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MediaPipe-Face-Detection-Quantized

Detect faces and locate facial features in real-time video and image streams.

Designed for sub-millisecond processing, this model predicts bounding boxes and pose skeletons (left eye, right eye, nose tip, mouth, left eye tragion, and right eye tragion) of faces in an image.

Model RepositoryHugging FaceResearch Paper

Technical Details

Input resolution:256x256
Number of output classes:6
Number of parameters (MediaPipeFaceDetector):135K
Model size (MediaPipeFaceDetector):255 KB
Number of parameters (MediaPipeFaceLandmarkDetector):603K
Model size (MediaPipeFaceLandmarkDetector):746 KB

Applicable Scenarios

  • Accessibility
  • Augmented 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.
  • quantized
    A “quantized” model can run in low or mixed precision, which can substantially reduce inference latency.

Supported Compute Chipsets

  • Snapdragon® X Elite