| {"env_info": "sys.platform: linux\nPython: 3.7.3 (default, Jan 22 2021, 20:04:44) [GCC 8.3.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: A100-SXM-80GB\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 11.3, V11.3.109\nGCC: x86_64-linux-gnu-gcc (Debian 8.3.0-6) 8.3.0\nPyTorch: 1.10.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX512\n - CUDA Runtime 11.3\n - NVCC architecture flags: -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\n - CuDNN 8.2\n - Magma 2.5.2\n - 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, \n\nTorchVision: 0.11.1+cu113\nOpenCV: 4.6.0\nMMCV: 1.6.1\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.3\nMMDetection: 2.25.2+87c120c", "config": "model = dict(\n type='MaskRCNN',\n backbone=dict(\n type='ResNet',\n depth=50,\n num_stages=4,\n out_indices=(0, 1, 2, 3),\n frozen_stages=1,\n norm_cfg=dict(type='SyncBN', requires_grad=True),\n norm_eval=True,\n style='pytorch',\n init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),\n neck=dict(\n type='FPN',\n in_channels=[256, 512, 1024, 2048],\n out_channels=256,\n num_outs=5,\n norm_cfg=dict(type='SyncBN', requires_grad=True)),\n rpn_head=dict(\n type='RPNHead',\n in_channels=256,\n feat_channels=256,\n anchor_generator=dict(\n type='AnchorGenerator',\n scales=[8],\n ratios=[0.5, 1.0, 2.0],\n strides=[4, 8, 16, 32, 64]),\n bbox_coder=dict(\n type='DeltaXYWHBBoxCoder',\n target_means=[0.0, 0.0, 0.0, 0.0],\n target_stds=[1.0, 1.0, 1.0, 1.0]),\n loss_cls=dict(\n type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n roi_head=dict(\n type='StandardRoIHead',\n bbox_roi_extractor=dict(\n type='SingleRoIExtractor',\n roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n out_channels=256,\n featmap_strides=[4, 8, 16, 32]),\n bbox_head=dict(\n type='Shared4Conv1FCBBoxHead',\n in_channels=256,\n fc_out_channels=1024,\n roi_feat_size=7,\n num_classes=20,\n bbox_coder=dict(\n type='DeltaXYWHBBoxCoder',\n target_means=[0.0, 0.0, 0.0, 0.0],\n target_stds=[0.1, 0.1, 0.2, 0.2]),\n reg_class_agnostic=False,\n loss_cls=dict(\n type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n mask_roi_extractor=None,\n mask_head=None),\n train_cfg=dict(\n rpn=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.7,\n neg_iou_thr=0.3,\n min_pos_iou=0.3,\n match_low_quality=True,\n ignore_iof_thr=-1),\n sampler=dict(\n type='RandomSampler',\n num=256,\n pos_fraction=0.5,\n neg_pos_ub=-1,\n add_gt_as_proposals=False),\n allowed_border=-1,\n pos_weight=-1,\n debug=False),\n rpn_proposal=dict(\n nms_pre=2000,\n max_per_img=1000,\n nms=dict(type='nms', iou_threshold=0.7),\n min_bbox_size=0),\n rcnn=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.5,\n neg_iou_thr=0.5,\n min_pos_iou=0.5,\n match_low_quality=True,\n ignore_iof_thr=-1),\n sampler=dict(\n type='RandomSampler',\n num=512,\n pos_fraction=0.25,\n neg_pos_ub=-1,\n add_gt_as_proposals=True),\n mask_size=28,\n pos_weight=-1,\n debug=False)),\n test_cfg=dict(\n rpn=dict(\n nms_pre=1000,\n max_per_img=1000,\n nms=dict(type='nms', iou_threshold=0.7),\n min_bbox_size=0),\n rcnn=dict(\n score_thr=0.05,\n