| {"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='RetinaNet',\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 start_level=1,\n add_extra_convs='on_input',\n num_outs=5,\n norm_cfg=dict(type='SyncBN', requires_grad=True)),\n bbox_head=dict(\n type='RetinaHead',\n num_classes=20,\n in_channels=256,\n stacked_convs=4,\n feat_channels=256,\n anchor_generator=dict(\n type='AnchorGenerator',\n octave_base_scale=4,\n scales_per_octave=3,\n ratios=[0.5, 1.0, 2.0],\n strides=[8, 16, 32, 64, 128]),\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='FocalLoss',\n use_sigmoid=True,\n gamma=2.0,\n alpha=0.25,\n loss_weight=1.0),\n loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n train_cfg=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.5,\n neg_iou_thr=0.4,\n min_pos_iou=0,\n ignore_iof_thr=-1),\n allowed_border=-1,\n pos_weight=-1,\n debug=False),\n test_cfg=dict(\n nms_pre=1000,\n min_bbox_size=0,\n score_thr=0.05,\n nms=dict(type='nms', iou_threshold=0.5),\n max_per_img=100))\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=0.015, 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_retinanet_mstrain-soft-teacher_sampler-2048_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_retinanet_12k_voc0712_lr1.5e-2_wd5e-5'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 42, "exp_name": "retinanet_mstrain_12k_voc0712.py", "hook_msgs": {}} | |
| {"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00148, "memory": 3221, "data_time": 0.00646, "loss_cls": 1.16285, "loss_bbox": 0.66144, "loss": 1.82429, "time": 0.08998} | |
| {"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00298, "memory": 3222, "data_time": 0.00569, "loss_cls": 1.15842, "loss_bbox": 0.5555, "loss": 1.71392, "time": 0.08556} | |
| {"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00448, "memory": 3223, "data_time": 0.00543, "loss_cls": 1.08913, "loss_bbox": 0.45169, "loss": 1.54083, "time": 0.11989} | |
| {"mode": "train", "epoch": 1, "iter": 200, "lr": 0.00598, "memory": 3223, "data_time": 0.00532, "loss_cls": 0.82578, "loss_bbox": 0.4111, "loss": 1.23688, "time": 0.08339} | |
| {"mode": "train", "epoch": 1, "iter": 250, "lr": 0.00748, "memory": 3223, "data_time": 0.00567, "loss_cls": 0.78451, "loss_bbox": 0.40734, "loss": 1.19185, "time": 0.08569} | |
| {"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00898, "memory": 3223, "data_time": 0.00545, "loss_cls": 0.73122, "loss_bbox": 0.39495, "loss": 1.12617, "time": 0.08289} | |
| {"mode": "train", "epoch": 1, "iter": 350, "lr": 0.01047, "memory": 3223, "data_time": 0.00551, "loss_cls": 0.65967, "loss_bbox": 0.38887, "loss": 1.04854, "time": 0.08505} | |
| {"mode": "train", "epoch": 1, "iter": 400, "lr": 0.01197, "memory": 3223, "data_time": 0.00562, "loss_cls": 0.63125, "loss_bbox": 0.37837, "loss": 1.00961, "time": 0.08237} | |
| {"mode": "train", "epoch": 1, "iter": 450, "lr": 0.01347, "memory": 3223, "data_time": 0.00543, "loss_cls": 0.84229, "loss_bbox": 0.4101, "loss": 1.25239, "time": 0.08435} | |
