metric_mean_iou {keras}

Computes the mean Intersection-Over-Union metric

Description

Computes the mean Intersection-Over-Union metric

Usage

metric_mean_iou(..., num_classes, name = NULL, dtype = NULL)

Arguments

...

Passed on to the underlying metric. Used for forwards and backwards compatibility.

num_classes

The possible number of labels the prediction task can have. This value must be provided, since a confusion matrix of dim c(num_classes, num_classes) will be allocated.

name

(Optional) string name of the metric instance.

dtype

(Optional) data type of the metric result.

Details

Mean Intersection-Over-Union is a common evaluation metric for semantic image segmentation, which first computes the IOU for each semantic class and then computes the average over classes. IOU is defined as follows:

  IOU = true_positive / (true_positive + false_positive + false_negative)

The predictions are accumulated in a confusion matrix, weighted by sample_weight and the metric is then calculated from it.

If sample_weight is NULL, weights default to 1. Use sample_weight of 0 to mask values.

Value

A (subclassed) Metric instance that can be passed directly to compile(metrics = ), or used as a standalone object. See ?Metric for example usage.

See Also

Other metrics: custom_metric(), metric_accuracy(), metric_auc(), metric_binary_accuracy(), metric_binary_crossentropy(), metric_categorical_accuracy(), metric_categorical_crossentropy(), metric_categorical_hinge(), metric_cosine_similarity(), metric_false_negatives(), metric_false_positives(), metric_hinge(), metric_kullback_leibler_divergence(), metric_logcosh_error(), metric_mean(), metric_mean_absolute_error(), metric_mean_absolute_percentage_error(), metric_mean_relative_error(), metric_mean_squared_error(), metric_mean_squared_logarithmic_error(), metric_mean_tensor(), metric_mean_wrapper(), metric_poisson(), metric_precision(), metric_precision_at_recall(), metric_recall(), metric_recall_at_precision(), metric_root_mean_squared_error(), metric_sensitivity_at_specificity(), metric_sparse_categorical_accuracy(), metric_sparse_categorical_crossentropy(), metric_sparse_top_k_categorical_accuracy(), metric_specificity_at_sensitivity(), metric_squared_hinge(), metric_sum(), metric_top_k_categorical_accuracy(), metric_true_negatives(), metric_true_positives()