tf.contrib.metrics.streaming_mean_iou()

tf.contrib.metrics.streaming_mean_iou(*args, **kwargs)

Calculate per-step mean Intersection-Over-Union (mIOU). (deprecated arguments)

SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-10-19. Instructions for updating: ignore_mask is being deprecated. Instead use weights with values 0.0 and 1.0 to mask values. For example, weights=tf.logical_not(mask).

Mean Intersection-Over-Union is a common evaluation metric for semantic image segmentation, which first computes the IOU for each semantic class and then computes the average over classes. IOU is defined as follows: IOU = true_positive / (true_positive + false_positive + false_negative). The predictions are accumulated in a confusion matrix, weighted by weights, and mIOU is then calculated from it.

For estimation of the metric over a stream of data, the function creates an update_op operation that updates these variables and returns the mean_iou.

If weights is None, weights default to 1. Use weights of 0 to mask values. Alternatively, if ignore_mask is not None, then mask values where ignore_mask is True.

Args: predictions: A tensor of prediction results for semantic labels, whose shape is [batch size] and type int32 or int64. The tensor will be flattened, if its rank > 1. labels: A tensor of ground truth labels with shape [batch size] and of type int32 or int64. The tensor will be flattened, if its rank > 1. num_classes: The possible number of labels the prediction task can have. This value must be provided, since a confusion matrix of dimension = [num_classes, num_classes] will be allocated. ignore_mask: An optional, bool Tensor whose shape matches predictions. weights: An optional Tensor whose shape is broadcastable to predictions. metrics_collections: An optional list of collections that mean_iou should be added to. updates_collections: An optional list of collections update_op should be added to. name: An optional variable_scope name.

Returns: mean_iou: A tensor representing the mean intersection-over-union. update_op: An operation that increments the confusion matrix.

Raises: ValueError: If predictions and labels have mismatched shapes, or if ignore_mask is not None and its shape doesn't match predictions, or if weights is not None and its shape doesn't match predictions, or if either metrics_collections or updates_collections are not a list or tuple.