text: _base_ = "./fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py"
  model = dict(
      pretrained="open-mmlab://resnext101_64x4d",
      backbone=dict(
          type="ResNeXt",
          depth=101,
          groups=64,
          base_width=4,
          num_stages=4,
          out_indices=(0, 1, 2, 3),
          frozen_stages=1,
          norm_cfg=dict(type="BN", requires_grad=True),
          norm_eval=True,
          style="pytorch",
      ),
  )
  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, 640), (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(
      samples_per_gpu=2,
      workers_per_gpu=2,
      train=dict(pipeline=train_pipeline),
      val=dict(pipeline=test_pipeline),
      test=dict(pipeline=test_pipeline),
  )
  # optimizer
  optimizer = dict(lr=0.01, paramwise_cfg=dict(bias_lr_mult=2.0, bias_decay_mult=0.0))
  optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
  # learning policy
  lr_config = dict(step=[16, 22])
  total_epochs = 24
  