Visual Anomaly Detector

R2026b

Train, evaluate, and compare deep learning anomaly detectors

Since R2026b

Description

The Visual Anomaly Detector app enables you to train, fine-tune, evaluate, and compare deep learning-based anomaly detection models for identifying defects in images.

The app supports Student-Teacher, PatchCore, FastFlow, and FCDD (fully convolutional data description) anomaly detector models. For more information on the characteristics and performance of these anomaly detection models, see Get Started with Anomaly Detection Using Deep Learning.

Using this app, you can:

• Load and partition labeled image data sets.

• Train and fine-tune deep learning anomaly detectors using Student-Teacher, PatchCore, FastFlow, or FCDD architectures.

• Evaluate performance with metrics such as confusion matrices, ROC curves, and precision-recall curves.

• Optimize detection thresholds using ROC curves and histograms.

• Visualize predictions with heatmaps showing anomaly locations.

• Compare multiple trained models to select the optimal detector.

• Perform inference on production data or live image streams using trained models.

• Export models, training functions, partitioned datastores, evaluation metrics and plots.

Open the Visual Anomaly Detector App

• MATLAB^® Toolstrip: On the Apps tab, under Image Processing and Computer Vision, click the app icon.

• MATLAB command prompt: Enter visualAnomalyDetector. For more information, see Programmatic Use.

Examples

Open App with Preloaded Data from Workspace

You can open the Visual Anomaly Detector app with pre-partitioned image datastores loaded from the MATLAB workspace.

Create image datastores for training, calibration, and test sets, and specify the anomaly class labels.

imdsTrain = imageDatastore("data/train",IncludeSubfolders=true,LabelSource="foldernames");
imdsCal = imageDatastore("data/calibration",IncludeSubfolders=true,LabelSource="foldernames");
imdsTest = imageDatastore("data/test",IncludeSubfolders=true,LabelSource="foldernames");
anomalyClasses = ["crack","scratch","contamination"];

Open the app with the preloaded data.

visualAnomalyDetector(imdsTrain,imdsCal,imdsTest,anomalyClasses)

The app opens with the data already loaded and partitioned, so you can immediately select a detector and begin training.

Related Examples

• Train and Evaluate Anomaly Detection Model Using Visual Anomaly Detector
• Detect Anomalies Using Visual Anomaly Detector
• Detect PCB Defects in Live Image Stream Using Visual Anomaly Detector

Parameters

Detector — Manage session, import training data, train detector model, evaluate performance, and export results

tab

Use the Detector tab to manage sessions, import training data, select and train anomaly detector models, evaluate performance, and export the trained model, training function, data, or results. The parameters and buttons in this tab are usable only after a session has been created or loaded.

Parameters/Buttons	Description
New Session	Create a new session. Select one of these options from the button menu: Train / Evaluate — Train and evaluate an anomaly detector model by importing training data using the Anomaly Detector: Train / Fine-tune / Evaluate Detector dialog box. Perform Detection — Perform inference with a trained detector on test data using the Anomaly Detector: Perform detection dialog box. Live Detection — Perform inference with a trained detector on a live image stream using the Connect Device dialog box. Using this option requires Image Acquisition Toolbox™.
Open Session	Load a previously saved session MAT file.
Save Session	Save the current session. Select one of these options from the list: Save — Save the current session to the existing MAT file. Save As — Save the current session as a new MAT file.
Visualize	Open the Browse Data tab to browse the imported and partitioned training, calibration, and test data.
StudentTeacher	Select the Student-Teacher anomaly detector and display its configurable parameters in the Hyperparameters panel.
PatchCore	Select the PatchCore anomaly detector and display its configurable parameters in the Hyperparameters panel.
FastFlow	Select the FastFlow anomaly detector and display its configurable parameters in the Hyperparameters panel.
FCDD	Select the FCDD (fully convolutional data description) anomaly detector and display its configurable parameters in the Hyperparameters panel.
Train	Start training the selected anomaly detector model on the imported training data. Training progress is displayed in the Training Progress panel. Click the button again to stop training.
Set Threshold	Open the Threshold panel and configure the anomaly detection threshold.
Confusion Matrix	Display the confusion matrix for the test data predictions at the current threshold.
ROC Curve	Display the receiver operating characteristic (ROC) curve showing the tradeoff between true positive rate and false positive rate across thresholds.
Histogram	Display the histogram of anomaly scores for normal and anomalous images in the test data.
Precision-Recall	Display the precision-recall curve for the test data predictions.
Summary	Display a summary table of evaluation metrics for the trained detector, including accuracy, precision, recall, and F1 score.
Classification Decision	Open the View Decision tab to visualize classification decisions for individual images, including anomaly heatmap overlays.
Compare Trained Detectors	Open the Compare Detectors tab to compare the performance of multiple trained detectors side by side.
Default Layout	Restore the app window to its default panel layout.
Export	Export data to the MATLAB workspace. Select one of these options from the button menu: Detector — Export the trained anomaly detector model. Training Function — Export a MATLAB function that reproduces the training workflow programmatically. Partitioned Datastore — Export the partitioned training, calibration, and test datastores. Performance Metrics — Export evaluation metrics and analysis plots.

