repvit_m1_1_coco.json

{"env_info": "sys.platform: linux\nPython: 3.8.16 (default, Mar  2 2023, 03:21:46) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA GeForce RTX 4090\nCUDA_HOME: /home/inspur/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.99\nGCC: gcc (Ubuntu 11.3.0-1ubuntu1~22.04.1) 11.3.0\nPyTorch: 2.0.1+cu117\nPyTorch compiling details: PyTorch built with:\n  - GCC 9.3\n  - C++ Version: 201703\n  - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)\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: AVX2\n  - CUDA Runtime 11.7\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_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 8.9.2  (built against CUDA 12.1)\n    - Built with CuDNN 8.5\n  - Magma 2.6.1\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -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 -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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 -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=2.0.1, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.15.2+cu117\nOpenCV: 4.7.0\nMMCV: 1.7.1\nMMCV Compiler: GCC 11.3\nMMCV CUDA Compiler: 11.7\nMMDetection: 2.28.2+25971f7", "config": "model = dict(\n    type='MaskRCNN',\n    pretrained='torchvision://resnet50',\n    backbone=dict(\n        type='repvit_m1_1',\n        depth=50,\n        num_stages=4,\n        out_indices=[2, 6, 20, 24],\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/repvit_m1_1_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_repvit_m1_1_fpn_1x_coco'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 0, "exp_name": "mask_rcnn_repvit_m1_1_fpn_1x_coco.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 2e-05, "memory": 5484, "data_time": 0.11493, "loss_rpn_cls": 0.53125, "loss_rpn_bbox": 0.17528, "loss_cls": 2.15711, "acc": 67.11523, "loss_bbox": 0.07881, "loss_mask": 1.82539, "loss": 4.76784, "time": 0.45501}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 4e-05, "memory": 5685, "data_time": 0.03757, "loss_rpn_cls": 0.2529, "loss_rpn_bbox": 0.1119, "loss_cls": 0.42318, "acc": 94.50195, "loss_bbox": 0.18693, "loss_mask": 0.78322, "loss": 1.75813, "time": 0.25931}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 6e-05, "memory": 5685, "data_time": 0.04388, "loss_rpn_cls": 0.22443, "loss_rpn_bbox": 0.10909, "loss_cls": 0.3672, "acc": 94.34106, "loss_bbox": 0.19103, "loss_mask": 0.70588, "loss": 1.59763, "time": 0.2502}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 8e-05, "memory": 5685, "data_time": 0.0412, "loss_rpn_cls": 0.18149, "loss_rpn_bbox": 0.11001, "loss_cls": 0.41935, "acc": 93.19458, "loss_bbox": 0.23987, "loss_mask": 0.68086, "loss": 1.63158, "time": 0.25473}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.0001, "memory": 5685, "data_time": 0.03801, "loss_rpn_cls": 0.13581, "loss_rpn_bbox": 0.10632, "loss_cls": 0.44377, "acc": 92.29712, "loss_bbox": 0.28241, "loss_mask": 0.64083, "loss": 1.60914, "time": 0.22581}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00012, "memory": 5766, "data_time": 0.04811, "loss_rpn_cls": 0.10375, "loss_rpn_bbox": 0.09649, "loss_cls": 0.48607, "acc": 90.94165, "loss_bbox": 0.34173, "loss_mask": 0.60045, "loss": 1.62849, "time": 0.23706}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00014, "memory": 5766, "data_time": 0.03541, "loss_rpn_cls": 0.09444, "loss_rpn_bbox": 0.0934, "loss_cls": 0.46625, "acc": 90.97437, "loss_bbox": 0.33575, "loss_mask": 0.55465, "loss": 1.54449, "time": 0.23098}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00016, "memory": 5872, "data_time": 0.04341, "loss_rpn_cls": 0.09834, "loss_rpn_bbox": 0.09409, "loss_cls": 0.47988, "acc": 90.21802, "loss_bbox": 0.35705, "loss_mask": 0.52511, "loss": 1.55448, "time": 0.23998}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00018, "memory": 5872, "data_time": 0.04044, "loss_rpn_cls": 0.08892, "loss_rpn_bbox": 0.08911, "loss_cls": 0.45759, "acc": 90.15674, "loss_bbox": 0.36044, "loss_mask": 0.48406, "loss": 1.4801, "time": 0.22401}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0002, "memory": 5872, "data_time": 0.03287, "loss_rpn_cls": 0.08567, "loss_rpn_bbox": 0.08671, "loss_cls": 0.45837, "acc": 90.03564, "loss_bbox": 0.36601, "loss_mask": 0.47189, "loss": 1.46865, "time": 0.22216}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0002, "memory": 5914, "data_time": 0.03586, "loss_rpn_cls": 0.08768, "loss_rpn_bbox": 0.08303, "loss_cls": 0.44763, "acc": 90.08301, "loss_bbox": 0.36176, "loss_mask": 0.44976, "loss": 1.42987, "time": 0.22477}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0002, "memory": 5914, "data_time": 0.0354, "loss_rpn_cls": 0.0858, "loss_rpn_bbox": 0.08417, "loss_cls": 0.42795, "acc": 89.92334, "loss_bbox": 0.3623, "loss_mask": 0.43589, "loss": 