2022/12/14 15:03:57 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] CUDA available: True numpy_random_seed: 1613385978 GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.3, V11.3.109 GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 PyTorch: 1.10.0 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) oneAPI Math Kernel Library Version 2021.3-Product Build 20210617 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX512 - CUDA Runtime 11.3 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 - CuDNN 8.2 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, 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 -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-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=1.10.0, 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: 0.11.0 OpenCV: 4.6.0 MMEngine: 0.3.1 Runtime environment: cudnn_benchmark: True mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None Distributed launcher: pytorch Distributed training: True GPU number: 8 ------------------------------------------------------------ 2022/12/14 15:03:58 - mmengine - INFO - Config: file_client_args = dict(backend='disk') model = dict( type='DBNet', backbone=dict( type='mmdet.ResNet', depth=18, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=-1, norm_cfg=dict(type='BN', requires_grad=True), init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18'), norm_eval=False, style='caffe'), neck=dict( type='FPNC', in_channels=[64, 128, 256, 512], 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_polygon=True, with_bbox=True, with_label=True), dict(type='FixInvalidPolygon'), dict(type='RemoveIgnored'), 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=(1333, 736), 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')) ] synthtext_textdet_data_root = 'data/synthtext' synthtext_textdet_train = dict( type='OCRDataset', data_root='data/synthtext', ann_file='textdet_train.json', 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_polygon=True, with_bbox=True, with_label=True), dict(type='FixInvalidPolygon'), dict(type='RemoveIgnored'), 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')) ]) 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=1000), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict( type='CheckpointHook', interval=10000, by_epoch=False, max_keep_ckpts=1), 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 = None test_evaluator = None 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=0.007, momentum=0.9, weight_decay=0.0001)) train_cfg = dict(type='IterBasedTrainLoop', max_iters=100000) test_cfg = None val_cfg = None param_scheduler = [ dict(type='PolyLR', power=0.9, eta_min=1e-07, by_epoch=False, end=100000) ] train_dataloader = dict( batch_size=2, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='OCRDataset', data_root='data/synthtext', ann_file='textdet_train.json', 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_polygon=True, with_bbox=True, with_label=True), dict(type='FixInvalidPolygon'), dict(type='RemoveIgnored'), 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')) ])) auto_scale_lr = dict(base_batch_size=16) launcher = 'pytorch' work_dir = 'work_dirs/db_r18_synthtext' 2022/12/14 15:03:58 - mmengine - INFO - Result has been saved to /mmocr/work_dirs/db_r18_synthtext/modules_statistic_results.json Name of parameter - Initialization information backbone.conv1.weight - torch.Size([64, 3, 7, 7]): PretrainedInit: load from torchvision://resnet18 backbone.bn1.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet18 backbone.bn1.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet18 backbone.layer1.0.conv1.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from torchvision://resnet18 backbone.layer1.0.bn1.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet18 backbone.layer1.0.bn1.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet18 backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from torchvision://resnet18 backbone.layer1.0.bn2.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet18 backbone.layer1.0.bn2.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet18 backbone.layer1.1.conv1.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from torchvision://resnet18 backbone.layer1.1.bn1.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet18 backbone.layer1.1.bn1.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet18 backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from torchvision://resnet18 backbone.layer1.1.bn2.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet18 backbone.layer1.1.bn2.