Browse Data — Browse imported and partitioned image datasets

tab

Use the Browse Data tab to browse and preview the imported and partitioned image datasets. This tab opens when you select Visualize on the Detector tab.

Parameters/Buttons	Description
Training Data	Display thumbnail images from the training data set in the image browser.
Calibration Data	Display thumbnail images from the calibration data set in the image browser.
Test Data	Display thumbnail images from the test data set in the image browser.
Thumbnail Size	Adjust the slider to change the size of thumbnail images displayed in the image browser.
Preview	Show or hide a larger preview of the selected image, along with other information, such as assigned label and actual label category.
Close Browse Data	Close the Browse Data tab and return to the Detector tab.

Anomaly Detector: Train / Fine-tune / Evaluate Detector — Import image data, configure data partitioning, and set up training and evaluation session

dialog box

Use the Anomaly Detector: Train / Fine-tune / Evaluate Detector dialog box to import image data and configure a new training session. This dialog box opens when you select New Session > Train / Evaluate on the Detector tab. The dialog box enables you to load data, assign labels, define data distribution, and optionally select pretrained detectors from the workspace.

Parameters/Buttons	Description
Load data from	Select the source of image data. Choose one of these options: Folder — Select a folder from the file system. The app automatically manages labels from the folder structure. Single Datastore — Import a single ImageDatastore object from the MATLAB workspace. Separate Datastores — Import pre-partitioned training, calibration, and test datastores separately from the MATLAB workspace.
Import ground truth labels	When the selected datastore does not contain labels, you can import ground truth labels separately from the workspace. This option is enabled automatically when labels are not detected in the selected datastore.
Assign labels to data categories	Assign each unique label in the imported data to either the Normal or Anomalous category. Labels assigned to the Anomalous category represent defective images. All other labels are treated as normal.
Workflow type	Select the workflow type: Train & Evaluate — Partition data into training, calibration, and test sets for training a new detector and evaluating its performance. Test — Use all data as a test set to evaluate a pretrained detector imported from the workspace. When you select this option, you must select a trained detector.
Define data ratio	Specify the data split ratios for partitioning the imported data into training, calibration, and test sets. The tree table displays the distribution per label category. You can edit individual values to customize the allocation.
Number/Percent	Choose whether to display the data distribution as absolute image counts or as percentages.
Default	Reset the data split ratios to their default values.
Select detector to import	Select one or more detectors from the MATLAB workspace to import into the session. The table includes all trained anomaly detector objects available in the base workspace. This option is optional for the Train & Evaluate workflow and required for the Test workflow. The app validates that the selected detector image size is compatible with the imported data.
Previous	Return to the previous step.
Next	Proceed to the next step. This button is enabled only after completing the required selections in the current step. On the final step, the button label changes to Done.

Anomaly Detector: Perform detection — Import trained detector and test data for performing inference

dialog box

Use the Anomaly Detector: Perform detection dialog box to import a pretrained anomaly detector model and test data for performing inference. This dialog box opens when you select New Session > Perform Detection.

Parameters/Buttons	Description
Import anomaly detector from workspace	Select trained anomaly detector object from the MATLAB workspace. The detector must be a studentTeacherAnomalyDetector, patchCoreAnomalyDetector, fastFlowAnomalyDetector, or fcddAnomalyDetector object.
Import data from	Select the source folder or datastore containing test images for inference.
Import	Load the trained model and test data, and start the inference session.

Hyperparameters — Configure model-specific and training hyperparameters for selected anomaly detector

panel

Use the Hyperparameters panel to configure model architecture and training hyperparameters for the selected anomaly detector. The available parameters change depending on which detector type you select.

