class_docstr: Internal implementation of Pascal object detection metrics.
class_docstr_tok: ["Internal","implementation","of","Pascal","object","detection","metrics","."]
code: def _initialize_detections(self):
      self.detection_keys = set()
      self.scores_per_class = [[] for _ in range(self.num_class)]
      self.tp_fp_labels_per_class = [[] for _ in range(self.num_class)]
      self.num_images_correctly_detected_per_class = np.zeros(
          self.num_class
      )
      self.average_precision_per_class = np.empty(
          self.num_class, dtype=float
      )
      self.average_precision_per_class.fill(np.nan)
      self.precisions_per_class = []
      self.recalls_per_class = []
      self.corloc_per_class = np.ones(self.num_class, dtype=float)
  
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 2 for calling the ObjectDetectionEvaluation obj's underlying member method code with example usage: obj._initialize_detections() without return types