recnext_m5_coco.json

{"env_info": "sys.platform: linux\nPython: 3.8.19 (default, Mar 20 2024, 19:58:24) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA GeForce RTX 4090 D\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 12.1, V12.1.105\nGCC: gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nPyTorch: 2.1.2\nPyTorch compiling details: PyTorch built with:\n  - GCC 9.3\n  - C++ Version: 201703\n  - Intel(R) oneAPI Math Kernel Library Version 2023.1-Product Build 20230303 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)\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 12.1\n  - NVCC architecture flags: -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_90,code=sm_90\n  - CuDNN 8.9.2\n  - Magma 2.6.1\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=8.9.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.2, 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, USE_ROCM=OFF, \n\nTorchVision: 0.16.2\nOpenCV: 4.10.0\nMMCV: 1.7.2\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 12.1\nMMDetection: 2.28.2+", "config": "model = dict(\n    type='MaskRCNN',\n    pretrained='torchvision://resnet50',\n    backbone=dict(\n        type='recnext_m5',\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(\n            type='Pretrained',\n            checkpoint='pretrain/recnext_m5_distill_300e.pth')),\n    neck=dict(\n        type='FPN',\n        in_channels=[80, 160, 320, 640],\n        out_channels=256,\n        num_outs=5),\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='Shared2FCBBoxHead',\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=dict(\n            type='SingleRoIExtractor',\n            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),\n            out_channels=256,\n            featmap_strides=[4, 8, 16, 32]),\n        mask_head=dict(\n            type='FCNMaskHead',\n            num_convs=4,\n            in_channels=256,\n            conv_out_channels=256,\n            num_classes=80,\n            loss_mask=dict(\n                type='CrossEntropyLoss', use_mask=True, 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=-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, with_mask=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', 'gt_masks'])\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, with_mask=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(\n                type='Collect',\n                keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])\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(metric=['bbox', 'segm'])\noptimizer = dict(type='AdamW', lr=0.0002, weight_decay=0.05)\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(\n    policy='step',\n    warmup='linear',\n    warmup_iters=500,\n    warmup_ratio=1e-06,\n    step=[8, 11])\nrunner = dict(type='EpochBasedRunner', max_epochs=12)\ncheckpoint_config = dict(interval=1)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [dict(type='NumClassCheckHook')]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nwork_dir = './work_dirs/mask_rcnn_recnext_m5_fpn_1x_coco'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 0, "exp_name": "mask_rcnn_recnext_m5_fpn_1x_coco.