text: dataset_type = 'CustomDataset'
  
  img_norm_cfg = dict(mean=[123.675, 116.28, 103.53],
                      std=[58.395, 57.12, 57.375],
                      to_rgb=True)
  
  img_scale = (544, 544)
  crop_size = (512, 512)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotations'),
      dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0), keep_ratio=False),
      dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
      dict(type='RandomFlip', prob=0.5, direction='horizontal'),
      dict(type='MaskCompose', prob=0.5, lambda_limits=(4, 16), keep_original=False,
           transforms=[
               dict(type='PhotoMetricDistortion'),
           ]),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
      dict(type='RandomRotate', prob=0.5, degree=30, pad_val=0, seg_pad_val=255),
      dict(type='ClassBordersExtractor', ignore_index=255),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_semantic_seg', 'gt_class_borders'])
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=img_scale,
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=False),
              dict(type='RandomFlip'),
              dict(type='Normalize', **img_norm_cfg),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img'])
          ]
      )
  ]
  
  data = dict(
      samples_per_gpu=8,
      workers_per_gpu=3,
      train=dict(
          type='RepeatDataset',
          adaptive_repeat=True,
          times=1,
          dataset=dict(
              type=dataset_type,
              img_dir='images/training',
              ann_dir='annotations/training',
              pipeline=train_pipeline
          )
      ),
      val=dict(
          type=dataset_type,
          img_dir='images/validation',
          ann_dir='annotations/validation',
          pipeline=test_pipeline
      ),
      test=dict(
          type=dataset_type,
          img_dir='images/validation',
          ann_dir='annotations/validation',
          pipeline=test_pipeline
      )
  )
  