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TensorFlow 1 version
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View source on GitHub
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Computes the mean Intersection-Over-Union metric.
Inherits From: Metric, Layer, Module
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.
If sample_weight is None, weights default to 1.
Use sample_weight of 0 to mask values.
Args | |
|---|---|
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. |
name
|
(Optional) string name of the metric instance. |
dtype
|
(Optional) data type of the metric result. |
Standalone usage:
# cm = [[1, 1],# [1, 1]]# sum_row = [2, 2], sum_col = [2, 2], true_positives = [1, 1]# iou = true_positives / (sum_row + sum_col - true_positives))# result = (1 / (2 + 2 - 1) + 1 / (2 + 2 - 1)) / 2 = 0.33m = tf.keras.metrics.MeanIoU(num_classes=2)m.update_state([0, 0, 1, 1], [0, 1, 0, 1])m.result().numpy()0.33333334
m.reset_states()m.update_state([0, 0, 1, 1], [0, 1, 0, 1],sample_weight=[0.3, 0.3, 0.3, 0.1])m.result().numpy()0.23809525
Usage with compile() API:
model.compile(
optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.MeanIoU(num_classes=2)])
Methods
reset_states
reset_states()
Resets all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
result
result()
Compute the mean intersection-over-union via the confusion matrix.
update_state
update_state(
y_true, y_pred, sample_weight=None
)
Accumulates the confusion matrix statistics.
| Args | |
|---|---|
y_true
|
The ground truth values. |
y_pred
|
The predicted values. |
sample_weight
|
Optional weighting of each example. Defaults to 1. Can be a
Tensor whose rank is either 0, or the same rank as y_true, and must
be broadcastable to y_true.
|
| Returns | |
|---|---|
| Update op. |