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[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.
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![Real-time Face Detection](mediapipe/docs/images/mobile/face_detection_android_gpu_small.gif)
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![Real-time Face Detection](mediapipe/docs/images/realtime_face_detection.gif)
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## Installation
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Follow these [instructions](mediapipe/docs/install.md).
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See mobile and desktop [examples](mediapipe/docs/examples.md).
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## Documentation
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On [MediaPipe Read-the-Docs](https://mediapipe.readthedocs.io/).
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[MediaPipe Read-the-Docs](https://mediapipe.readthedocs.io/).
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## Visualizing MediaPipe graphs
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A web-based visualizer is hosted on [MediaPipe Visualizer](https://mediapipe-viz.appspot.com/). Please also see instructions [here](mediapipe/docs/visualizer.md).
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## Publications
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* [MediaPipe: A Framework for Building Perception Pipelines](https://arxiv.org/) on [arXiv](https://arxiv.org/).
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* [MediaPipe: A Framework for Perceiving and Augmenting Reality](http://mixedreality.cs.cornell.edu/s/22_crv2_MediaPipe_CVPR_CV4ARVR_Workshop_2019_v2.pdf), extended abstract for [Third Workshop on Computer Vision for AR/VR](http://mixedreality.cs.cornell.edu/workshop/program).
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* [MediaPipe: A Framework for Perceiving and Augmenting Reality](http://mixedreality.cs.cornell.edu/s/22_crv2_MediaPipe_CVPR_CV4ARVR_Workshop_2019_v2.pdf), extended abstract for [Third Workshop on Computer Vision for AR/VR](https://sites.google.com/corp/view/perception-cv4arvr/mediapipe).
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* Full-length draft: [MediaPipe: A Framework for Building Perception Pipelines](https://tiny.cc/mediapipe_paper)
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## Contributing
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We welcome contributions. Please follow these [guidelines](./CONTRIBUTING.md).
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including, for instance, inference models and media processing functions. Sensory
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data such as audio and video streams enter the graph, and perceived descriptions
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such as object-localization and face-landmark streams exit the graph. An example
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graph that performs real-time face detection on mobile GPU is shown below.
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graph that performs real-time hair segmentation on mobile GPU is shown below.
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.. image:: images/mobile/face_detection_android_gpu.png
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.. image:: images/mobile/hair_segmentation_android_gpu.png
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:width: 400
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:alt: Example MediaPipe graph
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