tf.keras.metrics.IoU

Computes the Intersection-Over-Union metric for specific target classes.

Inherits From: Metric

tf.keras.metrics.IoU(
    num_classes,
    target_class_ids,
    name=None,
    dtype=None,
    ignore_class=None,
    sparse_y_true=True,
    sparse_y_pred=True,
    axis=-1
)

Formula:

iou = true_positives / (true_positives + false_positives + false_negatives)

Intersection-Over-Union is a common evaluation metric for semantic image segmentation.

To compute IoUs, 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.

Note, this class first computes IoUs for all individual classes, then returns the mean of IoUs for the classes that are specified by target_class_ids. If target_class_ids has only one id value, the IoU of that specific class is returned.

num_classes The possible number of labels the prediction task can have.
target_class_ids A tuple or list of target class ids for which the metric is returned. To compute IoU for a specific class, a list (or tuple) of a single id value should be provided.
name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.
ignore_class Optional integer. The ID of a class to be ignored during metric computation. This is useful, for example, in segmentation problems featuring a "void" class (commonly -1 or 255) in segmentation maps. By default (ignore_class=None), all classes are considered.
sparse_y_true Whether labels are encoded using integers or dense floating point vectors. If False, the argmax function is used to determine each sample's most likely associated label.
sparse_y_pred Whether predictions are encoded using integers or dense floating point vectors. If False, the argmax function is used to determine each sample's most likely associated label.
axis (Optional) -1 is the dimension containing the logits. Defaults to -1.

Examples:

# 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))
# iou = [0.33, 0.33]
m = keras.metrics.IoU(num_classes=2, target_class_ids=[0])
m.update_state([0, 0, 1, 1], [0, 1, 0, 1])
m.result()
0.33333334
 
m.reset_state()
m.update_state([0, 0, 1, 1], [0, 1, 0, 1],
               sample_weight=[0.3, 0.3, 0.3, 0.1])
# cm = [[0.3, 0.3],
#        [0.3, 0.1]]
# sum_row = [0.6, 0.4], sum_col = [0.6, 0.4],
# true_positives = [0.3, 0.1]
# iou = [0.33, 0.14]
m.result()
0.33333334

Usage with compile() API:

model.compile(
    optimizer='sgd',
    loss='mse',
    metrics=[keras.metrics.IoU(num_classes=2, target_class_ids=[0])])

dtype

variables

Methods

add_variable

View source

add_variable(
    shape, initializer, dtype=None, aggregation='sum', name=None
)

add_weight

View source

add_weight(
    shape=(), initializer=None, dtype=None, name=None
)

from_config

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@classmethod
from_config(
    config
)

get_config

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get_config()

Return the serializable config of the metric.

reset_state

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reset_state()

Reset all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

result

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result()

Compute the intersection-over-union via the confusion matrix.

stateless_reset_state

View source

stateless_reset_state()

stateless_result

View source

stateless_result(
    metric_variables
)

stateless_update_state

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stateless_update_state(
    metric_variables, *args, **kwargs
)

update_state

View source

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. Can be a Tensor whose rank is either 0, or the same as y_true, and must be broadcastable to y_true. Defaults to 1.

Returns
Update op.

__call__

View source

__call__(
    *args, **kwargs
)

Call self as a function.