Nvidia is one of the companies that is democratizing Machine Learning (ML) at the edge. This really excites me as an IoT engineer who wants to add ML capabilities to my applications e.g. ensure safety at an Industrial Plant to check for people in forbidden areas.
Nvidia has released Jetson and Xavier developer kits which are great for people who want to build really cool computer vision projects ranging from self-driving cars, robots, smart cameras and whatnot. The possibilities are endless. Huge shout out to pyimagesearch blog which has provided detailed setup instructions from time to time to get your embedded boards like Raspberry Pi and Jetson Nano ready for machine learning and computer vision projects. In my opinion, such tutorials really make developers' lives easier as they have a guide to follow to get their embedded boards ready. My blog is also inspired from pyimagesearch blog with some self improvisations.
Recently Nvidia launched a 2 GB variant of the Nvidia Jetson Nano variant which has enticed the developers to buy the kit and get their hands dirty to build cool projects. However, if the development environment is not setup correctly, the journey could be really frustrating and discouraging to move forward.
In this article, I am focussing how we could get our Jetson Nano developer kit ready for Computer Vision Deep Learning AI projects.
This tutorial is a long one and will definitely take a time to follow along. But I will say, it’s worth the effort and will give you a smooth start to your next AI project.
We will be installing OpenCV 4.5, TensorFlow 2.3, Keras and TensorRT models. As said, the whole process is very time-consuming, so patience is the key. However, the results are worth the effort. So, let’s begin.
At the time of writing this article, the Jetpack version is 4.4.1, but the steps will not be too different with future releases. Just a bit of tweaking might be needed with the dependent package versions.
- Jetson Nano DevKit (I am using the 4GB variant pre 2020)
- Barrel jack 5V 4A (20W) power supplies
- High speed micro SD card (64GB recommended)