text: _base_ = './yolov3_d53_mstrain-608_273e_coco.py'
  # dataset settings
  img_norm_cfg = dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotations', with_bbox=True),
      dict(
          type='Expand',
          mean=img_norm_cfg['mean'],
          to_rgb=img_norm_cfg['to_rgb'],
          ratio_range=(1, 2)),
      dict(
          type='MinIoURandomCrop',
          min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
          min_crop_size=0.3),
      dict(type='Resize', img_scale=[(320, 320), (416, 416)], keep_ratio=True),
      dict(type='RandomFlip', flip_ratio=0.5),
      dict(type='PhotoMetricDistortion'),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='Pad', size_divisor=32),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(416, 416),
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=True),
              dict(type='RandomFlip'),
              dict(type='Normalize', **img_norm_cfg),
              dict(type='Pad', size_divisor=32),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img'])
          ])
  ]
  data = dict(
      train=dict(pipeline=train_pipeline),
      val=dict(pipeline=test_pipeline),
      test=dict(pipeline=test_pipeline))
  
  # dataset settings
  dataset_type = 'CocoDataset'
  data_root = '../data/ht_cumt_rgbd/'
  classes=('person',)
  classes_miner=('miner',)
  data = dict(
      samples_per_gpu=48,
      workers_per_gpu=4,
      train=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_train2014.json',
          img_prefix=data_root + 'train2014/',
          pipeline=train_pipeline,
          classes=classes
      ),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2014.json',
          img_prefix=data_root + 'val2014/',
          pipeline=test_pipeline,
          classes=classes),
      test=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2014.json',
          img_prefix=data_root + 'val2014/',
          pipeline=test_pipeline,
          classes=classes))
  # optimizer
  optimizer = dict(type='SGD', lr=0.000750, momentum=0.9, weight_decay=0.0005)
  optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))