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.
Args |
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.
|
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.
|
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.
|
weights
|
Optional Tensor whose rank is either 0, or the same rank as
labels , and must be broadcastable to labels (i.e., all dimensions must
be either 1 , or the same as the corresponding labels dimension).
|
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
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.
|
RuntimeError
|
If eager execution is enabled.
|