nms=dict(type='nms', iou_threshold=0.5),\n max_per_img=100,\n mask_thr_binary=0.5)))\ndataset_type = 'VOCDataset'\ndata_root = 'data/VOCdevkit/'\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True),\n dict(\n type='Resize',\n img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),\n (1333, 608), (1333, 640), (1333, 672), (1333, 704),\n (1333, 736), (1333, 768), (1333, 800)],\n multiscale_mode='value',\n keep_ratio=True),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n]\ndata = dict(\n samples_per_gpu=2,\n workers_per_gpu=2,\n train=dict(\n type='VOCDataset',\n ann_file=[\n 'data/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt',\n 'data/VOCdevkit/VOC2012/ImageSets/Main/trainval.txt'\n ],\n img_prefix=['data/VOCdevkit/VOC2007/', 'data/VOCdevkit/VOC2012/'],\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True),\n dict(\n type='Resize',\n img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),\n (1333, 608), (1333, 640), (1333, 672), (1333, 704),\n (1333, 736), (1333, 768), (1333, 800)],\n multiscale_mode='value',\n keep_ratio=True),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n ]),\n val=dict(\n type='VOCDataset',\n ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',\n img_prefix='data/VOCdevkit/VOC2007/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]),\n test=dict(\n type='VOCDataset',\n ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',\n img_prefix='data/VOCdevkit/VOC2007/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]))\nevaluation = dict(interval=12000, metric='mAP', save_best='auto')\noptimizer = dict(type='SGD', lr=0.03, momentum=0.9, weight_decay=5e-05)\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(\n policy='step',\n warmup='linear',\n warmup_iters=500,\n warmup_ratio=0.001,\n step=[9000, 11000],\n by_epoch=False)\nrunner = dict(type='IterBasedRunner', max_iters=12000)\ncheckpoint_config = dict(interval=12000)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [\n dict(type='NumClassCheckHook'),\n dict(\n type='MMDetWandbHook',\n init_kwargs=dict(project='I2B', group='finetune'),\n interval=50,\n num_eval_images=0,\n log_checkpoint=False)\n]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = 'pretrain/selfsup_mask-rcnn_mstrain-soft-teacher_sampler-4096_temp0.5/final_model.pth'\nresume_from = None\nworkflow = [('train', 1)]\nopencv_num_threads = 0\nmp_start_method = 'fork'\nauto_scale_lr = dict(enable=False, base_batch_size=16)\ncustom_imports = None\nnorm_cfg = dict(type='SyncBN', requires_grad=True)\nwork_dir = 'work_dirs/finetune_mask-rcnn__12k_voc0712_lr3e-2_wd5e-5'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 42, "exp_name": "mask_rcnn_mstrain_12k_voc0712.py", "hook_msgs": {}} | |
| {"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00297, "memory": 3991, "data_time": 0.00644, "loss_rpn_cls": 0.41043, "loss_rpn_bbox": 0.03144, "loss_cls": 1.08654, "acc": 84.58247, "loss_bbox": 0.04802, "loss": 1.57643, "time": 0.11675} | |
| {"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00596, "memory": 3991, "data_time": 0.00565, "loss_rpn_cls": 0.10294, "loss_rpn_bbox": 0.0304, "loss_cls": 0.22905, "acc": 95.9438, "loss_bbox": 0.15879, "loss": 0.52118, "time": 0.112} | |
