text: from base_dirs import BASE_DATA_FOLDER
  
  dataset_type = 'CocoDataset'
  data_root = BASE_DATA_FOLDER+'/coco/'
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotations', with_bbox=True),
      dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
      dict(type='RandomFlip', flip_ratio=0.5),
      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=(1333, 800),
          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(
      samples_per_gpu=4,
      workers_per_gpu=2,
      train=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_trainCS2017.json',
          img_prefix=data_root + 'images/trainCS2017/',
          pipeline=train_pipeline),
      trainCS=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_trainCS2017.json',
          img_prefix=data_root + 'images/trainCS2017/',
          pipeline=test_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_valCS2017.json',
          img_prefix=data_root + 'images/valCS2017/',
          pipeline=test_pipeline),
      valCS=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_valCS2017.json',
          img_prefix=data_root + 'images/valCS2017/',
          pipeline=test_pipeline),
      testOS = dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'images/val2017/',
          pipeline=test_pipeline),
      testCS=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_testCS2017.json',
          img_prefix=data_root + 'images/testCS2017/',
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/instances_val2017.json',
          img_prefix=data_root + 'images/val2017/',
          pipeline=test_pipeline))
  evaluation = dict(interval=1, metric='bbox')
  