 data_time: 0.0028 memory: 1008 2022/11/02 14:29:27 - mmengine - INFO - Epoch(val) [220][365/500] eta: 0:00:05 time: 0.0448 data_time: 0.0028 memory: 1008 2022/11/02 14:29:27 - mmengine - INFO - Epoch(val) [220][370/500] eta: 0:00:05 time: 0.0418 data_time: 0.0029 memory: 1008 2022/11/02 14:29:27 - mmengine - INFO - Epoch(val) [220][375/500] eta: 0:00:05 time: 0.0400 data_time: 0.0033 memory: 1008 2022/11/02 14:29:28 - mmengine - INFO - Epoch(val) [220][380/500] eta: 0:00:05 time: 0.0480 data_time: 0.0035 memory: 1008 2022/11/02 14:29:28 - mmengine - INFO - Epoch(val) [220][385/500] eta: 0:00:05 time: 0.0489 data_time: 0.0032 memory: 1008 2022/11/02 14:29:28 - mmengine - INFO - Epoch(val) [220][390/500] eta: 0:00:04 time: 0.0429 data_time: 0.0029 memory: 1008 2022/11/02 14:29:28 - mmengine - INFO - Epoch(val) [220][395/500] eta: 0:00:04 time: 0.0396 data_time: 0.0028 memory: 1008 2022/11/02 14:29:28 - mmengine - INFO - Epoch(val) [220][400/500] eta: 0:00:04 time: 0.0420 data_time: 0.0029 memory: 1008 2022/11/02 14:29:29 - mmengine - INFO - Epoch(val) [220][405/500] eta: 0:00:04 time: 0.0456 data_time: 0.0031 memory: 1008 2022/11/02 14:29:29 - mmengine - INFO - Epoch(val) [220][410/500] eta: 0:00:04 time: 0.0491 data_time: 0.0035 memory: 1008 2022/11/02 14:29:29 - mmengine - INFO - Epoch(val) [220][415/500] eta: 0:00:04 time: 0.0467 data_time: 0.0035 memory: 1008 2022/11/02 14:29:30 - mmengine - INFO - Epoch(val) [220][420/500] eta: 0:00:05 time: 0.0654 data_time: 0.0241 memory: 1008 2022/11/02 14:29:30 - mmengine - INFO - Epoch(val) [220][425/500] eta: 0:00:05 time: 0.0671 data_time: 0.0236 memory: 1008 2022/11/02 14:29:30 - mmengine - INFO - Epoch(val) [220][430/500] eta: 0:00:02 time: 0.0415 data_time: 0.0026 memory: 1008 2022/11/02 14:29:30 - mmengine - INFO - Epoch(val) [220][435/500] eta: 0:00:02 time: 0.0419 data_time: 0.0034 memory: 1008 2022/11/02 14:29:30 - mmengine - INFO - Epoch(val) [220][440/500] eta: 0:00:03 time: 0.0507 data_time: 0.0045 memory: 1008 2022/11/02 14:29:31 - mmengine - INFO - Epoch(val) [220][445/500] eta: 0:00:03 time: 0.0561 data_time: 0.0043 memory: 1008 2022/11/02 14:29:31 - mmengine - INFO - Epoch(val) [220][450/500] eta: 0:00:02 time: 0.0476 data_time: 0.0032 memory: 1008 2022/11/02 14:29:31 - mmengine - INFO - Epoch(val) [220][455/500] eta: 0:00:02 time: 0.0382 data_time: 0.0029 memory: 1008 2022/11/02 14:29:31 - mmengine - INFO - Epoch(val) [220][460/500] eta: 0:00:01 time: 0.0353 data_time: 0.0024 memory: 1008 2022/11/02 14:29:31 - mmengine - INFO - Epoch(val) [220][465/500] eta: 0:00:01 time: 0.0359 data_time: 0.0021 memory: 1008 2022/11/02 14:29:32 - mmengine - INFO - Epoch(val) [220][470/500] eta: 0:00:01 time: 0.0375 data_time: 0.0023 memory: 1008 2022/11/02 14:29:32 - mmengine - INFO - Epoch(val) [220][475/500] eta: 0:00:01 time: 0.0367 data_time: 0.0027 memory: 1008 2022/11/02 14:29:32 - mmengine - INFO - Epoch(val) [220][480/500] eta: 0:00:00 time: 0.0401 data_time: 0.0035 memory: 1008 2022/11/02 14:29:32 - mmengine - INFO - Epoch(val) [220][485/500] eta: 0:00:00 time: 0.0405 data_time: 0.0035 memory: 1008 2022/11/02 14:29:32 - mmengine - INFO - Epoch(val) [220][490/500] eta: 0:00:00 time: 0.0411 data_time: 0.0033 memory: 1008 2022/11/02 14:29:33 - mmengine - INFO - Epoch(val) [220][495/500] eta: 0:00:00 time: 0.0452 data_time: 0.0055 memory: 1008 2022/11/02 14:29:33 - mmengine - INFO - Epoch(val) [220][500/500] eta: 0:00:00 time: 0.0397 data_time: 0.0047 memory: 1008 2022/11/02 14:29:33 - mmengine - INFO - Evaluating hmean-iou... 2022/11/02 14:29:33 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.8142, precision: 0.6916, hmean: 0.7479 2022/11/02 14:29:33 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.8142, precision: 0.7707, hmean: 0.7919 2022/11/02 14:29:33 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.8065, precision: 0.8191, hmean: 0.8127 2022/11/02 14:29:33 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.7872, precision: 0.8623, hmean: 0.8231 2022/11/02 14:29:33 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.7116, precision: 0.9129, hmean: 0.7998 2022/11/02 14:29:33 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.2898, precision: 0.9663, hmean: 0.4459 2022/11/02 14:29:33 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0005, precision: 1.0000, hmean: 0.0010 2022/11/02 14:29:33 - mmengine - INFO - Epoch(val) [220][500/500] icdar/precision: 0.8623 icdar/recall: 0.7872 icdar/hmean: 0.8231 2022/11/02 14:29:40 - mmengine - INFO - Epoch(train) [221][5/63] lr: 1.8023e-03 eta: 0:00:00 time: 0.8885 data_time: 0.2381 memory: 14901 loss: 1.7302 loss_prob: 0.9787 loss_thr: 0.5864 loss_db: 0.1652 2022/11/02 14:29:42 - mmengine - INFO - Epoch(train) [221][10/63] lr: 1.8023e-03 eta: 10:05:14 time: 0.9442 data_time: 0.2381 memory: 14901 loss: 1.8591 loss_prob: 1.0944 loss_thr: 0.5960 loss_db: 0.1687 2022/11/02 14:29:46 - mmengine - INFO - Epoch(train) [221][15/63] lr: 1.8023e-03 eta: 10:05:14 time: 0.5876 data_time: 0.0085 memory: 14901 loss: 1.8474 loss_prob: 1.0920 loss_thr: 0.5860 loss_db: 0.1694 2022/11/02 14:29:48 - mmengine - INFO - Epoch(train) [221][20/63] lr: 1.8023e-03 eta: 10:05:07 time: 0.5724 data_time: 0.0094 memory: 14901 loss: 1.7947 loss_prob: 1.0245 loss_thr: 0.6010 loss_db: 0.1693 2022/11/02 14:29:51 - mmengine - INFO - Epoch(train) [221][25/63] lr: 1.8023e-03 eta: 10:05:07 time: 0.5302 data_time: 0.0113 memory: 14901 loss: 1.7381 loss_prob: 0.9869 loss_thr: 0.5884 loss_db: 0.1629 2022/11/02 14:29:54 - mmengine - INFO - Epoch(train) [221][30/63] lr: 1.8023e-03 eta: 10:05:00 time: 0.5503 data_time: 0.0371 memory: 14901 loss: 1.6672 loss_prob: 0.9485 loss_thr: 0.5600 loss_db: 0.1587 2022/11/02 14:29:57 - mmengine - INFO - Epoch(train) [221][35/63] lr: 1.8023e-03 eta: 10:05:00 time: 0.5986 data_time: 0.0342 memory: 14901 loss: 1.8513 loss_prob: 1.0804 loss_thr: 0.5946 loss_db: 0.1763 2022/11/02 14:29:59 - mmengine - INFO - Epoch(train) [221][40/63] lr: 1.8023e-03 eta: 10:04:53 time: 0.5654 data_time: 0.0124 memory: 14901 loss: 1.9430 loss_prob: 1.1421 loss_thr: 0.6139 loss_db: 0.1870 2022/11/02 14:30:02 - mmengine - INFO - Epoch(train) [221][45/63] lr: 1.8023e-03 eta: 10:04:53 time: 0.5504 data_time: 0.0124 memory: 14901 loss: 1.9012 loss_prob: 1.1121 loss_thr: 0.6048 loss_db: 0.1842 2022/11/02 14:30:05 - mmengine - INFO - Epoch(train) [221][50/63] lr: 1.8023e-03 eta: 10:04:45 time: 0.5542 data_time: 0.0167 memory: 14901 loss: 1.8949 loss_prob: 1.0994 loss_thr: 0.6155 loss_db: 0.1799 2022/11/02 14:30:08 - mmengine - INFO - Epoch(train) [221][55/63] lr: 1.8023e-03 eta: 10:04:45 time: 0.5317 data_time: 0.0309 memory: 14901 loss: 1.8913 loss_prob: 1.0833 loss_thr: 0.6295 loss_db: 0.1786 2022/11/02 14:30:10 - mmengine - INFO - Epoch(train) [221][60/63] lr: 1.8023e-03 eta: 10:04:37 time: 0.5232 data_time: 0.0225 memory: 14901 loss: 1.8784 loss_prob: 1.0791 loss_thr: 0.6192 loss_db: 0.1801 2022/11/02 14:30:11 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:30:18 - mmengine - INFO - Epoch(train) [222][5/63] lr: 1.8007e-03 eta: 10:04:37 time: 0.9392 data_time: 0.2551 memory: 14901 loss: 1.9006 loss_prob: 1.0947 loss_thr: 0.6243 loss_db: 0.1817 2022/11/02 14:30:21 - mmengine - INFO - Epoch(train) [222][10/63] lr: 1.8007e-03 eta: 10:04:39 time: 0.9996 data_time: 0.2555 memory: 14901 loss: 1.8616 loss_prob: 1.0626 loss_thr: 0.6250 loss_db: 0.1739 2022/11/02 14:30:24 - mmengine - INFO - Epoch(train) [222][15/63] lr: 1.8007e-03 eta: 10:04:39 time: 0.5578 data_time: 0.0067 memory: 14901 loss: 1.9394 loss_prob: 1.1323 loss_thr: 0.6212 loss_db: 0.1860 2022/11/02 14:30:27 - mmengine - INFO - Epoch(train) [222][20/63] lr: 1.8007e-03 eta: 10:04:32 time: 0.5614 data_time: 0.0081 memory: 14901 loss: 2.0433 loss_prob: 1.2141 loss_thr: 0.6346 loss_db: 0.1946 2022/11/02 14:30:29 - mmengine - INFO - Epoch(train) [222][25/63] lr: 1.8007e-03 eta: 10:04:32 time: 0.5468 data_time: 0.0199 memory: 14901 loss: 1.8826 loss_prob: 1.0883 loss_thr: 0.6174 loss_db: 0.1769 2022/11/02 14:30:33 - mmengine - INFO - Epoch(train) [222][30/63] lr: 1.8007e-03 eta: 10:04:26 time: 0.5748 data_time: 0.0407 memory: 14901 loss: 1.7258 loss_prob: 0.9715 loss_thr: 0.5916 loss_db: 0.1627 2022/11/02 14:30:35 - mmengine - INFO - Epoch(train) [222][35/63] lr: 1.8007e-03 eta: 10:04:26 time: 0.5797 data_time: 0.0299 memory: 14901 loss: 1.7219 loss_prob: 0.9814 loss_thr: 0.5812 loss_db: 0.1592 2022/11/02 14:30:38 - mmengine - INFO - Epoch(train) [222][40/63] lr: 1.8007e-03 eta: 10:04:17 time: 0.5224 data_time: 0.0086 memory: 14901 loss: 1.7274 loss_prob: 0.9839 loss_thr: 0.5853 loss_db: 0.1582 2022/11/02 14:30:41 - mmengine - INFO - Epoch(train) [222][45/63] lr: 1.8007e-03 eta: 10:04:17 time: 0.5354 data_time: 0.0074 memory: 14901 loss: 1.7849 loss_prob: 1.0188 loss_thr: 0.5960 loss_db: 0.1701 2022/11/02 14:30:43 - mmengine - INFO - Epoch(train) [222][50/63] lr: 1.8007e-03 eta: 10:04:09 time: 0.5403 data_time: 0.0126 memory: 14901 loss: 1.8054 loss_prob: 1.0383 loss_thr: 0.5903 loss_db: 0.1768 2022/11/02 14:30:46 - mmengine - INFO - Epoch(train) [222][55/63] lr: 1.8007e-03 eta: 10:04:09 time: 0.5366 data_time: 0.0286 memory: 14901 loss: 1.7876 loss_prob: 1.0236 loss_thr: 0.5892 loss_db: 0.1749 2022/11/02 14:30:49 - mmengine - INFO - Epoch(train) [222][60/63] lr: 1.8007e-03 eta: 10:04:02 time: 0.5700 data_time: 0.0236 memory: 14901 loss: 1.7900 loss_prob: 1.0264 loss_thr: 0.5907 loss_db: 0.1730 2022/11/02 14:30:50 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:30:55 - mmengine - INFO - Epoch(train) [223][5/63] lr: 1.7990e-03 eta: 10:04:02 time: 0.7384 data_time: 0.2123 memory: 14901 loss: 1.9755 loss_prob: 1.1513 loss_thr: 0.6373 loss_db: 0.1869 2022/11/02 14:30:59 - mmengine - INFO - Epoch(train) [223][10/63] lr: 1.7990e-03 eta: 10:03:57 time: 0.8295 data_time: 0.2370 memory: 14901 loss: 1.9426 loss_prob: 1.1167 loss_thr: 0.6414 loss_db: 0.1845 2022/11/02 14:31:01 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:31:02 - mmengine - INFO - Epoch(train) [223][15/63] lr: 1.7990e-03 eta: 10:03:57 time: 0.6266 data_time: 0.0543 memory: 14901 loss: 1.8338 loss_prob: 1.0436 loss_thr: 0.6163 loss_db: 0.1739 2022/11/02 14:31:04 - mmengine - INFO - Epoch(train) [223][20/63] lr: 1.7990e-03 eta: 10:03:51 time: 0.5813 data_time: 0.0311 memory: 14901 loss: 1.9661 loss_prob: 1.1683 loss_thr: 0.6058 loss_db: 0.1919 2022/11/02 14:31:07 - mmengine - INFO - Epoch(train) [223][25/63] lr: 1.7990e-03 eta: 10:03:51 time: 0.5387 data_time: 0.0073 memory: 14901 loss: 1.9617 loss_prob: 1.1520 loss_thr: 0.6197 loss_db: 0.1900 2022/11/02 14:31:09 - mmengine - INFO - Epoch(train) [223][30/63] lr: 1.7990e-03 eta: 10:03:41 time: 0.4992 data_time: 0.0066 memory: 14901 loss: 1.9453 loss_prob: 1.1144 loss_thr: 0.6422 loss_db: 0.1887 2022/11/02 14:31:12 - mmengine - INFO - Epoch(train) [223][35/63] lr: 1.7990e-03 eta: 10:03:41 time: 0.5421 data_time: 0.0215 memory: 14901 loss: 2.0016 loss_prob: 1.1847 loss_thr: 0.6212 loss_db: 0.1957 2022/11/02 14:31:15 - mmengine - INFO - Epoch(train) [223][40/63] lr: 1.7990e-03 eta: 10:03:35 time: 0.5702 data_time: 0.0317 memory: 14901 loss: 1.9502 loss_prob: 1.1520 loss_thr: 0.6067 loss_db: 0.1916 2022/11/02 14:31:18 - mmengine - INFO - Epoch(train) [223][45/63] lr: 1.7990e-03 eta: 10:03:35 time: 0.5223 data_time: 0.0213 memory: 14901 loss: 1.9005 loss_prob: 1.0989 loss_thr: 0.6118 loss_db: 0.1898 2022/11/02 14:31:20 - mmengine - INFO - Epoch(train) [223][50/63] lr: 1.7990e-03 eta: 10:03:26 time: 0.5240 data_time: 0.0149 memory: 14901 loss: 1.8042 loss_prob: 1.0364 loss_thr: 0.5942 loss_db: 0.1736 2022/11/02 14:31:23 - mmengine - INFO - Epoch(train) [223][55/63] lr: 1.7990e-03 eta: 10:03:26 time: 0.5224 data_time: 0.0247 memory: 14901 loss: 1.8683 loss_prob: 1.0924 loss_thr: 0.5971 loss_db: 0.1788 2022/11/02 14:31:25 - mmengine - INFO - Epoch(train) [223][60/63] lr: 1.7990e-03 eta: 10:03:17 time: 0.5199 data_time: 0.0217 memory: 14901 loss: 1.8970 loss_prob: 1.1177 loss_thr: 0.5920 loss_db: 0.1872 2022/11/02 14:31:27 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:31:33 - mmengine - INFO - Epoch(train) [224][5/63] lr: 1.7974e-03 eta: 10:03:17 time: 0.8194 data_time: 0.2217 memory: 14901 loss: 1.8574 loss_prob: 1.0613 loss_thr: 0.6199 loss_db: 0.1762 2022/11/02 14:31:35 - mmengine - INFO - Epoch(train) [224][10/63] lr: 1.7974e-03 eta: 10:03:13 time: 0.8332 data_time: 0.2194 memory: 14901 loss: 1.9437 loss_prob: 1.1445 loss_thr: 0.6119 loss_db: 0.1873 2022/11/02 14:31:38 - mmengine - INFO - Epoch(train) [224][15/63] lr: 1.7974e-03 eta: 10:03:13 time: 0.5281 data_time: 0.0121 memory: 14901 loss: 1.9896 loss_prob: 1.1816 loss_thr: 0.6107 loss_db: 0.1973 2022/11/02 14:31:41 - mmengine - INFO - Epoch(train) [224][20/63] lr: 1.7974e-03 eta: 10:03:06 time: 0.5764 data_time: 0.0115 memory: 14901 loss: 2.0058 loss_prob: 1.1891 loss_thr: 0.6238 loss_db: 0.1929 2022/11/02 14:31:44 - mmengine - INFO - Epoch(train) [224][25/63] lr: 1.7974e-03 eta: 10:03:06 time: 0.6395 data_time: 0.0588 memory: 14901 loss: 1.9582 loss_prob: 1.1600 loss_thr: 0.6135 loss_db: 0.1847 2022/11/02 14:31:47 - mmengine - INFO - Epoch(train) [224][30/63] lr: 1.7974e-03 eta: 10:03:01 time: 0.5965 data_time: 0.0621 memory: 14901 loss: 2.0406 loss_prob: 1.2373 loss_thr: 0.6056 loss_db: 0.1976 2022/11/02 14:31:50 - mmengine - INFO - Epoch(train) [224][35/63] lr: 1.7974e-03 eta: 10:03:01 time: 0.5512 data_time: 0.0095 memory: 14901 loss: 2.0700 loss_prob: 1.2523 loss_thr: 0.6085 loss_db: 0.2093 2022/11/02 14:31:53 - mmengine - INFO - Epoch(train) [224][40/63] lr: 1.7974e-03 eta: 10:02:53 time: 0.5565 data_time: 0.0072 memory: 14901 loss: 2.2346 loss_prob: 1.3798 loss_thr: 0.6345 loss_db: 0.2204 2022/11/02 14:31:55 - mmengine - INFO - Epoch(train) [224][45/63] lr: 1.7974e-03 eta: 10:02:53 time: 0.5509 data_time: 0.0135 memory: 14901 loss: 2.1195 loss_prob: 1.3026 loss_thr: 0.6157 loss_db: 0.2012 2022/11/02 14:31:58 - mmengine - INFO - Epoch(train) [224][50/63] lr: 1.7974e-03 eta: 10:02:46 time: 0.5445 data_time: 0.0315 memory: 14901 loss: 1.7429 loss_prob: 1.0098 loss_thr: 0.5675 loss_db: 0.1656 2022/11/02 14:32:01 - mmengine - INFO - Epoch(train) [224][55/63] lr: 1.7974e-03 eta: 10:02:46 time: 0.5220 data_time: 0.0309 memory: 14901 loss: 1.8371 loss_prob: 1.0953 loss_thr: 0.5638 loss_db: 0.1780 2022/11/02 14:32:03 - mmengine - INFO - Epoch(train) [224][60/63] lr: 1.7974e-03 eta: 10:02:37 time: 0.5238 data_time: 0.0127 memory: 14901 loss: 1.8949 loss_prob: 1.1239 loss_thr: 0.5882 loss_db: 0.1828 2022/11/02 14:32:05 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:32:10 - mmengine - INFO - Epoch(train) [225][5/63] lr: 1.7957e-03 eta: 10:02:37 time: 0.7982 data_time: 0.2351 memory: 14901 loss: 2.0695 loss_prob: 1.2264 loss_thr: 0.6395 loss_db: 0.2035 2022/11/02 14:32:12 - mmengine - INFO - Epoch(train) [225][10/63] lr: 1.7957e-03 eta: 10:02:30 time: 0.7835 data_time: 0.2368 memory: 14901 loss: 1.9870 loss_prob: 1.1510 loss_thr: 0.6430 loss_db: 0.1930 2022/11/02 14:32:15 - mmengine - INFO - Epoch(train) [225][15/63] lr: 1.7957e-03 eta: 10:02:30 time: 0.4913 data_time: 0.0097 memory: 14901 loss: 1.7645 loss_prob: 1.0028 loss_thr: 0.5960 loss_db: 0.1657 2022/11/02 14:32:17 - mmengine - INFO - Epoch(train) [225][20/63] lr: 1.7957e-03 eta: 10:02:21 time: 0.5062 data_time: 0.0093 memory: 14901 loss: 1.7654 loss_prob: 1.0280 loss_thr: 0.5704 loss_db: 0.1669 2022/11/02 14:32:20 - mmengine - INFO - Epoch(train) [225][25/63] lr: 1.7957e-03 eta: 10:02:21 time: 0.5573 data_time: 0.0388 memory: 14901 loss: 1.7263 loss_prob: 0.9906 loss_thr: 0.5739 loss_db: 0.1618 2022/11/02 14:32:23 - mmengine - INFO - Epoch(train) [225][30/63] lr: 1.7957e-03 eta: 10:02:15 time: 0.5853 data_time: 0.0382 memory: 14901 loss: 1.7527 loss_prob: 0.9946 loss_thr: 0.5945 loss_db: 0.1636 2022/11/02 14:32:26 - mmengine - INFO - Epoch(train) [225][35/63] lr: 1.7957e-03 eta: 10:02:15 time: 0.5650 data_time: 0.0100 memory: 14901 loss: 1.8465 loss_prob: 1.0721 loss_thr: 0.6001 loss_db: 0.1744 2022/11/02 14:32:29 - mmengine - INFO - Epoch(train) [225][40/63] lr: 1.7957e-03 eta: 10:02:06 time: 0.5310 data_time: 0.0153 memory: 14901 loss: 1.8155 loss_prob: 1.0628 loss_thr: 0.5825 loss_db: 0.1702 2022/11/02 14:32:31 - mmengine - INFO - Epoch(train) [225][45/63] lr: 1.7957e-03 eta: 10:02:06 time: 0.5168 data_time: 0.0149 memory: 14901 loss: 1.8469 loss_prob: 1.0645 loss_thr: 0.6048 loss_db: 0.1776 2022/11/02 14:32:34 - mmengine - INFO - Epoch(train) [225][50/63] lr: 1.7957e-03 eta: 10:01:58 time: 0.5412 data_time: 0.0247 memory: 14901 loss: 1.8692 loss_prob: 1.0611 loss_thr: 0.6278 loss_db: 0.1804 2022/11/02 14:32:37 - mmengine - INFO - Epoch(train) [225][55/63] lr: 1.7957e-03 eta: 10:01:58 time: 0.5295 data_time: 0.0252 memory: 14901 loss: 1.8083 loss_prob: 1.0355 loss_thr: 0.6028 loss_db: 0.1700 2022/11/02 14:32:40 - mmengine - INFO - Epoch(train) [225][60/63] lr: 1.7957e-03 eta: 10:01:52 time: 0.5740 data_time: 0.0103 memory: 14901 loss: 1.8334 loss_prob: 1.0528 loss_thr: 0.6083 loss_db: 0.1722 2022/11/02 14:32:41 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:32:47 - mmengine - INFO - Epoch(train) [226][5/63] lr: 1.7941e-03 eta: 10:01:52 time: 0.9065 data_time: 0.2300 memory: 14901 loss: 1.9087 loss_prob: 1.1044 loss_thr: 0.6199 loss_db: 0.1844 2022/11/02 14:32:51 - mmengine - INFO - Epoch(train) [226][10/63] lr: 1.7941e-03 eta: 10:01:52 time: 0.9406 data_time: 0.2281 memory: 14901 loss: 1.8812 loss_prob: 1.1112 loss_thr: 0.5862 loss_db: 0.1838 2022/11/02 14:32:54 - mmengine - INFO - Epoch(train) [226][15/63] lr: 1.7941e-03 eta: 10:01:52 time: 0.6825 data_time: 0.0351 memory: 14901 loss: 1.7207 loss_prob: 0.9997 loss_thr: 0.5580 loss_db: 0.1630 2022/11/02 14:32:57 - mmengine - INFO - Epoch(train) [226][20/63] lr: 1.7941e-03 eta: 10:01:49 time: 0.6486 data_time: 0.0336 memory: 14901 loss: 1.7951 loss_prob: 1.0477 loss_thr: 0.5708 loss_db: 0.1766 2022/11/02 14:33:00 - mmengine - INFO - Epoch(train) [226][25/63] lr: 1.7941e-03 eta: 10:01:49 time: 0.5554 data_time: 0.0069 memory: 14901 loss: 1.8848 loss_prob: 1.1031 loss_thr: 0.5948 loss_db: 0.1869 2022/11/02 14:33:03 - mmengine - INFO - Epoch(train) [226][30/63] lr: 1.7941e-03 eta: 10:01:42 time: 0.5687 data_time: 0.0231 memory: 14901 loss: 1.8069 loss_prob: 1.0521 loss_thr: 0.5790 loss_db: 0.1758 2022/11/02 14:33:06 - mmengine - INFO - Epoch(train) [226][35/63] lr: 1.7941e-03 eta: 10:01:42 time: 0.6457 data_time: 0.0305 memory: 14901 loss: 1.8470 loss_prob: 1.0750 loss_thr: 0.5931 loss_db: 0.1788 2022/11/02 14:33:09 - mmengine - INFO - Epoch(train) [226][40/63] lr: 1.7941e-03 eta: 10:01:37 time: 0.6090 data_time: 0.0145 memory: 14901 loss: 1.8424 loss_prob: 1.0475 loss_thr: 0.6228 loss_db: 0.1722 2022/11/02 14:33:11 - mmengine - INFO - Epoch(train) [226][45/63] lr: 1.7941e-03 eta: 10:01:37 time: 0.5150 data_time: 0.0085 memory: 14901 loss: 1.7038 loss_prob: 0.9566 loss_thr: 0.5859 loss_db: 0.1613 2022/11/02 14:33:14 - mmengine - INFO - Epoch(train) [226][50/63] lr: 1.7941e-03 eta: 10:01:29 time: 0.5462 data_time: 0.0074 memory: 14901 loss: 1.8178 loss_prob: 1.0567 loss_thr: 0.5867 loss_db: 0.1743 2022/11/02 14:33:17 - mmengine - INFO - Epoch(train) [226][55/63] lr: 1.7941e-03 eta: 10:01:29 time: 0.5962 data_time: 0.0202 memory: 14901 loss: 1.7985 loss_prob: 1.0448 loss_thr: 0.5849 loss_db: 0.1687 2022/11/02 14:33:20 - mmengine - INFO - Epoch(train) [226][60/63] lr: 1.7941e-03 eta: 10:01:21 time: 0.5357 data_time: 0.0227 memory: 14901 loss: 1.7183 loss_prob: 0.9838 loss_thr: 0.5716 loss_db: 0.1630 2022/11/02 14:33:21 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:33:27 - mmengine - INFO - Epoch(train) [227][5/63] lr: 1.7924e-03 eta: 10:01:21 time: 0.8288 data_time: 0.2392 memory: 14901 loss: 1.8609 loss_prob: 1.0574 loss_thr: 0.6278 loss_db: 0.1757 2022/11/02 14:33:30 - mmengine - INFO - Epoch(train) [227][10/63] lr: 1.7924e-03 eta: 10:01:16 time: 0.8308 data_time: 0.2410 memory: 14901 loss: 1.8465 loss_prob: 1.0383 loss_thr: 0.6348 loss_db: 0.1735 2022/11/02 14:33:33 - mmengine - INFO - Epoch(train) [227][15/63] lr: 1.7924e-03 eta: 10:01:16 time: 0.5605 data_time: 0.0098 memory: 14901 loss: 1.7460 loss_prob: 0.9869 loss_thr: 0.5978 loss_db: 0.1614 2022/11/02 14:33:35 - mmengine - INFO - Epoch(train) [227][20/63] lr: 1.7924e-03 eta: 10:01:09 time: 0.5498 data_time: 0.0066 memory: 14901 loss: 1.7975 loss_prob: 1.0169 loss_thr: 0.6119 loss_db: 0.1687 2022/11/02 14:33:38 - mmengine - INFO - Epoch(train) [227][25/63] lr: 1.7924e-03 eta: 10:01:09 time: 0.5162 data_time: 0.0354 memory: 14901 loss: 1.8052 loss_prob: 1.0213 loss_thr: 0.6132 loss_db: 0.1707 2022/11/02 14:33:40 - mmengine - INFO - Epoch(train) [227][30/63] lr: 1.7924e-03 eta: 10:01:00 time: 0.5202 data_time: 0.0428 memory: 14901 loss: 1.7072 loss_prob: 0.9753 loss_thr: 0.5710 loss_db: 0.1608 2022/11/02 14:33:43 - mmengine - INFO - Epoch(train) [227][35/63] lr: 1.7924e-03 eta: 10:01:00 time: 0.5059 data_time: 0.0150 memory: 14901 loss: 1.7098 loss_prob: 0.9800 loss_thr: 0.5681 loss_db: 0.1616 2022/11/02 14:33:45 - mmengine - INFO - Epoch(train) [227][40/63] lr: 1.7924e-03 eta: 10:00:50 time: 0.4967 data_time: 0.0095 memory: 14901 loss: 1.8304 loss_prob: 1.0758 loss_thr: 0.5796 loss_db: 0.1750 2022/11/02 14:33:48 - mmengine - INFO - Epoch(train) [227][45/63] lr: 1.7924e-03 eta: 10:00:50 time: 0.5531 data_time: 0.0081 memory: 14901 loss: 1.8497 loss_prob: 1.1071 loss_thr: 0.5672 loss_db: 0.1754 2022/11/02 14:33:52 - mmengine - INFO - Epoch(train) [227][50/63] lr: 1.7924e-03 eta: 10:00:47 time: 0.6409 data_time: 0.0216 memory: 14901 loss: 1.7607 loss_prob: 1.0291 loss_thr: 0.5673 loss_db: 0.1643 2022/11/02 14:33:55 - mmengine - INFO - Epoch(train) [227][55/63] lr: 1.7924e-03 eta: 10:00:47 time: 0.6161 data_time: 0.0264 memory: 14901 loss: 1.7613 loss_prob: 1.0030 loss_thr: 0.5920 loss_db: 0.1664 2022/11/02 14:33:57 - mmengine - INFO - Epoch(train) [227][60/63] lr: 1.7924e-03 eta: 10:00:39 time: 0.5532 data_time: 0.0140 memory: 14901 loss: 1.8566 loss_prob: 1.0750 loss_thr: 0.6043 loss_db: 0.1773 2022/11/02 14:33:59 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:34:04 - mmengine - INFO - Epoch(train) [228][5/63] lr: 1.7907e-03 eta: 10:00:39 time: 0.8290 data_time: 0.2089 memory: 14901 loss: 1.7551 loss_prob: 1.0060 loss_thr: 0.5819 loss_db: 0.1672 2022/11/02 14:34:07 - mmengine - INFO - Epoch(train) [228][10/63] lr: 1.7907e-03 eta: 10:00:35 time: 0.8517 data_time: 0.2170 memory: 14901 loss: 1.9576 loss_prob: 1.1494 loss_thr: 0.6174 loss_db: 0.1907 2022/11/02 14:34:10 - mmengine - INFO - Epoch(train) [228][15/63] lr: 1.7907e-03 eta: 10:00:35 time: 0.5270 data_time: 0.0188 memory: 14901 loss: 2.0310 loss_prob: 1.2127 loss_thr: 0.6180 loss_db: 0.2002 2022/11/02 14:34:12 - mmengine - INFO - Epoch(train) [228][20/63] lr: 1.7907e-03 eta: 10:00:27 time: 0.5245 data_time: 0.0311 memory: 14901 loss: 1.9248 loss_prob: 1.1233 loss_thr: 0.6151 loss_db: 0.1864 2022/11/02 14:34:15 - mmengine - INFO - Epoch(train) [228][25/63] lr: 1.7907e-03 eta: 10:00:27 time: 0.5270 data_time: 0.0276 memory: 14901 loss: 1.8327 loss_prob: 1.0455 loss_thr: 0.6143 loss_db: 0.1729 2022/11/02 14:34:18 - mmengine - INFO - Epoch(train) [228][30/63] lr: 1.7907e-03 eta: 10:00:18 time: 0.5173 data_time: 0.0185 memory: 14901 loss: 1.7944 loss_prob: 1.0273 loss_thr: 0.5946 loss_db: 0.1725 2022/11/02 14:34:20 - mmengine - INFO - Epoch(train) [228][35/63] lr: 1.7907e-03 eta: 10:00:18 time: 0.5057 data_time: 0.0227 memory: 14901 loss: 1.8258 loss_prob: 1.0605 loss_thr: 0.5864 loss_db: 0.1789 2022/11/02 14:34:22 - mmengine - INFO - Epoch(train) [228][40/63] lr: 1.7907e-03 eta: 10:00:08 time: 0.4920 data_time: 0.0142 memory: 14901 loss: 1.8053 loss_prob: 1.0447 loss_thr: 0.5891 loss_db: 0.1715 2022/11/02 14:34:26 - mmengine - INFO - Epoch(train) [228][45/63] lr: 1.7907e-03 eta: 10:00:08 time: 0.5815 data_time: 0.0232 memory: 14901 loss: 1.7705 loss_prob: 1.0119 loss_thr: 0.5929 loss_db: 0.1658 2022/11/02 14:34:29 - mmengine - INFO - Epoch(train) [228][50/63] lr: 1.7907e-03 eta: 10:00:03 time: 0.6195 data_time: 0.0200 memory: 14901 loss: 1.7366 loss_prob: 0.9890 loss_thr: 0.5839 loss_db: 0.1637 2022/11/02 14:34:32 - mmengine - INFO - Epoch(train) [228][55/63] lr: 1.7907e-03 eta: 10:00:03 time: 0.6095 data_time: 0.0149 memory: 14901 loss: 1.6507 loss_prob: 0.9327 loss_thr: 0.5641 loss_db: 0.1539 2022/11/02 14:34:35 - mmengine - INFO - Epoch(train) [228][60/63] lr: 1.7907e-03 eta: 9:59:58 time: 0.5916 data_time: 0.0160 memory: 14901 loss: 1.6834 loss_prob: 0.9529 loss_thr: 0.5720 loss_db: 0.1585 2022/11/02 14:34:36 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:34:42 - mmengine - INFO - Epoch(train) [229][5/63] lr: 1.7891e-03 eta: 9:59:58 time: 0.8357 data_time: 0.2460 memory: 14901 loss: 1.8832 loss_prob: 1.0873 loss_thr: 0.6201 loss_db: 0.1758 2022/11/02 14:34:45 - mmengine - INFO - Epoch(train) [229][10/63] lr: 1.7891e-03 eta: 9:59:56 time: 0.8975 data_time: 0.2440 memory: 14901 loss: 1.8180 loss_prob: 1.0339 loss_thr: 0.6140 loss_db: 0.1701 2022/11/02 14:34:48 - mmengine - INFO - Epoch(train) [229][15/63] lr: 1.7891e-03 eta: 9:59:56 time: 0.5924 data_time: 0.0073 memory: 14901 loss: 1.7692 loss_prob: 0.9973 loss_thr: 0.6087 loss_db: 0.1632 2022/11/02 14:34:50 - mmengine - INFO - Epoch(train) [229][20/63] lr: 1.7891e-03 eta: 9:59:47 time: 0.5295 data_time: 0.0075 memory: 14901 loss: 1.7059 loss_prob: 0.9464 loss_thr: 0.6036 loss_db: 0.1559 2022/11/02 14:34:53 - mmengine - INFO - Epoch(train) [229][25/63] lr: 1.7891e-03 eta: 9:59:47 time: 0.5347 data_time: 0.0120 memory: 14901 loss: 1.7001 loss_prob: 0.9535 loss_thr: 0.5866 loss_db: 0.1600 2022/11/02 14:34:56 - mmengine - INFO - Epoch(train) [229][30/63] lr: 1.7891e-03 eta: 9:59:40 time: 0.5581 data_time: 0.0622 memory: 14901 loss: 1.7151 loss_prob: 0.9800 loss_thr: 0.5720 loss_db: 0.1631 2022/11/02 14:34:59 - mmengine - INFO - Epoch(train) [229][35/63] lr: 1.7891e-03 eta: 9:59:40 time: 0.5613 data_time: 0.0570 memory: 14901 loss: 1.7279 loss_prob: 0.9884 loss_thr: 0.5760 loss_db: 0.1635 2022/11/02 14:35:01 - mmengine - INFO - Epoch(train) [229][40/63] lr: 1.7891e-03 eta: 9:59:32 time: 0.5332 data_time: 0.0072 memory: 14901 loss: 1.7297 loss_prob: 0.9936 loss_thr: 0.5733 loss_db: 0.1627 2022/11/02 14:35:04 - mmengine - INFO - Epoch(train) [229][45/63] lr: 1.7891e-03 eta: 9:59:32 time: 0.5239 data_time: 0.0097 memory: 14901 loss: 1.7822 loss_prob: 1.0311 loss_thr: 0.5855 loss_db: 0.1656 2022/11/02 14:35:06 - mmengine - INFO - Epoch(train) [229][50/63] lr: 1.7891e-03 eta: 9:59:23 time: 0.5090 data_time: 0.0140 memory: 14901 loss: 1.8746 loss_prob: 1.0755 loss_thr: 0.6210 loss_db: 0.1781 2022/11/02 14:35:09 - mmengine - INFO - Epoch(train) [229][55/63] lr: 1.7891e-03 eta: 9:59:23 time: 0.5533 data_time: 0.0311 memory: 14901 loss: 1.8790 loss_prob: 1.0686 loss_thr: 0.6321 loss_db: 0.1783 2022/11/02 14:35:12 - mmengine - INFO - Epoch(train) [229][60/63] lr: 1.7891e-03 eta: 9:59:15 time: 0.5442 data_time: 0.0301 memory: 14901 loss: 1.9438 loss_prob: 1.1198 loss_thr: 0.6362 loss_db: 0.1878 2022/11/02 14:35:13 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:35:19 - mmengine - INFO - Epoch(train) [230][5/63] lr: 1.7874e-03 eta: 9:59:15 time: 0.7483 data_time: 0.2578 memory: 14901 loss: 2.9819 loss_prob: 1.9623 loss_thr: 0.7018 loss_db: 0.3178 2022/11/02 14:35:21 - mmengine - INFO - Epoch(train) [230][10/63] lr: 1.7874e-03 eta: 9:59:10 time: 0.8186 data_time: 0.2612 memory: 14901 loss: 2.9240 loss_prob: 1.9500 loss_thr: 0.6608 loss_db: 0.3132 2022/11/02 14:35:24 - mmengine - INFO - Epoch(train) [230][15/63] lr: 1.7874e-03 eta: 9:59:10 time: 0.5853 data_time: 0.0118 memory: 14901 loss: 2.7419 loss_prob: 1.7914 loss_thr: 0.6588 loss_db: 0.2917 2022/11/02 14:35:27 - mmengine - INFO - Epoch(train) [230][20/63] lr: 1.7874e-03 eta: 9:59:04 time: 0.5942 data_time: 0.0077 memory: 14901 loss: 2.6182 loss_prob: 1.6665 loss_thr: 0.6777 loss_db: 0.2740 2022/11/02 14:35:30 - mmengine - INFO - Epoch(train) [230][25/63] lr: 1.7874e-03 eta: 9:59:04 time: 0.5972 data_time: 0.0234 memory: 14901 loss: 2.4995 loss_prob: 1.5600 loss_thr: 0.6854 loss_db: 0.2541 2022/11/02 14:35:33 - mmengine - INFO - Epoch(train) [230][30/63] lr: 1.7874e-03 eta: 9:58:59 time: 0.5991 data_time: 0.0390 memory: 14901 loss: 2.2342 loss_prob: 1.3668 loss_thr: 0.6478 loss_db: 0.2196 2022/11/02 14:35:36 - mmengine - INFO - Epoch(train) [230][35/63] lr: 1.7874e-03 eta: 9:58:59 time: 0.5521 data_time: 0.0237 memory: 14901 loss: 2.1668 loss_prob: 1.3290 loss_thr: 0.6263 loss_db: 0.2115 2022/11/02 14:35:39 - mmengine - INFO - Epoch(train) [230][40/63] lr: 1.7874e-03 eta: 9:58:50 time: 0.5257 data_time: 0.0120 memory: 14901 loss: 2.0927 loss_prob: 1.2700 loss_thr: 0.6188 loss_db: 0.2039 2022/11/02 14:35:41 - mmengine - INFO - Epoch(train) [230][45/63] lr: 1.7874e-03 eta: 9:58:50 time: 0.5377 data_time: 0.0146 memory: 14901 loss: 2.1417 loss_prob: 1.2982 loss_thr: 0.6309 loss_db: 0.2127 2022/11/02 14:35:44 - mmengine - INFO - Epoch(train) [230][50/63] lr: 1.7874e-03 eta: 9:58:42 time: 0.5332 data_time: 0.0255 memory: 14901 loss: 2.2345 loss_prob: 1.3530 loss_thr: 0.6617 loss_db: 0.2199 2022/11/02 14:35:47 - mmengine - INFO - Epoch(train) [230][55/63] lr: 1.7874e-03 eta: 9:58:42 time: 0.5334 data_time: 0.0314 memory: 14901 loss: 2.0727 loss_prob: 1.2252 loss_thr: 0.6502 loss_db: 0.1973 2022/11/02 14:35:49 - mmengine - INFO - Epoch(train) [230][60/63] lr: 1.7874e-03 eta: 9:58:34 time: 0.5364 data_time: 0.0171 memory: 14901 loss: 1.8898 loss_prob: 1.0969 loss_thr: 0.6137 loss_db: 0.1792 2022/11/02 14:35:51 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:35:57 - mmengine - INFO - Epoch(train) [231][5/63] lr: 1.7858e-03 eta: 9:58:34 time: 0.8863 data_time: 0.2035 memory: 14901 loss: 1.8159 loss_prob: 1.0516 loss_thr: 0.5930 loss_db: 0.1713 2022/11/02 14:36:00 - mmengine - INFO - Epoch(train) [231][10/63] lr: 1.7858e-03 eta: 9:58:31 time: 0.8791 data_time: 0.2112 memory: 14901 loss: 1.7683 loss_prob: 1.0187 loss_thr: 0.5825 loss_db: 0.1670 2022/11/02 14:36:02 - mmengine - INFO - Epoch(train) [231][15/63] lr: 1.7858e-03 eta: 9:58:31 time: 0.5383 data_time: 0.0248 memory: 14901 loss: 1.7899 loss_prob: 1.0326 loss_thr: 0.5873 loss_db: 0.1699 2022/11/02 14:36:05 - mmengine - INFO - Epoch(train) [231][20/63] lr: 1.7858e-03 eta: 9:58:23 time: 0.5271 data_time: 0.0159 memory: 14901 loss: 1.8876 loss_prob: 1.1004 loss_thr: 0.6100 loss_db: 0.1772 2022/11/02 14:36:08 - mmengine - INFO - Epoch(train) [231][25/63] lr: 1.7858e-03 eta: 9:58:23 time: 0.5882 data_time: 0.0236 memory: 14901 loss: 2.0254 loss_prob: 1.2199 loss_thr: 0.6129 loss_db: 0.1925 2022/11/02 14:36:11 - mmengine - INFO - Epoch(train) [231][30/63] lr: 1.7858e-03 eta: 9:58:17 time: 0.5973 data_time: 0.0284 memory: 14901 loss: 1.9890 loss_prob: 1.1782 loss_thr: 0.6200 loss_db: 0.1908 2022/11/02 14:36:14 - mmengine - INFO - Epoch(train) [231][35/63] lr: 1.7858e-03 eta: 9:58:17 time: 0.5265 data_time: 0.0200 memory: 14901 loss: 1.7970 loss_prob: 1.0282 loss_thr: 0.6013 loss_db: 0.1675 2022/11/02 14:36:17 - mmengine - INFO - Epoch(train) [231][40/63] lr: 1.7858e-03 eta: 9:58:11 time: 0.5728 data_time: 0.0265 memory: 14901 loss: 1.8304 loss_prob: 1.0671 loss_thr: 0.5935 loss_db: 0.1698 2022/11/02 14:36:19 - mmengine - INFO - Epoch(train) [231][45/63] lr: 1.7858e-03 eta: 9:58:11 time: 0.5863 data_time: 0.0180 memory: 14901 loss: 1.8886 loss_prob: 1.0914 loss_thr: 0.6190 loss_db: 0.1781 2022/11/02 14:36:22 - mmengine - INFO - Epoch(train) [231][50/63] lr: 1.7858e-03 eta: 9:58:03 time: 0.5525 data_time: 0.0219 memory: 14901 loss: 1.7957 loss_prob: 1.0287 loss_thr: 0.5982 loss_db: 0.1688 2022/11/02 14:36:25 - mmengine - INFO - Epoch(train) [231][55/63] lr: 1.7858e-03 eta: 9:58:03 time: 0.5525 data_time: 0.0216 memory: 14901 loss: 1.8303 loss_prob: 1.0527 loss_thr: 0.6082 loss_db: 0.1694 2022/11/02 14:36:28 - mmengine - INFO - Epoch(train) [231][60/63] lr: 1.7858e-03 eta: 9:57:55 time: 0.5369 data_time: 0.0120 memory: 14901 loss: 1.8338 loss_prob: 1.0477 loss_thr: 0.6169 loss_db: 0.1691 2022/11/02 14:36:29 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:36:35 - mmengine - INFO - Epoch(train) [232][5/63] lr: 1.7841e-03 eta: 9:57:55 time: 0.8214 data_time: 0.2283 memory: 14901 loss: 1.7685 loss_prob: 1.0121 loss_thr: 0.5911 loss_db: 0.1653 2022/11/02 14:36:39 - mmengine - INFO - Epoch(train) [232][10/63] lr: 1.7841e-03 eta: 9:57:57 time: 0.9832 data_time: 0.2329 memory: 14901 loss: 1.6963 loss_prob: 0.9530 loss_thr: 0.5863 loss_db: 0.1570 2022/11/02 14:36:42 - mmengine - INFO - Epoch(train) [232][15/63] lr: 1.7841e-03 eta: 9:57:57 time: 0.7027 data_time: 0.0169 memory: 14901 loss: 1.7571 loss_prob: 1.0031 loss_thr: 0.5901 loss_db: 0.1639 2022/11/02 14:36:44 - mmengine - INFO - Epoch(train) [232][20/63] lr: 1.7841e-03 eta: 9:57:51 time: 0.5778 data_time: 0.0092 memory: 14901 loss: 1.8061 loss_prob: 1.0437 loss_thr: 0.5935 loss_db: 0.1689 2022/11/02 14:36:48 - mmengine - INFO - Epoch(train) [232][25/63] lr: 1.7841e-03 eta: 9:57:51 time: 0.5701 data_time: 0.0118 memory: 14901 loss: 1.8447 loss_prob: 1.0726 loss_thr: 0.5955 loss_db: 0.1766 2022/11/02 14:36:50 - mmengine - INFO - Epoch(train) [232][30/63] lr: 1.7841e-03 eta: 9:57:45 time: 0.6024 data_time: 0.0322 memory: 14901 loss: 1.9254 loss_prob: 1.1182 loss_thr: 0.6220 loss_db: 0.1851 2022/11/02 14:36:53 - mmengine - INFO - Epoch(train) [232][35/63] lr: 1.7841e-03 eta: 9:57:45 time: 0.5615 data_time: 0.0354 memory: 14901 loss: 1.8964 loss_prob: 1.0966 loss_thr: 0.6211 loss_db: 0.1786 2022/11/02 14:36:56 - mmengine - INFO - Epoch(train) [232][40/63] lr: 1.7841e-03 eta: 9:57:37 time: 0.5192 data_time: 0.0143 memory: 14901 loss: 1.9501 loss_prob: 1.1397 loss_thr: 0.6195 loss_db: 0.1908 2022/11/02 14:36:59 - mmengine - INFO - Epoch(train) [232][45/63] lr: 1.7841e-03 eta: 9:57:37 time: 0.5381 data_time: 0.0077 memory: 14901 loss: 1.9840 loss_prob: 1.1710 loss_thr: 0.6228 loss_db: 0.1902 2022/11/02 14:37:01 - mmengine - INFO - Epoch(train) [232][50/63] lr: 1.7841e-03 eta: 9:57:29 time: 0.5425 data_time: 0.0182 memory: 14901 loss: 1.8155 loss_prob: 1.0583 loss_thr: 0.5898 loss_db: 0.1674 2022/11/02 14:37:04 - mmengine - INFO - Epoch(train) [232][55/63] lr: 1.7841e-03 eta: 9:57:29 time: 0.5105 data_time: 0.0298 memory: 14901 loss: 1.9466 loss_prob: 1.1702 loss_thr: 0.5877 loss_db: 0.1887 2022/11/02 14:37:06 - mmengine - INFO - Epoch(train) [232][60/63] lr: 1.7841e-03 eta: 9:57:19 time: 0.5012 data_time: 0.0251 memory: 14901 loss: 2.1400 loss_prob: 1.3373 loss_thr: 0.6000 loss_db: 0.2027 2022/11/02 14:37:08 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:37:12 - mmengine - INFO - Epoch(train) [233][5/63] lr: 1.7825e-03 eta: 9:57:19 time: 0.7417 data_time: 0.2106 memory: 14901 loss: 1.9256 loss_prob: 1.1452 loss_thr: 0.5969 loss_db: 0.1835 2022/11/02 14:37:15 - mmengine - INFO - Epoch(train) [233][10/63] lr: 1.7825e-03 eta: 9:57:12 time: 0.7701 data_time: 0.2268 memory: 14901 loss: 1.9139 loss_prob: 1.1177 loss_thr: 0.6152 loss_db: 0.1810 2022/11/02 14:37:19 - mmengine - INFO - Epoch(train) [233][15/63] lr: 1.7825e-03 eta: 9:57:12 time: 0.6340 data_time: 0.0274 memory: 14901 loss: 1.7430 loss_prob: 0.9899 loss_thr: 0.5903 loss_db: 0.1628 2022/11/02 14:37:22 - mmengine - INFO - Epoch(train) [233][20/63] lr: 1.7825e-03 eta: 9:57:11 time: 0.7131 data_time: 0.0088 memory: 14901 loss: 1.7482 loss_prob: 0.9969 loss_thr: 0.5895 loss_db: 0.1618 2022/11/02 14:37:25 - mmengine - INFO - Epoch(train) [233][25/63] lr: 1.7825e-03 eta: 9:57:11 time: 0.6376 data_time: 0.0182 memory: 14901 loss: 1.8657 loss_prob: 1.0612 loss_thr: 0.6314 loss_db: 0.1731 2022/11/02 14:37:28 - mmengine - INFO - Epoch(train) [233][30/63] lr: 1.7825e-03 eta: 9:57:04 time: 0.5534 data_time: 0.0485 memory: 14901 loss: 1.8033 loss_prob: 1.0126 loss_thr: 0.6217 loss_db: 0.1689 2022/11/02 14:37:31 - mmengine - INFO - Epoch(train) [233][35/63] lr: 1.7825e-03 eta: 9:57:04 time: 0.5845 data_time: 0.0366 memory: 14901 loss: 1.7439 loss_prob: 0.9886 loss_thr: 0.5916 loss_db: 0.1638 2022/11/02 14:37:34 - mmengine - INFO - Epoch(train) [233][40/63] lr: 1.7825e-03 eta: 9:56:58 time: 0.5794 data_time: 0.0065 memory: 14901 loss: 1.9509 loss_prob: 1.1469 loss_thr: 0.6162 loss_db: 0.1877 2022/11/02 14:37:37 - mmengine - INFO - Epoch(train) [233][45/63] lr: 1.7825e-03 eta: 9:56:58 time: 0.5607 data_time: 0.0091 memory: 14901 loss: 1.9503 loss_prob: 1.1398 loss_thr: 0.6218 loss_db: 0.1887 2022/11/02 14:37:39 - mmengine - INFO - Epoch(train) [233][50/63] lr: 1.7825e-03 eta: 9:56:51 time: 0.5612 data_time: 0.0194 memory: 14901 loss: 1.9008 loss_prob: 1.1152 loss_thr: 0.6047 loss_db: 0.1810 2022/11/02 14:37:42 - mmengine - INFO - Epoch(train) [233][55/63] lr: 1.7825e-03 eta: 9:56:51 time: 0.5361 data_time: 0.0294 memory: 14901 loss: 2.0450 loss_prob: 1.2261 loss_thr: 0.6188 loss_db: 0.2000 2022/11/02 14:37:45 - mmengine - INFO - Epoch(train) [233][60/63] lr: 1.7825e-03 eta: 9:56:42 time: 0.5258 data_time: 0.0226 memory: 14901 loss: 1.8919 loss_prob: 1.0963 loss_thr: 0.6123 loss_db: 0.1833 2022/11/02 14:37:46 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:37:52 - mmengine - INFO - Epoch(train) [234][5/63] lr: 1.7808e-03 eta: 9:56:42 time: 0.8043 data_time: 0.2317 memory: 14901 loss: 1.8445 loss_prob: 1.0886 loss_thr: 0.5824 loss_db: 0.1735 2022/11/02 14:37:55 - mmengine - INFO - Epoch(train) [234][10/63] lr: 1.7808e-03 eta: 9:56:40 time: 0.8951 data_time: 0.2352 memory: 14901 loss: 1.9556 loss_prob: 1.1722 loss_thr: 0.5969 loss_db: 0.1865 2022/11/02 14:37:58 - mmengine - INFO - Epoch(train) [234][15/63] lr: 1.7808e-03 eta: 9:56:40 time: 0.6099 data_time: 0.0116 memory: 14901 loss: 1.7636 loss_prob: 1.0226 loss_thr: 0.5727 loss_db: 0.1683 2022/11/02 14:38:01 - mmengine - INFO - Epoch(train) [234][20/63] lr: 1.7808e-03 eta: 9:56:34 time: 0.5893 data_time: 0.0105 memory: 14901 loss: 1.7417 loss_prob: 0.9908 loss_thr: 0.5921 loss_db: 0.1589 2022/11/02 14:38:04 - mmengine - INFO - Epoch(train) [234][25/63] lr: 1.7808e-03 eta: 9:56:34 time: 0.5929 data_time: 0.0123 memory: 14901 loss: 1.7449 loss_prob: 0.9872 loss_thr: 0.5975 loss_db: 0.1601 2022/11/02 14:38:07 - mmengine - INFO - Epoch(train) [234][30/63] lr: 1.7808e-03 eta: 9:56:32 time: 0.6589 data_time: 0.0447 memory: 14901 loss: 1.7366 loss_prob: 0.9971 loss_thr: 0.5762 loss_db: 0.1634 2022/11/02 14:38:10 - mmengine - INFO - Epoch(train) [234][35/63] lr: 1.7808e-03 eta: 9:56:32 time: 0.6347 data_time: 0.0424 memory: 14901 loss: 1.7997 loss_prob: 1.0380 loss_thr: 0.5943 loss_db: 0.1673 2022/11/02 14:38:13 - mmengine - INFO - Epoch(train) [234][40/63] lr: 1.7808e-03 eta: 9:56:24 time: 0.5426 data_time: 0.0107 memory: 14901 loss: 1.7618 loss_prob: 0.9950 loss_thr: 0.5977 loss_db: 0.1690 2022/11/02 14:38:15 - mmengine - INFO - Epoch(train) [234][45/63] lr: 1.7808e-03 eta: 9:56:24 time: 0.5247 data_time: 0.0098 memory: 14901 loss: 1.9403 loss_prob: 1.1439 loss_thr: 0.6127 loss_db: 0.1838 2022/11/02 14:38:18 - mmengine - INFO - Epoch(train) [234][50/63] lr: 1.7808e-03 eta: 9:56:14 time: 0.4885 data_time: 0.0143 memory: 14901 loss: 1.9149 loss_prob: 1.1348 loss_thr: 0.6010 loss_db: 0.1791 2022/11/02 14:38:21 - mmengine - INFO - Epoch(train) [234][55/63] lr: 1.7808e-03 eta: 9:56:14 time: 0.5147 data_time: 0.0250 memory: 14901 loss: 1.6965 loss_prob: 0.9538 loss_thr: 0.5815 loss_db: 0.1612 2022/11/02 14:38:23 - mmengine - INFO - Epoch(train) [234][60/63] lr: 1.7808e-03 eta: 9:56:05 time: 0.5154 data_time: 0.0181 memory: 14901 loss: 1.7605 loss_prob: 1.0010 loss_thr: 0.5907 loss_db: 0.1687 2022/11/02 14:38:24 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:38:29 - mmengine - INFO - Epoch(train) [235][5/63] lr: 1.7791e-03 eta: 9:56:05 time: 0.6932 data_time: 0.2370 memory: 14901 loss: 1.8737 loss_prob: 1.0859 loss_thr: 0.6064 loss_db: 0.1815 2022/11/02 14:38:32 - mmengine - INFO - Epoch(train) [235][10/63] lr: 1.7791e-03 eta: 9:55:57 time: 0.7454 data_time: 0.2381 memory: 14901 loss: 1.8585 loss_prob: 1.0862 loss_thr: 0.5956 loss_db: 0.1766 2022/11/02 14:38:35 - mmengine - INFO - Epoch(train) [235][15/63] lr: 1.7791e-03 eta: 9:55:57 time: 0.5573 data_time: 0.0094 memory: 14901 loss: 1.9791 loss_prob: 1.1646 loss_thr: 0.6234 loss_db: 0.1912 2022/11/02 14:38:37 - mmengine - INFO - Epoch(train) [235][20/63] lr: 1.7791e-03 eta: 9:55:51 time: 0.5841 data_time: 0.0089 memory: 14901 loss: 1.9063 loss_prob: 1.1086 loss_thr: 0.6147 loss_db: 0.1830 2022/11/02 14:38:40 - mmengine - INFO - Epoch(train) [235][25/63] lr: 1.7791e-03 eta: 9:55:51 time: 0.5768 data_time: 0.0357 memory: 14901 loss: 1.7965 loss_prob: 1.0335 loss_thr: 0.5934 loss_db: 0.1696 2022/11/02 14:38:43 - mmengine - INFO - Epoch(train) [235][30/63] lr: 1.7791e-03 eta: 9:55:44 time: 0.5615 data_time: 0.0436 memory: 14901 loss: 1.7912 loss_prob: 1.0224 loss_thr: 0.5953 loss_db: 0.1735 2022/11/02 14:38:46 - mmengine - INFO - Epoch(train) [235][35/63] lr: 1.7791e-03 eta: 9:55:44 time: 0.5844 data_time: 0.0138 memory: 14901 loss: 1.7962 loss_prob: 1.0243 loss_thr: 0.5990 loss_db: 0.1729 2022/11/02 14:38:49 - mmengine - INFO - Epoch(train) [235][40/63] lr: 1.7791e-03 eta: 9:55:39 time: 0.6171 data_time: 0.0062 memory: 14901 loss: 1.7208 loss_prob: 0.9822 loss_thr: 0.5773 loss_db: 0.1612 2022/11/02 14:38:52 - mmengine - INFO - Epoch(train) [235][45/63] lr: 1.7791e-03 eta: 9:55:39 time: 0.5640 data_time: 0.0085 memory: 14901 loss: 1.7927 loss_prob: 1.0450 loss_thr: 0.5795 loss_db: 0.1683 2022/11/02 14:38:55 - mmengine - INFO - Epoch(train) [235][50/63] lr: 1.7791e-03 eta: 9:55:31 time: 0.5373 data_time: 0.0264 memory: 14901 loss: 1.8346 loss_prob: 1.0759 loss_thr: 0.5852 loss_db: 0.1735 2022/11/02 14:38:58 - mmengine - INFO - Epoch(train) [235][55/63] lr: 1.7791e-03 eta: 9:55:31 time: 0.5719 data_time: 0.0297 memory: 14901 loss: 1.6831 loss_prob: 0.9547 loss_thr: 0.5706 loss_db: 0.1578 2022/11/02 14:39:00 - mmengine - INFO - Epoch(train) [235][60/63] lr: 1.7791e-03 eta: 9:55:24 time: 0.5617 data_time: 0.0144 memory: 14901 loss: 1.7426 loss_prob: 0.9877 loss_thr: 0.5918 loss_db: 0.1631 2022/11/02 14:39:02 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:39:07 - mmengine - INFO - Epoch(train) [236][5/63] lr: 1.7775e-03 eta: 9:55:24 time: 0.8108 data_time: 0.2100 memory: 14901 loss: 1.7827 loss_prob: 1.0323 loss_thr: 0.5849 loss_db: 0.1655 2022/11/02 14:39:10 - mmengine - INFO - Epoch(train) [236][10/63] lr: 1.7775e-03 eta: 9:55:19 time: 0.8284 data_time: 0.2123 memory: 14901 loss: 1.7656 loss_prob: 1.0015 loss_thr: 0.5998 loss_db: 0.1643 2022/11/02 14:39:12 - mmengine - INFO - Epoch(train) [236][15/63] lr: 1.7775e-03 eta: 9:55:19 time: 0.5201 data_time: 0.0115 memory: 14901 loss: 1.7420 loss_prob: 0.9799 loss_thr: 0.5987 loss_db: 0.1634 2022/11/02 14:39:15 - mmengine - INFO - Epoch(train) [236][20/63] lr: 1.7775e-03 eta: 9:55:11 time: 0.5443 data_time: 0.0070 memory: 14901 loss: 1.6624 loss_prob: 0.9351 loss_thr: 0.5742 loss_db: 0.1532 2022/11/02 14:39:18 - mmengine - INFO - Epoch(train) [236][25/63] lr: 1.7775e-03 eta: 9:55:11 time: 0.5703 data_time: 0.0134 memory: 14901 loss: 1.6819 loss_prob: 0.9447 loss_thr: 0.5800 loss_db: 0.1571 2022/11/02 14:39:21 - mmengine - INFO - Epoch(train) [236][30/63] lr: 1.7775e-03 eta: 9:55:04 time: 0.5527 data_time: 0.0422 memory: 14901 loss: 1.7070 loss_prob: 0.9501 loss_thr: 0.5975 loss_db: 0.1593 2022/11/02 14:39:23 - mmengine - INFO - Epoch(train) [236][35/63] lr: 1.7775e-03 eta: 9:55:04 time: 0.5270 data_time: 0.0352 memory: 14901 loss: 1.7354 loss_prob: 0.9686 loss_thr: 0.6040 loss_db: 0.1628 2022/11/02 14:39:26 - mmengine - INFO - Epoch(train) [236][40/63] lr: 1.7775e-03 eta: 9:54:57 time: 0.5472 data_time: 0.0103 memory: 14901 loss: 1.7945 loss_prob: 1.0194 loss_thr: 0.6052 loss_db: 0.1699 2022/11/02 14:39:29 - mmengine - INFO - Epoch(train) [236][45/63] lr: 1.7775e-03 eta: 9:54:57 time: 0.5553 data_time: 0.0127 memory: 14901 loss: 1.7825 loss_prob: 1.0234 loss_thr: 0.5890 loss_db: 0.1701 2022/11/02 14:39:32 - mmengine - INFO - Epoch(train) [236][50/63] lr: 1.7775e-03 eta: 9:54:49 time: 0.5475 data_time: 0.0169 memory: 14901 loss: 1.7218 loss_prob: 0.9739 loss_thr: 0.5837 loss_db: 0.1642 2022/11/02 14:39:35 - mmengine - INFO - Epoch(train) [236][55/63] lr: 1.7775e-03 eta: 9:54:49 time: 0.6331 data_time: 0.0301 memory: 14901 loss: 1.6381 loss_prob: 0.9218 loss_thr: 0.5647 loss_db: 0.1516 2022/11/02 14:39:38 - mmengine - INFO - Epoch(train) [236][60/63] lr: 1.7775e-03 eta: 9:54:44 time: 0.6110 data_time: 0.0225 memory: 14901 loss: 1.5638 loss_prob: 0.8869 loss_thr: 0.5318 loss_db: 0.1451 2022/11/02 14:39:39 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:39:46 - mmengine - INFO - Epoch(train) [237][5/63] lr: 1.7758e-03 eta: 9:54:44 time: 0.9226 data_time: 0.2701 memory: 14901 loss: 1.7475 loss_prob: 0.9974 loss_thr: 0.5852 loss_db: 0.1649 2022/11/02 14:39:49 - mmengine - INFO - Epoch(train) [237][10/63] lr: 1.7758e-03 eta: 9:54:43 time: 0.9342 data_time: 0.2770 memory: 14901 loss: 1.7908 loss_prob: 1.0174 loss_thr: 0.6079 loss_db: 0.1655 2022/11/02 14:39:52 - mmengine - INFO - Epoch(train) [237][15/63] lr: 1.7758e-03 eta: 9:54:43 time: 0.5796 data_time: 0.0195 memory: 14901 loss: 1.7850 loss_prob: 1.0237 loss_thr: 0.5936 loss_db: 0.1677 2022/11/02 14:39:54 - mmengine - INFO - Epoch(train) [237][20/63] lr: 1.7758e-03 eta: 9:54:37 time: 0.5650 data_time: 0.0131 memory: 14901 loss: 1.8703 loss_prob: 1.0861 loss_thr: 0.6004 loss_db: 0.1838 2022/11/02 14:39:58 - mmengine - INFO - Epoch(train) [237][25/63] lr: 1.7758e-03 eta: 9:54:37 time: 0.5799 data_time: 0.0362 memory: 14901 loss: 1.8163 loss_prob: 1.0403 loss_thr: 0.6028 loss_db: 0.1731 2022/11/02 14:40:01 - mmengine - INFO - Epoch(train) [237][30/63] lr: 1.7758e-03 eta: 9:54:33 time: 0.6391 data_time: 0.0414 memory: 14901 loss: 1.6838 loss_prob: 0.9452 loss_thr: 0.5856 loss_db: 0.1529 2022/11/02 14:40:03 - mmengine - INFO - Epoch(train) [237][35/63] lr: 1.7758e-03 eta: 9:54:33 time: 0.5777 data_time: 0.0133 memory: 14901 loss: 1.6174 loss_prob: 0.9054 loss_thr: 0.5629 loss_db: 0.1490 2022/11/02 14:40:06 - mmengine - INFO - Epoch(train) [237][40/63] lr: 1.7758e-03 eta: 9:54:26 time: 0.5544 data_time: 0.0054 memory: 14901 loss: 1.6447 loss_prob: 0.9288 loss_thr: 0.5628 loss_db: 0.1531 2022/11/02 14:40:09 - mmengine - INFO - Epoch(train) [237][45/63] lr: 1.7758e-03 eta: 9:54:26 time: 0.5455 data_time: 0.0055 memory: 14901 loss: 1.8133 loss_prob: 1.0438 loss_thr: 0.6001 loss_db: 0.1694 2022/11/02 14:40:11 - mmengine - INFO - Epoch(train) [237][50/63] lr: 1.7758e-03 eta: 9:54:16 time: 0.5002 data_time: 0.0226 memory: 14901 loss: 1.8606 loss_prob: 1.0792 loss_thr: 0.6035 loss_db: 0.1780 2022/11/02 14:40:14 - mmengine - INFO - Epoch(train) [237][55/63] lr: 1.7758e-03 eta: 9:54:16 time: 0.4875 data_time: 0.0266 memory: 14901 loss: 1.8614 loss_prob: 1.0717 loss_thr: 0.6050 loss_db: 0.1847 2022/11/02 14:40:16 - mmengine - INFO - Epoch(train) [237][60/63] lr: 1.7758e-03 eta: 9:54:06 time: 0.4916 data_time: 0.0110 memory: 14901 loss: 2.0178 loss_prob: 1.1976 loss_thr: 0.6231 loss_db: 0.1972 2022/11/02 14:40:17 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:40:23 - mmengine - INFO - Epoch(train) [238][5/63] lr: 1.7742e-03 eta: 9:54:06 time: 0.7388 data_time: 0.2437 memory: 14901 loss: 1.9399 loss_prob: 1.1460 loss_thr: 0.6146 loss_db: 0.1793 2022/11/02 14:40:25 - mmengine - INFO - Epoch(train) [238][10/63] lr: 1.7742e-03 eta: 9:54:00 time: 0.8001 data_time: 0.2514 memory: 14901 loss: 1.8096 loss_prob: 1.0369 loss_thr: 0.6026 loss_db: 0.1701 2022/11/02 14:40:28 - mmengine - INFO - Epoch(train) [238][15/63] lr: 1.7742e-03 eta: 9:54:00 time: 0.5237 data_time: 0.0184 memory: 14901 loss: 1.8639 loss_prob: 1.0832 loss_thr: 0.6013 loss_db: 0.1794 2022/11/02 14:40:30 - mmengine - INFO - Epoch(train) [238][20/63] lr: 1.7742e-03 eta: 9:53:51 time: 0.5037 data_time: 0.0112 memory: 14901 loss: 1.7550 loss_prob: 1.0022 loss_thr: 0.5881 loss_db: 0.1648 2022/11/02 14:40:33 - mmengine - INFO - Epoch(train) [238][25/63] lr: 1.7742e-03 eta: 9:53:51 time: 0.5195 data_time: 0.0167 memory: 14901 loss: 1.7805 loss_prob: 1.0179 loss_thr: 0.5961 loss_db: 0.1664 2022/11/02 14:40:36 - mmengine - INFO - Epoch(train) [238][30/63] lr: 1.7742e-03 eta: 9:53:42 time: 0.5183 data_time: 0.0359 memory: 14901 loss: 1.8016 loss_prob: 1.0326 loss_thr: 0.5978 loss_db: 0.1712 2022/11/02 14:40:38 - mmengine - INFO - Epoch(train) [238][35/63] lr: 1.7742e-03 eta: 9:53:42 time: 0.5174 data_time: 0.0309 memory: 14901 loss: 1.7064 loss_prob: 0.9582 loss_thr: 0.5883 loss_db: 0.1599 2022/11/02 14:40:41 - mmengine - INFO - Epoch(train) [238][40/63] lr: 1.7742e-03 eta: 9:53:34 time: 0.5257 data_time: 0.0116 memory: 14901 loss: 1.7816 loss_prob: 1.0049 loss_thr: 0.6101 loss_db: 0.1667 2022/11/02 14:40:44 - mmengine - INFO - Epoch(train) [238][45/63] lr: 1.7742e-03 eta: 9:53:34 time: 0.6090 data_time: 0.0097 memory: 14901 loss: 1.7472 loss_prob: 0.9930 loss_thr: 0.5880 loss_db: 0.1662 2022/11/02 14:40:47 - mmengine - INFO - Epoch(train) [238][50/63] lr: 1.7742e-03 eta: 9:53:30 time: 0.6301 data_time: 0.0198 memory: 14901 loss: 1.6813 loss_prob: 0.9534 loss_thr: 0.5697 loss_db: 0.1582 2022/11/02 14:40:50 - mmengine - INFO - Epoch(train) [238][55/63] lr: 1.7742e-03 eta: 9:53:30 time: 0.5760 data_time: 0.0279 memory: 14901 loss: 1.8106 loss_prob: 1.0381 loss_thr: 0.6032 loss_db: 0.1693 2022/11/02 14:40:53 - mmengine - INFO - Epoch(train) [238][60/63] lr: 1.7742e-03 eta: 9:53:23 time: 0.5655 data_time: 0.0201 memory: 14901 loss: 1.8453 loss_prob: 1.0673 loss_thr: 0.6032 loss_db: 0.1748 2022/11/02 14:40:54 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:40:59 - mmengine - INFO - Epoch(train) [239][5/63] lr: 1.7725e-03 eta: 9:53:23 time: 0.7344 data_time: 0.2465 memory: 14901 loss: 1.6239 loss_prob: 0.9156 loss_thr: 0.5577 loss_db: 0.1506 2022/11/02 14:41:00 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:41:02 - mmengine - INFO - Epoch(train) [239][10/63] lr: 1.7725e-03 eta: 9:53:18 time: 0.8241 data_time: 0.2604 memory: 14901 loss: 1.5781 loss_prob: 0.8814 loss_thr: 0.5515 loss_db: 0.1451 2022/11/02 14:41:05 - mmengine - INFO - Epoch(train) [239][15/63] lr: 1.7725e-03 eta: 9:53:18 time: 0.5772 data_time: 0.0222 memory: 14901 loss: 1.6642 loss_prob: 0.9249 loss_thr: 0.5821 loss_db: 0.1572 2022/11/02 14:41:07 - mmengine - INFO - Epoch(train) [239][20/63] lr: 1.7725e-03 eta: 9:53:09 time: 0.5138 data_time: 0.0078 memory: 14901 loss: 1.6471 loss_prob: 0.9240 loss_thr: 0.5681 loss_db: 0.1550 2022/11/02 14:41:10 - mmengine - INFO - Epoch(train) [239][25/63] lr: 1.7725e-03 eta: 9:53:09 time: 0.5365 data_time: 0.0373 memory: 14901 loss: 1.7380 loss_prob: 0.9863 loss_thr: 0.5910 loss_db: 0.1607 2022/11/02 14:41:13 - mmengine - INFO - Epoch(train) [239][30/63] lr: 1.7725e-03 eta: 9:53:02 time: 0.5507 data_time: 0.0385 memory: 14901 loss: 1.7862 loss_prob: 1.0210 loss_thr: 0.5967 loss_db: 0.1685 2022/11/02 14:41:16 - mmengine - INFO - Epoch(train) [239][35/63] lr: 1.7725e-03 eta: 9:53:02 time: 0.5513 data_time: 0.0132 memory: 14901 loss: 1.7327 loss_prob: 0.9930 loss_thr: 0.5745 loss_db: 0.1652 2022/11/02 14:41:19 - mmengine - INFO - Epoch(train) [239][40/63] lr: 1.7725e-03 eta: 9:52:58 time: 0.6417 data_time: 0.0143 memory: 14901 loss: 1.8442 loss_prob: 1.0608 loss_thr: 0.6066 loss_db: 0.1768 2022/11/02 14:41:22 - mmengine - INFO - Epoch(train) [239][45/63] lr: 1.7725e-03 eta: 9:52:58 time: 0.6052 data_time: 0.0082 memory: 14901 loss: 1.8101 loss_prob: 1.0416 loss_thr: 0.5961 loss_db: 0.1724 2022/11/02 14:41:25 - mmengine - INFO - Epoch(train) [239][50/63] lr: 1.7725e-03 eta: 9:52:50 time: 0.5356 data_time: 0.0199 memory: 14901 loss: 1.8424 loss_prob: 1.0649 loss_thr: 0.6055 loss_db: 0.1720 2022/11/02 14:41:29 - mmengine - INFO - Epoch(train) [239][55/63] lr: 1.7725e-03 eta: 9:52:50 time: 0.6837 data_time: 0.0220 memory: 14901 loss: 1.8149 loss_prob: 1.0379 loss_thr: 0.6063 loss_db: 0.1708 2022/11/02 14:41:32 - mmengine - INFO - Epoch(train) [239][60/63] lr: 1.7725e-03 eta: 9:52:49 time: 0.7150 data_time: 0.0104 memory: 14901 loss: 1.7663 loss_prob: 0.9950 loss_thr: 0.6035 loss_db: 0.1678 2022/11/02 14:41:33 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:41:39 - mmengine - INFO - Epoch(train) [240][5/63] lr: 1.7708e-03 eta: 9:52:49 time: 0.8252 data_time: 0.2495 memory: 14901 loss: 1.7503 loss_prob: 0.9930 loss_thr: 0.5931 loss_db: 0.1642 2022/11/02 14:41:42 - mmengine - INFO - Epoch(train) [240][10/63] lr: 1.7708e-03 eta: 9:52:44 time: 0.8124 data_time: 0.2472 memory: 14901 loss: 1.7301 loss_prob: 0.9702 loss_thr: 0.6001 loss_db: 0.1598 2022/11/02 14:41:44 - mmengine - INFO - Epoch(train) [240][15/63] lr: 1.7708e-03 eta: 9:52:44 time: 0.4949 data_time: 0.0078 memory: 14901 loss: 1.6376 loss_prob: 0.9090 loss_thr: 0.5764 loss_db: 0.1522 2022/11/02 14:41:47 - mmengine - INFO - Epoch(train) [240][20/63] lr: 1.7708e-03 eta: 9:52:34 time: 0.5020 data_time: 0.0103 memory: 14901 loss: 1.7253 loss_prob: 0.9736 loss_thr: 0.5879 loss_db: 0.1639 2022/11/02 14:41:50 - mmengine - INFO - Epoch(train) [240][25/63] lr: 1.7708e-03 eta: 9:52:34 time: 0.5733 data_time: 0.0341 memory: 14901 loss: 1.8202 loss_prob: 1.0267 loss_thr: 0.6236 loss_db: 0.1699 2022/11/02 14:41:53 - mmengine - INFO - Epoch(train) [240][30/63] lr: 1.7708e-03 eta: 9:52:30 time: 0.6383 data_time: 0.0368 memory: 14901 loss: 1.7698 loss_prob: 0.9905 loss_thr: 0.6142 loss_db: 0.1651 2022/11/02 14:41:55 - mmengine - INFO - Epoch(train) [240][35/63] lr: 1.7708e-03 eta: 9:52:30 time: 0.5772 data_time: 0.0171 memory: 14901 loss: 1.7367 loss_prob: 0.9783 loss_thr: 0.5937 loss_db: 0.1647 2022/11/02 14:41:58 - mmengine - INFO - Epoch(train) [240][40/63] lr: 1.7708e-03 eta: 9:52:22 time: 0.5246 data_time: 0.0132 memory: 14901 loss: 1.6828 loss_prob: 0.9522 loss_thr: 0.5744 loss_db: 0.1563 2022/11/02 14:42:01 - mmengine - INFO - Epoch(train) [240][45/63] lr: 1.7708e-03 eta: 9:52:22 time: 0.5404 data_time: 0.0085 memory: 14901 loss: 1.6216 loss_prob: 0.9078 loss_thr: 0.5668 loss_db: 0.1471 2022/11/02 14:42:04 - mmengine - INFO - Epoch(train) [240][50/63] lr: 1.7708e-03 eta: 9:52:14 time: 0.5353 data_time: 0.0279 memory: 14901 loss: 1.7205 loss_prob: 0.9732 loss_thr: 0.5858 loss_db: 0.1615 2022/11/02 14:42:06 - mmengine - INFO - Epoch(train) [240][55/63] lr: 1.7708e-03 eta: 9:52:14 time: 0.5058 data_time: 0.0293 memory: 14901 loss: 1.7539 loss_prob: 1.0003 loss_thr: 0.5854 loss_db: 0.1682 2022/11/02 14:42:08 - mmengine - INFO - Epoch(train) [240][60/63] lr: 1.7708e-03 eta: 9:52:04 time: 0.4873 data_time: 0.0109 memory: 14901 loss: 1.6729 loss_prob: 0.9443 loss_thr: 0.5750 loss_db: 0.1535 2022/11/02 14:42:11 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:42:11 - mmengine - INFO - Saving checkpoint at 240 epochs 2022/11/02 14:42:15 - mmengine - INFO - Epoch(val) [240][5/500] eta: 9:52:04 time: 0.0424 data_time: 0.0058 memory: 14901 2022/11/02 14:42:15 - mmengine - INFO - Epoch(val) [240][10/500] eta: 0:00:21 time: 0.0444 data_time: 0.0059 memory: 1008 2022/11/02 14:42:15 - mmengine - INFO - Epoch(val) [240][15/500] eta: 0:00:21 time: 0.0373 data_time: 0.0025 memory: 1008 2022/11/02 14:42:15 - mmengine - INFO - Epoch(val) [240][20/500] eta: 0:00:16 time: 0.0353 data_time: 0.0023 memory: 1008 2022/11/02 14:42:16 - mmengine - INFO - Epoch(val) [240][25/500] eta: 0:00:16 time: 0.0364 data_time: 0.0022 memory: 1008 2022/11/02 14:42:16 - mmengine - INFO - Epoch(val) [240][30/500] eta: 0:00:19 time: 0.0424 data_time: 0.0029 memory: 1008 2022/11/02 14:42:16 - mmengine - INFO - Epoch(val) [240][35/500] eta: 0:00:19 time: 0.0409 data_time: 0.0030 memory: 1008 2022/11/02 14:42:16 - mmengine - INFO - Epoch(val) [240][40/500] eta: 0:00:19 time: 0.0430 data_time: 0.0032 memory: 1008 2022/11/02 14:42:16 - mmengine - INFO - Epoch(val) [240][45/500] eta: 0:00:19 time: 0.0457 data_time: 0.0033 memory: 1008 2022/11/02 14:42:17 - mmengine - INFO - Epoch(val) [240][50/500] eta: 0:00:39 time: 0.0878 data_time: 0.0493 memory: 1008 2022/11/02 14:42:17 - mmengine - INFO - Epoch(val) [240][55/500] eta: 0:00:39 time: 0.0885 data_time: 0.0488 memory: 1008 2022/11/02 14:42:18 - mmengine - INFO - Epoch(val) [240][60/500] eta: 0:00:17 time: 0.0405 data_time: 0.0019 memory: 1008 2022/11/02 14:42:18 - mmengine - INFO - Epoch(val) [240][65/500] eta: 0:00:17 time: 0.0409 data_time: 0.0023 memory: 1008 2022/11/02 14:42:18 - mmengine - INFO - Epoch(val) [240][70/500] eta: 0:00:17 time: 0.0417 data_time: 0.0026 memory: 1008 2022/11/02 14:42:18 - mmengine - INFO - Epoch(val) [240][75/500] eta: 0:00:17 time: 0.0382 data_time: 0.0024 memory: 1008 2022/11/02 14:42:19 - mmengine - INFO - Epoch(val) [240][80/500] eta: 0:00:25 time: 0.0597 data_time: 0.0268 memory: 1008 2022/11/02 14:42:19 - mmengine - INFO - Epoch(val) [240][85/500] eta: 0:00:25 time: 0.0580 data_time: 0.0265 memory: 1008 2022/11/02 14:42:19 - mmengine - INFO - Epoch(val) [240][90/500] eta: 0:00:15 time: 0.0390 data_time: 0.0023 memory: 1008 2022/11/02 14:42:19 - mmengine - INFO - Epoch(val) [240][95/500] eta: 0:00:15 time: 0.0420 data_time: 0.0029 memory: 1008 2022/11/02 14:42:19 - mmengine - INFO - Epoch(val) [240][100/500] eta: 0:00:15 time: 0.0386 data_time: 0.0029 memory: 1008 2022/11/02 14:42:20 - mmengine - INFO - Epoch(val) [240][105/500] eta: 0:00:15 time: 0.0378 data_time: 0.0028 memory: 1008 2022/11/02 14:42:20 - mmengine - INFO - Epoch(val) [240][110/500] eta: 0:00:13 time: 0.0353 data_time: 0.0024 memory: 1008 2022/11/02 14:42:20 - mmengine - INFO - Epoch(val) [240][115/500] eta: 0:00:13 time: 0.0362 data_time: 0.0025 memory: 1008 2022/11/02 14:42:20 - mmengine - INFO - Epoch(val) [240][120/500] eta: 0:00:14 time: 0.0393 data_time: 0.0029 memory: 1008 2022/11/02 14:42:20 - mmengine - INFO - Epoch(val) [240][125/500] eta: 0:00:14 time: 0.0367 data_time: 0.0026 memory: 1008 2022/11/02 14:42:20 - mmengine - INFO - Epoch(val) [240][130/500] eta: 0:00:13 time: 0.0357 data_time: 0.0023 memory: 1008 2022/11/02 14:42:21 - mmengine - INFO - Epoch(val) [240][135/500] eta: 0:00:13 time: 0.0362 data_time: 0.0023 memory: 1008 2022/11/02 14:42:21 - mmengine - INFO - Epoch(val) [240][140/500] eta: 0:00:13 time: 0.0386 data_time: 0.0023 memory: 1008 2022/11/02 14:42:21 - mmengine - INFO - Epoch(val) [240][145/500] eta: 0:00:13 time: 0.0481 data_time: 0.0034 memory: 1008 2022/11/02 14:42:21 - mmengine - INFO - Epoch(val) [240][150/500] eta: 0:00:16 time: 0.0465 data_time: 0.0036 memory: 1008 2022/11/02 14:42:22 - mmengine - INFO - Epoch(val) [240][155/500] eta: 0:00:16 time: 0.0425 data_time: 0.0024 memory: 1008 2022/11/02 14:42:22 - mmengine - INFO - Epoch(val) [240][160/500] eta: 0:00:14 time: 0.0428 data_time: 0.0025 memory: 1008 2022/11/02 14:42:22 - mmengine - INFO - Epoch(val) [240][165/500] eta: 0:00:14 time: 0.0394 data_time: 0.0025 memory: 1008 2022/11/02 14:42:22 - mmengine - INFO - Epoch(val) [240][170/500] eta: 0:00:13 time: 0.0418 data_time: 0.0025 memory: 1008 2022/11/02 14:42:22 - mmengine - INFO - Epoch(val) [240][175/500] eta: 0:00:13 time: 0.0396 data_time: 0.0027 memory: 1008 2022/11/02 14:42:23 - mmengine - INFO - Epoch(val) [240][180/500] eta: 0:00:11 time: 0.0374 data_time: 0.0027 memory: 1008 2022/11/02 14:42:23 - mmengine - INFO - Epoch(val) [240][185/500] eta: 0:00:11 time: 0.0421 data_time: 0.0028 memory: 1008 2022/11/02 14:42:23 - mmengine - INFO - Epoch(val) [240][190/500] eta: 0:00:12 time: 0.0417 data_time: 0.0027 memory: 1008 2022/11/02 14:42:23 - mmengine - INFO - Epoch(val) [240][195/500] eta: 0:00:12 time: 0.0375 data_time: 0.0025 memory: 1008 2022/11/02 14:42:23 - mmengine - INFO - Epoch(val) [240][200/500] eta: 0:00:13 time: 0.0453 data_time: 0.0026 memory: 1008 2022/11/02 14:42:24 - mmengine - INFO - Epoch(val) [240][205/500] eta: 0:00:13 time: 0.0448 data_time: 0.0026 memory: 1008 2022/11/02 14:42:24 - mmengine - INFO - Epoch(val) [240][210/500] eta: 0:00:10 time: 0.0348 data_time: 0.0025 memory: 1008 2022/11/02 14:42:24 - mmengine - INFO - Epoch(val) [240][215/500] eta: 0:00:10 time: 0.0374 data_time: 0.0024 memory: 1008 2022/11/02 14:42:24 - mmengine - INFO - Epoch(val) [240][220/500] eta: 0:00:11 time: 0.0418 data_time: 0.0062 memory: 1008 2022/11/02 14:42:24 - mmengine - INFO - Epoch(val) [240][225/500] eta: 0:00:11 time: 0.0414 data_time: 0.0058 memory: 1008 2022/11/02 14:42:25 - mmengine - INFO - Epoch(val) [240][230/500] eta: 0:00:09 time: 0.0364 data_time: 0.0018 memory: 1008 2022/11/02 14:42:25 - mmengine - INFO - Epoch(val) [240][235/500] eta: 0:00:09 time: 0.0348 data_time: 0.0021 memory: 1008 2022/11/02 14:42:25 - mmengine - INFO - Epoch(val) [240][240/500] eta: 0:00:10 time: 0.0399 data_time: 0.0028 memory: 1008 2022/11/02 14:42:25 - mmengine - INFO - Epoch(val) [240][245/500] eta: 0:00:10 time: 0.0404 data_time: 0.0030 memory: 1008 2022/11/02 14:42:25 - mmengine - INFO - Epoch(val) [240][250/500] eta: 0:00:09 time: 0.0371 data_time: 0.0025 memory: 1008 2022/11/02 14:42:25 - mmengine - INFO - Epoch(val) [240][255/500] eta: 0:00:09 time: 0.0350 data_time: 0.0023 memory: 1008 2022/11/02 14:42:26 - mmengine - INFO - Epoch(val) [240][260/500] eta: 0:00:08 time: 0.0369 data_time: 0.0024 memory: 1008 2022/11/02 14:42:26 - mmengine - INFO - Epoch(val) [240][265/500] eta: 0:00:08 time: 0.0414 data_time: 0.0036 memory: 1008 2022/11/02 14:42:26 - mmengine - INFO - Epoch(val) [240][270/500] eta: 0:00:09 time: 0.0426 data_time: 0.0035 memory: 1008 2022/11/02 14:42:26 - mmengine - INFO - Epoch(val) [240][275/500] eta: 0:00:09 time: 0.0402 data_time: 0.0026 memory: 1008 2022/11/02 14:42:26 - mmengine - INFO - Epoch(val) [240][280/500] eta: 0:00:08 time: 0.0387 data_time: 0.0024 memory: 1008 2022/11/02 14:42:27 - mmengine - INFO - Epoch(val) [240][285/500] eta: 0:00:08 time: 0.0365 data_time: 0.0022 memory: 1008 2022/11/02 14:42:27 - mmengine - INFO - Epoch(val) [240][290/500] eta: 0:00:07 time: 0.0377 data_time: 0.0022 memory: 1008 2022/11/02 14:42:27 - mmengine - INFO - Epoch(val) [240][295/500] eta: 0:00:07 time: 0.0417 data_time: 0.0025 memory: 1008 2022/11/02 14:42:27 - mmengine - INFO - Epoch(val) [240][300/500] eta: 0:00:07 time: 0.0380 data_time: 0.0024 memory: 1008 2022/11/02 14:42:27 - mmengine - INFO - Epoch(val) [240][305/500] eta: 0:00:07 time: 0.0349 data_time: 0.0023 memory: 1008 2022/11/02 14:42:28 - mmengine - INFO - Epoch(val) [240][310/500] eta: 0:00:07 time: 0.0374 data_time: 0.0024 memory: 1008 2022/11/02 14:42:28 - mmengine - INFO - Epoch(val) [240][315/500] eta: 0:00:07 time: 0.0394 data_time: 0.0022 memory: 1008 2022/11/02 14:42:28 - mmengine - INFO - Epoch(val) [240][320/500] eta: 0:00:06 time: 0.0371 data_time: 0.0022 memory: 1008 2022/11/02 14:42:28 - mmengine - INFO - Epoch(val) [240][325/500] eta: 0:00:06 time: 0.0476 data_time: 0.0024 memory: 1008 2022/11/02 14:42:28 - mmengine - INFO - Epoch(val) [240][330/500] eta: 0:00:08 time: 0.0489 data_time: 0.0024 memory: 1008 2022/11/02 14:42:29 - mmengine - INFO - Epoch(val) [240][335/500] eta: 0:00:08 time: 0.0359 data_time: 0.0024 memory: 1008 2022/11/02 14:42:29 - mmengine - INFO - Epoch(val) [240][340/500] eta: 0:00:07 time: 0.0454 data_time: 0.0025 memory: 1008 2022/11/02 14:42:29 - mmengine - INFO - Epoch(val) [240][345/500] eta: 0:00:07 time: 0.0478 data_time: 0.0026 memory: 1008 2022/11/02 14:42:29 - mmengine - INFO - Epoch(val) [240][350/500] eta: 0:00:06 time: 0.0427 data_time: 0.0027 memory: 1008 2022/11/02 14:42:30 - mmengine - INFO - Epoch(val) [240][355/500] eta: 0:00:06 time: 0.0410 data_time: 0.0024 memory: 1008 2022/11/02 14:42:30 - mmengine - INFO - Epoch(val) [240][360/500] eta: 0:00:05 time: 0.0396 data_time: 0.0024 memory: 1008 2022/11/02 14:42:30 - mmengine - INFO - Epoch(val) [240][365/500] eta: 0:00:05 time: 0.0423 data_time: 0.0028 memory: 1008 2022/11/02 14:42:30 - mmengine - INFO - Epoch(val) [240][370/500] eta: 0:00:04 time: 0.0369 data_time: 0.0027 memory: 1008 2022/11/02 14:42:30 - mmengine - INFO - Epoch(val) [240][375/500] eta: 0:00:04 time: 0.0347 data_time: 0.0025 memory: 1008 2022/11/02 14:42:30 - mmengine - INFO - Epoch(val) [240][380/500] eta: 0:00:04 time: 0.0374 data_time: 0.0025 memory: 1008 2022/11/02 14:42:31 - mmengine - INFO - Epoch(val) [240][385/500] eta: 0:00:04 time: 0.0369 data_time: 0.0023 memory: 1008 2022/11/02 14:42:31 - mmengine - INFO - Epoch(val) [240][390/500] eta: 0:00:04 time: 0.0414 data_time: 0.0024 memory: 1008 2022/11/02 14:42:31 - mmengine - INFO - Epoch(val) [240][395/500] eta: 0:00:04 time: 0.0410 data_time: 0.0023 memory: 1008 2022/11/02 14:42:31 - mmengine - INFO - Epoch(val) [240][400/500] eta: 0:00:03 time: 0.0346 data_time: 0.0021 memory: 1008 2022/11/02 14:42:31 - mmengine - INFO - Epoch(val) [240][405/500] eta: 0:00:03 time: 0.0368 data_time: 0.0022 memory: 1008 2022/11/02 14:42:32 - mmengine - INFO - Epoch(val) [240][410/500] eta: 0:00:03 time: 0.0387 data_time: 0.0023 memory: 1008 2022/11/02 14:42:32 - mmengine - INFO - Epoch(val) [240][415/500] eta: 0:00:03 time: 0.0364 data_time: 0.0021 memory: 1008 2022/11/02 14:42:33 - mmengine - INFO - Epoch(val) [240][420/500] eta: 0:00:08 time: 0.1081 data_time: 0.0774 memory: 1008 2022/11/02 14:42:33 - mmengine - INFO - Epoch(val) [240][425/500] eta: 0:00:08 time: 0.1103 data_time: 0.0774 memory: 1008 2022/11/02 14:42:33 - mmengine - INFO - Epoch(val) [240][430/500] eta: 0:00:02 time: 0.0385 data_time: 0.0023 memory: 1008 2022/11/02 14:42:33 - mmengine - INFO - Epoch(val) [240][435/500] eta: 0:00:02 time: 0.0369 data_time: 0.0024 memory: 1008 2022/11/02 14:42:33 - mmengine - INFO - Epoch(val) [240][440/500] eta: 0:00:02 time: 0.0367 data_time: 0.0023 memory: 1008 2022/11/02 14:42:34 - mmengine - INFO - Epoch(val) [240][445/500] eta: 0:00:02 time: 0.0396 data_time: 0.0025 memory: 1008 2022/11/02 14:42:34 - mmengine - INFO - Epoch(val) [240][450/500] eta: 0:00:01 time: 0.0396 data_time: 0.0025 memory: 1008 2022/11/02 14:42:34 - mmengine - INFO - Epoch(val) [240][455/500] eta: 0:00:01 time: 0.0397 data_time: 0.0025 memory: 1008 2022/11/02 14:42:34 - mmengine - INFO - Epoch(val) [240][460/500] eta: 0:00:01 time: 0.0412 data_time: 0.0030 memory: 1008 2022/11/02 14:42:34 - mmengine - INFO - Epoch(val) [240][465/500] eta: 0:00:01 time: 0.0386 data_time: 0.0030 memory: 1008 2022/11/02 14:42:35 - mmengine - INFO - Epoch(val) [240][470/500] eta: 0:00:01 time: 0.0361 data_time: 0.0025 memory: 1008 2022/11/02 14:42:35 - mmengine - INFO - Epoch(val) [240][475/500] eta: 0:00:01 time: 0.0374 data_time: 0.0027 memory: 1008 2022/11/02 14:42:35 - mmengine - INFO - Epoch(val) [240][480/500] eta: 0:00:00 time: 0.0369 data_time: 0.0027 memory: 1008 2022/11/02 14:42:35 - mmengine - INFO - Epoch(val) [240][485/500] eta: 0:00:00 time: 0.0347 data_time: 0.0023 memory: 1008 2022/11/02 14:42:35 - mmengine - INFO - Epoch(val) [240][490/500] eta: 0:00:00 time: 0.0383 data_time: 0.0023 memory: 1008 2022/11/02 14:42:36 - mmengine - INFO - Epoch(val) [240][495/500] eta: 0:00:00 time: 0.0449 data_time: 0.0026 memory: 1008 2022/11/02 14:42:36 - mmengine - INFO - Epoch(val) [240][500/500] eta: 0:00:00 time: 0.0406 data_time: 0.0027 memory: 1008 2022/11/02 14:42:36 - mmengine - INFO - Evaluating hmean-iou... 2022/11/02 14:42:36 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.7944, precision: 0.7346, hmean: 0.7634 2022/11/02 14:42:36 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.7939, precision: 0.8005, hmean: 0.7972 2022/11/02 14:42:36 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.7906, precision: 0.8360, hmean: 0.8127 2022/11/02 14:42:36 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.7727, precision: 0.8718, hmean: 0.8193 2022/11/02 14:42:36 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.7053, precision: 0.9043, hmean: 0.7925 2022/11/02 14:42:36 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.3394, precision: 0.9527, hmean: 0.5005 2022/11/02 14:42:36 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0019, precision: 1.0000, hmean: 0.0038 2022/11/02 14:42:36 - mmengine - INFO - Epoch(val) [240][500/500] icdar/precision: 0.8718 icdar/recall: 0.7727 icdar/hmean: 0.8193 2022/11/02 14:42:42 - mmengine - INFO - Epoch(train) [241][5/63] lr: 1.7692e-03 eta: 0:00:00 time: 0.9738 data_time: 0.2153 memory: 14901 loss: 1.6316 loss_prob: 0.9139 loss_thr: 0.5639 loss_db: 0.1538 2022/11/02 14:42:45 - mmengine - INFO - Epoch(train) [241][10/63] lr: 1.7692e-03 eta: 9:52:00 time: 0.8628 data_time: 0.2241 memory: 14901 loss: 1.6892 loss_prob: 0.9606 loss_thr: 0.5667 loss_db: 0.1619 2022/11/02 14:42:47 - mmengine - INFO - Epoch(train) [241][15/63] lr: 1.7692e-03 eta: 9:52:00 time: 0.5247 data_time: 0.0169 memory: 14901 loss: 1.7937 loss_prob: 1.0439 loss_thr: 0.5812 loss_db: 0.1687 2022/11/02 14:42:50 - mmengine - INFO - Epoch(train) [241][20/63] lr: 1.7692e-03 eta: 9:51:52 time: 0.5304 data_time: 0.0094 memory: 14901 loss: 1.7984 loss_prob: 1.0484 loss_thr: 0.5848 loss_db: 0.1652 2022/11/02 14:42:53 - mmengine - INFO - Epoch(train) [241][25/63] lr: 1.7692e-03 eta: 9:51:52 time: 0.6111 data_time: 0.0202 memory: 14901 loss: 1.7080 loss_prob: 0.9781 loss_thr: 0.5718 loss_db: 0.1581 2022/11/02 14:42:56 - mmengine - INFO - Epoch(train) [241][30/63] lr: 1.7692e-03 eta: 9:51:48 time: 0.6165 data_time: 0.0406 memory: 14901 loss: 1.7049 loss_prob: 0.9683 loss_thr: 0.5774 loss_db: 0.1592 2022/11/02 14:42:59 - mmengine - INFO - Epoch(train) [241][35/63] lr: 1.7692e-03 eta: 9:51:48 time: 0.5621 data_time: 0.0330 memory: 14901 loss: 1.7714 loss_prob: 1.0098 loss_thr: 0.5934 loss_db: 0.1682 2022/11/02 14:43:01 - mmengine - INFO - Epoch(train) [241][40/63] lr: 1.7692e-03 eta: 9:51:39 time: 0.5303 data_time: 0.0092 memory: 14901 loss: 1.6939 loss_prob: 0.9605 loss_thr: 0.5726 loss_db: 0.1607 2022/11/02 14:43:04 - mmengine - INFO - Epoch(train) [241][45/63] lr: 1.7692e-03 eta: 9:51:39 time: 0.5224 data_time: 0.0095 memory: 14901 loss: 1.6106 loss_prob: 0.9113 loss_thr: 0.5488 loss_db: 0.1506 2022/11/02 14:43:07 - mmengine - INFO - Epoch(train) [241][50/63] lr: 1.7692e-03 eta: 9:51:33 time: 0.5637 data_time: 0.0197 memory: 14901 loss: 1.7330 loss_prob: 0.9761 loss_thr: 0.5972 loss_db: 0.1597 2022/11/02 14:43:10 - mmengine - INFO - Epoch(train) [241][55/63] lr: 1.7692e-03 eta: 9:51:33 time: 0.5819 data_time: 0.0230 memory: 14901 loss: 1.7408 loss_prob: 0.9779 loss_thr: 0.6021 loss_db: 0.1608 2022/11/02 14:43:13 - mmengine - INFO - Epoch(train) [241][60/63] lr: 1.7692e-03 eta: 9:51:26 time: 0.5692 data_time: 0.0158 memory: 14901 loss: 1.6801 loss_prob: 0.9468 loss_thr: 0.5755 loss_db: 0.1578 2022/11/02 14:43:14 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:43:20 - mmengine - INFO - Epoch(train) [242][5/63] lr: 1.7675e-03 eta: 9:51:26 time: 0.8291 data_time: 0.2141 memory: 14901 loss: 1.8138 loss_prob: 1.0384 loss_thr: 0.6054 loss_db: 0.1699 2022/11/02 14:43:22 - mmengine - INFO - Epoch(train) [242][10/63] lr: 1.7675e-03 eta: 9:51:21 time: 0.8407 data_time: 0.2205 memory: 14901 loss: 1.8713 loss_prob: 1.0892 loss_thr: 0.6043 loss_db: 0.1778 2022/11/02 14:43:25 - mmengine - INFO - Epoch(train) [242][15/63] lr: 1.7675e-03 eta: 9:51:21 time: 0.5517 data_time: 0.0134 memory: 14901 loss: 1.8910 loss_prob: 1.1043 loss_thr: 0.6031 loss_db: 0.1837 2022/11/02 14:43:28 - mmengine - INFO - Epoch(train) [242][20/63] lr: 1.7675e-03 eta: 9:51:13 time: 0.5343 data_time: 0.0061 memory: 14901 loss: 1.7904 loss_prob: 1.0389 loss_thr: 0.5779 loss_db: 0.1736 2022/11/02 14:43:31 - mmengine - INFO - Epoch(train) [242][25/63] lr: 1.7675e-03 eta: 9:51:13 time: 0.5907 data_time: 0.0275 memory: 14901 loss: 1.8151 loss_prob: 1.0432 loss_thr: 0.5972 loss_db: 0.1747 2022/11/02 14:43:35 - mmengine - INFO - Epoch(train) [242][30/63] lr: 1.7675e-03 eta: 9:51:14 time: 0.7390 data_time: 0.0367 memory: 14901 loss: 1.7775 loss_prob: 1.0135 loss_thr: 0.5954 loss_db: 0.1686 2022/11/02 14:43:38 - mmengine - INFO - Epoch(train) [242][35/63] lr: 1.7675e-03 eta: 9:51:14 time: 0.6816 data_time: 0.0254 memory: 14901 loss: 1.5724 loss_prob: 0.8694 loss_thr: 0.5588 loss_db: 0.1443 2022/11/02 14:43:41 - mmengine - INFO - Epoch(train) [242][40/63] lr: 1.7675e-03 eta: 9:51:07 time: 0.5725 data_time: 0.0190 memory: 14901 loss: 1.6122 loss_prob: 0.8989 loss_thr: 0.5657 loss_db: 0.1477 2022/11/02 14:43:44 - mmengine - INFO - Epoch(train) [242][45/63] lr: 1.7675e-03 eta: 9:51:07 time: 0.5745 data_time: 0.0088 memory: 14901 loss: 1.8110 loss_prob: 1.0423 loss_thr: 0.5971 loss_db: 0.1716 2022/11/02 14:43:46 - mmengine - INFO - Epoch(train) [242][50/63] lr: 1.7675e-03 eta: 9:51:00 time: 0.5654 data_time: 0.0141 memory: 14901 loss: 1.8541 loss_prob: 1.0884 loss_thr: 0.5877 loss_db: 0.1780 2022/11/02 14:43:49 - mmengine - INFO - Epoch(train) [242][55/63] lr: 1.7675e-03 eta: 9:51:00 time: 0.5529 data_time: 0.0210 memory: 14901 loss: 1.7493 loss_prob: 1.0013 loss_thr: 0.5844 loss_db: 0.1636 2022/11/02 14:43:52 - mmengine - INFO - Epoch(train) [242][60/63] lr: 1.7675e-03 eta: 9:50:53 time: 0.5510 data_time: 0.0190 memory: 14901 loss: 1.7773 loss_prob: 1.0014 loss_thr: 0.6088 loss_db: 0.1671 2022/11/02 14:43:53 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:43:59 - mmengine - INFO - Epoch(train) [243][5/63] lr: 1.7659e-03 eta: 9:50:53 time: 0.8676 data_time: 0.2412 memory: 14901 loss: 1.7066 loss_prob: 0.9668 loss_thr: 0.5817 loss_db: 0.1581 2022/11/02 14:44:02 - mmengine - INFO - Epoch(train) [243][10/63] lr: 1.7659e-03 eta: 9:50:50 time: 0.8883 data_time: 0.2714 memory: 14901 loss: 1.6462 loss_prob: 0.9287 loss_thr: 0.5640 loss_db: 0.1536 2022/11/02 14:44:05 - mmengine - INFO - Epoch(train) [243][15/63] lr: 1.7659e-03 eta: 9:50:50 time: 0.5685 data_time: 0.0404 memory: 14901 loss: 1.6313 loss_prob: 0.9135 loss_thr: 0.5654 loss_db: 0.1524 2022/11/02 14:44:08 - mmengine - INFO - Epoch(train) [243][20/63] lr: 1.7659e-03 eta: 9:50:43 time: 0.5429 data_time: 0.0080 memory: 14901 loss: 1.7794 loss_prob: 1.0153 loss_thr: 0.5947 loss_db: 0.1694 2022/11/02 14:44:10 - mmengine - INFO - Epoch(train) [243][25/63] lr: 1.7659e-03 eta: 9:50:43 time: 0.5319 data_time: 0.0077 memory: 14901 loss: 1.8669 loss_prob: 1.0713 loss_thr: 0.6201 loss_db: 0.1755 2022/11/02 14:44:13 - mmengine - INFO - Epoch(train) [243][30/63] lr: 1.7659e-03 eta: 9:50:36 time: 0.5616 data_time: 0.0213 memory: 14901 loss: 1.7756 loss_prob: 1.0083 loss_thr: 0.5991 loss_db: 0.1682 2022/11/02 14:44:16 - mmengine - INFO - Epoch(train) [243][35/63] lr: 1.7659e-03 eta: 9:50:36 time: 0.5734 data_time: 0.0342 memory: 14901 loss: 1.6621 loss_prob: 0.9332 loss_thr: 0.5721 loss_db: 0.1568 2022/11/02 14:44:20 - mmengine - INFO - Epoch(train) [243][40/63] lr: 1.7659e-03 eta: 9:50:32 time: 0.6310 data_time: 0.0203 memory: 14901 loss: 1.6220 loss_prob: 0.9016 loss_thr: 0.5672 loss_db: 0.1532 2022/11/02 14:44:23 - mmengine - INFO - Epoch(train) [243][45/63] lr: 1.7659e-03 eta: 9:50:32 time: 0.6551 data_time: 0.0069 memory: 14901 loss: 1.7847 loss_prob: 1.0273 loss_thr: 0.5852 loss_db: 0.1721 2022/11/02 14:44:26 - mmengine - INFO - Epoch(train) [243][50/63] lr: 1.7659e-03 eta: 9:50:28 time: 0.6440 data_time: 0.0219 memory: 14901 loss: 1.8133 loss_prob: 1.0557 loss_thr: 0.5841 loss_db: 0.1735 2022/11/02 14:44:29 - mmengine - INFO - Epoch(train) [243][55/63] lr: 1.7659e-03 eta: 9:50:28 time: 0.6096 data_time: 0.0242 memory: 14901 loss: 1.6938 loss_prob: 0.9646 loss_thr: 0.5681 loss_db: 0.1610 2022/11/02 14:44:31 - mmengine - INFO - Epoch(train) [243][60/63] lr: 1.7659e-03 eta: 9:50:21 time: 0.5447 data_time: 0.0146 memory: 14901 loss: 1.8440 loss_prob: 1.0573 loss_thr: 0.6106 loss_db: 0.1761 2022/11/02 14:44:33 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:44:39 - mmengine - INFO - Epoch(train) [244][5/63] lr: 1.7642e-03 eta: 9:50:21 time: 0.8708 data_time: 0.2410 memory: 14901 loss: 1.8344 loss_prob: 1.0291 loss_thr: 0.6323 loss_db: 0.1729 2022/11/02 14:44:42 - mmengine - INFO - Epoch(train) [244][10/63] lr: 1.7642e-03 eta: 9:50:18 time: 0.8883 data_time: 0.2465 memory: 14901 loss: 1.8231 loss_prob: 1.0242 loss_thr: 0.6332 loss_db: 0.1657 2022/11/02 14:44:45 - mmengine - INFO - Epoch(train) [244][15/63] lr: 1.7642e-03 eta: 9:50:18 time: 0.5389 data_time: 0.0143 memory: 14901 loss: 1.6983 loss_prob: 0.9488 loss_thr: 0.5901 loss_db: 0.1593 2022/11/02 14:44:47 - mmengine - INFO - Epoch(train) [244][20/63] lr: 1.7642e-03 eta: 9:50:10 time: 0.5492 data_time: 0.0084 memory: 14901 loss: 1.8640 loss_prob: 1.0787 loss_thr: 0.6011 loss_db: 0.1841 2022/11/02 14:44:50 - mmengine - INFO - Epoch(train) [244][25/63] lr: 1.7642e-03 eta: 9:50:10 time: 0.5398 data_time: 0.0292 memory: 14901 loss: 1.9696 loss_prob: 1.1736 loss_thr: 0.6021 loss_db: 0.1939 2022/11/02 14:44:53 - mmengine - INFO - Epoch(train) [244][30/63] lr: 1.7642e-03 eta: 9:50:03 time: 0.5465 data_time: 0.0380 memory: 14901 loss: 1.8512 loss_prob: 1.0920 loss_thr: 0.5803 loss_db: 0.1789 2022/11/02 14:44:55 - mmengine - INFO - Epoch(train) [244][35/63] lr: 1.7642e-03 eta: 9:50:03 time: 0.5489 data_time: 0.0187 memory: 14901 loss: 1.7984 loss_prob: 1.0446 loss_thr: 0.5810 loss_db: 0.1728 2022/11/02 14:44:58 - mmengine - INFO - Epoch(train) [244][40/63] lr: 1.7642e-03 eta: 9:49:56 time: 0.5619 data_time: 0.0110 memory: 14901 loss: 1.8133 loss_prob: 1.0554 loss_thr: 0.5846 loss_db: 0.1733 2022/11/02 14:45:01 - mmengine - INFO - Epoch(train) [244][45/63] lr: 1.7642e-03 eta: 9:49:56 time: 0.6008 data_time: 0.0125 memory: 14901 loss: 1.7690 loss_prob: 1.0243 loss_thr: 0.5758 loss_db: 0.1689 2022/11/02 14:45:05 - mmengine - INFO - Epoch(train) [244][50/63] lr: 1.7642e-03 eta: 9:49:53 time: 0.6564 data_time: 0.0223 memory: 14901 loss: 1.7116 loss_prob: 0.9769 loss_thr: 0.5718 loss_db: 0.1629 2022/11/02 14:45:08 - mmengine - INFO - Epoch(train) [244][55/63] lr: 1.7642e-03 eta: 9:49:53 time: 0.6237 data_time: 0.0211 memory: 14901 loss: 1.7618 loss_prob: 1.0090 loss_thr: 0.5870 loss_db: 0.1657 2022/11/02 14:45:10 - mmengine - INFO - Epoch(train) [244][60/63] lr: 1.7642e-03 eta: 9:49:46 time: 0.5523 data_time: 0.0132 memory: 14901 loss: 1.8567 loss_prob: 1.0627 loss_thr: 0.6204 loss_db: 0.1736 2022/11/02 14:45:12 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:45:18 - mmengine - INFO - Epoch(train) [245][5/63] lr: 1.7625e-03 eta: 9:49:46 time: 0.8482 data_time: 0.2296 memory: 14901 loss: 1.8152 loss_prob: 1.0576 loss_thr: 0.5874 loss_db: 0.1702 2022/11/02 14:45:21 - mmengine - INFO - Epoch(train) [245][10/63] lr: 1.7625e-03 eta: 9:49:44 time: 0.9073 data_time: 0.2561 memory: 14901 loss: 1.7498 loss_prob: 0.9985 loss_thr: 0.5860 loss_db: 0.1652 2022/11/02 14:45:24 - mmengine - INFO - Epoch(train) [245][15/63] lr: 1.7625e-03 eta: 9:49:44 time: 0.5730 data_time: 0.0348 memory: 14901 loss: 1.6796 loss_prob: 0.9423 loss_thr: 0.5799 loss_db: 0.1573 2022/11/02 14:45:27 - mmengine - INFO - Epoch(train) [245][20/63] lr: 1.7625e-03 eta: 9:49:37 time: 0.5641 data_time: 0.0110 memory: 14901 loss: 1.7517 loss_prob: 0.9945 loss_thr: 0.5932 loss_db: 0.1639 2022/11/02 14:45:29 - mmengine - INFO - Epoch(train) [245][25/63] lr: 1.7625e-03 eta: 9:49:37 time: 0.5495 data_time: 0.0114 memory: 14901 loss: 1.8572 loss_prob: 1.0535 loss_thr: 0.6296 loss_db: 0.1741 2022/11/02 14:45:32 - mmengine - INFO - Epoch(train) [245][30/63] lr: 1.7625e-03 eta: 9:49:30 time: 0.5708 data_time: 0.0261 memory: 14901 loss: 1.7483 loss_prob: 0.9759 loss_thr: 0.6092 loss_db: 0.1631 2022/11/02 14:45:35 - mmengine - INFO - Epoch(train) [245][35/63] lr: 1.7625e-03 eta: 9:49:30 time: 0.6044 data_time: 0.0294 memory: 14901 loss: 1.6337 loss_prob: 0.9129 loss_thr: 0.5660 loss_db: 0.1547 2022/11/02 14:45:38 - mmengine - INFO - Epoch(train) [245][40/63] lr: 1.7625e-03 eta: 9:49:24 time: 0.5675 data_time: 0.0118 memory: 14901 loss: 1.6515 loss_prob: 0.9190 loss_thr: 0.5752 loss_db: 0.1573 2022/11/02 14:45:41 - mmengine - INFO - Epoch(train) [245][45/63] lr: 1.7625e-03 eta: 9:49:24 time: 0.5550 data_time: 0.0079 memory: 14901 loss: 1.7110 loss_prob: 0.9642 loss_thr: 0.5847 loss_db: 0.1620 2022/11/02 14:45:44 - mmengine - INFO - Epoch(train) [245][50/63] lr: 1.7625e-03 eta: 9:49:16 time: 0.5529 data_time: 0.0083 memory: 14901 loss: 1.6913 loss_prob: 0.9663 loss_thr: 0.5699 loss_db: 0.1551 2022/11/02 14:45:47 - mmengine - INFO - Epoch(train) [245][55/63] lr: 1.7625e-03 eta: 9:49:16 time: 0.5966 data_time: 0.0254 memory: 14901 loss: 1.7180 loss_prob: 0.9713 loss_thr: 0.5911 loss_db: 0.1555 2022/11/02 14:45:49 - mmengine - INFO - Epoch(train) [245][60/63] lr: 1.7625e-03 eta: 9:49:10 time: 0.5663 data_time: 0.0235 memory: 14901 loss: 1.7842 loss_prob: 1.0048 loss_thr: 0.6161 loss_db: 0.1633 2022/11/02 14:45:51 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:45:57 - mmengine - INFO - Epoch(train) [246][5/63] lr: 1.7609e-03 eta: 9:49:10 time: 0.8433 data_time: 0.2186 memory: 14901 loss: 1.8414 loss_prob: 1.0544 loss_thr: 0.6153 loss_db: 0.1717 2022/11/02 14:45:59 - mmengine - INFO - Epoch(train) [246][10/63] lr: 1.7609e-03 eta: 9:49:06 time: 0.8681 data_time: 0.2185 memory: 14901 loss: 1.6960 loss_prob: 0.9579 loss_thr: 0.5783 loss_db: 0.1597 2022/11/02 14:46:02 - mmengine - INFO - Epoch(train) [246][15/63] lr: 1.7609e-03 eta: 9:49:06 time: 0.5449 data_time: 0.0056 memory: 14901 loss: 1.7278 loss_prob: 0.9713 loss_thr: 0.5938 loss_db: 0.1627 2022/11/02 14:46:05 - mmengine - INFO - Epoch(train) [246][20/63] lr: 1.7609e-03 eta: 9:48:59 time: 0.5481 data_time: 0.0076 memory: 14901 loss: 1.6577 loss_prob: 0.9330 loss_thr: 0.5688 loss_db: 0.1558 2022/11/02 14:46:08 - mmengine - INFO - Epoch(train) [246][25/63] lr: 1.7609e-03 eta: 9:48:59 time: 0.5532 data_time: 0.0287 memory: 14901 loss: 1.6390 loss_prob: 0.9175 loss_thr: 0.5699 loss_db: 0.1516 2022/11/02 14:46:10 - mmengine - INFO - Epoch(train) [246][30/63] lr: 1.7609e-03 eta: 9:48:50 time: 0.5251 data_time: 0.0369 memory: 14901 loss: 1.7661 loss_prob: 1.0035 loss_thr: 0.5985 loss_db: 0.1641 2022/11/02 14:46:13 - mmengine - INFO - Epoch(train) [246][35/63] lr: 1.7609e-03 eta: 9:48:50 time: 0.5018 data_time: 0.0171 memory: 14901 loss: 1.7675 loss_prob: 1.0149 loss_thr: 0.5879 loss_db: 0.1647 2022/11/02 14:46:15 - mmengine - INFO - Epoch(train) [246][40/63] lr: 1.7609e-03 eta: 9:48:42 time: 0.5289 data_time: 0.0070 memory: 14901 loss: 1.7407 loss_prob: 1.0070 loss_thr: 0.5649 loss_db: 0.1688 2022/11/02 14:46:18 - mmengine - INFO - Epoch(train) [246][45/63] lr: 1.7609e-03 eta: 9:48:42 time: 0.5644 data_time: 0.0075 memory: 14901 loss: 1.8247 loss_prob: 1.0571 loss_thr: 0.5886 loss_db: 0.1790 2022/11/02 14:46:21 - mmengine - INFO - Epoch(train) [246][50/63] lr: 1.7609e-03 eta: 9:48:35 time: 0.5511 data_time: 0.0202 memory: 14901 loss: 1.8670 loss_prob: 1.0829 loss_thr: 0.6086 loss_db: 0.1755 2022/11/02 14:46:23 - mmengine - INFO - Epoch(train) [246][55/63] lr: 1.7609e-03 eta: 9:48:35 time: 0.5217 data_time: 0.0289 memory: 14901 loss: 1.8345 loss_prob: 1.0610 loss_thr: 0.6043 loss_db: 0.1692 2022/11/02 14:46:26 - mmengine - INFO - Epoch(train) [246][60/63] lr: 1.7609e-03 eta: 9:48:27 time: 0.5263 data_time: 0.0179 memory: 14901 loss: 1.7128 loss_prob: 0.9633 loss_thr: 0.5865 loss_db: 0.1630 2022/11/02 14:46:27 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:46:32 - mmengine - INFO - Epoch(train) [247][5/63] lr: 1.7592e-03 eta: 9:48:27 time: 0.7161 data_time: 0.2249 memory: 14901 loss: 1.7410 loss_prob: 0.9993 loss_thr: 0.5726 loss_db: 0.1691 2022/11/02 14:46:35 - mmengine - INFO - Epoch(train) [247][10/63] lr: 1.7592e-03 eta: 9:48:19 time: 0.7603 data_time: 0.2410 memory: 14901 loss: 1.7831 loss_prob: 1.0257 loss_thr: 0.5822 loss_db: 0.1751 2022/11/02 14:46:38 - mmengine - INFO - Epoch(train) [247][15/63] lr: 1.7592e-03 eta: 9:48:19 time: 0.5795 data_time: 0.0235 memory: 14901 loss: 2.2488 loss_prob: 1.3967 loss_thr: 0.6200 loss_db: 0.2321 2022/11/02 14:46:40 - mmengine - INFO - Epoch(train) [247][20/63] lr: 1.7592e-03 eta: 9:48:11 time: 0.5461 data_time: 0.0073 memory: 14901 loss: 2.4133 loss_prob: 1.5140 loss_thr: 0.6527 loss_db: 0.2466 2022/11/02 14:46:43 - mmengine - INFO - Epoch(train) [247][25/63] lr: 1.7592e-03 eta: 9:48:11 time: 0.5455 data_time: 0.0259 memory: 14901 loss: 2.2796 loss_prob: 1.3555 loss_thr: 0.6964 loss_db: 0.2277 2022/11/02 14:46:46 - mmengine - INFO - Epoch(train) [247][30/63] lr: 1.7592e-03 eta: 9:48:04 time: 0.5593 data_time: 0.0332 memory: 14901 loss: 2.3347 loss_prob: 1.3990 loss_thr: 0.7018 loss_db: 0.2339 2022/11/02 14:46:49 - mmengine - INFO - Epoch(train) [247][35/63] lr: 1.7592e-03 eta: 9:48:04 time: 0.5133 data_time: 0.0255 memory: 14901 loss: 2.0944 loss_prob: 1.2534 loss_thr: 0.6387 loss_db: 0.2023 2022/11/02 14:46:51 - mmengine - INFO - Epoch(train) [247][40/63] lr: 1.7592e-03 eta: 9:47:57 time: 0.5342 data_time: 0.0187 memory: 14901 loss: 2.4912 loss_prob: 1.5591 loss_thr: 0.6834 loss_db: 0.2487 2022/11/02 14:46:54 - mmengine - INFO - Epoch(train) [247][45/63] lr: 1.7592e-03 eta: 9:47:57 time: 0.5682 data_time: 0.0087 memory: 14901 loss: 2.5986 loss_prob: 1.6263 loss_thr: 0.7138 loss_db: 0.2585 2022/11/02 14:46:57 - mmengine - INFO - Epoch(train) [247][50/63] lr: 1.7592e-03 eta: 9:47:51 time: 0.5975 data_time: 0.0174 memory: 14901 loss: 2.0148 loss_prob: 1.1809 loss_thr: 0.6448 loss_db: 0.1891 2022/11/02 14:47:00 - mmengine - INFO - Epoch(train) [247][55/63] lr: 1.7592e-03 eta: 9:47:51 time: 0.5873 data_time: 0.0257 memory: 14901 loss: 2.0165 loss_prob: 1.1693 loss_thr: 0.6616 loss_db: 0.1856 2022/11/02 14:47:03 - mmengine - INFO - Epoch(train) [247][60/63] lr: 1.7592e-03 eta: 9:47:46 time: 0.6129 data_time: 0.0253 memory: 14901 loss: 1.9626 loss_prob: 1.1417 loss_thr: 0.6412 loss_db: 0.1797 2022/11/02 14:47:05 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:47:10 - mmengine - INFO - Epoch(train) [248][5/63] lr: 1.7576e-03 eta: 9:47:46 time: 0.7631 data_time: 0.2452 memory: 14901 loss: 1.8218 loss_prob: 1.0494 loss_thr: 0.6009 loss_db: 0.1715 2022/11/02 14:47:14 - mmengine - INFO - Epoch(train) [248][10/63] lr: 1.7576e-03 eta: 9:47:46 time: 0.9517 data_time: 0.2498 memory: 14901 loss: 1.8542 loss_prob: 1.0749 loss_thr: 0.6030 loss_db: 0.1763 2022/11/02 14:47:17 - mmengine - INFO - Epoch(train) [248][15/63] lr: 1.7576e-03 eta: 9:47:46 time: 0.7250 data_time: 0.0105 memory: 14901 loss: 1.8145 loss_prob: 1.0437 loss_thr: 0.6003 loss_db: 0.1706 2022/11/02 14:47:20 - mmengine - INFO - Epoch(train) [248][20/63] lr: 1.7576e-03 eta: 9:47:42 time: 0.6340 data_time: 0.0123 memory: 14901 loss: 1.8455 loss_prob: 1.0679 loss_thr: 0.6030 loss_db: 0.1746 2022/11/02 14:47:24 - mmengine - INFO - Epoch(train) [248][25/63] lr: 1.7576e-03 eta: 9:47:42 time: 0.6720 data_time: 0.0450 memory: 14901 loss: 1.8818 loss_prob: 1.1072 loss_thr: 0.5977 loss_db: 0.1768 2022/11/02 14:47:27 - mmengine - INFO - Epoch(train) [248][30/63] lr: 1.7576e-03 eta: 9:47:37 time: 0.6175 data_time: 0.0391 memory: 14901 loss: 1.8401 loss_prob: 1.0784 loss_thr: 0.5866 loss_db: 0.1751 2022/11/02 14:47:29 - mmengine - INFO - Epoch(train) [248][35/63] lr: 1.7576e-03 eta: 9:47:37 time: 0.5188 data_time: 0.0110 memory: 14901 loss: 1.7743 loss_prob: 1.0288 loss_thr: 0.5757 loss_db: 0.1699 2022/11/02 14:47:32 - mmengine - INFO - Epoch(train) [248][40/63] lr: 1.7576e-03 eta: 9:47:28 time: 0.5050 data_time: 0.0129 memory: 14901 loss: 1.8135 loss_prob: 1.0578 loss_thr: 0.5841 loss_db: 0.1716 2022/11/02 14:47:34 - mmengine - INFO - Epoch(train) [248][45/63] lr: 1.7576e-03 eta: 9:47:28 time: 0.5179 data_time: 0.0113 memory: 14901 loss: 1.8995 loss_prob: 1.1115 loss_thr: 0.6061 loss_db: 0.1819 2022/11/02 14:47:37 - mmengine - INFO - Epoch(train) [248][50/63] lr: 1.7576e-03 eta: 9:47:20 time: 0.5299 data_time: 0.0237 memory: 14901 loss: 2.0007 loss_prob: 1.1941 loss_thr: 0.6110 loss_db: 0.1955 2022/11/02 14:47:40 - mmengine - INFO - Epoch(train) [248][55/63] lr: 1.7576e-03 eta: 9:47:20 time: 0.5477 data_time: 0.0213 memory: 14901 loss: 1.9060 loss_prob: 1.1157 loss_thr: 0.6064 loss_db: 0.1839 2022/11/02 14:47:42 - mmengine - INFO - Epoch(train) [248][60/63] lr: 1.7576e-03 eta: 9:47:12 time: 0.5357 data_time: 0.0101 memory: 14901 loss: 1.7554 loss_prob: 0.9847 loss_thr: 0.6036 loss_db: 0.1671 2022/11/02 14:47:44 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:47:50 - mmengine - INFO - Epoch(train) [249][5/63] lr: 1.7559e-03 eta: 9:47:12 time: 0.8242 data_time: 0.2014 memory: 14901 loss: 1.7497 loss_prob: 1.0072 loss_thr: 0.5736 loss_db: 0.1689 2022/11/02 14:47:52 - mmengine - INFO - Epoch(train) [249][10/63] lr: 1.7559e-03 eta: 9:47:08 time: 0.8526 data_time: 0.2045 memory: 14901 loss: 1.7403 loss_prob: 0.9976 loss_thr: 0.5778 loss_db: 0.1648 2022/11/02 14:47:55 - mmengine - INFO - Epoch(train) [249][15/63] lr: 1.7559e-03 eta: 9:47:08 time: 0.5673 data_time: 0.0153 memory: 14901 loss: 2.1983 loss_prob: 1.3428 loss_thr: 0.6403 loss_db: 0.2152 2022/11/02 14:47:58 - mmengine - INFO - Epoch(train) [249][20/63] lr: 1.7559e-03 eta: 9:47:00 time: 0.5506 data_time: 0.0169 memory: 14901 loss: 2.4574 loss_prob: 1.5208 loss_thr: 0.6951 loss_db: 0.2415 2022/11/02 14:48:01 - mmengine - INFO - Epoch(train) [249][25/63] lr: 1.7559e-03 eta: 9:47:00 time: 0.5391 data_time: 0.0174 memory: 14901 loss: 2.0162 loss_prob: 1.1951 loss_thr: 0.6280 loss_db: 0.1931 2022/11/02 14:48:04 - mmengine - INFO - Epoch(train) [249][30/63] lr: 1.7559e-03 eta: 9:46:54 time: 0.5675 data_time: 0.0327 memory: 14901 loss: 1.7468 loss_prob: 1.0026 loss_thr: 0.5777 loss_db: 0.1665 2022/11/02 14:48:07 - mmengine - INFO - Epoch(train) [249][35/63] lr: 1.7559e-03 eta: 9:46:54 time: 0.6177 data_time: 0.0358 memory: 14901 loss: 1.9365 loss_prob: 1.1354 loss_thr: 0.6126 loss_db: 0.1885 2022/11/02 14:48:10 - mmengine - INFO - Epoch(train) [249][40/63] lr: 1.7559e-03 eta: 9:46:48 time: 0.6011 data_time: 0.0151 memory: 14901 loss: 1.8888 loss_prob: 1.1074 loss_thr: 0.6006 loss_db: 0.1808 2022/11/02 14:48:12 - mmengine - INFO - Epoch(train) [249][45/63] lr: 1.7559e-03 eta: 9:46:48 time: 0.5250 data_time: 0.0103 memory: 14901 loss: 1.8084 loss_prob: 1.0507 loss_thr: 0.5858 loss_db: 0.1719 2022/11/02 14:48:15 - mmengine - INFO - Epoch(train) [249][50/63] lr: 1.7559e-03 eta: 9:46:40 time: 0.5279 data_time: 0.0159 memory: 14901 loss: 2.0856 loss_prob: 1.2470 loss_thr: 0.6349 loss_db: 0.2036 2022/11/02 14:48:18 - mmengine - INFO - Epoch(train) [249][55/63] lr: 1.7559e-03 eta: 9:46:40 time: 0.5407 data_time: 0.0247 memory: 14901 loss: 2.0244 loss_prob: 1.2040 loss_thr: 0.6226 loss_db: 0.1978 2022/11/02 14:48:21 - mmengine - INFO - Epoch(train) [249][60/63] lr: 1.7559e-03 eta: 9:46:34 time: 0.5817 data_time: 0.0250 memory: 14901 loss: 1.9270 loss_prob: 1.1297 loss_thr: 0.6081 loss_db: 0.1892 2022/11/02 14:48:22 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:48:28 - mmengine - INFO - Epoch(train) [250][5/63] lr: 1.7542e-03 eta: 9:46:34 time: 0.8458 data_time: 0.2415 memory: 14901 loss: 1.8699 loss_prob: 1.0910 loss_thr: 0.5998 loss_db: 0.1792 2022/11/02 14:48:31 - mmengine - INFO - Epoch(train) [250][10/63] lr: 1.7542e-03 eta: 9:46:31 time: 0.8780 data_time: 0.2474 memory: 14901 loss: 1.8509 loss_prob: 1.0765 loss_thr: 0.5980 loss_db: 0.1764 2022/11/02 14:48:33 - mmengine - INFO - Epoch(train) [250][15/63] lr: 1.7542e-03 eta: 9:46:31 time: 0.5506 data_time: 0.0215 memory: 14901 loss: 1.7940 loss_prob: 1.0230 loss_thr: 0.5997 loss_db: 0.1713 2022/11/02 14:48:36 - mmengine - INFO - Epoch(train) [250][20/63] lr: 1.7542e-03 eta: 9:46:23 time: 0.5377 data_time: 0.0108 memory: 14901 loss: 1.8201 loss_prob: 1.0528 loss_thr: 0.5937 loss_db: 0.1737 2022/11/02 14:48:39 - mmengine - INFO - Epoch(train) [250][25/63] lr: 1.7542e-03 eta: 9:46:23 time: 0.5307 data_time: 0.0098 memory: 14901 loss: 1.7957 loss_prob: 1.0396 loss_thr: 0.5848 loss_db: 0.1714 2022/11/02 14:48:43 - mmengine - INFO - Epoch(train) [250][30/63] lr: 1.7542e-03 eta: 9:46:21 time: 0.6772 data_time: 0.0467 memory: 14901 loss: 1.7935 loss_prob: 1.0224 loss_thr: 0.6018 loss_db: 0.1693 2022/11/02 14:48:46 - mmengine - INFO - Epoch(train) [250][35/63] lr: 1.7542e-03 eta: 9:46:21 time: 0.7391 data_time: 0.0454 memory: 14901 loss: 1.8328 loss_prob: 1.0515 loss_thr: 0.6097 loss_db: 0.1716 2022/11/02 14:48:49 - mmengine - INFO - Epoch(train) [250][40/63] lr: 1.7542e-03 eta: 9:46:15 time: 0.5903 data_time: 0.0112 memory: 14901 loss: 1.7522 loss_prob: 1.0044 loss_thr: 0.5820 loss_db: 0.1658 2022/11/02 14:48:52 - mmengine - INFO - Epoch(train) [250][45/63] lr: 1.7542e-03 eta: 9:46:15 time: 0.5455 data_time: 0.0091 memory: 14901 loss: 1.7237 loss_prob: 0.9666 loss_thr: 0.5953 loss_db: 0.1618 2022/11/02 14:48:55 - mmengine - INFO - Epoch(train) [250][50/63] lr: 1.7542e-03 eta: 9:46:09 time: 0.5811 data_time: 0.0228 memory: 14901 loss: 1.8142 loss_prob: 1.0201 loss_thr: 0.6264 loss_db: 0.1678 2022/11/02 14:48:57 - mmengine - INFO - Epoch(train) [250][55/63] lr: 1.7542e-03 eta: 9:46:09 time: 0.5607 data_time: 0.0282 memory: 14901 loss: 1.8381 loss_prob: 1.0510 loss_thr: 0.6145 loss_db: 0.1725 2022/11/02 14:49:00 - mmengine - INFO - Epoch(train) [250][60/63] lr: 1.7542e-03 eta: 9:46:01 time: 0.5287 data_time: 0.0170 memory: 14901 loss: 1.7472 loss_prob: 0.9987 loss_thr: 0.5826 loss_db: 0.1659 2022/11/02 14:49:01 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:49:08 - mmengine - INFO - Epoch(train) [251][5/63] lr: 1.7526e-03 eta: 9:46:01 time: 0.9139 data_time: 0.2490 memory: 14901 loss: 1.6598 loss_prob: 0.9237 loss_thr: 0.5845 loss_db: 0.1516 2022/11/02 14:49:10 - mmengine - INFO - Epoch(train) [251][10/63] lr: 1.7526e-03 eta: 9:45:58 time: 0.9053 data_time: 0.2514 memory: 14901 loss: 1.7365 loss_prob: 0.9671 loss_thr: 0.6075 loss_db: 0.1620 2022/11/02 14:49:13 - mmengine - INFO - Epoch(train) [251][15/63] lr: 1.7526e-03 eta: 9:45:58 time: 0.5300 data_time: 0.0131 memory: 14901 loss: 1.7690 loss_prob: 0.9959 loss_thr: 0.6051 loss_db: 0.1680 2022/11/02 14:49:16 - mmengine - INFO - Epoch(train) [251][20/63] lr: 1.7526e-03 eta: 9:45:53 time: 0.5847 data_time: 0.0124 memory: 14901 loss: 1.7650 loss_prob: 0.9891 loss_thr: 0.6123 loss_db: 0.1636 2022/11/02 14:49:19 - mmengine - INFO - Epoch(train) [251][25/63] lr: 1.7526e-03 eta: 9:45:53 time: 0.5546 data_time: 0.0167 memory: 14901 loss: 1.7377 loss_prob: 0.9747 loss_thr: 0.5986 loss_db: 0.1644 2022/11/02 14:49:22 - mmengine - INFO - Epoch(train) [251][30/63] lr: 1.7526e-03 eta: 9:45:46 time: 0.5767 data_time: 0.0393 memory: 14901 loss: 1.7809 loss_prob: 1.0285 loss_thr: 0.5794 loss_db: 0.1730 2022/11/02 14:49:24 - mmengine - INFO - Epoch(train) [251][35/63] lr: 1.7526e-03 eta: 9:45:46 time: 0.5725 data_time: 0.0330 memory: 14901 loss: 1.7423 loss_prob: 1.0056 loss_thr: 0.5701 loss_db: 0.1666 2022/11/02 14:49:27 - mmengine - INFO - Epoch(train) [251][40/63] lr: 1.7526e-03 eta: 9:45:37 time: 0.5103 data_time: 0.0101 memory: 14901 loss: 1.6800 loss_prob: 0.9489 loss_thr: 0.5724 loss_db: 0.1587 2022/11/02 14:49:31 - mmengine - INFO - Epoch(train) [251][45/63] lr: 1.7526e-03 eta: 9:45:37 time: 0.6579 data_time: 0.0105 memory: 14901 loss: 1.6450 loss_prob: 0.9225 loss_thr: 0.5699 loss_db: 0.1527 2022/11/02 14:49:34 - mmengine - INFO - Epoch(train) [251][50/63] lr: 1.7526e-03 eta: 9:45:36 time: 0.7090 data_time: 0.0259 memory: 14901 loss: 1.5970 loss_prob: 0.8894 loss_thr: 0.5601 loss_db: 0.1475 2022/11/02 14:49:37 - mmengine - INFO - Epoch(train) [251][55/63] lr: 1.7526e-03 eta: 9:45:36 time: 0.5814 data_time: 0.0266 memory: 14901 loss: 1.6088 loss_prob: 0.9056 loss_thr: 0.5523 loss_db: 0.1509 2022/11/02 14:49:40 - mmengine - INFO - Epoch(train) [251][60/63] lr: 1.7526e-03 eta: 9:45:28 time: 0.5320 data_time: 0.0135 memory: 14901 loss: 1.5877 loss_prob: 0.8985 loss_thr: 0.5431 loss_db: 0.1461 2022/11/02 14:49:41 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:49:46 - mmengine - INFO - Epoch(train) [252][5/63] lr: 1.7509e-03 eta: 9:45:28 time: 0.7843 data_time: 0.2544 memory: 14901 loss: 1.7102 loss_prob: 0.9685 loss_thr: 0.5832 loss_db: 0.1585 2022/11/02 14:49:49 - mmengine - INFO - Epoch(train) [252][10/63] lr: 1.7509e-03 eta: 9:45:23 time: 0.8327 data_time: 0.2529 memory: 14901 loss: 1.7051 loss_prob: 0.9685 loss_thr: 0.5790 loss_db: 0.1575 2022/11/02 14:49:52 - mmengine - INFO - Epoch(train) [252][15/63] lr: 1.7509e-03 eta: 9:45:23 time: 0.5325 data_time: 0.0102 memory: 14901 loss: 1.6376 loss_prob: 0.9228 loss_thr: 0.5630 loss_db: 0.1518 2022/11/02 14:49:55 - mmengine - INFO - Epoch(train) [252][20/63] lr: 1.7509e-03 eta: 9:45:15 time: 0.5340 data_time: 0.0102 memory: 14901 loss: 1.6812 loss_prob: 0.9597 loss_thr: 0.5671 loss_db: 0.1544 2022/11/02 14:49:58 - mmengine - INFO - Epoch(train) [252][25/63] lr: 1.7509e-03 eta: 9:45:15 time: 0.5958 data_time: 0.0408 memory: 14901 loss: 1.6817 loss_prob: 0.9616 loss_thr: 0.5651 loss_db: 0.1550 2022/11/02 14:50:01 - mmengine - INFO - Epoch(train) [252][30/63] lr: 1.7509e-03 eta: 9:45:10 time: 0.6084 data_time: 0.0408 memory: 14901 loss: 1.6149 loss_prob: 0.9095 loss_thr: 0.5529 loss_db: 0.1525 2022/11/02 14:50:04 - mmengine - INFO - Epoch(train) [252][35/63] lr: 1.7509e-03 eta: 9:45:10 time: 0.5828 data_time: 0.0098 memory: 14901 loss: 1.7167 loss_prob: 0.9902 loss_thr: 0.5611 loss_db: 0.1653 2022/11/02 14:50:06 - mmengine - INFO - Epoch(train) [252][40/63] lr: 1.7509e-03 eta: 9:45:04 time: 0.5718 data_time: 0.0128 memory: 14901 loss: 1.6876 loss_prob: 0.9590 loss_thr: 0.5695 loss_db: 0.1592 2022/11/02 14:50:09 - mmengine - INFO - Epoch(train) [252][45/63] lr: 1.7509e-03 eta: 9:45:04 time: 0.5597 data_time: 0.0104 memory: 14901 loss: 1.6287 loss_prob: 0.8993 loss_thr: 0.5816 loss_db: 0.1479 2022/11/02 14:50:12 - mmengine - INFO - Epoch(train) [252][50/63] lr: 1.7509e-03 eta: 9:44:57 time: 0.5653 data_time: 0.0226 memory: 14901 loss: 1.6779 loss_prob: 0.9298 loss_thr: 0.5943 loss_db: 0.1539 2022/11/02 14:50:15 - mmengine - INFO - Epoch(train) [252][55/63] lr: 1.7509e-03 eta: 9:44:57 time: 0.5853 data_time: 0.0225 memory: 14901 loss: 1.6291 loss_prob: 0.9028 loss_thr: 0.5759 loss_db: 0.1504 2022/11/02 14:50:18 - mmengine - INFO - Epoch(train) [252][60/63] lr: 1.7509e-03 eta: 9:44:51 time: 0.5800 data_time: 0.0097 memory: 14901 loss: 1.6066 loss_prob: 0.8894 loss_thr: 0.5679 loss_db: 0.1493 2022/11/02 14:50:19 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:50:26 - mmengine - INFO - Epoch(train) [253][5/63] lr: 1.7492e-03 eta: 9:44:51 time: 0.8997 data_time: 0.2254 memory: 14901 loss: 1.6444 loss_prob: 0.9088 loss_thr: 0.5811 loss_db: 0.1545 2022/11/02 14:50:29 - mmengine - INFO - Epoch(train) [253][10/63] lr: 1.7492e-03 eta: 9:44:49 time: 0.9243 data_time: 0.2396 memory: 14901 loss: 1.6331 loss_prob: 0.8952 loss_thr: 0.5856 loss_db: 0.1523 2022/11/02 14:50:31 - mmengine - INFO - Epoch(train) [253][15/63] lr: 1.7492e-03 eta: 9:44:49 time: 0.5850 data_time: 0.0250 memory: 14901 loss: 1.5749 loss_prob: 0.8696 loss_thr: 0.5602 loss_db: 0.1452 2022/11/02 14:50:34 - mmengine - INFO - Epoch(train) [253][20/63] lr: 1.7492e-03 eta: 9:44:41 time: 0.5140 data_time: 0.0082 memory: 14901 loss: 1.5597 loss_prob: 0.8868 loss_thr: 0.5263 loss_db: 0.1465 2022/11/02 14:50:37 - mmengine - INFO - Epoch(train) [253][25/63] lr: 1.7492e-03 eta: 9:44:41 time: 0.5071 data_time: 0.0192 memory: 14901 loss: 1.6882 loss_prob: 0.9717 loss_thr: 0.5526 loss_db: 0.1639 2022/11/02 14:50:39 - mmengine - INFO - Epoch(train) [253][30/63] lr: 1.7492e-03 eta: 9:44:32 time: 0.5255 data_time: 0.0399 memory: 14901 loss: 1.8033 loss_prob: 1.0378 loss_thr: 0.5936 loss_db: 0.1719 2022/11/02 14:50:42 - mmengine - INFO - Epoch(train) [253][35/63] lr: 1.7492e-03 eta: 9:44:32 time: 0.5393 data_time: 0.0356 memory: 14901 loss: 1.7404 loss_prob: 0.9961 loss_thr: 0.5818 loss_db: 0.1625 2022/11/02 14:50:44 - mmengine - INFO - Epoch(train) [253][40/63] lr: 1.7492e-03 eta: 9:44:24 time: 0.5214 data_time: 0.0159 memory: 14901 loss: 1.7886 loss_prob: 1.0407 loss_thr: 0.5761 loss_db: 0.1718 2022/11/02 14:50:47 - mmengine - INFO - Epoch(train) [253][45/63] lr: 1.7492e-03 eta: 9:44:24 time: 0.5163 data_time: 0.0111 memory: 14901 loss: 1.8885 loss_prob: 1.1042 loss_thr: 0.6023 loss_db: 0.1820 2022/11/02 14:50:51 - mmengine - INFO - Epoch(train) [253][50/63] lr: 1.7492e-03 eta: 9:44:20 time: 0.6265 data_time: 0.0238 memory: 14901 loss: 1.7622 loss_prob: 0.9980 loss_thr: 0.5954 loss_db: 0.1687 2022/11/02 14:50:53 - mmengine - INFO - Epoch(train) [253][55/63] lr: 1.7492e-03 eta: 9:44:20 time: 0.6367 data_time: 0.0265 memory: 14901 loss: 1.6786 loss_prob: 0.9462 loss_thr: 0.5727 loss_db: 0.1597 2022/11/02 14:50:56 - mmengine - INFO - Epoch(train) [253][60/63] lr: 1.7492e-03 eta: 9:44:12 time: 0.5287 data_time: 0.0141 memory: 14901 loss: 1.7204 loss_prob: 0.9735 loss_thr: 0.5867 loss_db: 0.1602 2022/11/02 14:50:57 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:51:03 - mmengine - INFO - Epoch(train) [254][5/63] lr: 1.7476e-03 eta: 9:44:12 time: 0.7866 data_time: 0.2337 memory: 14901 loss: 1.6846 loss_prob: 0.9550 loss_thr: 0.5702 loss_db: 0.1594 2022/11/02 14:51:06 - mmengine - INFO - Epoch(train) [254][10/63] lr: 1.7476e-03 eta: 9:44:08 time: 0.8738 data_time: 0.2534 memory: 14901 loss: 1.6955 loss_prob: 0.9676 loss_thr: 0.5679 loss_db: 0.1601 2022/11/02 14:51:09 - mmengine - INFO - Epoch(train) [254][15/63] lr: 1.7476e-03 eta: 9:44:08 time: 0.6265 data_time: 0.0291 memory: 14901 loss: 1.7130 loss_prob: 0.9709 loss_thr: 0.5862 loss_db: 0.1558 2022/11/02 14:51:12 - mmengine - INFO - Epoch(train) [254][20/63] lr: 1.7476e-03 eta: 9:44:02 time: 0.5771 data_time: 0.0086 memory: 14901 loss: 1.7279 loss_prob: 0.9877 loss_thr: 0.5790 loss_db: 0.1612 2022/11/02 14:51:14 - mmengine - INFO - Epoch(train) [254][25/63] lr: 1.7476e-03 eta: 9:44:02 time: 0.5397 data_time: 0.0118 memory: 14901 loss: 1.6447 loss_prob: 0.9323 loss_thr: 0.5595 loss_db: 0.1529 2022/11/02 14:51:17 - mmengine - INFO - Epoch(train) [254][30/63] lr: 1.7476e-03 eta: 9:43:55 time: 0.5625 data_time: 0.0393 memory: 14901 loss: 1.6563 loss_prob: 0.9174 loss_thr: 0.5903 loss_db: 0.1486 2022/11/02 14:51:20 - mmengine - INFO - Epoch(train) [254][35/63] lr: 1.7476e-03 eta: 9:43:55 time: 0.6060 data_time: 0.0334 memory: 14901 loss: 1.7363 loss_prob: 0.9539 loss_thr: 0.6249 loss_db: 0.1576 2022/11/02 14:51:23 - mmengine - INFO - Epoch(train) [254][40/63] lr: 1.7476e-03 eta: 9:43:49 time: 0.5831 data_time: 0.0081 memory: 14901 loss: 1.6950 loss_prob: 0.9366 loss_thr: 0.5987 loss_db: 0.1596 2022/11/02 14:51:26 - mmengine - INFO - Epoch(train) [254][45/63] lr: 1.7476e-03 eta: 9:43:49 time: 0.5232 data_time: 0.0098 memory: 14901 loss: 1.7109 loss_prob: 0.9640 loss_thr: 0.5858 loss_db: 0.1611 2022/11/02 14:51:29 - mmengine - INFO - Epoch(train) [254][50/63] lr: 1.7476e-03 eta: 9:43:41 time: 0.5392 data_time: 0.0163 memory: 14901 loss: 1.7993 loss_prob: 1.0145 loss_thr: 0.6201 loss_db: 0.1647 2022/11/02 14:51:31 - mmengine - INFO - Epoch(train) [254][55/63] lr: 1.7476e-03 eta: 9:43:41 time: 0.5596 data_time: 0.0347 memory: 14901 loss: 1.7676 loss_prob: 0.9912 loss_thr: 0.6109 loss_db: 0.1655 2022/11/02 14:51:34 - mmengine - INFO - Epoch(train) [254][60/63] lr: 1.7476e-03 eta: 9:43:34 time: 0.5462 data_time: 0.0263 memory: 14901 loss: 1.6936 loss_prob: 0.9533 loss_thr: 0.5810 loss_db: 0.1592 2022/11/02 14:51:34 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:51:35 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:51:40 - mmengine - INFO - Epoch(train) [255][5/63] lr: 1.7459e-03 eta: 9:43:34 time: 0.7367 data_time: 0.2456 memory: 14901 loss: 1.6553 loss_prob: 0.9313 loss_thr: 0.5711 loss_db: 0.1529 2022/11/02 14:51:44 - mmengine - INFO - Epoch(train) [255][10/63] lr: 1.7459e-03 eta: 9:43:32 time: 0.9165 data_time: 0.2658 memory: 14901 loss: 1.7040 loss_prob: 0.9617 loss_thr: 0.5835 loss_db: 0.1588 2022/11/02 14:51:47 - mmengine - INFO - Epoch(train) [255][15/63] lr: 1.7459e-03 eta: 9:43:32 time: 0.6656 data_time: 0.0261 memory: 14901 loss: 1.6110 loss_prob: 0.8992 loss_thr: 0.5615 loss_db: 0.1503 2022/11/02 14:51:50 - mmengine - INFO - Epoch(train) [255][20/63] lr: 1.7459e-03 eta: 9:43:24 time: 0.5470 data_time: 0.0071 memory: 14901 loss: 1.5459 loss_prob: 0.8533 loss_thr: 0.5493 loss_db: 0.1432 2022/11/02 14:51:53 - mmengine - INFO - Epoch(train) [255][25/63] lr: 1.7459e-03 eta: 9:43:24 time: 0.5619 data_time: 0.0172 memory: 14901 loss: 1.7159 loss_prob: 0.9723 loss_thr: 0.5805 loss_db: 0.1631 2022/11/02 14:51:56 - mmengine - INFO - Epoch(train) [255][30/63] lr: 1.7459e-03 eta: 9:43:19 time: 0.6051 data_time: 0.0383 memory: 14901 loss: 1.8016 loss_prob: 1.0300 loss_thr: 0.6003 loss_db: 0.1714 2022/11/02 14:51:58 - mmengine - INFO - Epoch(train) [255][35/63] lr: 1.7459e-03 eta: 9:43:19 time: 0.5693 data_time: 0.0319 memory: 14901 loss: 1.9940 loss_prob: 1.2085 loss_thr: 0.5999 loss_db: 0.1856 2022/11/02 14:52:01 - mmengine - INFO - Epoch(train) [255][40/63] lr: 1.7459e-03 eta: 9:43:11 time: 0.5239 data_time: 0.0093 memory: 14901 loss: 2.0471 loss_prob: 1.2471 loss_thr: 0.6106 loss_db: 0.1894 2022/11/02 14:52:04 - mmengine - INFO - Epoch(train) [255][45/63] lr: 1.7459e-03 eta: 9:43:11 time: 0.5551 data_time: 0.0096 memory: 14901 loss: 1.9299 loss_prob: 1.1416 loss_thr: 0.6071 loss_db: 0.1812 2022/11/02 14:52:07 - mmengine - INFO - Epoch(train) [255][50/63] lr: 1.7459e-03 eta: 9:43:03 time: 0.5342 data_time: 0.0220 memory: 14901 loss: 1.9013 loss_prob: 1.1347 loss_thr: 0.5803 loss_db: 0.1863 2022/11/02 14:52:09 - mmengine - INFO - Epoch(train) [255][55/63] lr: 1.7459e-03 eta: 9:43:03 time: 0.5434 data_time: 0.0283 memory: 14901 loss: 1.8117 loss_prob: 1.0508 loss_thr: 0.5836 loss_db: 0.1774 2022/11/02 14:52:12 - mmengine - INFO - Epoch(train) [255][60/63] lr: 1.7459e-03 eta: 9:42:56 time: 0.5476 data_time: 0.0204 memory: 14901 loss: 1.7541 loss_prob: 0.9890 loss_thr: 0.5986 loss_db: 0.1664 2022/11/02 14:52:13 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:52:19 - mmengine - INFO - Epoch(train) [256][5/63] lr: 1.7443e-03 eta: 9:42:56 time: 0.8181 data_time: 0.2618 memory: 14901 loss: 1.7458 loss_prob: 0.9897 loss_thr: 0.5902 loss_db: 0.1659 2022/11/02 14:52:22 - mmengine - INFO - Epoch(train) [256][10/63] lr: 1.7443e-03 eta: 9:42:51 time: 0.8334 data_time: 0.2634 memory: 14901 loss: 1.8012 loss_prob: 1.0189 loss_thr: 0.6104 loss_db: 0.1720 2022/11/02 14:52:24 - mmengine - INFO - Epoch(train) [256][15/63] lr: 1.7443e-03 eta: 9:42:51 time: 0.5287 data_time: 0.0286 memory: 14901 loss: 1.7652 loss_prob: 0.9904 loss_thr: 0.6097 loss_db: 0.1651 2022/11/02 14:52:28 - mmengine - INFO - Epoch(train) [256][20/63] lr: 1.7443e-03 eta: 9:42:48 time: 0.6601 data_time: 0.0289 memory: 14901 loss: 1.7197 loss_prob: 0.9656 loss_thr: 0.5934 loss_db: 0.1606 2022/11/02 14:52:31 - mmengine - INFO - Epoch(train) [256][25/63] lr: 1.7443e-03 eta: 9:42:48 time: 0.6796 data_time: 0.0082 memory: 14901 loss: 1.6395 loss_prob: 0.9096 loss_thr: 0.5779 loss_db: 0.1520 2022/11/02 14:52:35 - mmengine - INFO - Epoch(train) [256][30/63] lr: 1.7443e-03 eta: 9:42:43 time: 0.6334 data_time: 0.0258 memory: 14901 loss: 1.5946 loss_prob: 0.8915 loss_thr: 0.5537 loss_db: 0.1494 2022/11/02 14:52:37 - mmengine - INFO - Epoch(train) [256][35/63] lr: 1.7443e-03 eta: 9:42:43 time: 0.5943 data_time: 0.0253 memory: 14901 loss: 1.7066 loss_prob: 0.9640 loss_thr: 0.5811 loss_db: 0.1615 2022/11/02 14:52:40 - mmengine - INFO - Epoch(train) [256][40/63] lr: 1.7443e-03 eta: 9:42:35 time: 0.5157 data_time: 0.0124 memory: 14901 loss: 1.6821 loss_prob: 0.9265 loss_thr: 0.5990 loss_db: 0.1566 2022/11/02 14:52:42 - mmengine - INFO - Epoch(train) [256][45/63] lr: 1.7443e-03 eta: 9:42:35 time: 0.5298 data_time: 0.0158 memory: 14901 loss: 1.8149 loss_prob: 1.0189 loss_thr: 0.6263 loss_db: 0.1697 2022/11/02 14:52:45 - mmengine - INFO - Epoch(train) [256][50/63] lr: 1.7443e-03 eta: 9:42:27 time: 0.5473 data_time: 0.0209 memory: 14901 loss: 2.0200 loss_prob: 1.2247 loss_thr: 0.5917 loss_db: 0.2036 2022/11/02 14:52:48 - mmengine - INFO - Epoch(train) [256][55/63] lr: 1.7443e-03 eta: 9:42:27 time: 0.5493 data_time: 0.0211 memory: 14901 loss: 3.7791 loss_prob: 2.6954 loss_thr: 0.6954 loss_db: 0.3883 2022/11/02 14:52:51 - mmengine - INFO - Epoch(train) [256][60/63] lr: 1.7443e-03 eta: 9:42:21 time: 0.5614 data_time: 0.0088 memory: 14901 loss: 4.6506 loss_prob: 3.3164 loss_thr: 0.8488 loss_db: 0.4853 2022/11/02 14:52:52 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:52:58 - mmengine - INFO - Epoch(train) [257][5/63] lr: 1.7426e-03 eta: 9:42:21 time: 0.8320 data_time: 0.2310 memory: 14901 loss: 3.2603 loss_prob: 2.1466 loss_thr: 0.7510 loss_db: 0.3627 2022/11/02 14:53:01 - mmengine - INFO - Epoch(train) [257][10/63] lr: 1.7426e-03 eta: 9:42:16 time: 0.8358 data_time: 0.2284 memory: 14901 loss: 2.8645 loss_prob: 1.8150 loss_thr: 0.7510 loss_db: 0.2985 2022/11/02 14:53:03 - mmengine - INFO - Epoch(train) [257][15/63] lr: 1.7426e-03 eta: 9:42:16 time: 0.5487 data_time: 0.0112 memory: 14901 loss: 2.6177 loss_prob: 1.6274 loss_thr: 0.7278 loss_db: 0.2626 2022/11/02 14:53:06 - mmengine - INFO - Epoch(train) [257][20/63] lr: 1.7426e-03 eta: 9:42:08 time: 0.5453 data_time: 0.0138 memory: 14901 loss: 2.4892 loss_prob: 1.5622 loss_thr: 0.6767 loss_db: 0.2503 2022/11/02 14:53:09 - mmengine - INFO - Epoch(train) [257][25/63] lr: 1.7426e-03 eta: 9:42:08 time: 0.5724 data_time: 0.0162 memory: 14901 loss: 2.5192 loss_prob: 1.5711 loss_thr: 0.6904 loss_db: 0.2577 2022/11/02 14:53:12 - mmengine - INFO - Epoch(train) [257][30/63] lr: 1.7426e-03 eta: 9:42:02 time: 0.5777 data_time: 0.0434 memory: 14901 loss: 2.6065 loss_prob: 1.6202 loss_thr: 0.7154 loss_db: 0.2709 2022/11/02 14:53:15 - mmengine - INFO - Epoch(train) [257][35/63] lr: 1.7426e-03 eta: 9:42:02 time: 0.5570 data_time: 0.0395 memory: 14901 loss: 2.5256 loss_prob: 1.5592 loss_thr: 0.7047 loss_db: 0.2618 2022/11/02 14:53:17 - mmengine - INFO - Epoch(train) [257][40/63] lr: 1.7426e-03 eta: 9:41:54 time: 0.5253 data_time: 0.0073 memory: 14901 loss: 2.3333 loss_prob: 1.4253 loss_thr: 0.6729 loss_db: 0.2351 2022/11/02 14:53:19 - mmengine - INFO - Epoch(train) [257][45/63] lr: 1.7426e-03 eta: 9:41:54 time: 0.4791 data_time: 0.0054 memory: 14901 loss: 2.2559 loss_prob: 1.3924 loss_thr: 0.6391 loss_db: 0.2244 2022/11/02 14:53:23 - mmengine - INFO - Epoch(train) [257][50/63] lr: 1.7426e-03 eta: 9:41:49 time: 0.6133 data_time: 0.0309 memory: 14901 loss: 2.2184 loss_prob: 1.3694 loss_thr: 0.6339 loss_db: 0.2151 2022/11/02 14:53:26 - mmengine - INFO - Epoch(train) [257][55/63] lr: 1.7426e-03 eta: 9:41:49 time: 0.6378 data_time: 0.0317 memory: 14901 loss: 2.1899 loss_prob: 1.3338 loss_thr: 0.6484 loss_db: 0.2077 2022/11/02 14:53:29 - mmengine - INFO - Epoch(train) [257][60/63] lr: 1.7426e-03 eta: 9:41:42 time: 0.5479 data_time: 0.0119 memory: 14901 loss: 2.1289 loss_prob: 1.2829 loss_thr: 0.6386 loss_db: 0.2074 2022/11/02 14:53:30 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:53:37 - mmengine - INFO - Epoch(train) [258][5/63] lr: 1.7409e-03 eta: 9:41:42 time: 0.8867 data_time: 0.2740 memory: 14901 loss: 2.1367 loss_prob: 1.2596 loss_thr: 0.6728 loss_db: 0.2043 2022/11/02 14:53:39 - mmengine - INFO - Epoch(train) [258][10/63] lr: 1.7409e-03 eta: 9:41:39 time: 0.8931 data_time: 0.2829 memory: 14901 loss: 2.2038 loss_prob: 1.3149 loss_thr: 0.6798 loss_db: 0.2090 2022/11/02 14:53:42 - mmengine - INFO - Epoch(train) [258][15/63] lr: 1.7409e-03 eta: 9:41:39 time: 0.5385 data_time: 0.0155 memory: 14901 loss: 2.2113 loss_prob: 1.3451 loss_thr: 0.6558 loss_db: 0.2104 2022/11/02 14:53:45 - mmengine - INFO - Epoch(train) [258][20/63] lr: 1.7409e-03 eta: 9:41:32 time: 0.5613 data_time: 0.0080 memory: 14901 loss: 2.1346 loss_prob: 1.2953 loss_thr: 0.6378 loss_db: 0.2014 2022/11/02 14:53:48 - mmengine - INFO - Epoch(train) [258][25/63] lr: 1.7409e-03 eta: 9:41:32 time: 0.5685 data_time: 0.0332 memory: 14901 loss: 2.1025 loss_prob: 1.2676 loss_thr: 0.6331 loss_db: 0.2018 2022/11/02 14:53:51 - mmengine - INFO - Epoch(train) [258][30/63] lr: 1.7409e-03 eta: 9:41:28 time: 0.6367 data_time: 0.0321 memory: 14901 loss: 1.9289 loss_prob: 1.1269 loss_thr: 0.6147 loss_db: 0.1872 2022/11/02 14:53:54 - mmengine - INFO - Epoch(train) [258][35/63] lr: 1.7409e-03 eta: 9:41:28 time: 0.6055 data_time: 0.0093 memory: 14901 loss: 1.8262 loss_prob: 1.0518 loss_thr: 0.6041 loss_db: 0.1704 2022/11/02 14:53:57 - mmengine - INFO - Epoch(train) [258][40/63] lr: 1.7409e-03 eta: 9:41:22 time: 0.6042 data_time: 0.0084 memory: 14901 loss: 1.8712 loss_prob: 1.0998 loss_thr: 0.5942 loss_db: 0.1772 2022/11/02 14:54:01 - mmengine - INFO - Epoch(train) [258][45/63] lr: 1.7409e-03 eta: 9:41:22 time: 0.7006 data_time: 0.0059 memory: 14901 loss: 1.9962 loss_prob: 1.1919 loss_thr: 0.6105 loss_db: 0.1937 2022/11/02 14:54:03 - mmengine - INFO - Epoch(train) [258][50/63] lr: 1.7409e-03 eta: 9:41:17 time: 0.5988 data_time: 0.0204 memory: 14901 loss: 2.0001 loss_prob: 1.2008 loss_thr: 0.6045 loss_db: 0.1948 2022/11/02 14:54:06 - mmengine - INFO - Epoch(train) [258][55/63] lr: 1.7409e-03 eta: 9:41:17 time: 0.5158 data_time: 0.0221 memory: 14901 loss: 1.9741 loss_prob: 1.1685 loss_thr: 0.6130 loss_db: 0.1925 2022/11/02 14:54:09 - mmengine - INFO - Epoch(train) [258][60/63] lr: 1.7409e-03 eta: 9:41:09 time: 0.5423 data_time: 0.0129 memory: 14901 loss: 1.9989 loss_prob: 1.1796 loss_thr: 0.6254 loss_db: 0.1939 2022/11/02 14:54:10 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:54:16 - mmengine - INFO - Epoch(train) [259][5/63] lr: 1.7393e-03 eta: 9:41:09 time: 0.9020 data_time: 0.2406 memory: 14901 loss: 1.8354 loss_prob: 1.0556 loss_thr: 0.6031 loss_db: 0.1767 2022/11/02 14:54:19 - mmengine - INFO - Epoch(train) [259][10/63] lr: 1.7393e-03 eta: 9:41:06 time: 0.8825 data_time: 0.2417 memory: 14901 loss: 2.0644 loss_prob: 1.2397 loss_thr: 0.6211 loss_db: 0.2036 2022/11/02 14:54:22 - mmengine - INFO - Epoch(train) [259][15/63] lr: 1.7393e-03 eta: 9:41:06 time: 0.5761 data_time: 0.0122 memory: 14901 loss: 2.1710 loss_prob: 1.3183 loss_thr: 0.6353 loss_db: 0.2174 2022/11/02 14:54:25 - mmengine - INFO - Epoch(train) [259][20/63] lr: 1.7393e-03 eta: 9:41:00 time: 0.5931 data_time: 0.0127 memory: 14901 loss: 2.0858 loss_prob: 1.2449 loss_thr: 0.6373 loss_db: 0.2035 2022/11/02 14:54:28 - mmengine - INFO - Epoch(train) [259][25/63] lr: 1.7393e-03 eta: 9:41:00 time: 0.5625 data_time: 0.0198 memory: 14901 loss: 2.0771 loss_prob: 1.2450 loss_thr: 0.6345 loss_db: 0.1976 2022/11/02 14:54:31 - mmengine - INFO - Epoch(train) [259][30/63] lr: 1.7393e-03 eta: 9:40:54 time: 0.5708 data_time: 0.0542 memory: 14901 loss: 1.8043 loss_prob: 1.0416 loss_thr: 0.5965 loss_db: 0.1661 2022/11/02 14:54:33 - mmengine - INFO - Epoch(train) [259][35/63] lr: 1.7393e-03 eta: 9:40:54 time: 0.5730 data_time: 0.0448 memory: 14901 loss: 1.7413 loss_prob: 0.9944 loss_thr: 0.5836 loss_db: 0.1634 2022/11/02 14:54:36 - mmengine - INFO - Epoch(train) [259][40/63] lr: 1.7393e-03 eta: 9:40:48 time: 0.5715 data_time: 0.0134 memory: 14901 loss: 1.9330 loss_prob: 1.1418 loss_thr: 0.6042 loss_db: 0.1870 2022/11/02 14:54:39 - mmengine - INFO - Epoch(train) [259][45/63] lr: 1.7393e-03 eta: 9:40:48 time: 0.5560 data_time: 0.0125 memory: 14901 loss: 1.9404 loss_prob: 1.1353 loss_thr: 0.6198 loss_db: 0.1853 2022/11/02 14:54:42 - mmengine - INFO - Epoch(train) [259][50/63] lr: 1.7393e-03 eta: 9:40:43 time: 0.6130 data_time: 0.0195 memory: 14901 loss: 1.9178 loss_prob: 1.1103 loss_thr: 0.6267 loss_db: 0.1808 2022/11/02 14:54:45 - mmengine - INFO - Epoch(train) [259][55/63] lr: 1.7393e-03 eta: 9:40:43 time: 0.6016 data_time: 0.0239 memory: 14901 loss: 1.9567 loss_prob: 1.1460 loss_thr: 0.6250 loss_db: 0.1857 2022/11/02 14:54:48 - mmengine - INFO - Epoch(train) [259][60/63] lr: 1.7393e-03 eta: 9:40:36 time: 0.5661 data_time: 0.0133 memory: 14901 loss: 1.9731 loss_prob: 1.1594 loss_thr: 0.6241 loss_db: 0.1897 2022/11/02 14:54:49 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:54:55 - mmengine - INFO - Epoch(train) [260][5/63] lr: 1.7376e-03 eta: 9:40:36 time: 0.8390 data_time: 0.3169 memory: 14901 loss: 1.8556 loss_prob: 1.0803 loss_thr: 0.6035 loss_db: 0.1718 2022/11/02 14:54:57 - mmengine - INFO - Epoch(train) [260][10/63] lr: 1.7376e-03 eta: 9:40:30 time: 0.8164 data_time: 0.3129 memory: 14901 loss: 1.7936 loss_prob: 1.0454 loss_thr: 0.5803 loss_db: 0.1679 2022/11/02 14:55:00 - mmengine - INFO - Epoch(train) [260][15/63] lr: 1.7376e-03 eta: 9:40:30 time: 0.5319 data_time: 0.0101 memory: 14901 loss: 1.8725 loss_prob: 1.0997 loss_thr: 0.5982 loss_db: 0.1746 2022/11/02 14:55:03 - mmengine - INFO - Epoch(train) [260][20/63] lr: 1.7376e-03 eta: 9:40:24 time: 0.5720 data_time: 0.0118 memory: 14901 loss: 1.8629 loss_prob: 1.0838 loss_thr: 0.6018 loss_db: 0.1773 2022/11/02 14:55:06 - mmengine - INFO - Epoch(train) [260][25/63] lr: 1.7376e-03 eta: 9:40:24 time: 0.5841 data_time: 0.0495 memory: 14901 loss: 1.8997 loss_prob: 1.1049 loss_thr: 0.6116 loss_db: 0.1832 2022/11/02 14:55:09 - mmengine - INFO - Epoch(train) [260][30/63] lr: 1.7376e-03 eta: 9:40:16 time: 0.5406 data_time: 0.0489 memory: 14901 loss: 1.9123 loss_prob: 1.1273 loss_thr: 0.6028 loss_db: 0.1822 2022/11/02 14:55:11 - mmengine - INFO - Epoch(train) [260][35/63] lr: 1.7376e-03 eta: 9:40:16 time: 0.4950 data_time: 0.0079 memory: 14901 loss: 1.8212 loss_prob: 1.0565 loss_thr: 0.5934 loss_db: 0.1713 2022/11/02 14:55:14 - mmengine - INFO - Epoch(train) [260][40/63] lr: 1.7376e-03 eta: 9:40:08 time: 0.5373 data_time: 0.0068 memory: 14901 loss: 1.9104 loss_prob: 1.1125 loss_thr: 0.6161 loss_db: 0.1818 2022/11/02 14:55:17 - mmengine - INFO - Epoch(train) [260][45/63] lr: 1.7376e-03 eta: 9:40:08 time: 0.5622 data_time: 0.0088 memory: 14901 loss: 1.8593 loss_prob: 1.0833 loss_thr: 0.5985 loss_db: 0.1775 2022/11/02 14:55:19 - mmengine - INFO - Epoch(train) [260][50/63] lr: 1.7376e-03 eta: 9:40:01 time: 0.5406 data_time: 0.0280 memory: 14901 loss: 1.6906 loss_prob: 0.9643 loss_thr: 0.5662 loss_db: 0.1602 2022/11/02 14:55:22 - mmengine - INFO - Epoch(train) [260][55/63] lr: 1.7376e-03 eta: 9:40:01 time: 0.4940 data_time: 0.0311 memory: 14901 loss: 1.7532 loss_prob: 0.9932 loss_thr: 0.5976 loss_db: 0.1624 2022/11/02 14:55:24 - mmengine - INFO - Epoch(train) [260][60/63] lr: 1.7376e-03 eta: 9:39:51 time: 0.4714 data_time: 0.0128 memory: 14901 loss: 1.8501 loss_prob: 1.0486 loss_thr: 0.6327 loss_db: 0.1688 2022/11/02 14:55:25 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:55:25 - mmengine - INFO - Saving checkpoint at 260 epochs 2022/11/02 14:55:29 - mmengine - INFO - Epoch(val) [260][5/500] eta: 9:39:51 time: 0.0463 data_time: 0.0062 memory: 14901 2022/11/02 14:55:29 - mmengine - INFO - Epoch(val) [260][10/500] eta: 0:00:23 time: 0.0472 data_time: 0.0058 memory: 1008 2022/11/02 14:55:29 - mmengine - INFO - Epoch(val) [260][15/500] eta: 0:00:23 time: 0.0373 data_time: 0.0020 memory: 1008 2022/11/02 14:55:29 - mmengine - INFO - Epoch(val) [260][20/500] eta: 0:00:17 time: 0.0374 data_time: 0.0022 memory: 1008 2022/11/02 14:55:30 - mmengine - INFO - Epoch(val) [260][25/500] eta: 0:00:17 time: 0.0362 data_time: 0.0023 memory: 1008 2022/11/02 14:55:30 - mmengine - INFO - Epoch(val) [260][30/500] eta: 0:00:19 time: 0.0413 data_time: 0.0027 memory: 1008 2022/11/02 14:55:30 - mmengine - INFO - Epoch(val) [260][35/500] eta: 0:00:19 time: 0.0439 data_time: 0.0030 memory: 1008 2022/11/02 14:55:30 - mmengine - INFO - Epoch(val) [260][40/500] eta: 0:00:21 time: 0.0460 data_time: 0.0030 memory: 1008 2022/11/02 14:55:31 - mmengine - INFO - Epoch(val) [260][45/500] eta: 0:00:21 time: 0.0452 data_time: 0.0030 memory: 1008 2022/11/02 14:55:32 - mmengine - INFO - Epoch(val) [260][50/500] eta: 0:00:50 time: 0.1127 data_time: 0.0751 memory: 1008 2022/11/02 14:55:32 - mmengine - INFO - Epoch(val) [260][55/500] eta: 0:00:50 time: 0.1123 data_time: 0.0744 memory: 1008 2022/11/02 14:55:32 - mmengine - INFO - Epoch(val) [260][60/500] eta: 0:00:16 time: 0.0372 data_time: 0.0018 memory: 1008 2022/11/02 14:55:32 - mmengine - INFO - Epoch(val) [260][65/500] eta: 0:00:16 time: 0.0389 data_time: 0.0021 memory: 1008 2022/11/02 14:55:32 - mmengine - INFO - Epoch(val) [260][70/500] eta: 0:00:17 time: 0.0402 data_time: 0.0025 memory: 1008 2022/11/02 14:55:32 - mmengine - INFO - Epoch(val) [260][75/500] eta: 0:00:17 time: 0.0381 data_time: 0.0024 memory: 1008 2022/11/02 14:55:33 - mmengine - INFO - Epoch(val) [260][80/500] eta: 0:00:46 time: 0.1107 data_time: 0.0757 memory: 1008 2022/11/02 14:55:34 - mmengine - INFO - Epoch(val) [260][85/500] eta: 0:00:46 time: 0.1081 data_time: 0.0754 memory: 1008 2022/11/02 14:55:34 - mmengine - INFO - Epoch(val) [260][90/500] eta: 0:00:16 time: 0.0413 data_time: 0.0023 memory: 1008 2022/11/02 14:55:34 - mmengine - INFO - Epoch(val) [260][95/500] eta: 0:00:16 time: 0.0445 data_time: 0.0027 memory: 1008 2022/11/02 14:55:34 - mmengine - INFO - Epoch(val) [260][100/500] eta: 0:00:15 time: 0.0376 data_time: 0.0023 memory: 1008 2022/11/02 14:55:34 - mmengine - INFO - Epoch(val) [260][105/500] eta: 0:00:15 time: 0.0369 data_time: 0.0025 memory: 1008 2022/11/02 14:55:35 - mmengine - INFO - Epoch(val) [260][110/500] eta: 0:00:15 time: 0.0385 data_time: 0.0031 memory: 1008 2022/11/02 14:55:35 - mmengine - INFO - Epoch(val) [260][115/500] eta: 0:00:15 time: 0.0388 data_time: 0.0030 memory: 1008 2022/11/02 14:55:35 - mmengine - INFO - Epoch(val) [260][120/500] eta: 0:00:14 time: 0.0375 data_time: 0.0025 memory: 1008 2022/11/02 14:55:35 - mmengine - INFO - Epoch(val) [260][125/500] eta: 0:00:14 time: 0.0363 data_time: 0.0025 memory: 1008 2022/11/02 14:55:35 - mmengine - INFO - Epoch(val) [260][130/500] eta: 0:00:13 time: 0.0359 data_time: 0.0024 memory: 1008 2022/11/02 14:55:35 - mmengine - INFO - Epoch(val) [260][135/500] eta: 0:00:13 time: 0.0359 data_time: 0.0023 memory: 1008 2022/11/02 14:55:36 - mmengine - INFO - Epoch(val) [260][140/500] eta: 0:00:12 time: 0.0360 data_time: 0.0022 memory: 1008 2022/11/02 14:55:36 - mmengine - INFO - Epoch(val) [260][145/500] eta: 0:00:12 time: 0.0395 data_time: 0.0023 memory: 1008 2022/11/02 14:55:36 - mmengine - INFO - Epoch(val) [260][150/500] eta: 0:00:14 time: 0.0419 data_time: 0.0027 memory: 1008 2022/11/02 14:55:36 - mmengine - INFO - Epoch(val) [260][155/500] eta: 0:00:14 time: 0.0430 data_time: 0.0025 memory: 1008 2022/11/02 14:55:36 - mmengine - INFO - Epoch(val) [260][160/500] eta: 0:00:14 time: 0.0416 data_time: 0.0022 memory: 1008 2022/11/02 14:55:37 - mmengine - INFO - Epoch(val) [260][165/500] eta: 0:00:14 time: 0.0393 data_time: 0.0023 memory: 1008 2022/11/02 14:55:37 - mmengine - INFO - Epoch(val) [260][170/500] eta: 0:00:12 time: 0.0390 data_time: 0.0023 memory: 1008 2022/11/02 14:55:37 - mmengine - INFO - Epoch(val) [260][175/500] eta: 0:00:12 time: 0.0353 data_time: 0.0022 memory: 1008 2022/11/02 14:55:37 - mmengine - INFO - Epoch(val) [260][180/500] eta: 0:00:10 time: 0.0343 data_time: 0.0022 memory: 1008 2022/11/02 14:55:37 - mmengine - INFO - Epoch(val) [260][185/500] eta: 0:00:10 time: 0.0376 data_time: 0.0023 memory: 1008 2022/11/02 14:55:38 - mmengine - INFO - Epoch(val) [260][190/500] eta: 0:00:12 time: 0.0399 data_time: 0.0024 memory: 1008 2022/11/02 14:55:38 - mmengine - INFO - Epoch(val) [260][195/500] eta: 0:00:12 time: 0.0389 data_time: 0.0025 memory: 1008 2022/11/02 14:55:38 - mmengine - INFO - Epoch(val) [260][200/500] eta: 0:00:13 time: 0.0443 data_time: 0.0026 memory: 1008 2022/11/02 14:55:38 - mmengine - INFO - Epoch(val) [260][205/500] eta: 0:00:13 time: 0.0443 data_time: 0.0026 memory: 1008 2022/11/02 14:55:38 - mmengine - INFO - Epoch(val) [260][210/500] eta: 0:00:10 time: 0.0379 data_time: 0.0026 memory: 1008 2022/11/02 14:55:39 - mmengine - INFO - Epoch(val) [260][215/500] eta: 0:00:10 time: 0.0419 data_time: 0.0029 memory: 1008 2022/11/02 14:55:39 - mmengine - INFO - Epoch(val) [260][220/500] eta: 0:00:11 time: 0.0427 data_time: 0.0030 memory: 1008 2022/11/02 14:55:39 - mmengine - INFO - Epoch(val) [260][225/500] eta: 0:00:11 time: 0.0408 data_time: 0.0028 memory: 1008 2022/11/02 14:55:39 - mmengine - INFO - Epoch(val) [260][230/500] eta: 0:00:10 time: 0.0379 data_time: 0.0025 memory: 1008 2022/11/02 14:55:39 - mmengine - INFO - Epoch(val) [260][235/500] eta: 0:00:10 time: 0.0374 data_time: 0.0024 memory: 1008 2022/11/02 14:55:40 - mmengine - INFO - Epoch(val) [260][240/500] eta: 0:00:10 time: 0.0387 data_time: 0.0024 memory: 1008 2022/11/02 14:55:40 - mmengine - INFO - Epoch(val) [260][245/500] eta: 0:00:10 time: 0.0371 data_time: 0.0023 memory: 1008 2022/11/02 14:55:40 - mmengine - INFO - Epoch(val) [260][250/500] eta: 0:00:09 time: 0.0379 data_time: 0.0024 memory: 1008 2022/11/02 14:55:40 - mmengine - INFO - Epoch(val) [260][255/500] eta: 0:00:09 time: 0.0367 data_time: 0.0024 memory: 1008 2022/11/02 14:55:40 - mmengine - INFO - Epoch(val) [260][260/500] eta: 0:00:08 time: 0.0359 data_time: 0.0023 memory: 1008 2022/11/02 14:55:41 - mmengine - INFO - Epoch(val) [260][265/500] eta: 0:00:08 time: 0.0357 data_time: 0.0022 memory: 1008 2022/11/02 14:55:43 - mmengine - INFO - Epoch(val) [260][270/500] eta: 0:00:49 time: 0.2149 data_time: 0.1781 memory: 1008 2022/11/02 14:55:43 - mmengine - INFO - Epoch(val) [260][275/500] eta: 0:00:49 time: 0.2188 data_time: 0.1781 memory: 1008 2022/11/02 14:55:43 - mmengine - INFO - Epoch(val) [260][280/500] eta: 0:00:09 time: 0.0426 data_time: 0.0026 memory: 1008 2022/11/02 14:55:43 - mmengine - INFO - Epoch(val) [260][285/500] eta: 0:00:09 time: 0.0410 data_time: 0.0027 memory: 1008 2022/11/02 14:55:43 - mmengine - INFO - Epoch(val) [260][290/500] eta: 0:00:08 time: 0.0406 data_time: 0.0025 memory: 1008 2022/11/02 14:55:44 - mmengine - INFO - Epoch(val) [260][295/500] eta: 0:00:08 time: 0.0414 data_time: 0.0024 memory: 1008 2022/11/02 14:55:44 - mmengine - INFO - Epoch(val) [260][300/500] eta: 0:00:07 time: 0.0381 data_time: 0.0023 memory: 1008 2022/11/02 14:55:44 - mmengine - INFO - Epoch(val) [260][305/500] eta: 0:00:07 time: 0.0360 data_time: 0.0023 memory: 1008 2022/11/02 14:55:44 - mmengine - INFO - Epoch(val) [260][310/500] eta: 0:00:07 time: 0.0372 data_time: 0.0023 memory: 1008 2022/11/02 14:55:44 - mmengine - INFO - Epoch(val) [260][315/500] eta: 0:00:07 time: 0.0422 data_time: 0.0024 memory: 1008 2022/11/02 14:55:45 - mmengine - INFO - Epoch(val) [260][320/500] eta: 0:00:07 time: 0.0406 data_time: 0.0025 memory: 1008 2022/11/02 14:55:45 - mmengine - INFO - Epoch(val) [260][325/500] eta: 0:00:07 time: 0.0482 data_time: 0.0027 memory: 1008 2022/11/02 14:55:45 - mmengine - INFO - Epoch(val) [260][330/500] eta: 0:00:08 time: 0.0481 data_time: 0.0026 memory: 1008 2022/11/02 14:55:45 - mmengine - INFO - Epoch(val) [260][335/500] eta: 0:00:08 time: 0.0377 data_time: 0.0026 memory: 1008 2022/11/02 14:55:46 - mmengine - INFO - Epoch(val) [260][340/500] eta: 0:00:07 time: 0.0492 data_time: 0.0027 memory: 1008 2022/11/02 14:55:46 - mmengine - INFO - Epoch(val) [260][345/500] eta: 0:00:07 time: 0.0490 data_time: 0.0026 memory: 1008 2022/11/02 14:55:46 - mmengine - INFO - Epoch(val) [260][350/500] eta: 0:00:06 time: 0.0452 data_time: 0.0032 memory: 1008 2022/11/02 14:55:46 - mmengine - INFO - Epoch(val) [260][355/500] eta: 0:00:06 time: 0.0423 data_time: 0.0029 memory: 1008 2022/11/02 14:55:46 - mmengine - INFO - Epoch(val) [260][360/500] eta: 0:00:04 time: 0.0356 data_time: 0.0021 memory: 1008 2022/11/02 14:55:47 - mmengine - INFO - Epoch(val) [260][365/500] eta: 0:00:04 time: 0.0388 data_time: 0.0022 memory: 1008 2022/11/02 14:55:47 - mmengine - INFO - Epoch(val) [260][370/500] eta: 0:00:04 time: 0.0374 data_time: 0.0026 memory: 1008 2022/11/02 14:55:47 - mmengine - INFO - Epoch(val) [260][375/500] eta: 0:00:04 time: 0.0346 data_time: 0.0028 memory: 1008 2022/11/02 14:55:47 - mmengine - INFO - Epoch(val) [260][380/500] eta: 0:00:04 time: 0.0391 data_time: 0.0028 memory: 1008 2022/11/02 14:55:47 - mmengine - INFO - Epoch(val) [260][385/500] eta: 0:00:04 time: 0.0396 data_time: 0.0026 memory: 1008 2022/11/02 14:55:47 - mmengine - INFO - Epoch(val) [260][390/500] eta: 0:00:04 time: 0.0367 data_time: 0.0025 memory: 1008 2022/11/02 14:55:48 - mmengine - INFO - Epoch(val) [260][395/500] eta: 0:00:04 time: 0.0371 data_time: 0.0025 memory: 1008 2022/11/02 14:55:48 - mmengine - INFO - Epoch(val) [260][400/500] eta: 0:00:03 time: 0.0366 data_time: 0.0025 memory: 1008 2022/11/02 14:55:48 - mmengine - INFO - Epoch(val) [260][405/500] eta: 0:00:03 time: 0.0369 data_time: 0.0023 memory: 1008 2022/11/02 14:55:48 - mmengine - INFO - Epoch(val) [260][410/500] eta: 0:00:03 time: 0.0393 data_time: 0.0026 memory: 1008 2022/11/02 14:55:48 - mmengine - INFO - Epoch(val) [260][415/500] eta: 0:00:03 time: 0.0390 data_time: 0.0029 memory: 1008 2022/11/02 14:55:49 - mmengine - INFO - Epoch(val) [260][420/500] eta: 0:00:03 time: 0.0466 data_time: 0.0142 memory: 1008 2022/11/02 14:55:49 - mmengine - INFO - Epoch(val) [260][425/500] eta: 0:00:03 time: 0.0459 data_time: 0.0136 memory: 1008 2022/11/02 14:55:49 - mmengine - INFO - Epoch(val) [260][430/500] eta: 0:00:02 time: 0.0357 data_time: 0.0020 memory: 1008 2022/11/02 14:55:49 - mmengine - INFO - Epoch(val) [260][435/500] eta: 0:00:02 time: 0.0348 data_time: 0.0022 memory: 1008 2022/11/02 14:55:49 - mmengine - INFO - Epoch(val) [260][440/500] eta: 0:00:02 time: 0.0358 data_time: 0.0021 memory: 1008 2022/11/02 14:55:50 - mmengine - INFO - Epoch(val) [260][445/500] eta: 0:00:02 time: 0.0390 data_time: 0.0024 memory: 1008 2022/11/02 14:55:50 - mmengine - INFO - Epoch(val) [260][450/500] eta: 0:00:02 time: 0.0402 data_time: 0.0026 memory: 1008 2022/11/02 14:55:50 - mmengine - INFO - Epoch(val) [260][455/500] eta: 0:00:02 time: 0.0406 data_time: 0.0026 memory: 1008 2022/11/02 14:55:50 - mmengine - INFO - Epoch(val) [260][460/500] eta: 0:00:01 time: 0.0401 data_time: 0.0028 memory: 1008 2022/11/02 14:55:50 - mmengine - INFO - Epoch(val) [260][465/500] eta: 0:00:01 time: 0.0387 data_time: 0.0030 memory: 1008 2022/11/02 14:55:51 - mmengine - INFO - Epoch(val) [260][470/500] eta: 0:00:01 time: 0.0404 data_time: 0.0028 memory: 1008 2022/11/02 14:55:51 - mmengine - INFO - Epoch(val) [260][475/500] eta: 0:00:01 time: 0.0399 data_time: 0.0026 memory: 1008 2022/11/02 14:55:51 - mmengine - INFO - Epoch(val) [260][480/500] eta: 0:00:00 time: 0.0387 data_time: 0.0027 memory: 1008 2022/11/02 14:55:51 - mmengine - INFO - Epoch(val) [260][485/500] eta: 0:00:00 time: 0.0395 data_time: 0.0027 memory: 1008 2022/11/02 14:55:51 - mmengine - INFO - Epoch(val) [260][490/500] eta: 0:00:00 time: 0.0408 data_time: 0.0024 memory: 1008 2022/11/02 14:55:52 - mmengine - INFO - Epoch(val) [260][495/500] eta: 0:00:00 time: 0.0503 data_time: 0.0038 memory: 1008 2022/11/02 14:55:52 - mmengine - INFO - Epoch(val) [260][500/500] eta: 0:00:00 time: 0.0527 data_time: 0.0068 memory: 1008 2022/11/02 14:55:52 - mmengine - INFO - Evaluating hmean-iou... 2022/11/02 14:55:52 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.7944, precision: 0.7060, hmean: 0.7476 2022/11/02 14:55:52 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.7944, precision: 0.7674, hmean: 0.7807 2022/11/02 14:55:52 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.7896, precision: 0.8147, hmean: 0.8020 2022/11/02 14:55:52 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.7732, precision: 0.8543, hmean: 0.8117 2022/11/02 14:55:52 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.7025, precision: 0.8940, hmean: 0.7867 2022/11/02 14:55:52 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.3115, precision: 0.9529, hmean: 0.4695 2022/11/02 14:55:52 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0005, precision: 1.0000, hmean: 0.0010 2022/11/02 14:55:52 - mmengine - INFO - Epoch(val) [260][500/500] icdar/precision: 0.8543 icdar/recall: 0.7732 icdar/hmean: 0.8117 2022/11/02 14:55:58 - mmengine - INFO - Epoch(train) [261][5/63] lr: 1.7359e-03 eta: 0:00:00 time: 0.8805 data_time: 0.2805 memory: 14901 loss: 1.6661 loss_prob: 0.9250 loss_thr: 0.5895 loss_db: 0.1517 2022/11/02 14:56:01 - mmengine - INFO - Epoch(train) [261][10/63] lr: 1.7359e-03 eta: 9:39:50 time: 0.9433 data_time: 0.2801 memory: 14901 loss: 1.6444 loss_prob: 0.9290 loss_thr: 0.5625 loss_db: 0.1529 2022/11/02 14:56:04 - mmengine - INFO - Epoch(train) [261][15/63] lr: 1.7359e-03 eta: 9:39:50 time: 0.5784 data_time: 0.0075 memory: 14901 loss: 1.7546 loss_prob: 0.9949 loss_thr: 0.5948 loss_db: 0.1650 2022/11/02 14:56:07 - mmengine - INFO - Epoch(train) [261][20/63] lr: 1.7359e-03 eta: 9:39:42 time: 0.5345 data_time: 0.0071 memory: 14901 loss: 1.7190 loss_prob: 0.9701 loss_thr: 0.5873 loss_db: 0.1616 2022/11/02 14:56:10 - mmengine - INFO - Epoch(train) [261][25/63] lr: 1.7359e-03 eta: 9:39:42 time: 0.5491 data_time: 0.0387 memory: 14901 loss: 1.6336 loss_prob: 0.9110 loss_thr: 0.5732 loss_db: 0.1495 2022/11/02 14:56:12 - mmengine - INFO - Epoch(train) [261][30/63] lr: 1.7359e-03 eta: 9:39:34 time: 0.5370 data_time: 0.0375 memory: 14901 loss: 1.7155 loss_prob: 0.9737 loss_thr: 0.5816 