logs/orcnn_ssp_dotav10.json

{"env_info": "sys.platform: linux\nPython: 3.8.0 (default, Nov  6 2019, 21:49:08) [GCC 7.3.0]\nCUDA available: True\nGPU 0,1,2,3: NVIDIA GeForce RTX 3090\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 11.2, V11.2.67\nGCC: gcc (Ubuntu 9.5.0-1ubuntu1~22.04) 9.5.0\nPyTorch: 1.11.0+cu113\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\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 11.3\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 8.2\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, 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 -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -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-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=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0+cu113\nOpenCV: 4.8.1\nMMCV: 1.7.0\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.3\nMMRotate: 0.3.4+bd27831", "config": "dataset_type = 'DOTADataset'\ndata_root = 'datasets/dota/split_ss_dota_v10/'\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='pseudo_labels/ssp_dotav10_hybrid/',\n        img_prefix='datasets/dota/split_ss_dota_v10/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='datasets/dota/split_ss_dota_v10/trainval/annfiles/',\n        img_prefix='datasets/dota/split_ss_dota_v10/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='datasets/dota/split_ss_dota_v10/test/images/',\n        img_prefix='datasets/dota/split_ss_dota_v10/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=1, metric='mAP')\noptimizer = dict(type='SGD', lr=0.02, 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=1)\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)]\nopencv_num_threads = 0\nmp_start_method = 'fork'\nangle_version = 'le90'\nclasses = ('plane', 'baseball-diamond', 'bridge', 'ground-track-field',\n           'small-vehicle', 'large-vehicle', 'ship', 'tennis-court',\n           'basketball-court', 'storage-tank', 'soccer-ball-field',\n           'roundabout', 'harbor', 'swimming-pool', 'helicopter')\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                gpu_assign_thr=600,\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                gpu_assign_thr=600,\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)))\nwork_dir = './work_dirs/orcnn_ssp_dotav10'\nauto_resume = False\ngpu_ids = range(0, 4)\n", "seed": 2423, "exp_name": "orcnn_ssp_dotav10.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00797, "memory": 6624, "data_time": 0.05823, "loss_rpn_cls": 0.40073, "loss_rpn_bbox": 0.25186, "loss_cls": 0.50309, "acc": 92.90137, "loss_bbox": 0.14215, "loss": 1.29783, "grad_norm": 8.04984, "time": 0.35713}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00931, "memory": 6624, "data_time": 0.00371, "loss_rpn_cls": 0.21128, "loss_rpn_bbox": 0.20246, "loss_cls": 0.31006, "acc": 94.1543, "loss_bbox": 0.22193, "loss": 0.94573, "grad_norm": 2.17127, "time": 0.27208}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.01064, "memory": 6624, "data_time": 0.0038, "loss_rpn_cls": 0.20688, "loss_rpn_bbox": 0.22697, "loss_cls": 0.37541, "acc": 92.77881, "loss_bbox": 0.30877, "loss": 1.11803, "grad_norm": 2.4164, "time": 0.26998}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.01197, "memory": 6624, "data_time": 0.00368, "loss_rpn_cls": 0.1785, "loss_rpn_bbox": 0.21443, "loss_cls": 0.34946, "acc": 92.58398, "loss_bbox": 0.2995, "loss": 1.0419, "grad_norm": 2.14517, "time": 0.27136}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.01331, "memory": 