class_docstr: A class to evaluate detections using weighted PASCAL metrics.
  
  Weighted PASCAL metrics computes the mean average precision as the average
  precision given the scores and tp_fp_labels of all classes. In comparison,
  PASCAL metrics computes the mean average precision as the mean of the
  per-class average precisions.
  
  This definition is very similar to the mean of the per-class average
  precisions weighted by class frequency. However, they are typically not the
  same as the average precision is not a linear function of the scores and
  tp_fp_labels.
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code: def __init__(self, categories, matching_iou_threshold=0.5):
      super(WeightedPascalDetectionEvaluator, self).__init__(
          categories,
          matching_iou_threshold=matching_iou_threshold,
          evaluate_corlocs=False,
          metric_prefix="WeightedPascalBoxes",
          use_weighted_mean_ap=True,
      )
  
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 WeightedPascalDetectionEvaluator object from inherited base classes: ObjectDetectionEvaluator with example: obj = WeightedPascalDetectionEvaluator(categories, matching_iou_threshold)