text: _base_ = [
      '../_base_/models/faster_rcnn_r50_fpn.py',
      '../_base_/datasets/coco_detection.py',
      '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
  ]
  
  model = dict(
      roi_head=dict(
          bbox_head=dict(
              type='Shared2FCBBoxHead',
              in_channels=256,
              fc_out_channels=1024,
              roi_feat_size=7,
              num_classes=2,
              bbox_coder=dict(
                  type='DeltaXYWHBBoxCoder',
                  target_means=[0., 0., 0., 0.],
                  target_stds=[0.1, 0.1, 0.2, 0.2]),
              reg_class_agnostic=False,
              loss_cls=dict(
                  type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
              loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))))
  
  dataset_type = 'ErosiveUlcer'
  data_root = './data'
  
  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=2,
      workers_per_gpu=2,
      train=dict(
          type=dataset_type,
          ann_file='/data3/zzhang/annotation/erosiveulcer_fine/train.json',
          img_prefix='/data2/dataset/gastric_object_detection/erosive',
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file='/data3/zzhang/annotation/erosiveulcer_fine/test.json',
          img_prefix='/data2/dataset/gastric_object_detection/erosive',
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          ann_file='/data3/zzhang/annotation/erosiveulcer_fine/test.json',
          img_prefix='/data2/dataset/gastric_object_detection/erosive',
          pipeline=test_pipeline))
  evaluation = dict(metric=['bbox'])