class_docstr: A class to evaluate detections using Open Images V2 metrics.
  
  Open Images V2 introduce group_of type of bounding boxes and this metric
  handles those boxes appropriately.
class_docstr_tok: ["A","class","to","evaluate","detections","using","Open","Images","V2","metrics",".","Open","Images","V2","introduce","group_of","type","of","bounding","boxes","and","this","metric","handles","those","boxes","appropriately","."]
docstr: Constructor.
  
  Args:
    categories: A list of dicts, each of which has the following keys -
      'id': (required) an integer id uniquely identifying this category.
      'name': (required) string representing category name e.g., 'cat', 'dog'.
    matching_iou_threshold: IOU threshold to use for matching groundtruth
      boxes to detection boxes.
    evaluate_corlocs: if True, additionally evaluates and returns CorLoc.
code: def __init__(
      self,
      categories,
      matching_iou_threshold=0.5,
      evaluate_corlocs=False,
  ):
      """Constructor.
  
      Args:
        categories: A list of dicts, each of which has the following keys -
          'id': (required) an integer id uniquely identifying this category.
          'name': (required) string representing category name e.g., 'cat', 'dog'.
        matching_iou_threshold: IOU threshold to use for matching groundtruth
          boxes to detection boxes.
        evaluate_corlocs: if True, additionally evaluates and returns CorLoc.
      """
      super(OpenImagesDetectionEvaluator, self).__init__(
          categories,
          matching_iou_threshold,
          evaluate_corlocs,
          metric_prefix="OpenImagesV2",
      )
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import collections
  import logging
  import numpy
  from abc import ABCMeta, abstractmethod
  from .None import label_map_util, metrics, per_image_evaluation, standard_fields
label_desc: Use image node_id 1 to create a new OpenImagesDetectionEvaluator object from inherited base classes: ObjectDetectionEvaluator with example: obj = OpenImagesDetectionEvaluator(categories, matching_iou_threshold, evaluate_corlocs)