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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.

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

Qualcomm AI Hub Models uses Qualcomm AI Hub Workbench to compile, profile, and evaluate this model. Sign up to run these models on a hosted Qualcomm® device.

Quick Start

Use our lightweight command-line interface to inspect and download MediaPipe-Hand-Detection:

pip install qai_hub_models_cli # (the CLI is also available with the qai-hub-models package)

# Inspect the model and list the available download options
qai-hub-models info MediaPipe-Hand-Detection

# Print performance and accuracy metrics
qai-hub-models perf MediaPipe-Hand-Detection
qai-hub-models numerics MediaPipe-Hand-Detection

# Download a ready-to-deploy asset
qai-hub-models fetch MediaPipe-Hand-Detection --runtime tflite --precision float

See the CLI README for the full list of commands and filters.

Setup

1. Install the package

Install the package via pip:

# NOTE: 3.10 <= PYTHON_VERSION < 3.14 is supported.
pip install qai-hub-models

2. Configure Qualcomm® AI Hub Workbench

Sign-in to Qualcomm® AI Hub Workbench with your Qualcomm® ID. Once signed in navigate to Account -> Settings -> API Token.

With this API token, you can configure your client to run models on the cloud hosted devices.

qai-hub configure --api_token API_TOKEN

Navigate to docs for more information.

Run CLI Demo

Run the following simple CLI demo to verify the model is working end to end:

python -m qai_hub_models.models.mediapipe_hand.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.

By default, the demo will run locally in PyTorch. Pass --eval-mode on-device to the demo script to run the model on a cloud-hosted target device.

Export for on-device deployment

To run the model on Qualcomm® devices, you must export the model for use with an edge runtime such as TensorFlow Lite, ONNX Runtime, or Qualcomm AI Engine Direct. Use the following command to export the model:

qai-hub-models export mediapipe_hand --target-runtime tflite --precision float

Additional options are documented with the --help option.

License

  • The license for the original implementation of MediaPipe-Hand-Detection can be found here.

References

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