{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla PG503-216\nCUDA_HOME: /usr/local/cuda\nNVCC: Build cuda_11.2.r11.2/compiler.29618528_0\nGCC: gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44)\nPyTorch: 1.8.0\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v1.7.0 (Git Hash 7aed236906b1f7a05c0917e5257a1af05e9ff683)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime 11.1\n  - 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\n  - CuDNN 8.0.5\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -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=1.8.0, 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: 0.9.0\nOpenCV: 4.5.5\nMMCV: 1.4.5\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.1\nMMRotate: 0.1.0+", "config": "dataset_type = 'DOTADataset'\ndata_root = '/mnt/lustre/zhouyue.vendor/split_1024_dota1_0/'\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='RResize', img_scale=(1024, 1024)),\n    dict(\n        type='RRandomFlip',\n        flip_ratio=[0.25, 0.25, 0.25],\n        direction=['horizontal', 'vertical', 'diagonal'],\n        version='le90'),\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=(1024, 1024),\n        flip=False,\n        transforms=[\n            dict(type='RResize'),\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'])\n        ])\n]\ndata = dict(\n    samples_per_gpu=2,\n    workers_per_gpu=2,\n    train=dict(\n        type='DOTADataset',\n        ann_file=\n        '/mnt/lustre/zhouyue.vendor/split_1024_dota1_0/trainval/annfiles/',\n        img_prefix=\n        '/mnt/lustre/zhouyue.vendor/split_1024_dota1_0/trainval/images/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='LoadAnnotations', with_bbox=True),\n            dict(type='RResize', img_scale=(1024, 1024)),\n            dict(\n                type='RRandomFlip',\n                flip_ratio=[0.25, 0.25, 0.25],\n                direction=['horizontal', 'vertical', 'diagonal'],\n                version='le90'),\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        version='le90'),\n    val=dict(\n        type='DOTADataset',\n        ann_file=\n        '/mnt/lustre/zhouyue.vendor/split_1024_dota1_0/trainval/annfiles/',\n        img_prefix=\n        '/mnt/lustre/zhouyue.vendor/split_1024_dota1_0/trainval/images/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(1024, 1024),\n                flip=False,\n                transforms=[\n                    dict(type='RResize'),\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'])\n                ])\n        ],\n        version='le90'),\n    test=dict(\n        type='DOTADataset',\n        ann_file='/mnt/lustre/zhouyue.vendor/split_1024_dota1_0/test/images/',\n        img_prefix='/mnt/lustre/zhouyue.vendor/split_1024_dota1_0/test/images/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(1024, 1024),\n                flip=False,\n                transforms=[\n                    dict(type='RResize'),\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'])\n                ])\n        ],\n        version='le90'))\nevaluation = dict(interval=12, metric='mAP')\noptimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)\noptimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))\nlr_config = dict(\n    policy='step',\n    warmup='linear',\n    warmup_iters=500,\n    warmup_ratio=0.3333333333333333,\n    step=[8, 11])\nrunner = dict(type='EpochBasedRunner', max_epochs=12)\ncheckpoint_config = dict(interval=12)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nangle_version = 'le90'\nmodel = dict(\n    type='OrientedRCNN',\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='BN', 