Q-engineering

@programmeddeath1 , It's very hard to diagnose the issue, due to the lack of debug info generated. Changing the root partition should be of any effect. Once loaded, it will...

@programmeddeath1 , You have three options. 1) Use a default JetPack 4.6, default OpenCV (no CUDA), an **OLD** version of ncnn. You get the benchmark speeds and YoloV7 without any...

I've used a ready-made model by [Xiang Shin Wuu](https://github.com/xiang-wuu). You might take a look [here](https://blog.csdn.net/weixin_40293999/article/details/130977968)

You're welcome

Very sharp. It doesn't matter that much. If you compare the outcome, the YoloV7 anchors are even better positioned than the tiny counterparts.  

Dear @evanshlom, No. YoloV7-tiny is still used. Hence the impressive inference times. Only the algorithm finding the boxes is feed by YoloV7 parameters. Which, surprisingly, gives better results in this...

A Raspberry Pi has not the computer power to train a neural network. You have to use a powerful PC with a (NVIDIA) GPU card. Or use the some cloud...

I've used an [already converted model](https://github.com/xiang-wuu/ncnn-android-yolov7/tree/master/app/src/main/assets). There are guides on the net explaining how to convert onnx to ncnn. However, it may be somewhat cumbersome, due to the dynamic input...

Here are the instructions. ``` sudo chown root:root / /lib sudo apt purge ubuntu-desktop -y && sudo apt autoremove -y && sudo apt autoclean sudo apt-get remove nautilus nautilus-* gnome-power-manager...

Thanks for all the work. I've made a link to your repo in the README.md. https://github.com/Qengineering/Jetson-Nano-Ubuntu-20-image/tree/main#ros