EUnsupervisedSegmenterMetrics Class

Collection of metrics used to evaluate the state of an EUnsupervisedSegmenter.
A metric is a value summarizing the quality of a collection of unsupervised segmentation results (see EUnsupervisedSegmenterResult) with respect to their ground truth.
New results can be added to the object individually with EUnsupervisedSegmenterMetrics::AddResult or collectively with EUnsupervisedSegmenterMetrics::AddMetrics.

EUnsupervisedSegmenterMetrics contains two types of metrics: unsupervised metrics that are computed only on good images and supervised/defect detection metrics that are computed on both good and bad images. The defect detection metrics are accessible only when results for bad images were added to the object. When supervised metrics are accessible, EUnsupervisedSegmenterMetrics::IsTotallyUnsupervised is false.
There is only one unsupervised metric: the error (see EUnsupervisedSegmenterMetrics::Error).
See EDeepLearningDefectDetectionMetrics for a description of the defect detection metrics.

Base Class:EDeepLearningDefectDetectionMetrics

Namespace: Euresys::Open_eVision::EasyDeepLearning

Methods

Adds the given result to the metric for error computation. The result must be computed from a good image that was corrupted during training.
Adds the other metrics to the current metrics of this object.
Adds the given result with the corresponding ground truth label to the metrics.
Constructs an EUnsupervisedSegmenterMetrics object.
Gets the average score of defective images.
Gets the average score of good images.
Best achievable weighted accuracy.
The weighted accuracy is the weighted average of the true positive rate and the true negative rate (which is equal to 1 minus the false positive rate). See EROCPoint.
The classification threshold corresponding to this accuracy is given by EUnsupervisedSegmenterMetrics::GetBestWeightedAccuracyClassificationThreshold.
Classification threshold giving the best achievable weighted accuracy (see EUnsupervisedSegmenterMetrics::GetBestWeightedAccuracy).
The error of the segmenter.
This metric is only available in the metrics computed during a training which are accessible through EUnsupervisedSegmenter::GetValidationMetrics.
The error, which is also called the loss, is the quantity that is minimized during the training of the deep neural network.
Whether this metrics has results from only good images (true) or from both good and defective images (false).
Some metrics are accessible only if EUnsupervisedSegmenterMetrics::IsTotallyUnsupervised is false.
Indicates whether the object contains at least one result.
Loads an unsupervised segmentation metric. The given ESerializer must have been created for reading.
Assignment operator.
Removes the given result from the metric for error computation. The result must be computed from a good image that was corrupted during training.
Removes the given result with the corresponding ground truth label to the metrics.
Saves an unsupervised segmentation metric. The given ESerializer must have been created for writing.

EUnsupervisedSegmenterMetrics Class

Collection of metrics used to evaluate the state of an EUnsupervisedSegmenter.
A metric is a value summarizing the quality of a collection of unsupervised segmentation results (see EUnsupervisedSegmenterResult) with respect to their ground truth.
New results can be added to the object individually with EUnsupervisedSegmenterMetrics::AddResult or collectively with EUnsupervisedSegmenterMetrics::AddMetrics.

EUnsupervisedSegmenterMetrics contains two types of metrics: unsupervised metrics that are computed only on good images and supervised/defect detection metrics that are computed on both good and bad images. The defect detection metrics are accessible only when results for bad images were added to the object. When supervised metrics are accessible, EUnsupervisedSegmenterMetrics::IsTotallyUnsupervised is false.
There is only one unsupervised metric: the error (see EUnsupervisedSegmenterMetrics::Error).
See EDeepLearningDefectDetectionMetrics for a description of the defect detection metrics.

Base Class:EDeepLearningDefectDetectionMetrics

Namespace: Euresys.Open_eVision.EasyDeepLearning

Properties

Gets the average score of defective images.
Gets the average score of good images.
The error of the segmenter.
This metric is only available in the metrics computed during a training which are accessible through EUnsupervisedSegmenter::GetValidationMetrics.
The error, which is also called the loss, is the quantity that is minimized during the training of the deep neural network.

Methods

Adds the given result to the metric for error computation. The result must be computed from a good image that was corrupted during training.
Adds the other metrics to the current metrics of this object.
Adds the given result with the corresponding ground truth label to the metrics.
Constructs an EUnsupervisedSegmenterMetrics object.
Best achievable weighted accuracy.
The weighted accuracy is the weighted average of the true positive rate and the true negative rate (which is equal to 1 minus the false positive rate). See EROCPoint.
The classification threshold corresponding to this accuracy is given by EUnsupervisedSegmenterMetrics::GetBestWeightedAccuracyClassificationThreshold.
Classification threshold giving the best achievable weighted accuracy (see EUnsupervisedSegmenterMetrics::GetBestWeightedAccuracy).
Whether this metrics has results from only good images (true) or from both good and defective images (false).
Some metrics are accessible only if EUnsupervisedSegmenterMetrics::IsTotallyUnsupervised is false.
Indicates whether the object contains at least one result.
Loads an unsupervised segmentation metric. The given ESerializer must have been created for reading.
Assignment operator.
Removes the given result from the metric for error computation. The result must be computed from a good image that was corrupted during training.
Removes the given result with the corresponding ground truth label to the metrics.
Saves an unsupervised segmentation metric. The given ESerializer must have been created for writing.