metric_iou {keras3} | R Documentation |
Computes the Intersection-Over-Union metric for specific target classes.
Description
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 NULL
, 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.
Usage
metric_iou(
...,
num_classes,
target_class_ids,
name = NULL,
dtype = NULL,
ignore_class = NULL,
sparse_y_true = TRUE,
sparse_y_pred = TRUE,
axis = -1L
)
Arguments
... |
For forward/backward compatability. |
num_classes |
The possible number of labels the prediction task can have. |
target_class_ids |
A list of target class ids for which the metric is returned. To compute IoU for a specific class, a list 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 ( |
sparse_y_true |
Whether labels are encoded using integers or
dense floating point vectors. If |
sparse_y_pred |
Whether predictions are encoded using integers or
dense floating point vectors. If |
axis |
(Optional) -1 is the dimension containing the logits.
Defaults to |
Value
a Metric
instance is returned. The Metric
instance can be passed
directly to compile(metrics = )
, or used as a standalone object. See
?Metric
for example usage.
Examples
Standalone usage:
m <- metric_iou(num_classes = 2L, target_class_ids = list(0L)) m$update_state(c(0, 0, 1, 1), c(0, 1, 0, 1)) m$result()
## tf.Tensor(0.3333333, shape=(), dtype=float32)
m$reset_state() m$update_state(c(0, 0, 1, 1), c(0, 1, 0, 1), sample_weight = c(0.3, 0.3, 0.3, 0.1)) m$result()
## tf.Tensor(0.33333325, shape=(), dtype=float32)
Usage with compile()
API:
model %>% compile( optimizer = 'sgd', loss = 'mse', metrics = list(metric_iou(num_classes = 2L, target_class_ids = list(0L))))
See Also
Other iou metrics:
metric_binary_iou()
metric_mean_iou()
metric_one_hot_iou()
metric_one_hot_mean_iou()
Other metrics:
Metric()
custom_metric()
metric_auc()
metric_binary_accuracy()
metric_binary_crossentropy()
metric_binary_focal_crossentropy()
metric_binary_iou()
metric_categorical_accuracy()
metric_categorical_crossentropy()
metric_categorical_focal_crossentropy()
metric_categorical_hinge()
metric_cosine_similarity()
metric_f1_score()
metric_false_negatives()
metric_false_positives()
metric_fbeta_score()
metric_hinge()
metric_huber()
metric_kl_divergence()
metric_log_cosh()
metric_log_cosh_error()
metric_mean()
metric_mean_absolute_error()
metric_mean_absolute_percentage_error()
metric_mean_iou()
metric_mean_squared_error()
metric_mean_squared_logarithmic_error()
metric_mean_wrapper()
metric_one_hot_iou()
metric_one_hot_mean_iou()
metric_poisson()
metric_precision()
metric_precision_at_recall()
metric_r2_score()
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()