| {"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+a7ef785", "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=80,\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 = 'CocoDataset'\ndata_root = 'data/coco/'\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(type='Resize', img_scale=(1333, 800), 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='CocoDataset',\n ann_file='data/coco/annotations/instances_train2017.json',\n img_prefix='data/coco/train2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True),\n dict(type='Resize', img_scale=(1333, 800), 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='CocoDataset',\n ann_file='data/coco/annotations/instances_val2017.json',\n img_prefix='data/coco/val2017/',\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='CocoDataset',\n ann_file='data/coco/annotations/instances_val2017.json',\n img_prefix='data/coco/val2017/',\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(\n interval=12000, metric='bbox', save_best='auto', gpu_collect=True)\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='semi-coco'),\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_faster-rcnn_12k_coco'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 42, "exp_name": "faster_rcnn_fpn_12k_semi-coco.py", "hook_msgs": {}} | |
| {"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00297, "memory": 4029, "data_time": 0.00871, "loss_rpn_cls": 0.5004, "loss_rpn_bbox": 0.10321, "loss_cls": 1.45083, "acc": 83.72046, "loss_bbox": 0.04998, "loss": 2.10442, "time": 0.13065} | |
| {"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00596, "memory": 4062, "data_time": 0.00742, "loss_rpn_cls": 0.22724, "loss_rpn_bbox": 0.0963, "loss_cls": 0.47765, "acc": 93.14209, "loss_bbox": 0.23758, "loss": 1.03878, "time": 0.12515} | |
| {"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00896, "memory": 4062, "data_time": 0.00716, "loss_rpn_cls": 0.15045, "loss_rpn_bbox": 0.08741, "loss_cls": 0.49875, "acc": 91.99438, "loss_bbox": 0.28502, "loss": 1.02164, "time": 0.14318} | |
| {"mode": "train", "epoch": 1, "iter": 200, "lr": 0.01196, "memory": 4062, "data_time": 0.00739, "loss_rpn_cls": 0.09823, "loss_rpn_bbox": 0.08639, "loss_cls": 0.58495, "acc": 89.73608, "loss_bbox": 0.37037, "loss": 1.13995, "time": 0.12398} | |
| {"mode": "train", "epoch": 1, "iter": 250, "lr": 0.01496, "memory": 4062, "data_time": 0.00718, "loss_rpn_cls": 0.09426, "loss_rpn_bbox": 0.08374, "loss_cls": 0.5577, "acc": 89.79077, "loss_bbox": 0.3555, "loss": 1.0912, "time": 0.12554} | |
| {"mode": "train", "epoch": 1, "iter": 300, "lr": 0.01795, "memory": 4062, "data_time": 0.00719, "loss_rpn_cls": 0.08925, "loss_rpn_bbox": 0.08519, "loss_cls": 0.55754, "acc": 89.90454, "loss_bbox": 0.3238, "loss": 1.05578, "time": 0.12821} | |
| {"mode": "train", "epoch": 1, "iter": 350, "lr": 0.02095, "memory": 4062, "data_time": 0.00695, "loss_rpn_cls": 0.08902, "loss_rpn_bbox": 0.08342, "loss_cls": 0.52477, "acc": 90.13989, "loss_bbox": 0.30322, "loss": 1.00043, "time": 0.12548} | |
| {"mode": "train", "epoch": 1, "iter": 400, "lr": 0.02395, "memory": 4062, "data_time": 0.00707, "loss_rpn_cls": 0.08871, "loss_rpn_bbox": 0.08562, "loss_cls": 0.50535, "acc": 89.89819, "loss_bbox": 0.30567, "loss": 0.98535, "time": 0.12565} | |
| {"mode": "train", "epoch": 1, "iter": 450, "lr": 0.02694, "memory": 4062, "data_time": 0.0071, "loss_rpn_cls": 0.0788, "loss_rpn_bbox": 0.07577, "loss_cls": 0.46605, "acc": 90.59253, "loss_bbox": 0.28171, "loss": 0.90233, "time": 0.12647} | |
