You can use a labeling app and Computer Vision Toolbox™ objects and functions to train algorithms from ground truth data. Use the labeling app to interactively label ground truth data in a video, image sequence, image collection, or custom data source. Then, use the labeled data to create training data to train an object detector or to train a semantic segmentation network.
Load data for labeling
Label data and select an automation algorithm: Create ROI and scene labels within the app. For more details, see:
You can choose from one of the built-in algorithms or create your own custom algorithm to label objects in your data. To learn how to create your own automation algorithm, see Create Automation Algorithm for Labeling.
Export labels: After labeling your data, you can
export the labels to the workspace or save them to a file. The labels are exported
groundTruth object. If your data
source consists of multiple image collections, label the entire set of image
collections to obtain an array of
objects. For details about sharing
objects, see Share and Store Labeled Ground Truth Data.
Create training data: To create training data
groundTruth object, use one of these
For objects created using a video file or custom data source, the
pixelLabelTrainingData functions write images to disk for
groundTruth. Sample the ground truth data by specifying
a sampling factor. Sampling mitigates overtraining an object detector on similar
Object detectors — Use one of several Computer Vision Toolbox object detectors. For a list of detectors, see Object Detection Using Features and Object Detection using Deep Learning. For object detectors specific to automated driving, see the Automated Driving Toolbox object detectors listed in Visual Perception (Automated Driving Toolbox).
Semantic segmentation network — For details on training a semantic segmentation network, see Getting Started with Semantic Segmentation Using Deep Learning.