![MediaPipe](mediapipe/docs/images/mediapipe_small.png?raw=true "MediaPipe logo")
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#### We will be [presenting at CVPR 2019](https://sites.google.com/corp/view/perception-cv4arvr/mediapipe) on June 17~20 in Long Beach, CA. Come join us!

[MediaPipe](http://g.co/mediapipe) is a framework for building multimodal (eg. video, audio, any time series data) applied ML pipelines. With MediaPipe, a perception pipeline can be built as a graph of modular components, including, for instance, inference models (e.g., TensorFlow, TFLite) and media processing functions.

![Real-time Face Detection](mediapipe/docs/images/realtime_face_detection.gif)

## Installation
Follow these [instructions](mediapipe/docs/install.md).

## Getting started
See mobile and desktop [examples](mediapipe/docs/examples.md).

## Documentation
[MediaPipe Read-the-Docs](https://mediapipe.readthedocs.io/).

## Visualizing MediaPipe graphs
A web-based visualizer is hosted on [MediaPipe Visualizer](https://mediapipe-viz.appspot.com/). Please also see instructions [here](mediapipe/docs/visualizer.md).

## Publications
* [MediaPipe: A Framework for Building Perception Pipelines](https://tiny.cc/mediapipe_paper) (draft)

## Contributing
We welcome contributions. Please follow these [guidelines](./CONTRIBUTING.md).

We use GitHub issues for tracking requests and bugs. Please post questions to the MediaPipe Stack Overflow with a 'mediapipe' tag.