class_docstr: Evaluate semantic segmentation
  
  Args:
      num_classes (int): number of classes
      ignore_index (int, optional): value in semantic segmentation ground truth. Predictions for the
      corresponding pixels should be ignored. Default: 255.
class_docstr_tok: ["Evaluate","semantic","segmentation","Args",":","num_classes","(","int",")",":","number","of","classes","ignore_index","(","int",",","optional",")",":","value","in","semantic","segmentation","ground","truth",".","Predictions","for","the","corresponding","pixels","should","be","ignored",".","Default",":","255","."]
code: def __init__(self, num_classes, ignore_index=255):
      self._num_classes = num_classes
      self._ignore_index = ignore_index
      self._N = num_classes + 1  # store ignore label in the last class
  
      self._conf_matrix = np.zeros((self._N, self._N), dtype=np.int64)
  
imports: from collections import OrderedDict
  import numpy
label_desc: Use image node_id 1 to create a new SemanticEvaluator object  with example: obj = SemanticEvaluator(num_classes, ignore_index)