recnext_m3_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_m3',\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_m3_distill_300e.pth')),\n    neck=dict(\n        type='FPN',\n        in_channels=[64, 128, 256, 512],\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_m3_fpn_1x_coco'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 0, "exp_name": "mask_rcnn_recnext_m3_fpn_1x_coco.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 2e-05, "memory": 5515, "data_time": 0.09472, "loss_rpn_cls": 0.56654, "loss_rpn_bbox": 0.25733, "loss_cls": 3.43003, "acc": 54.8645, "loss_bbox": 0.03387, "loss_mask": 3.33527, "loss": 7.62303, "time": 0.79276}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 4e-05, "memory": 5515, "data_time": 0.02214, "loss_rpn_cls": 0.28923, "loss_rpn_bbox": 0.19049, "loss_cls": 0.31238, "acc": 97.12476, "loss_bbox": 0.06647, "loss_mask": 1.24, "loss": 2.09857, "time": 0.6206}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 6e-05, "memory": 5515, "data_time": 0.02436, "loss_rpn_cls": 0.219, "loss_rpn_bbox": 0.1483, "loss_cls": 0.37297, "acc": 94.64014, "loss_bbox": 0.17601, "loss_mask": 0.77275, "loss": 1.68903, "time": 0.68493}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 8e-05, "memory": 5621, "data_time": 0.02508, "loss_rpn_cls": 0.17856, "loss_rpn_bbox": 0.13182, "loss_cls": 0.39671, "acc": 93.72314, "loss_bbox": 0.21497, "loss_mask": 0.71021, "loss": 1.63227, "time": 0.73228}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.0001, "memory": 5621, "data_time": 0.02611, "loss_rpn_cls": 0.15149, "loss_rpn_bbox": 0.12061, "loss_cls": 0.41551, "acc": 93.05396, "loss_bbox": 0.24658, "loss_mask": 0.67376, "loss": 1.60795, "time": 0.75488}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00012, "memory": 5670, "data_time": 0.03293, "loss_rpn_cls": 0.12077, "loss_rpn_bbox": 0.10665, "loss_cls": 0.44819, "acc": 91.99731, "loss_bbox": 0.29099, "loss_mask": 0.64106, "loss": 1.60766, "time": 0.77408}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00014, "memory": 5724, "data_time": 0.02697, "loss_rpn_cls": 0.10565, "loss_rpn_bbox": 0.09861, "loss_cls": 0.44779, "acc": 91.61206, "loss_bbox": 0.31092, "loss_mask": 0.60341, "loss": 1.56639, "time": 0.77123}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00016, "memory": 5862, "data_time": 0.03196, "loss_rpn_cls": 0.10359, "loss_rpn_bbox": 0.09732, "loss_cls": 0.47608, "acc": 90.55908, "loss_bbox": 0.34608, "loss_mask": 0.56826, "loss": 1.59133, "time": 0.8031}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00018, "memory": 5862, "data_time": 0.02776, "loss_rpn_cls": 0.09422, "loss_rpn_bbox": 0.09171, "loss_cls": 0.45393, "acc": 90.49609, "loss_bbox": 0.34707, "loss_mask": 0.52863, "loss": 1.51557, "time": 0.79301}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0002, "memory": 5862, "data_time": 0.02223, "loss_rpn_cls": 0.08711, "loss_rpn_bbox": 0.08684, "loss_cls": 0.46147, "acc": 90.10425, "loss_bbox": 0.36289, "loss_mask": 0.4955, "loss": 1.49381, "time": 0.78622}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0002, "memory": 5869, "data_time": 0.02188, "loss_rpn_cls": 0.08596, "loss_rpn_bbox": 0.08203, "loss_cls": 0.44062, "acc": 90.23657, "loss_bbox": 0.35638, "loss_mask": 0.46365, "loss": 1.42865, "time": 0.80946}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0002, "memory": 5940, "data_time": 0.0191, "loss_rpn_cls": 0.08345, "loss_rpn_bbox": 0.08292, "loss_cls": 0.43529, "acc": 89.87744, "loss_bbox": 0.35656, "loss_mask": 0.44554, "loss": 1.40375, "time": 0.81665}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0002, "memory": 5940, "data_time": 0.02833, "loss_rpn_cls": 0.07955, "loss_rpn_bbox": 0.08521, "loss_cls": 0.43451, "acc": 89.66968, "loss_bbox": 0.369, "loss_mask": 0.43365, "loss": 1.40192, "time": 0.82939}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0002, "memory": 5940, "data_time": 0.02234, "loss_rpn_cls": 0.08056, "loss_rpn_bbox": 0.08339, "loss_cls": 0.41666, "acc": 89.60059, "loss_bbox": 0.37013, "loss_mask": 0.41988, "loss": 1.37062, "time": 0.83885}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0002, "memory": 5940, "data_time": 