loss_db: 0.1601 2022/11/02 14:56:15 - mmengine - INFO - Epoch(train) [261][35/63] lr: 1.7359e-03 eta: 9:39:34 time: 0.4929 data_time: 0.0130 memory: 14901 loss: 1.7029 loss_prob: 0.9643 loss_thr: 0.5788 loss_db: 0.1598 2022/11/02 14:56:17 - mmengine - INFO - Epoch(train) [261][40/63] lr: 1.7359e-03 eta: 9:39:26 time: 0.5234 data_time: 0.0140 memory: 14901 loss: 1.8069 loss_prob: 1.0490 loss_thr: 0.5879 loss_db: 0.1701 2022/11/02 14:56:20 - mmengine - INFO - Epoch(train) [261][45/63] lr: 1.7359e-03 eta: 9:39:26 time: 0.5314 data_time: 0.0074 memory: 14901 loss: 1.9658 loss_prob: 1.1758 loss_thr: 0.6010 loss_db: 0.1890 2022/11/02 14:56:23 - mmengine - INFO - Epoch(train) [261][50/63] lr: 1.7359e-03 eta: 9:39:19 time: 0.5644 data_time: 0.0222 memory: 14901 loss: 1.9882 loss_prob: 1.1735 loss_thr: 0.6206 loss_db: 0.1942 2022/11/02 14:56:26 - mmengine - INFO - Epoch(train) [261][55/63] lr: 1.7359e-03 eta: 9:39:19 time: 0.5601 data_time: 0.0245 memory: 14901 loss: 1.8899 loss_prob: 1.0966 loss_thr: 0.6097 loss_db: 0.1836 2022/11/02 14:56:29 - mmengine - INFO - Epoch(train) [261][60/63] lr: 1.7359e-03 eta: 9:39:12 time: 0.5516 data_time: 0.0124 memory: 14901 loss: 1.8495 loss_prob: 1.0760 loss_thr: 0.5957 loss_db: 0.1777 2022/11/02 14:56:30 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:56:35 - mmengine - INFO - Epoch(train) [262][5/63] lr: 1.7343e-03 eta: 9:39:12 time: 0.7702 data_time: 0.2134 memory: 14901 loss: 1.9717 loss_prob: 1.1597 loss_thr: 0.6218 loss_db: 0.1902 2022/11/02 14:56:38 - mmengine - INFO - Epoch(train) [262][10/63] lr: 1.7343e-03 eta: 9:39:07 time: 0.8404 data_time: 0.2218 memory: 14901 loss: 1.8745 loss_prob: 1.0891 loss_thr: 0.6057 loss_db: 0.1797 2022/11/02 14:56:41 - mmengine - INFO - Epoch(train) [262][15/63] lr: 1.7343e-03 eta: 9:39:07 time: 0.5698 data_time: 0.0166 memory: 14901 loss: 1.7135 loss_prob: 0.9896 loss_thr: 0.5613 loss_db: 0.1627 2022/11/02 14:56:43 - mmengine - INFO - Epoch(train) [262][20/63] lr: 1.7343e-03 eta: 9:38:59 time: 0.5125 data_time: 0.0123 memory: 14901 loss: 1.6974 loss_prob: 0.9814 loss_thr: 0.5541 loss_db: 0.1619 2022/11/02 14:56:46 - mmengine - INFO - Epoch(train) [262][25/63] lr: 1.7343e-03 eta: 9:38:59 time: 0.5320 data_time: 0.0410 memory: 14901 loss: 1.9283 loss_prob: 1.1303 loss_thr: 0.6118 loss_db: 0.1862 2022/11/02 14:56:49 - mmengine - INFO - Epoch(train) [262][30/63] lr: 1.7343e-03 eta: 9:38:51 time: 0.5319 data_time: 0.0448 memory: 14901 loss: 1.9597 loss_prob: 1.1497 loss_thr: 0.6182 loss_db: 0.1919 2022/11/02 14:56:51 - mmengine - INFO - Epoch(train) [262][35/63] lr: 1.7343e-03 eta: 9:38:51 time: 0.5304 data_time: 0.0178 memory: 14901 loss: 1.6832 loss_prob: 0.9499 loss_thr: 0.5732 loss_db: 0.1600 2022/11/02 14:56:54 - mmengine - INFO - Epoch(train) [262][40/63] lr: 1.7343e-03 eta: 9:38:43 time: 0.5441 data_time: 0.0087 memory: 14901 loss: 1.6597 loss_prob: 0.9236 loss_thr: 0.5836 loss_db: 0.1525 2022/11/02 14:56:56 - mmengine - INFO - Epoch(train) [262][45/63] lr: 1.7343e-03 eta: 9:38:43 time: 0.5077 data_time: 0.0078 memory: 14901 loss: 1.7198 loss_prob: 0.9757 loss_thr: 0.5806 loss_db: 0.1635 2022/11/02 14:56:59 - mmengine - INFO - Epoch(train) [262][50/63] lr: 1.7343e-03 eta: 9:38:34 time: 0.4831 data_time: 0.0226 memory: 14901 loss: 1.6600 loss_prob: 0.9351 loss_thr: 0.5694 loss_db: 0.1556 2022/11/02 14:57:01 - mmengine - INFO - Epoch(train) [262][55/63] lr: 1.7343e-03 eta: 9:38:34 time: 0.4848 data_time: 0.0211 memory: 14901 loss: 1.7759 loss_prob: 1.0191 loss_thr: 0.5937 loss_db: 0.1632 2022/11/02 14:57:04 - mmengine - INFO - Epoch(train) [262][60/63] lr: 1.7343e-03 eta: 9:38:25 time: 0.5056 data_time: 0.0098 memory: 14901 loss: 1.8574 loss_prob: 1.0831 loss_thr: 0.5968 loss_db: 0.1775 2022/11/02 14:57:05 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:57:11 - mmengine - INFO - Epoch(train) [263][5/63] lr: 1.7326e-03 eta: 9:38:25 time: 0.7998 data_time: 0.2200 memory: 14901 loss: 2.0703 loss_prob: 1.2668 loss_thr: 0.6035 loss_db: 0.2000 2022/11/02 14:57:14 - mmengine - INFO - Epoch(train) [263][10/63] lr: 1.7326e-03 eta: 9:38:21 time: 0.8522 data_time: 0.2213 memory: 14901 loss: 2.1488 loss_prob: 1.3320 loss_thr: 0.6059 loss_db: 0.2109 2022/11/02 14:57:16 - mmengine - INFO - Epoch(train) [263][15/63] lr: 1.7326e-03 eta: 9:38:21 time: 0.5270 data_time: 0.0111 memory: 14901 loss: 1.8649 loss_prob: 1.1044 loss_thr: 0.5811 loss_db: 0.1795 2022/11/02 14:57:19 - mmengine - INFO - Epoch(train) [263][20/63] lr: 1.7326e-03 eta: 9:38:15 time: 0.5836 data_time: 0.0104 memory: 14901 loss: 1.8408 loss_prob: 1.0696 loss_thr: 0.5968 loss_db: 0.1743 2022/11/02 14:57:22 - mmengine - INFO - Epoch(train) [263][25/63] lr: 1.7326e-03 eta: 9:38:15 time: 0.5958 data_time: 0.0215 memory: 14901 loss: 1.7762 loss_prob: 1.0219 loss_thr: 0.5857 loss_db: 0.1685 2022/11/02 14:57:25 - mmengine - INFO - Epoch(train) [263][30/63] lr: 1.7326e-03 eta: 9:38:07 time: 0.5496 data_time: 0.0410 memory: 14901 loss: 1.7595 loss_prob: 1.0115 loss_thr: 0.5784 loss_db: 0.1696 2022/11/02 14:57:27 - mmengine - INFO - Epoch(train) [263][35/63] lr: 1.7326e-03 eta: 9:38:07 time: 0.5398 data_time: 0.0252 memory: 14901 loss: 1.8657 loss_prob: 1.0874 loss_thr: 0.5987 loss_db: 0.1795 2022/11/02 14:57:30 - mmengine - INFO - Epoch(train) [263][40/63] lr: 1.7326e-03 eta: 9:37:59 time: 0.5169 data_time: 0.0086 memory: 14901 loss: 1.8775 loss_prob: 1.1047 loss_thr: 0.5903 loss_db: 0.1824 2022/11/02 14:57:32 - mmengine - INFO - Epoch(train) [263][45/63] lr: 1.7326e-03 eta: 9:37:59 time: 0.5014 data_time: 0.0094 memory: 14901 loss: 1.8440 loss_prob: 1.0772 loss_thr: 0.5839 loss_db: 0.1830 2022/11/02 14:57:35 - mmengine - INFO - Epoch(train) [263][50/63] lr: 1.7326e-03 eta: 9:37:49 time: 0.4857 data_time: 0.0152 memory: 14901 loss: 1.8481 loss_prob: 1.0805 loss_thr: 0.5860 loss_db: 0.1815 2022/11/02 14:57:38 - mmengine - INFO - Epoch(train) [263][55/63] lr: 1.7326e-03 eta: 9:37:49 time: 0.5286 data_time: 0.0286 memory: 14901 loss: 2.2238 loss_prob: 1.3725 loss_thr: 0.6340 loss_db: 0.2173 2022/11/02 14:57:40 - mmengine - INFO - Epoch(train) [263][60/63] lr: 1.7326e-03 eta: 9:37:42 time: 0.5447 data_time: 0.0221 memory: 14901 loss: 2.2439 loss_prob: 1.3782 loss_thr: 0.6466 loss_db: 0.2191 2022/11/02 14:57:42 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:57:47 - mmengine - INFO - Epoch(train) [264][5/63] lr: 1.7309e-03 eta: 9:37:42 time: 0.7635 data_time: 0.2226 memory: 14901 loss: 2.1417 loss_prob: 1.3347 loss_thr: 0.5913 loss_db: 0.2157 2022/11/02 14:57:50 - mmengine - INFO - Epoch(train) [264][10/63] lr: 1.7309e-03 eta: 9:37:36 time: 0.8117 data_time: 0.2257 memory: 14901 loss: 2.2736 loss_prob: 1.4442 loss_thr: 0.6035 loss_db: 0.2259 2022/11/02 14:57:53 - mmengine - INFO - Epoch(train) [264][15/63] lr: 1.7309e-03 eta: 9:37:36 time: 0.5666 data_time: 0.0231 memory: 14901 loss: 2.1549 loss_prob: 1.3236 loss_thr: 0.6192 loss_db: 0.2121 2022/11/02 14:57:55 - mmengine - INFO - Epoch(train) [264][20/63] lr: 1.7309e-03 eta: 9:37:28 time: 0.5225 data_time: 0.0178 memory: 14901 loss: 2.0988 loss_prob: 1.2518 loss_thr: 0.6436 loss_db: 0.2034 2022/11/02 14:57:58 - mmengine - INFO - Epoch(train) [264][25/63] lr: 1.7309e-03 eta: 9:37:28 time: 0.4983 data_time: 0.0092 memory: 14901 loss: 2.0805 loss_prob: 1.2324 loss_thr: 0.6442 loss_db: 0.2039 2022/11/02 14:58:01 - mmengine - INFO - Epoch(train) [264][30/63] lr: 1.7309e-03 eta: 9:37:23 time: 0.6065 data_time: 0.0398 memory: 14901 loss: 2.0742 loss_prob: 1.2493 loss_thr: 0.6111 loss_db: 0.2138 2022/11/02 14:58:03 - mmengine - INFO - Epoch(train) [264][35/63] lr: 1.7309e-03 eta: 9:37:23 time: 0.5650 data_time: 0.0357 memory: 14901 loss: 2.0607 loss_prob: 1.2318 loss_thr: 0.6264 loss_db: 0.2024 2022/11/02 14:58:06 - mmengine - INFO - Epoch(train) [264][40/63] lr: 1.7309e-03 eta: 9:37:13 time: 0.4888 data_time: 0.0066 memory: 14901 loss: 2.0422 loss_prob: 1.2001 loss_thr: 0.6495 loss_db: 0.1926 2022/11/02 14:58:09 - mmengine - INFO - Epoch(train) [264][45/63] lr: 1.7309e-03 eta: 9:37:13 time: 0.5550 data_time: 0.0116 memory: 14901 loss: 2.0625 loss_prob: 1.2115 loss_thr: 0.6514 loss_db: 0.1996 2022/11/02 14:58:11 - mmengine - INFO - Epoch(train) [264][50/63] lr: 1.7309e-03 eta: 9:37:06 time: 0.5376 data_time: 0.0174 memory: 14901 loss: 1.8534 loss_prob: 1.0668 loss_thr: 0.6121 loss_db: 0.1745 2022/11/02 14:58:14 - mmengine - INFO - Epoch(train) [264][55/63] lr: 1.7309e-03 eta: 9:37:06 time: 0.5015 data_time: 0.0250 memory: 14901 loss: 1.7401 loss_prob: 1.0158 loss_thr: 0.5657 loss_db: 0.1587 2022/11/02 14:58:16 - mmengine - INFO - Epoch(train) [264][60/63] lr: 1.7309e-03 eta: 9:36:57 time: 0.4964 data_time: 0.0207 memory: 14901 loss: 1.8129 loss_prob: 1.0608 loss_thr: 0.5830 loss_db: 0.1692 2022/11/02 14:58:18 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:58:22 - mmengine - INFO - Epoch(train) [265][5/63] lr: 1.7293e-03 eta: 9:36:57 time: 0.7189 data_time: 0.2218 memory: 14901 loss: 1.8364 loss_prob: 1.0455 loss_thr: 0.6168 loss_db: 0.1742 2022/11/02 14:58:26 - mmengine - INFO - Epoch(train) [265][10/63] lr: 1.7293e-03 eta: 9:36:51 time: 0.8091 data_time: 0.2241 memory: 14901 loss: 1.8431 loss_prob: 1.0539 loss_thr: 0.6178 loss_db: 0.1714 2022/11/02 14:58:28 - mmengine - INFO - Epoch(train) [265][15/63] lr: 1.7293e-03 eta: 9:36:51 time: 0.5572 data_time: 0.0085 memory: 14901 loss: 1.9801 loss_prob: 1.1548 loss_thr: 0.6370 loss_db: 0.1884 2022/11/02 14:58:31 - mmengine - INFO - Epoch(train) [265][20/63] lr: 1.7293e-03 eta: 9:36:42 time: 0.5105 data_time: 0.0070 memory: 14901 loss: 1.9532 loss_prob: 1.1297 loss_thr: 0.6315 loss_db: 0.1921 2022/11/02 14:58:34 - mmengine - INFO - Epoch(train) [265][25/63] lr: 1.7293e-03 eta: 9:36:42 time: 0.5823 data_time: 0.0424 memory: 14901 loss: 1.8060 loss_prob: 1.0296 loss_thr: 0.6040 loss_db: 0.1724 2022/11/02 14:58:37 - mmengine - INFO - Epoch(train) [265][30/63] lr: 1.7293e-03 eta: 9:36:36 time: 0.5846 data_time: 0.0401 memory: 14901 loss: 1.7771 loss_prob: 1.0219 loss_thr: 0.5912 loss_db: 0.1640 2022/11/02 14:58:39 - mmengine - INFO - Epoch(train) [265][35/63] lr: 1.7293e-03 eta: 9:36:36 time: 0.5023 data_time: 0.0069 memory: 14901 loss: 1.7615 loss_prob: 1.0177 loss_thr: 0.5790 loss_db: 0.1649 2022/11/02 14:58:42 - mmengine - INFO - Epoch(train) [265][40/63] lr: 1.7293e-03 eta: 9:36:30 time: 0.5894 data_time: 0.0072 memory: 14901 loss: 1.6735 loss_prob: 0.9598 loss_thr: 0.5567 loss_db: 0.1570 2022/11/02 14:58:45 - mmengine - INFO - Epoch(train) [265][45/63] lr: 1.7293e-03 eta: 9:36:30 time: 0.6329 data_time: 0.0123 memory: 14901 loss: 1.7468 loss_prob: 1.0068 loss_thr: 0.5719 loss_db: 0.1681 2022/11/02 14:58:48 - mmengine - INFO - Epoch(train) [265][50/63] lr: 1.7293e-03 eta: 9:36:23 time: 0.5462 data_time: 0.0316 memory: 14901 loss: 1.8120 loss_prob: 1.0572 loss_thr: 0.5793 loss_db: 0.1755 2022/11/02 14:58:51 - mmengine - INFO - Epoch(train) [265][55/63] lr: 1.7293e-03 eta: 9:36:23 time: 0.5675 data_time: 0.0243 memory: 14901 loss: 1.8733 loss_prob: 1.0855 loss_thr: 0.6108 loss_db: 0.1770 2022/11/02 14:58:54 - mmengine - INFO - Epoch(train) [265][60/63] lr: 1.7293e-03 eta: 9:36:18 time: 0.6104 data_time: 0.0087 memory: 14901 loss: 1.9693 loss_prob: 1.1417 loss_thr: 0.6397 loss_db: 0.1879 2022/11/02 14:58:55 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:59:00 - mmengine - INFO - Epoch(train) [266][5/63] lr: 1.7276e-03 eta: 9:36:18 time: 0.7378 data_time: 0.2060 memory: 14901 loss: 1.8660 loss_prob: 1.0859 loss_thr: 0.6012 loss_db: 0.1789 2022/11/02 14:59:04 - mmengine - INFO - Epoch(train) [266][10/63] lr: 1.7276e-03 eta: 9:36:13 time: 0.8491 data_time: 0.2489 memory: 14901 loss: 1.7465 loss_prob: 1.0093 loss_thr: 0.5709 loss_db: 0.1663 2022/11/02 14:59:07 - mmengine - INFO - Epoch(train) [266][15/63] lr: 1.7276e-03 eta: 9:36:13 time: 0.6317 data_time: 0.0483 memory: 14901 loss: 1.7782 loss_prob: 1.0256 loss_thr: 0.5844 loss_db: 0.1682 2022/11/02 14:59:09 - mmengine - INFO - Epoch(train) [266][20/63] lr: 1.7276e-03 eta: 9:36:07 time: 0.5809 data_time: 0.0082 memory: 14901 loss: 1.8736 loss_prob: 1.0998 loss_thr: 0.5905 loss_db: 0.1833 2022/11/02 14:59:12 - mmengine - INFO - Epoch(train) [266][25/63] lr: 1.7276e-03 eta: 9:36:07 time: 0.5511 data_time: 0.0094 memory: 14901 loss: 1.8195 loss_prob: 1.0569 loss_thr: 0.5888 loss_db: 0.1738 2022/11/02 14:59:15 - mmengine - INFO - Epoch(train) [266][30/63] lr: 1.7276e-03 eta: 9:36:00 time: 0.5505 data_time: 0.0148 memory: 14901 loss: 1.7883 loss_prob: 1.0246 loss_thr: 0.6006 loss_db: 0.1632 2022/11/02 14:59:17 - mmengine - INFO - Epoch(train) [266][35/63] lr: 1.7276e-03 eta: 9:36:00 time: 0.5149 data_time: 0.0132 memory: 14901 loss: 1.9401 loss_prob: 1.1411 loss_thr: 0.6160 loss_db: 0.1829 2022/11/02 14:59:20 - mmengine - INFO - Epoch(train) [266][40/63] lr: 1.7276e-03 eta: 9:35:50 time: 0.4765 data_time: 0.0047 memory: 14901 loss: 1.8399 loss_prob: 1.0709 loss_thr: 0.5929 loss_db: 0.1762 2022/11/02 14:59:23 - mmengine - INFO - Epoch(train) [266][45/63] lr: 1.7276e-03 eta: 9:35:50 time: 0.5116 data_time: 0.0070 memory: 14901 loss: 1.6636 loss_prob: 0.9487 loss_thr: 0.5585 loss_db: 0.1565 2022/11/02 14:59:26 - mmengine - INFO - Epoch(train) [266][50/63] lr: 1.7276e-03 eta: 9:35:47 time: 0.6561 data_time: 0.0102 memory: 14901 loss: 1.7045 loss_prob: 0.9745 loss_thr: 0.5691 loss_db: 0.1609 2022/11/02 14:59:29 - mmengine - INFO - Epoch(train) [266][55/63] lr: 1.7276e-03 eta: 9:35:47 time: 0.6551 data_time: 0.0403 memory: 14901 loss: 1.7699 loss_prob: 1.0023 loss_thr: 0.5964 loss_db: 0.1712 2022/11/02 14:59:32 - mmengine - INFO - Epoch(train) [266][60/63] lr: 1.7276e-03 eta: 9:35:41 time: 0.5704 data_time: 0.0396 memory: 14901 loss: 1.7287 loss_prob: 0.9752 loss_thr: 0.5880 loss_db: 0.1655 2022/11/02 14:59:34 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 14:59:40 - mmengine - INFO - Epoch(train) [267][5/63] lr: 1.7260e-03 eta: 9:35:41 time: 0.9102 data_time: 0.2647 memory: 14901 loss: 1.6662 loss_prob: 0.9547 loss_thr: 0.5532 loss_db: 0.1583 2022/11/02 14:59:43 - mmengine - INFO - Epoch(train) [267][10/63] lr: 1.7260e-03 eta: 9:35:37 time: 0.8730 data_time: 0.2669 memory: 14901 loss: 1.6613 loss_prob: 0.9423 loss_thr: 0.5618 loss_db: 0.1571 2022/11/02 14:59:46 - mmengine - INFO - Epoch(train) [267][15/63] lr: 1.7260e-03 eta: 9:35:37 time: 0.5668 data_time: 0.0081 memory: 14901 loss: 1.6973 loss_prob: 0.9605 loss_thr: 0.5795 loss_db: 0.1574 2022/11/02 14:59:49 - mmengine - INFO - Epoch(train) [267][20/63] lr: 1.7260e-03 eta: 9:35:31 time: 0.5946 data_time: 0.0076 memory: 14901 loss: 1.7317 loss_prob: 1.0036 loss_thr: 0.5707 loss_db: 0.1573 2022/11/02 14:59:51 - mmengine - INFO - Epoch(train) [267][25/63] lr: 1.7260e-03 eta: 9:35:31 time: 0.5544 data_time: 0.0318 memory: 14901 loss: 1.6513 loss_prob: 0.9518 loss_thr: 0.5483 loss_db: 0.1512 2022/11/02 14:59:54 - mmengine - INFO - Epoch(train) [267][30/63] lr: 1.7260e-03 eta: 9:35:24 time: 0.5443 data_time: 0.0473 memory: 14901 loss: 1.6192 loss_prob: 0.9171 loss_thr: 0.5504 loss_db: 0.1517 2022/11/02 14:59:57 - mmengine - INFO - Epoch(train) [267][35/63] lr: 1.7260e-03 eta: 9:35:24 time: 0.5254 data_time: 0.0272 memory: 14901 loss: 1.6262 loss_prob: 0.9277 loss_thr: 0.5487 loss_db: 0.1497 2022/11/02 14:59:59 - mmengine - INFO - Epoch(train) [267][40/63] lr: 1.7260e-03 eta: 9:35:16 time: 0.5277 data_time: 0.0128 memory: 14901 loss: 1.7362 loss_prob: 1.0075 loss_thr: 0.5720 loss_db: 0.1567 2022/11/02 15:00:02 - mmengine - INFO - Epoch(train) [267][45/63] lr: 1.7260e-03 eta: 9:35:16 time: 0.5092 data_time: 0.0085 memory: 14901 loss: 1.8296 loss_prob: 1.0580 loss_thr: 0.6033 loss_db: 0.1683 2022/11/02 15:00:04 - mmengine - INFO - Epoch(train) [267][50/63] lr: 1.7260e-03 eta: 9:35:08 time: 0.5195 data_time: 0.0211 memory: 14901 loss: 1.7028 loss_prob: 0.9651 loss_thr: 0.5775 loss_db: 0.1601 2022/11/02 15:00:07 - mmengine - INFO - Epoch(train) [267][55/63] lr: 1.7260e-03 eta: 9:35:08 time: 0.5403 data_time: 0.0243 memory: 14901 loss: 1.7090 loss_prob: 0.9712 loss_thr: 0.5794 loss_db: 0.1585 2022/11/02 15:00:11 - mmengine - INFO - Epoch(train) [267][60/63] lr: 1.7260e-03 eta: 9:35:02 time: 0.6068 data_time: 0.0094 memory: 14901 loss: 1.6803 loss_prob: 0.9503 loss_thr: 0.5727 loss_db: 0.1573 2022/11/02 15:00:12 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:00:18 - mmengine - INFO - Epoch(train) [268][5/63] lr: 1.7243e-03 eta: 9:35:02 time: 0.8875 data_time: 0.2427 memory: 14901 loss: 1.6962 loss_prob: 0.9635 loss_thr: 0.5750 loss_db: 0.1578 2022/11/02 15:00:21 - mmengine - INFO - Epoch(train) [268][10/63] lr: 1.7243e-03 eta: 9:35:00 time: 0.9089 data_time: 0.2443 memory: 14901 loss: 1.7937 loss_prob: 1.0541 loss_thr: 0.5712 loss_db: 0.1683 2022/11/02 15:00:24 - mmengine - INFO - Epoch(train) [268][15/63] lr: 1.7243e-03 eta: 9:35:00 time: 0.5706 data_time: 0.0157 memory: 14901 loss: 1.6738 loss_prob: 0.9690 loss_thr: 0.5495 loss_db: 0.1553 2022/11/02 15:00:26 - mmengine - INFO - Epoch(train) [268][20/63] lr: 1.7243e-03 eta: 9:34:52 time: 0.5332 data_time: 0.0165 memory: 14901 loss: 1.5459 loss_prob: 0.8515 loss_thr: 0.5547 loss_db: 0.1396 2022/11/02 15:00:29 - mmengine - INFO - Epoch(train) [268][25/63] lr: 1.7243e-03 eta: 9:34:52 time: 0.5262 data_time: 0.0378 memory: 14901 loss: 1.6401 loss_prob: 0.9170 loss_thr: 0.5694 loss_db: 0.1536 2022/11/02 15:00:32 - mmengine - INFO - Epoch(train) [268][30/63] lr: 1.7243e-03 eta: 9:34:45 time: 0.5602 data_time: 0.0394 memory: 14901 loss: 1.7061 loss_prob: 0.9703 loss_thr: 0.5703 loss_db: 0.1655 2022/11/02 15:00:34 - mmengine - INFO - Epoch(train) [268][35/63] lr: 1.7243e-03 eta: 9:34:45 time: 0.5422 data_time: 0.0147 memory: 14901 loss: 1.6856 loss_prob: 0.9610 loss_thr: 0.5644 loss_db: 0.1603 2022/11/02 15:00:37 - mmengine - INFO - Epoch(train) [268][40/63] lr: 1.7243e-03 eta: 9:34:37 time: 0.5194 data_time: 0.0097 memory: 14901 loss: 1.7272 loss_prob: 0.9882 loss_thr: 0.5779 loss_db: 0.1611 2022/11/02 15:00:40 - mmengine - INFO - Epoch(train) [268][45/63] lr: 1.7243e-03 eta: 9:34:37 time: 0.5220 data_time: 0.0071 memory: 14901 loss: 1.8897 loss_prob: 1.1032 loss_thr: 0.6063 loss_db: 0.1802 2022/11/02 15:00:42 - mmengine - INFO - Epoch(train) [268][50/63] lr: 1.7243e-03 eta: 9:34:29 time: 0.5340 data_time: 0.0233 memory: 14901 loss: 1.8691 loss_prob: 1.0881 loss_thr: 0.6034 loss_db: 0.1777 2022/11/02 15:00:45 - mmengine - INFO - Epoch(train) [268][55/63] lr: 1.7243e-03 eta: 9:34:29 time: 0.5450 data_time: 0.0233 memory: 14901 loss: 1.8590 loss_prob: 1.0763 loss_thr: 0.6105 loss_db: 0.1722 2022/11/02 15:00:48 - mmengine - INFO - Epoch(train) [268][60/63] lr: 1.7243e-03 eta: 9:34:23 time: 0.5777 data_time: 0.0073 memory: 14901 loss: 2.0516 loss_prob: 1.2175 loss_thr: 0.6485 loss_db: 0.1856 2022/11/02 15:00:50 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:00:57 - mmengine - INFO - Epoch(train) [269][5/63] lr: 1.7226e-03 eta: 9:34:23 time: 1.0106 data_time: 0.2327 memory: 14901 loss: 2.2933 loss_prob: 1.4136 loss_thr: 0.6575 loss_db: 0.2223 2022/11/02 15:01:00 - mmengine - INFO - Epoch(train) [269][10/63] lr: 1.7226e-03 eta: 9:34:25 time: 1.0281 data_time: 0.2353 memory: 14901 loss: 2.3880 loss_prob: 1.5283 loss_thr: 0.6275 loss_db: 0.2322 2022/11/02 15:01:03 - mmengine - INFO - Epoch(train) [269][15/63] lr: 1.7226e-03 eta: 9:34:25 time: 0.5875 data_time: 0.0113 memory: 14901 loss: 1.9502 loss_prob: 1.1537 loss_thr: 0.6097 loss_db: 0.1867 2022/11/02 15:01:05 - mmengine - INFO - Epoch(train) [269][20/63] lr: 1.7226e-03 eta: 9:34:17 time: 0.5431 data_time: 0.0067 memory: 14901 loss: 1.8227 loss_prob: 1.0387 loss_thr: 0.6074 loss_db: 0.1767 2022/11/02 15:01:09 - mmengine - INFO - Epoch(train) [269][25/63] lr: 1.7226e-03 eta: 9:34:17 time: 0.6282 data_time: 0.0166 memory: 14901 loss: 1.8221 loss_prob: 1.0427 loss_thr: 0.6077 loss_db: 0.1718 2022/11/02 15:01:12 - mmengine - INFO - Epoch(train) [269][30/63] lr: 1.7226e-03 eta: 9:34:14 time: 0.6546 data_time: 0.0403 memory: 14901 loss: 1.8151 loss_prob: 1.0386 loss_thr: 0.6055 loss_db: 0.1710 2022/11/02 15:01:15 - mmengine - INFO - Epoch(train) [269][35/63] lr: 1.7226e-03 eta: 9:34:14 time: 0.5431 data_time: 0.0341 memory: 14901 loss: 1.7477 loss_prob: 0.9951 loss_thr: 0.5897 loss_db: 0.1628 2022/11/02 15:01:17 - mmengine - INFO - Epoch(train) [269][40/63] lr: 1.7226e-03 eta: 9:34:05 time: 0.5119 data_time: 0.0104 memory: 14901 loss: 1.7112 loss_prob: 0.9676 loss_thr: 0.5853 loss_db: 0.1583 2022/11/02 15:01:20 - mmengine - INFO - Epoch(train) [269][45/63] lr: 1.7226e-03 eta: 9:34:05 time: 0.5089 data_time: 0.0085 memory: 14901 loss: 1.8544 loss_prob: 1.0625 loss_thr: 0.6107 loss_db: 0.1812 2022/11/02 15:01:23 - mmengine - INFO - Epoch(train) [269][50/63] lr: 1.7226e-03 eta: 9:33:59 time: 0.5652 data_time: 0.0215 memory: 14901 loss: 1.8641 loss_prob: 1.0684 loss_thr: 0.6130 loss_db: 0.1828 2022/11/02 15:01:26 - mmengine - INFO - Epoch(train) [269][55/63] lr: 1.7226e-03 eta: 9:33:59 time: 0.5963 data_time: 0.0292 memory: 14901 loss: 1.7767 loss_prob: 1.0170 loss_thr: 0.5923 loss_db: 0.1674 2022/11/02 15:01:28 - mmengine - INFO - Epoch(train) [269][60/63] lr: 1.7226e-03 eta: 9:33:52 time: 0.5512 data_time: 0.0185 memory: 14901 loss: 1.7130 loss_prob: 0.9822 loss_thr: 0.5734 loss_db: 0.1574 2022/11/02 15:01:30 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:01:35 - mmengine - INFO - Epoch(train) [270][5/63] lr: 1.7210e-03 eta: 9:33:52 time: 0.7795 data_time: 0.2246 memory: 14901 loss: 1.7969 loss_prob: 1.0346 loss_thr: 0.5918 loss_db: 0.1704 2022/11/02 15:01:38 - mmengine - INFO - Epoch(train) [270][10/63] lr: 1.7210e-03 eta: 9:33:46 time: 0.8116 data_time: 0.2286 memory: 14901 loss: 1.8053 loss_prob: 1.0355 loss_thr: 0.5980 loss_db: 0.1719 2022/11/02 15:01:41 - mmengine - INFO - Epoch(train) [270][15/63] lr: 1.7210e-03 eta: 9:33:46 time: 0.5766 data_time: 0.0190 memory: 14901 loss: 1.7070 loss_prob: 0.9716 loss_thr: 0.5766 loss_db: 0.1588 2022/11/02 15:01:44 - mmengine - INFO - Epoch(train) [270][20/63] lr: 1.7210e-03 eta: 9:33:40 time: 0.5952 data_time: 0.0216 memory: 14901 loss: 1.7906 loss_prob: 1.0255 loss_thr: 0.5955 loss_db: 0.1696 2022/11/02 15:01:47 - mmengine - INFO - Epoch(train) [270][25/63] lr: 1.7210e-03 eta: 9:33:40 time: 0.6537 data_time: 0.0379 memory: 14901 loss: 1.9341 loss_prob: 1.1281 loss_thr: 0.6151 loss_db: 0.1908 2022/11/02 15:01:50 - mmengine - INFO - Epoch(train) [270][30/63] lr: 1.7210e-03 eta: 9:33:35 time: 0.6231 data_time: 0.0340 memory: 14901 loss: 1.6493 loss_prob: 0.9341 loss_thr: 0.5576 loss_db: 0.1575 2022/11/02 15:01:53 - mmengine - INFO - Epoch(train) [270][35/63] lr: 1.7210e-03 eta: 9:33:35 time: 0.5360 data_time: 0.0085 memory: 14901 loss: 1.5976 loss_prob: 0.8926 loss_thr: 0.5604 loss_db: 0.1447 2022/11/02 15:01:56 - mmengine - INFO - Epoch(train) [270][40/63] lr: 1.7210e-03 eta: 9:33:29 time: 0.5655 data_time: 0.0130 memory: 14901 loss: 1.7054 loss_prob: 0.9725 loss_thr: 0.5745 loss_db: 0.1584 2022/11/02 15:01:59 - mmengine - INFO - Epoch(train) [270][45/63] lr: 1.7210e-03 eta: 9:33:29 time: 0.5807 data_time: 0.0130 memory: 14901 loss: 1.7565 loss_prob: 1.0090 loss_thr: 0.5809 loss_db: 0.1666 2022/11/02 15:02:02 - mmengine - INFO - Epoch(train) [270][50/63] lr: 1.7210e-03 eta: 9:33:23 time: 0.5996 data_time: 0.0211 memory: 14901 loss: 1.7902 loss_prob: 1.0194 loss_thr: 0.6044 loss_db: 0.1663 2022/11/02 15:02:03 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:02:04 - mmengine - INFO - Epoch(train) [270][55/63] lr: 1.7210e-03 eta: 9:33:23 time: 0.5771 data_time: 0.0218 memory: 14901 loss: 1.6850 loss_prob: 0.9400 loss_thr: 0.5888 loss_db: 0.1561 2022/11/02 15:02:08 - mmengine - INFO - Epoch(train) [270][60/63] lr: 1.7210e-03 eta: 9:33:18 time: 0.6019 data_time: 0.0066 memory: 14901 loss: 1.7014 loss_prob: 0.9636 loss_thr: 0.5756 loss_db: 0.1622 2022/11/02 15:02:09 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:02:15 - mmengine - INFO - Epoch(train) [271][5/63] lr: 1.7193e-03 eta: 9:33:18 time: 0.8205 data_time: 0.2280 memory: 14901 loss: 2.0443 loss_prob: 1.2355 loss_thr: 0.6128 loss_db: 0.1960 2022/11/02 15:02:18 - mmengine - INFO - Epoch(train) [271][10/63] lr: 1.7193e-03 eta: 9:33:13 time: 0.8357 data_time: 0.2335 memory: 14901 loss: 2.0345 loss_prob: 1.2272 loss_thr: 0.6184 loss_db: 0.1889 2022/11/02 15:02:20 - mmengine - INFO - Epoch(train) [271][15/63] lr: 1.7193e-03 eta: 9:33:13 time: 0.5595 data_time: 0.0159 memory: 14901 loss: 1.7100 loss_prob: 0.9703 loss_thr: 0.5818 loss_db: 0.1578 2022/11/02 15:02:23 - mmengine - INFO - Epoch(train) [271][20/63] lr: 1.7193e-03 eta: 9:33:06 time: 0.5425 data_time: 0.0070 memory: 14901 loss: 1.7526 loss_prob: 0.9854 loss_thr: 0.6028 loss_db: 0.1644 2022/11/02 15:02:27 - mmengine - INFO - Epoch(train) [271][25/63] lr: 1.7193e-03 eta: 9:33:06 time: 0.6691 data_time: 0.0291 memory: 14901 loss: 1.7336 loss_prob: 0.9737 loss_thr: 0.5966 loss_db: 0.1632 2022/11/02 15:02:30 - mmengine - INFO - Epoch(train) [271][30/63] lr: 1.7193e-03 eta: 9:33:02 time: 0.6630 data_time: 0.0382 memory: 14901 loss: 1.6972 loss_prob: 0.9666 loss_thr: 0.5695 loss_db: 0.1612 2022/11/02 15:02:32 - mmengine - INFO - Epoch(train) [271][35/63] lr: 1.7193e-03 eta: 9:33:02 time: 0.4906 data_time: 0.0232 memory: 14901 loss: 1.7344 loss_prob: 0.9941 loss_thr: 0.5749 loss_db: 0.1654 2022/11/02 15:02:36 - mmengine - INFO - Epoch(train) [271][40/63] lr: 1.7193e-03 eta: 9:32:57 time: 0.5906 data_time: 0.0159 memory: 14901 loss: 1.9354 loss_prob: 1.1475 loss_thr: 0.5947 loss_db: 0.1932 2022/11/02 15:02:38 - mmengine - INFO - Epoch(train) [271][45/63] lr: 1.7193e-03 eta: 9:32:57 time: 0.6466 data_time: 0.0075 memory: 14901 loss: 2.0344 loss_prob: 1.2267 loss_thr: 0.5996 loss_db: 0.2081 2022/11/02 15:02:41 - mmengine - INFO - Epoch(train) [271][50/63] lr: 1.7193e-03 eta: 9:32:50 time: 0.5593 data_time: 0.0185 memory: 14901 loss: 1.8821 loss_prob: 1.1078 loss_thr: 0.5916 loss_db: 0.1826 2022/11/02 15:02:44 - mmengine - INFO - Epoch(train) [271][55/63] lr: 1.7193e-03 eta: 9:32:50 time: 0.5235 data_time: 0.0285 memory: 14901 loss: 1.7478 loss_prob: 1.0013 loss_thr: 0.5836 loss_db: 0.1630 2022/11/02 15:02:47 - mmengine - INFO - Epoch(train) [271][60/63] lr: 1.7193e-03 eta: 9:32:43 time: 0.5567 data_time: 0.0167 memory: 14901 loss: 1.7548 loss_prob: 0.9969 loss_thr: 0.5904 loss_db: 0.1675 2022/11/02 15:02:48 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:02:53 - mmengine - INFO - Epoch(train) [272][5/63] lr: 1.7176e-03 eta: 9:32:43 time: 0.7259 data_time: 0.1978 memory: 14901 loss: 2.0258 loss_prob: 1.2099 loss_thr: 0.6204 loss_db: 0.1956 2022/11/02 15:02:56 - mmengine - INFO - Epoch(train) [272][10/63] lr: 1.7176e-03 eta: 9:32:35 time: 0.7577 data_time: 0.2138 memory: 14901 loss: 1.9723 loss_prob: 1.1722 loss_thr: 0.6076 loss_db: 0.1925 2022/11/02 15:02:58 - mmengine - INFO - Epoch(train) [272][15/63] lr: 1.7176e-03 eta: 9:32:35 time: 0.5532 data_time: 0.0241 memory: 14901 loss: 1.8016 loss_prob: 1.0505 loss_thr: 0.5784 loss_db: 0.1727 2022/11/02 15:03:01 - mmengine - INFO - Epoch(train) [272][20/63] lr: 1.7176e-03 eta: 9:32:28 time: 0.5669 data_time: 0.0107 memory: 14901 loss: 1.6929 loss_prob: 0.9473 loss_thr: 0.5889 loss_db: 0.1567 2022/11/02 15:03:04 - mmengine - INFO - Epoch(train) [272][25/63] lr: 1.7176e-03 eta: 9:32:28 time: 0.5442 data_time: 0.0170 memory: 14901 loss: 1.8316 loss_prob: 1.0550 loss_thr: 0.6013 loss_db: 0.1752 2022/11/02 15:03:07 - mmengine - INFO - Epoch(train) [272][30/63] lr: 1.7176e-03 eta: 9:32:21 time: 0.5413 data_time: 0.0395 memory: 14901 loss: 1.7729 loss_prob: 1.0347 loss_thr: 0.5662 loss_db: 0.1720 2022/11/02 15:03:09 - mmengine - INFO - Epoch(train) [272][35/63] lr: 1.7176e-03 eta: 9:32:21 time: 0.5480 data_time: 0.0385 memory: 14901 loss: 1.7702 loss_prob: 1.0431 loss_thr: 0.5626 loss_db: 0.1645 2022/11/02 15:03:12 - mmengine - INFO - Epoch(train) [272][40/63] lr: 1.7176e-03 eta: 9:32:15 time: 0.5803 data_time: 0.0127 memory: 14901 loss: 1.8176 loss_prob: 1.0804 loss_thr: 0.5689 loss_db: 0.1682 2022/11/02 15:03:16 - mmengine - INFO - Epoch(train) [272][45/63] lr: 1.7176e-03 eta: 9:32:15 time: 0.6369 data_time: 0.0101 memory: 14901 loss: 1.7122 loss_prob: 0.9796 loss_thr: 0.5700 loss_db: 0.1626 2022/11/02 15:03:18 - mmengine - INFO - Epoch(train) [272][50/63] lr: 1.7176e-03 eta: 9:32:09 time: 0.5740 data_time: 0.0269 memory: 14901 loss: 1.6669 loss_prob: 0.9324 loss_thr: 0.5786 loss_db: 0.1560 2022/11/02 15:03:21 - mmengine - INFO - Epoch(train) [272][55/63] lr: 1.7176e-03 eta: 9:32:09 time: 0.5241 data_time: 0.0350 memory: 14901 loss: 2.0150 loss_prob: 1.1938 loss_thr: 0.6266 loss_db: 0.1946 2022/11/02 15:03:23 - mmengine - INFO - Epoch(train) [272][60/63] lr: 1.7176e-03 eta: 9:32:00 time: 0.5203 data_time: 0.0225 memory: 14901 loss: 2.0217 loss_prob: 1.2142 loss_thr: 0.6082 loss_db: 0.1994 2022/11/02 15:03:25 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:03:30 - mmengine - INFO - Epoch(train) [273][5/63] lr: 1.7160e-03 eta: 9:32:00 time: 0.7676 data_time: 0.2378 memory: 14901 loss: 1.7928 loss_prob: 1.0345 loss_thr: 0.5856 loss_db: 0.1727 2022/11/02 15:03:34 - mmengine - INFO - Epoch(train) [273][10/63] lr: 1.7160e-03 eta: 9:31:58 time: 0.9068 data_time: 0.3158 memory: 14901 loss: 1.8206 loss_prob: 1.0533 loss_thr: 0.5923 loss_db: 0.1750 2022/11/02 15:03:37 - mmengine - INFO - Epoch(train) [273][15/63] lr: 1.7160e-03 eta: 9:31:58 time: 0.6673 data_time: 0.0888 memory: 14901 loss: 1.7136 loss_prob: 0.9642 loss_thr: 0.5924 loss_db: 0.1570 2022/11/02 15:03:39 - mmengine - INFO - Epoch(train) [273][20/63] lr: 1.7160e-03 eta: 9:31:51 time: 0.5506 data_time: 0.0119 memory: 14901 loss: 1.7357 loss_prob: 0.9814 loss_thr: 0.5945 loss_db: 0.1599 2022/11/02 15:03:42 - mmengine - INFO - Epoch(train) [273][25/63] lr: 1.7160e-03 eta: 9:31:51 time: 0.4964 data_time: 0.0071 memory: 14901 loss: 1.7991 loss_prob: 1.0336 loss_thr: 0.5926 loss_db: 0.1729 2022/11/02 15:03:44 - mmengine - INFO - Epoch(train) [273][30/63] lr: 1.7160e-03 eta: 9:31:41 time: 0.4904 data_time: 0.0063 memory: 14901 loss: 1.7389 loss_prob: 0.9930 loss_thr: 0.5781 loss_db: 0.1677 2022/11/02 15:03:47 - mmengine - INFO - Epoch(train) [273][35/63] lr: 1.7160e-03 eta: 9:31:41 time: 0.5105 data_time: 0.0094 memory: 14901 loss: 1.6775 loss_prob: 0.9385 loss_thr: 0.5811 loss_db: 0.1578 2022/11/02 15:03:50 - mmengine - INFO - Epoch(train) [273][40/63] lr: 1.7160e-03 eta: 9:31:34 time: 0.5374 data_time: 0.0129 memory: 14901 loss: 1.7587 loss_prob: 0.9875 loss_thr: 0.6070 loss_db: 0.1642 2022/11/02 15:03:52 - mmengine - INFO - Epoch(train) [273][45/63] lr: 1.7160e-03 eta: 9:31:34 time: 0.5126 data_time: 0.0091 memory: 14901 loss: 1.8314 loss_prob: 1.0615 loss_thr: 0.6013 loss_db: 0.1687 2022/11/02 15:03:54 - mmengine - INFO - Epoch(train) [273][50/63] lr: 1.7160e-03 eta: 9:31:24 time: 0.4715 data_time: 0.0068 memory: 14901 loss: 1.9019 loss_prob: 1.1181 loss_thr: 0.6047 loss_db: 0.1791 2022/11/02 15:03:57 - mmengine - INFO - Epoch(train) [273][55/63] lr: 1.7160e-03 eta: 9:31:24 time: 0.5242 data_time: 0.0134 memory: 14901 loss: 1.7747 loss_prob: 1.0060 loss_thr: 0.6030 loss_db: 0.1658 2022/11/02 15:04:00 - mmengine - INFO - Epoch(train) [273][60/63] lr: 1.7160e-03 eta: 9:31:17 time: 0.5540 data_time: 0.0199 memory: 14901 loss: 1.5983 loss_prob: 0.8854 loss_thr: 0.5644 loss_db: 0.1485 2022/11/02 15:04:01 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:04:06 - mmengine - INFO - Epoch(train) [274][5/63] lr: 1.7143e-03 eta: 9:31:17 time: 0.7439 data_time: 0.2377 memory: 14901 loss: 1.7901 loss_prob: 1.0322 loss_thr: 0.5900 loss_db: 0.1679 2022/11/02 15:04:09 - mmengine - INFO - Epoch(train) [274][10/63] lr: 1.7143e-03 eta: 9:31:11 time: 0.8136 data_time: 0.2302 memory: 14901 loss: 1.7266 loss_prob: 0.9754 loss_thr: 0.5869 loss_db: 0.1643 2022/11/02 15:04:12 - mmengine - INFO - Epoch(train) [274][15/63] lr: 1.7143e-03 eta: 9:31:11 time: 0.6019 data_time: 0.0067 memory: 14901 loss: 1.7033 loss_prob: 0.9617 loss_thr: 0.5805 loss_db: 0.1610 2022/11/02 15:04:15 - mmengine - INFO - Epoch(train) [274][20/63] lr: 1.7143e-03 eta: 9:31:04 time: 0.5534 data_time: 0.0099 memory: 14901 loss: 1.6963 loss_prob: 0.9721 loss_thr: 0.5630 loss_db: 0.1613 2022/11/02 15:04:19 - mmengine - INFO - Epoch(train) [274][25/63] lr: 1.7143e-03 eta: 9:31:04 time: 0.6265 data_time: 0.0326 memory: 14901 loss: 1.6904 loss_prob: 0.9674 loss_thr: 0.5652 loss_db: 0.1578 2022/11/02 15:04:21 - mmengine - INFO - Epoch(train) [274][30/63] lr: 1.7143e-03 eta: 9:30:59 time: 0.6184 data_time: 0.0395 memory: 14901 loss: 1.7147 loss_prob: 0.9788 loss_thr: 0.5768 loss_db: 0.1591 2022/11/02 15:04:24 - mmengine - INFO - Epoch(train) [274][35/63] lr: 1.7143e-03 eta: 9:30:59 time: 0.5535 data_time: 0.0161 memory: 14901 loss: 1.7702 loss_prob: 1.0130 loss_thr: 0.5897 loss_db: 0.1675 2022/11/02 15:04:27 - mmengine - INFO - Epoch(train) [274][40/63] lr: 1.7143e-03 eta: 9:30:52 time: 0.5443 data_time: 0.0092 memory: 14901 loss: 1.7437 loss_prob: 0.9943 loss_thr: 0.5801 loss_db: 0.1694 2022/11/02 15:04:30 - mmengine - INFO - Epoch(train) [274][45/63] lr: 1.7143e-03 eta: 9:30:52 time: 0.5760 data_time: 0.0166 memory: 14901 loss: 1.7961 loss_prob: 1.0404 loss_thr: 0.5814 