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Image Labeler

Label images for computer vision applications


The Image Labeler app enables you to label ground truth data in a collection of images. Using the app, you can:

The Image Labeler app supports all image file formats supported by imread. To add additional file formats to imread, use imformats.

Open the Image Labeler App

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

  • MATLAB command prompt: Enter imageLabeler.

Programmatic Use

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imageLabeler opens a new session of the app, enabling you to label ground truth data in images.

imageLabeler(imageFolder) opens the app and loads all the images from the folder named imageFolder.

imageLabeler(imageDatastore) opens the app and reads all of the images from an imageDatastore object. The imageDatastore files are read using imread. For example, to open the app with a collection of stop sign images:

   stopSignsFolder = fullfile(toolboxdir('vision'),'visiondata','stopSignImages');
   imds = imageDatastore(stopSignsFolder)

imageLabeler(sessionFile) opens the app and loads a saved Image Labeler session, sessionFile. The sessionFile input contains the path and file name. The MAT-file that sessionFile points to contains the saved session.


The Image Labeler app provides built-in algorithms that you can use to automate labeling. From the app toolstrip, click Select Algorithm and then select an automation algorithm.

Built-In Automation AlgorithmDescription

ACF People Detector

Detect and label people using a pretrained detector based on aggregate channel features (ACF). With this algorithm, you do not need to draw any ROI labels.

ACF Vehicle Detector (requires Automated Driving System Toolbox™)

Detect and label vehicles using a pretrained detector based on ACF. With this algorithm, you do not need to draw any ROI labels.

Introduced in R2018a