text: _base_ = [
      '../_base_/models/rpn_r50_fpn.py', '../_base_/datasets/coco_detection.py',
      '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
  ]
  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, with_label=False),
      # dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
      dict(type='Resize', img_scale=(200, 200), 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']),
  ]
  data = dict(train=dict(pipeline=train_pipeline))
  evaluation = dict(interval=1, metric='proposal_fast')
  