anomalyDetectionMetrics
Description
An anomalyDetectionMetrics
object encapsulates anomaly detection
quality metrics for a set of images. Images of anomalies belong to the positive
(true
) class. Normal images belong to the negative
(false
) class.
Note
This functionality requires the Automated Visual Inspection Library for Computer Vision Toolbox™. You can install the Automated Visual Inspection Library for Computer Vision Toolbox from Add-On Explorer. For more information about installing add-ons, see Get and Manage Add-Ons.
Creation
Create an anomalyDetectionMetrics
object using the evaluateAnomalyDetection
function.
Properties
ConfusionMatrix
— Confusion matrix
table
This property is read-only.
Confusion matrix, specified as a 2-by-2 table. Each table element (i, j) is the count of images known to belong to class i but predicted to belong to class j.
NormalizedConfusionMatrix
— Normalized confusion matrix
table
This property is read-only.
Normalized confusion matrix, specified as a 2-by-2 table.
NormalizedConfusionMatrix
represents a confusion matrix
normalized by the number of images known to belong to each class. Each table element
(i, j) is the count of images known to belong to
class i but predicted to belong to class j,
divided by the total number of images predicted in class i. Elements
are in the range [0, 1].
DataSetMetrics
— Metrics aggregated over data set
table
This property is read-only.
Metrics aggregated over the data set, specified as a 1-by-8 table with columns corresponding to these metrics:
GlobalAccuracy
— Ratio of correctly classified images to total images, regardless of classMeanAccuracy
— Ratio of correctly classified images of each class to total images, averaged over all classesPrecision
— Positive predictive valueRecall
— True positive (TP) rateSpecificity
— True negative (TN) rateF1Score
— Harmonic mean of precision and recallFalsePositiveRate
— False positive (FP) rate, or falloutFalseNegativeRate
— False negative (FN) rate, or miss
ClassMetrics
— Class metrics
table
This property is read-only.
Anomaly detection metrics for each class, specified as a 2-by-2 table. The table has rows "Normal" and "Anomaly" and columns corresponding to these metrics:
Accuracy
— Fraction of correctly classified images for each class (normal or anomaly).AccuracyPerSubClass
— Fraction of correctly classified images for each subclass within the normal and anomaly classes. The value ofAccuracyPerSubClass
is a table with C rows and one column, where C is the number of subclasses.
Examples
Calculate Metrics for Trained FCDD Anomaly Detector
Load test images and corresponding labels, then create a datastore that reads the test data. The data set consists of grayscale images of handwritten digits 0–9.
[Xtest,gtLabels] = digitTest4DArrayData; dsTest = arrayDatastore(Xtest,IterationDimension=4);
Load a pretrained FCDD anomaly detector. This detector has been trained to classify the digit 8 as normal and all other digits as anomalies. Therefore, specify the set of anomaly labels as the set of digits between 0 and 9, excluding 8.
load("digit8AnomalyDetector.mat"); anomalyLabels = setdiff(string(0:9),"8");
Classify each test image as normal or anomalous.
predLabels = classify(detector,dsTest);
Calculate metrics for the anomaly detection results.
metrics = evaluateAnomalyDetection(predLabels,gtLabels,anomalyLabels)
Evaluating anomaly detection results ------------------------------------ * Finalizing... Done. * Data set metrics: GlobalAccuracy MeanAccuracy Precision Recall Specificity F1Score FalsePositiveRate FalseNegativeRate ______________ ____________ _________ ______ ___________ _______ _________________ _________________ 0.7662 0.839 0.98971 0.748 0.93 0.85204 0.07 0.252
metrics = anomalyDetectionMetrics with properties: ConfusionMatrix: [2x2 table] NormalizedConfusionMatrix: [2x2 table] DataSetMetrics: [1x8 table] ClassMetrics: [2x2 table]
Display the confusion matrix.
cm = metrics.ConfusionMatrix
cm=2×2 table
Normal Anomaly
______ _______
Normal 465 35
Anomaly 1134 3366
Version History
Introduced in R2022b
See Also
MATLAB Command
You clicked a link that corresponds to this MATLAB command:
Run the command by entering it in the MATLAB Command Window. Web browsers do not support MATLAB commands.
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list:
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Americas
- América Latina (Español)
- Canada (English)
- United States (English)
Europe
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)