Hand detection: Datasets & Models

Manually annotating hand positions or relying on hardware controllers and depth sensors is expensive, slow, and doesn't scale to the range of devices fitness and AR users actually own. A trained hand detection model turns any ordinary RGB camera into an input device, letting you count reps, verify exercise form, or drive touchless AR interactions without extra hardware. It also removes the brittleness of rule-based skin-color heuristics that break under different lighting or backgrounds. Building on an existing hand dataset means you can prototype and validate the workflow in days rather than collecting and labeling images from scratch.

You get consistent, frame-by-frame hand localization that runs on the cameras your users already have, from phones to AR headsets. RF-DETR delivers strong detection accuracy on small and overlapping objects like fingers and partially visible hands while remaining practical to deploy at the edge. Because it starts from a labeled dataset you can extend with your own footage, the model scales cleanly from a single demo to a production fitness or AR pipeline.

1. Upload an image: Drop any photo into the widget above to try hand detection for free, right on this page.
2. Preview instant results: The model detects hand, open palm, fist, pointing finger, two hands 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 RF-DETR 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.