How to Train Ultralytics YOLO26 Semantic Segmentation Model on Custom Dataset | Ultralytics Platform

Semantic segmentation is one of the most powerful computer vision techniques for scene understanding. Instead of simply detecting objects, it assigns a class label to every pixel in an image, enabling AI systems to understand surfaces, regions, and object categories at a much deeper level. In this video, we explore semantic segmentation using Ultralytics YOLO26 and a wooden tools dataset. Along the way, we'll discuss how semantic segmentation works, train a model on a custom dataset using Ultralytics Platform, analyze its performance, and see the results on real video footage. You'll also learn the key differences between semantic segmentation and instance segmentation, two commonly confused computer vision tasks that serve different purposes in real-world applications. Whether you're working on industrial inspection, robotics, automation, or scene understanding, this tutorial provides a practical introduction to building and evaluating semantic segmentation models with YOLO26. Chapters: 00:00 - Introduction to semantic segmentation 00:38 - Semantic segmentation documentation overview 01:05 - Wooden-tools dataset overview 01:49 - Training YOLO26 on the dataset 03:22 - Analyzing training metrics 04:16 - Semantic segmentation demo 05:03 - Running video inference with YOLO26 05:45 - Semantic vs instance segmentation 06:50 - Conclusion and key takeaways 🔗 Read more about semantic segmentation ➡️ https://docs.ultralytics.com/tasks/se... Ultralytics YOLO Resources: 💻 GitHub Repository: https://github.com/ultralytics/ 📚 Documentation: https://docs.ultralytics.com/ #yolo26 #semanticsegmentation #computervision #visionai #imagesegmentation #deeplearning #ultralytics