YOLOv11: How to Train for Object Detection on a Custom Dataset | Step-by-step guide

Master YOLOv11 object detection with this complete tutorial. From finding datasets to labeling images, training the model, and deploying it for real-world use, this guide has you covered. Learn to train on your local machine or Google Colab and get your custom object detection model up and running. Chapters: - 00:00:00 Introduction to YOLOv11 - 00:00:55 Finding Free Annotated Datasets for YOLOv11 - 00:02:14 Image Labeling for YOLOv11 - 00:09:05 Setting Up Your Local YOLOv11 Training Environment - 00:15:44 Understanding YOLO Annotation Formats - 00:27:22 Training YOLOv11 Locally - 00:34:03 YOLOv11Training Hyperparameters - 00:38:04 Evaluating Your YOLOv11 Model's Performance - 00:43:30 Running Inference with Your Trained YOLOv11 Model - 00:48:19 YOLOv11 Training in Google Colab - 01:00:41 Saving Your Fine-Tuned YOLOv11 Model Weights - 01:04:32 Deploying Your YOLOv11 Model - 01:11:07 Conclusion Resources: - Roboflow: https://roboflow.com - ⭐ Notebooks GitHub: https://github.com/roboflow/notebooks - ⭐ Supervision GitHub: https://github.com/roboflow/supervision - 🏞️ TFT-ID dataset: https://universe.roboflow.com/huyifei/tft-id - 📓 YOLOv11 object detection model training notebook: https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolo11-object-detection-on-custom-dataset.ipynb - 🗞 YOLOv11 object detection model training blog post: https://blog.roboflow.com/yolov11-how-to-train-custom-data/ Stay updated with the projects I'm working on at https://github.com/roboflow and https://github.com/SkalskiP! ⭐ #yolo #yolov11 #yolo11 #objectdetection