EDeepLearningDefectDetectionMetrics::GetConfusion
Confusion value of one label with another.
The confusion value of a label with another is the number of images belonging to this label that are classified as belonging to the other label.
For a EDeepLearningDefectDetectionMetrics there are only 2 labels (good and defective) so the confusion matrix is only composed of 4 values which are called matrix element (see EDeepLearningDefectDetectionMetrics).
The confusion matrix is computed for a given threshold (see EUnsupervisedSegmenter::ClassificationThreshold) which means an index can be passed to the method (see EDeepLearningDefectDetectionMetrics::NumberOfClassifiers).
Namespace: Euresys::Open_eVision::EasyDeepLearning
[C++]
OEV_UINT32 GetConfusion(
Euresys::Open_eVision::EasyDeepLearning::EConfusionMatrixElement element,
int index
)
Parameters
element
The element from which to obtain the confusion value
index
The index of the classifier to use. If the index is '-1', the index corresponding to EUnsupervisedSegmenter::ClassificationThreshold will be used.
EDeepLearningDefectDetectionMetrics.GetConfusion
Confusion value of one label with another.
The confusion value of a label with another is the number of images belonging to this label that are classified as belonging to the other label.
For a EDeepLearningDefectDetectionMetrics there are only 2 labels (good and defective) so the confusion matrix is only composed of 4 values which are called matrix element (see EDeepLearningDefectDetectionMetrics).
The confusion matrix is computed for a given threshold (see EUnsupervisedSegmenter::ClassificationThreshold) which means an index can be passed to the method (see EDeepLearningDefectDetectionMetrics::NumberOfClassifiers).
Namespace: Euresys.Open_eVision.EasyDeepLearning
[C#]
uint GetConfusion(
Euresys.Open_eVision.EasyDeepLearning.EConfusionMatrixElement element,
int index
)
Parameters
element
The element from which to obtain the confusion value
index
The index of the classifier to use. If the index is '-1', the index corresponding to EUnsupervisedSegmenter::ClassificationThreshold will be used.