Keypoint detection
Spotting specific landmarks — eyes, joints, edges — in an image so the model knows where important parts are.
Think of it like
Like marking constellations on a star map: the model learns to highlight the brightest points in an image.
Example
A pose estimator locates your shoulders, elbows, and knees so a fitness app can check your form during a workout.
How it actually works
The model outputs coordinates for each landmark, often with confidence scores. Accuracy matters downstream — a missed shoulder throws off everything built on it. Modern approaches use heatmaps or regression heads to predict positions end-to-end.
For product teams
Unlocks use cases that need to understand *where* things are, not just *what* they are — from fitness tracking to gesture control.
For engineers
Output is typically a list of (x, y) coordinates or a heatmap per keypoint; training uses L2 loss or MSE on predicted positions.
Related
- Closely paired with object detection and bounding boxes for spatial reasoning.
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