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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.
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)])
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.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.
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
.Update op.