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This week you'll learn about Introduction to OpenCV AI Kit (OAK).

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Deep learning (DL) has a myriad of interesting concepts that come up every day. Understanding and coding these concepts are important parts of a DL researcher's journey. Still, we sometimes forget about another important piece of this puzzle: Deep Learning in practical applications. 

One of the reasons deep learning is so important is the way it helps bring change to human society. So applying the concepts to something practical is as important as the idea that gives rise to the concept. 

(source: xkcd)

It is easy to be an obnoxious conceptual theoretical physicist, but what matters is how you make your work a reality. 

To make our lives as DL researchers easier, several modular AI systems have been created to help us with various tasks: more computation power, assessment of models on real-life data, etc. 

Today, we will introduce the OpenCV AI Kit: a family of powerful edge devices capable of running extremely heavy neural networks and providing other CV helper functionalities like depth estimation. 

The big picture: OpenCV AI Kit (OAK) is a set of modular open-source ecosystems consisting of MIT-licensed hardware and an AI accelerator. 

How it works: As mentioned above, OAK aims to help us run powerful neural networks on edge devices as well as help us tackle a variety of other real-time computer vision problems. Tasks like depth estimation, running inference on object detection models, real-time segmentation, etc., can easily be done using the OAK family. 

Our thoughts: This blog is just an introduction to the available OAK systems and an overview of the tasks it can accomplish. 

Yes, but: For a more nuanced approach to understanding how you can utilize OAK, stick with us till the end of this series.

Stay smart: Stay tuned for what we have next in this series! 

Click here to read the full tutorial

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