return {
    "meta": {
        "env_info": "sys.platform: linux\nPython: 3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: Tesla PG503-216\nCUDA_HOME: /mnt/lustre/share/cuda-10.2\nNVCC: Cuda compilation tools, release 10.2, V10.2.89\nGCC: gcc (GCC) 5.4.0\nPyTorch: 1.8.0\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) oneAPI Math Kernel Library Version 2021.3-Product Build 20210617 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v1.7.0 (Git Hash 7aed236906b1f7a05c0917e5257a1af05e9ff683)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime 11.1\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_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_37,code=compute_37\n  - CuDNN 8.0.5\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, 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 -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-variable -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=1.8.0, 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, \n\nTorchVision: 0.9.0\nOpenCV: 4.5.4-dev\nMMCV: 1.3.18\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.1\nMMDetection: 2.18.1+d9697f3",
        "config": "optimizer = dict(\n    type='SGD',\n    lr=0.01,\n    momentum=0.9,\n    weight_decay=0.0005,\n    nesterov=True,\n    paramwise_cfg=dict(norm_decay_mult=0.0, bias_decay_mult=0.0))\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(\n    policy='YOLOX',\n    warmup='exp',\n    by_epoch=False,\n    warmup_by_epoch=True,\n    warmup_ratio=1,\n    warmup_iters=5,\n    num_last_epochs=15,\n    min_lr_ratio=0.05)\nrunner = dict(type='EpochBasedRunner', max_epochs=300)\ncheckpoint_config = dict(interval=10)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [\n    dict(type='YOLOXModeSwitchHook', num_last_epochs=15, priority=48),\n    dict(type='SyncNormHook', num_last_epochs=15, interval=10, priority=48),\n    dict(\n        type='ExpMomentumEMAHook',\n        resume_from=None,\n        momentum=0.0001,\n        priority=49)\n]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nimg_scale = (640, 640)\nmodel = dict(\n    type='YOLOX',\n    input_size=(640, 640),\n    random_size_range=(15, 25),\n    random_size_interval=10,\n    backbone=dict(type='CSPDarknet', deepen_factor=1.0, widen_factor=1.0),\n    neck=dict(\n        type='YOLOXPAFPN',\n        in_channels=[256, 512, 1024],\n        out_channels=256,\n        num_csp_blocks=3),\n    bbox_head=dict(\n        type='YOLOXHead', num_classes=80, in_channels=256, feat_channels=256),\n    train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)),\n    test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))\ndata_root = 'data/coco/'\ndataset_type = 'CocoDataset'\ntrain_pipeline = [\n    dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0),\n    dict(\n        type='RandomAffine', scaling_ratio_range=(0.1, 2),\n        border=(-320, -320)),\n    dict(\n        type='MixUp',\n        img_scale=(640, 640),\n        ratio_range=(0.8, 1.6),\n        pad_val=114.0),\n    dict(type='YOLOXHSVRandomAug'),\n    dict(type='RandomFlip', flip_ratio=0.5),\n    dict(type='Resize', img_scale=(640, 640), keep_ratio=True),\n    dict(\n        type='Pad',\n        pad_to_square=True,\n        pad_val=dict(img=(114.0, 114.0, 114.0))),\n    dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),\n    dict(type='DefaultFormatBundle'),\n    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n]\ntrain_dataset = dict(\n    type='MultiImageMixDataset',\n    dataset=dict(\n        type='CocoDataset',\n        ann_file='data/coco/annotations/instances_train2017.json',\n        img_prefix='data/coco/train2017/',\n        pipeline=[\n            dict(\n                type='LoadImageFromFile',\n                file_client_args=dict(\n                    backend='petrel',\n                    path_mapping=dict({\n                        '.data/coco/':\n                        's3://openmmlab/datasets/detection/coco/',\n                        'data/coco/':\n                        's3://openmmlab/datasets/detection/coco/'\n                    }))),\n            dict(type='LoadAnnotations', with_bbox=True)\n        ],\n        filter_empty_gt=False),\n    pipeline=[\n        dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0),\n        dict(\n            type='RandomAffine',\n            scaling_ratio_range=(0.1, 2),\n            border=(-320, -320)),\n        dict(\n            type='MixUp',\n            img_scale=(640, 640),\n            ratio_range=(0.8, 1.6),\n            pad_val=114.0),\n        dict(type='YOLOXHSVRandomAug'),\n        dict(type='RandomFlip', flip_ratio=0.5),\n        dict(type='Resize', img_scale=(640, 640), keep_ratio=True),\n        dict(\n            type='Pad',\n            pad_to_square=True,\n            pad_val=dict(img=(114.0, 114.0, 114.0))),\n        dict(\n            type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),\n        dict(type='DefaultFormatBundle'),\n        dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n    ])\ntest_pipeline = [\n    dict(\n        type='LoadImageFromFile',\n        file_client_args=dict(\n            backend='petrel',\n            path_mapping=dict({\n                '.data/coco/':\n                's3://openmmlab/datasets/detection/coco/',\n                'data/coco/':\n                's3://openmmlab/datasets/detection/coco/'\n            }))),\n    dict(\n        type='MultiScaleFlipAug',\n        img_scale=(640, 640),\n        flip=False,\n        transforms=[\n            dict(type='Resize', keep_ratio=True),\n            dict(type='RandomFlip'),\n            dict(\n                type='Pad',\n                pad_to_square=True,\n                pad_val=dict(img=(114.0, 114.0, 114.0))),\n            dict(type='DefaultFormatBundle'),\n            dict(type='Collect', keys=['img'])\n        ])\n]\ndata = dict(\n    samples_per_gpu=8,\n    workers_per_gpu=4,\n    persistent_workers=True,\n    train=dict(\n        type='MultiImageMixDataset',\n        dataset=dict(\n            type='CocoDataset',\n            ann_file='data/coco/annotations/instances_train2017.json',\n            