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.

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 )] )

dtype

variables

Methods

add_variable

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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__

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__call__( *args, **kwargs )

Call self as a function.