Decoding a YOLO Core ML Object Detector Using Array Operations

Jarek Szczegielniak

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24 Nov 2020CPOL3 min read

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In the next article, we’ll do the same but with array operations. This will allow us to include the decoding logic directly in the model.

Here we’ll decode the YOLO Core ML Model using array manipulations (vectorization) to get rid of loops. Understanding how it works will allow us to add this logic to the Core ML model in the next article.

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

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

Written By

Jarek Szczegielniak

Architect

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.

Decoding a YOLO Core ML Object Detector Using Array Operations

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