Computes the mean Intersection-Over-Union metric
metric_mean_iou(..., num_classes, name = NULL, dtype = NULL)Passed on to the underlying metric. Used for forwards and backwards compatibility.
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.
(Optional) string name of the metric instance.
(Optional) data type of the metric result.
A (subclassed) Metric instance that can be passed directly to
compile(metrics = ), or used as a standalone object. See ?Metric for
example usage.
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.
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_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_mean(),
metric_poisson(),
metric_precision_at_recall(),
metric_precision(),
metric_recall_at_precision(),
metric_recall(),
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()