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet18 backbone.layer2.0.conv1.weight - torch.Size([128, 64, 3, 3]): PretrainedInit: load from torchvision://resnet18 backbone.layer2.0.bn1.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet18 backbone.layer2.0.bn1.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet18 backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from torchvision://resnet18 backbone.layer2.0.bn2.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet18 backbone.layer2.0.bn2.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet18 backbone.layer2.0.downsample.0.weight - torch.Size([128, 64, 1, 1]): PretrainedInit: load from torchvision://resnet18 backbone.layer2.0.downsample.1.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet18 backbone.layer2.0.downsample.1.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet18 backbone.layer2.1.conv1.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from torchvision://resnet18 backbone.layer2.1.bn1.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet18 backbone.layer2.1.bn1.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet18 backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from torchvision://resnet18 backbone.layer2.1.bn2.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet18 backbone.layer2.1.bn2.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet18 backbone.layer3.0.conv1.weight - torch.Size([256, 128, 3, 3]): PretrainedInit: load from torchvision://resnet18 backbone.layer3.0.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet18 backbone.layer3.0.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet18 backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet18 backbone.layer3.0.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet18 backbone.layer3.0.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet18 backbone.layer3.0.downsample.0.weight - torch.Size([256, 128, 1, 1]): PretrainedInit: load from torchvision://resnet18 backbone.layer3.0.downsample.1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet18 backbone.layer3.0.downsample.1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet18 backbone.layer3.1.conv1.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet18 backbone.layer3.1.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet18 backbone.layer3.1.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet18 backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet18 backbone.layer3.1.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet18 backbone.layer3.1.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet18 backbone.layer4.0.conv1.weight - torch.Size([512, 256, 3, 3]): PretrainedInit: load from torchvision://resnet18 backbone.layer4.0.bn1.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet18 backbone.layer4.0.bn1.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet18 backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from torchvision://resnet18 backbone.layer4.0.bn2.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet18 backbone.layer4.0.bn2.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet18 backbone.layer4.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): PretrainedInit: load from torchvision://resnet18 backbone.layer4.0.downsample.1.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet18 backbone.layer4.0.downsample.1.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet18 backbone.layer4.1.conv1.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from torchvision://resnet18 backbone.layer4.1.bn1.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet18 backbone.layer4.1.bn1.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet18 backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from torchvision://resnet18 backbone.layer4.1.bn2.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet18 backbone.layer4.1.bn2.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet18 neck.lateral_convs.0.conv.weight - torch.Size([256, 64, 1, 1]): Initialized by user-defined `init_weights` in ConvModule neck.lateral_convs.1.conv.weight - torch.Size([256, 128, 1, 1]): Initialized by user-defined `init_weights` in ConvModule neck.lateral_convs.2.conv.weight - torch.Size([256, 256, 1, 1]): Initialized by user-defined `init_weights` in ConvModule neck.lateral_convs.3.conv.weight - torch.Size([256, 512, 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 2022/12/14 15:05:43 - mmengine - INFO - Checkpoints will be saved to /mmocr/work_dirs/db_r18_synthtext. 