To follow along with this tutorial, you will need a Raspberry Pi 4 or 400. You will need to run the 64-bit Ubuntu operating system. The Raspberry Pi i

How to Deploy a YOLOv8 Model to a Raspberry Pi

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2023-01-25 10:30:04

To follow along with this tutorial, you will need a Raspberry Pi 4 or 400. You will need to run the 64-bit Ubuntu operating system.

The Raspberry Pi is a useful edge deployment device for many computer vision applications and use cases. For applications that operate at lower frame rates, from motion-triggered security systems to wildlife surveying, a Pi is an excellent choice for a device on which to deploy your application. Pis are small and you can deploy a state-of-the-art YOLOv8 computer vision model on your Pi.

Notably, you can run models on a Pi without an internet connection while still executing logic on your model inference results.

In this guide, we’re going to walk through how to deploy a computer vision model to a Raspberry Pi. We’ll be deploying a model built on Roboflow that we will deploy to a local Docker container. By the end of the guide, we’ll have a working computer vision model ready to use on our Pi.

In this guide, we are going to build a model that can detect a retail cooler that has an empty space. Such a model could be used as part of a system to inform retail workers when shelves are empty. This information could be used to ensure that, on busy days, cooled products are well stocked.