class_docstr: A class to load datasets, evaluate results for a datast split (e.g., "coco_train_2017")
  
  To use your own dataset that's not in COCO format, write a subclass that
  implements the interfaces.
docstr: Args:
      results (list[dict]): the inference results as dicts.
          Each dict corresponds to one __instance__. It contains the following keys:
  
          image_id (str): the id that matches `inference_roidbs`.
          category_id (int): the category prediction, in range [1, #category]
          bbox (list[float]): x1, y1, x2, y2
          score (float):
          segmentation: the segmentation mask in COCO's rle format.
      output (str): the output file or directory to optionally save the results to.
  
  Returns:
      dict: the evaluation results.
code: def eval_inference_results(self, results, output=None):
      """
      Args:
          results (list[dict]): the inference results as dicts.
              Each dict corresponds to one __instance__. It contains the following keys:
  
              image_id (str): the id that matches `inference_roidbs`.
              category_id (int): the category prediction, in range [1, #category]
              bbox (list[float]): x1, y1, x2, y2
              score (float):
              segmentation: the segmentation mask in COCO's rle format.
          output (str): the output file or directory to optionally save the results to.
  
      Returns:
          dict: the evaluation results.
      """
      raise NotImplementedError()
  
file_path: tensorpack/examples/FasterRCNN/dataset/dataset.py
imports: from collections import defaultdict
label_desc: Use image node_id 3 for calling the DatasetSplit obj's underlying member method code with example usage: obj.eval_inference_results(results, output) without return types