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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.

0.68ms
Inference Time
0-51MB
Memory Usage
152NPU
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

Supported Mobile Form Factors

  • Phone
  • Tablet

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 Mobile Devices

  • Google Pixel 3
  • Google Pixel 3a
  • Google Pixel 3a XL
  • Google Pixel 4
  • Google Pixel 4a
  • Google Pixel 5a 5G
  • Samsung Galaxy S21
  • Samsung Galaxy S21 Ultra
  • Samsung Galaxy S21+
  • Samsung Galaxy S22 5G
  • Samsung Galaxy S22 Ultra 5G
  • Samsung Galaxy S22+ 5G
  • Samsung Galaxy S23
  • Samsung Galaxy S23 Ultra
  • Samsung Galaxy S23+
  • Samsung Galaxy S24
  • Samsung Galaxy S24 Ultra
  • Samsung Galaxy S24+
  • Samsung Galaxy Tab S8
  • Xiaomi 12
  • Xiaomi 12 Pro

Supported Mobile Chipsets

  • Snapdragon® 8 Gen 1 Mobile
  • Snapdragon® 8 Gen 2 Mobile
  • Snapdragon® 8 Gen 3 Mobile
  • Snapdragon® 888 Mobile