Detector Hyperparameters

Detector	Parameters	Description
Student-Teacher	Network	Network size for the Student-Teacher model
	Normalization Data Ratio	Ratio of training data for anomaly map normalization. Value ranges from 0.0 to 1.0.
PatchCore	Pretrained Backbone Network	Backbone network for feature extraction
FastFlow	Pretrained Encoder Network	Encoder network for feature extraction
	Number of downsampling operations	Number of downsampling layers
FCDD	Pretrained Encoder Network	Encoder network for feature extraction
	Number of downsampling operations	Number of downsampling layers
	Freeze Backbone	Whether to freeze the backbone network weights during training

For more information on model hyperparameters, see trainStudentTeacherAnomalyDetector, trainPatchCoreAnomalyDetector, trainFastFlowAnomalyDetector, and trainFCDDAnomalyDetector.

Training Hyperparameters

Parameters	Description
Compression Ratio	Ratio for core-set compression. Value ranges from 0.0 to 1.0.
Mini-Batch Size	Number of images processed in each mini-batch during feature extraction
Execution Environment	Hardware for training
Reset Input Normalization	Whether to reset input normalization statistics
Subsampling Method	Strategy for subsampling the core set
Initial Learn Rate	Initial learning rate for the optimizer
Max Epochs	Maximum number of training epochs
Stochastic Solver	Optimization algorithm
Learn Rate Schedule	Learning rate decay schedule
Learn Rate Drop Factor	Factor to reduce the learning rate. Value ranges from 0 to 1.
Learn Rate Drop Period	Number of epochs between learning rate drops
Shuffle	Data shuffling strategy
Momentum	Momentum value for SGDM optimizer
Gradient Decay Factor	Decay rate for first moment estimate (beta1) in Adam optimizer
Squared Gradient Decay Factor	Decay rate for second moment estimate
Epsilon	Small constant for numerical stability
Validation Frequency	Number of iterations between validation evaluations
Validation Patience	Number of validation checks to wait before stopping early
Output Network	Network to return after training
L2 Regularization	L2 regularization factor (weight decay)
Batch Normalization Statistics	Method for computing batch normalization statistics
Gradient Threshold	Threshold for gradient clipping
Gradient Threshold Method	Method for gradient clipping
Execution Environment	Hardware for training
Preprocessing Environment	Data preprocessing parallelization
Acceleration	Automatic acceleration for training
Checkpoint Path	Folder path to save training checkpoints
Checkpoint Frequency	How often to save checkpoints
Checkpoint Frequency Unit	Unit for checkpoint frequency

For more information on these training hyperparameters, see trainingOptions (Deep Learning Toolbox).

Threshold — Configure anomaly detection threshold

panel

Use the Threshold panel to configure and visualize the anomaly detection threshold. This panel opens when you select Set Threshold on the Detector tab. The threshold determines the decision boundary between normal and anomalous predictions.

Parameters/Buttons	Description
Predefined Threshold	Select one of these threshold method options: Max F1 Score — Maximize the balance between precision and recall. Youdens Index — Maximize the combined sensitivity and specificity. Nearest Zero-One ROC — Select the threshold closest to the ideal point (0,1) on the ROC curve. Custom — Specify a user-defined threshold value.
Threshold Value	Numeric threshold value for classification. This value is automatically set when you select a predefined method. You can enter a custom value when Custom is selected.
Data Selection	Select the data for computing the threshold. Calibration — Use only calibration data for threshold computation. Calibration + Test — Use the combined calibration and test data for threshold computation.
ROC Curve	Display ROC curve with the current threshold position indicated on the curve.
Histogram	Display the score distribution histogram with the threshold line overlaid, showing separation between normal and anomalous scores.

You can adjust the view and export these plots, by clicking the three dots at the top-right corner of the corresponding plots.

View Decision — Visualize classification decisions and anomaly heatmaps for individual images

tab

Use the View Decision tab to visualize classification decisions for individual images, including anomaly heatmap overlays. This tab opens when you select Classification Decision on the Detector tab.

Parameters/Buttons	Description
Normalize Anomaly Score	Open a dialog box for configuring anomaly score normalization settings.
Show Heatmap	Toggle the visibility of the anomaly heatmap overlay on images.
Blend Type	Select a blending method for the heatmap overlay. Use "Proportional" to blend the heatmap intensity proportionally with the image, or "Equal" for equal blending.
Colormap	Select the colormap for the anomaly heatmap.
Opacity	Adjust the opacity of the heatmap overlay. Range is 1 to 100.
Thumbnail Size	Adjust the size of image thumbnails in the decision browser.
Preview	Show or hide a larger preview of the selected image with heatmap overlay.
Close View Decision	Close the View Decision tab and return to the Detector tab.

