Ultralytics YOLO11 | Training, Inference, Benchmarking, and Deployment Explained! 🚀 - How to Train Ultralytics YOLO11 on the DOTA Dataset for Oriented Bounding Boxes in Google Colab 🚁

Training Oriented Bounding Boxes (OBB) with Ultralytics YOLO11 just got easier. When standard bounding boxes fall short, especially for rotated or skewed objects in aerial imagery, the OBB technique provides precise localization. In this step-by-step tutorial, you’ll dive into the OBB capabilities of the Ultralytics Python package, explore the powerful DOTA dataset, and learn how to train your own YOLO11 model in Google Colab with ease. Key highlights: 00:00 - Introduction to Oriented Bounding Boxes and why they matter 00:33 - Exploring the Ultralytics OBB task documentation 02:27 - Why the DOTA dataset is ideal for OBB training 03:15 - Step-by-step guide to training YOLO11 on the DOTA dataset in Google Colab 08:08 - Conclusion and final thoughts on OBB training and its benefits Read more ➡️ https://docs.ultralytics.com/datasets/obb/dota-v2/ Ultralytics Resources: 🏢 About Us: https://ultralytics.com/about 💼 Join Our Team: https://ultralytics.com/work 📞 Contact Us: https://ultralytics.com/contact 💬 Discord Community: https://discord.com/invite/ultralytics 📄 Ultralytics License: https://ultralytics.com/license #computervision #objectdetection #ai #machinelearning #deeplearning #yolo #ultralytics #obb