text: _base_ = "./retinanet_r50_fpn_1x_coco.py"
  model = dict(
      pretrained="open-mmlab://detectron2/resnet50_caffe",
      backbone=dict(norm_cfg=dict(requires_grad=False), norm_eval=True, style="caffe"),
  )
  # use caffe img_norm
  img_norm_cfg = dict(mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
  train_pipeline = [
      dict(type="LoadImageFromFile"),
      dict(type="LoadAnnotations", with_bbox=True),
      dict(
          type="Resize",
          img_scale=[
              (1333, 640),
              (1333, 672),
              (1333, 704),
              (1333, 736),
              (1333, 768),
              (1333, 800),
          ],
          multiscale_mode="value",
          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(
      train=dict(pipeline=train_pipeline),
      val=dict(pipeline=test_pipeline),
      test=dict(pipeline=test_pipeline),
  )
  