class_docstr: Evaluate detection result of a single image.
class_docstr_tok: ["Evaluate","detection","result","of","a","single","image","."]
docstr: Initialized PerImageEvaluation by evaluation parameters.
  
  Args:
    num_groundtruth_classes: Number of ground truth object classes
    matching_iou_threshold: A ratio of area intersection to union, which is
        the threshold to consider whether a detection is true positive or not
docstr_tok: ["Initialized","PerImageEvaluation","by","evaluation","parameters",".","Args",":","num_groundtruth_classes",":","Number","of","ground","truth","object","classes","matching_iou_threshold",":","A","ratio","of","area","intersection","to","union",",","which","is","the","threshold","to","consider","whether","a","detection","is","true","positive","or","not"]
code: def __init__(
      self, num_groundtruth_classes, matching_iou_threshold=0.5
  ):
      """Initialized PerImageEvaluation by evaluation parameters.
  
      Args:
        num_groundtruth_classes: Number of ground truth object classes
        matching_iou_threshold: A ratio of area intersection to union, which is
            the threshold to consider whether a detection is true positive or not
      """
      self.matching_iou_threshold = matching_iou_threshold
      self.num_groundtruth_classes = num_groundtruth_classes
  
file_path: SlowFast/ava_evaluation/per_image_evaluation.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import numpy
  from .None import np_box_list, np_box_list_ops, np_box_mask_list, np_box_mask_list_ops
label_desc: Use image node_id 1 to create a new PerImageEvaluation object  with example: obj = PerImageEvaluation(num_groundtruth_classes, matching_iou_threshold)