Jetson Nano Developer Kit is a small, powerful single-board computer that lets you run multiple neural networks in parallel for applications like image classification, object detection, segmentation, and speech processing.
Jetson Nano is powered by a 1.4-GHz quad-core ARM A57 CPU, 128-core Nvidia Maxwell GPU and 4 GB of RAM. It has four USB type-A ports, including one that is USB 3.0, both HDMI and DisplayPort out for video and a gigabit Ethernet connector. There’s an onboard CSI camera slot, though you can also connect cameras via USB. Also, like the Raspberry Pi, the Jetson Nano has 40 GPIO (general input / output) pins which you can use to attach to lights, motors and sensors. Unfortunately, the Jetson Nano does not have onboard wireless and bluetooth connectivity. A micro USB port is used to connect it to a power, but there’s also a barrel connector you can use with an optional high-power supply that provides 4 amps for more intensive tasks.The CPU comes with a heatsink on top of it, but you can attach an optional fan on top of the sink if you’re going to be performing processor-intensive tasks that require more cooling.
The retail price for the Jetson Nano is 99 USD, which is almost two times more expensive than the Raspberry Pi, but at the same time it opens up much more opportunities through the use of Nvidia GPUs.
Before boot up your NVIDIA Jetson Nano you need below items. I bought my Jetson Nano by the following link, which includes full kit with acrylic case and etc.
After Etcher finishes to flash, insert the microSD card into the slot on the underside of the Jetson Nano module.
The Jetson Nano Developer Kit will take you through some initial setup, including:
After logging in, you will see the following desktop of NVIDIA Jetson.
Sometimes it happens that your Jetson Nano will shut itself down. This happened to me on my first attempt. A 2.5A supply should help to avoid that problem. You can use the official Raspberry Pi power supply for the Pi model 3B, since that provides 2.5A.
I hope you found this guide useful and thanks for reading. In one of my upcoming tutorials, I will demonstrate how to optimize and deploy deep learning models trained in the public cloud on Jetson Nano. If you have any questions or feedback? Leave a comment below. If you like this post, please support me by subscribing to my blog.