|TensorFlow 1 version||View source on GitHub|
Computes the mean Intersection-Over-Union metric.
See Migration guide for more details.
tf.keras.metrics.MeanIoU( num_classes, name=None, dtype=None )
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
sample_weight and the metric is then calculated from it.
None, weights default to 1.
sample_weight of 0 to mask values.
||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.|
||(Optional) string name of the metric instance.|