recnext_m4_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_m4',\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_m4_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_m4_fpn_1x_coco'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 0, "exp_name": "mask_rcnn_recnext_m4_fpn_1x_coco.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 2e-05, "memory": 7988, "data_time": 0.09877, "loss_rpn_cls": 0.59666, "loss_rpn_bbox": 0.29868, "loss_cls": 1.19372, "acc": 75.47974, "loss_bbox": 0.03968, "loss_mask": 3.66495, "loss": 5.79369, "time": 0.8497}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 4e-05, "memory": 8154, "data_time": 0.02167, "loss_rpn_cls": 0.31013, "loss_rpn_bbox": 0.1765, "loss_cls": 0.2636, "acc": 97.01685, "loss_bbox": 0.07637, "loss_mask": 1.14923, "loss": 1.97583, "time": 0.70641}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 6e-05, "memory": 8154, "data_time": 0.02592, "loss_rpn_cls": 0.23418, "loss_rpn_bbox": 0.13856, "loss_cls": 0.37142, "acc": 94.36987, "loss_bbox": 0.1882, "loss_mask": 0.7508, "loss": 1.68316, "time": 0.78518}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 8e-05, "memory": 8154, "data_time": 0.02429, "loss_rpn_cls": 0.18882, "loss_rpn_bbox": 0.12816, "loss_cls": 0.38889, "acc": 93.67871, "loss_bbox": 0.21628, "loss_mask": 0.71208, "loss": 1.63422, "time": 0.79774}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.0001, "memory": 8154, "data_time": 0.02259, "loss_rpn_cls": 0.15163, "loss_rpn_bbox": 0.11837, "loss_cls": 0.40619, "acc": 93.0564, "loss_bbox": 0.24597, "loss_mask": 0.68195, "loss": 1.60411, "time": 0.78576}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00012, "memory": 8298, "data_time": 0.03172, "loss_rpn_cls": 0.12137, "loss_rpn_bbox": 0.10433, "loss_cls": 0.43397, "acc": 92.18652, "loss_bbox": 0.28277, "loss_mask": 0.65107, "loss": 1.59351, "time": 0.84309}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00014, "memory": 8298, "data_time": 0.02468, "loss_rpn_cls": 0.10513, "loss_rpn_bbox": 0.09646, "loss_cls": 0.43823, "acc": 91.73828, "loss_bbox": 0.30341, "loss_mask": 0.61541, "loss": 1.55863, "time": 0.82129}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00016, "memory": 8374, "data_time": 0.029, "loss_rpn_cls": 0.10287, "loss_rpn_bbox": 0.09574, "loss_cls": 0.46744, "acc": 90.64673, "loss_bbox": 0.34274, "loss_mask": 0.57718, "loss": 1.58597, "time": 0.85122}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00018, "memory": 8374, "data_time": 0.02468, "loss_rpn_cls": 0.09158, "loss_rpn_bbox": 0.0901, "loss_cls": 0.4586, "acc": 90.4126, "loss_bbox": 0.34892, "loss_mask": 0.54356, "loss": 1.53277, "time": 0.89019}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0002, "memory": 8443, "data_time": 0.02011, "loss_rpn_cls": 0.08564, "loss_rpn_bbox": 0.08604, "loss_cls": 0.45295, "acc": 90.31763, "loss_bbox": 0.35886, "loss_mask": 0.51408, "loss": 1.49757, "time": 0.8563}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0002, "memory": 8443, "data_time": 0.0251, "loss_rpn_cls": 0.08278, "loss_rpn_bbox": 0.08115, "loss_cls": 0.44764, "acc": 90.02051, "loss_bbox": 0.36111, "loss_mask": 0.47296, "loss": 1.44565, "time": 0.85487}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0002, "memory": 8452, "data_time": 0.02089, "loss_rpn_cls": 0.08291, "loss_rpn_bbox": 0.0823, "loss_cls": 0.44177, "acc": 89.69727, "loss_bbox": 0.36445, "loss_mask": 0.45879, "loss": 1.43023, "time": 0.92943}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0002, "memory": 8452, "data_time": 0.02975, "loss_rpn_cls": 0.07877, "loss_rpn_bbox": 0.08472, "loss_cls": 0.43076, "acc": 89.71973, "loss_bbox": 0.36341, "loss_mask": 0.43627, "loss": 1.39392, "time": 0.90327}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0002, "memory": 8452, "data_time": 0.02355, "loss_rpn_cls": 0.07683, "loss_rpn_bbox": 0.08238, "loss_cls": 0.41705, "acc": 89.48047, "loss_bbox": 0.37653, "loss_mask": 0.42069, "loss": 1.37348, "time": 0.88347}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0002, "memory": 8452, "data_time": 0.0207, "loss_rpn_cls": 0.07312, "loss_rpn_bbox": 