Yoga pose classification: Datasets & Models

Manually reviewing whether a user is in the correct pose is impossible to do at scale, and rule-based approaches built on raw joint angles are brittle across different body types, camera angles, and clothing. A trained classification model learns these variations directly from data, giving consistent yoga pose detection without hand-coding thresholds for every asana. For fitness and AR app teams, this means you can ship real-time feedback, session tracking, and guided flows without staffing manual review or building fragile heuristics. The result is a component you can drop into an existing camera pipeline and iterate on as you add new poses.

A ViT classifier delivers accurate pose predictions that hold up across lighting conditions and camera setups, and it integrates with the phone and webcam feeds fitness apps already use. Because it runs as a classification step, it is lightweight enough for on-device or edge inference and scales cleanly as you expand your pose library. You can retrain on your own labeled images to improve accuracy for your specific user base and studio environments.

1. Upload an image: Drop any photo into the widget above to try yoga pose classification for free, right on this page.
2. Preview instant results: The model detects person in yoga pose, yoga mat, downward dog pose, warrior pose, tree pose within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
3. Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train Classification (ViT) on your own images for production accuracy.
4. Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.