Jetson-Nano-image

Jetson Nano DNN image

A Jetson Nano image with OpenCV, TensorFlow and Pytorch

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Installation.

• Get a 32 GB (minimal) SD-card which will hold the image.
• Download the JetsonNano.img.xz image (7.2 GByte!) from our server.
• Flash the image on the SD card with the Imager or balenaEtcher.
• According to issue #17, only flash the xz directly, not an unzipped img image.
• Insert the SD card in your Jetson Nano 4 GB RAM and enjoy.
• Username: jetson
• Password: jetson
• JetsonNano.img.xz md5sum: 621F2E4E0B7775E5293E2186C96E91AA

GDrive.

In some parts of the world, getting a good, solid connection to Sync is difficult. That is why we’ve also provided a copy on Google Drive. However, Google Drive limits the number of daily downloads. Please be considerate and use Google Drive only if necessary.

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Tips.

• If you are in need of extra space, you can delete the opencv and the opencv_contrib folder from the SD card. They are no longer needed since all libraries are placed in the /usr/ directory.
• Use a tool like GParted sudo apt-get install gparted to expand the image to larger SD cards. We recommend a minimum of 64 GB. Deep learning simply requires a lot of space.
  
  

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Pre-installed frameworks.

• JetPack 4.6.0
• OpenCV 4.5.3
• TensorFLow 2.4.1
• TensorFlow Addons 0.13.0-dev
• Pytorch 1.8.1
• TorchVision 0.9.1
• LibTorch 1.8.1
• ncnn 20210720
• MNN 1.2.1
• JTOP 3.1.1
• TeamViewer aarch64 15.24.5

TensorFlow 2.5 and above require CUDA 11. CUDA version 11 cannot be installed on a Jetson Nano due to incompatibility between the GPU and low-level software at this time; hence, TensorFlow 2.4.1. Only when NVIDIA releases a JetPack with CUDA 11 will we be able to upgrade TensorFlow.

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OpenCV + TensorFlow.

Importing both TensorFlow and OpenCV in Python can throw the error: cannot allocate memory in static TLS block.
This behaviour only occurs on an aarch64 system and is caused by the OpenMP memory requirements not being met.
For more information, see GitHub ticket #14884.

There are a few solutions. The easiest is to import OpenCV at the beginning, as shown above.
The other is disabling OpenMP by setting the -DBUILD_OPENMP and -DWITH_OPENMP flags OFF.
Where possible, OpenCV will now use the default pthread or the TBB engine for parallelisation.
We don’t recommend it. Not all OpenCV algorithms automatically switch to pthread.
Our advice is to import OpenCV into Python first before anything else.

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