» API reference / Metrics / Image segmentation metrics

# Image segmentation metrics

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### `MeanIoU` class

``````tf.keras.metrics.MeanIoU(
num_classes: int,
name: Optional[str] = None,
dtype: Union[str, tensorflow.python.framework.dtypes.DType, NoneType] = None,
ignore_class: Optional[int] = None,
sparse_y_true: bool = True,
sparse_y_pred: bool = True,
axis: int = -1,
)
``````

Computes the mean Intersection-Over-Union metric.

General definition and computation:

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

For an individual class, the IoU metric is defined as follows:

``````iou = true_positives / (true_positives + false_positives + false_negatives)
``````

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.

Arguments

• 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 `tf.argmax` function will be 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 `tf.argmax` function will be used to determine each sample's most likely associated label.
• axis: (Optional) Defaults to -1. The dimension containing the logits.

Standalone usage:

``````>>> # 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 = tf.keras.metrics.MeanIoU(num_classes=2)
>>> m.update_state([0, 0, 1, 1], [0, 1, 0, 1])
>>> m.result().numpy()
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=[tf.keras.metrics.MeanIoU(num_classes=2)])
``````