| {"mode": "train", "epoch": 1, "iter": 500, "lr": 0.01497, "memory": 3223, "data_time": 0.00564, "loss_cls": 0.90531, "loss_bbox": 0.48745, "loss": 1.39276, "time": 0.08146} | |
| {"mode": "train", "epoch": 1, "iter": 550, "lr": 0.015, "memory": 3223, "data_time": 0.00568, "loss_cls": 0.76802, "loss_bbox": 0.42043, "loss": 1.18845, "time": 0.08651} | |
| {"mode": "train", "epoch": 1, "iter": 600, "lr": 0.015, "memory": 3223, "data_time": 0.0057, "loss_cls": 0.76047, "loss_bbox": 0.40146, "loss": 1.16193, "time": 0.08419} | |
| {"mode": "train", "epoch": 1, "iter": 650, "lr": 0.015, "memory": 3223, "data_time": 0.00553, "loss_cls": 0.81023, "loss_bbox": 0.39659, "loss": 1.20682, "time": 0.08533} | |
| {"mode": "train", "epoch": 1, "iter": 700, "lr": 0.015, "memory": 3224, "data_time": 0.00552, "loss_cls": 0.64596, "loss_bbox": 0.36867, "loss": 1.01464, "time": 0.0838} | |
| {"mode": "train", "epoch": 1, "iter": 750, "lr": 0.015, "memory": 3224, "data_time": 0.00565, "loss_cls": 0.65355, "loss_bbox": 0.35524, "loss": 1.00879, "time": 0.0857} | |
| {"mode": "train", "epoch": 1, "iter": 800, "lr": 0.015, "memory": 3224, "data_time": 0.00573, "loss_cls": 0.58584, "loss_bbox": 0.35988, "loss": 0.94571, "time": 0.08564} | |
| {"mode": "train", "epoch": 1, "iter": 850, "lr": 0.015, "memory": 3224, "data_time": 0.0056, "loss_cls": 0.57241, "loss_bbox": 0.34075, "loss": 0.91316, "time": 0.08053} | |
| {"mode": "train", "epoch": 1, "iter": 900, "lr": 0.015, "memory": 3224, "data_time": 0.00591, "loss_cls": 0.54976, "loss_bbox": 0.34491, "loss": 0.89467, "time": 0.08217} | |
| {"mode": "train", "epoch": 1, "iter": 950, "lr": 0.015, "memory": 3224, "data_time": 0.00571, "loss_cls": 0.53236, "loss_bbox": 0.34115, "loss": 0.8735, "time": 0.08164} | |
| {"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.015, "memory": 3224, "data_time": 0.00546, "loss_cls": 0.5129, "loss_bbox": 0.31934, "loss": 0.83224, "time": 0.08014} | |
| {"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.015, "memory": 3224, "data_time": 0.00561, "loss_cls": 0.51722, "loss_bbox": 0.32834, "loss": 0.84555, "time": 0.08025} | |
| {"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.015, "memory": 3224, "data_time": 0.00577, "loss_cls": 0.49092, "loss_bbox": 0.32066, "loss": 0.81158, "time": 0.08099} | |
| {"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.015, "memory": 3224, "data_time": 0.00587, "loss_cls": 0.50335, "loss_bbox": 0.32718, "loss": 0.83054, "time": 0.0861} | |
| {"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.015, "memory": 3224, "data_time": 0.00573, "loss_cls": 0.48893, "loss_bbox": 0.32396, "loss": 0.81289, "time": 0.08041} | |
| {"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.015, "memory": 3224, "data_time": 0.00572, "loss_cls": 0.45359, "loss_bbox": 0.31847, "loss": 0.77206, "time": 0.10601} | |
| {"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.015, "memory": 3224, "data_time": 0.00544, "loss_cls": 0.46386, "loss_bbox": 0.30648, "loss": 0.77034, "time": 0.08085} | |
| {"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.015, "memory": 3224, "data_time": 0.00554, "loss_cls": 0.43709, "loss_bbox": 0.30441, "loss": 0.7415, "time": 0.08235} | |
