text: # model settings
  model = dict(
      type='ImageClassifier',
      backbone=dict(
          type='SwinTransformer', arch='base', img_size=224, drop_path_rate=0.5,
          init_cfg=dict(
              type='Pretrained',
              checkpoint='https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_base_patch4_window7_224_22kto1k-f967f799.pth',
              prefix='backbone',
          )
      ),
      neck=dict(type='GlobalAveragePooling'),
      head=dict(
          type='LinearClsHead',
          num_classes=11,
          in_channels=1024,
          init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
          loss=dict(
              type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
          cal_acc=False),
      init_cfg=[
          dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
          dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
      ],
      train_cfg=dict(augments=[
          dict(type='BatchMixup', alpha=0.8, num_classes=11, prob=0.5),
          dict(type='BatchCutMix', alpha=1.0, num_classes=11, prob=0.5)
      ]))
  