Ultralytics YOLO11 | Training, Inference, Benchmarking, and Deployment Explained! 🚀 - How to Train Ultralytics YOLO11 on HomeObjects-3K Dataset | Detection, Validation & ONNX Export 🚀

Let’s explore a dataset with 3,000+ high-resolution images and 12 common household object classes, including sofa, chair, door, table, and more. This tutorial shows you how to train a YOLO11 model on this diverse dataset, evaluate its performance using mAP, precision, and recall, and visualize results with a confusion matrix. You’ll also learn how to run inference and export the trained model to ONNX format, making it ready for deployment in real-world AI applications. Chapters: 00:00 - HomeObjects-3K dataset introduction 00:13 - Dataset documentation walkthrough 00:53 - Dataset structure, classes, and applications 01:48 - Dataset YAML and annotations mosaic 02:07 - Ultralytics notebooks repository overview 03:15 - Installing Ultralytics package in Google Colab 03:44 - Training Ultralytics YOLO11 model on the dataset 06:08 - Validation results discussion: mAP, precision, recall, and confusion matrix 08:51 - Running prediction with the trained model 09:39 - Exporting the trained model in the ONNX format 11:08 - Conclusion and summary 🔗 Notebook ➡️ https://github.com/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-homeobjects-dataset.ipynb 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 #ai #homeobjects #objectdetection #onnx #machinelearning #deeplearning #computervision #dataset #googlecolab