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 2e-05, "memory": 12162, "data_time": 0.10124, "loss_rpn_cls": 0.57897, "loss_rpn_bbox": 0.29447, "loss_cls": 2.44604, "acc": 64.22388, "loss_bbox": 0.01761, "loss_mask": 4.91424, "loss": 8.25132, "time": 0.92458}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 4e-05, "memory": 12163, "data_time": 0.02269, "loss_rpn_cls": 0.28703, "loss_rpn_bbox": 0.18774, "loss_cls": 0.31387, "acc": 97.12085, "loss_bbox": 0.06702, "loss_mask": 1.42078, "loss": 2.27644, "time": 0.76004}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 6e-05, "memory": 12163, "data_time": 0.02567, "loss_rpn_cls": 0.22962, "loss_rpn_bbox": 0.14398, "loss_cls": 0.33011, "acc": 95.22852, "loss_bbox": 0.14907, "loss_mask": 0.80762, "loss": 1.6604, "time": 0.8231}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 8e-05, "memory": 12385, "data_time": 0.02589, "loss_rpn_cls": 0.18987, "loss_rpn_bbox": 0.13163, "loss_cls": 0.39689, "acc": 93.6875, "loss_bbox": 0.21744, "loss_mask": 0.72614, "loss": 1.66196, "time": 0.89824}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.0001, "memory": 12385, "data_time": 0.02519, "loss_rpn_cls": 0.15527, "loss_rpn_bbox": 0.1194, "loss_cls": 0.41932, "acc": 92.98706, "loss_bbox": 0.24804, "loss_mask": 0.68844, "loss": 1.63048, "time": 0.89776}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00012, "memory": 12385, "data_time": 0.03471, "loss_rpn_cls": 0.1227, "loss_rpn_bbox": 0.10586, "loss_cls": 0.44483, "acc": 92.12915, "loss_bbox": 0.28665, "loss_mask": 0.65717, "loss": 1.61721, "time": 0.92565}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00014, "memory": 12385, "data_time": 0.02526, "loss_rpn_cls": 0.10488, "loss_rpn_bbox": 0.09935, "loss_cls": 0.43134, "acc": 91.91187, "loss_bbox": 0.29848, "loss_mask": 0.62319, "loss": 1.55725, "time": 0.88532}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00016, "memory": 12529, "data_time": 0.0292, "loss_rpn_cls": 0.10313, "loss_rpn_bbox": 0.09747, "loss_cls": 0.45338, "acc": 91.02222, "loss_bbox": 0.32965, "loss_mask": 0.59179, "loss": 1.57542, "time": 0.94602}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00018, "memory": 12529, "data_time": 0.02445, "loss_rpn_cls": 0.09184, "loss_rpn_bbox": 0.09088, "loss_cls": 0.43942, "acc": 90.86279, "loss_bbox": 0.33565, "loss_mask": 0.55791, "loss": 1.5157, "time": 0.9278}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0002, "memory": 12578, "data_time": 0.02051, "loss_rpn_cls": 0.08447, "loss_rpn_bbox": 0.08646, "loss_cls": 0.44453, "acc": 90.48901, "loss_bbox": 0.35417, "loss_mask": 0.5292, "loss": 1.49883, "time": 0.94028}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0002, "memory": 12578, "data_time": 0.02376, "loss_rpn_cls": 0.08305, "loss_rpn_bbox": 0.08177, "loss_cls": 0.4422, "acc": 90.17944, "loss_bbox": 0.36451, "loss_mask": 0.48558, "loss": 1.45712, "time": 0.97768}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0002, "memory": 12607, "data_time": 0.01941, "loss_rpn_cls": 0.08311, "loss_rpn_bbox": 0.08325, "loss_cls": 0.43304, "acc": 89.95972, "loss_bbox": 0.36422, "loss_mask": 0.46565, "loss": 1.42927, "time": 0.97188}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0002, "memory": 12638, "data_time": 0.02978, "loss_rpn_cls": 0.07759, "loss_rpn_bbox": 0.08572, "loss_cls": 0.42348, "acc": 89.80713, "loss_bbox": 0.37104, "loss_mask": 0.44599, "loss": 1.40383, "time": 0.97074}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0002, "memory": 12638, "data_time": 0.02518, "loss_rpn_cls": 0.07676, "loss_rpn_bbox": 0.08301, "loss_cls": 0.41757, "acc": 89.50806, "loss_bbox": 0.37909, "loss_mask": 0.42959, "loss": 1.38602, "time": 0.95867}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0002, "memory": 12638, "data_time": 0.02265, "loss_rpn_cls": 0.07527, "loss_rpn_bbox": 0.08091, "loss_cls": 0.40125, "acc": 89.79858, "loss_bbox": 0.36332, "loss_mask": 0.4197, "loss": 1.34045, "time": 0.96547}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0002, "memory": 12650, "data_time": 0.02834, "loss_rpn_cls": 0.06983, "loss_rpn_bbox": 0.08026, "loss_cls": 0.41159, "acc": 89.37476, "loss_bbox": 0.37519, "loss_mask": 0.41078, "loss": 1.34765, "time": 0.99249}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0002, "memory": 