| {"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00896, "memory": 3992, "data_time": 0.00552, "loss_rpn_cls": 0.06976, "loss_rpn_bbox": 0.03024, "loss_cls": 0.23172, "acc": 95.49007, "loss_bbox": 0.17311, "loss": 0.50484, "time": 0.11235} | |
| {"mode": "train", "epoch": 1, "iter": 200, "lr": 0.01196, "memory": 3995, "data_time": 0.00534, "loss_rpn_cls": 0.05468, "loss_rpn_bbox": 0.02682, "loss_cls": 0.22995, "acc": 95.1844, "loss_bbox": 0.17774, "loss": 0.48919, "time": 0.11386} | |
| {"mode": "train", "epoch": 1, "iter": 250, "lr": 0.01496, "memory": 3995, "data_time": 0.00556, "loss_rpn_cls": 0.03784, "loss_rpn_bbox": 0.02613, "loss_cls": 0.25751, "acc": 94.17487, "loss_bbox": 0.18684, "loss": 0.50832, "time": 0.11232} | |
| {"mode": "train", "epoch": 1, "iter": 300, "lr": 0.01795, "memory": 3995, "data_time": 0.00554, "loss_rpn_cls": 0.03023, "loss_rpn_bbox": 0.02683, "loss_cls": 0.2374, "acc": 94.24235, "loss_bbox": 0.17474, "loss": 0.4692, "time": 0.11367} | |
| {"mode": "train", "epoch": 1, "iter": 350, "lr": 0.02095, "memory": 3995, "data_time": 0.00574, "loss_rpn_cls": 0.03024, "loss_rpn_bbox": 0.02549, "loss_cls": 0.2147, "acc": 94.58972, "loss_bbox": 0.16195, "loss": 0.43238, "time": 0.11257} | |
| {"mode": "train", "epoch": 1, "iter": 400, "lr": 0.02395, "memory": 3995, "data_time": 0.00575, "loss_rpn_cls": 0.02962, "loss_rpn_bbox": 0.02628, "loss_cls": 0.20662, "acc": 94.50053, "loss_bbox": 0.16455, "loss": 0.42707, "time": 0.1129} | |
| {"mode": "train", "epoch": 1, "iter": 450, "lr": 0.02694, "memory": 3995, "data_time": 0.00554, "loss_rpn_cls": 0.02807, "loss_rpn_bbox": 0.02585, "loss_cls": 0.19557, "acc": 94.61617, "loss_bbox": 0.16118, "loss": 0.41067, "time": 0.11173} | |
| {"mode": "train", "epoch": 1, "iter": 500, "lr": 0.02994, "memory": 3995, "data_time": 0.00564, "loss_rpn_cls": 0.0275, "loss_rpn_bbox": 0.02652, "loss_cls": 0.19759, "acc": 94.51387, "loss_bbox": 0.16369, "loss": 0.4153, "time": 0.11064} | |
| {"mode": "train", "epoch": 1, "iter": 550, "lr": 0.03, "memory": 3995, "data_time": 0.00571, "loss_rpn_cls": 0.02621, "loss_rpn_bbox": 0.0237, "loss_cls": 0.19653, "acc": 94.49793, "loss_bbox": 0.16558, "loss": 0.41202, "time": 0.11319} | |
| {"mode": "train", "epoch": 1, "iter": 600, "lr": 0.03, "memory": 3995, "data_time": 0.00568, "loss_rpn_cls": 0.02619, "loss_rpn_bbox": 0.02437, "loss_cls": 0.19264, "acc": 94.49541, "loss_bbox": 0.16481, "loss": 0.40801, "time": 0.11242} | |
| {"mode": "train", "epoch": 1, "iter": 650, "lr": 0.03, "memory": 3995, "data_time": 0.0058, "loss_rpn_cls": 0.02561, "loss_rpn_bbox": 0.02533, "loss_cls": 0.17946, "acc": 94.84409, "loss_bbox": 0.16391, "loss": 0.39431, "time": 0.11165} | |
| {"mode": "train", "epoch": 1, "iter": 700, "lr": 0.03, "memory": 3995, "data_time": 0.00556, "loss_rpn_cls": 0.02681, "loss_rpn_bbox": 0.0249, "loss_cls": 0.18208, "acc": 94.75084, "loss_bbox": 0.16257, "loss": 0.39636, "time": 0.11304} | |
