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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.

Snapdragon® X Elite
1.04ms
Inference Time
1MB
Memory Usage
196NPU
Layers

Technical Details

Input resolution:256x256
Number of parameters (MediaPipeHandDetector):1.76M
Model size (MediaPipeHandDetector):6.76 MB
Number of parameters (MediaPipeHandLandmarkDetector):2.01M
Model size (MediaPipeHandLandmarkDetector):7.71 MB

Applicable Scenarios

  • Gesture Control
  • Virtual Reality
  • Gaming

Licenses

Source Model:APACHE-2.0
Deployable Model:AI Model Hub License

Tags

  • real-time
    A “real-time” model can typically achieve 5-60 predictions per second. This translates to latency ranging up to 200 ms per prediction.

Supported Compute Chipsets

  • Snapdragon® X Elite