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    rpn_head=dict(\n        type='OrientedRPNHead',\n        in_channels=256,\n        feat_channels=256,\n        version='le90',\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='MidpointOffsetCoder',\n            angle_range='le90',\n            target_means=[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n            target_stds=[1.0, 1.0, 1.0, 1.0, 0.5, 0.5]),\n        loss_cls=dict(\n            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n        loss_bbox=dict(\n            type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),\n    roi_head=dict(\n        type='OrientedStandardRoIHead',\n        bbox_roi_extractor=dict(\n            type='RotatedSingleRoIExtractor',\n            roi_layer=dict(\n                type='RoIAlignRotated',\n                out_size=7,\n                sample_num=2,\n                clockwise=True),\n            out_channels=256,\n            featmap_strides=[4, 8, 16, 32]),\n        bbox_head=dict(\n            type='RotatedShared2FCBBoxHead',\n            in_channels=256,\n            fc_out_channels=1024,\n            roi_feat_size=7,\n            num_classes=15,\n            bbox_coder=dict(\n                type='DeltaXYWHAOBBoxCoder',\n                angle_range='le90',\n                norm_factor=None,\n                edge_swap=True,\n                proj_xy=True,\n                target_means=(0.0, 0.0, 0.0, 0.0, 0.0),\n                target_stds=(0.1, 0.1, 0.2, 0.2, 0.1)),\n            reg_class_agnostic=True,\n            loss_cls=dict(\n                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),\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=0,\n            pos_weight=-1,\n            debug=False),\n        rpn_proposal=dict(\n            nms_pre=2000,\n            max_per_img=2000,\n            nms=dict(type='nms', iou_threshold=0.8),\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=False,\n                iou_calculator=dict(type='RBboxOverlaps2D'),\n                ignore_iof_thr=-1),\n            sampler=dict(\n                type='RRandomSampler',\n                num=512,\n                pos_fraction=0.25,\n                neg_pos_ub=-1,\n                add_gt_as_proposals=True),\n            pos_weight=-1,\n            debug=False)),\n    test_cfg=dict(\n        rpn=dict(\n            nms_pre=2000,\n            max_per_img=2000,\n            nms=dict(type='nms', iou_threshold=0.8),\n            min_bbox_size=0),\n        rcnn=dict(\n            nms_pre=2000,\n            min_bbox_size=0,\n            score_thr=0.05,\n            nms=dict(iou_thr=0.1),\n            max_per_img=2000)))\nfp16 = dict(loss_scale='dynamic')\nwork_dir = 'work_dirs/oriented_rcnn_r50_fpn_fp16_1x_dota_le90'\nauto_resume = False\ngpu_ids = range(0, 1)\n", "seed": 1002054415, "exp_name": "oriented_rcnn_r50_fpn_fp16_1x_dota_le90.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00199, "memory": 7257, "data_time": 3.52311, "loss_rpn_cls": 0.48213, "loss_rpn_bbox": 0.24753, "loss_cls": 0.62053, "acc": 88.82031, "loss_bbox": 0.08943, "loss": 1.43962, "grad_norm": Infinity, "time": 3.76809}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00233, "memory": 7257, "data_time": 0.01287, "loss_rpn_cls": 0.29759, "loss_rpn_bbox": 0.2632, "loss_cls": 0.28731, "acc": 95.13281, "loss_bbox": 0.13402, "loss": 0.98212, "grad_norm": 4.67642, "time": 0.23445}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00266, "memory": 7257, "data_time": 0.01204, "loss_rpn_cls": 0.25954, "loss_rpn_bbox": 0.27566, "loss_cls": 