Image segmentation metrics

[source]

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

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)])