Thank you, thank you, thank you! I've been struggling to get my YOLOv8 + RPi 4 + Coral TPU up and running. I'm going to follow your tutorial this weekend and see if I can get everything working. Exciting!!
Hello Koby, i have a problem when i run the code like you did at 28:41 my RP4 reboot, i do not know why i have tried many time and it reboot again , i need to finish your tutorial so i could use my usb web cam to for live object detection, thank you in advance Edit: the RP does not reboot it closed remotely
Thank you so much for this amazing tutorial, it helped a lot with my project! Could you please help me with some informations about how I could use this setup for an USB webcam live inference object detection? Instead of being on .mp4 video files. I tried using the ultralytics script, but it didn’t work. Thanks again!
one issue I can't seem to get around when going back to Python 3.9 is that I cannot get libcamera to work for the pi camera module 3...either coral doesn't work with 3.11 or libcamera doesn't work with 3.9...driving me crazy!
Hi! We are creating a system that classifies tomato ripeness levels using image processing in CNN architecture with the YOLOv8 model. We are using Raspberry Pi 4 OS with 4GB RAM and we have encountered a problem - the system has 2-3 minute delay/lag in classifying the ripeness level. Would you happen to have any recommendation/suggestion sir on this problem?
Thanks for the comment. The mentioned error occurs when the USB accelerator is connected to your Raspberry Pi when the code is executed. If it was connected then there is a loose contact just like in my case while working with pi5. You can change the port/ make sure you have firm USB contact.
Hello Koby. Many thanks for this tutorial. I was earlier using RPI4 (32 bit legacy os) along with google coral accelerator and using edge tpu i was able to recognize object with my pi camera. I think it was using SSD model. I want to extend by using YOLO model and referring to this project. I am having a short query that this model work on edge tpu correct since we are using coral accelerator
Hello Koby. Can you help me pls? I want to run an object detection model on my raspberry pi 4 with a Coral USB accelerator and a model 3 wide camera. But I am having big problems with FPS currently only 3-4fps. Can you recommend me the best approach? I need to detect objects at a long distance but I have only class 1. I have been using mobilenet ssd fpn lite 320 and 640. I need to have minimum 25fps to close my project. I would appreciate any help. Thank you.
switched to a usb camera, but still struggling with getting it to run the model with a video feed. Is there an example code somewhere that does this using this model? No matter what I try I still get the "ValueError: Failed to load delegate from libedgetpu.so.1" error.
ok so the cord that comes with the coral USB is crap. Replaced it and the TPU is actually recognized. ...facepalm...BUT I'm still struggling to find the right code to detect properly.
Thank you, thank you, thank you!
I've been struggling to get my YOLOv8 + RPi 4 + Coral TPU up and running. I'm going to follow your tutorial this weekend and see if I can get everything working. Exciting!!
That sounds great. Post here if you facing any issues.
Thank you for this amazing tutorial
Glad it was helpful!
Hello Koby, i have a problem when i run the code like you did at 28:41 my RP4 reboot, i do not know why i have tried many time and it reboot again , i need to finish your tutorial so i could use my usb web cam to for live object detection, thank you in advance
Edit: the RP does not reboot it closed remotely
Thank you so much for this amazing tutorial, it helped a lot with my project!
Could you please help me with some informations about how I could use this setup for an USB webcam live inference object detection? Instead of being on .mp4 video files. I tried using the ultralytics script, but it didn’t work. Thanks again!
Hey! For a USB webcam, you can specify 0 or 1 or (your USB camera index) as the input and everything works the same with your camera.
Please provide context for the Ultralytics script and error.
@davidyarko7300 please help me
Thank for your tutorial
maybe it's have possible to deploy YOLOv7 model on TPU with RPI4
I will try it when i'm get some time
You are most welcome.
Thank you for your video. How much FPS did you get for YOLOv8 and YOLOv9 respectively?
We did get several FPS in this video depending on the process type and model size.
Some combinations gave the peek 60FPS, and 30FPS for the most part.
Do you think this would work on the google coral devboard too?
Hello Koby..Thanks for this video...I just bought dual edge coral TPU but in pci case for raspberry pi 5. . Can not this be used with this library?
Can i use this set up for Raspberrypi zero 2w
one issue I can't seem to get around when going back to Python 3.9 is that I cannot get libcamera to work for the pi camera module 3...either coral doesn't work with 3.11 or libcamera doesn't work with 3.9...driving me crazy!
Hi, Can you help me with this error.
silvatpu-linux-setup
Illegal instruction
pip uninstall torch
pip uninstall torchvision
pip install torch==2.0.1
pip install torchvision==0.15.2
github.com/ultralytics/hub/issues/787#issuecomment-2263946340
Hi! We are creating a system that classifies tomato ripeness levels using image processing in CNN architecture with the YOLOv8 model. We are using Raspberry Pi 4 OS with 4GB RAM and we have encountered a problem - the system has 2-3 minute delay/lag in classifying the ripeness level. Would you happen to have any recommendation/suggestion sir on this problem?
My first recommendation to you is this video, you could get the edge tpu USB accelerator to help you speed up the process.
Please help me in that , i have a rpi 4 with 64 bit os , and i want to use camera module ip , with script ultrlytics
Can I create my own model using yolov8? How do I convert it to tflite?
35:24
Thank you. The explanation was very good. But I have one problem. At this time 35:24 , I keep getting the same error. I could not solve it.
Thanks for the comment. The mentioned error occurs when the USB accelerator is connected to your Raspberry Pi when the code is executed. If it was connected then there is a loose contact just like in my case while working with pi5. You can change the port/ make sure you have firm USB contact.
Thank you, the problem has been solved❤
nice video~
Hello Koby. Many thanks for this tutorial. I was earlier using RPI4 (32 bit legacy os) along with google coral accelerator and using edge tpu i was able to recognize object with my pi camera. I think it was using SSD model.
I want to extend by using YOLO model and referring to this project. I am having a short query that this model work on edge tpu correct since we are using coral accelerator
Hi Aman, Can you please share any link where Rpi4 (bookworm) + Coral+ Pi camera is used for detection using any YOLO model?
Hello Koby. Can you help me pls? I want to run an object detection model on my raspberry pi 4 with a Coral USB accelerator and a model 3 wide camera. But I am having big problems with FPS currently only 3-4fps. Can you recommend me the best approach? I need to detect objects at a long distance but I have only class 1. I have been using mobilenet ssd fpn lite 320 and 640. I need to have minimum 25fps to close my project. I would appreciate any help. Thank you.
Same with steam deck 6fps 9 classes I had to resize to 240x240 2 classes now I get 22fps. I need 45fps min
Great video. Thanks!
switched to a usb camera, but still struggling with getting it to run the model with a video feed. Is there an example code somewhere that does this using this model? No matter what I try I still get the "ValueError: Failed to load delegate from libedgetpu.so.1" error.
ok so the cord that comes with the coral USB is crap. Replaced it and the TPU is actually recognized. ...facepalm...BUT I'm still struggling to find the right code to detect properly.