loss_db: 0.1743 2022/11/02 15:04:32 - mmengine - INFO - Epoch(train) [274][50/63] lr: 1.7143e-03 eta: 9:30:46 time: 0.5794 data_time: 0.0283 memory: 14901 loss: 1.8820 loss_prob: 1.1140 loss_thr: 0.5863 loss_db: 0.1817 2022/11/02 15:04:35 - mmengine - INFO - Epoch(train) [274][55/63] lr: 1.7143e-03 eta: 9:30:46 time: 0.5241 data_time: 0.0292 memory: 14901 loss: 1.7493 loss_prob: 1.0218 loss_thr: 0.5559 loss_db: 0.1716 2022/11/02 15:04:38 - mmengine - INFO - Epoch(train) [274][60/63] lr: 1.7143e-03 eta: 9:30:40 time: 0.5969 data_time: 0.0143 memory: 14901 loss: 1.6619 loss_prob: 0.9506 loss_thr: 0.5517 loss_db: 0.1596 2022/11/02 15:04:40 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:04:47 - mmengine - INFO - Epoch(train) [275][5/63] lr: 1.7126e-03 eta: 9:30:40 time: 0.9127 data_time: 0.2851 memory: 14901 loss: 1.7908 loss_prob: 1.0538 loss_thr: 0.5670 loss_db: 0.1699 2022/11/02 15:04:49 - mmengine - INFO - Epoch(train) [275][10/63] lr: 1.7126e-03 eta: 9:30:38 time: 0.9296 data_time: 0.2878 memory: 14901 loss: 1.8652 loss_prob: 1.1007 loss_thr: 0.5823 loss_db: 0.1821 2022/11/02 15:04:52 - mmengine - INFO - Epoch(train) [275][15/63] lr: 1.7126e-03 eta: 9:30:38 time: 0.5009 data_time: 0.0103 memory: 14901 loss: 1.8312 loss_prob: 1.0755 loss_thr: 0.5763 loss_db: 0.1793 2022/11/02 15:04:54 - mmengine - INFO - Epoch(train) [275][20/63] lr: 1.7126e-03 eta: 9:30:30 time: 0.5207 data_time: 0.0060 memory: 14901 loss: 1.7321 loss_prob: 0.9986 loss_thr: 0.5711 loss_db: 0.1624 2022/11/02 15:04:57 - mmengine - INFO - Epoch(train) [275][25/63] lr: 1.7126e-03 eta: 9:30:30 time: 0.5745 data_time: 0.0397 memory: 14901 loss: 1.8336 loss_prob: 1.0779 loss_thr: 0.5836 loss_db: 0.1722 2022/11/02 15:05:00 - mmengine - INFO - Epoch(train) [275][30/63] lr: 1.7126e-03 eta: 9:30:23 time: 0.5387 data_time: 0.0399 memory: 14901 loss: 1.7704 loss_prob: 1.0270 loss_thr: 0.5770 loss_db: 0.1664 2022/11/02 15:05:02 - mmengine - INFO - Epoch(train) [275][35/63] lr: 1.7126e-03 eta: 9:30:23 time: 0.5025 data_time: 0.0127 memory: 14901 loss: 1.8525 loss_prob: 1.0825 loss_thr: 0.5954 loss_db: 0.1745 2022/11/02 15:05:05 - mmengine - INFO - Epoch(train) [275][40/63] lr: 1.7126e-03 eta: 9:30:15 time: 0.5227 data_time: 0.0117 memory: 14901 loss: 1.9040 loss_prob: 1.1304 loss_thr: 0.5935 loss_db: 0.1801 2022/11/02 15:05:08 - mmengine - INFO - Epoch(train) [275][45/63] lr: 1.7126e-03 eta: 9:30:15 time: 0.5136 data_time: 0.0057 memory: 14901 loss: 1.8264 loss_prob: 1.0690 loss_thr: 0.5796 loss_db: 0.1778 2022/11/02 15:05:10 - mmengine - INFO - Epoch(train) [275][50/63] lr: 1.7126e-03 eta: 9:30:07 time: 0.5258 data_time: 0.0225 memory: 14901 loss: 2.0407 loss_prob: 1.2596 loss_thr: 0.5873 loss_db: 0.1938 2022/11/02 15:05:13 - mmengine - INFO - Epoch(train) [275][55/63] lr: 1.7126e-03 eta: 9:30:07 time: 0.5542 data_time: 0.0232 memory: 14901 loss: 1.9838 loss_prob: 1.2196 loss_thr: 0.5850 loss_db: 0.1792 2022/11/02 15:05:16 - mmengine - INFO - Epoch(train) [275][60/63] lr: 1.7126e-03 eta: 9:30:00 time: 0.5711 data_time: 0.0111 memory: 14901 loss: 1.6314 loss_prob: 0.9222 loss_thr: 0.5577 loss_db: 0.1515 2022/11/02 15:05:18 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:05:24 - mmengine - INFO - Epoch(train) [276][5/63] lr: 1.7110e-03 eta: 9:30:00 time: 0.9232 data_time: 0.2645 memory: 14901 loss: 1.9771 loss_prob: 1.1902 loss_thr: 0.5909 loss_db: 0.1959 2022/11/02 15:05:27 - mmengine - INFO - Epoch(train) [276][10/63] lr: 1.7110e-03 eta: 9:29:57 time: 0.8998 data_time: 0.2721 memory: 14901 loss: 2.0308 loss_prob: 1.2276 loss_thr: 0.6049 loss_db: 0.1983 2022/11/02 15:05:29 - mmengine - INFO - Epoch(train) [276][15/63] lr: 1.7110e-03 eta: 9:29:57 time: 0.5116 data_time: 0.0159 memory: 14901 loss: 1.9248 loss_prob: 1.1140 loss_thr: 0.6319 loss_db: 0.1789 2022/11/02 15:05:32 - mmengine - INFO - Epoch(train) [276][20/63] lr: 1.7110e-03 eta: 9:29:49 time: 0.5121 data_time: 0.0079 memory: 14901 loss: 2.0143 loss_prob: 1.1774 loss_thr: 0.6431 loss_db: 0.1938 2022/11/02 15:05:35 - mmengine - INFO - Epoch(train) [276][25/63] lr: 1.7110e-03 eta: 9:29:49 time: 0.5449 data_time: 0.0150 memory: 14901 loss: 2.0824 loss_prob: 1.2614 loss_thr: 0.6150 loss_db: 0.2060 2022/11/02 15:05:37 - mmengine - INFO - Epoch(train) [276][30/63] lr: 1.7110e-03 eta: 9:29:41 time: 0.5338 data_time: 0.0326 memory: 14901 loss: 1.8191 loss_prob: 1.0783 loss_thr: 0.5706 loss_db: 0.1702 2022/11/02 15:05:40 - mmengine - INFO - Epoch(train) [276][35/63] lr: 1.7110e-03 eta: 9:29:41 time: 0.5270 data_time: 0.0265 memory: 14901 loss: 1.5835 loss_prob: 0.8896 loss_thr: 0.5478 loss_db: 0.1461 2022/11/02 15:05:42 - mmengine - INFO - Epoch(train) [276][40/63] lr: 1.7110e-03 eta: 9:29:33 time: 0.5180 data_time: 0.0102 memory: 14901 loss: 1.6911 loss_prob: 0.9644 loss_thr: 0.5665 loss_db: 0.1602 2022/11/02 15:05:46 - mmengine - INFO - Epoch(train) [276][45/63] lr: 1.7110e-03 eta: 9:29:33 time: 0.5866 data_time: 0.0090 memory: 14901 loss: 1.7014 loss_prob: 0.9701 loss_thr: 0.5740 loss_db: 0.1573 2022/11/02 15:05:49 - mmengine - INFO - Epoch(train) [276][50/63] lr: 1.7110e-03 eta: 9:29:31 time: 0.6910 data_time: 0.0296 memory: 14901 loss: 1.7213 loss_prob: 0.9839 loss_thr: 0.5778 loss_db: 0.1595 2022/11/02 15:05:52 - mmengine - INFO - Epoch(train) [276][55/63] lr: 1.7110e-03 eta: 9:29:31 time: 0.6127 data_time: 0.0322 memory: 14901 loss: 1.9032 loss_prob: 1.1257 loss_thr: 0.5963 loss_db: 0.1812 2022/11/02 15:05:55 - mmengine - INFO - Epoch(train) [276][60/63] lr: 1.7110e-03 eta: 9:29:23 time: 0.5369 data_time: 0.0121 memory: 14901 loss: 2.0040 loss_prob: 1.2195 loss_thr: 0.5872 loss_db: 0.1973 2022/11/02 15:05:56 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:06:02 - mmengine - INFO - Epoch(train) [277][5/63] lr: 1.7093e-03 eta: 9:29:23 time: 0.7965 data_time: 0.2366 memory: 14901 loss: 1.6931 loss_prob: 0.9974 loss_thr: 0.5331 loss_db: 0.1627 2022/11/02 15:06:05 - mmengine - INFO - Epoch(train) [277][10/63] lr: 1.7093e-03 eta: 9:29:19 time: 0.8611 data_time: 0.2344 memory: 14901 loss: 1.6780 loss_prob: 0.9476 loss_thr: 0.5755 loss_db: 0.1550 2022/11/02 15:06:08 - mmengine - INFO - Epoch(train) [277][15/63] lr: 1.7093e-03 eta: 9:29:19 time: 0.5939 data_time: 0.0110 memory: 14901 loss: 1.7458 loss_prob: 0.9863 loss_thr: 0.5987 loss_db: 0.1607 2022/11/02 15:06:10 - mmengine - INFO - Epoch(train) [277][20/63] lr: 1.7093e-03 eta: 9:29:12 time: 0.5638 data_time: 0.0261 memory: 14901 loss: 1.8079 loss_prob: 1.0393 loss_thr: 0.6002 loss_db: 0.1684 2022/11/02 15:06:13 - mmengine - INFO - Epoch(train) [277][25/63] lr: 1.7093e-03 eta: 9:29:12 time: 0.5656 data_time: 0.0216 memory: 14901 loss: 1.7564 loss_prob: 1.0113 loss_thr: 0.5798 loss_db: 0.1653 2022/11/02 15:06:16 - mmengine - INFO - Epoch(train) [277][30/63] lr: 1.7093e-03 eta: 9:29:06 time: 0.5869 data_time: 0.0309 memory: 14901 loss: 1.7190 loss_prob: 0.9684 loss_thr: 0.5901 loss_db: 0.1605 2022/11/02 15:06:19 - mmengine - INFO - Epoch(train) [277][35/63] lr: 1.7093e-03 eta: 9:29:06 time: 0.5627 data_time: 0.0316 memory: 14901 loss: 1.7071 loss_prob: 0.9503 loss_thr: 0.5981 loss_db: 0.1587 2022/11/02 15:06:21 - mmengine - INFO - Epoch(train) [277][40/63] lr: 1.7093e-03 eta: 9:28:58 time: 0.5255 data_time: 0.0130 memory: 14901 loss: 1.5579 loss_prob: 0.8718 loss_thr: 0.5408 loss_db: 0.1453 2022/11/02 15:06:25 - mmengine - INFO - Epoch(train) [277][45/63] lr: 1.7093e-03 eta: 9:28:58 time: 0.5813 data_time: 0.0122 memory: 14901 loss: 1.5736 loss_prob: 0.8811 loss_thr: 0.5480 loss_db: 0.1445 2022/11/02 15:06:27 - mmengine - INFO - Epoch(train) [277][50/63] lr: 1.7093e-03 eta: 9:28:53 time: 0.5916 data_time: 0.0212 memory: 14901 loss: 1.6827 loss_prob: 0.9392 loss_thr: 0.5869 loss_db: 0.1567 2022/11/02 15:06:30 - mmengine - INFO - Epoch(train) [277][55/63] lr: 1.7093e-03 eta: 9:28:53 time: 0.5377 data_time: 0.0222 memory: 14901 loss: 1.8201 loss_prob: 1.0243 loss_thr: 0.6259 loss_db: 0.1699 2022/11/02 15:06:33 - mmengine - INFO - Epoch(train) [277][60/63] lr: 1.7093e-03 eta: 9:28:45 time: 0.5311 data_time: 0.0082 memory: 14901 loss: 1.7312 loss_prob: 0.9766 loss_thr: 0.5941 loss_db: 0.1606 2022/11/02 15:06:34 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:06:39 - mmengine - INFO - Epoch(train) [278][5/63] lr: 1.7076e-03 eta: 9:28:45 time: 0.7383 data_time: 0.2413 memory: 14901 loss: 1.7906 loss_prob: 1.0349 loss_thr: 0.5820 loss_db: 0.1736 2022/11/02 15:06:42 - mmengine - INFO - Epoch(train) [278][10/63] lr: 1.7076e-03 eta: 9:28:39 time: 0.8236 data_time: 0.2413 memory: 14901 loss: 1.8108 loss_prob: 1.0586 loss_thr: 0.5804 loss_db: 0.1717 2022/11/02 15:06:45 - mmengine - INFO - Epoch(train) [278][15/63] lr: 1.7076e-03 eta: 9:28:39 time: 0.6082 data_time: 0.0074 memory: 14901 loss: 1.6153 loss_prob: 0.9192 loss_thr: 0.5441 loss_db: 0.1520 2022/11/02 15:06:48 - mmengine - INFO - Epoch(train) [278][20/63] lr: 1.7076e-03 eta: 9:28:34 time: 0.5887 data_time: 0.0117 memory: 14901 loss: 1.5919 loss_prob: 0.8896 loss_thr: 0.5540 loss_db: 0.1484 2022/11/02 15:06:51 - mmengine - INFO - Epoch(train) [278][25/63] lr: 1.7076e-03 eta: 9:28:34 time: 0.5834 data_time: 0.0368 memory: 14901 loss: 1.6926 loss_prob: 0.9561 loss_thr: 0.5827 loss_db: 0.1539 2022/11/02 15:06:54 - mmengine - INFO - Epoch(train) [278][30/63] lr: 1.7076e-03 eta: 9:28:27 time: 0.5585 data_time: 0.0377 memory: 14901 loss: 1.6860 loss_prob: 0.9619 loss_thr: 0.5689 loss_db: 0.1553 2022/11/02 15:06:57 - mmengine - INFO - Epoch(train) [278][35/63] lr: 1.7076e-03 eta: 9:28:27 time: 0.5703 data_time: 0.0130 memory: 14901 loss: 1.7313 loss_prob: 0.9995 loss_thr: 0.5634 loss_db: 0.1684 2022/11/02 15:06:59 - mmengine - INFO - Epoch(train) [278][40/63] lr: 1.7076e-03 eta: 9:28:19 time: 0.5424 data_time: 0.0084 memory: 14901 loss: 1.7709 loss_prob: 1.0270 loss_thr: 0.5710 loss_db: 0.1729 2022/11/02 15:07:02 - mmengine - INFO - Epoch(train) [278][45/63] lr: 1.7076e-03 eta: 9:28:19 time: 0.5841 data_time: 0.0100 memory: 14901 loss: 1.7315 loss_prob: 0.9977 loss_thr: 0.5697 loss_db: 0.1640 2022/11/02 15:07:05 - mmengine - INFO - Epoch(train) [278][50/63] lr: 1.7076e-03 eta: 9:28:15 time: 0.6227 data_time: 0.0324 memory: 14901 loss: 1.6840 loss_prob: 0.9601 loss_thr: 0.5647 loss_db: 0.1592 2022/11/02 15:07:08 - mmengine - INFO - Epoch(train) [278][55/63] lr: 1.7076e-03 eta: 9:28:15 time: 0.5526 data_time: 0.0321 memory: 14901 loss: 1.6828 loss_prob: 0.9598 loss_thr: 0.5621 loss_db: 0.1609 2022/11/02 15:07:11 - mmengine - INFO - Epoch(train) [278][60/63] lr: 1.7076e-03 eta: 9:28:08 time: 0.5510 data_time: 0.0116 memory: 14901 loss: 1.7525 loss_prob: 1.0155 loss_thr: 0.5698 loss_db: 0.1673 2022/11/02 15:07:12 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:07:18 - mmengine - INFO - Epoch(train) [279][5/63] lr: 1.7060e-03 eta: 9:28:08 time: 0.8308 data_time: 0.2371 memory: 14901 loss: 2.0014 loss_prob: 1.1940 loss_thr: 0.6141 loss_db: 0.1933 2022/11/02 15:07:21 - mmengine - INFO - Epoch(train) [279][10/63] lr: 1.7060e-03 eta: 9:28:03 time: 0.8535 data_time: 0.2372 memory: 14901 loss: 1.9062 loss_prob: 1.1215 loss_thr: 0.5997 loss_db: 0.1850 2022/11/02 15:07:24 - mmengine - INFO - Epoch(train) [279][15/63] lr: 1.7060e-03 eta: 9:28:03 time: 0.5525 data_time: 0.0077 memory: 14901 loss: 1.6731 loss_prob: 0.9449 loss_thr: 0.5728 loss_db: 0.1554 2022/11/02 15:07:26 - mmengine - INFO - Epoch(train) [279][20/63] lr: 1.7060e-03 eta: 9:27:57 time: 0.5730 data_time: 0.0132 memory: 14901 loss: 1.7936 loss_prob: 1.0373 loss_thr: 0.5849 loss_db: 0.1714 2022/11/02 15:07:29 - mmengine - INFO - Epoch(train) [279][25/63] lr: 1.7060e-03 eta: 9:27:57 time: 0.5755 data_time: 0.0408 memory: 14901 loss: 1.7284 loss_prob: 0.9913 loss_thr: 0.5760 loss_db: 0.1611 2022/11/02 15:07:33 - mmengine - INFO - Epoch(train) [279][30/63] lr: 1.7060e-03 eta: 9:27:53 time: 0.6474 data_time: 0.0424 memory: 14901 loss: 1.6921 loss_prob: 0.9533 loss_thr: 0.5850 loss_db: 0.1538 2022/11/02 15:07:36 - mmengine - INFO - Epoch(train) [279][35/63] lr: 1.7060e-03 eta: 9:27:53 time: 0.6284 data_time: 0.0209 memory: 14901 loss: 1.9109 loss_prob: 1.1352 loss_thr: 0.5919 loss_db: 0.1838 2022/11/02 15:07:38 - mmengine - INFO - Epoch(train) [279][40/63] lr: 1.7060e-03 eta: 9:27:46 time: 0.5506 data_time: 0.0136 memory: 14901 loss: 1.8589 loss_prob: 1.1052 loss_thr: 0.5757 loss_db: 0.1780 2022/11/02 15:07:41 - mmengine - INFO - Epoch(train) [279][45/63] lr: 1.7060e-03 eta: 9:27:46 time: 0.5269 data_time: 0.0099 memory: 14901 loss: 1.7477 loss_prob: 0.9979 loss_thr: 0.5917 loss_db: 0.1582 2022/11/02 15:07:44 - mmengine - INFO - Epoch(train) [279][50/63] lr: 1.7060e-03 eta: 9:27:39 time: 0.5683 data_time: 0.0235 memory: 14901 loss: 1.7670 loss_prob: 1.0006 loss_thr: 0.6043 loss_db: 0.1621 2022/11/02 15:07:47 - mmengine - INFO - Epoch(train) [279][55/63] lr: 1.7060e-03 eta: 9:27:39 time: 0.5790 data_time: 0.0236 memory: 14901 loss: 1.8345 loss_prob: 1.0549 loss_thr: 0.6068 loss_db: 0.1728 2022/11/02 15:07:49 - mmengine - INFO - Epoch(train) [279][60/63] lr: 1.7060e-03 eta: 9:27:31 time: 0.5089 data_time: 0.0120 memory: 14901 loss: 1.9078 loss_prob: 1.1098 loss_thr: 0.6191 loss_db: 0.1789 2022/11/02 15:07:50 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:07:56 - mmengine - INFO - Epoch(train) [280][5/63] lr: 1.7043e-03 eta: 9:27:31 time: 0.7741 data_time: 0.1963 memory: 14901 loss: 1.6753 loss_prob: 0.9477 loss_thr: 0.5785 loss_db: 0.1491 2022/11/02 15:07:58 - mmengine - INFO - Epoch(train) [280][10/63] lr: 1.7043e-03 eta: 9:27:24 time: 0.7913 data_time: 0.1966 memory: 14901 loss: 1.8270 loss_prob: 1.0411 loss_thr: 0.6145 loss_db: 0.1714 2022/11/02 15:08:01 - mmengine - INFO - Epoch(train) [280][15/63] lr: 1.7043e-03 eta: 9:27:24 time: 0.4969 data_time: 0.0113 memory: 14901 loss: 1.7560 loss_prob: 0.9934 loss_thr: 0.5953 loss_db: 0.1673 2022/11/02 15:08:03 - mmengine - INFO - Epoch(train) [280][20/63] lr: 1.7043e-03 eta: 9:27:16 time: 0.5021 data_time: 0.0147 memory: 14901 loss: 1.6678 loss_prob: 0.9330 loss_thr: 0.5759 loss_db: 0.1589 2022/11/02 15:08:06 - mmengine - INFO - Epoch(train) [280][25/63] lr: 1.7043e-03 eta: 9:27:16 time: 0.5107 data_time: 0.0276 memory: 14901 loss: 1.7566 loss_prob: 0.9928 loss_thr: 0.5972 loss_db: 0.1665 2022/11/02 15:08:09 - mmengine - INFO - Epoch(train) [280][30/63] lr: 1.7043e-03 eta: 9:27:07 time: 0.5144 data_time: 0.0305 memory: 14901 loss: 1.8651 loss_prob: 1.0780 loss_thr: 0.6096 loss_db: 0.1775 2022/11/02 15:08:11 - mmengine - INFO - Epoch(train) [280][35/63] lr: 1.7043e-03 eta: 9:27:07 time: 0.4958 data_time: 0.0193 memory: 14901 loss: 1.8222 loss_prob: 1.0583 loss_thr: 0.5897 loss_db: 0.1741 2022/11/02 15:08:13 - mmengine - INFO - Epoch(train) [280][40/63] lr: 1.7043e-03 eta: 9:26:58 time: 0.4950 data_time: 0.0239 memory: 14901 loss: 1.7671 loss_prob: 1.0182 loss_thr: 0.5771 loss_db: 0.1718 2022/11/02 15:08:16 - mmengine - INFO - Epoch(train) [280][45/63] lr: 1.7043e-03 eta: 9:26:58 time: 0.5099 data_time: 0.0235 memory: 14901 loss: 1.8864 loss_prob: 1.1060 loss_thr: 0.6031 loss_db: 0.1772 2022/11/02 15:08:19 - mmengine - INFO - Epoch(train) [280][50/63] lr: 1.7043e-03 eta: 9:26:51 time: 0.5291 data_time: 0.0226 memory: 14901 loss: 1.8992 loss_prob: 1.1077 loss_thr: 0.6158 loss_db: 0.1757 2022/11/02 15:08:21 - mmengine - INFO - Epoch(train) [280][55/63] lr: 1.7043e-03 eta: 9:26:51 time: 0.5437 data_time: 0.0221 memory: 14901 loss: 1.8584 loss_prob: 1.0648 loss_thr: 0.6169 loss_db: 0.1767 2022/11/02 15:08:24 - mmengine - INFO - Epoch(train) [280][60/63] lr: 1.7043e-03 eta: 9:26:43 time: 0.5279 data_time: 0.0198 memory: 14901 loss: 2.0122 loss_prob: 1.2037 loss_thr: 0.6165 loss_db: 0.1920 2022/11/02 15:08:25 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:08:25 - mmengine - INFO - Saving checkpoint at 280 epochs 2022/11/02 15:08:29 - mmengine - INFO - Epoch(val) [280][5/500] eta: 9:26:43 time: 0.0497 data_time: 0.0078 memory: 14901 2022/11/02 15:08:29 - mmengine - INFO - Epoch(val) [280][10/500] eta: 0:00:21 time: 0.0448 data_time: 0.0046 memory: 1008 2022/11/02 15:08:29 - mmengine - INFO - Epoch(val) [280][15/500] eta: 0:00:21 time: 0.0387 data_time: 0.0021 memory: 1008 2022/11/02 15:08:30 - mmengine - INFO - Epoch(val) [280][20/500] eta: 0:00:18 time: 0.0376 data_time: 0.0023 memory: 1008 2022/11/02 15:08:30 - mmengine - INFO - Epoch(val) [280][25/500] eta: 0:00:18 time: 0.0376 data_time: 0.0023 memory: 1008 2022/11/02 15:08:30 - mmengine - INFO - Epoch(val) [280][30/500] eta: 0:00:20 time: 0.0442 data_time: 0.0025 memory: 1008 2022/11/02 15:08:30 - mmengine - INFO - Epoch(val) [280][35/500] eta: 0:00:20 time: 0.0427 data_time: 0.0024 memory: 1008 2022/11/02 15:08:30 - mmengine - INFO - Epoch(val) [280][40/500] eta: 0:00:19 time: 0.0415 data_time: 0.0024 memory: 1008 2022/11/02 15:08:31 - mmengine - INFO - Epoch(val) [280][45/500] eta: 0:00:19 time: 0.0433 data_time: 0.0024 memory: 1008 2022/11/02 15:08:31 - mmengine - INFO - Epoch(val) [280][50/500] eta: 0:00:44 time: 0.0986 data_time: 0.0606 memory: 1008 2022/11/02 15:08:32 - mmengine - INFO - Epoch(val) [280][55/500] eta: 0:00:44 time: 0.1005 data_time: 0.0602 memory: 1008 2022/11/02 15:08:32 - mmengine - INFO - Epoch(val) [280][60/500] eta: 0:00:17 time: 0.0393 data_time: 0.0017 memory: 1008 2022/11/02 15:08:32 - mmengine - INFO - Epoch(val) [280][65/500] eta: 0:00:17 time: 0.0430 data_time: 0.0024 memory: 1008 2022/11/02 15:08:32 - mmengine - INFO - Epoch(val) [280][70/500] eta: 0:00:20 time: 0.0466 data_time: 0.0029 memory: 1008 2022/11/02 15:08:32 - mmengine - INFO - Epoch(val) [280][75/500] eta: 0:00:20 time: 0.0396 data_time: 0.0027 memory: 1008 2022/11/02 15:08:33 - mmengine - INFO - Epoch(val) [280][80/500] eta: 0:00:15 time: 0.0357 data_time: 0.0025 memory: 1008 2022/11/02 15:08:33 - mmengine - INFO - Epoch(val) [280][85/500] eta: 0:00:15 time: 0.0346 data_time: 0.0025 memory: 1008 2022/11/02 15:08:33 - mmengine - INFO - Epoch(val) [280][90/500] eta: 0:00:16 time: 0.0394 data_time: 0.0025 memory: 1008 2022/11/02 15:08:33 - mmengine - INFO - Epoch(val) [280][95/500] eta: 0:00:16 time: 0.0434 data_time: 0.0026 memory: 1008 2022/11/02 15:08:33 - mmengine - INFO - Epoch(val) [280][100/500] eta: 0:00:15 time: 0.0388 data_time: 0.0026 memory: 1008 2022/11/02 15:08:34 - mmengine - INFO - Epoch(val) [280][105/500] eta: 0:00:15 time: 0.0373 data_time: 0.0028 memory: 1008 2022/11/02 15:08:34 - mmengine - INFO - Epoch(val) [280][110/500] eta: 0:00:15 time: 0.0394 data_time: 0.0027 memory: 1008 2022/11/02 15:08:34 - mmengine - INFO - Epoch(val) [280][115/500] eta: 0:00:15 time: 0.0402 data_time: 0.0024 memory: 1008 2022/11/02 15:08:34 - mmengine - INFO - Epoch(val) [280][120/500] eta: 0:00:15 time: 0.0413 data_time: 0.0024 memory: 1008 2022/11/02 15:08:34 - mmengine - INFO - Epoch(val) [280][125/500] eta: 0:00:15 time: 0.0386 data_time: 0.0023 memory: 1008 2022/11/02 15:08:35 - mmengine - INFO - Epoch(val) [280][130/500] eta: 0:00:12 time: 0.0348 data_time: 0.0023 memory: 1008 2022/11/02 15:08:35 - mmengine - INFO - Epoch(val) [280][135/500] eta: 0:00:12 time: 0.0380 data_time: 0.0025 memory: 1008 2022/11/02 15:08:35 - mmengine - INFO - Epoch(val) [280][140/500] eta: 0:00:14 time: 0.0395 data_time: 0.0025 memory: 1008 2022/11/02 15:08:35 - mmengine - INFO - Epoch(val) [280][145/500] eta: 0:00:14 time: 0.0403 data_time: 0.0023 memory: 1008 2022/11/02 15:08:35 - mmengine - INFO - Epoch(val) [280][150/500] eta: 0:00:15 time: 0.0437 data_time: 0.0025 memory: 1008 2022/11/02 15:08:36 - mmengine - INFO - Epoch(val) [280][155/500] eta: 0:00:15 time: 0.0483 data_time: 0.0025 memory: 1008 2022/11/02 15:08:36 - mmengine - INFO - Epoch(val) [280][160/500] eta: 0:00:16 time: 0.0489 data_time: 0.0025 memory: 1008 2022/11/02 15:08:36 - mmengine - INFO - Epoch(val) [280][165/500] eta: 0:00:16 time: 0.0429 data_time: 0.0024 memory: 1008 2022/11/02 15:08:36 - mmengine - INFO - Epoch(val) [280][170/500] eta: 0:00:14 time: 0.0427 data_time: 0.0023 memory: 1008 2022/11/02 15:08:37 - mmengine - INFO - Epoch(val) [280][175/500] eta: 0:00:14 time: 0.0430 data_time: 0.0026 memory: 1008 2022/11/02 15:08:37 - mmengine - INFO - Epoch(val) [280][180/500] eta: 0:00:12 time: 0.0399 data_time: 0.0025 memory: 1008 2022/11/02 15:08:37 - mmengine - INFO - Epoch(val) [280][185/500] eta: 0:00:12 time: 0.0400 data_time: 0.0023 memory: 1008 2022/11/02 15:08:37 - mmengine - INFO - Epoch(val) [280][190/500] eta: 0:00:12 time: 0.0408 data_time: 0.0023 memory: 1008 2022/11/02 15:08:37 - mmengine - INFO - Epoch(val) [280][195/500] eta: 0:00:12 time: 0.0395 data_time: 0.0023 memory: 1008 2022/11/02 15:08:38 - mmengine - INFO - Epoch(val) [280][200/500] eta: 0:00:13 time: 0.0441 data_time: 0.0023 memory: 1008 2022/11/02 15:08:38 - mmengine - INFO - Epoch(val) [280][205/500] eta: 0:00:13 time: 0.0426 data_time: 0.0021 memory: 1008 2022/11/02 15:08:38 - mmengine - INFO - Epoch(val) [280][210/500] eta: 0:00:11 time: 0.0388 data_time: 0.0022 memory: 1008 2022/11/02 15:08:38 - mmengine - INFO - Epoch(val) [280][215/500] eta: 0:00:11 time: 0.0407 data_time: 0.0028 memory: 1008 2022/11/02 15:08:38 - mmengine - INFO - Epoch(val) [280][220/500] eta: 0:00:10 time: 0.0377 data_time: 0.0026 memory: 1008 2022/11/02 15:08:39 - mmengine - INFO - Epoch(val) [280][225/500] eta: 0:00:10 time: 0.0392 data_time: 0.0021 memory: 1008 2022/11/02 15:08:39 - mmengine - INFO - Epoch(val) [280][230/500] eta: 0:00:10 time: 0.0377 data_time: 0.0022 memory: 1008 2022/11/02 15:08:39 - mmengine - INFO - Epoch(val) [280][235/500] eta: 0:00:10 time: 0.0366 data_time: 0.0022 memory: 1008 2022/11/02 15:08:39 - mmengine - INFO - Epoch(val) [280][240/500] eta: 0:00:10 time: 0.0398 data_time: 0.0023 memory: 1008 2022/11/02 15:08:39 - mmengine - INFO - Epoch(val) [280][245/500] eta: 0:00:10 time: 0.0364 data_time: 0.0022 memory: 1008 2022/11/02 15:08:40 - mmengine - INFO - Epoch(val) [280][250/500] eta: 0:00:09 time: 0.0380 data_time: 0.0020 memory: 1008 2022/11/02 15:08:40 - mmengine - INFO - Epoch(val) [280][255/500] eta: 0:00:09 time: 0.0391 data_time: 0.0021 memory: 1008 2022/11/02 15:08:40 - mmengine - INFO - Epoch(val) [280][260/500] eta: 0:00:08 time: 0.0368 data_time: 0.0023 memory: 1008 2022/11/02 15:08:40 - mmengine - INFO - Epoch(val) [280][265/500] eta: 0:00:08 time: 0.0413 data_time: 0.0024 memory: 1008 2022/11/02 15:08:40 - mmengine - INFO - Epoch(val) [280][270/500] eta: 0:00:09 time: 0.0424 data_time: 0.0025 memory: 1008 2022/11/02 15:08:40 - mmengine - INFO - Epoch(val) [280][275/500] eta: 0:00:09 time: 0.0369 data_time: 0.0023 memory: 1008 2022/11/02 15:08:41 - mmengine - INFO - Epoch(val) [280][280/500] eta: 0:00:08 time: 0.0388 data_time: 0.0023 memory: 1008 2022/11/02 15:08:41 - mmengine - INFO - Epoch(val) [280][285/500] eta: 0:00:08 time: 0.0394 data_time: 0.0023 memory: 1008 2022/11/02 15:08:41 - mmengine - INFO - Epoch(val) [280][290/500] eta: 0:00:08 time: 0.0392 data_time: 0.0023 memory: 1008 2022/11/02 15:08:41 - mmengine - INFO - Epoch(val) [280][295/500] eta: 0:00:08 time: 0.0427 data_time: 0.0027 memory: 1008 2022/11/02 15:08:41 - mmengine - INFO - Epoch(val) [280][300/500] eta: 0:00:08 time: 0.0406 data_time: 0.0030 memory: 1008 2022/11/02 15:08:42 - mmengine - INFO - Epoch(val) [280][305/500] eta: 0:00:08 time: 0.0381 data_time: 0.0027 memory: 1008 2022/11/02 15:08:42 - mmengine - INFO - Epoch(val) [280][310/500] eta: 0:00:07 time: 0.0420 data_time: 0.0031 memory: 1008 2022/11/02 15:08:42 - mmengine - INFO - Epoch(val) [280][315/500] eta: 0:00:07 time: 0.0457 data_time: 0.0031 memory: 1008 2022/11/02 15:08:42 - mmengine - INFO - Epoch(val) [280][320/500] eta: 0:00:07 time: 0.0414 data_time: 0.0025 memory: 1008 2022/11/02 15:08:43 - mmengine - INFO - Epoch(val) [280][325/500] eta: 0:00:07 time: 0.0549 data_time: 0.0024 memory: 1008 2022/11/02 15:08:43 - mmengine - INFO - Epoch(val) [280][330/500] eta: 0:00:09 time: 0.0540 data_time: 0.0025 memory: 1008 2022/11/02 15:08:43 - mmengine - INFO - Epoch(val) [280][335/500] eta: 0:00:09 time: 0.0364 data_time: 0.0026 memory: 1008 2022/11/02 15:08:43 - mmengine - INFO - Epoch(val) [280][340/500] eta: 0:00:08 time: 0.0536 data_time: 0.0026 memory: 1008 2022/11/02 15:08:44 - mmengine - INFO - Epoch(val) [280][345/500] eta: 0:00:08 time: 0.0558 data_time: 0.0025 memory: 1008 2022/11/02 15:08:44 - mmengine - INFO - Epoch(val) [280][350/500] eta: 0:00:07 time: 0.0498 data_time: 0.0027 memory: 1008 2022/11/02 15:08:44 - mmengine - INFO - Epoch(val) [280][355/500] eta: 0:00:07 time: 0.0490 data_time: 0.0026 memory: 1008 2022/11/02 15:08:44 - mmengine - INFO - Epoch(val) [280][360/500] eta: 0:00:06 time: 0.0440 data_time: 0.0027 memory: 1008 2022/11/02 15:08:45 - mmengine - INFO - Epoch(val) [280][365/500] eta: 0:00:06 time: 0.0471 data_time: 0.0031 memory: 1008 2022/11/02 15:08:45 - mmengine - INFO - Epoch(val) [280][370/500] eta: 0:00:05 time: 0.0408 data_time: 0.0027 memory: 1008 2022/11/02 15:08:45 - mmengine - INFO - Epoch(val) [280][375/500] eta: 0:00:05 time: 0.0355 data_time: 0.0024 memory: 1008 2022/11/02 15:08:45 - mmengine - INFO - Epoch(val) [280][380/500] eta: 0:00:04 time: 0.0377 data_time: 0.0023 memory: 1008 2022/11/02 15:08:45 - mmengine - INFO - Epoch(val) [280][385/500] eta: 0:00:04 time: 0.0401 data_time: 0.0023 memory: 1008 2022/11/02 15:08:46 - mmengine - INFO - Epoch(val) [280][390/500] eta: 0:00:04 time: 0.0394 data_time: 0.0023 memory: 1008 2022/11/02 15:08:46 - mmengine - INFO - Epoch(val) [280][395/500] eta: 0:00:04 time: 0.0375 data_time: 0.0022 memory: 1008 2022/11/02 15:08:46 - mmengine - INFO - Epoch(val) [280][400/500] eta: 0:00:03 time: 0.0374 data_time: 0.0023 memory: 1008 2022/11/02 15:08:46 - mmengine - INFO - Epoch(val) [280][405/500] eta: 0:00:03 time: 0.0383 data_time: 0.0023 memory: 1008 2022/11/02 15:08:46 - mmengine - INFO - Epoch(val) [280][410/500] eta: 0:00:03 time: 0.0403 data_time: 0.0022 memory: 1008 2022/11/02 15:08:46 - mmengine - INFO - Epoch(val) [280][415/500] eta: 0:00:03 time: 0.0394 data_time: 0.0023 memory: 1008 2022/11/02 15:08:47 - mmengine - INFO - Epoch(val) [280][420/500] eta: 0:00:08 time: 0.1096 data_time: 0.0766 memory: 1008 2022/11/02 15:08:48 - mmengine - INFO - Epoch(val) [280][425/500] eta: 0:00:08 time: 0.1118 data_time: 0.0764 memory: 1008 2022/11/02 15:08:48 - mmengine - INFO - Epoch(val) [280][430/500] eta: 0:00:02 time: 0.0390 data_time: 0.0021 memory: 1008 2022/11/02 15:08:48 - mmengine - INFO - Epoch(val) [280][435/500] eta: 0:00:02 time: 0.0389 data_time: 0.0023 memory: 1008 2022/11/02 15:08:48 - mmengine - INFO - Epoch(val) [280][440/500] eta: 0:00:02 time: 0.0399 data_time: 0.0025 memory: 1008 2022/11/02 15:08:48 - mmengine - INFO - Epoch(val) [280][445/500] eta: 0:00:02 time: 0.0404 data_time: 0.0027 memory: 1008 2022/11/02 15:08:49 - mmengine - INFO - Epoch(val) [280][450/500] eta: 0:00:02 time: 0.0415 data_time: 0.0028 memory: 1008 2022/11/02 15:08:49 - mmengine - INFO - Epoch(val) [280][455/500] eta: 0:00:02 time: 0.0424 data_time: 0.0029 memory: 1008 2022/11/02 15:08:49 - mmengine - INFO - Epoch(val) [280][460/500] eta: 0:00:01 time: 0.0436 data_time: 0.0032 memory: 1008 2022/11/02 15:08:49 - mmengine - INFO - Epoch(val) [280][465/500] eta: 0:00:01 time: 0.0413 data_time: 0.0036 memory: 1008 2022/11/02 15:08:49 - mmengine - INFO - Epoch(val) [280][470/500] eta: 0:00:01 time: 0.0411 data_time: 0.0033 memory: 1008 2022/11/02 15:08:50 - mmengine - INFO - Epoch(val) [280][475/500] eta: 0:00:01 time: 0.0432 data_time: 0.0030 memory: 1008 2022/11/02 15:08:50 - mmengine - INFO - Epoch(val) [280][480/500] eta: 0:00:00 time: 0.0412 data_time: 0.0030 memory: 1008 2022/11/02 15:08:50 - mmengine - INFO - Epoch(val) [280][485/500] eta: 0:00:00 time: 0.0377 data_time: 0.0024 memory: 1008 2022/11/02 15:08:50 - mmengine - INFO - Epoch(val) [280][490/500] eta: 0:00:00 time: 0.0400 data_time: 0.0022 memory: 1008 2022/11/02 15:08:50 - mmengine - INFO - Epoch(val) [280][495/500] eta: 0:00:00 time: 0.0434 data_time: 0.0024 memory: 1008 2022/11/02 15:08:51 - mmengine - INFO - Epoch(val) [280][500/500] eta: 0:00:00 time: 0.0390 data_time: 0.0023 memory: 1008 2022/11/02 15:08:51 - mmengine - INFO - Evaluating hmean-iou... 2022/11/02 15:08:51 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.8166, precision: 0.5342, hmean: 0.6458 2022/11/02 15:08:51 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.8166, precision: 0.6350, hmean: 0.7144 2022/11/02 15:08:51 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.8151, precision: 0.7060, hmean: 0.7566 2022/11/02 15:08:51 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.8031, precision: 0.7805, hmean: 0.7916 2022/11/02 15:08:51 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.7626, precision: 0.8539, hmean: 0.8057 2022/11/02 15:08:51 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.4242, precision: 0.9402, hmean: 0.5846 2022/11/02 15:08:51 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0014, precision: 0.7500, hmean: 0.0029 2022/11/02 15:08:51 - mmengine - INFO - Epoch(val) [280][500/500] icdar/precision: 0.8539 icdar/recall: 0.7626 icdar/hmean: 0.8057 2022/11/02 15:08:56 - mmengine - INFO - Epoch(train) [281][5/63] lr: 1.7026e-03 eta: 0:00:00 time: 0.7368 data_time: 0.2182 memory: 14901 loss: 2.1322 loss_prob: 1.2990 loss_thr: 0.6234 loss_db: 0.2098 2022/11/02 15:08:58 - mmengine - INFO - Epoch(train) [281][10/63] lr: 1.7026e-03 eta: 9:26:35 time: 0.7577 data_time: 0.2145 memory: 14901 loss: 1.8069 loss_prob: 1.0273 loss_thr: 0.6119 loss_db: 0.1677 2022/11/02 15:09:01 - mmengine - INFO - Epoch(train) [281][15/63] lr: 1.7026e-03 eta: 9:26:35 time: 0.4761 data_time: 0.0103 memory: 14901 loss: 1.8302 loss_prob: 1.0538 loss_thr: 0.6064 loss_db: 0.1700 2022/11/02 15:09:03 - mmengine - INFO - Epoch(train) [281][20/63] lr: 1.7026e-03 eta: 9:26:25 time: 0.4725 data_time: 0.0103 memory: 14901 loss: 1.9062 loss_prob: 1.1048 loss_thr: 0.6221 loss_db: 0.1793 2022/11/02 15:09:06 - mmengine - INFO - Epoch(train) [281][25/63] lr: 1.7026e-03 eta: 9:26:25 time: 0.5243 data_time: 0.0130 memory: 14901 loss: 1.8310 loss_prob: 1.0614 loss_thr: 0.5956 loss_db: 0.1740 2022/11/02 15:09:09 - mmengine - INFO - Epoch(train) [281][30/63] lr: 1.7026e-03 eta: 9:26:19 time: 0.5663 data_time: 0.0420 memory: 14901 loss: 1.7553 loss_prob: 1.0169 loss_thr: 0.5709 loss_db: 0.1675 2022/11/02 15:09:11 - mmengine - INFO - Epoch(train) [281][35/63] lr: 1.7026e-03 eta: 9:26:19 time: 0.5364 data_time: 0.0339 memory: 14901 loss: 1.7823 loss_prob: 1.0108 loss_thr: 0.6042 loss_db: 0.1673 2022/11/02 15:09:14 - mmengine - INFO - Epoch(train) [281][40/63] lr: 1.7026e-03 eta: 9:26:10 time: 0.5015 data_time: 0.0051 memory: 14901 loss: 1.7765 loss_prob: 1.0087 loss_thr: 0.6003 loss_db: 0.1675 2022/11/02 15:09:16 - mmengine - INFO - Epoch(train) [281][45/63] lr: 1.7026e-03 eta: 9:26:10 time: 0.5116 data_time: 0.0091 memory: 14901 loss: 1.6617 loss_prob: 0.9385 loss_thr: 0.5689 loss_db: 0.1543 2022/11/02 15:09:19 - mmengine - INFO - Epoch(train) [281][50/63] lr: 1.7026e-03 eta: 9:26:02 time: 0.5200 data_time: 0.0187 memory: 14901 loss: 1.6967 loss_prob: 0.9589 loss_thr: 0.5819 loss_db: 0.1560 2022/11/02 15:09:22 - mmengine - INFO - Epoch(train) [281][55/63] lr: 1.7026e-03 eta: 9:26:02 time: 0.5742 data_time: 0.0261 memory: 14901 loss: 1.7173 loss_prob: 0.9788 loss_thr: 0.5776 loss_db: 0.1610 2022/11/02 15:09:24 - mmengine - INFO - Epoch(train) [281][60/63] lr: 1.7026e-03 eta: 9:25:55 time: 0.5529 data_time: 0.0207 memory: 14901 loss: 1.6282 loss_prob: 0.9120 loss_thr: 0.5673 loss_db: 0.1488 2022/11/02 15:09:26 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:09:32 - mmengine - INFO - Epoch(train) [282][5/63] lr: 1.7010e-03 eta: 9:25:55 time: 0.8457 data_time: 0.2537 memory: 14901 loss: 1.7147 loss_prob: 0.9557 loss_thr: 0.6001 loss_db: 0.1589 2022/11/02 15:09:35 - mmengine - INFO - Epoch(train) [282][10/63] lr: 1.7010e-03 eta: 9:25:52 time: 0.8828 data_time: 0.2552 memory: 14901 loss: 1.5876 loss_prob: 0.8909 loss_thr: 0.5510 loss_db: 0.1456 2022/11/02 15:09:37 - mmengine - INFO - Epoch(train) [282][15/63] lr: 1.7010e-03 eta: 9:25:52 time: 0.5144 data_time: 0.0112 memory: 14901 loss: 1.6448 loss_prob: 0.9289 loss_thr: 0.5642 loss_db: 0.1517 2022/11/02 15:09:40 - mmengine - INFO - Epoch(train) [282][20/63] lr: 1.7010e-03 eta: 9:25:45 time: 0.5728 data_time: 0.0173 memory: 14901 loss: 1.6643 loss_prob: 0.9331 loss_thr: 0.5782 loss_db: 0.1530 2022/11/02 15:09:43 - mmengine - INFO - Epoch(train) [282][25/63] lr: 1.7010e-03 eta: 9:25:45 time: 0.5533 data_time: 0.0192 memory: 14901 loss: 1.5940 loss_prob: 0.8883 loss_thr: 0.5571 loss_db: 0.1486 2022/11/02 15:09:45 - mmengine - INFO - Epoch(train) [282][30/63] lr: 1.7010e-03 eta: 9:25:37 time: 0.5207 data_time: 0.0281 memory: 14901 loss: 1.5357 loss_prob: 0.8572 loss_thr: 0.5335 loss_db: 0.1450 2022/11/02 15:09:48 - mmengine - INFO - Epoch(train) [282][35/63] lr: 1.7010e-03 eta: 9:25:37 time: 0.5191 data_time: 0.0229 memory: 14901 loss: 1.6902 loss_prob: 0.9673 loss_thr: 0.5633 loss_db: 0.1597 2022/11/02 15:09:50 - mmengine - INFO - Epoch(train) [282][40/63] lr: 1.7010e-03 eta: 9:25:28 time: 0.4881 data_time: 0.0114 memory: 14901 loss: 1.8754 loss_prob: 1.0841 loss_thr: 0.6122 loss_db: 