7231, "data_time": 0.00369, "loss_rpn_cls": 0.15458, "loss_rpn_bbox": 0.20955, "loss_cls": 0.35878, "acc": 91.28564, "loss_bbox": 0.37612, "loss": 1.09902, "grad_norm": 2.05485, "time": 0.2721}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.01464, "memory": 7231, "data_time": 0.00354, "loss_rpn_cls": 0.1444, "loss_rpn_bbox": 0.20706, "loss_cls": 0.35816, "acc": 90.63525, "loss_bbox": 0.3832, "loss": 1.09282, "grad_norm": 2.27273, "time": 0.2724}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.01597, "memory": 7231, "data_time": 0.00358, "loss_rpn_cls": 0.12332, "loss_rpn_bbox": 0.19498, "loss_cls": 0.34746, "acc": 90.78857, "loss_bbox": 0.40289, "loss": 1.06865, "grad_norm": 2.14061, "time": 0.27551}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.01731, "memory": 7231, "data_time": 0.00408, "loss_rpn_cls": 0.11965, "loss_rpn_bbox": 0.22321, "loss_cls": 0.36881, "acc": 89.38672, "loss_bbox": 0.47248, "loss": 1.18416, "grad_norm": 2.15391, "time": 0.28509}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.01864, "memory": 7231, "data_time": 0.00343, "loss_rpn_cls": 0.1206, "loss_rpn_bbox": 0.2012, "loss_cls": 0.32966, "acc": 90.52734, "loss_bbox": 0.43605, "loss": 1.08751, "grad_norm": 2.31913, "time": 0.27704}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.01997, "memory": 7231, "data_time": 0.00369, "loss_rpn_cls": 0.11211, "loss_rpn_bbox": 0.19672, "loss_cls": 0.35196, "acc": 89.80127, "loss_bbox": 0.46431, "loss": 1.12509, "grad_norm": 2.23356, "time": 0.47458}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.02, "memory": 7231, "data_time": 0.00364, "loss_rpn_cls": 0.11631, "loss_rpn_bbox": 0.20332, "loss_cls": 0.3593, "acc": 89.05127, "loss_bbox": 0.48122, "loss": 1.16016, "grad_norm": 2.31258, "time": 0.28131}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.02, "memory": 7231, "data_time": 0.00376, "loss_rpn_cls": 0.10335, "loss_rpn_bbox": 0.20555, "loss_cls": 0.37104, "acc": 88.56445, "loss_bbox": 0.49973, "loss": 1.17967, "grad_norm": 2.36765, "time": 0.29119}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.02, "memory": 7231, "data_time": 0.00372, "loss_rpn_cls": 0.10547, "loss_rpn_bbox": 0.19267, "loss_cls": 0.36475, "acc": 88.78223, "loss_bbox": 0.49534, "loss": 1.15823, "grad_norm": 2.19196, "time": 0.29319}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.02, "memory": 7231, "data_time": 0.00368, "loss_rpn_cls": 0.0925, "loss_rpn_bbox": 0.17899, "loss_cls": 0.33112, "acc": 89.61035, "loss_bbox": 0.47455, "loss": 1.07716, "grad_norm": 2.11465, "time": 0.28828}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.02, "memory": 7231, "data_time": 0.00364, "loss_rpn_cls": 0.09415, "loss_rpn_bbox": 0.1856, "loss_cls": 0.33949, "acc": 89.53955, "loss_bbox": 0.46418, "loss": 1.08341, "grad_norm": 2.19436, "time": 0.48071}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.02, "memory": 7231, "data_time": 0.00349, "loss_rpn_cls": 0.08653, "loss_rpn_bbox": 0.17734, "loss_cls": 0.32108, "acc": 89.82324, "loss_bbox": 0.42288, "loss": 1.00782, "grad_norm": 2.01464, "time": 0.29113}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.02, "memory": 7231, "data_time": 0.00368, "loss_rpn_cls": 0.08531, "loss_rpn_bbox": 0.18427, "loss_cls": 0.32536, "acc": 89.26953, "loss_bbox": 0.47661, "loss": 1.07155, "grad_norm": 2.07047, "time": 0.47688}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.02, "memory": 7231, "data_time": 0.00381, "loss_rpn_cls": 0.07984, "loss_rpn_bbox": 0.176, "loss_cls": 0.30855, "acc": 