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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.

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This model is currently not supported on any Automotive chipset.

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Model RepositoryHugging FaceResearch Paper

Technical Details

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

Applicable Scenarios

  • Gesture Control
  • Virtual Reality
  • Gaming

License

Model:APACHE-2.0

Tags

  • real-time

Supported Automotive Devices

  • SA7255P ADP
  • SA8255P ADP
  • SA8295P ADP
  • SA8650P ADP
  • SA8775P ADP

Supported Automotive Chipsets

  • Qualcomm® SA7255P
  • Qualcomm® SA8295P
  • Qualcomm® SA8775P

Related Models

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View details for the MediaPipe-Face-Detection model.

MediaPipe-Face-Detection

Detect faces and locate facial features in real-time video and image streams.

View details for the MediaPipe-Pose-Estimation model.

MediaPipe-Pose-Estimation

Detect and track human face, hand, and torso in real-time images and video streams.

View details for the MediaPipe-Selfie-Segmentation model.

MediaPipe-Selfie-Segmentation

Segments the person from background in a selfie image and realtime background segmentation in video conferencing.

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