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MediaPipe-Hand-Detection

Real‑time hand detection optimized for mobile and edge.

The MediaPipe Hand Landmark Detector is a machine learning pipeline that predicts bounding boxes and pose skeletons of hands in an image.

Model RepositoryHugging FaceResearch Paper

Technical Details

Input resolution:256x256
Number of parameters (HandDetector):1.76M
Model size (HandDetector) (float):6.75 MB
Number of parameters (HandLandmarkDetector):2.01M
Model size (HandLandmarkDetector) (float):7.70 MB

Applicable Scenarios

  • Gesture Control
  • Virtual Reality
  • Gaming

Licenses

Source Model:APACHE-2.0
Deployable Model:AI-HUB-MODELS-LICENSE

Tags

  • real-time

Supported Automotive Devices

  • SA7255P ADP
  • SA8255 (Proxy)
  • SA8295P ADP
  • SA8650 (Proxy)
  • SA8775P ADP

Supported Automotive Chipsets

  • Qualcomm® SA7255P
  • Qualcomm® SA8255P (Proxy)
  • Qualcomm® SA8295P
  • Qualcomm® SA8650P (Proxy)
  • Qualcomm® SA8775P

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View details for the MediaPipe-Selfie-Segmentation model.

MediaPipe-Selfie-Segmentation

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