0.3257, "acc": 94.50195, "loss_bbox": 0.17275, "loss": 1.03365, "grad_norm": 4.31072, "time": 0.22777}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.00299, "memory": 7257, "data_time": 0.01293, "loss_rpn_cls": 0.23987, "loss_rpn_bbox": 0.25713, "loss_cls": 0.32541, "acc": 93.76758, "loss_bbox": 0.25195, "loss": 1.07437, "grad_norm": 4.29975, "time": 0.24398}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.00333, "memory": 7257, "data_time": 0.01288, "loss_rpn_cls": 0.23703, "loss_rpn_bbox": 0.26567, "loss_cls": 0.38109, "acc": 92.06055, "loss_bbox": 0.29285, "loss": 1.17664, "grad_norm": 4.74712, "time": 0.22679}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00366, "memory": 7257, "data_time": 0.01233, "loss_rpn_cls": 0.20889, "loss_rpn_bbox": 0.26987, "loss_cls": 0.31074, "acc": 93.71289, "loss_bbox": 0.24454, "loss": 1.03403, "grad_norm": 4.00968, "time": 0.24297}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00399, "memory": 7257, "data_time": 0.01289, "loss_rpn_cls": 0.18468, "loss_rpn_bbox": 0.2639, "loss_cls": 0.29306, "acc": 92.85156, "loss_bbox": 0.25408, "loss": 0.99572, "grad_norm": 3.98683, "time": 0.22627}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00433, "memory": 7257, "data_time": 0.01259, "loss_rpn_cls": 0.18752, "loss_rpn_bbox": 0.2724, "loss_cls": 0.32599, "acc": 92.97852, "loss_bbox": 0.22192, "loss": 1.00783, "grad_norm": 4.18683, "time": 0.24141}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00466, "memory": 7257, "data_time": 0.01212, "loss_rpn_cls": 0.1671, "loss_rpn_bbox": 0.25162, "loss_cls": 0.38193, "acc": 91.08203, "loss_bbox": 0.36136, "loss": 1.16201, "grad_norm": 4.78547, "time": 0.22817}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.00499, "memory": 7520, "data_time": 0.0118, "loss_rpn_cls": 0.14046, "loss_rpn_bbox": 0.21739, "loss_cls": 0.32497, "acc": 92.28711, "loss_bbox": 0.30136, "loss": 0.98417, "grad_norm": 4.18886, "time": 0.22637}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.005, "memory": 7520, "data_time": 0.01219, "loss_rpn_cls": 0.11939, "loss_rpn_bbox": 0.19204, "loss_cls": 0.31505, "acc": 91.45508, "loss_bbox": 0.33742, "loss": 0.96389, "grad_norm": 4.31438, "time": 0.24235}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.005, "memory": 7520, "data_time": 0.01095, "loss_rpn_cls": 0.13842, "loss_rpn_bbox": 0.23923, "loss_cls": 0.43101, "acc": 89.18555, "loss_bbox": 0.4026, "loss": 1.21126, "grad_norm": 5.53567, "time": 0.2511}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.005, "memory": 7520, "data_time": 0.01242, "loss_rpn_cls": 0.12089, "loss_rpn_bbox": 0.2039, "loss_cls": 0.34912, "acc": 90.89648, "loss_bbox": 0.3361, "loss": 1.01001, "grad_norm": 4.37282, "time": 0.23674}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.005, "memory": 7520, "data_time": 0.01163, "loss_rpn_cls": 0.11991, "loss_rpn_bbox": 0.2066, "loss_cls": 0.31064, "acc": 91.67773, "loss_bbox": 0.30093, "loss": 0.93809, "grad_norm": 4.33684, "time": 0.23275}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.005, "memory": 7520, "data_time": 0.01368, "loss_rpn_cls": 0.10633, "loss_rpn_bbox": 0.21309, "loss_cls": 0.33459, "acc": 90.70312, "loss_bbox": 0.38066, "loss": 1.03467, "grad_norm": 4.65049, "time": 0.24503}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.005, "memory": 7520, "data_time": 0.01233, "loss_rpn_cls": 0.11196, "loss_rpn_bbox": 0.1813, "loss_cls": 0.29673, "acc": 92.16211, "loss_bbox": 0.27611, "loss": 0.86609, "grad_norm": 3.98199, "time": 0.23298}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.005, "memory": 7520, "data_time": 0.01306, "loss_rpn_cls": 