text: from maskrcnn_benchmark.data import datasets
  
  from .coco import coco_evaluation
  from .voc import voc_evaluation
  
  
  def evaluate(dataset, predictions, output_folder, **kwargs):
      """evaluate dataset using different methods based on dataset type.
      Args:
          dataset: Dataset object
          predictions(list[BoxList]): each item in the list represents the
              prediction results for one image.
          output_folder: output folder, to save evaluation files or results.
          **kwargs: other args.
      Returns:
          evaluation result
      """
      args = dict(
          dataset=dataset, predictions=predictions, output_folder=output_folder, **kwargs
      )
      if isinstance(dataset, datasets.COCODataset):
          return coco_evaluation(**args)
      elif isinstance(dataset, datasets.PascalVOCDataset):
          return voc_evaluation(**args)
      elif isinstance(dataset, datasets.JINNANDataset):
          return coco_evaluation(**args)
      else:
          dataset_name = dataset.__class__.__name__
          raise NotImplementedError("Unsupported dataset type {}.".format(dataset_name))
  