Image for Rock 4 SE RK 3399

[ajay01994]

Hi ,could you please also help me. with releasing the same model for Rock pi 4SE ,RK 3399 architecture.this would be really helpful since there are supply chain issues with Raspberry pi

https://forum.radxa.com/c/rockpi4

[Qengineering]

Dear @ajay01994,

If you look at Radxa's website, you are facing the same journey as I did.
Install a Debian operating system. I used the native Radxa Debian, as being as close to the hardware as possible.
Install OpenCV as described in our guide, just like the ncnn framework.
Not much can go wrong.

[ajay01994]

Hi ,

Thanks for the reply , do you have a model zoo for YOLO V5,V7,V8 for RK 3399 , Also could you help with TNN how to install that ?

[Qengineering]

The RK3399 has no NPU on board. You can use the ncnn models made for the Raspberry Pi. Or any other framework, like MNN, TNN, TensorFlow-Lite.
Please see the updated guide on how to install TNN on a ARM board like your Rock Pi 4 SE.

[ajay01994]

Hi yes it worked for Rk 3399 thanks a lot !

I also bought a rk3588 with NPU , and was able to run yolov5 , do you have inference code for yolo v7,v8 for NPU ,and help with steps to install and inference on NPU for these models

[apanand14]

First of all great work @Qengineering . I appreciate your efforts.

Hi Ajay! can you tell me how can I run yolov5 as a demo on rk3588 with NPU. Thank you in advance.

And yes @Qengineering , I'm also interested in yolov8-seg inference with NPU. Thank you in advance you too!!

[Qengineering]

I will port the NPU examples for the RK3566 to the RK3588 and upload them to GitHub next week.

[apanand14]

I have already ran build-linux_RK3588.sh file on rknpu2 examples but later I do not know what to do to make running yolov5 demo . If you guide me then it would be great. Thank you in advance

[apanand14]

@Qengineering Finally, I ran successfully yolov5 demo on the image you provided. Now, I would like to test yolov8_seg based on RK3588 whenever you put it. I'm really looking forward to testing it. Thank you once again!

[Qengineering]

Please find: https://github.com/Qengineering/YoloV8-seg-NPU

[apanand14]

Hi @Qengineering , thank you so much for it. But I'm not able to download Rock 5 image from your sync site. This new image contains yolov8 examples right? And One more thing, if I would like to run my custom trained model inference on Rock 5b with NPU then should I change in .cpp right? and the model itself. Is there anything should I need to tale care of or?

[Qengineering]

Sorry for the inconvenience. The nightly upload failed. Just restart it again.

I let you know when the link is active.

[Qengineering]

To get your custom model up and running, you need to use the rknn_toolkit2 toolkit, installed on a Linux PC, you can port the model to an INT8 or FP16 rknn model. Please use the example that best fits your architecture. For instance, ~/rknn-toolkit2/rknn-toolkit2/examples/tensorflow. Here, use test.py to port your *.pd model to *.rknn. Sometimes you need to modify test.py a little to get everything working. Also, not every model can be ported to the NPU domain. And, of course, some models don't act well with INT8. Once running, postprocces.cpp is location where you alter the code to get your outputs. (Mostly)
Best to install the toolkit in a virtual environment, as it will pull in many thrid party packages, like TensorFlow, Pytorch, PaddlePaddle etc.

[Qengineering]

The link is up and running.

[apanand14]

Thank you!! Sure! I will try it and will update you or ask you if I need any assistance. One more thing about the image. I'm using the rock5b image already from this repo. is it sufficient to test new examples or should I use this Rock5_ubuntu22.img for yolov8 seg?