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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
    0-50MB
    Memory Usage
    152NPU
    Layers
    See more metrics
    Model RepositoryHugging FaceResearch Paper

    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 Form Factors

    • Phone
    • Tablet
    • IoT

    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 Devices

    • Google Pixel 3
    • Google Pixel 3a
    • Google Pixel 3a XL
    • Google Pixel 4
    • Google Pixel 4a
    • Google Pixel 5a 5G
    • QCS8550 (Proxy)
    • 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 Chipsets

    • Qualcomm® QCS8550
    • Snapdragon® 8 Gen 1 Mobile
    • Snapdragon® 8 Gen 2 Mobile
    • Snapdragon® 8 Gen 3 Mobile
    • Snapdragon® 888 Mobile