Ultralytics YOLO11 | Training, Inference, Benchmarking, and Deployment Explained! 🚀 - How to Train Ultralytics YOLO11 on the KITTI Dataset | Object Detection, Inference & ONNX Export 🚀🤯

The KITTI dataset is one of the most widely used benchmarks in autonomous driving and computer vision research. In this tutorial, we’ll show how to train Ultralytics YOLO11 on the KITTI dataset, perform inference, and export your trained model to ONNX for deployment. You’ll learn how to install the Ultralytics Python package in Google Colab and use the dataset YAML to train YOLO11 step by step. We’ll also analyze model metrics like mAP, precision, and recall, and see how to run real-time inference on raw driving footage. Chapters: 00:00 - Introduction to KITTI dataset 02:09 - Understanding the dataset structure 02:31 - Installing Ultralytics package in Google Colab 02:51 - Dataset YAML file overview 03:17 - Training YOLO11 on KITTI dataset 06:28 - Inference demo on raw video stream 07:27 - Understanding model training metrics 09:44 - Running predictions with the trained model 10:32 - Exporting the trained model to ONNX format 11:22 - Conclusion and key takeaways 🔗 Dataset ➡️ https://docs.ultralytics.com/datasets/detect/kitti/ 🔗 Notebook ➡️ https://github.com/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-kitti-detection-dataset.ipynb 🔗 Raw video for testing ➡️ https://drive.google.com/drive/folders/1uWuIH-G7TPVBgiIX7Jxn2tJM3KqJWxXq?usp=sharing Ultralytics YOLO Resources: 💻 GitHub Repository: https://github.com/ultralytics/ 📚 Documentation: https://docs.ultralytics.com/ #kittidataset #yolo11 #ultralytics #objectdetection #computervision #onnx