| {"mode": "train", "epoch": 1, "iter": 500, "lr": 0.02994, "memory": 4062, "data_time": 0.00728, "loss_rpn_cls": 0.08262, "loss_rpn_bbox": 0.08365, "loss_cls": 0.46881, "acc": 90.15234, "loss_bbox": 0.29078, "loss": 0.92586, "time": 0.12555} | |
| {"mode": "train", "epoch": 1, "iter": 550, "lr": 0.03, "memory": 4062, "data_time": 0.00691, "loss_rpn_cls": 0.07897, "loss_rpn_bbox": 0.07984, "loss_cls": 0.47414, "acc": 89.82837, "loss_bbox": 0.29201, "loss": 0.92497, "time": 0.12599} | |
| {"mode": "train", "epoch": 1, "iter": 600, "lr": 0.03, "memory": 4062, "data_time": 0.00735, "loss_rpn_cls": 0.08161, "loss_rpn_bbox": 0.07705, "loss_cls": 0.43686, "acc": 90.50366, "loss_bbox": 0.27477, "loss": 0.87029, "time": 0.12599} | |
| {"mode": "train", "epoch": 1, "iter": 650, "lr": 0.03, "memory": 4062, "data_time": 0.007, "loss_rpn_cls": 0.0801, "loss_rpn_bbox": 0.08214, "loss_cls": 0.46587, "acc": 89.79932, "loss_bbox": 0.28519, "loss": 0.91331, "time": 0.12464} | |
| {"mode": "train", "epoch": 1, "iter": 700, "lr": 0.03, "memory": 4062, "data_time": 0.00712, "loss_rpn_cls": 0.08007, "loss_rpn_bbox": 0.07933, "loss_cls": 0.42723, "acc": 90.28564, "loss_bbox": 0.28151, "loss": 0.86815, "time": 0.12475} | |
| {"mode": "train", "epoch": 1, "iter": 750, "lr": 0.03, "memory": 4062, "data_time": 0.00714, "loss_rpn_cls": 0.07753, "loss_rpn_bbox": 0.07738, "loss_cls": 0.42494, "acc": 90.28223, "loss_bbox": 0.28204, "loss": 0.86189, "time": 0.1241} | |
| {"mode": "train", "epoch": 1, "iter": 800, "lr": 0.03, "memory": 4062, "data_time": 0.00713, "loss_rpn_cls": 0.07466, "loss_rpn_bbox": 0.0783, "loss_cls": 0.41823, "acc": 90.23804, "loss_bbox": 0.28505, "loss": 0.85623, "time": 0.12462} | |
| {"mode": "train", "epoch": 1, "iter": 850, "lr": 0.03, "memory": 4062, "data_time": 0.00684, "loss_rpn_cls": 0.0773, "loss_rpn_bbox": 0.07389, "loss_cls": 0.40628, "acc": 90.45337, "loss_bbox": 0.27241, "loss": 0.82988, "time": 0.12465} | |
| {"mode": "train", "epoch": 1, "iter": 900, "lr": 0.03, "memory": 4062, "data_time": 0.0071, "loss_rpn_cls": 0.0739, "loss_rpn_bbox": 0.07948, "loss_cls": 0.40342, "acc": 90.31104, "loss_bbox": 0.27638, "loss": 0.83318, "time": 0.12678} | |
| {"mode": "train", "epoch": 1, "iter": 950, "lr": 0.03, "memory": 4062, "data_time": 0.00672, "loss_rpn_cls": 0.07605, "loss_rpn_bbox": 0.0801, "loss_cls": 0.40499, "acc": 90.37134, "loss_bbox": 0.2765, "loss": 0.83765, "time": 0.12749} | |
| {"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.03, "memory": 4062, "data_time": 0.0075, "loss_rpn_cls": 0.0721, "loss_rpn_bbox": 0.07509, "loss_cls": 0.39642, "acc": 90.32324, "loss_bbox": 0.27627, "loss": 0.81989, "time": 0.12939} | |
| {"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.03, "memory": 4062, "data_time": 0.00713, "loss_rpn_cls": 0.07725, "loss_rpn_bbox": 0.07399, "loss_cls": 0.39299, "acc": 90.41211, "loss_bbox": 0.27577, "loss": 0.82, "time": 0.12668} | |
| {"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.03, "memory": 4062, "data_time": 0.00728, "loss_rpn_cls": 0.07213, "loss_rpn_bbox": 0.07631, "loss_cls": 0.40113, "acc": 90.19849, "loss_bbox": 0.28057, "loss": 0.83014, "time": 0.12435} | |
| {"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.03, "memory": 4062, "data_time": 0.00728, "loss_rpn_cls": 0.07204, "loss_rpn_bbox": 0.07066, "loss_cls": 0.3779, "acc": 90.53369, "loss_bbox": 0.27757, "loss": 0.79818, "time": 0.12447} | |
| {"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.03, "memory": 4062, "data_time": 0.00712, "loss_rpn_cls": 0.07314, "loss_rpn_bbox": 0.0754, "loss_cls": 0.37992, "acc": 90.42578, "loss_bbox": 0.26983, "loss": 0.79829, "time": 0.12701} | |
