YOLO11: How to Train for Object Detection on a Custom Dataset - Step-by-Step Guide

Overview

Dive into a comprehensive video tutorial on training YOLO11 for object detection on custom datasets. Learn the entire process from finding and labeling datasets to deploying the model for real-world applications. Explore techniques for training on local machines and Google Colab, understand YOLO annotation formats, and master hyperparameter tuning. Gain insights into evaluating model performance, running inference, and saving fine-tuned weights. Access valuable resources including GitHub repositories, datasets, and training notebooks to enhance your YOLO11 object detection skills.

Syllabus

- Introduction to YOLO11
- Finding Free Annotated Datasets for YOLO11
- Image Labeling for YOLO11
- Setting Up Your Local YOLO11 Training Environment
- Understanding YOLO Annotation Formats
- Training YOLO11 Locally
- YOLO11Training Hyperparameters
- Evaluating Your YOLO11 Model's Performance
- Running Inference with Your Trained YOLO11 Model
- YOLOv11 Training in Google Colab
- Saving Your Fine-Tuned YOLO11 Model Weights
- Deploying Your YOLOv11 Model
- Conclusion