0.08093, "loss_cls": 0.4059, "acc": 89.63281, "loss_bbox": 0.3617, "loss_mask": 0.41225, "loss": 1.3339, "time": 0.89066}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0002, "memory": 8462, "data_time": 0.02811, "loss_rpn_cls": 0.06874, "loss_rpn_bbox": 0.08077, "loss_cls": 0.41245, "acc": 89.40454, "loss_bbox": 0.37178, "loss_mask": 0.40557, "loss": 1.3393, "time": 0.87534}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0002, "memory": 8462, "data_time": 0.02881, "loss_rpn_cls": 0.06964, "loss_rpn_bbox": 0.0768, "loss_cls": 0.38286, "acc": 89.79492, "loss_bbox": 0.35383, "loss_mask": 0.39498, "loss": 1.27811, "time": 0.87489}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0002, "memory": 8462, "data_time": 0.02424, "loss_rpn_cls": 0.06909, "loss_rpn_bbox": 0.07608, "loss_cls": 0.39148, "acc": 89.6604, "loss_bbox": 0.35772, "loss_mask": 0.3964, "loss": 1.29077, "time": 0.88238}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0002, "memory": 8462, "data_time": 0.02455, "loss_rpn_cls": 0.06635, "loss_rpn_bbox": 0.07518, "loss_cls": 0.38462, "acc": 89.88818, "loss_bbox": 0.34826, "loss_mask": 0.38498, "loss": 1.2594, "time": 0.87231}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0002, "memory": 8462, "data_time": 0.0274, "loss_rpn_cls": 0.06893, "loss_rpn_bbox": 0.0738, "loss_cls": 0.36704, "acc": 90.03564, "loss_bbox": 0.34023, "loss_mask": 0.37656, "loss": 1.22656, "time": 0.87442}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0002, "memory": 8462, "data_time": 0.02497, "loss_rpn_cls": 0.06391, "loss_rpn_bbox": 0.07293, "loss_cls": 0.36893, "acc": 89.979, "loss_bbox": 0.34337, "loss_mask": 0.3755, "loss": 1.22464, "time": 0.90916}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.0002, "memory": 8462, "data_time": 0.02644, "loss_rpn_cls": 0.06067, "loss_rpn_bbox": 0.0696, "loss_cls": 0.35798, "acc": 90.10132, "loss_bbox": 0.33454, "loss_mask": 0.36701, "loss": 1.1898, "time": 0.8705}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.0002, "memory": 8462, "data_time": 0.0269, "loss_rpn_cls": 0.06299, "loss_rpn_bbox": 0.07713, "loss_cls": 0.37286, "acc": 89.62378, "loss_bbox": 0.35562, "loss_mask": 0.36806, "loss": 1.23667, "time": 0.90249}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.0002, "memory": 8462, "data_time": 0.03125, "loss_rpn_cls": 0.06501, "loss_rpn_bbox": 0.07727, "loss_cls": 0.36193, "acc": 89.67578, "loss_bbox": 0.35153, "loss_mask": 0.365, "loss": 1.22074, "time": 0.89248}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.0002, "memory": 8462, "data_time": 0.02769, "loss_rpn_cls": 0.05876, "loss_rpn_bbox": 0.06736, "loss_cls": 0.354, "acc": 90.27026, "loss_bbox": 0.33147, "loss_mask": 0.35883, "loss": 1.17041, "time": 0.97648}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.0002, "memory": 8462, "data_time": 0.02698, "loss_rpn_cls": 0.0583, "loss_rpn_bbox": 0.06929, "loss_cls": 0.34741, "acc": 90.25195, "loss_bbox": 0.33183, "loss_mask": 0.35527, "loss": 1.16212, "time": 0.97812}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.0002, "memory": 8462, "data_time": 0.02578, "loss_rpn_cls": 0.06395, "loss_rpn_bbox": 0.07498, "loss_cls": 0.35629, "acc": 89.86426, "loss_bbox": 0.3432, "loss_mask": 0.35108, "loss": 1.1895, "time": 0.95223}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.0002, "memory": 8462, "data_time": 0.02727, "loss_rpn_cls": 0.06083, "loss_rpn_bbox": 0.06836, "loss_cls": 0.33051, "acc": 90.63623, "loss_bbox": 0.31912, "loss_mask": 0.33931, "loss": 1.11812, "time": 0.88282}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.0002, "memory": 8462, "data_time": 0.02358, "loss_rpn_cls": 0.05746, "loss_rpn_bbox": 0.06944, "loss_cls": 0.34161, "acc": 90.15674, "loss_bbox": 0.33538, "loss_mask": 0.33942, "loss": 1.14331, "time": 0.91616}
{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.0002, "memory": 8462, "data_time": 0.03139, "loss_rpn_cls": 0.06441, "loss_rpn_bbox": 0.07546, "loss_cls": 0.33291, "acc": 90.35645, "loss_bbox": 0.33211, "loss_mask": 0.34784, "loss": 1.15272, "time": 0.87709}
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