1.39611, "time": 0.22921}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0002, "memory": 5914, "data_time": 0.04643, "loss_rpn_cls": 0.08152, "loss_rpn_bbox": 0.08739, "loss_cls": 0.43011, "acc": 89.7854, "loss_bbox": 0.36421, "loss_mask": 0.42014, "loss": 1.38336, "time": 0.23036}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0002, "memory": 5932, "data_time": 0.03909, "loss_rpn_cls": 0.08153, "loss_rpn_bbox": 0.08408, "loss_cls": 0.41312, "acc": 89.56372, "loss_bbox": 0.37063, "loss_mask": 0.4186, "loss": 1.36797, "time": 0.22349}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0002, "memory": 5932, "data_time": 0.03728, "loss_rpn_cls": 0.07783, "loss_rpn_bbox": 0.08302, "loss_cls": 0.40903, "acc": 89.63501, "loss_bbox": 0.36463, "loss_mask": 0.40655, "loss": 1.34108, "time": 0.22942}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0002, "memory": 5932, "data_time": 0.04123, "loss_rpn_cls": 0.07502, "loss_rpn_bbox": 0.08324, "loss_cls": 0.41193, "acc": 89.5249, "loss_bbox": 0.36948, "loss_mask": 0.40897, "loss": 1.34865, "time": 0.21881}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0002, "memory": 5932, "data_time": 0.04313, "loss_rpn_cls": 0.07307, "loss_rpn_bbox": 0.07937, "loss_cls": 0.38904, "acc": 89.71875, "loss_bbox": 0.3573, "loss_mask": 0.39409, "loss": 1.29287, "time": 0.22877}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0002, "memory": 5932, "data_time": 0.03936, "loss_rpn_cls": 0.07364, "loss_rpn_bbox": 0.07946, "loss_cls": 0.39475, "acc": 89.65527, "loss_bbox": 0.36034, "loss_mask": 0.39728, "loss": 1.30546, "time": 0.2158}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0002, "memory": 5932, "data_time": 0.03842, "loss_rpn_cls": 0.07313, "loss_rpn_bbox": 0.07734, "loss_cls": 0.39265, "acc": 89.66455, "loss_bbox": 0.35397, "loss_mask": 0.38329, "loss": 1.28039, "time": 0.21532}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0002, "memory": 5936, "data_time": 0.04323, "loss_rpn_cls": 0.07288, "loss_rpn_bbox": 0.07643, "loss_cls": 0.37378, "acc": 89.92383, "loss_bbox": 0.34456, "loss_mask": 0.37956, "loss": 1.24721, "time": 0.22676}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0002, "memory": 5936, "data_time": 0.04121, "loss_rpn_cls": 0.07134, "loss_rpn_bbox": 0.07546, "loss_cls": 0.3729, "acc": 89.88843, "loss_bbox": 0.34647, "loss_mask": 0.37744, "loss": 1.24361, "time": 0.22407}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.0002, "memory": 5936, "data_time": 0.04542, "loss_rpn_cls": 0.06559, "loss_rpn_bbox": 0.07308, "loss_cls": 0.36851, "acc": 89.98267, "loss_bbox": 0.34278, "loss_mask": 0.37697, "loss": 1.22692, "time": 0.21262}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.0002, "memory": 5936, "data_time": 0.04357, "loss_rpn_cls": 0.0712, "loss_rpn_bbox": 0.0808, "loss_cls": 0.38398, "acc": 89.51953, "loss_bbox": 0.35933, "loss_mask": 0.37571, "loss": 1.27102, "time": 0.21876}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.0002, "memory": 5936, "data_time": 0.04852, "loss_rpn_cls": 0.0722, "loss_rpn_bbox": 0.08073, "loss_cls": 0.37505, "acc": 89.47705, "loss_bbox": 0.36045, "loss_mask": 0.37496, "loss": 1.26338, "time": 0.22206}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.0002, "memory": 5936, "data_time": 0.04288, "loss_rpn_cls": 0.06499, "loss_rpn_bbox": 0.07143, "loss_cls": 0.36126, "acc": 90.20068, "loss_bbox": 0.33195, "loss_mask": 0.36521, "loss": 1.19484, "time": 0.21172}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.0002, "memory": 5936, "data_time": 0.04545, "loss_rpn_cls": 0.06526, "loss_rpn_bbox": 0.0733, "loss_cls": 0.36175, "acc": 90.00073, "loss_bbox": 0.33686, "loss_mask": 0.36024, "loss": 1.19741, "time": 0.21082}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.0002, "memory": 5936, "data_time": 0.04031, "loss_rpn_cls": 0.07245, "loss_rpn_bbox": 0.0794, "loss_cls": 0.36601, "acc": 89.76782, "loss_bbox": 0.34344, "loss_mask": 0.35565, "loss": 1.21695, "time": 0.21426}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.0002, "memory": 5936, "data_time": 0.0399, "loss_rpn_cls": 0.06673, "loss_rpn_bbox": 0.07326, "loss_cls": 0.34296, "acc": 90.51123, "loss_bbox": 0.32437, "loss_mask": 0.35109, "loss": 1.15842, "time": 0.20436}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.0002, "memory": 5936, "data_time": 0.03708, "loss_rpn_cls": 0.06306, "loss_rpn_bbox": 0.07435, "loss_cls": 0.36285, "acc": 89.646, "loss_bbox": 0.35021, "loss_mask": 0.34748, "loss": 1.19795, "time": 0.20476}
{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.0002, "memory": 5936, "data_time": 0.04808, "loss_rpn_cls": 0.07364, "loss_rpn_bbox": 0.08142, "loss_cls": 0.34656, "acc": 90.08008, "loss_bbox": 0.33642, "loss_mask": 0.35705, "loss": 1.1951, "time": 0.21209}
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