How to Segment Carparts with Ultralytics Platform | Train, Deploy & Inference | Ultralytics YOLO26 🚀

In this tutorial, we demonstrate how to build a complete segmentation pipeline using Ultralytics YOLO26 and the Ultralytics Platform. We begin with an overview of the car-parts segmentation dataset and train a YOLO26 model directly on the platform. You’ll learn how to monitor training results, run predictions, export the trained model, and understand the deployment workflow. Finally, we take the trained model and run inference locally using the Ultralytics Python package, showing how to test the model locally before deployment.

Chapters
00:00 - Introduction to carparts segmentation
00:42 - Carparts dataset overview in the Platform
02:10 - Training YOLO26 on the carparts dataset in the Platform
03:15 - Model training results overview in the Platform
03:46 - Running predictions with trained model on the Platform
04:00 - Exporting the trained model
04:12 - Model deployment overview
05:03 - Running local inference with Ultralytics Python package
06:54 - Conclusion and key takeaways

🔗 Carparts segmentation dataset ➡️ https://platform.ultralytics.com/muhammadrizwanmunawar/datasets/carparts-seg

Ultralytics YOLO Resources:
💻 GitHub Repository: https://github.com/ultralytics/
📚 Documentation: https://docs.ultralytics.com/

#yolo26 #imagesegmentation #carparts #ultralytics #computervision #visionai

Видео How to Segment Carparts with Ultralytics Platform | Train, Deploy & Inference | Ultralytics YOLO26 🚀 канала Ultralytics