Monday, February 6, 2023

New Tutorial: Training a Custom Image Classification Network for OAK-D

Hi,

This week you'll learn about Training a Custom Image Classification Network for OAK-D.

Throughout this series, we have been gushing about the beauty of the OAK family. The last few tutorials served as an extensive introduction and catalog for the OAK family, but now it's time for some hands-on initiative. Training models and doing research on your high-performance desktop computers is one thing, but building an actual real-life AI-based solution on edge devices is something else entirely.

We hear about Artificial Intelligence (AI) being used in real-life applications almost daily. We know the problem statements and the approach (models, dataset) that we would use to tackle the problem. But we often overlook another key piece: the bridge that connects our model to be used in a real-life setting.

Our next few blogs will deal with training models for deployment into our OAK-D edge device. In this blog post, we will train a Custom Image Classification Network for future deployment on our edge device.

The big picture: OAK-D can be used for a variety of tasks. But it is important to learn the basic interface and protocols, enabling us to use OAK-D however we require. Today, we build a Custom Image Classification Network that identifies vegetables from images.

How it works: If you are a seasoned PyImageSearch user, don't let the elementary-sounding task fool you. We are dealing with edge devices here, so you have to ensure our model is the right amount of light and is as accurate as possible.

Our thoughts: If you want to learn how to work on deep learning with edge devices, this blog post is foundational for what is to come!

Yes, but: Don't stick to just one task. Try to think of other problems and train models on those statements for future deployment.

Stay smart: Don't pass on the opportunity to master yet another craft in the AI domain, taming the little but powerful pocket behemoth: the OAK family of edge devices.

Click here to read the full tutorial

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