Export Custom Trained Ultralytics YOLOv8 Model and Run Live Inference on Webcam | Episode 4

Welcome to Episode 4 of our Ultralytics YOLOv8 series! Join Nicolai Nielsen as he walks you through the process of exporting your custom-trained Ultralytics YOLOv8 model and running live inference using a webcam. This video is packed with practical insights and step-by-step instructions perfect for anyone looking to leverage computer vision and AI technologies in their projects.

🔍 In this episode, you'll learn:
- How to export a trained YOLOv8 model from Google Colab
- Steps to perform object detection using a custom Python script
- Live inference techniques with a webcam
- Practical tips for integrating YOLOv8 into your own applications

Using the YOLOv8 medium model, Nicolai demonstrates the entire workflow, from model training to live object detection. You'll see how to handle different data sources, such as NumPy arrays, PIL images, and video sources, and how to set confidence scores to filter predictions.

📚 For more details, check out these resources:

Don't miss out on the upcoming videos where we'll dive deeper into object tracking and advanced customizations. Be sure to like, subscribe, and hit the notification bell to stay updated with the latest in AI and computer vision from Ultralytics!

#YOLOv8 #Ultralytics #ComputerVision #AI #MachineLearning #ObjectDetection #Python

[Meloia]

Aí, Nicolai killing it in Episode 4! 🎸 I'm curious, how do you handle model performance on a webcam with varying light conditions? Will it samba through the shadows or miss a beat? Share the gospel on tweaking YOLOv8 for unpredictable environments!

[AlexChen-fy]

Loving the vibe of this series! Quick question—how does YOLOv8 perform in terms of latency when running live inference on a low-power device like a Raspberry Pi or Jetson Nano? Are there any optimization tricks or hardware acceleration techniques you'd recommend for squeezing out faster FPS in real-time applications? 😎

[os-EmilyW]

Fascinating video, Nicolai! Could you delve a bit deeper into the performance implications of running live inference on a webcam? Specifically, how does the model's complexity impact real-time processing speeds, and are there any notable trade-offs between accuracy and efficiency?

[Sasha-nx]

Fabulous breakdown on deploying YOLOv8 for live inference, Nicolai! How would you optimize this workflow for a low-power, edge device like a Raspberry Pi, especially when handling different custom datasets? #ModelEfficiency #EdgeComputing

[Smitthy-kd]

Hey Nicolai, awesome vid! Quick question: if I want to use the exported YOLOv8 model for a real-time application beyond just a webcam (like drones or autonomous vehicles), what tweaks or optimizations would you recommend to handle the increased data and maintain performance?

[id]

Hi, I don't seem to see the link for the Colab for this episode, has it been taken down?

[Smitthy-kd]

This is awesome stuff! Quick question: How does the performance of the YOLOv8 model hold up across different lighting conditions when doing live webcam inference? My webcam setups always seem to struggle in low light!?

[ruchisharma]

after installing python, cv and ultralytics on raspberry pi 4 and then downloading my trained model from google colab, can i directly run it on raspberry pi for object detection?

[alimohsen]

Thank you for this excellent offer. After training the model and creating the best.pt file (in yolov8), how can I convert this model to a tflite model?

[nadjareifer]

Hi :) ... thanks for the great video ... just one question ... when I run the code, a video (mp4) from the webcam transmission is saved in an additional folder detect/predict, but unfortunately it gets corrupted every time and it can't be opened ... is there another easy way to save a video from the webcam transmission?

[nurdalizadarisman]

if train model inside ultralytics website, which platform should i choose to export (pytorch, onnx, etc)?

[rubakh]

If I use this approach to retrain the model with 1 or 2 new classes, does it mean that the resulting model can detect the pre-trained objects in addition to the new objects?

[ethanlu]

Hello, Sir! Why i cant open the camera of my laptop when i type { result = model(source = 1, like you, could you teach me how to do ? 😭

[m]

What export formats are available? And I how do I know which one to use for my use case?

[AHMADAFIFBINHILMIUPM]

hye! im working on a custom dataset and i use the yolov8m-seg.pt variation. unfortunately, whenever i tried to use it on web cam, it shows the raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'Segment' object has no attribute 'detect'. i hope i can get the solution for this

[romroc]

Thanks for your interesting videos. In the documentation they say onnx and openvino is 3x faster on cpu. I tested with my trained yolov8x model, with imgsz 4032, it executes in the same time as original .pt format. Can you confirm it's faster in your tests?

[kuaranir]

Once I run it, the new windows with video doesnt appear in PyCharm, but in VSCode is all right (((

[engammar]

Did you also import OpenCV for the web cam functionality or just the code in the video? Thanks

[mahrukhhafeez]

Hey. I want to download the model that i have trained on my dataset to detect ingredients on Google Colab but the weights are stored in my google drive. that i mounted with the notebook. because I have to make an Android application using this YOLOV8 model. How can I do that? Integration with the app in Android Studio. Is there a tutorial or something? Any guidelines would help. I have a project due next week.

[ramazan-yerlikaya]

How can i get python files and source code ?