Ultralytics YOLO11 | Training, Inference, Benchmarking, and Deployment Explained! 🚀 - How to Use Baidu's RT-DETR for Object Detection | Inference and Benchmarking with Ultralytics 🚀

Get started with RT-DETR, a vision transformer-based real-time object detection model. This tutorial covers the full workflow: exploring the RT-DETR documentation, running inference in Google Colab, and benchmarking model performance. We also explain the role of half precision, discuss benchmark results, and place RT-DETR in the context of a complete computer vision pipeline. Chapters: 00:00 - Introduction to the RT-DETR model 00:44 - RT-DETR documentation walkthrough 02:17 - Running inference with RT-DETR in Google Colab 06:00 - Benchmarking the RT-DETR model 09:05 - Advantages of using half precision in benchmarking 09:30 - Analyzing RT-DETR benchmark results 11:03 - Overview of the computer vision pipeline 11:34 - Conclusion and key takeaways 🔗 Explore more ➡️ https://docs.ultralytics.com/models/rtdetr/ Ultralytics YOLO Resources: 💻 GitHub Repository: https://github.com/ultralytics/ 📚 Documentation: https://docs.ultralytics.com/ #rtdetr #objectdetection #computervision #transformers #ultralytics #machinelearning