How to Train a YOLOv8 Oriented Bounding Box (OBB) Model

James Gallagher's guide provides a detailed walkthrough on training a YOLOv8 Oriented Bounding Box (OBB) model for object detection, particularly focusing on solar panels. Unlike traditional models that produce horizontally-aligned bounding boxes, the YOLOv8 OBB model offers a more precise fit for angled objects by using oriented bounding boxes. The guide begins with data collection from Roboflow Universe, specifically utilizing the Aerial Solar Panels dataset, which initially contains standard bounding box annotations requiring relabeling with polygons for OBB training. It then guides users through setting up a project in Roboflow, annotating images with the polygon tool, creating a dataset version, and exporting data for use in a Google Colab notebook. The training process involves using the ultralytics library to run the model for 100 epochs, followed by testing the model's performance using the supervision package to visualize results. The guide emphasizes the advantages of oriented bounding boxes in providing tighter alignment around objects of interest, demonstrating the improved accuracy compared to traditional bounding box models.