I didn’t try directly run the yolov8 in rock5b.
You need to run the yolov8 in a PC and convert the model (some modifications) using RKNN toolkit2.
Then, run the converted model in rock5b.
You can use this command to clone the yolov8
https://github.com/ultralytics/ultralytics.git
You may want to try the yolov5 examples in the rknn-toolkit2 before working on YoloV8.
I managed to get it just after posting, but yes still have reboot issues. I will try v5.
Seems I have found a solution. Running the board using the Ameridroid power adapter, I had previously been using a USB-A to USB-C cable. Currently using a USB-C to USB-C and it trained all 3 epochs without a reboot.
Nice! If there is any problems, don’t hesitate to let me know.
convert yolov8 onnx to rknn, show error when run it on rk3588, error message:
failed to submit!, op id: 173, op name: Add:Add_243
convert script setting:
This is a beginner question.
Is the rk3588 model (.rknn) created on an AMD64 linux machine (installed rknn-toolkit2)?
Also, if you simulate it (.rknn) on Rock5b (installed rknpu2),
Do I have to run it on rock’s debian11 instead of armbian? Is it not possible to simulate on armbian?
Good day!
I tried exactly the same (yolov8s.pt to onnx, than to rknn)
And got these error http://joxi.ru/KAgPBgXhEwg59r
The same is with yolov5 and any version of yolov8
Please try to follow your insturtions now, maybe you will get the same negative result
Hi, can you share a screenshot of your onnx model using the https://netron.app/?
Also, please find the node Add_243 and take a screenshot of that node with the connected nodes.
Something like this:
Hi:) @Luke_Mira
Yes. Some people also managed to create the .rknn in rock 5B but I never tried that.
Do you mean run the neural network model in the physical NPU?
Sorry, I’m using my customized Ubuntu. I didn’t try it in debian or armbian.
Maybe ask the radxa admins could get a better answer?
Hi,
It looks like your script isn’t run the inference simulation.
Can you check the code of rknn is configured as following?
rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform='rk3588')
I’m attempting to use OpenCV for a webcam stream and feed it to a yolov8 model I have for face detection.
Without using OpenCV, I am able to run (in python):
model= YOLO("mymodel.pt")
model.predict(source="1", show=True)
However this has pretty bad frames and I wanted to give OpenCV a try for more customization.
I can use OpenCV and open the webcam and even show it. However, using:
ret, frame = cv2.read()
results = model(frame)
I receive an error:
The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
trying to use any() or all() results in an error that a file ‘None’ or ‘True’ does not exist. I’m not quite sure what’s happening, but I have found plenty of examples of OpenCV webcam frames being sent to YOLO this way without such an error.
Hi,
I didn’t try the YOLO+webcam before.
It looks like the camera device id, e.g., /dev/video20 is not properly specified in the both cases.
Can you check the camera device id is correct?
I checked, all is correct, exactly from your post
Can you try to make rknn right now with your instruction?
From step 1 (git clone …ultralytics)
It’s working on my desktop.
Can you share export.py and test.py files?
From ultralytics and rknn toolkit
Can you do the same with yolov5s.pt?
I missed your points.
If I understand correctly, you’re using the YoloV5/YoloV8s model generated from ultralytics, not the example one from rknn toolkit2.
For this step, you need replace the post-processing code, where the error comes from, in the test.py.
Yes, the video source is correct. /dev/video1 is the source, and it can be accessed how I showed there. The first code snippet runs perfectly fine other than poor frames. I can access the video source using OpenCV just fine as well using:
cap = cv2.VideoCapture(1) or cap = cv2.VideoCapture('/dev/video1')
The issue is when passing the OpenCV frame to model().
What do you mean?
What code i need to replace? Step4 http://joxi.ru/BA01G5WfMeBzer?
All of these is done
Yes, i take onnx, that i got on step 2 and try to convert it to rknn
Can you do the same?
Got these error, when tried to execute rknn on rk3588 board
E RKNN: [02:30:45.402] failed to submit!, op id: 190, op name: Add:/model.24/Add_2, flags: 0x5, task start: 942, task number: 36, run task counter: 18, int status: 0
Traceback (most recent call last):
File “/home/sitnikov/YOLOv5-RK3588-Python/inference_npu.py”, line 218, in
input1_data = outputs[1]
IndexError: list index out of range
Hi, you may need to convert the cv.Mat to data structure for the model input.
This link has the example usage of using OpenCV.
Let me know if it works for you.