Compare Detectors — Compare performance of multiple trained anomaly detectors

tab

Use the Compare Detectors tab to compare the performance of multiple trained anomaly detectors side by side. This tab opens when you select Compare Trained Detectors on the Detector tab.

Parameters/Buttons	Description
Model Selection	Select which models to include in the comparison from the left panel which lists all trained detectors. Use Select All to include all detectors or Remove All to clear the selection. The comparison views update dynamically as you change the selection.
Summary	Display a comparison summary table of evaluation metrics for the selected detectors. The table shows metrics such as Global Accuracy, Mean Accuracy, Precision, Recall, Specificity, F1 Score, False Positive Rate, False Negative Rate, and Model Type. Click Select Parameter to customize which metrics appear as columns in the table. Click Export to Workspace to export the summary table to the MATLAB workspace as a table.
Result Plot	Display a bar chart comparing a selected metric across all selected detectors. Use the Select parameter to compare list to choose the metric to plot. Available metrics include Global Accuracy, Mean Accuracy, Precision, Recall, Specificity, F1 Score, False Positive Rate, and False Negative Rate.
ROC Curve	Display overlaid ROC curves for all selected detectors on a single plot, enabling direct comparison of the tradeoff between true positive rate and false positive rate across detectors.
Confusion Matrix	Display confusion matrices for all selected detectors in a tiled layout for side-by-side comparison of classification results.
Precision-Recall	Display overlaid precision-recall curves for all selected detectors on a single plot, enabling direct comparison of the tradeoff between precision and recall across detectors.
Close Compare	Close the Compare Detectors tab and return to the Detector tab.

Connect Device — Connect to image acquisition device for live anomaly detection

dialog box

Use the Connect Device dialog box to connect to an image acquisition device for live anomaly detection. This dialog box opens when you select New Session > Live Detection. This feature requires Image Acquisition Toolbox.

Parameters/Buttons	Description
Select Device	Select an image acquisition device (camera) for capturing live images. Use the refresh button to refresh the list of available devices.
Select Pixel Format	Select the pixel format of the image stream.
Connect	Establish a connection to the selected device and begin the live detection session.

Live Detection — Perform real-time anomaly detection on live image streams from connected device

tab

Use the Live Detection tab to perform real-time anomaly detection on live image streams captured from a connected image acquisition device. This tab opens when you select New Session > Live Detection and connect a device using the Connect Device dialog box. This feature requires Image Acquisition Toolbox.

Parameters/Buttons	Description
Detector	Select a trained anomaly detector from the MATLAB workspace. The list includes all anomaly detector objects available in the base workspace. Use the refresh button to refresh the list of available anomaly detectors from the MATLAB workspace.
Start	Begin capturing images from the connected device and running the selected anomaly detector on each captured frame in real time. The button changes to Stop while detection is active.
Heat Map	Toggle the anomaly heatmap overlay on the live image stream. Options are "On" or "Off" (default). This control is enabled only while detection is active.
Opacity	Adjust the opacity of the heatmap overlay on the live image. Range is 1 to 100. Default is 60. This control is enabled only while detection is active.
Switch Device	Adjust the device properties such as the connected device and pixel format.
Device Properties	Adjust the properties of the connected device, such as the ROI, frame rate, and saturation.
Live Image Display	View the live image stream from the connected device with real-time classification annotations. The app annotates each frame with the detection result (Good or Bad) and the anomaly score.

Programmatic Use

visualAnomalyDetector opens the Visual Anomaly Detector app.

visualAnomalyDetector(imdsTrain,imdsCal,imdsTest,anomalyClasses) opens the Visual Anomaly Detector app with pre-partitioned image data loaded from the workspace.

• imdsTrain — Image datastore containing training images, specified as an ImageDatastore object.

• imdsCal — Image datastore containing calibration images used for threshold optimization, specified as an ImageDatastore object.

• imdsTest — Image datastore containing test images for evaluating detector performance, specified as an ImageDatastore object.

• anomalyClasses — Names of the classes that represent anomalous images, specified as a string array or categorical array. The app treats all other class labels in the datastores as normal.

visualAnomalyDetector close closes the Visual Anomaly Detector app.

Version History

Introduced in R2026b