0.1791 2022/11/02 15:09:53 - mmengine - INFO - Epoch(train) [282][45/63] lr: 1.7010e-03 eta: 9:25:28 time: 0.4918 data_time: 0.0138 memory: 14901 loss: 1.7914 loss_prob: 1.0273 loss_thr: 0.5939 loss_db: 0.1701 2022/11/02 15:09:56 - mmengine - INFO - Epoch(train) [282][50/63] lr: 1.7010e-03 eta: 9:25:21 time: 0.5404 data_time: 0.0224 memory: 14901 loss: 1.7368 loss_prob: 0.9911 loss_thr: 0.5814 loss_db: 0.1643 2022/11/02 15:09:59 - mmengine - INFO - Epoch(train) [282][55/63] lr: 1.7010e-03 eta: 9:25:21 time: 0.5807 data_time: 0.0207 memory: 14901 loss: 1.7121 loss_prob: 0.9710 loss_thr: 0.5774 loss_db: 0.1637 2022/11/02 15:10:01 - mmengine - INFO - Epoch(train) [282][60/63] lr: 1.7010e-03 eta: 9:25:13 time: 0.5256 data_time: 0.0100 memory: 14901 loss: 1.7536 loss_prob: 1.0136 loss_thr: 0.5716 loss_db: 0.1684 2022/11/02 15:10:02 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:10:07 - mmengine - INFO - Epoch(train) [283][5/63] lr: 1.6993e-03 eta: 9:25:13 time: 0.6530 data_time: 0.2082 memory: 14901 loss: 1.7306 loss_prob: 0.9844 loss_thr: 0.5829 loss_db: 0.1633 2022/11/02 15:10:09 - mmengine - INFO - Epoch(train) [283][10/63] lr: 1.6993e-03 eta: 9:25:02 time: 0.6625 data_time: 0.2046 memory: 14901 loss: 1.7926 loss_prob: 1.0490 loss_thr: 0.5740 loss_db: 0.1697 2022/11/02 15:10:11 - mmengine - INFO - Epoch(train) [283][15/63] lr: 1.6993e-03 eta: 9:25:02 time: 0.4660 data_time: 0.0096 memory: 14901 loss: 1.6970 loss_prob: 0.9763 loss_thr: 0.5596 loss_db: 0.1611 2022/11/02 15:10:14 - mmengine - INFO - Epoch(train) [283][20/63] lr: 1.6993e-03 eta: 9:24:54 time: 0.5056 data_time: 0.0107 memory: 14901 loss: 1.6528 loss_prob: 0.9252 loss_thr: 0.5701 loss_db: 0.1576 2022/11/02 15:10:18 - mmengine - INFO - Epoch(train) [283][25/63] lr: 1.6993e-03 eta: 9:24:54 time: 0.6298 data_time: 0.0201 memory: 14901 loss: 1.5899 loss_prob: 0.8789 loss_thr: 0.5626 loss_db: 0.1485 2022/11/02 15:10:20 - mmengine - INFO - Epoch(train) [283][30/63] lr: 1.6993e-03 eta: 9:24:49 time: 0.6145 data_time: 0.0397 memory: 14901 loss: 1.7048 loss_prob: 0.9800 loss_thr: 0.5674 loss_db: 0.1574 2022/11/02 15:10:22 - mmengine - INFO - Epoch(train) [283][35/63] lr: 1.6993e-03 eta: 9:24:49 time: 0.4932 data_time: 0.0256 memory: 14901 loss: 1.9409 loss_prob: 1.1631 loss_thr: 0.5986 loss_db: 0.1792 2022/11/02 15:10:25 - mmengine - INFO - Epoch(train) [283][40/63] lr: 1.6993e-03 eta: 9:24:39 time: 0.4787 data_time: 0.0110 memory: 14901 loss: 1.8153 loss_prob: 1.0575 loss_thr: 0.5894 loss_db: 0.1684 2022/11/02 15:10:27 - mmengine - INFO - Epoch(train) [283][45/63] lr: 1.6993e-03 eta: 9:24:39 time: 0.4687 data_time: 0.0140 memory: 14901 loss: 1.7501 loss_prob: 0.9980 loss_thr: 0.5857 loss_db: 0.1664 2022/11/02 15:10:30 - mmengine - INFO - Epoch(train) [283][50/63] lr: 1.6993e-03 eta: 9:24:30 time: 0.4754 data_time: 0.0149 memory: 14901 loss: 1.8796 loss_prob: 1.0896 loss_thr: 0.6084 loss_db: 0.1816 2022/11/02 15:10:32 - mmengine - INFO - Epoch(train) [283][55/63] lr: 1.6993e-03 eta: 9:24:30 time: 0.5005 data_time: 0.0291 memory: 14901 loss: 1.7381 loss_prob: 0.9967 loss_thr: 0.5758 loss_db: 0.1656 2022/11/02 15:10:35 - mmengine - INFO - Epoch(train) [283][60/63] lr: 1.6993e-03 eta: 9:24:21 time: 0.5008 data_time: 0.0249 memory: 14901 loss: 1.6563 loss_prob: 0.9292 loss_thr: 0.5710 loss_db: 0.1562 2022/11/02 15:10:36 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:10:41 - mmengine - INFO - Epoch(train) [284][5/63] lr: 1.6976e-03 eta: 9:24:21 time: 0.7294 data_time: 0.2655 memory: 14901 loss: 1.7448 loss_prob: 0.9889 loss_thr: 0.5945 loss_db: 0.1613 2022/11/02 15:10:43 - mmengine - INFO - Epoch(train) [284][10/63] lr: 1.6976e-03 eta: 9:24:14 time: 0.7676 data_time: 0.2717 memory: 14901 loss: 1.6661 loss_prob: 0.9397 loss_thr: 0.5742 loss_db: 0.1523 2022/11/02 15:10:46 - mmengine - INFO - Epoch(train) [284][15/63] lr: 1.6976e-03 eta: 9:24:14 time: 0.5412 data_time: 0.0113 memory: 14901 loss: 1.7585 loss_prob: 0.9948 loss_thr: 0.5981 loss_db: 0.1656 2022/11/02 15:10:49 - mmengine - INFO - Epoch(train) [284][20/63] lr: 1.6976e-03 eta: 9:24:06 time: 0.5364 data_time: 0.0078 memory: 14901 loss: 1.8385 loss_prob: 1.0559 loss_thr: 0.6089 loss_db: 0.1736 2022/11/02 15:10:53 - mmengine - INFO - Epoch(train) [284][25/63] lr: 1.6976e-03 eta: 9:24:06 time: 0.6375 data_time: 0.0148 memory: 14901 loss: 1.8151 loss_prob: 1.0562 loss_thr: 0.5871 loss_db: 0.1718 2022/11/02 15:10:56 - mmengine - INFO - Epoch(train) [284][30/63] lr: 1.6976e-03 eta: 9:24:04 time: 0.7058 data_time: 0.0410 memory: 14901 loss: 1.7553 loss_prob: 1.0093 loss_thr: 0.5775 loss_db: 0.1685 2022/11/02 15:10:59 - mmengine - INFO - Epoch(train) [284][35/63] lr: 1.6976e-03 eta: 9:24:04 time: 0.6001 data_time: 0.0365 memory: 14901 loss: 1.6626 loss_prob: 0.9349 loss_thr: 0.5701 loss_db: 0.1576 2022/11/02 15:11:01 - mmengine - INFO - Epoch(train) [284][40/63] lr: 1.6976e-03 eta: 9:23:57 time: 0.5358 data_time: 0.0118 memory: 14901 loss: 1.6358 loss_prob: 0.9204 loss_thr: 0.5613 loss_db: 0.1541 2022/11/02 15:11:04 - mmengine - INFO - Epoch(train) [284][45/63] lr: 1.6976e-03 eta: 9:23:57 time: 0.4857 data_time: 0.0100 memory: 14901 loss: 1.8063 loss_prob: 1.0452 loss_thr: 0.5859 loss_db: 0.1752 2022/11/02 15:11:07 - mmengine - INFO - Epoch(train) [284][50/63] lr: 1.6976e-03 eta: 9:23:49 time: 0.5229 data_time: 0.0207 memory: 14901 loss: 1.8101 loss_prob: 1.0474 loss_thr: 0.5876 loss_db: 0.1751 2022/11/02 15:11:10 - mmengine - INFO - Epoch(train) [284][55/63] lr: 1.6976e-03 eta: 9:23:49 time: 0.6432 data_time: 0.0227 memory: 14901 loss: 1.6886 loss_prob: 0.9640 loss_thr: 0.5632 loss_db: 0.1614 2022/11/02 15:11:13 - mmengine - INFO - Epoch(train) [284][60/63] lr: 1.6976e-03 eta: 9:23:45 time: 0.6453 data_time: 0.0120 memory: 14901 loss: 1.7368 loss_prob: 0.9930 loss_thr: 0.5837 loss_db: 0.1602 2022/11/02 15:11:15 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:11:20 - mmengine - INFO - Epoch(train) [285][5/63] lr: 1.6960e-03 eta: 9:23:45 time: 0.7568 data_time: 0.2440 memory: 14901 loss: 1.6746 loss_prob: 0.9316 loss_thr: 0.5916 loss_db: 0.1514 2022/11/02 15:11:22 - mmengine - INFO - Epoch(train) [285][10/63] lr: 1.6960e-03 eta: 9:23:37 time: 0.7506 data_time: 0.2405 memory: 14901 loss: 1.7367 loss_prob: 0.9736 loss_thr: 0.6033 loss_db: 0.1597 2022/11/02 15:11:25 - mmengine - INFO - Epoch(train) [285][15/63] lr: 1.6960e-03 eta: 9:23:37 time: 0.5338 data_time: 0.0061 memory: 14901 loss: 1.8622 loss_prob: 1.0825 loss_thr: 0.6091 loss_db: 0.1706 2022/11/02 15:11:28 - mmengine - INFO - Epoch(train) [285][20/63] lr: 1.6960e-03 eta: 9:23:32 time: 0.6187 data_time: 0.0098 memory: 14901 loss: 1.8053 loss_prob: 1.0520 loss_thr: 0.5839 loss_db: 0.1694 2022/11/02 15:11:31 - mmengine - INFO - Epoch(train) [285][25/63] lr: 1.6960e-03 eta: 9:23:32 time: 0.6399 data_time: 0.0469 memory: 14901 loss: 1.8225 loss_prob: 1.0526 loss_thr: 0.5964 loss_db: 0.1736 2022/11/02 15:11:34 - mmengine - INFO - Epoch(train) [285][30/63] lr: 1.6960e-03 eta: 9:23:25 time: 0.5418 data_time: 0.0448 memory: 14901 loss: 1.7428 loss_prob: 0.9965 loss_thr: 0.5829 loss_db: 0.1635 2022/11/02 15:11:37 - mmengine - INFO - Epoch(train) [285][35/63] lr: 1.6960e-03 eta: 9:23:25 time: 0.5256 data_time: 0.0073 memory: 14901 loss: 1.5909 loss_prob: 0.8768 loss_thr: 0.5693 loss_db: 0.1448 2022/11/02 15:11:39 - mmengine - INFO - Epoch(train) [285][40/63] lr: 1.6960e-03 eta: 9:23:17 time: 0.5086 data_time: 0.0097 memory: 14901 loss: 1.6391 loss_prob: 0.9216 loss_thr: 0.5680 loss_db: 0.1495 2022/11/02 15:11:42 - mmengine - INFO - Epoch(train) [285][45/63] lr: 1.6960e-03 eta: 9:23:17 time: 0.5048 data_time: 0.0105 memory: 14901 loss: 1.7274 loss_prob: 0.9932 loss_thr: 0.5688 loss_db: 0.1653 2022/11/02 15:11:44 - mmengine - INFO - Epoch(train) [285][50/63] lr: 1.6960e-03 eta: 9:23:08 time: 0.5155 data_time: 0.0245 memory: 14901 loss: 1.7455 loss_prob: 0.9991 loss_thr: 0.5791 loss_db: 0.1673 2022/11/02 15:11:47 - mmengine - INFO - Epoch(train) [285][55/63] lr: 1.6960e-03 eta: 9:23:08 time: 0.4972 data_time: 0.0242 memory: 14901 loss: 1.7541 loss_prob: 1.0101 loss_thr: 0.5797 loss_db: 0.1643 2022/11/02 15:11:49 - mmengine - INFO - Epoch(train) [285][60/63] lr: 1.6960e-03 eta: 9:22:59 time: 0.4719 data_time: 0.0060 memory: 14901 loss: 1.6522 loss_prob: 0.9440 loss_thr: 0.5530 loss_db: 0.1552 2022/11/02 15:11:50 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:11:56 - mmengine - INFO - Epoch(train) [286][5/63] lr: 1.6943e-03 eta: 9:22:59 time: 0.7579 data_time: 0.2408 memory: 14901 loss: 1.6646 loss_prob: 0.9438 loss_thr: 0.5630 loss_db: 0.1578 2022/11/02 15:11:58 - mmengine - INFO - Epoch(train) [286][10/63] lr: 1.6943e-03 eta: 9:22:53 time: 0.8099 data_time: 0.2451 memory: 14901 loss: 1.7018 loss_prob: 0.9586 loss_thr: 0.5809 loss_db: 0.1623 2022/11/02 15:12:01 - mmengine - INFO - Epoch(train) [286][15/63] lr: 1.6943e-03 eta: 9:22:53 time: 0.5205 data_time: 0.0108 memory: 14901 loss: 1.7971 loss_prob: 1.0271 loss_thr: 0.5971 loss_db: 0.1729 2022/11/02 15:12:03 - mmengine - INFO - Epoch(train) [286][20/63] lr: 1.6943e-03 eta: 9:22:44 time: 0.4912 data_time: 0.0063 memory: 14901 loss: 1.8632 loss_prob: 1.0858 loss_thr: 0.5999 loss_db: 0.1775 2022/11/02 15:12:06 - mmengine - INFO - Epoch(train) [286][25/63] lr: 1.6943e-03 eta: 9:22:44 time: 0.5718 data_time: 0.0295 memory: 14901 loss: 1.7616 loss_prob: 1.0167 loss_thr: 0.5785 loss_db: 0.1664 2022/11/02 15:12:09 - mmengine - INFO - Epoch(train) [286][30/63] lr: 1.6943e-03 eta: 9:22:38 time: 0.5816 data_time: 0.0439 memory: 14901 loss: 1.6994 loss_prob: 0.9754 loss_thr: 0.5591 loss_db: 0.1649 2022/11/02 15:12:12 - mmengine - INFO - Epoch(train) [286][35/63] lr: 1.6943e-03 eta: 9:22:38 time: 0.5019 data_time: 0.0217 memory: 14901 loss: 1.7079 loss_prob: 0.9768 loss_thr: 0.5678 loss_db: 0.1633 2022/11/02 15:12:14 - mmengine - INFO - Epoch(train) [286][40/63] lr: 1.6943e-03 eta: 9:22:30 time: 0.5203 data_time: 0.0115 memory: 14901 loss: 1.7687 loss_prob: 1.0202 loss_thr: 0.5849 loss_db: 0.1636 2022/11/02 15:12:17 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:12:17 - mmengine - INFO - Epoch(train) [286][45/63] lr: 1.6943e-03 eta: 9:22:30 time: 0.5283 data_time: 0.0093 memory: 14901 loss: 1.7998 loss_prob: 1.0602 loss_thr: 0.5649 loss_db: 0.1747 2022/11/02 15:12:20 - mmengine - INFO - Epoch(train) [286][50/63] lr: 1.6943e-03 eta: 9:22:23 time: 0.5431 data_time: 0.0252 memory: 14901 loss: 1.6933 loss_prob: 0.9886 loss_thr: 0.5372 loss_db: 0.1676 2022/11/02 15:12:22 - mmengine - INFO - Epoch(train) [286][55/63] lr: 1.6943e-03 eta: 9:22:23 time: 0.5315 data_time: 0.0280 memory: 14901 loss: 1.6162 loss_prob: 0.9262 loss_thr: 0.5378 loss_db: 0.1521 2022/11/02 15:12:25 - mmengine - INFO - Epoch(train) [286][60/63] lr: 1.6943e-03 eta: 9:22:14 time: 0.4944 data_time: 0.0091 memory: 14901 loss: 1.6765 loss_prob: 0.9521 loss_thr: 0.5709 loss_db: 0.1535 2022/11/02 15:12:26 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:12:32 - mmengine - INFO - Epoch(train) [287][5/63] lr: 1.6926e-03 eta: 9:22:14 time: 0.8556 data_time: 0.2776 memory: 14901 loss: 1.6716 loss_prob: 0.9433 loss_thr: 0.5705 loss_db: 0.1578 2022/11/02 15:12:35 - mmengine - INFO - Epoch(train) [287][10/63] lr: 1.6926e-03 eta: 9:22:10 time: 0.8843 data_time: 0.2810 memory: 14901 loss: 1.6072 loss_prob: 0.9024 loss_thr: 0.5566 loss_db: 0.1482 2022/11/02 15:12:37 - mmengine - INFO - Epoch(train) [287][15/63] lr: 1.6926e-03 eta: 9:22:10 time: 0.5194 data_time: 0.0111 memory: 14901 loss: 1.6517 loss_prob: 0.9383 loss_thr: 0.5579 loss_db: 0.1556 2022/11/02 15:12:40 - mmengine - INFO - Epoch(train) [287][20/63] lr: 1.6926e-03 eta: 9:22:02 time: 0.5099 data_time: 0.0106 memory: 14901 loss: 1.6075 loss_prob: 0.9041 loss_thr: 0.5500 loss_db: 0.1533 2022/11/02 15:12:43 - mmengine - INFO - Epoch(train) [287][25/63] lr: 1.6926e-03 eta: 9:22:02 time: 0.5260 data_time: 0.0399 memory: 14901 loss: 1.6253 loss_prob: 0.8983 loss_thr: 0.5754 loss_db: 0.1517 2022/11/02 15:12:45 - mmengine - INFO - Epoch(train) [287][30/63] lr: 1.6926e-03 eta: 9:21:54 time: 0.5316 data_time: 0.0376 memory: 14901 loss: 1.6479 loss_prob: 0.9139 loss_thr: 0.5815 loss_db: 0.1525 2022/11/02 15:12:48 - mmengine - INFO - Epoch(train) [287][35/63] lr: 1.6926e-03 eta: 9:21:54 time: 0.5557 data_time: 0.0062 memory: 14901 loss: 1.6783 loss_prob: 0.9439 loss_thr: 0.5790 loss_db: 0.1554 2022/11/02 15:12:51 - mmengine - INFO - Epoch(train) [287][40/63] lr: 1.6926e-03 eta: 9:21:49 time: 0.5974 data_time: 0.0086 memory: 14901 loss: 1.7465 loss_prob: 0.9884 loss_thr: 0.5945 loss_db: 0.1635 2022/11/02 15:12:54 - mmengine - INFO - Epoch(train) [287][45/63] lr: 1.6926e-03 eta: 9:21:49 time: 0.5499 data_time: 0.0092 memory: 14901 loss: 1.7912 loss_prob: 1.0349 loss_thr: 0.5870 loss_db: 0.1693 2022/11/02 15:12:56 - mmengine - INFO - Epoch(train) [287][50/63] lr: 1.6926e-03 eta: 9:21:42 time: 0.5448 data_time: 0.0231 memory: 14901 loss: 1.7334 loss_prob: 0.9971 loss_thr: 0.5714 loss_db: 0.1649 2022/11/02 15:12:59 - mmengine - INFO - Epoch(train) [287][55/63] lr: 1.6926e-03 eta: 9:21:42 time: 0.5371 data_time: 0.0252 memory: 14901 loss: 1.6676 loss_prob: 0.9637 loss_thr: 0.5476 loss_db: 0.1562 2022/11/02 15:13:02 - mmengine - INFO - Epoch(train) [287][60/63] lr: 1.6926e-03 eta: 9:21:35 time: 0.5604 data_time: 0.0097 memory: 14901 loss: 1.6334 loss_prob: 0.9354 loss_thr: 0.5460 loss_db: 0.1521 2022/11/02 15:13:03 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:13:09 - mmengine - INFO - Epoch(train) [288][5/63] lr: 1.6910e-03 eta: 9:21:35 time: 0.7736 data_time: 0.2247 memory: 14901 loss: 1.4628 loss_prob: 0.7995 loss_thr: 0.5280 loss_db: 0.1353 2022/11/02 15:13:12 - mmengine - INFO - Epoch(train) [288][10/63] lr: 1.6910e-03 eta: 9:21:30 time: 0.8297 data_time: 0.2264 memory: 14901 loss: 1.5043 loss_prob: 0.8268 loss_thr: 0.5390 loss_db: 0.1385 2022/11/02 15:13:14 - mmengine - INFO - Epoch(train) [288][15/63] lr: 1.6910e-03 eta: 9:21:30 time: 0.5195 data_time: 0.0115 memory: 14901 loss: 1.5720 loss_prob: 0.8724 loss_thr: 0.5526 loss_db: 0.1469 2022/11/02 15:13:16 - mmengine - INFO - Epoch(train) [288][20/63] lr: 1.6910e-03 eta: 9:21:21 time: 0.4934 data_time: 0.0078 memory: 14901 loss: 1.4512 loss_prob: 0.7950 loss_thr: 0.5229 loss_db: 0.1333 2022/11/02 15:13:19 - mmengine - INFO - Epoch(train) [288][25/63] lr: 1.6910e-03 eta: 9:21:21 time: 0.4994 data_time: 0.0236 memory: 14901 loss: 1.5021 loss_prob: 0.8431 loss_thr: 0.5170 loss_db: 0.1419 2022/11/02 15:13:22 - mmengine - INFO - Epoch(train) [288][30/63] lr: 1.6910e-03 eta: 9:21:13 time: 0.5312 data_time: 0.0403 memory: 14901 loss: 1.7346 loss_prob: 1.0084 loss_thr: 0.5606 loss_db: 0.1656 2022/11/02 15:13:25 - mmengine - INFO - Epoch(train) [288][35/63] lr: 1.6910e-03 eta: 9:21:13 time: 0.6145 data_time: 0.0251 memory: 14901 loss: 1.7347 loss_prob: 0.9915 loss_thr: 0.5790 loss_db: 0.1641 2022/11/02 15:13:28 - mmengine - INFO - Epoch(train) [288][40/63] lr: 1.6910e-03 eta: 9:21:09 time: 0.6366 data_time: 0.0063 memory: 14901 loss: 1.7510 loss_prob: 0.9960 loss_thr: 0.5881 loss_db: 0.1669 2022/11/02 15:13:31 - mmengine - INFO - Epoch(train) [288][45/63] lr: 1.6910e-03 eta: 9:21:09 time: 0.5408 data_time: 0.0071 memory: 14901 loss: 1.7714 loss_prob: 1.0134 loss_thr: 0.5928 loss_db: 0.1652 2022/11/02 15:13:33 - mmengine - INFO - Epoch(train) [288][50/63] lr: 1.6910e-03 eta: 9:21:01 time: 0.5209 data_time: 0.0170 memory: 14901 loss: 1.6662 loss_prob: 0.9366 loss_thr: 0.5769 loss_db: 0.1527 2022/11/02 15:13:36 - mmengine - INFO - Epoch(train) [288][55/63] lr: 1.6910e-03 eta: 9:21:01 time: 0.5707 data_time: 0.0273 memory: 14901 loss: 1.6829 loss_prob: 0.9500 loss_thr: 0.5813 loss_db: 0.1516 2022/11/02 15:13:39 - mmengine - INFO - Epoch(train) [288][60/63] lr: 1.6910e-03 eta: 9:20:54 time: 0.5474 data_time: 0.0177 memory: 14901 loss: 1.7179 loss_prob: 0.9764 loss_thr: 0.5845 loss_db: 0.1570 2022/11/02 15:13:40 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:13:46 - mmengine - INFO - Epoch(train) [289][5/63] lr: 1.6893e-03 eta: 9:20:54 time: 0.8467 data_time: 0.2327 memory: 14901 loss: 1.7193 loss_prob: 0.9897 loss_thr: 0.5631 loss_db: 0.1665 2022/11/02 15:13:50 - mmengine - INFO - Epoch(train) [289][10/63] lr: 1.6893e-03 eta: 9:20:52 time: 0.9526 data_time: 0.2324 memory: 14901 loss: 1.6083 loss_prob: 0.9134 loss_thr: 0.5452 loss_db: 0.1497 2022/11/02 15:13:52 - mmengine - INFO - Epoch(train) [289][15/63] lr: 1.6893e-03 eta: 9:20:52 time: 0.5880 data_time: 0.0070 memory: 14901 loss: 1.6994 loss_prob: 0.9730 loss_thr: 0.5664 loss_db: 0.1600 2022/11/02 15:13:55 - mmengine - INFO - Epoch(train) [289][20/63] lr: 1.6893e-03 eta: 9:20:44 time: 0.5035 data_time: 0.0055 memory: 14901 loss: 1.7631 loss_prob: 1.0072 loss_thr: 0.5927 loss_db: 0.1632 2022/11/02 15:13:58 - mmengine - INFO - Epoch(train) [289][25/63] lr: 1.6893e-03 eta: 9:20:44 time: 0.5370 data_time: 0.0297 memory: 14901 loss: 1.6872 loss_prob: 0.9447 loss_thr: 0.5856 loss_db: 0.1570 2022/11/02 15:14:01 - mmengine - INFO - Epoch(train) [289][30/63] lr: 1.6893e-03 eta: 9:20:40 time: 0.6481 data_time: 0.0456 memory: 14901 loss: 1.6855 loss_prob: 0.9393 loss_thr: 0.5881 loss_db: 0.1581 2022/11/02 15:14:04 - mmengine - INFO - Epoch(train) [289][35/63] lr: 1.6893e-03 eta: 9:20:40 time: 0.6141 data_time: 0.0239 memory: 14901 loss: 1.6996 loss_prob: 0.9502 loss_thr: 0.5918 loss_db: 0.1575 2022/11/02 15:14:06 - mmengine - INFO - Epoch(train) [289][40/63] lr: 1.6893e-03 eta: 9:20:32 time: 0.5079 data_time: 0.0077 memory: 14901 loss: 1.6467 loss_prob: 0.9153 loss_thr: 0.5771 loss_db: 0.1543 2022/11/02 15:14:08 - mmengine - INFO - Epoch(train) [289][45/63] lr: 1.6893e-03 eta: 9:20:32 time: 0.4693 data_time: 0.0049 memory: 14901 loss: 1.6784 loss_prob: 0.9434 loss_thr: 0.5745 loss_db: 0.1605 2022/11/02 15:14:11 - mmengine - INFO - Epoch(train) [289][50/63] lr: 1.6893e-03 eta: 9:20:23 time: 0.5015 data_time: 0.0193 memory: 14901 loss: 1.7318 loss_prob: 0.9880 loss_thr: 0.5795 loss_db: 0.1643 2022/11/02 15:14:14 - mmengine - INFO - Epoch(train) [289][55/63] lr: 1.6893e-03 eta: 9:20:23 time: 0.5410 data_time: 0.0309 memory: 14901 loss: 1.7218 loss_prob: 0.9719 loss_thr: 0.5894 loss_db: 0.1605 2022/11/02 15:14:16 - mmengine - INFO - Epoch(train) [289][60/63] lr: 1.6893e-03 eta: 9:20:15 time: 0.5109 data_time: 0.0192 memory: 14901 loss: 1.6398 loss_prob: 0.9143 loss_thr: 0.5736 loss_db: 0.1519 2022/11/02 15:14:18 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:14:24 - mmengine - INFO - Epoch(train) [290][5/63] lr: 1.6876e-03 eta: 9:20:15 time: 0.8482 data_time: 0.2227 memory: 14901 loss: 1.5858 loss_prob: 0.8835 loss_thr: 0.5526 loss_db: 0.1497 2022/11/02 15:14:26 - mmengine - INFO - Epoch(train) [290][10/63] lr: 1.6876e-03 eta: 9:20:11 time: 0.8824 data_time: 0.2296 memory: 14901 loss: 1.5965 loss_prob: 0.8951 loss_thr: 0.5505 loss_db: 0.1509 2022/11/02 15:14:29 - mmengine - INFO - Epoch(train) [290][15/63] lr: 1.6876e-03 eta: 9:20:11 time: 0.5326 data_time: 0.0151 memory: 14901 loss: 1.5829 loss_prob: 0.8783 loss_thr: 0.5574 loss_db: 0.1472 2022/11/02 15:14:32 - mmengine - INFO - Epoch(train) [290][20/63] lr: 1.6876e-03 eta: 9:20:03 time: 0.5126 data_time: 0.0106 memory: 14901 loss: 1.6368 loss_prob: 0.9135 loss_thr: 0.5723 loss_db: 0.1510 2022/11/02 15:14:34 - mmengine - INFO - Epoch(train) [290][25/63] lr: 1.6876e-03 eta: 9:20:03 time: 0.5238 data_time: 0.0273 memory: 14901 loss: 1.6403 loss_prob: 0.9164 loss_thr: 0.5721 loss_db: 0.1518 2022/11/02 15:14:38 - mmengine - INFO - Epoch(train) [290][30/63] lr: 1.6876e-03 eta: 9:19:58 time: 0.5984 data_time: 0.0418 memory: 14901 loss: 1.7072 loss_prob: 0.9880 loss_thr: 0.5572 loss_db: 0.1620 2022/11/02 15:14:40 - mmengine - INFO - Epoch(train) [290][35/63] lr: 1.6876e-03 eta: 9:19:58 time: 0.5481 data_time: 0.0218 memory: 14901 loss: 1.7183 loss_prob: 0.9963 loss_thr: 0.5572 loss_db: 0.1648 2022/11/02 15:14:43 - mmengine - INFO - Epoch(train) [290][40/63] lr: 1.6876e-03 eta: 9:19:50 time: 0.5349 data_time: 0.0081 memory: 14901 loss: 1.5934 loss_prob: 0.8925 loss_thr: 0.5525 loss_db: 0.1484 2022/11/02 15:14:45 - mmengine - INFO - Epoch(train) [290][45/63] lr: 1.6876e-03 eta: 9:19:50 time: 0.5339 data_time: 0.0097 memory: 14901 loss: 1.7143 loss_prob: 0.9747 loss_thr: 0.5820 loss_db: 0.1577 2022/11/02 15:14:48 - mmengine - INFO - Epoch(train) [290][50/63] lr: 1.6876e-03 eta: 9:19:41 time: 0.4923 data_time: 0.0194 memory: 14901 loss: 1.7723 loss_prob: 1.0092 loss_thr: 0.5952 loss_db: 0.1679 2022/11/02 15:14:50 - mmengine - INFO - Epoch(train) [290][55/63] lr: 1.6876e-03 eta: 9:19:41 time: 0.5138 data_time: 0.0375 memory: 14901 loss: 1.7297 loss_prob: 0.9862 loss_thr: 0.5750 loss_db: 0.1685 2022/11/02 15:14:53 - mmengine - INFO - Epoch(train) [290][60/63] lr: 1.6876e-03 eta: 9:19:34 time: 0.5218 data_time: 0.0276 memory: 14901 loss: 1.7707 loss_prob: 1.0085 loss_thr: 0.5923 loss_db: 0.1700 2022/11/02 15:14:54 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:15:02 - mmengine - INFO - Epoch(train) [291][5/63] lr: 1.6859e-03 eta: 9:19:34 time: 0.9841 data_time: 0.2108 memory: 14901 loss: 1.6932 loss_prob: 0.9599 loss_thr: 0.5756 loss_db: 0.1577 2022/11/02 15:15:05 - mmengine - INFO - Epoch(train) [291][10/63] lr: 1.6859e-03 eta: 9:19:36 time: 1.0758 data_time: 0.2262 memory: 14901 loss: 1.5898 loss_prob: 0.8859 loss_thr: 0.5542 loss_db: 0.1498 2022/11/02 15:15:09 - mmengine - INFO - Epoch(train) [291][15/63] lr: 1.6859e-03 eta: 9:19:36 time: 0.6710 data_time: 0.0249 memory: 14901 loss: 1.5809 loss_prob: 0.8749 loss_thr: 0.5587 loss_db: 0.1473 2022/11/02 15:15:11 - mmengine - INFO - Epoch(train) [291][20/63] lr: 1.6859e-03 eta: 9:19:30 time: 0.5826 data_time: 0.0099 memory: 14901 loss: 1.5872 loss_prob: 0.8825 loss_thr: 0.5593 loss_db: 0.1454 2022/11/02 15:15:14 - mmengine - INFO - Epoch(train) [291][25/63] lr: 1.6859e-03 eta: 9:19:30 time: 0.4983 data_time: 0.0277 memory: 14901 loss: 1.7178 loss_prob: 0.9762 loss_thr: 0.5841 loss_db: 0.1575 2022/11/02 15:15:16 - mmengine - INFO - Epoch(train) [291][30/63] lr: 1.6859e-03 eta: 9:19:22 time: 0.5226 data_time: 0.0301 memory: 14901 loss: 1.7861 loss_prob: 1.0267 loss_thr: 0.5909 loss_db: 0.1685 2022/11/02 15:15:19 - mmengine - INFO - Epoch(train) [291][35/63] lr: 1.6859e-03 eta: 9:19:22 time: 0.5104 data_time: 0.0193 memory: 14901 loss: 1.6818 loss_prob: 0.9602 loss_thr: 0.5622 loss_db: 0.1594 2022/11/02 15:15:22 - mmengine - INFO - Epoch(train) [291][40/63] lr: 1.6859e-03 eta: 9:19:15 time: 0.5625 data_time: 0.0163 memory: 14901 loss: 1.6749 loss_prob: 0.9456 loss_thr: 0.5746 loss_db: 0.1547 2022/11/02 15:15:24 - mmengine - INFO - Epoch(train) [291][45/63] lr: 1.6859e-03 eta: 9:19:15 time: 0.5447 data_time: 0.0073 memory: 14901 loss: 1.6418 loss_prob: 0.9056 loss_thr: 0.5861 loss_db: 0.1500 2022/11/02 15:15:27 - mmengine - INFO - Epoch(train) [291][50/63] lr: 1.6859e-03 eta: 9:19:08 time: 0.5474 data_time: 0.0199 memory: 14901 loss: 1.7026 loss_prob: 0.9450 loss_thr: 0.5984 loss_db: 0.1592 2022/11/02 15:15:30 - mmengine - INFO - Epoch(train) [291][55/63] lr: 1.6859e-03 eta: 9:19:08 time: 0.5539 data_time: 0.0211 memory: 14901 loss: 1.7474 loss_prob: 0.9897 loss_thr: 0.5934 loss_db: 0.1644 2022/11/02 15:15:32 - mmengine - INFO - Epoch(train) [291][60/63] lr: 1.6859e-03 eta: 9:19:00 time: 0.5079 data_time: 0.0139 memory: 14901 loss: 1.7098 loss_prob: 0.9644 loss_thr: 0.5853 loss_db: 0.1601 2022/11/02 15:15:34 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:15:39 - mmengine - INFO - Epoch(train) [292][5/63] lr: 1.6843e-03 eta: 9:19:00 time: 0.7738 data_time: 0.2025 memory: 14901 loss: 1.7684 loss_prob: 1.0097 loss_thr: 0.5896 loss_db: 0.1691 2022/11/02 15:15:42 - mmengine - INFO - Epoch(train) [292][10/63] lr: 1.6843e-03 eta: 9:18:56 time: 0.8637 data_time: 0.2044 memory: 14901 loss: 1.6678 loss_prob: 0.9530 loss_thr: 0.5594 loss_db: 0.1554 2022/11/02 15:15:45 - mmengine - INFO - Epoch(train) [292][15/63] lr: 1.6843e-03 eta: 9:18:56 time: 0.5889 data_time: 0.0156 memory: 14901 loss: 1.5722 loss_prob: 0.8904 loss_thr: 0.5370 loss_db: 0.1448 2022/11/02 15:15:48 - mmengine - INFO - Epoch(train) [292][20/63] lr: 1.6843e-03 eta: 9:18:49 time: 0.5563 data_time: 0.0095 memory: 14901 loss: 1.6200 loss_prob: 0.9137 loss_thr: 0.5550 loss_db: 0.1513 2022/11/02 15:15:51 - mmengine - INFO - Epoch(train) [292][25/63] lr: 1.6843e-03 eta: 9:18:49 time: 0.5563 data_time: 0.0185 memory: 14901 loss: 1.6238 loss_prob: 0.9076 loss_thr: 0.5646 loss_db: 0.1516 2022/11/02 15:15:53 - mmengine - INFO - Epoch(train) [292][30/63] lr: 1.6843e-03 eta: 9:18:41 time: 0.5121 data_time: 0.0304 memory: 14901 loss: 1.5544 loss_prob: 0.8674 loss_thr: 0.5428 loss_db: 0.1442 2022/11/02 15:15:56 - mmengine - INFO - Epoch(train) [292][35/63] lr: 1.6843e-03 eta: 9:18:41 time: 0.5059 data_time: 0.0255 memory: 14901 loss: 1.7079 loss_prob: 0.9968 loss_thr: 0.5534 loss_db: 0.1576 2022/11/02 15:15:58 - mmengine - INFO - Epoch(train) [292][40/63] lr: 1.6843e-03 eta: 9:18:33 time: 0.5408 data_time: 0.0141 memory: 14901 loss: 1.7732 loss_prob: 1.0453 loss_thr: 0.5617 loss_db: 0.1662 2022/11/02 15:16:01 - mmengine - INFO - Epoch(train) [292][45/63] lr: 1.6843e-03 eta: 9:18:33 time: 0.5135 data_time: 0.0059 memory: 14901 loss: 1.7008 loss_prob: 0.9879 loss_thr: 0.5500 loss_db: 0.1629 2022/11/02 15:16:04 - mmengine - INFO - Epoch(train) [292][50/63] lr: 1.6843e-03 eta: 9:18:26 time: 0.5315 data_time: 0.0185 memory: 14901 loss: 1.6645 loss_prob: 0.9353 loss_thr: 0.5738 loss_db: 0.1554 2022/11/02 15:16:06 - mmengine - INFO - Epoch(train) [292][55/63] lr: 1.6843e-03 eta: 9:18:26 time: 0.5636 data_time: 0.0276 memory: 14901 loss: 1.6830 loss_prob: 0.9279 loss_thr: 0.6007 loss_db: 0.1544 2022/11/02 15:16:09 - mmengine - INFO - Epoch(train) [292][60/63] lr: 1.6843e-03 eta: 9:18:18 time: 0.5254 data_time: 0.0177 memory: 14901 loss: 1.6055 loss_prob: 0.8971 loss_thr: 0.5581 loss_db: 0.1503 2022/11/02 15:16:10 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:16:17 - mmengine - INFO - Epoch(train) [293][5/63] lr: 1.6826e-03 eta: 9:18:18 time: 0.8462 data_time: 0.2268 memory: 14901 loss: 1.5618 loss_prob: 0.8671 loss_thr: 0.5512 loss_db: 0.1436 2022/11/02 15:16:19 - mmengine - INFO - Epoch(train) [293][10/63] lr: 1.6826e-03 eta: 9:18:15 time: 0.9133 data_time: 0.2291 memory: 14901 loss: 1.5490 loss_prob: 0.8654 loss_thr: 0.5421 loss_db: 0.1415 2022/11/02 15:16:23 - mmengine - INFO - Epoch(train) [293][15/63] lr: 1.6826e-03 eta: 9:18:15 time: 0.6028 data_time: 0.0115 memory: 14901 loss: 1.6241 loss_prob: 0.9110 loss_thr: 0.5613 loss_db: 0.1518 2022/11/02 15:16:25 - mmengine - INFO - Epoch(train) [293][20/63] lr: 1.6826e-03 eta: 9:18:11 time: 0.6210 data_time: 0.0076 memory: 14901 loss: 1.6422 loss_prob: 0.9352 loss_thr: 0.5512 loss_db: 0.1558 2022/11/02 15:16:28 - mmengine - INFO - Epoch(train) [293][25/63] lr: 1.6826e-03 eta: 9:18:11 time: 0.5652 data_time: 0.0228 memory: 14901 loss: 1.6598 loss_prob: 0.9524 loss_thr: 0.5509 loss_db: 0.1565 2022/11/02 15:16:31 - mmengine - INFO - Epoch(train) [293][30/63] lr: 1.6826e-03 eta: 9:18:03 time: 0.5382 data_time: 0.0416 memory: 14901 loss: 1.6763 loss_prob: 0.9573 loss_thr: 0.5615 loss_db: 0.1576 2022/11/02 15:16:33 - mmengine - INFO - Epoch(train) [293][35/63] lr: 1.6826e-03 eta: 9:18:03 time: 0.5015 data_time: 0.0282 memory: 14901 loss: 1.6631 loss_prob: 0.9403 loss_thr: 0.5669 loss_db: 0.1559 2022/11/02 15:16:35 - mmengine - INFO - Epoch(train) [293][40/63] lr: 1.6826e-03 eta: 9:17:53 time: 0.4607 data_time: 0.0090 memory: 14901 loss: 1.7884 loss_prob: 1.0231 loss_thr: 0.5998 loss_db: 0.1655 2022/11/02 15:16:38 - mmengine - INFO - Epoch(train) [293][45/63] lr: 1.6826e-03 eta: 9:17:53 time: 0.5095 data_time: 0.0100 memory: 14901 loss: 1.7659 loss_prob: 1.0105 loss_thr: 0.5941 loss_db: 0.1613 2022/11/02 15:16:42 - mmengine - INFO - Epoch(train) [293][50/63] lr: 1.6826e-03 eta: 9:17:48 time: 0.6039 data_time: 0.0196 memory: 14901 loss: 1.7087 loss_prob: 0.9779 loss_thr: 0.5710 loss_db: 0.1598 2022/11/02 15:16:44 - mmengine - INFO - Epoch(train) [293][55/63] lr: 1.6826e-03 eta: 9:17:48 time: 0.5557 data_time: 0.0265 memory: 14901 loss: 1.6722 loss_prob: 0.9507 loss_thr: 0.5647 loss_db: 0.1568 2022/11/02 15:16:46 - mmengine - INFO - Epoch(train) [293][60/63] lr: 1.6826e-03 eta: 9:17:39 time: 0.4767 data_time: 0.0178 memory: 14901 loss: 1.6427 loss_prob: 0.9188 loss_thr: 0.5740 loss_db: 0.1499 2022/11/02 15:16:47 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:16:52 - mmengine - INFO - Epoch(train) [294][5/63] lr: 1.6809e-03 eta: 9:17:39 time: 0.6895 data_time: 0.2122 memory: 14901 loss: 1.7154 loss_prob: 0.9700 loss_thr: 0.5834 loss_db: 0.1620 2022/11/02 15:16:55 - mmengine - INFO - Epoch(train) [294][10/63] lr: 1.6809e-03 eta: 9:17:32 time: 0.7772 data_time: 0.2125 memory: 14901 loss: 1.6724 loss_prob: 0.9506 loss_thr: 0.5657 loss_db: 0.1562 2022/11/02 15:16:58 - mmengine - INFO - Epoch(train) [294][15/63] lr: 1.6809e-03 eta: 9:17:32 time: 0.5396 data_time: 0.0133 memory: 14901 loss: 1.6576 loss_prob: 0.9427 loss_thr: 0.5576 loss_db: 0.1574 2022/11/02 15:17:01 - mmengine - INFO - Epoch(train) [294][20/63] lr: 1.6809e-03 eta: 9:17:25 time: 0.5427 data_time: 0.0133 memory: 14901 loss: 1.6301 loss_prob: 0.9208 loss_thr: 0.5526 loss_db: 0.1567 2022/11/02 15:17:03 - mmengine - INFO - Epoch(train) [294][25/63] lr: 1.6809e-03 eta: 9:17:25 time: 0.5521 data_time: 0.0156 memory: 14901 loss: 1.6267 loss_prob: 0.9138 loss_thr: 0.5604 loss_db: 0.1525 2022/11/02 15:17:07 - mmengine - INFO - Epoch(train) [294][30/63] lr: 1.6809e-03 eta: 9:17:20 time: 0.6377 data_time: 0.0323 memory: 14901 loss: 1.7263 loss_prob: 0.9676 loss_thr: 0.5949 loss_db: 0.1638 2022/11/02 15:17:09 - mmengine - INFO - Epoch(train) [294][35/63] lr: 1.6809e-03 eta: 9:17:20 time: 0.6257 data_time: 0.0232 memory: 14901 loss: 1.6856 loss_prob: 0.9431 loss_thr: 0.5836 loss_db: 0.1588 2022/11/02 15:17:13 - mmengine - INFO - Epoch(train) [294][40/63] lr: 1.6809e-03 eta: 9:17:15 time: 0.6040 data_time: 0.0141 memory: 14901 loss: 1.5910 loss_prob: 0.8912 loss_thr: 0.5536 loss_db: 0.1463 2022/11/02 15:17:16 - mmengine - INFO - Epoch(train) [294][45/63] lr: 1.6809e-03 eta: 9:17:15 time: 0.6593 data_time: 0.0146 memory: 14901 loss: 1.6316 loss_prob: 0.9094 loss_thr: 0.5715 loss_db: 0.1507 2022/11/02 15:17:19 - mmengine - INFO - Epoch(train) [294][50/63] lr: 1.6809e-03 eta: 9:17:08 time: 0.5469 data_time: 0.0132 memory: 14901 loss: 1.6645 loss_prob: 0.9252 loss_thr: 0.5831 loss_db: 0.1562 2022/11/02 15:17:21 - mmengine - INFO - Epoch(train) [294][55/63] lr: 1.6809e-03 eta: 9:17:08 time: 0.5097 data_time: 0.0255 memory: 14901 loss: 1.5980 loss_prob: 0.8940 loss_thr: 0.5565 loss_db: 0.1475 2022/11/02 15:17:24 - mmengine - INFO - Epoch(train) [294][60/63] lr: 1.6809e-03 eta: 9:17:00 time: 0.5153 data_time: 0.0186 memory: 14901 loss: 1.5997 loss_prob: 0.8945 loss_thr: 0.5586 loss_db: 0.1466 2022/11/02 15:17:25 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:17:31 - mmengine - INFO - Epoch(train) [295][5/63] lr: 1.6793e-03 eta: 9:17:00 time: 0.8250 data_time: 0.2201 memory: 14901 loss: 1.6816 loss_prob: 0.9545 loss_thr: 0.5719 loss_db: 0.1553 2022/11/02 15:17:34 - mmengine - INFO - Epoch(train) [295][10/63] lr: 1.6793e-03 eta: 9:16:55 time: 0.8407 data_time: 0.2211 memory: 14901 loss: 1.7245 loss_prob: 0.9835 loss_thr: 0.5806 loss_db: 0.1604 2022/11/02 15:17:36 - mmengine - INFO - Epoch(train) [295][15/63] lr: 1.6793e-03 eta: 9:16:55 time: 0.5391 data_time: 0.0177 memory: 14901 loss: 1.6688 loss_prob: 0.9446 loss_thr: 0.5646 loss_db: 0.1595 2022/11/02 15:17:39 - mmengine - INFO - Epoch(train) [295][20/63] lr: 1.6793e-03 eta: 9:16:47 time: 0.5095 data_time: 0.0115 memory: 14901 loss: 1.6288 loss_prob: 0.9164 loss_thr: 0.5587 loss_db: 0.1537 2022/11/02 15:17:42 - mmengine - INFO - Epoch(train) [295][25/63] lr: 1.6793e-03 eta: 9:16:47 time: 0.5097 data_time: 0.0215 memory: 14901 loss: 1.6059 loss_prob: 0.8950 loss_thr: 0.5615 loss_db: 0.1493 2022/11/02 15:17:44 - mmengine - INFO - Epoch(train) [295][30/63] lr: 1.6793e-03 eta: 9:16:40 time: 0.5511 data_time: 0.0345 memory: 14901 loss: 1.7350 loss_prob: 1.0029 loss_thr: 0.5702 loss_db: 0.1619 2022/11/02 15:17:48 - mmengine - INFO - Epoch(train) [295][35/63] lr: 1.6793e-03 eta: 9:16:40 time: 0.6110 data_time: 0.0248 memory: 14901 loss: 1.7008 loss_prob: 0.9766 loss_thr: 0.5664 loss_db: 0.1578 2022/11/02 15:17:50 - mmengine - INFO - Epoch(train) [295][40/63] lr: 1.6793e-03 eta: 9:16:33 time: 0.5556 data_time: 0.0175 memory: 14901 loss: 1.8467 loss_prob: 1.0799 loss_thr: 0.5869 loss_db: 0.1798 2022/11/02 15:17:53 - mmengine - INFO - Epoch(train) [295][45/63] lr: 1.6793e-03 eta: 9:16:33 time: 0.5001 data_time: 0.0124 memory: 14901 loss: 1.8452 loss_prob: 1.0819 loss_thr: 0.5853 loss_db: 0.1780 2022/11/02 15:17:56 - mmengine - INFO - Epoch(train) [295][50/63] lr: 1.6793e-03 eta: 9:16:27 time: 0.5760 data_time: 0.0200 memory: 14901 loss: 1.5943 loss_prob: 0.8979 loss_thr: 0.5479 loss_db: 0.1485 2022/11/02 15:17:58 - mmengine - INFO - Epoch(train) [295][55/63] lr: 1.6793e-03 eta: 9:16:27 time: 0.5607 data_time: 0.0236 memory: 14901 loss: 1.7595 loss_prob: 1.0089 loss_thr: 0.5815 