tf.keras.metrics.BinaryIoU

Computes the Intersection-Over-Union metric for class 0 and/or 1.

Inherits From: IoU, Metric

tf.keras.metrics.BinaryIoU(
    target_class_ids=(0, 1), threshold=0.5, name=None, dtype=None
)

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.

This class can be used to compute IoUs for a binary classification task where the predictions are provided as logits. First a threshold is applied to the predicted values such that those that are below the threshold are converted to class 0 and those that are above the threshold are converted to class 1.

IoUs for classes 0 and 1 are then computed, the mean of IoUs for the classes that are specified by target_class_ids is returned.

Note: with threshold=0, this metric has the same behavior as IoU.

Args
target_class_ids	A tuple or list of target class ids for which the metric is returned. Options are [0], [1], or [0, 1]. With [0] (or [1]), the IoU metric for class 0 (or class 1, respectively) is returned. With [0, 1], the mean of IoUs for the two classes is returned.
threshold	A threshold that applies to the prediction logits to convert them to either predicted class 0 if the logit is below threshold or predicted class 1 if the logit is above threshold.
name	(Optional) string name of the metric instance.
dtype	(Optional) data type of the metric result.

Example:

Example:

m = keras.metrics.BinaryIoU(target_class_ids=[0, 1], threshold=0.3)
m.update_state([0, 1, 0, 1], [0.1, 0.2, 0.4, 0.7])
m.result()
0.33333334

m.reset_state()
m.update_state([0, 1, 0, 1], [0.1, 0.2, 0.4, 0.7],
               sample_weight=[0.2, 0.3, 0.4, 0.1])
# cm = [[0.2, 0.4],
#        [0.3, 0.1]]
# sum_row = [0.6, 0.4], sum_col = [0.5, 0.5],
# true_positives = [0.2, 0.1]
# iou = [0.222, 0.125]
m.result()
0.17361112

Usage with compile() API:

model.compile(
    optimizer='sgd',
    loss='mse',
    metrics=[keras.metrics.BinaryIoU(
        target_class_ids=[0],
        threshold=0.5
    )]
)

Attributes
dtype
variables

Methods

add_variable

View source

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

add_weight

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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

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stateless_reset_state()

stateless_result

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stateless_result(
    metric_variables
)

stateless_update_state

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stateless_update_state(
    metric_variables, *args, **kwargs
)

update_state

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update_state(
    y_true, y_pred, sample_weight=None
)

Accumulates the confusion matrix statistics.

Before the confusion matrix is updated, the predicted values are thresholded to be: 0 for values that are smaller than the threshold 1 for values that are larger or equal to the threshold

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.