| {"mode": "train", "epoch": 1, "iter": 750, "lr": 0.03, "memory": 3995, "data_time": 0.00556, "loss_rpn_cls": 0.02305, "loss_rpn_bbox": 0.02381, "loss_cls": 0.17837, "acc": 94.85941, "loss_bbox": 0.1554, "loss": 0.38064, "time": 0.1142} | |
| {"mode": "train", "epoch": 1, "iter": 800, "lr": 0.03, "memory": 3995, "data_time": 0.00565, "loss_rpn_cls": 0.02559, "loss_rpn_bbox": 0.02504, "loss_cls": 0.1712, "acc": 95.01142, "loss_bbox": 0.15679, "loss": 0.37862, "time": 0.11286} | |
| {"mode": "train", "epoch": 1, "iter": 850, "lr": 0.03, "memory": 3995, "data_time": 0.00603, "loss_rpn_cls": 0.02292, "loss_rpn_bbox": 0.02453, "loss_cls": 0.17492, "acc": 94.82509, "loss_bbox": 0.16082, "loss": 0.38319, "time": 0.11357} | |
| {"mode": "train", "epoch": 1, "iter": 900, "lr": 0.03, "memory": 3995, "data_time": 0.00601, "loss_rpn_cls": 0.02541, "loss_rpn_bbox": 0.02451, "loss_cls": 0.16358, "acc": 94.99489, "loss_bbox": 0.1574, "loss": 0.3709, "time": 0.11277} | |
| {"mode": "train", "epoch": 1, "iter": 950, "lr": 0.03, "memory": 3995, "data_time": 0.00568, "loss_rpn_cls": 0.02353, "loss_rpn_bbox": 0.02362, "loss_cls": 0.16549, "acc": 94.90694, "loss_bbox": 0.16323, "loss": 0.37588, "time": 0.1138} | |
| {"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.03, "memory": 3995, "data_time": 0.00566, "loss_rpn_cls": 0.02596, "loss_rpn_bbox": 0.02283, "loss_cls": 0.15184, "acc": 95.35421, "loss_bbox": 0.14761, "loss": 0.34824, "time": 0.11104} | |
| {"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.03, "memory": 3995, "data_time": 0.00624, "loss_rpn_cls": 0.02226, "loss_rpn_bbox": 0.02232, "loss_cls": 0.15488, "acc": 95.17919, "loss_bbox": 0.15263, "loss": 0.35209, "time": 0.11326} | |
| {"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.03, "memory": 3995, "data_time": 0.0057, "loss_rpn_cls": 0.02119, "loss_rpn_bbox": 0.02169, "loss_cls": 0.15774, "acc": 94.94709, "loss_bbox": 0.15923, "loss": 0.35986, "time": 0.11268} | |
| {"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.03, "memory": 3995, "data_time": 0.006, "loss_rpn_cls": 0.0218, "loss_rpn_bbox": 0.02127, "loss_cls": 0.1472, "acc": 95.34287, "loss_bbox": 0.14878, "loss": 0.33906, "time": 0.11321} | |
| {"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.03, "memory": 3995, "data_time": 0.00615, "loss_rpn_cls": 0.02182, "loss_rpn_bbox": 0.02347, "loss_cls": 0.16316, "acc": 94.93938, "loss_bbox": 0.16, "loss": 0.36845, "time": 0.11361} | |
| {"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.03, "memory": 3995, "data_time": 0.00581, "loss_rpn_cls": 0.02133, "loss_rpn_bbox": 0.02247, "loss_cls": 0.16057, "acc": 94.90527, "loss_bbox": 0.16563, "loss": 0.36999, "time": 0.11263} | |
| {"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.03, "memory": 3995, "data_time": 0.006, "loss_rpn_cls": 0.01998, "loss_rpn_bbox": 0.02045, "loss_cls": 0.14458, "acc": 95.52483, "loss_bbox": 0.14612, "loss": 0.33113, "time": 0.11225} | |
| {"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.03, "memory": 3995, "data_time": 0.0057, "loss_rpn_cls": 0.02182, "loss_rpn_bbox": 0.02188, "loss_cls": 0.15621, "acc": 95.08521, "loss_bbox": 0.15699, "loss": 0.35689, "time": 0.11065} | |