89.74023, "loss_bbox": 0.44101, "loss": 1.0054, "grad_norm": 1.98565, "time": 0.28861}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.02, "memory": 7231, "data_time": 0.00389, "loss_rpn_cls": 0.10127, "loss_rpn_bbox": 0.18995, "loss_cls": 0.33336, "acc": 89.20264, "loss_bbox": 0.46497, "loss": 1.08954, "grad_norm": 2.20284, "time": 0.29274}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.02, "memory": 7231, "data_time": 0.00365, "loss_rpn_cls": 0.09286, "loss_rpn_bbox": 0.17899, "loss_cls": 0.30193, "acc": 89.82666, "loss_bbox": 0.43926, "loss": 1.01304, "grad_norm": 1.92941, "time": 0.29183}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.02, "memory": 7231, "data_time": 0.00354, "loss_rpn_cls": 0.08832, "loss_rpn_bbox": 0.17115, "loss_cls": 0.30844, "acc": 89.8501, "loss_bbox": 0.43209, "loss": 1.0, "grad_norm": 1.86717, "time": 0.29167}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.02, "memory": 7231, "data_time": 0.00385, "loss_rpn_cls": 0.07651, "loss_rpn_bbox": 0.1673, "loss_cls": 0.30349, "acc": 89.70361, "loss_bbox": 0.43565, "loss": 0.98294, "grad_norm": 1.90374, "time": 0.28857}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.02, "memory": 7231, "data_time": 0.00386, "loss_rpn_cls": 0.07843, "loss_rpn_bbox": 0.17997, "loss_cls": 0.32146, "acc": 89.22363, "loss_bbox": 0.45426, "loss": 1.03412, "grad_norm": 1.91347, "time": 0.29066}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.02, "memory": 7231, "data_time": 0.00363, "loss_rpn_cls": 0.08229, "loss_rpn_bbox": 0.17358, "loss_cls": 0.32333, "acc": 89.23535, "loss_bbox": 0.44772, "loss": 1.02691, "grad_norm": 2.01611, "time": 0.30191}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.02, "memory": 7231, "data_time": 0.00367, "loss_rpn_cls": 0.07639, "loss_rpn_bbox": 0.19278, "loss_cls": 0.31087, "acc": 89.06201, "loss_bbox": 0.45798, "loss": 1.03802, "grad_norm": 2.02427, "time": 0.29165}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.02, "memory": 7231, "data_time": 0.00371, "loss_rpn_cls": 0.0699, "loss_rpn_bbox": 0.17141, "loss_cls": 0.29497, "acc": 89.72656, "loss_bbox": 0.45384, "loss": 0.99011, "grad_norm": 1.91267, "time": 0.29314}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.02, "memory": 7231, "data_time": 0.00375, "loss_rpn_cls": 0.07802, "loss_rpn_bbox": 0.20129, "loss_cls": 0.31992, "acc": 88.67139, "loss_bbox": 0.48148, "loss": 1.08071, "grad_norm": 2.05437, "time": 0.29013}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.02, "memory": 7231, "data_time": 0.00377, "loss_rpn_cls": 0.07238, "loss_rpn_bbox": 0.18842, "loss_cls": 0.29443, "acc": 89.52148, "loss_bbox": 0.45739, "loss": 1.01263, "grad_norm": 1.85585, "time": 0.29489}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.02, "memory": 7231, "data_time": 0.00365, "loss_rpn_cls": 0.07386, "loss_rpn_bbox": 0.17768, "loss_cls": 0.29572, "acc": 89.49854, "loss_bbox": 0.46457, "loss": 1.01183, "grad_norm": 1.90363, "time": 0.29593}
{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.02, "memory": 7231, "data_time": 0.0039, "loss_rpn_cls": 0.07722, "loss_rpn_bbox": 0.18888, "loss_cls": 0.29703, "acc": 89.3999, "loss_bbox": 0.47486, "loss": 1.03799, "grad_norm": 1.84502, "time": 0.2921}
{"mode": "train", "epoch": 1, "iter": 1550, "lr": 0.02, "memory": 7231, "data_time": 0.00358, "loss_rpn_cls": 0.06547, "loss_rpn_bbox": 0.17953, "loss_cls": 0.31346, "acc": 88.72656, "loss_bbox": 0.47813, "loss": 1.03659, "grad_norm": 1.9367, "time": 0.48931}
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