How to Train a YOLOv11 Object Detection Model on a Custom Dataset

YOLOv11, launched by Ultralytics on September 27, 2024, is an advanced computer vision model designed for a variety of tasks such as object detection, segmentation, and classification. It boasts a higher mean Average Precision (mAP) on the COCO dataset while using 22% fewer parameters than its predecessor YOLOv8m, making it suitable for real-time applications. The guide by James Gallagher provides a comprehensive walkthrough on training a YOLOv11 model with a custom dataset using Roboflow and Google Colab. The process involves creating a labeled dataset, exporting it in the YOLOv11 PyTorch TXT format, and running training with YOLO11n weights. The guide also covers evaluating the model on test images and deploying it to Roboflow for broader access. This tutorial is supplemented by a Colab notebook, offering users a practical, hands-on experience in training and deploying computer vision models.