text: # model settings
  model = dict(
      type='RetinaNet',
      backbone=dict(
          type='ResNet',
          depth=50,
          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',
          init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
      neck=dict(
          type='FPN',
          in_channels=[256, 512, 1024, 2048],
          out_channels=256,
          start_level=1,
          add_extra_convs='on_input',
          num_outs=5),
      bbox_head=dict(
          type='RetinaHead',
          num_classes=11,
          in_channels=256,
          stacked_convs=4,
          feat_channels=256,
          anchor_generator=dict(
              type='AnchorGenerator',
              octave_base_scale=4,
              scales_per_octave=3,
              ratios=[0.5, 1.0, 2.0],
              strides=[8, 16, 32, 64, 128]),
          bbox_coder=dict(
              type='DeltaXYWHBBoxCoder',
              target_means=[.0, .0, .0, .0],
              target_stds=[1.0, 1.0, 1.0, 1.0]),
          loss_cls=dict(
              type='FocalLoss',
              use_sigmoid=True,
              gamma=2.0,
              alpha=0.25,
              loss_weight=1.0),
          loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
      # model training and testing settings
      train_cfg=dict(
          assigner=dict(
              type='MaxIoUAssigner',
              pos_iou_thr=0.5,
              neg_iou_thr=0.4,
              min_pos_iou=0,
              ignore_iof_thr=-1),
          allowed_border=-1,
          pos_weight=-1,
          debug=False),
      test_cfg=dict(
          nms_pre=1000,
          min_bbox_size=0,
          score_thr=0.05,
          nms=dict(type='nms', iou_threshold=0.5),
          max_per_img=100))
  