Image segmentation metrics

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IoU class

keras.metrics.IoU( num_classes, target_class_ids, name=None, dtype=None, ignore_class=None, sparse_y_true=True, sparse_y_pred=True, axis=-1, ) 

Computes the Intersection-Over-Union metric for specific target classes.

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, this class first computes IoUs for all individual classes, then returns the mean of IoUs for the classes that are specified by target_class_ids. If target_class_ids has only one id value, the IoU of that specific class is returned.

Arguments

• num_classes: The possible number of labels the prediction task can have.
• target_class_ids: A tuple or list of target class ids for which the metric is returned. To compute IoU for a specific class, a list (or tuple) of a single id value should be provided.
• 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) -1 is the dimension containing the logits. Defaults to -1.

Examples

>>> # 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)) >>> # iou = [0.33, 0.33] >>> m = keras.metrics.IoU(num_classes=2, target_class_ids=[0]) >>> 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]) >>> # cm = [[0.3, 0.3], >>> # [0.3, 0.1]] >>> # sum_row = [0.6, 0.4], sum_col = [0.6, 0.4], >>> # true_positives = [0.3, 0.1] >>> # iou = [0.33, 0.14] >>> m.result() 0.33333334 

Usage with compile() API:

model.compile( optimizer='sgd', loss='mse', metrics=[keras.metrics.IoU(num_classes=2, target_class_ids=[0])]) 

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BinaryIoU class

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

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

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.

Arguments

• 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

>>> 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 )] ) 

[source]

OneHotIoU class

keras.metrics.OneHotIoU( num_classes, target_class_ids, name=None, dtype=None, ignore_class=None, sparse_y_pred=False, axis=-1, ) 

Computes the Intersection-Over-Union metric for one-hot encoded labels.

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 IoU for multi-class classification tasks where the labels are one-hot encoded (the last axis should have one dimension per class). Note that the predictions should also have the same shape. To compute the IoU, first the labels and predictions are converted back into integer format by taking the argmax over the class axis. Then the same computation steps as for the base IoU class apply.

Note, if there is only one channel in the labels and predictions, this class is the same as class IoU. In this case, use IoU instead.

Also, make sure that num_classes is equal to the number of classes in the data, to avoid a "labels out of bound" error when the confusion matrix is computed.

Arguments

• num_classes: The possible number of labels the prediction task can have.
• target_class_ids: A tuple or list of target class ids for which the metric is returned. To compute IoU for a specific class, a list (or tuple) of a single id value should be provided.
• 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_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

>>> y_true = np.array([[0, 0, 1], [1, 0, 0], [0, 1, 0], [1, 0, 0]]) >>> y_pred = np.array([[0.2, 0.3, 0.5], [0.1, 0.2, 0.7], [0.5, 0.3, 0.1], ... [0.1, 0.4, 0.5]]) >>> sample_weight = [0.1, 0.2, 0.3, 0.4] >>> m = keras.metrics.OneHotIoU(num_classes=3, target_class_ids=[0, 2]) >>> m.update_state( ... y_true=y_true, y_pred=y_pred, sample_weight=sample_weight) >>> # cm = [[0, 0, 0.2+0.4], >>> # [0.3, 0, 0], >>> # [0, 0, 0.1]] >>> # sum_row = [0.3, 0, 0.7], sum_col = [0.6, 0.3, 0.1] >>> # true_positives = [0, 0, 0.1] >>> # single_iou = true_positives / (sum_row + sum_col - true_positives)) >>> # mean_iou = (0 / (0.3 + 0.6 - 0) + 0.1 / (0.7 + 0.1 - 0.1)) / 2 >>> m.result() 0.071 

Usage with compile() API:

model.compile( optimizer='sgd', loss='mse', metrics=[keras.metrics.OneHotIoU( num_classes=3, target_class_id=[1] )] ) 

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OneHotMeanIoU class

keras.metrics.OneHotMeanIoU( num_classes, name=None, dtype=None, ignore_class=None, sparse_y_pred=False, axis=-1 ) 

Computes mean Intersection-Over-Union metric for one-hot encoded labels.

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 the mean IoU for multi-class classification tasks where the labels are one-hot encoded (the last axis should have one dimension per class). Note that the predictions should also have the same shape. To compute the mean IoU, first the labels and predictions are converted back into integer format by taking the argmax over the class axis. Then the same computation steps as for the base MeanIoU class apply.

Note, if there is only one channel in the labels and predictions, this class is the same as class MeanIoU. In this case, use MeanIoU instead.

Also, make sure that num_classes is equal to the number of classes in the data, to avoid a "labels out of bound" error when the confusion matrix is computed.

Arguments

• num_classes: The possible number of labels the prediction task can have.
• 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_pred: Whether predictions are encoded using natural numbers or probability distribution vectors. If False, the argmax function will be used to determine each sample's most likely associated label.
• axis: (Optional) The dimension containing the logits. Defaults to -1.

Example

>>> y_true = np.array([[0, 0, 1], [1, 0, 0], [0, 1, 0], [1, 0, 0]]) >>> y_pred = np.array([[0.2, 0.3, 0.5], [0.1, 0.2, 0.7], [0.5, 0.3, 0.1], ... [0.1, 0.4, 0.5]]) >>> sample_weight = [0.1, 0.2, 0.3, 0.4] >>> m = keras.metrics.OneHotMeanIoU(num_classes=3) >>> m.update_state( ... y_true=y_true, y_pred=y_pred, sample_weight=sample_weight) >>> # cm = [[0, 0, 0.2+0.4], >>> # [0.3, 0, 0], >>> # [0, 0, 0.1]] >>> # sum_row = [0.3, 0, 0.7], sum_col = [0.6, 0.3, 0.1] >>> # true_positives = [0, 0, 0.1] >>> # single_iou = true_positives / (sum_row + sum_col - true_positives)) >>> # mean_iou = (0 + 0 + 0.1 / (0.7 + 0.1 - 0.1)) / 3 >>> m.result() 0.048 

Usage with compile() API:

model.compile( optimizer='sgd', loss='mse', metrics=[keras.metrics.OneHotMeanIoU(num_classes=3)]) 

[source]

MeanIoU class

keras.metrics.MeanIoU( num_classes, name=None, dtype=None, ignore_class=None, sparse_y_true=True, sparse_y_pred=True, axis=-1, ) 

Computes the mean Intersection-Over-Union metric.

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.

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 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

>>> # 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)])