text: # model settings
  norm_cfg = dict(type='SyncBN', requires_grad=True)
  model = dict(
      type='EncoderDecoder',
      pretrained=None,
      backbone=dict(
          type='UNet',
          in_channels=3,
          base_channels=64,
          num_stages=5,
          strides=(1, 1, 1, 1, 1),
          enc_num_convs=(2, 2, 2, 2, 2),
          dec_num_convs=(2, 2, 2, 2),
          downsamples=(True, True, True, True),
          enc_dilations=(1, 1, 1, 1, 1),
          dec_dilations=(1, 1, 1, 1),
          with_cp=False,
          conv_cfg=None,
          norm_cfg=norm_cfg,
          act_cfg=dict(type='ReLU'),
          upsample_cfg=dict(type='InterpConv'),
          norm_eval=False),
      decode_head=dict(
          type='PSPHead',
          in_channels=64,
          in_index=4,
          channels=16,
          pool_scales=(1, 2, 3, 6),
          dropout_ratio=0.1,
          num_classes=2,
          norm_cfg=norm_cfg,
          align_corners=False,
          loss_decode=dict(
              type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
      auxiliary_head=dict(
          type='FCNHead',
          in_channels=128,
          in_index=3,
          channels=64,
          num_convs=1,
          concat_input=False,
          dropout_ratio=0.1,
          num_classes=2,
          norm_cfg=norm_cfg,
          align_corners=False,
          loss_decode=dict(
              type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
  # model training and testing settings
  train_cfg = dict()
  test_cfg = dict(mode='slide', crop_size=256, stride=170)
  