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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Jetson Nano with Ubuntu 20.04 image
| Date | Stars |
|---|---|
| 2026-07-24 | 968 |
| 2026-07-25 | 970 |
| 2026-07-28 | 970 |
| 2026-07-30 | 970 |
| 2026-08-06 | 970 |
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growth rate 0.00%/day
# Jetson Nano with Ubuntu 20.04 OS image <br/><br/> <br/><br/> <br/><br/> ## A Jetson Nano - Ubuntu 20.04 image with OpenCV, TensorFlow and Pytorch [](https://opensource.org/licenses/BSD-3-Clause)<br/><br/> ### Update 9-17-2023. - Refresh Ubuntu 20.04. - Add WiFi support (https://github.com/Qengineering/Jetson-Nano-Ubuntu-20-image/issues/28). - Reduce xz archive. ### Update 9-6-2023. - Added a new split image. ### Update 7-15-2023. - Refresh Ubuntu 20.04. - Update OpenCV (**4.8.0**) - Update PyTorch (**1.13.0**) - Update TorchVision (**0.14.0**) - New: TensorRT (**8.0.1.6**) ### Update 7-13-2023. - Added an installation wheel for TensorRT 8.0.1.6+cuda10.2. The version is synchronous with the C++ version found on the image. Newer versions of TensorRT require CUDA 11 or later, which is not supported on a Jetson Nano. (thanks to [Teemu Heikkilä](https://github.com/theikkila)) ### Tip 3-10-2023. - Connected to the net for the first time? Wait for the Software Updater and let it refresh your operating system. ### Update 7-30-2022. - Added bare overclocked Ubuntu 20.04 image. ### Update 7-26-2022. - Refresh Ubuntu 20.04 - Update OpenCV (**4.6.0**) - Update PyTorch (**1.12.0**) - Update TorchVision (**0.13.0**) - New xz archive (size reduction of 26%) - New download site (Gdrive has a limited number of downloads per day). ### Update 1-30-2022. - Add **Jtop** (thanks to [SkrilaxCZ](https://github.com/rbonghi/jetson_stats/issues/173)) ------------ ## Installation. - Get a 32 GB (minimal) SD card to hold the image. - Download the image `JetsonNanoUb20_3b.img.xz` (**8.7 GByte!**) from our [server](https://storage.qengineering.eu/JetsonNanoUb20_3b.img.xz). - Flash the image on the SD card with the [Imager](https://www.raspberrypi.org/software/) or [balenaEtcher](https://www.balena.io/etcher/). - Given [issue #101](https://github.com/Qengineering/Jetson-Nano-Ubuntu-20-image/issues/101#) the Imager works sometimes better than the balenaEtcher. - According to [issue #17](https://github.com/Qengineering/Jetson-Nano-Ubuntu-20-image/issues/17#) only flash the xz directly, not an unzipped img image. - Insert the SD card in your Jetson Nano and enjoy. - Password: ***jetson*** - JetsonNanoUb20_3b.img.xz md5sum: D738F1FE20088A1BDBD10E2358B512F7 ### GDrive. In some parts of the world, getting a good solid connection to is difficult. That is why we've also provided a copy on [Google Drive](https://drive.google.com/file/d/1L2H_sQC_kSILrcJteWg7htKxJirtDsZ9/view?usp=sharing). However, Google Drive limits the number of daily downloads, which is much lower than our average daily download volume. Please be considerate and use Google Drive only if necessary. #### Tip:<br> The SD card is overflowing with software; more than 21 GByte! With a 32 GB card, you don't have enough space to work decently.<br> Therefore, flash the image on an SD card of 64 or more and use GParted (`$ sudo apt-get install gparted`) to enlarge the partition.<br> Or use the method of Doeke Wartena: https://github.com/Qengineering/Jetson-Nano-Ubuntu-20-image/issues/125. ------------ ### Split image. Due to the large image (9.3 GB), the download may take quite some time. It makes downloading vulnerable.<br/> That's why we split the file into smaller chunks. These are more manageable than one huge download.<br/> If you prefer this partial download over one large one, download the following 14 files (700 MB each) and place them in one folder.</br> - [JetsonNanoUb20_3b.img.xz.001](https://storage.qengineering.eu/Nano/JetsonNanoUb20_3b.img.xz.001) - [JetsonNanoUb20_3b.img.xz.002](https://storage.qengineering.eu/Nano/JetsonNanoUb20_3b.img.xz.002) - [JetsonNanoUb20_3b.img.xz.003](https://storag
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:bf7641a763fc1045, topic:deep-learning, topic:pytorch, topic:tensorflow