0.12446, "loss_rpn_bbox": 0.2132, "loss_cls": 0.31386, "acc": 90.59375, "loss_bbox": 0.35469, "loss": 1.0062, "grad_norm": 4.2759, "time": 0.24951}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.005, "memory": 7520, "data_time": 0.01243, "loss_rpn_cls": 0.09506, "loss_rpn_bbox": 0.17461, "loss_cls": 0.30108, "acc": 91.40625, "loss_bbox": 0.29542, "loss": 0.86616, "grad_norm": 3.99883, "time": 0.24123}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.005, "memory": 7520, "data_time": 0.01288, "loss_rpn_cls": 0.1057, "loss_rpn_bbox": 0.16374, "loss_cls": 0.31413, "acc": 90.18359, "loss_bbox": 0.35714, "loss": 0.9407, "grad_norm": 4.46256, "time": 0.22617}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.005, "memory": 7520, "data_time": 0.011, "loss_rpn_cls": 0.09867, "loss_rpn_bbox": 0.14772, "loss_cls": 0.29302, "acc": 91.64258, "loss_bbox": 0.29304, "loss": 0.83244, "grad_norm": 4.19537, "time": 0.23417}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.005, "memory": 7520, "data_time": 0.01123, "loss_rpn_cls": 0.09724, "loss_rpn_bbox": 0.16097, "loss_cls": 0.31656, "acc": 90.50781, "loss_bbox": 0.35806, "loss": 0.93282, "grad_norm": 4.22808, "time": 0.22181}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.005, "memory": 7520, "data_time": 0.01274, "loss_rpn_cls": 0.08645, "loss_rpn_bbox": 0.18205, "loss_cls": 0.3042, "acc": 90.74609, "loss_bbox": 0.37264, "loss": 0.94534, "grad_norm": 4.25118, "time": 0.23607}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.005, "memory": 7520, "data_time": 0.012, "loss_rpn_cls": 0.1235, "loss_rpn_bbox": 0.17159, "loss_cls": 0.31995, "acc": 90.28125, "loss_bbox": 0.35672, "loss": 0.97176, "grad_norm": 4.41918, "time": 0.24102}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.005, "memory": 7520, "data_time": 0.01152, "loss_rpn_cls": 0.10133, "loss_rpn_bbox": 0.18251, "loss_cls": 0.25378, "acc": 92.07227, "loss_bbox": 0.30979, "loss": 0.8474, "grad_norm": 4.1917, "time": 0.22896}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.005, "memory": 7520, "data_time": 0.01247, "loss_rpn_cls": 0.09632, "loss_rpn_bbox": 0.15836, "loss_cls": 0.28556, "acc": 91.57227, "loss_bbox": 0.29339, "loss": 0.83363, "grad_norm": 4.02405, "time": 0.24416}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.005, "memory": 7520, "data_time": 0.01247, "loss_rpn_cls": 0.11337, "loss_rpn_bbox": 0.16718, "loss_cls": 0.26807, "acc": 92.16797, "loss_bbox": 0.28323, "loss": 0.83185, "grad_norm": 4.01951, "time": 0.23243}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.005, "memory": 7520, "data_time": 0.01174, "loss_rpn_cls": 0.09118, "loss_rpn_bbox": 0.18574, "loss_cls": 0.26575, "acc": 91.44141, "loss_bbox": 0.29565, "loss": 0.83833, "grad_norm": 3.70898, "time": 0.24188}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.005, "memory": 7520, "data_time": 0.01378, "loss_rpn_cls": 0.08794, "loss_rpn_bbox": 0.14171, "loss_cls": 0.29503, "acc": 91.18164, "loss_bbox": 0.31035, "loss": 0.83503, "grad_norm": 3.98614, "time": 0.24448}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.005, "memory": 7520, "data_time": 0.01218, "loss_rpn_cls": 0.10144, "loss_rpn_bbox": 0.21503, "loss_cls": 0.29763, "acc": 90.45703, "loss_bbox": 0.31763, "loss": 0.93172, "grad_norm": 4.0424, "time": 0.24485}
{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.005, "memory": 7520, "data_time": 0.01171, "loss_rpn_cls": 0.06323, "loss_rpn_bbox": 0.13684, "loss_cls": 0.26808, "acc": 91.60156, "loss_bbox": 0.31904, "loss": 0.78719, "grad_norm": 3.68738, "time": 0.22942}
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