tf.keras.metrics.MeanIoU

Computes the mean Intersection-Over-Union metric.

Inherits From: IoU, Metric

tf.keras.metrics.MeanIoU( num_classes, 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 that this class first computes IoUs for all individual classes, then returns the mean of these values.

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.
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) The dimension containing the logits. Defaults to -1.

Example:

Example:

# 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 m = keras.metrics.MeanIoU(num_classes=2) 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]) m.result().numpy() 0.23809525

Usage with compile() API:

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

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.

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.