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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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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 Compute Devices

  • Snapdragon X Elite CRD
  • Snapdragon X Plus 8-Core CRD
  • Snapdragon X2 Elite CRD

Supported Compute Chipsets

  • Snapdragon® X Elite
  • Snapdragon® X Plus 8-Core
  • Snapdragon® X2 Elite

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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