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__()
 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.
        
keras_retinanet/callbacks/eval.py