| {"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.015, "memory": 3224, "data_time": 0.00566, "loss_cls": 0.4519, "loss_bbox": 0.31337, "loss": 0.76527, "time": 0.08293} | |
| {"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.015, "memory": 3224, "data_time": 0.00553, "loss_cls": 0.43094, "loss_bbox": 0.30545, "loss": 0.7364, "time": 0.07969} | |
| {"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.015, "memory": 3224, "data_time": 0.00592, "loss_cls": 0.43839, "loss_bbox": 0.31672, "loss": 0.75511, "time": 0.08167} | |
| {"mode": "train", "epoch": 1, "iter": 1550, "lr": 0.015, "memory": 3224, "data_time": 0.00546, "loss_cls": 0.43553, "loss_bbox": 0.30691, "loss": 0.74244, "time": 0.08057} | |
| {"mode": "train", "epoch": 1, "iter": 1600, "lr": 0.015, "memory": 3224, "data_time": 0.00561, "loss_cls": 0.40553, "loss_bbox": 0.30886, "loss": 0.71439, "time": 0.07974} | |
| {"mode": "train", "epoch": 1, "iter": 1650, "lr": 0.015, "memory": 3224, "data_time": 0.00562, "loss_cls": 0.40692, "loss_bbox": 0.30499, "loss": 0.71191, "time": 0.07977} | |
| {"mode": "train", "epoch": 1, "iter": 1700, "lr": 0.015, "memory": 3224, "data_time": 0.00565, "loss_cls": 0.40038, "loss_bbox": 0.30546, "loss": 0.70585, "time": 0.08124} | |
| {"mode": "train", "epoch": 1, "iter": 1750, "lr": 0.015, "memory": 3224, "data_time": 0.00553, "loss_cls": 0.41832, "loss_bbox": 0.30419, "loss": 0.72251, "time": 0.08518} | |
| {"mode": "train", "epoch": 1, "iter": 1800, "lr": 0.015, "memory": 3224, "data_time": 0.00579, "loss_cls": 0.40231, "loss_bbox": 0.30225, "loss": 0.70456, "time": 0.08349} | |
| {"mode": "train", "epoch": 1, "iter": 1850, "lr": 0.015, "memory": 3224, "data_time": 0.00576, "loss_cls": 0.37605, "loss_bbox": 0.29564, "loss": 0.6717, "time": 0.08002} | |
| {"mode": "train", "epoch": 1, "iter": 1900, "lr": 0.015, "memory": 3224, "data_time": 0.00596, "loss_cls": 0.40054, "loss_bbox": 0.30438, "loss": 0.70491, "time": 0.08584} | |
| {"mode": "train", "epoch": 1, "iter": 1950, "lr": 0.015, "memory": 3224, "data_time": 0.0057, "loss_cls": 0.39986, "loss_bbox": 0.29539, "loss": 0.69524, "time": 0.08385} | |
| {"mode": "train", "epoch": 1, "iter": 2000, "lr": 0.015, "memory": 3224, "data_time": 0.0056, "loss_cls": 0.37848, "loss_bbox": 0.29018, "loss": 0.66866, "time": 0.08386} | |
| {"mode": "train", "epoch": 1, "iter": 2050, "lr": 0.015, "memory": 3224, "data_time": 0.00582, "loss_cls": 0.35862, "loss_bbox": 0.29024, "loss": 0.64886, "time": 0.08042} | |
| {"mode": "train", "epoch": 1, "iter": 2100, "lr": 0.015, "memory": 3224, "data_time": 0.00577, "loss_cls": 0.38244, "loss_bbox": 0.2926, "loss": 0.67504, "time": 0.08178} | |
| {"mode": "train", "epoch": 1, "iter": 2150, "lr": 0.015, "memory": 3224, "data_time": 0.00577, "loss_cls": 0.35512, "loss_bbox": 0.27947, "loss": 0.63459, "time": 0.08113} | |
| {"mode": "train", "epoch": 1, "iter": 2200, "lr": 0.015, "memory": 3224, "data_time": 0.00584, "loss_cls": 0.34082, "loss_bbox": 0.28299, "loss": 0.6238, "time": 0.08397} | |
