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.
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-timeA “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