class_docstr: Evaluation callback for arbitrary datasets.
      
class_docstr_tok: ["Evaluation","callback","for","arbitrary","datasets","."]
docstr: Evaluate a given dataset using a given model at the end of every epoch during training.
  
  # Arguments
      generator        : The generator that represents the dataset to evaluate.
      iou_threshold    : The threshold used to consider when a detection is positive or negative.
      score_threshold  : The score confidence threshold to use for detections.
      max_detections   : The maximum number of detections to use per image.
      save_path        : The path to save images with visualized detections to.
      tensorboard      : Instance of keras.callbacks.TensorBoard used to log the mAP value.
      weighted_average : Compute the mAP using the weighted average of precisions among classes.
      verbose          : Set the verbosity level, by default this is set to 1.
docstr_tok: ["Evaluate","a","given","dataset","using","a","given","model","at","the","end","of","every","epoch","during","training",".","#","Arguments","generator",":","The","generator","that","represents","the","dataset","to","evaluate",".","iou_threshold",":","The","threshold","used","to","consider","when","a","detection","is","positive","or","negative",".","score_threshold",":","The","score","confidence","threshold","to","use","for","detections",".","max_detections",":","The","maximum","number","of","detections","to","use","per","image",".","save_path",":","The","path","to","save","images","with","visualized","detections","to",".","tensorboard",":","Instance","of","keras.callbacks.TensorBoard","used","to","log","the","mAP","value",".","weighted_average",":","Compute","the","mAP","using","the","weighted","average","of","precisions","among","classes",".","verbose",":","Set","the","verbosity","level",",","by","default","this","is","set","to","1","."]
code: def __init__(
      self,
      generator,
      iou_threshold=0.5,
      score_threshold=0.05,
      max_detections=100,
      save_path=None,
      tensorboard=None,
      weighted_average=False,
      verbose=1,
  ):
      """Evaluate a given dataset using a given model at the end of every epoch during training.
  
      # Arguments
          generator        : The generator that represents the dataset to evaluate.
          iou_threshold    : The threshold used to consider when a detection is positive or negative.
          score_threshold  : The score confidence threshold to use for detections.
          max_detections   : The maximum number of detections to use per image.
          save_path        : The path to save images with visualized detections to.
          tensorboard      : Instance of keras.callbacks.TensorBoard used to log the mAP value.
          weighted_average : Compute the mAP using the weighted average of precisions among classes.
          verbose          : Set the verbosity level, by default this is set to 1.
      """
      self.generator = generator
      self.iou_threshold = iou_threshold
      self.score_threshold = score_threshold
      self.max_detections = max_detections
      self.save_path = save_path
      self.tensorboard = tensorboard
      self.weighted_average = weighted_average
      self.verbose = verbose
  
      super(Evaluate, self).__init__()
  
imports: from tensorflow import keras
  from ..utils.eval import evaluate
label_desc: Use image node_id 1 to create a new Evaluate object from inherited base classes: keras.callbacks with example: obj = Evaluate(generator, iou_threshold, score_threshold, max_detections, save_path, tensorboard, weighted_average, verbose)