| {"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.03, "memory": 3995, "data_time": 0.00579, "loss_rpn_cls": 0.02226, "loss_rpn_bbox": 0.02149, "loss_cls": 0.1555, "acc": 95.15674, "loss_bbox": 0.15612, "loss": 0.35537, "time": 0.11239} | |
| {"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.03, "memory": 3995, "data_time": 0.00565, "loss_rpn_cls": 0.02105, "loss_rpn_bbox": 0.02309, "loss_cls": 0.15521, "acc": 95.05029, "loss_bbox": 0.15922, "loss": 0.35857, "time": 0.11099} | |
| {"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.03, "memory": 3995, "data_time": 0.00585, "loss_rpn_cls": 0.02237, "loss_rpn_bbox": 0.02267, "loss_cls": 0.16152, "acc": 94.90433, "loss_bbox": 0.16257, "loss": 0.36914, "time": 0.11216} | |
| {"mode": "train", "epoch": 1, "iter": 1550, "lr": 0.03, "memory": 3995, "data_time": 0.00575, "loss_rpn_cls": 0.02116, "loss_rpn_bbox": 0.0225, "loss_cls": 0.15811, "acc": 95.14237, "loss_bbox": 0.15299, "loss": 0.35477, "time": 0.11156} | |
| {"mode": "train", "epoch": 1, "iter": 1600, "lr": 0.03, "memory": 3995, "data_time": 0.00588, "loss_rpn_cls": 0.02113, "loss_rpn_bbox": 0.02233, "loss_cls": 0.15422, "acc": 95.05477, "loss_bbox": 0.16012, "loss": 0.3578, "time": 0.1111} | |
| {"mode": "train", "epoch": 1, "iter": 1650, "lr": 0.03, "memory": 3995, "data_time": 0.00571, "loss_rpn_cls": 0.01862, "loss_rpn_bbox": 0.02049, "loss_cls": 0.14403, "acc": 95.29903, "loss_bbox": 0.1535, "loss": 0.33663, "time": 0.11174} | |
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| {"mode": "train", "epoch": 1, "iter": 11750, "lr": 0.0003, "memory": 3995, "data_time": 0.00569, "loss_rpn_cls": 0.00787, "loss_rpn_bbox": 0.01678, "loss_cls": 0.07745, "acc": 96.98307, "loss_bbox": 0.11778, "loss": 0.21988, "time": 0.11492} | |
| {"mode": "train", "epoch": 1, "iter": 11800, "lr": 0.0003, "memory": 3995, "data_time": 0.00611, "loss_rpn_cls": 0.00691, "loss_rpn_bbox": 0.01579, "loss_cls": 0.07497, "acc": 97.09582, "loss_bbox": 0.10819, "loss": 0.20587, "time": 0.11348} | |
| {"mode": "train", "epoch": 1, "iter": 11850, "lr": 0.0003, "memory": 3995, "data_time": 0.00572, "loss_rpn_cls": 0.00789, "loss_rpn_bbox": 0.01631, "loss_cls": 0.0756, "acc": 97.08119, "loss_bbox": 0.11148, "loss": 0.21129, "time": 0.11214} | |
| {"mode": "train", "epoch": 1, "iter": 11900, "lr": 0.0003, "memory": 3995, "data_time": 0.00576, "loss_rpn_cls": 0.00665, "loss_rpn_bbox": 0.01629, "loss_cls": 0.07343, "acc": 97.19099, "loss_bbox": 0.10649, "loss": 0.20287, "time": 0.11253} | |
| {"mode": "train", "epoch": 1, "iter": 11950, "lr": 0.0003, "memory": 3995, "data_time": 0.00609, "loss_rpn_cls": 0.00792, "loss_rpn_bbox": 0.01633, "loss_cls": 0.07778, "acc": 96.98358, "loss_bbox": 0.11344, "loss": 0.21547, "time": 0.11297} | |
| {"mode": "train", "epoch": 1, "iter": 12000, "lr": 0.0003, "memory": 3995, "data_time": 0.00577, "loss_rpn_cls": 0.0072, "loss_rpn_bbox": 0.0156, "loss_cls": 0.07421, "acc": 97.14434, "loss_bbox": 0.1089, "loss": 0.2059, "time": 0.12536} | |
| {"mode": "val", "epoch": 1, "iter": 619, "lr": 0.0003, "mAP": 0.82919, "AP50": 0.829} | |