text: _base_ = [
      "../_base_/datasets/coco_detection.py",
      "../_base_/schedules/schedule_1x.py",
      "../_base_/default_runtime.py",
  ]
  # model settings
  model = dict(
      type="FOVEA",
      pretrained="torchvision://resnet50",
      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",
      ),
      neck=dict(
          type="FPN",
          in_channels=[256, 512, 1024, 2048],
          out_channels=256,
          start_level=1,
          num_outs=5,
          add_extra_convs="on_input",
      ),
      bbox_head=dict(
          type="FoveaHead",
          num_classes=80,
          in_channels=256,
          stacked_convs=4,
          feat_channels=256,
          strides=[8, 16, 32, 64, 128],
          base_edge_list=[16, 32, 64, 128, 256],
          scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)),
          sigma=0.4,
          with_deform=False,
          loss_cls=dict(
              type="FocalLoss", use_sigmoid=True, gamma=1.50, alpha=0.4, loss_weight=1.0
          ),
          loss_bbox=dict(type="SmoothL1Loss", beta=0.11, loss_weight=1.0),
      ),
  )
  # training and testing settings
  train_cfg = {}
  test_cfg = dict(
      nms_pre=1000,
      score_thr=0.05,
      nms=dict(type="nms", iou_threshold=0.5),
      max_per_img=100,
  )
  data = dict(samples_per_gpu=4, workers_per_gpu=4)
  # optimizer
  optimizer = dict(type="SGD", lr=0.01, momentum=0.9, weight_decay=0.0001)
  