2022/12/14 15:09:02 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 15:09:02 - mmengine - INFO - Epoch(train) [1][1000/53672] lr: 6.9370e-03 eta: 5:27:26 time: 0.1695 data_time: 0.0031 memory: 9076 loss: 2.8583 loss_prob: 1.8730 loss_thr: 0.6763 loss_db: 0.3091 2022/12/14 15:12:16 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 15:12:16 - mmengine - INFO - Epoch(train) [1][2000/53672] lr: 6.8739e-03 eta: 5:21:01 time: 0.1878 data_time: 0.0033 memory: 1015 loss: 2.7586 loss_prob: 1.7915 loss_thr: 0.6624 loss_db: 0.3047 2022/12/14 15:15:30 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 15:15:30 - mmengine - INFO - Epoch(train) [1][3000/53672] lr: 6.8108e-03 eta: 5:16:03 time: 0.2032 data_time: 0.0033 memory: 1015 loss: 2.4919 loss_prob: 1.5855 loss_thr: 0.6488 loss_db: 0.2576 2022/12/14 15:18:43 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 15:18:43 - mmengine - INFO - Epoch(train) [1][4000/53672] lr: 6.7476e-03 eta: 5:11:41 time: 0.1970 data_time: 0.0032 memory: 1015 loss: 2.2711 loss_prob: 1.4271 loss_thr: 0.6125 loss_db: 0.2315 2022/12/14 15:21:54 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 15:21:54 - mmengine - INFO - Epoch(train) [1][5000/53672] lr: 6.6843e-03 eta: 5:07:28 time: 0.2083 data_time: 0.0033 memory: 1015 loss: 2.6359 loss_prob: 1.6524 loss_thr: 0.6597 loss_db: 0.3237 2022/12/14 15:25:07 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 15:25:07 - mmengine - INFO - Epoch(train) [1][6000/53672] lr: 6.6209e-03 eta: 5:03:57 time: 0.1734 data_time: 0.0039 memory: 1015 loss: 2.1752 loss_prob: 1.3766 loss_thr: 0.5701 loss_db: 0.2285 2022/12/14 15:28:21 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 15:28:21 - mmengine - INFO - Epoch(train) [1][7000/53672] lr: 6.5575e-03 eta: 5:00:41 time: 0.2205 data_time: 0.0037 memory: 1015 loss: 1.9366 loss_prob: 1.2111 loss_thr: 0.5319 loss_db: 0.1936 2022/12/14 15:31:36 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 15:31:36 - mmengine - INFO - Epoch(train) [1][8000/53672] lr: 6.4940e-03 eta: 4:57:29 time: 0.1818 data_time: 0.0036 memory: 1015 loss: 2.0852 loss_prob: 1.3017 loss_thr: 0.5698 loss_db: 0.2137 2022/12/14 15:34:50 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 15:34:50 - mmengine - INFO - Epoch(train) [1][9000/53672] lr: 6.4304e-03 eta: 4:54:16 time: 0.1868 data_time: 0.0041 memory: 1015 loss: 2.3493 loss_prob: 1.4776 loss_thr: 0.5973 loss_db: 0.2744 2022/12/14 15:38:06 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 15:38:06 - mmengine - INFO - Epoch(train) [1][10000/53672] lr: 6.3668e-03 eta: 4:51:24 time: 0.2049 data_time: 0.0034 memory: 1015 loss: 2.4274 loss_prob: 1.4837 loss_thr: 0.6462 loss_db: 0.2975 2022/12/14 15:38:06 - mmengine - INFO - Saving checkpoint at 10000 iterations 2022/12/14 15:41:22 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 15:41:22 - mmengine - INFO - Epoch(train) [1][11000/53672] lr: 6.3031e-03 eta: 4:48:19 time: 0.2064 data_time: 0.0034 memory: 1015 loss: 2.0044 loss_prob: 1.2500 loss_thr: 0.5543 loss_db: 0.2000 2022/12/14 15:44:38 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 15:44:38 - mmengine - INFO - Epoch(train) [1][12000/53672] lr: 6.2393e-03 eta: 4:45:21 time: 0.1693 data_time: 0.0037 memory: 1015 loss: 1.8546 loss_prob: 1.1358 loss_thr: 0.5284 loss_db: 0.1903 2022/12/14 15:47:51 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 15:47:51 - mmengine - INFO - Epoch(train) [1][13000/53672] lr: 6.1755e-03 eta: 4:41:53 time: 0.1779 data_time: 0.0038 memory: 1015 loss: 2.7259 loss_prob: 1.8064 loss_thr: 0.6390 loss_db: 0.2805 2022/12/14 15:51:00 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 15:51:00 - mmengine - INFO - Epoch(train) [1][14000/53672] lr: 6.1116e-03 eta: 4:38:05 time: 0.1835 data_time: 0.0037 memory: 1015 loss: 2.5466 loss_prob: 1.6112 loss_thr: 0.6723 loss_db: 0.2630 2022/12/14 15:54:11 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 15:54:11 - mmengine - INFO - Epoch(train) [1][15000/53672] lr: 6.0476e-03 eta: 4:34:32 time: 0.1925 data_time: 0.0036 memory: 1015 loss: 2.2838 loss_prob: 1.4728 loss_thr: 0.5639 loss_db: 0.2471 2022/12/14 15:57:23 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 15:57:23 - mmengine - INFO - Epoch(train) [1][16000/53672] lr: 5.9835e-03 eta: 4:31:11 time: 0.1848 data_time: 0.0036 memory: 1015 loss: 1.9855 loss_prob: 1.2709 loss_thr: 0.5199 loss_db: 0.1947 2022/12/14 16:00:33 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 16:00:33 - mmengine - INFO - Epoch(train) [1][17000/53672] lr: 5.9193e-03 eta: 4:27:38 time: 0.1852 data_time: 0.0036 memory: 1015 loss: 1.7284 loss_prob: 1.0720 loss_thr: 0.4870 loss_db: 0.1694 2022/12/14 16:03:46 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 16:03:46 - mmengine - INFO - Epoch(train) [1][18000/53672] lr: 5.8551e-03 eta: 4:24:21 time: 0.2197 data_time: 0.0036 memory: 1015 loss: 2.0180 loss_prob: 1.2890 loss_thr: 0.5259 loss_db: 0.2032 2022/12/14 16:06:57 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 16:06:57 - mmengine - INFO - Epoch(train) [1][19000/53672] lr: 5.7908e-03 eta: 4:20:58 time: 0.1893 data_time: 0.0037 memory: 1015 loss: 1.8249 loss_prob: 1.1133 loss_thr: 0.5347 loss_db: 0.1768 2022/12/14 16:10:07 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 16:10:07 - mmengine - INFO - Epoch(train) [1][20000/53672] lr: 5.7264e-03 eta: 4:17:34 time: 0.2045 data_time: 0.0036 memory: 1015 loss: 2.2146 loss_prob: 1.3802 loss_thr: 0.5905 loss_db: 0.2440 2022/12/14 16:10:07 - mmengine - INFO - Saving checkpoint at 20000 iterations 2022/12/14 16:13:18 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 16:13:18 - mmengine - INFO - Epoch(train) [1][21000/53672] lr: 5.6620e-03 eta: 4:14:10 time: 0.1813 data_time: 0.0037 memory: 1015 loss: 2.0559 loss_prob: 1.2781 loss_thr: 0.5587 loss_db: 0.2190 2022/12/14 16:16:32 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 16:16:32 - mmengine - INFO - Epoch(train) [1][22000/53672] lr: 5.5974e-03 eta: 4:10:59 time: 0.1891 data_time: 0.0037 memory: 1015 loss: 1.7579 loss_prob: 1.0811 loss_thr: 0.4994 loss_db: 0.1774 2022/12/14 16:19:43 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 16:19:43 - mmengine - INFO - Epoch(train) [1][23000/53672] lr: 5.5328e-03 eta: 4:07:41 time: 0.1919 data_time: 0.0036 memory: 1015 loss: 1.9201 loss_prob: 1.1780 loss_thr: 0.5532 loss_db: 0.1889 2022/12/14 16:22:55 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 16:22:55 - mmengine - INFO - Epoch(train) [1][24000/53672] lr: 5.4681e-03 eta: 4:04:23 time: 0.1845 data_time: 0.0036 memory: 1015 loss: 2.0389 loss_prob: 1.2630 loss_thr: 0.5723 loss_db: 0.2035 2022/12/14 16:26:07 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 16:26:07 - mmengine - INFO - Epoch(train) [1][25000/53672] lr: 5.4033e-03 eta: 4:01:08 time: 0.2045 data_time: 0.0036 memory: 1015 loss: 2.1091 loss_prob: 1.3360 loss_thr: 0.5549 loss_db: 0.2181 2022/12/14 16:29:23 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 16:29:23 - mmengine - INFO - Epoch(train) [1][26000/53672] lr: 5.3384e-03 eta: 3:58:04 time: 0.1883 data_time: 0.0038 memory: 1015 loss: 2.0151 loss_prob: 1.2631 loss_thr: 0.5486 loss_db: 0.2033 2022/12/14 16:32:33 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 16:32:33 - mmengine - INFO - Epoch(train) [1][27000/53672] lr: 5.2734e-03 eta: 3:54:44 time: 0.1925 data_time: 0.0036 memory: 1015 loss: 2.0035 loss_prob: 1.2539 loss_thr: 0.5450 loss_db: 0.2046 2022/12/14 16:35:46 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 16:35:46 - mmengine - INFO - Epoch(train) [1][28000/53672] lr: 5.2084e-03 eta: 3:51:30 time: 0.1991 data_time: 0.0038 memory: 1015 loss: 1.9778 loss_prob: 1.2307 loss_thr: 0.5620 loss_db: 0.1851 2022/12/14 16:39:00 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 16:39:00 - mmengine - INFO - Epoch(train) [1][29000/53672] lr: 5.1432e-03 eta: 3:48:18 time: 0.1986 data_time: 0.0038 memory: 1015 loss: 1.6462 loss_prob: 0.9947 loss_thr: 0.4919 loss_db: 0.1595 2022/12/14 16:42:13 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 16:42:13 - mmengine - INFO - Epoch(train) [1][30000/53672] lr: 5.0780e-03 eta: 3:45:06 time: 0.2467 data_time: 0.0037 memory: 1015 loss: 1.5359 loss_prob: 0.9142 loss_thr: 0.4719 loss_db: 0.1498 2022/12/14 16:42:13 - mmengine - INFO - Saving checkpoint at 30000 iterations 2022/12/14 16:45:27 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 16:45:27 - mmengine - INFO - Epoch(train) [1][31000/53672] lr: 5.0127e-03 eta: 3:41:55 time: 0.2165 data_time: 0.0036 memory: 1015 loss: 1.5677 loss_prob: 0.9426 loss_thr: 0.4789 loss_db: 0.1461 2022/12/14 16:48:40 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 16:48:40 - mmengine - INFO - Epoch(train) [1][32000/53672] lr: 4.9472e-03 eta: 3:38:43 time: 0.1849 data_time: 0.0037 memory: 1015 loss: 1.7416 loss_prob: 1.0743 loss_thr: 0.4947 loss_db: 0.1727 2022/12/14 16:51:52 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 16:51:52 - mmengine - INFO - Epoch(train) [1][33000/53672] lr: 4.8817e-03 eta: 3:35:26 time: 0.1769 data_time: 0.0039 memory: 1015 loss: 2.2715 loss_prob: 1.4667 loss_thr: 0.5625 loss_db: 0.2424 2022/12/14 16:55:07 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 16:55:07 - mmengine - INFO - Epoch(train) [1][34000/53672] lr: 4.8161e-03 eta: 3:32:17 time: 0.1804 data_time: 0.0038 memory: 1015 loss: 1.7974 loss_prob: 1.1338 