loss_db: 0.1691 2022/11/02 15:18:01 - mmengine - INFO - Epoch(train) [295][60/63] lr: 1.6793e-03 eta: 9:16:20 time: 0.5425 data_time: 0.0121 memory: 14901 loss: 1.7293 loss_prob: 0.9825 loss_thr: 0.5811 loss_db: 0.1657 2022/11/02 15:18:03 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:18:09 - mmengine - INFO - Epoch(train) [296][5/63] lr: 1.6776e-03 eta: 9:16:20 time: 0.8247 data_time: 0.2343 memory: 14901 loss: 1.6581 loss_prob: 0.9319 loss_thr: 0.5733 loss_db: 0.1529 2022/11/02 15:18:11 - mmengine - INFO - Epoch(train) [296][10/63] lr: 1.6776e-03 eta: 9:16:14 time: 0.8265 data_time: 0.2333 memory: 14901 loss: 1.6112 loss_prob: 0.8967 loss_thr: 0.5676 loss_db: 0.1470 2022/11/02 15:18:14 - mmengine - INFO - Epoch(train) [296][15/63] lr: 1.6776e-03 eta: 9:16:14 time: 0.5301 data_time: 0.0121 memory: 14901 loss: 1.6171 loss_prob: 0.8974 loss_thr: 0.5698 loss_db: 0.1499 2022/11/02 15:18:17 - mmengine - INFO - Epoch(train) [296][20/63] lr: 1.6776e-03 eta: 9:16:08 time: 0.5825 data_time: 0.0218 memory: 14901 loss: 1.6563 loss_prob: 0.9234 loss_thr: 0.5769 loss_db: 0.1560 2022/11/02 15:18:20 - mmengine - INFO - Epoch(train) [296][25/63] lr: 1.6776e-03 eta: 9:16:08 time: 0.5912 data_time: 0.0328 memory: 14901 loss: 1.6663 loss_prob: 0.9318 loss_thr: 0.5770 loss_db: 0.1575 2022/11/02 15:18:22 - mmengine - INFO - Epoch(train) [296][30/63] lr: 1.6776e-03 eta: 9:16:01 time: 0.5292 data_time: 0.0421 memory: 14901 loss: 1.8602 loss_prob: 1.0800 loss_thr: 0.6076 loss_db: 0.1726 2022/11/02 15:18:25 - mmengine - INFO - Epoch(train) [296][35/63] lr: 1.6776e-03 eta: 9:16:01 time: 0.5264 data_time: 0.0395 memory: 14901 loss: 1.8311 loss_prob: 1.0720 loss_thr: 0.5894 loss_db: 0.1697 2022/11/02 15:18:28 - mmengine - INFO - Epoch(train) [296][40/63] lr: 1.6776e-03 eta: 9:15:53 time: 0.5386 data_time: 0.0195 memory: 14901 loss: 1.6067 loss_prob: 0.9070 loss_thr: 0.5511 loss_db: 0.1486 2022/11/02 15:18:30 - mmengine - INFO - Epoch(train) [296][45/63] lr: 1.6776e-03 eta: 9:15:53 time: 0.5196 data_time: 0.0125 memory: 14901 loss: 1.6990 loss_prob: 0.9542 loss_thr: 0.5857 loss_db: 0.1591 2022/11/02 15:18:33 - mmengine - INFO - Epoch(train) [296][50/63] lr: 1.6776e-03 eta: 9:15:45 time: 0.5186 data_time: 0.0163 memory: 14901 loss: 1.8010 loss_prob: 1.0260 loss_thr: 0.6022 loss_db: 0.1727 2022/11/02 15:18:37 - mmengine - INFO - Epoch(train) [296][55/63] lr: 1.6776e-03 eta: 9:15:45 time: 0.6379 data_time: 0.0242 memory: 14901 loss: 1.6787 loss_prob: 0.9538 loss_thr: 0.5630 loss_db: 0.1619 2022/11/02 15:18:39 - mmengine - INFO - Epoch(train) [296][60/63] lr: 1.6776e-03 eta: 9:15:41 time: 0.6315 data_time: 0.0161 memory: 14901 loss: 1.5824 loss_prob: 0.8860 loss_thr: 0.5470 loss_db: 0.1495 2022/11/02 15:18:41 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:18:46 - mmengine - INFO - Epoch(train) [297][5/63] lr: 1.6759e-03 eta: 9:15:41 time: 0.8534 data_time: 0.2646 memory: 14901 loss: 1.5980 loss_prob: 0.8922 loss_thr: 0.5578 loss_db: 0.1480 2022/11/02 15:18:49 - mmengine - INFO - Epoch(train) [297][10/63] lr: 1.6759e-03 eta: 9:15:36 time: 0.8635 data_time: 0.2697 memory: 14901 loss: 1.7228 loss_prob: 0.9825 loss_thr: 0.5786 loss_db: 0.1618 2022/11/02 15:18:52 - mmengine - INFO - Epoch(train) [297][15/63] lr: 1.6759e-03 eta: 9:15:36 time: 0.5144 data_time: 0.0147 memory: 14901 loss: 1.7625 loss_prob: 1.0138 loss_thr: 0.5842 loss_db: 0.1645 2022/11/02 15:18:55 - mmengine - INFO - Epoch(train) [297][20/63] lr: 1.6759e-03 eta: 9:15:29 time: 0.5369 data_time: 0.0054 memory: 14901 loss: 1.6760 loss_prob: 0.9451 loss_thr: 0.5764 loss_db: 0.1545 2022/11/02 15:18:57 - mmengine - INFO - Epoch(train) [297][25/63] lr: 1.6759e-03 eta: 9:15:29 time: 0.5769 data_time: 0.0281 memory: 14901 loss: 1.5706 loss_prob: 0.8767 loss_thr: 0.5473 loss_db: 0.1466 2022/11/02 15:19:00 - mmengine - INFO - Epoch(train) [297][30/63] lr: 1.6759e-03 eta: 9:15:22 time: 0.5487 data_time: 0.0475 memory: 14901 loss: 1.5687 loss_prob: 0.8863 loss_thr: 0.5362 loss_db: 0.1462 2022/11/02 15:19:03 - mmengine - INFO - Epoch(train) [297][35/63] lr: 1.6759e-03 eta: 9:15:22 time: 0.5307 data_time: 0.0277 memory: 14901 loss: 1.5547 loss_prob: 0.8659 loss_thr: 0.5471 loss_db: 0.1418 2022/11/02 15:19:06 - mmengine - INFO - Epoch(train) [297][40/63] lr: 1.6759e-03 eta: 9:15:16 time: 0.5705 data_time: 0.0113 memory: 14901 loss: 1.5329 loss_prob: 0.8405 loss_thr: 0.5525 loss_db: 0.1399 2022/11/02 15:19:08 - mmengine - INFO - Epoch(train) [297][45/63] lr: 1.6759e-03 eta: 9:15:16 time: 0.5594 data_time: 0.0102 memory: 14901 loss: 1.5256 loss_prob: 0.8360 loss_thr: 0.5501 loss_db: 0.1394 2022/11/02 15:19:11 - mmengine - INFO - Epoch(train) [297][50/63] lr: 1.6759e-03 eta: 9:15:07 time: 0.4973 data_time: 0.0244 memory: 14901 loss: 1.6124 loss_prob: 0.8903 loss_thr: 0.5710 loss_db: 0.1511 2022/11/02 15:19:14 - mmengine - INFO - Epoch(train) [297][55/63] lr: 1.6759e-03 eta: 9:15:07 time: 0.5391 data_time: 0.0261 memory: 14901 loss: 1.6204 loss_prob: 0.9085 loss_thr: 0.5578 loss_db: 0.1541 2022/11/02 15:19:16 - mmengine - INFO - Epoch(train) [297][60/63] lr: 1.6759e-03 eta: 9:15:00 time: 0.5349 data_time: 0.0106 memory: 14901 loss: 1.5887 loss_prob: 0.9071 loss_thr: 0.5340 loss_db: 0.1477 2022/11/02 15:19:18 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:19:23 - mmengine - INFO - Epoch(train) [298][5/63] lr: 1.6743e-03 eta: 9:15:00 time: 0.8311 data_time: 0.2397 memory: 14901 loss: 1.6159 loss_prob: 0.9081 loss_thr: 0.5611 loss_db: 0.1468 2022/11/02 15:19:26 - mmengine - INFO - Epoch(train) [298][10/63] lr: 1.6743e-03 eta: 9:14:54 time: 0.8057 data_time: 0.2452 memory: 14901 loss: 1.6062 loss_prob: 0.9005 loss_thr: 0.5523 loss_db: 0.1534 2022/11/02 15:19:29 - mmengine - INFO - Epoch(train) [298][15/63] lr: 1.6743e-03 eta: 9:14:54 time: 0.5228 data_time: 0.0161 memory: 14901 loss: 1.6439 loss_prob: 0.9390 loss_thr: 0.5479 loss_db: 0.1570 2022/11/02 15:19:31 - mmengine - INFO - Epoch(train) [298][20/63] lr: 1.6743e-03 eta: 9:14:46 time: 0.5331 data_time: 0.0141 memory: 14901 loss: 1.6013 loss_prob: 0.8999 loss_thr: 0.5515 loss_db: 0.1498 2022/11/02 15:19:34 - mmengine - INFO - Epoch(train) [298][25/63] lr: 1.6743e-03 eta: 9:14:46 time: 0.5772 data_time: 0.0160 memory: 14901 loss: 1.6843 loss_prob: 0.9683 loss_thr: 0.5620 loss_db: 0.1541 2022/11/02 15:19:38 - mmengine - INFO - Epoch(train) [298][30/63] lr: 1.6743e-03 eta: 9:14:43 time: 0.6646 data_time: 0.0384 memory: 14901 loss: 1.6993 loss_prob: 0.9809 loss_thr: 0.5630 loss_db: 0.1553 2022/11/02 15:19:41 - mmengine - INFO - Epoch(train) [298][35/63] lr: 1.6743e-03 eta: 9:14:43 time: 0.6161 data_time: 0.0366 memory: 14901 loss: 1.5897 loss_prob: 0.8830 loss_thr: 0.5562 loss_db: 0.1505 2022/11/02 15:19:43 - mmengine - INFO - Epoch(train) [298][40/63] lr: 1.6743e-03 eta: 9:14:36 time: 0.5459 data_time: 0.0137 memory: 14901 loss: 1.5795 loss_prob: 0.8803 loss_thr: 0.5526 loss_db: 0.1466 2022/11/02 15:19:46 - mmengine - INFO - Epoch(train) [298][45/63] lr: 1.6743e-03 eta: 9:14:36 time: 0.5544 data_time: 0.0118 memory: 14901 loss: 1.5960 loss_prob: 0.8952 loss_thr: 0.5562 loss_db: 0.1446 2022/11/02 15:19:49 - mmengine - INFO - Epoch(train) [298][50/63] lr: 1.6743e-03 eta: 9:14:28 time: 0.5315 data_time: 0.0232 memory: 14901 loss: 1.6212 loss_prob: 0.9093 loss_thr: 0.5621 loss_db: 0.1498 2022/11/02 15:19:51 - mmengine - INFO - Epoch(train) [298][55/63] lr: 1.6743e-03 eta: 9:14:28 time: 0.5349 data_time: 0.0205 memory: 14901 loss: 1.7003 loss_prob: 0.9802 loss_thr: 0.5590 loss_db: 0.1611 2022/11/02 15:19:54 - mmengine - INFO - Epoch(train) [298][60/63] lr: 1.6743e-03 eta: 9:14:21 time: 0.5445 data_time: 0.0093 memory: 14901 loss: 1.6604 loss_prob: 0.9401 loss_thr: 0.5664 loss_db: 0.1539 2022/11/02 15:19:55 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:20:02 - mmengine - INFO - Epoch(train) [299][5/63] lr: 1.6726e-03 eta: 9:14:21 time: 0.8664 data_time: 0.2152 memory: 14901 loss: 1.6021 loss_prob: 0.9007 loss_thr: 0.5557 loss_db: 0.1457 2022/11/02 15:20:05 - mmengine - INFO - Epoch(train) [299][10/63] lr: 1.6726e-03 eta: 9:14:20 time: 0.9690 data_time: 0.2161 memory: 14901 loss: 1.6121 loss_prob: 0.8999 loss_thr: 0.5628 loss_db: 0.1495 2022/11/02 15:20:08 - mmengine - INFO - Epoch(train) [299][15/63] lr: 1.6726e-03 eta: 9:14:20 time: 0.6297 data_time: 0.0086 memory: 14901 loss: 1.6005 loss_prob: 0.8828 loss_thr: 0.5631 loss_db: 0.1546 2022/11/02 15:20:11 - mmengine - INFO - Epoch(train) [299][20/63] lr: 1.6726e-03 eta: 9:14:12 time: 0.5355 data_time: 0.0092 memory: 14901 loss: 1.5812 loss_prob: 0.8791 loss_thr: 0.5529 loss_db: 0.1492 2022/11/02 15:20:13 - mmengine - INFO - Epoch(train) [299][25/63] lr: 1.6726e-03 eta: 9:14:12 time: 0.5391 data_time: 0.0097 memory: 14901 loss: 1.7057 loss_prob: 0.9832 loss_thr: 0.5638 loss_db: 0.1587 2022/11/02 15:20:17 - mmengine - INFO - Epoch(train) [299][30/63] lr: 1.6726e-03 eta: 9:14:07 time: 0.5978 data_time: 0.0350 memory: 14901 loss: 1.7466 loss_prob: 1.0033 loss_thr: 0.5771 loss_db: 0.1662 2022/11/02 15:20:19 - mmengine - INFO - Epoch(train) [299][35/63] lr: 1.6726e-03 eta: 9:14:07 time: 0.5743 data_time: 0.0398 memory: 14901 loss: 1.5844 loss_prob: 0.8858 loss_thr: 0.5502 loss_db: 0.1484 2022/11/02 15:20:22 - mmengine - INFO - Epoch(train) [299][40/63] lr: 1.6726e-03 eta: 9:13:59 time: 0.5290 data_time: 0.0120 memory: 14901 loss: 1.6516 loss_prob: 0.9241 loss_thr: 0.5745 loss_db: 0.1531 2022/11/02 15:20:25 - mmengine - INFO - Epoch(train) [299][45/63] lr: 1.6726e-03 eta: 9:13:59 time: 0.5504 data_time: 0.0108 memory: 14901 loss: 1.6031 loss_prob: 0.8875 loss_thr: 0.5636 loss_db: 0.1520 2022/11/02 15:20:27 - mmengine - INFO - Epoch(train) [299][50/63] lr: 1.6726e-03 eta: 9:13:53 time: 0.5558 data_time: 0.0252 memory: 14901 loss: 1.6104 loss_prob: 0.9000 loss_thr: 0.5585 loss_db: 0.1519 2022/11/02 15:20:30 - mmengine - INFO - Epoch(train) [299][55/63] lr: 1.6726e-03 eta: 9:13:53 time: 0.5271 data_time: 0.0204 memory: 14901 loss: 1.6628 loss_prob: 0.9230 loss_thr: 0.5875 loss_db: 0.1523 2022/11/02 15:20:33 - mmengine - INFO - Epoch(train) [299][60/63] lr: 1.6726e-03 eta: 9:13:46 time: 0.5479 data_time: 0.0125 memory: 14901 loss: 1.5771 loss_prob: 0.8724 loss_thr: 0.5628 loss_db: 0.1418 2022/11/02 15:20:34 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:20:40 - mmengine - INFO - Epoch(train) [300][5/63] lr: 1.6709e-03 eta: 9:13:46 time: 0.7958 data_time: 0.2522 memory: 14901 loss: 1.6831 loss_prob: 0.9477 loss_thr: 0.5767 loss_db: 0.1587 2022/11/02 15:20:43 - mmengine - INFO - Epoch(train) [300][10/63] lr: 1.6709e-03 eta: 9:13:40 time: 0.8134 data_time: 0.2521 memory: 14901 loss: 1.7374 loss_prob: 0.9867 loss_thr: 0.5912 loss_db: 0.1596 2022/11/02 15:20:45 - mmengine - INFO - Epoch(train) [300][15/63] lr: 1.6709e-03 eta: 9:13:40 time: 0.5548 data_time: 0.0089 memory: 14901 loss: 1.6137 loss_prob: 0.9243 loss_thr: 0.5425 loss_db: 0.1469 2022/11/02 15:20:49 - mmengine - INFO - Epoch(train) [300][20/63] lr: 1.6709e-03 eta: 9:13:35 time: 0.6214 data_time: 0.0097 memory: 14901 loss: 1.4965 loss_prob: 0.8267 loss_thr: 0.5313 loss_db: 0.1385 2022/11/02 15:20:51 - mmengine - INFO - Epoch(train) [300][25/63] lr: 1.6709e-03 eta: 9:13:35 time: 0.6108 data_time: 0.0135 memory: 14901 loss: 1.5299 loss_prob: 0.8539 loss_thr: 0.5331 loss_db: 0.1429 2022/11/02 15:20:55 - mmengine - INFO - Epoch(train) [300][30/63] lr: 1.6709e-03 eta: 9:13:29 time: 0.5871 data_time: 0.0396 memory: 14901 loss: 1.6093 loss_prob: 0.9030 loss_thr: 0.5579 loss_db: 0.1484 2022/11/02 15:20:57 - mmengine - INFO - Epoch(train) [300][35/63] lr: 1.6709e-03 eta: 9:13:29 time: 0.5625 data_time: 0.0326 memory: 14901 loss: 1.6934 loss_prob: 0.9380 loss_thr: 0.5996 loss_db: 0.1558 2022/11/02 15:20:59 - mmengine - INFO - Epoch(train) [300][40/63] lr: 1.6709e-03 eta: 9:13:20 time: 0.4787 data_time: 0.0054 memory: 14901 loss: 1.7464 loss_prob: 0.9797 loss_thr: 0.5995 loss_db: 0.1672 2022/11/02 15:21:02 - mmengine - INFO - Epoch(train) [300][45/63] lr: 1.6709e-03 eta: 9:13:20 time: 0.4977 data_time: 0.0087 memory: 14901 loss: 1.6386 loss_prob: 0.9215 loss_thr: 0.5623 loss_db: 0.1547 2022/11/02 15:21:05 - mmengine - INFO - Epoch(train) [300][50/63] lr: 1.6709e-03 eta: 9:13:12 time: 0.5265 data_time: 0.0180 memory: 14901 loss: 1.6309 loss_prob: 0.9229 loss_thr: 0.5552 loss_db: 0.1528 2022/11/02 15:21:07 - mmengine - INFO - Epoch(train) [300][55/63] lr: 1.6709e-03 eta: 9:13:12 time: 0.5146 data_time: 0.0235 memory: 14901 loss: 1.5768 loss_prob: 0.8840 loss_thr: 0.5453 loss_db: 0.1474 2022/11/02 15:21:10 - mmengine - INFO - Epoch(train) [300][60/63] lr: 1.6709e-03 eta: 9:13:05 time: 0.5333 data_time: 0.0171 memory: 14901 loss: 1.5569 loss_prob: 0.8595 loss_thr: 0.5530 loss_db: 0.1444 2022/11/02 15:21:11 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:21:11 - mmengine - INFO - Saving checkpoint at 300 epochs 2022/11/02 15:21:15 - mmengine - INFO - Epoch(val) [300][5/500] eta: 9:13:05 time: 0.0429 data_time: 0.0046 memory: 14901 2022/11/02 15:21:15 - mmengine - INFO - Epoch(val) [300][10/500] eta: 0:00:22 time: 0.0466 data_time: 0.0058 memory: 1008 2022/11/02 15:21:16 - mmengine - INFO - Epoch(val) [300][15/500] eta: 0:00:22 time: 0.0387 data_time: 0.0034 memory: 1008 2022/11/02 15:21:16 - mmengine - INFO - Epoch(val) [300][20/500] eta: 0:00:16 time: 0.0351 data_time: 0.0021 memory: 1008 2022/11/02 15:21:16 - mmengine - INFO - Epoch(val) [300][25/500] eta: 0:00:16 time: 0.0388 data_time: 0.0031 memory: 1008 2022/11/02 15:21:16 - mmengine - INFO - Epoch(val) [300][30/500] eta: 0:00:19 time: 0.0417 data_time: 0.0033 memory: 1008 2022/11/02 15:21:16 - mmengine - INFO - Epoch(val) [300][35/500] eta: 0:00:19 time: 0.0400 data_time: 0.0026 memory: 1008 2022/11/02 15:21:17 - mmengine - INFO - Epoch(val) [300][40/500] eta: 0:00:19 time: 0.0422 data_time: 0.0026 memory: 1008 2022/11/02 15:21:17 - mmengine - INFO - Epoch(val) [300][45/500] eta: 0:00:19 time: 0.0413 data_time: 0.0024 memory: 1008 2022/11/02 15:21:17 - mmengine - INFO - Epoch(val) [300][50/500] eta: 0:00:18 time: 0.0418 data_time: 0.0035 memory: 1008 2022/11/02 15:21:17 - mmengine - INFO - Epoch(val) [300][55/500] eta: 0:00:18 time: 0.0439 data_time: 0.0040 memory: 1008 2022/11/02 15:21:17 - mmengine - INFO - Epoch(val) [300][60/500] eta: 0:00:16 time: 0.0386 data_time: 0.0028 memory: 1008 2022/11/02 15:21:18 - mmengine - INFO - Epoch(val) [300][65/500] eta: 0:00:16 time: 0.0420 data_time: 0.0026 memory: 1008 2022/11/02 15:21:18 - mmengine - INFO - Epoch(val) [300][70/500] eta: 0:00:18 time: 0.0431 data_time: 0.0027 memory: 1008 2022/11/02 15:21:18 - mmengine - INFO - Epoch(val) [300][75/500] eta: 0:00:18 time: 0.0377 data_time: 0.0027 memory: 1008 2022/11/02 15:21:18 - mmengine - INFO - Epoch(val) [300][80/500] eta: 0:00:22 time: 0.0525 data_time: 0.0205 memory: 1008 2022/11/02 15:21:19 - mmengine - INFO - Epoch(val) [300][85/500] eta: 0:00:22 time: 0.0538 data_time: 0.0201 memory: 1008 2022/11/02 15:21:19 - mmengine - INFO - Epoch(val) [300][90/500] eta: 0:00:17 time: 0.0433 data_time: 0.0025 memory: 1008 2022/11/02 15:21:19 - mmengine - INFO - Epoch(val) [300][95/500] eta: 0:00:17 time: 0.0452 data_time: 0.0029 memory: 1008 2022/11/02 15:21:19 - mmengine - INFO - Epoch(val) [300][100/500] eta: 0:00:15 time: 0.0395 data_time: 0.0028 memory: 1008 2022/11/02 15:21:19 - mmengine - INFO - Epoch(val) [300][105/500] eta: 0:00:15 time: 0.0420 data_time: 0.0031 memory: 1008 2022/11/02 15:21:20 - mmengine - INFO - Epoch(val) [300][110/500] eta: 0:00:16 time: 0.0423 data_time: 0.0029 memory: 1008 2022/11/02 15:21:20 - mmengine - INFO - Epoch(val) [300][115/500] eta: 0:00:16 time: 0.0404 data_time: 0.0025 memory: 1008 2022/11/02 15:21:20 - mmengine - INFO - Epoch(val) [300][120/500] eta: 0:00:15 time: 0.0414 data_time: 0.0027 memory: 1008 2022/11/02 15:21:20 - mmengine - INFO - Epoch(val) [300][125/500] eta: 0:00:15 time: 0.0376 data_time: 0.0024 memory: 1008 2022/11/02 15:21:20 - mmengine - INFO - Epoch(val) [300][130/500] eta: 0:00:13 time: 0.0369 data_time: 0.0025 memory: 1008 2022/11/02 15:21:21 - mmengine - INFO - Epoch(val) [300][135/500] eta: 0:00:13 time: 0.0390 data_time: 0.0029 memory: 1008 2022/11/02 15:21:21 - mmengine - INFO - Epoch(val) [300][140/500] eta: 0:00:14 time: 0.0392 data_time: 0.0028 memory: 1008 2022/11/02 15:21:21 - mmengine - INFO - Epoch(val) [300][145/500] eta: 0:00:14 time: 0.0403 data_time: 0.0026 memory: 1008 2022/11/02 15:21:21 - mmengine - INFO - Epoch(val) [300][150/500] eta: 0:00:15 time: 0.0439 data_time: 0.0028 memory: 1008 2022/11/02 15:21:21 - mmengine - INFO - Epoch(val) [300][155/500] eta: 0:00:15 time: 0.0475 data_time: 0.0030 memory: 1008 2022/11/02 15:21:22 - mmengine - INFO - Epoch(val) [300][160/500] eta: 0:00:16 time: 0.0483 data_time: 0.0028 memory: 1008 2022/11/02 15:21:22 - mmengine - INFO - Epoch(val) [300][165/500] eta: 0:00:16 time: 0.0455 data_time: 0.0027 memory: 1008 2022/11/02 15:21:22 - mmengine - INFO - Epoch(val) [300][170/500] eta: 0:00:15 time: 0.0485 data_time: 0.0029 memory: 1008 2022/11/02 15:21:22 - mmengine - INFO - Epoch(val) [300][175/500] eta: 0:00:15 time: 0.0442 data_time: 0.0028 memory: 1008 2022/11/02 15:21:23 - mmengine - INFO - Epoch(val) [300][180/500] eta: 0:00:11 time: 0.0360 data_time: 0.0024 memory: 1008 2022/11/02 15:21:23 - mmengine - INFO - Epoch(val) [300][185/500] eta: 0:00:11 time: 0.0389 data_time: 0.0024 memory: 1008 2022/11/02 15:21:23 - mmengine - INFO - Epoch(val) [300][190/500] eta: 0:00:12 time: 0.0395 data_time: 0.0026 memory: 1008 2022/11/02 15:21:23 - mmengine - INFO - Epoch(val) [300][195/500] eta: 0:00:12 time: 0.0373 data_time: 0.0026 memory: 1008 2022/11/02 15:21:23 - mmengine - INFO - Epoch(val) [300][200/500] eta: 0:00:14 time: 0.0483 data_time: 0.0028 memory: 1008 2022/11/02 15:21:24 - mmengine - INFO - Epoch(val) [300][205/500] eta: 0:00:14 time: 0.0486 data_time: 0.0033 memory: 1008 2022/11/02 15:21:24 - mmengine - INFO - Epoch(val) [300][210/500] eta: 0:00:10 time: 0.0377 data_time: 0.0029 memory: 1008 2022/11/02 15:21:24 - mmengine - INFO - Epoch(val) [300][215/500] eta: 0:00:10 time: 0.0400 data_time: 0.0026 memory: 1008 2022/11/02 15:21:24 - mmengine - INFO - Epoch(val) [300][220/500] eta: 0:00:11 time: 0.0404 data_time: 0.0030 memory: 1008 2022/11/02 15:21:24 - mmengine - INFO - Epoch(val) [300][225/500] eta: 0:00:11 time: 0.0400 data_time: 0.0027 memory: 1008 2022/11/02 15:21:25 - mmengine - INFO - Epoch(val) [300][230/500] eta: 0:00:10 time: 0.0388 data_time: 0.0022 memory: 1008 2022/11/02 15:21:25 - mmengine - INFO - Epoch(val) [300][235/500] eta: 0:00:10 time: 0.0365 data_time: 0.0021 memory: 1008 2022/11/02 15:21:25 - mmengine - INFO - Epoch(val) [300][240/500] eta: 0:00:09 time: 0.0377 data_time: 0.0023 memory: 1008 2022/11/02 15:21:25 - mmengine - INFO - Epoch(val) [300][245/500] eta: 0:00:09 time: 0.0366 data_time: 0.0025 memory: 1008 2022/11/02 15:21:25 - mmengine - INFO - Epoch(val) [300][250/500] eta: 0:00:08 time: 0.0355 data_time: 0.0024 memory: 1008 2022/11/02 15:21:26 - mmengine - INFO - Epoch(val) [300][255/500] eta: 0:00:08 time: 0.0395 data_time: 0.0027 memory: 1008 2022/11/02 15:21:26 - mmengine - INFO - Epoch(val) [300][260/500] eta: 0:00:10 time: 0.0433 data_time: 0.0033 memory: 1008 2022/11/02 15:21:26 - mmengine - INFO - Epoch(val) [300][265/500] eta: 0:00:10 time: 0.0473 data_time: 0.0033 memory: 1008 2022/11/02 15:21:26 - mmengine - INFO - Epoch(val) [300][270/500] eta: 0:00:10 time: 0.0465 data_time: 0.0031 memory: 1008 2022/11/02 15:21:26 - mmengine - INFO - Epoch(val) [300][275/500] eta: 0:00:10 time: 0.0429 data_time: 0.0026 memory: 1008 2022/11/02 15:21:27 - mmengine - INFO - Epoch(val) [300][280/500] eta: 0:00:09 time: 0.0447 data_time: 0.0027 memory: 1008 2022/11/02 15:21:27 - mmengine - INFO - Epoch(val) [300][285/500] eta: 0:00:09 time: 0.0427 data_time: 0.0028 memory: 1008 2022/11/02 15:21:27 - mmengine - INFO - Epoch(val) [300][290/500] eta: 0:00:09 time: 0.0456 data_time: 0.0028 memory: 1008 2022/11/02 15:21:27 - mmengine - INFO - Epoch(val) [300][295/500] eta: 0:00:09 time: 0.0522 data_time: 0.0031 memory: 1008 2022/11/02 15:21:28 - mmengine - INFO - Epoch(val) [300][300/500] eta: 0:00:10 time: 0.0549 data_time: 0.0038 memory: 1008 2022/11/02 15:21:28 - mmengine - INFO - Epoch(val) [300][305/500] eta: 0:00:10 time: 0.0499 data_time: 0.0041 memory: 1008 2022/11/02 15:21:28 - mmengine - INFO - Epoch(val) [300][310/500] eta: 0:00:08 time: 0.0432 data_time: 0.0032 memory: 1008 2022/11/02 15:21:28 - mmengine - INFO - Epoch(val) [300][315/500] eta: 0:00:08 time: 0.0455 data_time: 0.0026 memory: 1008 2022/11/02 15:21:29 - mmengine - INFO - Epoch(val) [300][320/500] eta: 0:00:08 time: 0.0446 data_time: 0.0028 memory: 1008 2022/11/02 15:21:29 - mmengine - INFO - Epoch(val) [300][325/500] eta: 0:00:08 time: 0.0546 data_time: 0.0027 memory: 1008 2022/11/02 15:21:29 - mmengine - INFO - Epoch(val) [300][330/500] eta: 0:00:08 time: 0.0525 data_time: 0.0025 memory: 1008 2022/11/02 15:21:29 - mmengine - INFO - Epoch(val) [300][335/500] eta: 0:00:08 time: 0.0364 data_time: 0.0026 memory: 1008 2022/11/02 15:21:30 - mmengine - INFO - Epoch(val) [300][340/500] eta: 0:00:09 time: 0.0618 data_time: 0.0032 memory: 1008 2022/11/02 15:21:30 - mmengine - INFO - Epoch(val) [300][345/500] eta: 0:00:09 time: 0.0648 data_time: 0.0030 memory: 1008 2022/11/02 15:21:30 - mmengine - INFO - Epoch(val) [300][350/500] eta: 0:00:06 time: 0.0434 data_time: 0.0022 memory: 1008 2022/11/02 15:21:30 - mmengine - INFO - Epoch(val) [300][355/500] eta: 0:00:06 time: 0.0411 data_time: 0.0023 memory: 1008 2022/11/02 15:21:31 - mmengine - INFO - Epoch(val) [300][360/500] eta: 0:00:05 time: 0.0422 data_time: 0.0027 memory: 1008 2022/11/02 15:21:31 - mmengine - INFO - Epoch(val) [300][365/500] eta: 0:00:05 time: 0.0447 data_time: 0.0031 memory: 1008 2022/11/02 15:21:31 - mmengine - INFO - Epoch(val) [300][370/500] eta: 0:00:04 time: 0.0383 data_time: 0.0028 memory: 1008 2022/11/02 15:21:31 - mmengine - INFO - Epoch(val) [300][375/500] eta: 0:00:04 time: 0.0396 data_time: 0.0033 memory: 1008 2022/11/02 15:21:31 - mmengine - INFO - Epoch(val) [300][380/500] eta: 0:00:05 time: 0.0425 data_time: 0.0034 memory: 1008 2022/11/02 15:21:32 - mmengine - INFO - Epoch(val) [300][385/500] eta: 0:00:05 time: 0.0420 data_time: 0.0025 memory: 1008 2022/11/02 15:21:32 - mmengine - INFO - Epoch(val) [300][390/500] eta: 0:00:05 time: 0.0493 data_time: 0.0044 memory: 1008 2022/11/02 15:21:32 - mmengine - INFO - Epoch(val) [300][395/500] eta: 0:00:05 time: 0.0460 data_time: 0.0045 memory: 1008 2022/11/02 15:21:32 - mmengine - INFO - Epoch(val) [300][400/500] eta: 0:00:04 time: 0.0405 data_time: 0.0027 memory: 1008 2022/11/02 15:21:32 - mmengine - INFO - Epoch(val) [300][405/500] eta: 0:00:04 time: 0.0434 data_time: 0.0026 memory: 1008 2022/11/02 15:21:33 - mmengine - INFO - Epoch(val) [300][410/500] eta: 0:00:03 time: 0.0437 data_time: 0.0027 memory: 1008 2022/11/02 15:21:33 - mmengine - INFO - Epoch(val) [300][415/500] eta: 0:00:03 time: 0.0420 data_time: 0.0028 memory: 1008 2022/11/02 15:21:33 - mmengine - INFO - Epoch(val) [300][420/500] eta: 0:00:03 time: 0.0404 data_time: 0.0029 memory: 1008 2022/11/02 15:21:33 - mmengine - INFO - Epoch(val) [300][425/500] eta: 0:00:03 time: 0.0411 data_time: 0.0029 memory: 1008 2022/11/02 15:21:33 - mmengine - INFO - Epoch(val) [300][430/500] eta: 0:00:02 time: 0.0382 data_time: 0.0025 memory: 1008 2022/11/02 15:21:34 - mmengine - INFO - Epoch(val) [300][435/500] eta: 0:00:02 time: 0.0379 data_time: 0.0028 memory: 1008 2022/11/02 15:21:34 - mmengine - INFO - Epoch(val) [300][440/500] eta: 0:00:02 time: 0.0390 data_time: 0.0029 memory: 1008 2022/11/02 15:21:34 - mmengine - INFO - Epoch(val) [300][445/500] eta: 0:00:02 time: 0.0389 data_time: 0.0025 memory: 1008 2022/11/02 15:21:34 - mmengine - INFO - Epoch(val) [300][450/500] eta: 0:00:02 time: 0.0401 data_time: 0.0024 memory: 1008 2022/11/02 15:21:34 - mmengine - INFO - Epoch(val) [300][455/500] eta: 0:00:02 time: 0.0407 data_time: 0.0026 memory: 1008 2022/11/02 15:21:35 - mmengine - INFO - Epoch(val) [300][460/500] eta: 0:00:01 time: 0.0377 data_time: 0.0025 memory: 1008 2022/11/02 15:21:35 - mmengine - INFO - Epoch(val) [300][465/500] eta: 0:00:01 time: 0.0352 data_time: 0.0023 memory: 1008 2022/11/02 15:21:35 - mmengine - INFO - Epoch(val) [300][470/500] eta: 0:00:01 time: 0.0362 data_time: 0.0024 memory: 1008 2022/11/02 15:21:35 - mmengine - INFO - Epoch(val) [300][475/500] eta: 0:00:01 time: 0.0356 data_time: 0.0025 memory: 1008 2022/11/02 15:21:35 - mmengine - INFO - Epoch(val) [300][480/500] eta: 0:00:00 time: 0.0382 data_time: 0.0026 memory: 1008 2022/11/02 15:21:36 - mmengine - INFO - Epoch(val) [300][485/500] eta: 0:00:00 time: 0.0379 data_time: 0.0024 memory: 1008 2022/11/02 15:21:36 - mmengine - INFO - Epoch(val) [300][490/500] eta: 0:00:00 time: 0.0386 data_time: 0.0024 memory: 1008 2022/11/02 15:21:36 - mmengine - INFO - Epoch(val) [300][495/500] eta: 0:00:00 time: 0.0439 data_time: 0.0027 memory: 1008 2022/11/02 15:21:36 - mmengine - INFO - Epoch(val) [300][500/500] eta: 0:00:00 time: 0.0395 data_time: 0.0026 memory: 1008 2022/11/02 15:21:36 - mmengine - INFO - Evaluating hmean-iou... 2022/11/02 15:21:36 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.8146, precision: 0.7154, hmean: 0.7618 2022/11/02 15:21:36 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.8146, precision: 0.7779, hmean: 0.7959 2022/11/02 15:21:36 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.8122, precision: 0.8134, hmean: 0.8128 2022/11/02 15:21:36 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.8021, precision: 0.8544, hmean: 0.8274 2022/11/02 15:21:36 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.7713, precision: 0.8950, hmean: 0.8285 2022/11/02 15:21:36 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.4627, precision: 0.9496, hmean: 0.6222 2022/11/02 15:21:36 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0077, precision: 1.0000, hmean: 0.0153 2022/11/02 15:21:36 - mmengine - INFO - Epoch(val) [300][500/500] icdar/precision: 0.8950 icdar/recall: 0.7713 icdar/hmean: 0.8285 2022/11/02 15:21:43 - mmengine - INFO - Epoch(train) [301][5/63] lr: 1.6692e-03 eta: 0:00:00 time: 0.8664 data_time: 0.2546 memory: 14901 loss: 1.4024 loss_prob: 0.7669 loss_thr: 0.5056 loss_db: 0.1300 2022/11/02 15:21:45 - mmengine - INFO - Epoch(train) [301][10/63] lr: 1.6692e-03 eta: 9:13:01 time: 0.8859 data_time: 0.2533 memory: 14901 loss: 1.4850 loss_prob: 0.8304 loss_thr: 0.5147 loss_db: 0.1398 2022/11/02 15:21:48 - mmengine - INFO - Epoch(train) [301][15/63] lr: 1.6692e-03 eta: 9:13:01 time: 0.5711 data_time: 0.0078 memory: 14901 loss: 1.5149 loss_prob: 0.8495 loss_thr: 0.5273 loss_db: 0.1382 2022/11/02 15:21:51 - mmengine - INFO - Epoch(train) [301][20/63] lr: 1.6692e-03 eta: 9:12:56 time: 0.6173 data_time: 0.0111 memory: 14901 loss: 1.5266 loss_prob: 0.8439 loss_thr: 0.5421 loss_db: 0.1406 2022/11/02 15:21:54 - mmengine - INFO - Epoch(train) [301][25/63] lr: 1.6692e-03 eta: 9:12:56 time: 0.5929 data_time: 0.0421 memory: 14901 loss: 1.4331 loss_prob: 0.7707 loss_thr: 0.5309 loss_db: 0.1316 2022/11/02 15:21:57 - mmengine - INFO - Epoch(train) [301][30/63] lr: 1.6692e-03 eta: 9:12:50 time: 0.5715 data_time: 0.0532 memory: 14901 loss: 1.4127 loss_prob: 0.7674 loss_thr: 0.5139 loss_db: 0.1314 2022/11/02 15:22:00 - mmengine - INFO - Epoch(train) [301][35/63] lr: 1.6692e-03 eta: 9:12:50 time: 0.6164 data_time: 0.0235 memory: 14901 loss: 1.5354 loss_prob: 0.8502 loss_thr: 0.5390 loss_db: 0.1462 2022/11/02 15:22:03 - mmengine - INFO - Epoch(train) [301][40/63] lr: 1.6692e-03 eta: 9:12:44 time: 0.5817 data_time: 0.0112 memory: 14901 loss: 1.5161 loss_prob: 0.8364 loss_thr: 0.5375 loss_db: 0.1422 2022/11/02 15:22:06 - mmengine - INFO - Epoch(train) [301][45/63] lr: 1.6692e-03 eta: 9:12:44 time: 0.5453 data_time: 0.0077 memory: 14901 loss: 1.6971 loss_prob: 0.9606 loss_thr: 0.5771 loss_db: 0.1594 2022/11/02 15:22:09 - mmengine - INFO - Epoch(train) [301][50/63] lr: 1.6692e-03 eta: 9:12:38 time: 0.5744 data_time: 0.0248 memory: 14901 loss: 1.8023 loss_prob: 1.0409 loss_thr: 0.5915 loss_db: 0.1699 2022/11/02 15:22:11 - mmengine - INFO - Epoch(train) [301][55/63] lr: 1.6692e-03 eta: 9:12:38 time: 0.5302 data_time: 0.0271 memory: 14901 loss: 1.8091 loss_prob: 1.0598 loss_thr: 0.5827 loss_db: 0.1666 2022/11/02 15:22:15 - mmengine - INFO - Epoch(train) [301][60/63] lr: 1.6692e-03 eta: 9:12:33 time: 0.6107 data_time: 0.0080 memory: 14901 loss: 1.7358 loss_prob: 1.0018 loss_thr: 0.5741 loss_db: 0.1598 2022/11/02 15:22:16 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:22:23 - mmengine - INFO - Epoch(train) [302][5/63] lr: 1.6676e-03 eta: 9:12:33 time: 0.9007 data_time: 0.3027 memory: 14901 loss: 1.6522 loss_prob: 0.9253 loss_thr: 0.5722 loss_db: 0.1547 2022/11/02 15:22:25 - mmengine - INFO - Epoch(train) [302][10/63] lr: 1.6676e-03 eta: 9:12:30 time: 0.9376 data_time: 0.3021 memory: 14901 loss: 1.6053 loss_prob: 0.8950 loss_thr: 0.5623 loss_db: 0.1480 2022/11/02 15:22:28 - mmengine - INFO - Epoch(train) [302][15/63] lr: 1.6676e-03 eta: 9:12:30 time: 0.5275 data_time: 0.0085 memory: 14901 loss: 1.7173 loss_prob: 0.9898 loss_thr: 0.5678 loss_db: 0.1597 2022/11/02 15:22:31 - mmengine - INFO - Epoch(train) [302][20/63] lr: 1.6676e-03 eta: 9:12:22 time: 0.5032 data_time: 0.0105 memory: 14901 loss: 1.8106 loss_prob: 1.0437 loss_thr: 0.6012 loss_db: 0.1658 2022/11/02 15:22:33 - mmengine - INFO - Epoch(train) [302][25/63] lr: 1.6676e-03 eta: 9:12:22 time: 0.5432 data_time: 0.0411 memory: 14901 loss: 1.7501 loss_prob: 0.9737 loss_thr: 0.6166 loss_db: 0.1598 2022/11/02 15:22:36 - mmengine - INFO - Epoch(train) [302][30/63] lr: 1.6676e-03 eta: 9:12:16 time: 0.5741 data_time: 0.0415 memory: 14901 loss: 1.7082 loss_prob: 0.9677 loss_thr: 0.5808 loss_db: 0.1597 2022/11/02 15:22:39 - mmengine - INFO - Epoch(train) [302][35/63] lr: 1.6676e-03 eta: 9:12:16 time: 0.5797 data_time: 0.0100 memory: 14901 loss: 1.6129 loss_prob: 0.9123 loss_thr: 0.5501 loss_db: 0.1505 2022/11/02 15:22:40 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:22:42 - mmengine - INFO - Epoch(train) [302][40/63] lr: 1.6676e-03 eta: 9:12:10 time: 0.5779 data_time: 0.0088 memory: 14901 loss: 1.5967 loss_prob: 0.8763 loss_thr: 0.5733 loss_db: 0.1471 2022/11/02 15:22:45 - mmengine - INFO - Epoch(train) [302][45/63] lr: 1.6676e-03 eta: 9:12:10 time: 0.5473 data_time: 0.0101 memory: 14901 loss: 1.5871 loss_prob: 0.8686 loss_thr: 0.5733 loss_db: 0.1452 2022/11/02 15:22:47 - mmengine - INFO - Epoch(train) [302][50/63] lr: 1.6676e-03 eta: 9:12:03 time: 0.5405 data_time: 0.0260 memory: 14901 loss: 1.5465 loss_prob: 0.8603 loss_thr: 0.5431 loss_db: 0.1431 2022/11/02 15:22:50 - mmengine - INFO - Epoch(train) [302][55/63] lr: 1.6676e-03 eta: 9:12:03 time: 0.5213 data_time: 0.0263 memory: 14901 loss: 1.6853 loss_prob: 0.9695 loss_thr: 0.5598 loss_db: 0.1560 2022/11/02 15:22:52 - mmengine - INFO - Epoch(train) [302][60/63] lr: 1.6676e-03 eta: 9:11:54 time: 0.4980 data_time: 0.0117 memory: 14901 loss: 1.6250 loss_prob: 0.9088 loss_thr: 0.5653 loss_db: 0.1508 2022/11/02 15:22:54 - mmengine - INFO - Exp name: dbnet_resnet50_1200e_icdar2015_20221102_115917 2022/11/02 15:23:01 - mmengine - INFO - Epoch(train) [303][5/63] lr: 1.6659e-03 eta: 9:11:54 time: 0.9645 data_time: 0.2790 memory: 14901 loss: 1.6435 loss_prob: 0.9250 loss_thr: 0.5641 loss_db: 0.1543 2022/11/02 15:23:05 - mmengine - INFO - Epoch(train) [303][10/63] lr: 1.6659e-03 eta: 9:11:56 time: 1.0779 data_time: 0.2820 memory: 14901 loss: 1.6520 loss_prob: 0.9326 loss_thr: 0.5647 loss_db: 0.1547 2022/11/02 15:23:07 - mmengine - INFO - Epoch(train) [303][15/63] lr: 1.6659e-03 eta: 9:11:56 time: 0.6208 data_time: 0.0093 memory: 14901 loss: 1.5800 loss_prob: 0.8842 loss_thr: 0.5471 loss_db: 0.1488 2022/11/02 15:23:10 - mmengine - INFO - Epoch(train) [303][20/63] lr: 1.6659e-03 eta: 9:11:49 time: 0.5541 data_time: 0.0079 memory: 14901 loss: 1.8204 loss_prob: 1.0877 loss_thr: 0.5589 loss_db: 0.1738 2022/11/02 15:23:13 - mmengine - INFO - Epoch(train) [303][25/63] lr: 1.6659e-03 eta: 9:11:49 time: 0.6108 data_time: 0.0500 memory: 14901 loss: 1.9076 loss_prob: 1.1560 loss_thr: 0.5733 loss_db: 0.1783 2022/11/02 15:23:16 - mmengine - INFO - Epoch(train) [303][30/63] lr: 1.6659e-03 eta: 9:11:44 time: 0.6214 data_time: 0.0521 memory: 14901 loss: 2.0443 loss_prob: 1.2504 loss_thr: 0.5883 loss_db: 0.2056 2022/11/02 15:23:19 - mmengine - INFO - Epoch(train) [303][35/63] lr: 1.6659e-03 eta: 9:11:44 time: 0.5542 data_time: 0.0124 memory: 14901 loss: 2.0795 loss_prob: 1.2688 loss_thr: 0.5986 loss_db: 0.2120 2022/11/02 15:23:22 - mmengine - INFO - Epoch(train) [303][40/63] lr: 1.6659e-03 eta: 9:11:37 time: 0.5430 data_time: 0.0095 memory: 14901 loss: 1.9608 loss_prob: 1.1611 loss_thr: 0.6165 loss_db: 0.1832 2022/11/02 15:23:24 - mmengine - INFO - Epoch(train) [303][45/63] lr: 1.6659e-03 eta: 9:11:37 time: 0.5344 data_time: 0.0062 memory: 14901 loss: 2.0833 loss_prob: 1.2461 loss_thr: 0.6385 loss_db: 0.1987 2022/11/02 15:23:27 - mmengine - INFO - Epoch(train) [303][50/63] lr: 1.6659e-03 eta: 9:11:30 time: 0.