| {"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.03, "memory": 4062, "data_time": 0.00698, "loss_rpn_cls": 0.06889, "loss_rpn_bbox": 0.07831, "loss_cls": 0.37209, "acc": 90.4668, "loss_bbox": 0.27312, "loss": 0.79242, "time": 0.12628} | |
| {"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.03, "memory": 4062, "data_time": 0.00695, "loss_rpn_cls": 0.066, "loss_rpn_bbox": 0.07, "loss_cls": 0.37616, "acc": 90.38892, "loss_bbox": 0.27619, "loss": 0.78835, "time": 0.12492} | |
| {"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.03, "memory": 4062, "data_time": 0.00674, "loss_rpn_cls": 0.07232, "loss_rpn_bbox": 0.07786, "loss_cls": 0.3755, "acc": 90.26514, "loss_bbox": 0.28396, "loss": 0.80964, "time": 0.12501} | |
| {"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.03, "memory": 4062, "data_time": 0.00682, "loss_rpn_cls": 0.07325, "loss_rpn_bbox": 0.07771, "loss_cls": 0.3831, "acc": 90.22974, "loss_bbox": 0.28444, "loss": 0.81849, "time": 0.1243} | |
| {"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.03, "memory": 4062, "data_time": 0.00669, "loss_rpn_cls": 0.07464, "loss_rpn_bbox": 0.07715, "loss_cls": 0.37253, "acc": 90.42407, "loss_bbox": 0.27741, "loss": 0.80173, "time": 0.12416} | |
| {"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.03, "memory": 4062, "data_time": 0.00658, "loss_rpn_cls": 0.07007, "loss_rpn_bbox": 0.07278, "loss_cls": 0.37162, "acc": 90.31934, "loss_bbox": 0.27955, "loss": 0.79402, "time": 0.12608} | |
| {"mode": "train", "epoch": 1, "iter": 1550, "lr": 0.03, "memory": 4062, "data_time": 0.00681, "loss_rpn_cls": 0.0702, "loss_rpn_bbox": 0.07832, "loss_cls": 0.36688, "acc": 90.49585, "loss_bbox": 0.27146, "loss": 0.78686, "time": 0.12784} | |
| {"mode": "train", "epoch": 1, "iter": 1600, "lr": 0.03, "memory": 4062, "data_time": 0.00682, "loss_rpn_cls": 0.06484, "loss_rpn_bbox": 0.06892, "loss_cls": 0.36144, "acc": 90.59204, "loss_bbox": 0.27358, "loss": 0.76878, "time": 0.12424} | |
| {"mode": "train", "epoch": 1, "iter": 1650, "lr": 0.03, "memory": 4062, "data_time": 0.00657, "loss_rpn_cls": 0.06564, "loss_rpn_bbox": 0.07224, "loss_cls": 0.34756, "acc": 91.01074, "loss_bbox": 0.25937, "loss": 0.74482, "time": 0.12565} | |
| {"mode": "train", "epoch": 1, "iter": 1700, "lr": 0.03, "memory": 4062, "data_time": 0.00668, "loss_rpn_cls": 0.06691, "loss_rpn_bbox": 0.07381, "loss_cls": 0.35868, "acc": 90.59082, "loss_bbox": 0.27199, "loss": 0.77139, "time": 0.12504} | |
| {"mode": "train", "epoch": 1, "iter": 1750, "lr": 0.03, "memory": 4062, "data_time": 0.00673, "loss_rpn_cls": 0.06968, "loss_rpn_bbox": 0.0748, "loss_cls": 0.37488, "acc": 90.18408, "loss_bbox": 0.29048, "loss": 0.80985, "time": 0.12586} | |
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| {"mode": "train", "epoch": 1, "iter": 11900, "lr": 0.0003, "memory": 4086, "data_time": 0.00679, "loss_rpn_cls": 0.04002, "loss_rpn_bbox": 0.05604, "loss_cls": 0.25288, "acc": 91.80713, "loss_bbox": 0.24632, "loss": 0.59526, "time": 0.1242} | |
| {"mode": "train", "epoch": 1, "iter": 11950, "lr": 0.0003, "memory": 4086, "data_time": 0.00669, "loss_rpn_cls": 0.03918, "loss_rpn_bbox": 0.05533, "loss_cls": 0.25328, "acc": 91.90625, "loss_bbox": 0.24626, "loss": 0.59404, "time": 0.1232} | |
| {"mode": "train", "epoch": 1, "iter": 12000, "lr": 0.0003, "memory": 4086, "data_time": 0.00689, "loss_rpn_cls": 0.04113, "loss_rpn_bbox": 0.05834, "loss_cls": 0.25107, "acc": 92.00781, "loss_bbox": 0.24318, "loss": 0.59372, "time": 0.13826} | |
| {"mode": "val", "epoch": 1, "iter": 625, "lr": 0.0003, "bbox_mAP": 0.306, "bbox_mAP_50": 0.49, "bbox_mAP_75": 0.327, "bbox_mAP_s": 0.169, "bbox_mAP_m": 0.336, "bbox_mAP_l": 0.395, "bbox_mAP_copypaste": "0.306 0.490 0.327 0.169 0.336 0.395"} | |