loss_thr: 0.4792 loss_db: 0.1844 2022/12/14 16:58:21 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 16:58:21 - mmengine - INFO - Epoch(train) [1][35000/53672] lr: 4.7504e-03 eta: 3:29:06 time: 0.2003 data_time: 0.0039 memory: 1015 loss: 1.5225 loss_prob: 0.8962 loss_thr: 0.4859 loss_db: 0.1405 2022/12/14 17:01:36 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 17:01:36 - mmengine - INFO - Epoch(train) [1][36000/53672] lr: 4.6845e-03 eta: 3:25:56 time: 0.1814 data_time: 0.0039 memory: 1015 loss: 2.0511 loss_prob: 1.2866 loss_thr: 0.5623 loss_db: 0.2022 2022/12/14 17:04:51 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 17:04:51 - mmengine - INFO - Epoch(train) [1][37000/53672] lr: 4.6186e-03 eta: 3:22:47 time: 0.2012 data_time: 0.0039 memory: 1015 loss: 1.6865 loss_prob: 1.0527 loss_thr: 0.4730 loss_db: 0.1609 2022/12/14 17:08:06 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 17:08:06 - mmengine - INFO - Epoch(train) [1][38000/53672] lr: 4.5526e-03 eta: 3:19:37 time: 0.1919 data_time: 0.0039 memory: 1015 loss: 2.0060 loss_prob: 1.2693 loss_thr: 0.5261 loss_db: 0.2107 2022/12/14 17:11:22 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 17:11:22 - mmengine - INFO - Epoch(train) [1][39000/53672] lr: 4.4864e-03 eta: 3:16:28 time: 0.1656 data_time: 0.0040 memory: 1015 loss: 1.8342 loss_prob: 1.1668 loss_thr: 0.4814 loss_db: 0.1859 2022/12/14 17:14:36 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 17:14:36 - mmengine - INFO - Epoch(train) [1][40000/53672] lr: 4.4202e-03 eta: 3:13:15 time: 0.2194 data_time: 0.0038 memory: 1015 loss: 1.8426 loss_prob: 1.1391 loss_thr: 0.5229 loss_db: 0.1806 2022/12/14 17:14:36 - mmengine - INFO - Saving checkpoint at 40000 iterations 2022/12/14 17:17:52 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 17:17:52 - mmengine - INFO - Epoch(train) [1][41000/53672] lr: 4.3538e-03 eta: 3:10:06 time: 0.1739 data_time: 0.0039 memory: 1015 loss: 1.7162 loss_prob: 1.0567 loss_thr: 0.4810 loss_db: 0.1784 2022/12/14 17:21:04 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 17:21:04 - mmengine - INFO - Epoch(train) [1][42000/53672] lr: 4.2874e-03 eta: 3:06:51 time: 0.1894 data_time: 0.0038 memory: 1015 loss: 1.6172 loss_prob: 0.9768 loss_thr: 0.4828 loss_db: 0.1577 2022/12/14 17:24:20 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 17:24:20 - mmengine - INFO - Epoch(train) [1][43000/53672] lr: 4.2208e-03 eta: 3:03:40 time: 0.2077 data_time: 0.0039 memory: 1015 loss: 1.5931 loss_prob: 0.9801 loss_thr: 0.4553 loss_db: 0.1578 2022/12/14 17:27:35 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 17:27:35 - mmengine - INFO - Epoch(train) [1][44000/53672] lr: 4.1541e-03 eta: 3:00:29 time: 0.1999 data_time: 0.0037 memory: 1015 loss: 1.5645 loss_prob: 0.9647 loss_thr: 0.4469 loss_db: 0.1530 2022/12/14 17:30:50 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 17:30:50 - mmengine - INFO - Epoch(train) [1][45000/53672] lr: 4.0873e-03 eta: 2:57:17 time: 0.2093 data_time: 0.0039 memory: 1015 loss: 1.2994 loss_prob: 0.7624 loss_thr: 0.4054 loss_db: 0.1316 2022/12/14 17:34:04 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 17:34:04 - mmengine - INFO - Epoch(train) [1][46000/53672] lr: 4.0203e-03 eta: 2:54:05 time: 0.1836 data_time: 0.0037 memory: 1015 loss: 1.5688 loss_prob: 0.9832 loss_thr: 0.4455 loss_db: 0.1402 2022/12/14 17:37:17 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 17:37:17 - mmengine - INFO - Epoch(train) [1][47000/53672] lr: 3.9533e-03 eta: 2:50:50 time: 0.2016 data_time: 0.0037 memory: 1015 loss: 1.6053 loss_prob: 0.9869 loss_thr: 0.4657 loss_db: 0.1527 2022/12/14 17:40:33 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 17:40:33 - mmengine - INFO - Epoch(train) [1][48000/53672] lr: 3.8861e-03 eta: 2:47:40 time: 0.1656 data_time: 0.0039 memory: 1015 loss: 1.5760 loss_prob: 0.9577 loss_thr: 0.4617 loss_db: 0.1566 2022/12/14 17:43:49 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 17:43:49 - mmengine - INFO - Epoch(train) [1][49000/53672] lr: 3.8187e-03 eta: 2:44:28 time: 0.1868 data_time: 0.0038 memory: 1015 loss: 1.5324 loss_prob: 0.9380 loss_thr: 0.4447 loss_db: 0.1497 2022/12/14 17:47:03 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 17:47:03 - mmengine - INFO - Epoch(train) [1][50000/53672] lr: 3.7513e-03 eta: 2:41:16 time: 0.1941 data_time: 0.0039 memory: 1015 loss: 1.7778 loss_prob: 1.0884 loss_thr: 0.5189 loss_db: 0.1705 2022/12/14 17:47:03 - mmengine - INFO - Saving checkpoint at 50000 iterations 2022/12/14 17:50:17 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 17:50:17 - mmengine - INFO - Epoch(train) [1][51000/53672] lr: 3.6837e-03 eta: 2:38:03 time: 0.1978 data_time: 0.0038 memory: 1015 loss: 1.6568 loss_prob: 1.0106 loss_thr: 0.4789 loss_db: 0.1673 2022/12/14 17:53:33 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 17:53:33 - mmengine - INFO - Epoch(train) [1][52000/53672] lr: 3.6160e-03 eta: 2:34:51 time: 0.1903 data_time: 0.0041 memory: 1015 loss: 1.3486 loss_prob: 0.7877 loss_thr: 0.4347 loss_db: 0.1262 2022/12/14 17:56:51 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 17:56:51 - mmengine - INFO - Epoch(train) [1][53000/53672] lr: 3.5481e-03 eta: 2:31:41 time: 0.2092 data_time: 0.0042 memory: 1015 loss: 1.7135 loss_prob: 1.0208 loss_thr: 0.5276 loss_db: 0.1650 2022/12/14 17:59:00 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 18:00:06 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 18:00:06 - mmengine - INFO - Epoch(train) [1][54000/53672] lr: 3.4801e-03 eta: 2:28:29 time: 0.2258 data_time: 0.0038 memory: 1015 loss: 1.4703 loss_prob: 0.8956 loss_thr: 0.4316 loss_db: 0.1431 2022/12/14 18:03:22 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 18:03:22 - mmengine - INFO - Epoch(train) [1][55000/53672] lr: 3.4119e-03 eta: 2:25:17 time: 0.1921 data_time: 0.0039 memory: 1015 loss: 2.1898 loss_prob: 1.4035 loss_thr: 0.5679 loss_db: 0.2184 2022/12/14 18:06:38 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 18:06:38 - mmengine - INFO - Epoch(train) [1][56000/53672] lr: 3.3436e-03 eta: 2:22:05 time: 0.2181 data_time: 0.0037 memory: 1015 loss: 2.2032 loss_prob: 1.3883 loss_thr: 0.5809 loss_db: 0.2340 2022/12/14 18:09:52 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 18:09:52 - mmengine - INFO - Epoch(train) [1][57000/53672] lr: 3.2751e-03 eta: 2:18:51 time: 0.1796 data_time: 0.0036 memory: 1015 loss: 1.6210 loss_prob: 0.9812 loss_thr: 0.4845 loss_db: 0.1553 2022/12/14 18:13:06 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 18:13:06 - mmengine - INFO - Epoch(train) [1][58000/53672] lr: 3.2065e-03 eta: 2:15:38 time: 0.1831 data_time: 0.0037 memory: 1015 loss: 1.3739 loss_prob: 0.8198 loss_thr: 0.4188 loss_db: 0.1353 2022/12/14 18:16:18 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 18:16:18 - mmengine - INFO - Epoch(train) [1][59000/53672] lr: 3.1377e-03 eta: 2:12:22 time: 0.1854 data_time: 0.0038 memory: 1015 loss: 1.7284 loss_prob: 1.0636 loss_thr: 0.4978 loss_db: 0.1669 2022/12/14 18:19:30 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 18:19:30 - mmengine - INFO - Epoch(train) [1][60000/53672] lr: 3.0688e-03 eta: 2:09:07 time: 0.1930 data_time: 0.0037 memory: 1015 loss: 2.0769 loss_prob: 1.2748 loss_thr: 0.5671 loss_db: 0.2350 2022/12/14 18:19:30 - mmengine - INFO - Saving checkpoint at 60000 iterations 2022/12/14 18:22:45 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 18:22:45 - mmengine - INFO - Epoch(train) [1][61000/53672] lr: 2.9996e-03 eta: 2:05:55 time: 0.1757 data_time: 0.0037 memory: 1015 loss: 1.6899 loss_prob: 1.0247 loss_thr: 0.4928 loss_db: 0.1724 2022/12/14 18:26:00 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 18:26:00 - mmengine - INFO - Epoch(train) [1][62000/53672] lr: 2.9303e-03 eta: 2:02:42 time: 0.1833 data_time: 0.0036 memory: 1015 loss: 1.5741 loss_prob: 0.9446 loss_thr: 0.4824 loss_db: 0.1471 2022/12/14 18:29:14 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 18:29:14 - mmengine - INFO - Epoch(train) [1][63000/53672] lr: 2.8608e-03 eta: 1:59:28 time: 0.1693 data_time: 0.0040 memory: 1015 loss: 1.4830 loss_prob: 0.8722 loss_thr: 0.4700 loss_db: 0.1408 2022/12/14 18:32:28 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 18:32:28 - mmengine - INFO - Epoch(train) [1][64000/53672] lr: 2.7912e-03 eta: 1:56:14 time: 0.2210 data_time: 0.0037 memory: 1015 loss: 1.4018 loss_prob: 0.8438 loss_thr: 0.4149 loss_db: 0.1430 2022/12/14 18:35:42 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 18:35:42 - mmengine - INFO - Epoch(train) [1][65000/53672] lr: 2.7213e-03 eta: 1:53:00 time: 0.2146 data_time: 0.0038 memory: 1015 loss: 1.6843 loss_prob: 1.0149 loss_thr: 0.5042 loss_db: 0.1652 2022/12/14 18:38:58 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 18:38:58 - mmengine - INFO - Epoch(train) [1][66000/53672] lr: 2.6512e-03 eta: 1:49:48 time: 0.2131 data_time: 0.0037 memory: 1015 loss: 2.1105 loss_prob: 1.3466 loss_thr: 0.5480 loss_db: 0.2159 2022/12/14 18:42:14 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 18:42:14 - mmengine - INFO - Epoch(train) [1][67000/53672] lr: 2.5809e-03 eta: 1:46:35 time: 0.2026 data_time: 0.0038 memory: 1015 loss: 1.9915 loss_prob: 1.2256 loss_thr: 0.5616 loss_db: 0.2043 2022/12/14 