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Decoding a Core ML YOLO Object Detector

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23 Nov 2020CPOL4 min read 7.1K   82  
In this article, we will decode the Core ML YOLO Model by transforming an array of abstract numbers to a human-readable form.
Here we’ll discuss how to convert the output values into the detected object labels, confidence scores, and the corresponding boxes.

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This article is part of the series 'Mobile Neural Networks on iOS with Core ML - Part 2 View All

License

This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)


Written By
Architect
Poland Poland
Jarek has two decades of professional experience in software architecture and development, machine learning, business and system analysis, logistics, and business process optimization.
He is passionate about creating software solutions with complex logic, especially with the application of AI.

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