Automate Ground Truth Labeling for Object Detection
Versión 1.0.0.3 (1,69 KB) por
Alon Feldman
This file allows to use a pretrained Object Detection algorithm, to automate ground truth labeling in the Ground Truth Labeler app.
To speed up the creation of a data base for training an Object Detector, we can use the automation feature in the MATLAB Image Labeler App. My goal was to automate the labeling process when training a custom YOLOv3 Object Detector.
Defining and using a custom automation algorithm requires you to create a class first. Once your class is created, save it into the appropriate folder. The instructions to do that are here.
Some algorithm types are available, such as vehicle detection and semantic segmentation, but there is no multi-label object detector.
This code had to be written, saved in a specific location, and run before it could be used.
With this code, you can open the Image Labler App, choose this automation algorithm, and use a pre-trained model to label more data faster.
Note that the Threshold for any algorithm is set as 0.5. you may change this number in the code if you like (on line 44).
Citar como
Alon Feldman, Shai Kendler, Barak Fishbain (2021). Automate Ground Truth Labeling for Object Detection (https://www.mathworks.com/matlabcentral/fileexchange/102689), MATLAB Central File Exchange. Retrieved November 30, 2021.
Compatibilidad con la versión de MATLAB
Se creó con
R2021b
Compatible con cualquier versión desde R2021b
Compatibilidad con las plataformas
Windows macOS LinuxEtiquetas
Agradecimientos
Inspirado por: Computer Vision Toolbox Model for YOLO v3 Object Detection
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