{ }
View source on GitHub |
Calculate per-step mean Intersection-Over-Union (mIOU).
tf.compat.v1.metrics.mean_iou(
labels,
predictions,
num_classes,
weights=None,
metrics_collections=None,
updates_collections=None,
name=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 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.
Returns | |
---|---|
mean_iou
|
A Tensor representing the mean intersection-over-union.
|
update_op
|
An operation that increments the confusion matrix. |