18:45:30 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 18:45:30 - mmengine - INFO - Epoch(train) [1][68000/53672] lr: 2.5104e-03 eta: 1:43:22 time: 0.1958 data_time: 0.0039 memory: 1015 loss: 1.6346 loss_prob: 0.9874 loss_thr: 0.4885 loss_db: 0.1587 2022/12/14 18:48:44 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 18:48:44 - mmengine - INFO - Epoch(train) [1][69000/53672] lr: 2.4397e-03 eta: 1:40:08 time: 0.1974 data_time: 0.0037 memory: 1015 loss: 2.4991 loss_prob: 1.6898 loss_thr: 0.5606 loss_db: 0.2488 2022/12/14 18:52:01 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 18:52:01 - mmengine - INFO - Epoch(train) [1][70000/53672] lr: 2.3688e-03 eta: 1:36:56 time: 0.1914 data_time: 0.0038 memory: 1015 loss: 1.6344 loss_prob: 0.9977 loss_thr: 0.4740 loss_db: 0.1627 2022/12/14 18:52:01 - mmengine - INFO - Saving checkpoint at 70000 iterations 2022/12/14 18:55:14 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 18:55:14 - mmengine - INFO - Epoch(train) [1][71000/53672] lr: 2.2976e-03 eta: 1:33:41 time: 0.2014 data_time: 0.0037 memory: 1015 loss: 1.6173 loss_prob: 0.9776 loss_thr: 0.4846 loss_db: 0.1550 2022/12/14 18:58:28 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 18:58:28 - mmengine - INFO - Epoch(train) [1][72000/53672] lr: 2.2262e-03 eta: 1:30:28 time: 0.1741 data_time: 0.0038 memory: 1015 loss: 1.4174 loss_prob: 0.8652 loss_thr: 0.4212 loss_db: 0.1310 2022/12/14 19:01:41 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 19:01:41 - mmengine - INFO - Epoch(train) [1][73000/53672] lr: 2.1545e-03 eta: 1:27:13 time: 0.1869 data_time: 0.0038 memory: 1015 loss: 2.0395 loss_prob: 1.2633 loss_thr: 0.5659 loss_db: 0.2104 2022/12/14 19:04:55 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 19:04:55 - mmengine - INFO - Epoch(train) [1][74000/53672] lr: 2.0825e-03 eta: 1:24:00 time: 0.1811 data_time: 0.0038 memory: 1015 loss: 1.7801 loss_prob: 1.0793 loss_thr: 0.5267 loss_db: 0.1741 2022/12/14 19:08:12 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 19:08:12 - mmengine - INFO - Epoch(train) [1][75000/53672] lr: 2.0103e-03 eta: 1:20:47 time: 0.2112 data_time: 0.0040 memory: 1015 loss: 1.6451 loss_prob: 1.0001 loss_thr: 0.4805 loss_db: 0.1645 2022/12/14 19:11:24 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 19:11:24 - mmengine - INFO - Epoch(train) [1][76000/53672] lr: 1.9378e-03 eta: 1:17:32 time: 0.1943 data_time: 0.0039 memory: 1015 loss: 1.6129 loss_prob: 0.9645 loss_thr: 0.4892 loss_db: 0.1592 2022/12/14 19:14:40 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 19:14:40 - mmengine - INFO - Epoch(train) [1][77000/53672] lr: 1.8650e-03 eta: 1:14:19 time: 0.2296 data_time: 0.0039 memory: 1015 loss: 1.6139 loss_prob: 0.9917 loss_thr: 0.4637 loss_db: 0.1586 2022/12/14 19:17:56 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 19:17:56 - mmengine - INFO - Epoch(train) [1][78000/53672] lr: 1.7918e-03 eta: 1:11:06 time: 0.1719 data_time: 0.0039 memory: 1015 loss: 1.4180 loss_prob: 0.8683 loss_thr: 0.4177 loss_db: 0.1319 2022/12/14 19:21:08 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 19:21:08 - mmengine - INFO - Epoch(train) [1][79000/53672] lr: 1.7184e-03 eta: 1:07:51 time: 0.1983 data_time: 0.0038 memory: 1015 loss: 1.3979 loss_prob: 0.8460 loss_thr: 0.4228 loss_db: 0.1291 2022/12/14 19:24:27 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 19:24:27 - mmengine - INFO - Epoch(train) [1][80000/53672] lr: 1.6446e-03 eta: 1:04:38 time: 0.2176 data_time: 0.0040 memory: 1015 loss: 1.6343 loss_prob: 0.9770 loss_thr: 0.4824 loss_db: 0.1749 2022/12/14 19:24:27 - mmengine - INFO - Saving checkpoint at 80000 iterations 2022/12/14 19:27:39 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 19:27:39 - mmengine - INFO - Epoch(train) [1][81000/53672] lr: 1.5704e-03 eta: 1:01:24 time: 0.1791 data_time: 0.0038 memory: 1015 loss: 1.6334 loss_prob: 1.0004 loss_thr: 0.4800 loss_db: 0.1529 2022/12/14 19:30:55 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 19:30:55 - mmengine - INFO - Epoch(train) [1][82000/53672] lr: 1.4958e-03 eta: 0:58:11 time: 0.2159 data_time: 0.0036 memory: 1015 loss: 1.2683 loss_prob: 0.7464 loss_thr: 0.3996 loss_db: 0.1223 2022/12/14 19:34:13 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 19:34:13 - mmengine - INFO - Epoch(train) [1][83000/53672] lr: 1.4208e-03 eta: 0:54:57 time: 0.2064 data_time: 0.0039 memory: 1015 loss: 1.4831 loss_prob: 0.9091 loss_thr: 0.4328 loss_db: 0.1412 2022/12/14 19:37:28 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 19:37:28 - mmengine - INFO - Epoch(train) [1][84000/53672] lr: 1.3454e-03 eta: 0:51:44 