Raspberry Pi projects
Image recognition
Get into machine learning and train your Pi to recognise and classify other Pis, without having to write a single line of code.
We’re going to train our Raspberry Pi to identify other Raspberry Pis (or other objects) with machine learning (ML). Why is this important? An example of an industrial application for this type of ML is identifying defects in circuit boards. As circuit boards exit the assembly line, a machine can be trained to identify a defective circuit board for troubleshooting by a human.
Tom’s Hardware has some neat machine learning and artificial intelligence tutorials, including facial recognition and face mask identification. In those projects, all of the training images were stored locally on the Raspberry Pi and the model training took a long time because it was also performed on the Pi. In this tutorial, we’ll use a web platform called Edge Impulse to create and train our model to alleviate a few processing cycles from our Pi. Another advantage of Edge Impulse is the ease of uploading training images, which can be done from a smartphone (and without having to involve an app).
We’ll use BalenaCloudOS instead of the standard Raspberry Pi OS since the folks at Balena have pre-built an API call to Edge Impulse. The previous facial recognition and face mask identification tutorials also required tedious command line package installs and Python code. This project eliminates all terminal commands and instead utilises an intuitive GUI.