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This upgrade requires a Phillips #2 screwdriver, the wireless module, and antennas (not to mention about 10-20 minutes of your time)
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You must install the WiFi module and antennas under the main heatsink on your Jetson Nano. WiFi module such as the Intel Dual Band Wireless-Ac 8265 W/Bt(Intel 8265NGW) and 2x Molex Flex 2042811100 Flex Antennas ( Figure 5, bottom-center).Pictured is the Geekworm Dual Band USB 1200m
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No tools are required and it is portable to other devices.
When flashing has successfully completed, you are ready to move on to Step #2. Figure 3: Flashing NVIDIA’s Jetpack image to a microSD card with balenaEtcher is one of the first steps for configuring your Nano for computer vision and deep learning. From there, fire up balenaEtcher and proceed to flash. Insert the microSD into the card reader, and then plug the card reader into a USB port on your computer. We recommend either a 32GB or 64GB microSD card (SanDisk’s 98MB/s cards are high quality, and Amazon carries them if they are a distributor in your locale). You will need a suitable microSD card and microSD reader hardware. Once both (1) your Nano Jetpack image is downloaded, and (2) balenaEtcher is installed, you are ready to flash the image to a microSD. You will use it to flash your Nano image to a microSD card.
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While your Nano SD image is downloading, go ahead and download and install balenaEtcher, a disk image flashing tool: Figure 2: Download and install balenaEtcher for your OS. We recommend the Jetpack 4.2 for compatibility with the Complete Bundle of Raspberry Pi for Computer Vision (our recommendation will inevitably change in the future). Go ahead and start your download here, ensuring that you download the “Jetson Nano Developer Kit SD Card image” as shown in the following screenshot: Figure 1: The first step to configure your NVIDIA Jetson Nano for computer vision and deep learning is to download the Jetpack SD card image. You will need the microSD flashed and ready to go to follow along with the next steps. In this step, we will download NVIDIA’s Jetpack 4.2 Ubuntu-based OS image and flash it to a microSD. Step #1: Flash NVIDIA’s Jetson Nano Developer Kit. If you encounter a problem with the final testing step, then you may need to go back and resolve it or worse, start back at the very first step and endure another 2-5 days of pain and suffering through the configuration tutorial to get up and running (but don’t worry, I present an alternative at the end of the 16 steps). We will also test our Nano’s camera with OpenCV to ensure that we can access our video stream. Once we are done, we will test our system to ensure it is configured properly and that TensorFlow/Keras and OpenCV are operating as intended.
Prepare yourself for a long, grueling process - you may need 2-5 days of your time to configure your Nano following this guide. In this tutorial, we’ll work through 16 steps to configure your Jetson Nano for computer vision and deep learning. While it is a very capable machine, configuring it is not (complex machines are typically not easy to configure). The NVIDIA Jetson Nano packs 472GFLOPS of computational horsepower.
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Looking for the source code to this post? Jump Right To The Downloads Section How to configure your NVIDIA Jetson Nano for Computer Vision and Deep Learning