12650, "data_time": 0.02744, "loss_rpn_cls": 0.06726, "loss_rpn_bbox": 0.07761, "loss_cls": 0.37995, "acc": 89.79419, "loss_bbox": 0.35797, "loss_mask": 0.39951, "loss": 1.2823, "time": 0.97778}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0002, "memory": 12650, "data_time": 0.02657, "loss_rpn_cls": 0.0672, "loss_rpn_bbox": 0.07625, "loss_cls": 0.38689, "acc": 89.57227, "loss_bbox": 0.36795, "loss_mask": 0.39877, "loss": 1.29706, "time": 0.98183}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0002, "memory": 12650, "data_time": 0.02311, "loss_rpn_cls": 0.06711, "loss_rpn_bbox": 0.07549, "loss_cls": 0.38648, "acc": 89.74829, "loss_bbox": 0.35581, "loss_mask": 0.3858, "loss": 1.2707, "time": 0.97188}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0002, "memory": 12674, "data_time": 0.02746, "loss_rpn_cls": 0.06845, "loss_rpn_bbox": 0.07472, "loss_cls": 0.36588, "acc": 90.01465, "loss_bbox": 0.34573, "loss_mask": 0.37982, "loss": 1.2346, "time": 0.98772}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0002, "memory": 12674, "data_time": 0.02561, "loss_rpn_cls": 0.06298, "loss_rpn_bbox": 0.07263, "loss_cls": 0.37298, "acc": 89.70996, "loss_bbox": 0.35303, "loss_mask": 0.38013, "loss": 1.24175, "time": 0.97374}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.0002, "memory": 12674, "data_time": 0.02983, "loss_rpn_cls": 0.05739, "loss_rpn_bbox": 0.07047, "loss_cls": 0.36001, "acc": 90.09863, "loss_bbox": 0.34073, "loss_mask": 0.37596, "loss": 1.20457, "time": 0.96129}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.0002, "memory": 12674, "data_time": 0.02903, "loss_rpn_cls": 0.06295, "loss_rpn_bbox": 0.07717, "loss_cls": 0.36869, "acc": 89.53223, "loss_bbox": 0.36114, "loss_mask": 0.37077, "loss": 1.24072, "time": 0.94649}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.0002, "memory": 12674, "data_time": 0.03182, "loss_rpn_cls": 0.0637, "loss_rpn_bbox": 0.07799, "loss_cls": 0.35399, "acc": 89.78296, "loss_bbox": 0.3577, "loss_mask": 0.36653, "loss": 1.21991, "time": 1.06328}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.0002, "memory": 12674, "data_time": 0.02828, "loss_rpn_cls": 0.05807, "loss_rpn_bbox": 0.06787, "loss_cls": 0.34622, "acc": 90.38989, "loss_bbox": 0.33206, "loss_mask": 0.36218, "loss": 1.1664, "time": 0.96566}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.0002, "memory": 12674, "data_time": 0.02429, "loss_rpn_cls": 0.05814, "loss_rpn_bbox": 0.06962, "loss_cls": 0.34804, "acc": 90.1394, "loss_bbox": 0.33864, "loss_mask": 0.3567, "loss": 1.17112, "time": 0.99535}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.0002, "memory": 12674, "data_time": 0.02394, "loss_rpn_cls": 0.0665, "loss_rpn_bbox": 0.07562, "loss_cls": 0.35131, "acc": 89.86963, "loss_bbox": 0.3435, "loss_mask": 0.35274, "loss": 1.18967, "time": 0.98932}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.0002, "memory": 12674, "data_time": 0.0244, "loss_rpn_cls": 0.05906, "loss_rpn_bbox": 0.0691, "loss_cls": 0.32348, "acc": 90.71362, "loss_bbox": 0.31946, "loss_mask": 0.34349, "loss": 1.11459, "time": 0.97381}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.0002, "memory": 12674, "data_time": 0.02341, "loss_rpn_cls": 0.05796, "loss_rpn_bbox": 0.06964, "loss_cls": 0.34236, "acc": 89.99854, "loss_bbox": 0.34552, "loss_mask": 0.34113, "loss": 1.15661, "time": 0.97425}
{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.0002, "memory": 12674, "data_time": 0.03132, "loss_rpn_cls": 0.0628, "loss_rpn_bbox": 0.07617, "loss_cls": 0.33131, "acc": 90.26465, "loss_bbox": 0.33496, "loss_mask": 0.34677, "loss": 1.15201, "time": 0.98079}
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