README.md

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.

This is based on the implementation of MediaPipe-Face-Detection-Quantized found here. This repository contains scripts for optimized on-device export suitable to run on Qualcomm® devices. More details on model performance accross various devices, can be found here.

Sign up to start using Qualcomm AI Hub and run these models on a hosted Qualcomm® device.

Example & Usage

Install the package via pip:

pip install "qai_hub_models[mediapipe_face_quantized]"

Once installed, run the following simple CLI demo:

python -m qai_hub_models.models.mediapipe_face_quantized.demo

More details on the CLI tool can be found with the --help option. See demo.py for sample usage of the model including pre/post processing scripts. Please refer to our general instructions on using models for more usage instructions.

Export for on-device deployment

This repository contains export scripts that produce a model optimized for on-device deployment. This can be run as follows:

python -m qai_hub_models.models.mediapipe_face_quantized.export

Additional options are documented with the --help option. Note that the above script requires access to Deployment instructions for Qualcomm® AI Hub.

License

• The license for the original implementation of MediaPipe-Face-Detection-Quantized can be found here.
• The license for the compiled assets for on-device deployment can be found here

References

• BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs
• Source Model Implementation

Community

• Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
• For questions or feedback please reach out to us.