text: _base_ = '../dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py'
  
  # We also need to change the num_classes in head to match the dataset's annotation
  
  
  # Modify dataset related settings
  dataset_type = 'COCODataset'
  classes = ('1','2','3','4','5','6','7',) 
  #classes =  ('0',)
  data = dict(
      train=dict(
          img_prefix='./dataset/train_image',
          classes=classes,
          ann_file='./dataset/train_label_multi'),
      val=dict(
          img_prefix='./dataset/train_image',
          classes=classes,
          ann_file='./dataset/val_label_multi'),
      test=dict(
          img_prefix='./dataset/train_image',
          classes=classes,
          ann_file='./dataset/val_label_multi_1.json'))
  
  # We can use the pre-trained Mask RCNN model to obtain higher performance
  load_from = 'checkpoints/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco-e75f90c8.pth'