How to Train a Custom YOLOv8 Pose Estimation Model

James Gallagher's blog post provides a detailed guide on training a custom YOLOv8 keypoint detection model, specifically for identifying key points of a glue stick to determine its orientation. Keypoint detection, also known as pose estimation, is a technique used to identify specific points on images, applicable in various scenarios such as assembly line orientation checks and human pose estimation. The guide outlines the process from installing the necessary dependencies, like the ultralytics and roboflow Python packages, to creating keypoint annotations using Roboflow, generating a dataset, and training the model in a notebook. The article emphasizes the importance of labeling keypoints accurately and provides a step-by-step approach, including logic to calculate object orientation by measuring angles between identified keypoints. It concludes by demonstrating how the trained model can evaluate whether a glue stick is positioned correctly, using a custom angle tolerance, and suggests that similar logic can be adapted for other applications like human pose detection.