0.02166, "loss_rpn_cls": 0.07727, "loss_rpn_bbox": 0.08101, "loss_cls": 0.40487, "acc": 89.7334, "loss_bbox": 0.36064, "loss_mask": 0.41567, "loss": 1.33945, "time": 0.81558}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0002, "memory": 5940, "data_time": 0.02556, "loss_rpn_cls": 0.07275, "loss_rpn_bbox": 0.08089, "loss_cls": 0.41122, "acc": 89.55859, "loss_bbox": 0.37126, "loss_mask": 0.40713, "loss": 1.34325, "time": 0.82361}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0002, "memory": 5940, "data_time": 0.02715, "loss_rpn_cls": 0.07192, "loss_rpn_bbox": 0.07834, "loss_cls": 0.38255, "acc": 89.93652, "loss_bbox": 0.34825, "loss_mask": 0.39553, "loss": 1.27659, "time": 0.82197}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0002, "memory": 5956, "data_time": 0.02713, "loss_rpn_cls": 0.07012, "loss_rpn_bbox": 0.07705, "loss_cls": 0.38627, "acc": 89.76025, "loss_bbox": 0.3558, "loss_mask": 0.40331, "loss": 1.29255, "time": 0.83084}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0002, "memory": 5956, "data_time": 0.02321, "loss_rpn_cls": 0.07132, "loss_rpn_bbox": 0.0762, "loss_cls": 0.3933, "acc": 89.72998, "loss_bbox": 0.34874, "loss_mask": 0.38799, "loss": 1.27755, "time": 0.82072}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0002, "memory": 5956, "data_time": 0.02681, "loss_rpn_cls": 0.07241, "loss_rpn_bbox": 0.07528, "loss_cls": 0.36562, "acc": 90.17163, "loss_bbox": 0.33565, "loss_mask": 0.38327, "loss": 1.23223, "time": 0.79159}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0002, "memory": 5956, "data_time": 0.02547, "loss_rpn_cls": 0.06699, "loss_rpn_bbox": 0.07345, "loss_cls": 0.37825, "acc": 89.79199, "loss_bbox": 0.34676, "loss_mask": 0.38226, "loss": 1.24771, "time": 0.84703}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.0002, "memory": 5956, "data_time": 0.02729, "loss_rpn_cls": 0.06185, "loss_rpn_bbox": 0.07092, "loss_cls": 0.36743, "acc": 90.06812, "loss_bbox": 0.33887, "loss_mask": 0.37384, "loss": 1.21291, "time": 0.8346}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.0002, "memory": 5956, "data_time": 0.02593, "loss_rpn_cls": 0.06793, "loss_rpn_bbox": 0.07827, "loss_cls": 0.38178, "acc": 89.43359, "loss_bbox": 0.35745, "loss_mask": 0.37692, "loss": 1.26235, "time": 0.84853}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.0002, "memory": 5957, "data_time": 0.02954, "loss_rpn_cls": 0.06976, "loss_rpn_bbox": 0.07825, "loss_cls": 0.36772, "acc": 89.70215, "loss_bbox": 0.35729, "loss_mask": 0.37122, "loss": 1.24425, "time": 0.82717}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.0002, "memory": 5957, "data_time": 0.02623, "loss_rpn_cls": 0.06029, "loss_rpn_bbox": 0.06852, "loss_cls": 0.35934, "acc": 90.229, "loss_bbox": 0.33547, "loss_mask": 0.36935, "loss": 1.19297, "time": 0.79405}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.0002, "memory": 5957, "data_time": 0.02817, "loss_rpn_cls": 0.06081, "loss_rpn_bbox": 0.07125, "loss_cls": 0.34972, "acc": 90.30811, "loss_bbox": 0.33491, "loss_mask": 0.35994, "loss": 1.17663, "time": 0.84578}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.0002, "memory": 5957, "data_time": 0.02425, "loss_rpn_cls": 0.0675, "loss_rpn_bbox": 0.07592, "loss_cls": 0.35911, "acc": 89.87671, "loss_bbox": 0.34075, "loss_mask": 0.35904, "loss": 1.20231, "time": 0.81697}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.0002, "memory": 5957, "data_time": 0.02436, "loss_rpn_cls": 0.06418, "loss_rpn_bbox": 0.07024, "loss_cls": 0.34002, "acc": 90.57935, "loss_bbox": 0.32281, "loss_mask": 0.34944, "loss": 1.14668, "time": 0.81547}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.0002, "memory": 5957, "data_time": 0.02221, "loss_rpn_cls": 0.06303, "loss_rpn_bbox": 0.07142, "loss_cls": 0.34979, "acc": 90.09106, "loss_bbox": 0.34518, "loss_mask": 0.34537, "loss": 1.17477, "time": 0.8289}
{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.0002, "memory": 5957, "data_time": 0.02922, "loss_rpn_cls": 0.06915, "loss_rpn_bbox": 0.07768, "loss_cls": 0.34218, "acc": 90.19458, "loss_bbox": 0.33426, "loss_mask": 0.35484, "loss": 1.17812, "time": 0.84382}
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