text: _base_ = [
      '../_base_/datasets/coco500_detection_augm.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 = dict()
  test_cfg = dict(
      nms_pre=1000,
      score_thr=0.05,
      nms=dict(type='nms', iou_thr=0.5),
      max_per_img=100)
  
  # optimizer
  optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
  
  # learning policy
  lr_config = dict(step=[60, 80])
  total_epochs = 100
  