img_prefix='data/coco/train2017/',\n            pipeline=[\n                dict(\n                    type='LoadImageFromFile',\n                    file_client_args=dict(\n                        backend='petrel',\n                        path_mapping=dict({\n                            '.data/coco/':\n                            's3://openmmlab/datasets/detection/coco/',\n                            'data/coco/':\n                            's3://openmmlab/datasets/detection/coco/'\n                        }))),\n                dict(type='LoadAnnotations', with_bbox=True)\n            ],\n            filter_empty_gt=False),\n        pipeline=[\n            dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0),\n            dict(\n                type='RandomAffine',\n                scaling_ratio_range=(0.1, 2),\n                border=(-320, -320)),\n            dict(\n                type='MixUp',\n                img_scale=(640, 640),\n                ratio_range=(0.8, 1.6),\n                pad_val=114.0),\n            dict(type='YOLOXHSVRandomAug'),\n            dict(type='RandomFlip', flip_ratio=0.5),\n            dict(type='Resize', img_scale=(640, 640), keep_ratio=True),\n            dict(\n                type='Pad',\n                pad_to_square=True,\n                pad_val=dict(img=(114.0, 114.0, 114.0))),\n            dict(\n                type='FilterAnnotations',\n                min_gt_bbox_wh=(1, 1),\n                keep_empty=False),\n            dict(type='DefaultFormatBundle'),\n            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\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(\n                type='LoadImageFromFile',\n                file_client_args=dict(\n                    backend='petrel',\n                    path_mapping=dict({\n                        '.data/coco/':\n                        's3://openmmlab/datasets/detection/coco/',\n                        'data/coco/':\n                        's3://openmmlab/datasets/detection/coco/'\n                    }))),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(640, 640),\n                flip=False,\n                transforms=[\n                    dict(type='Resize', keep_ratio=True),\n                    dict(type='RandomFlip'),\n                    dict(\n                        type='Pad',\n                        pad_to_square=True,\n                        pad_val=dict(img=(114.0, 114.0, 114.0))),\n                    dict(type='DefaultFormatBundle'),\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(\n                type='LoadImageFromFile',\n                file_client_args=dict(\n                    backend='petrel',\n                    path_mapping=dict({\n                        '.data/coco/':\n                        's3://openmmlab/datasets/detection/coco/',\n                        'data/coco/':\n                        's3://openmmlab/datasets/detection/coco/'\n                    }))),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(640, 640),\n                flip=False,\n                transforms=[\n                    dict(type='Resize', keep_ratio=True),\n                    dict(type='RandomFlip'),\n                    dict(\n                        type='Pad',\n                        pad_to_square=True,\n                        pad_val=dict(img=(114.0, 114.0, 114.0))),\n                    dict(type='DefaultFormatBundle'),\n                    dict(type='Collect', keys=['img'])\n                ])\n        ]))\nmax_epochs = 300\nnum_last_epochs = 15\ninterval = 10\nevaluation = dict(\n    save_best='auto', interval=10, dynamic_intervals=[(285, 1)], metric='bbox')\nwork_dir = '/mnt/lustre/share_data/huanghaian/work_dir/yolox_l_1124'\ngpu_ids = range(0, 8)\n",
        "seed": 937171196,
        "exp_name": "yolox_l_8x8_300e_coco.py",
        "epoch": 289,
        "iter": 534361,
        "mmcv_version": "1.3.18",
        "time": "Tue Nov 30 00:07:05 2021",
        "CLASSES": (
            "person",
            "bicycle",
            "car",
            "motorcycle",
            "airplane",
            "bus",
            "train",
            "truck",
            "boat",
            "traffic light",
            "fire hydrant",
            "stop sign",
            "parking meter",
            "bench",
            "bird",
            "cat",
            "dog",
            "horse",
            "sheep",
            "cow",
            "elephant",
            "bear",
            "zebra",
            "giraffe",
            "backpack",
            "umbrella",
            "handbag",
            "tie",
            "suitcase",
            "frisbee",
            "skis",
            "snowboard",
            "sports ball",
            "kite",
            "baseball bat",
            "baseball glove",
            "skateboard",
            "surfboard",
            "tennis racket",
            "bottle",
            "wine glass",
            "cup",
            "fork",
            "knife",
            "spoon",
            "bowl",
            "banana",
            "apple",
            "sandwich",
            "orange",
            "broccoli",
            "carrot",
            "hot dog",
            "pizza",
            "donut",
            "cake",
            "chair",
            "couch",
            "potted plant",
            "bed",
            "dining table",
            "toilet",
            "tv",
            "laptop",
            "mouse",
            "remote",
            "keyboard",
            "cell phone",
            "microwave",
            "oven",
            "toaster",
            "sink",
            "refrigerator",
            "book",
            "clock",
            "vase",
            "scissors",
            "teddy bear",
            "hair drier",
            "toothbrush",
        ),
        "hook_msgs": {
            "last_ckpt": "/mnt/lustre/share_data/huanghaian/work_dir/yolox_l_1124/epoch_280.pth",
            "best_score": 0.491,
            "best_ckpt": "/mnt/lustre/share_data/huanghaian/work_dir/yolox_l_1124/best_bbox_mAP_epoch_289.pth",
        },
    }
}