| {"mode": "train", "epoch": 1, "iter": 2250, "lr": 0.015, "memory": 3224, "data_time": 0.00565, "loss_cls": 0.34476, "loss_bbox": 0.28227, "loss": 0.62703, "time": 0.08082} | |
| {"mode": "train", "epoch": 1, "iter": 2300, "lr": 0.015, "memory": 3224, "data_time": 0.00585, "loss_cls": 0.3532, "loss_bbox": 0.28375, "loss": 0.63696, "time": 0.08128} | |
| {"mode": "train", "epoch": 1, "iter": 2350, "lr": 0.015, "memory": 3224, "data_time": 0.00553, "loss_cls": 0.35495, "loss_bbox": 0.28728, "loss": 0.64223, "time": 0.08142} | |
| {"mode": "train", "epoch": 1, "iter": 2400, "lr": 0.015, "memory": 3224, "data_time": 0.00556, "loss_cls": 0.3385, "loss_bbox": 0.28629, "loss": 0.62479, "time": 0.08011} | |
| {"mode": "train", "epoch": 1, "iter": 2450, "lr": 0.015, "memory": 3224, "data_time": 0.00544, "loss_cls": 0.33517, "loss_bbox": 0.27911, "loss": 0.61428, "time": 0.08015} | |
| {"mode": "train", "epoch": 1, "iter": 2500, "lr": 0.015, "memory": 3224, "data_time": 0.00579, "loss_cls": 0.32928, "loss_bbox": 0.28163, "loss": 0.61091, "time": 0.07983} | |
| {"mode": "train", "epoch": 1, "iter": 2550, "lr": 0.015, "memory": 3224, "data_time": 0.00591, "loss_cls": 0.34592, "loss_bbox": 0.2878, "loss": 0.63372, "time": 0.08036} | |
| {"mode": "train", "epoch": 1, "iter": 2600, "lr": 0.015, "memory": 3224, "data_time": 0.00583, "loss_cls": 0.33784, "loss_bbox": 0.2843, "loss": 0.62215, "time": 0.08175} | |
| {"mode": "train", "epoch": 1, "iter": 2650, "lr": 0.015, "memory": 3224, "data_time": 0.0056, "loss_cls": 0.33472, "loss_bbox": 0.27858, "loss": 0.6133, "time": 0.08317} | |
| {"mode": "train", "epoch": 1, "iter": 2700, "lr": 0.015, "memory": 3224, "data_time": 0.00561, "loss_cls": 0.31628, "loss_bbox": 0.27683, "loss": 0.59312, "time": 0.07941} | |
| {"mode": "train", "epoch": 1, "iter": 2750, "lr": 0.015, "memory": 3224, "data_time": 0.00566, "loss_cls": 0.34986, "loss_bbox": 0.27511, "loss": 0.62497, "time": 0.081} | |
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| {"mode": "train", "epoch": 1, "iter": 11500, "lr": 0.00015, "memory": 3224, "data_time": 0.00556, "loss_cls": 0.1207, "loss_bbox": 0.16788, "loss": 0.28858, "time": 0.0804} | |
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| {"mode": "train", "epoch": 1, "iter": 11850, "lr": 0.00015, "memory": 3224, "data_time": 0.00561, "loss_cls": 0.1308, "loss_bbox": 0.18031, "loss": 0.31112, "time": 0.08005} | |
| {"mode": "train", "epoch": 1, "iter": 11900, "lr": 0.00015, "memory": 3224, "data_time": 0.00564, "loss_cls": 0.12696, "loss_bbox": 0.1711, "loss": 0.29806, "time": 0.0806} | |
| {"mode": "train", "epoch": 1, "iter": 11950, "lr": 0.00015, "memory": 3224, "data_time": 0.00584, "loss_cls": 0.13343, "loss_bbox": 0.18106, "loss": 0.31449, "time": 0.08243} | |
| {"mode": "train", "epoch": 1, "iter": 12000, "lr": 0.00015, "memory": 3224, "data_time": 0.00575, "loss_cls": 0.13279, "loss_bbox": 0.17728, "loss": 0.31006, "time": 0.09617} | |
| {"mode": "val", "epoch": 1, "iter": 619, "lr": 0.00015, "mAP": 0.80415, "AP50": 0.804} | |