Keypoint estimation (also called pose detection) locates specific points of interest on objects or bodies within images. Instead of a bounding box, the model predicts the x/y coordinates of each defined keypoint, producing a skeleton overlay. This tutorial walks through training a YOLOv8 keypoint model on Datature Nexus, using a golf swing as the example case.
What This Tutorial Covers
- Uploading images and defining keypoint skeletons
- Labeling keypoints on joints and body parts
- Configuring the YOLOv8 keypoint detection architecture
- Running the training and reviewing skeleton predictions
- Evaluating keypoint accuracy on test images
How Keypoint Detection Works
The model learns two things simultaneously: where the person or object is (a bounding box) and where each defined point sits within that box (keypoint coordinates). For human pose, that typically means 17 points covering the nose, eyes, ears, shoulders, elbows, wrists, hips, knees, and ankles. For custom applications, you define whatever points matter for your task.
Where Keypoint Estimation Gets Used
Sports biomechanics: analyzing athlete form, comparing swing mechanics, tracking joint angles during movement. Physical therapy: measuring range of motion, tracking recovery progress. Robotics: understanding human posture for safe human-robot interaction. Retail: virtual try-on systems that need body landmark positions. Animal behavior research: tracking animal poses in wildlife footage without manual frame-by-frame annotation.
Anywhere you need to track the spatial arrangement of specific body or object parts, keypoint detection is the right tool.