time: 0.2141 data_time: 0.0037 memory: 1015 loss: 1.3968 loss_prob: 0.8194 loss_thr: 0.4436 loss_db: 0.1339 2022/12/14 19:40:47 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 19:40:47 - mmengine - INFO - Epoch(train) [1][85000/53672] lr: 1.2694e-03 eta: 0:48:30 time: 0.2094 data_time: 0.0038 memory: 1015 loss: 1.6190 loss_prob: 0.9954 loss_thr: 0.4667 loss_db: 0.1570 2022/12/14 19:44:02 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 19:44:02 - mmengine - INFO - Epoch(train) [1][86000/53672] lr: 1.1930e-03 eta: 0:45:16 time: 0.1834 data_time: 0.0037 memory: 1015 loss: 1.5777 loss_prob: 0.9806 loss_thr: 0.4474 loss_db: 0.1496 2022/12/14 19:47:19 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 19:47:19 - mmengine - INFO - Epoch(train) [1][87000/53672] lr: 1.1161e-03 eta: 0:42:03 time: 0.1824 data_time: 0.0038 memory: 1015 loss: 1.4386 loss_prob: 0.8779 loss_thr: 0.4237 loss_db: 0.1370 2022/12/14 19:50:34 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 19:50:34 - mmengine - INFO - Epoch(train) [1][88000/53672] lr: 1.0385e-03 eta: 0:38:49 time: 0.2107 data_time: 0.0038 memory: 1015 loss: 1.4497 loss_prob: 0.8769 loss_thr: 0.4281 loss_db: 0.1447 2022/12/14 19:53:48 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 19:53:48 - mmengine - INFO - Epoch(train) [1][89000/53672] lr: 9.6027e-04 eta: 0:35:35 time: 0.1906 data_time: 0.0039 memory: 1015 loss: 1.3752 loss_prob: 0.8170 loss_thr: 0.4290 loss_db: 0.1293 2022/12/14 19:57:01 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 19:57:01 - mmengine - INFO - Epoch(train) [1][90000/53672] lr: 8.8134e-04 eta: 0:32:20 time: 0.1819 data_time: 0.0042 memory: 1015 loss: 1.8122 loss_prob: 1.1474 loss_thr: 0.4973 loss_db: 0.1675 2022/12/14 19:57:01 - mmengine - INFO - Saving checkpoint at 90000 iterations 2022/12/14 20:00:18 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 20:00:18 - mmengine - INFO - Epoch(train) [1][91000/53672] lr: 8.0162e-04 eta: 0:29:07 time: 0.1911 data_time: 0.0040 memory: 1015 loss: 1.3495 loss_prob: 0.8193 loss_thr: 0.4063 loss_db: 0.1240 2022/12/14 20:03:35 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 20:03:35 - mmengine - INFO - Epoch(train) [1][92000/53672] lr: 7.2100e-04 eta: 0:25:53 time: 0.1886 data_time: 0.0038 memory: 1015 loss: 1.5934 loss_prob: 0.9818 loss_thr: 0.4569 loss_db: 0.1547 2022/12/14 20:06:53 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 20:06:53 - mmengine - INFO - Epoch(train) [1][93000/53672] lr: 6.3937e-04 eta: 0:22:39 time: 0.1684 data_time: 0.0039 memory: 1015 loss: 1.4330 loss_prob: 0.8490 loss_thr: 0.4526 loss_db: 0.1314 2022/12/14 20:10:11 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 20:10:11 - mmengine - INFO - Epoch(train) [1][94000/53672] lr: 5.5656e-04 eta: 0:19:25 time: 0.1959 data_time: 0.0038 memory: 1015 loss: 1.6754 loss_prob: 1.0165 loss_thr: 0.4888 loss_db: 0.1701 2022/12/14 20:13:28 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 20:13:28 - mmengine - INFO - Epoch(train) [1][95000/53672] lr: 4.7235e-04 eta: 0:16:11 time: 0.2230 data_time: 0.0039 memory: 1015 loss: 1.5351 loss_prob: 0.9144 loss_thr: 0.4729 loss_db: 0.1478 2022/12/14 20:16:46 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 20:16:46 - mmengine - INFO - Epoch(train) [1][96000/53672] lr: 3.8642e-04 eta: 0:12:57 time: 0.1935 data_time: 0.0039 memory: 1015 loss: 1.7014 loss_prob: 1.0405 loss_thr: 0.4830 loss_db: 0.1779 2022/12/14 20:20:00 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 20:20:00 - mmengine - INFO - Epoch(train) [1][97000/53672] lr: 2.9830e-04 eta: 0:09:42 time: 0.1945 data_time: 0.0037 memory: 1015 loss: 1.6087 loss_prob: 0.9960 loss_thr: 0.4430 loss_db: 0.1698 2022/12/14 20:23:17 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 20:23:17 - mmengine - INFO - Epoch(train) [1][98000/53672] lr: 2.0712e-04 eta: 0:06:28 time: 0.1978 data_time: 0.0038 memory: 1015 loss: 1.7042 loss_prob: 1.0585 loss_thr: 0.4694 loss_db: 0.1764 2022/12/14 20:26:33 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 20:26:33 - mmengine - INFO - Epoch(train) [1][99000/53672] lr: 1.1104e-04 eta: 0:03:14 time: 0.2028 data_time: 0.0039 memory: 1015 loss: 2.0578 loss_prob: 1.3635 loss_thr: 0.4959 loss_db: 0.1984 2022/12/14 20:29:47 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_100k_synthtext_20221214_150351 2022/12/14 20:29:47 - mmengine - INFO - Epoch(train) [1][100000/53672] lr: 1.0000e-07 eta: 0:00:00 time: 0.2474 data_time: 0.0039 memory: 1015 loss: 1.4956 loss_prob: 0.9037 loss_thr: 0.4454 loss_db: 0.1466 2022/12/14 20:29:47 - mmengine - INFO - Saving checkpoint at 100000 iterations