TensorFlow 2 version | View source on GitHub |
Computes the mean Intersection-Over-Union metric.
Inherits From: Metric
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.
Usage:
m = tf.keras.metrics.MeanIoU(num_classes=2)
m.update_state([0, 0, 1, 1], [0, 1, 0, 1])
# 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 = 0.33
print('Final result: ', m.result().numpy()) # Final result: 0.33
Usage with tf.keras API:
model = tf.keras.Model(inputs, outputs)
model.compile(
'sgd',
loss='mse',
metrics=[tf.keras.metrics.MeanIoU(num_classes=2)])
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. |
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. |