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Attention: We have moved to https://developers.google.com/mediapipe as the primary developer documentation site for MediaPipe as of April 3, 2023.

MediaPipe

Attention: MediaPipe Solutions Preview is an early release. Learn more.

On-device machine learning for everyone

Delight your customers with innovative machine learning features. MediaPipe contains everything that you need to customize and deploy to mobile (Android, iOS), web, desktop, edge devices, and IoT, effortlessly.

Get started

You can get started with MediaPipe Solutions by by checking out any of the developer guides for vision, text, and audio tasks. If you need help setting up a development environment for use with MediaPipe Tasks, check out the setup guides for Android, web apps, and Python.

Solutions

MediaPipe Solutions provides a suite of libraries and tools for you to quickly apply artificial intelligence (AI) and machine learning (ML) techniques in your applications. You can plug these solutions into your applications immediately, customize them to your needs, and use them across multiple development platforms. MediaPipe Solutions is part of the MediaPipe open source project, so you can further customize the solutions code to meet your application needs.

These libraries and resources provide the core functionality for each MediaPipe Solution:

  • MediaPipe Tasks: Cross-platform APIs and libraries for deploying solutions. Learn more.
  • MediaPipe models: Pre-trained, ready-to-run models for use with each solution.

These tools let you customize and evaluate solutions:

  • MediaPipe Model Maker: Customize models for solutions with your data. Learn more.
  • MediaPipe Studio: Visualize, evaluate, and benchmark solutions in your browser. Learn more.

Legacy solutions

We have ended support for these MediaPipe Legacy Solutions as of March 1, 2023. All other MediaPipe Legacy Solutions will be upgraded to a new MediaPipe Solution. See the Solutions guide for details. The code repository and prebuilt binaries for all MediaPipe Legacy Solutions will continue to be provided on an as-is basis.

For more on the legacy solutions, see the documentation.

Framework

To start using MediaPipe Framework, install MediaPipe Framework and start building example applications in C++, Android, and iOS.

MediaPipe Framework is the low-level component used to build efficient on-device machine learning pipelines, similar to the premade MediaPipe Solutions.

Before using MediaPipe Framework, familiarize yourself with the following key Framework concepts:

Community

  • Slack community for MediaPipe users.
  • Discuss - General community discussion around MediaPipe.
  • Awesome MediaPipe - A curated list of awesome MediaPipe related frameworks, libraries and software.

Contributing

We welcome contributions. Please follow these guidelines.

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

Privacy Notice

Last modified: June 5, 2026

When you use MediaPipe Tasks, processing of the input data (e.g. images, video, text) takes place on device, and MediaPipe does not send that input data to Google servers. As a result, you can use our MediaPipe Tasks APIs for processing data that should not leave the device.

MediaPipe Tasks APIs send metrics about the performance and utilization of the APIs in your app to Google. Google uses this metrics data to measure performance, usage, debug, maintain and improve the MediaPipe Tasks, as further described in our Privacy Policy.

You are responsible for obtaining informed consent from your app users about Google's processing of MediaPipe metrics data as required by applicable law.

Resources

Publications

Videos

Release files for mediapipe 1.1.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distributions (wheels)

Table of built distributions (wheels) for mediapipe 1.1.0
File
mediapipe-1.1.0-py3-none-win_arm64.whl Python 3 none Windows ARM64 Details
mediapipe-1.1.0-py3-none-win_amd64.whl Python 3 none Windows x86-64 Details
mediapipe-1.1.0-py3-none-manylinux_2_28_x86_64.whl Python 3 none Linux glibc 2.28+ x86-64 Details
mediapipe-1.1.0-py3-none-manylinux_2_28_aarch64.whl Python 3 none Linux glibc 2.28+ ARM64 Details
mediapipe-1.1.0-py3-none-macosx_11_0_arm64.whl Python 3 none macOS 11.0+ ARM64 Details

Total release size: 164.0 MB

Release files / mediapipe-1.1.0-py3-none-win_arm64.whl

Download URL mediapipe-1.1.0-py3-none-win_arm64.whl
Size 19.9 MB
Tags Python 3 Windows ARM64
SHA-256 checksum
How to use checksums
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BLAKE2b-256 checksum
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No
Uploaded via twine/7.0.0 CPython/3.13.15

Release files / mediapipe-1.1.0-py3-none-win_amd64.whl

Download URL mediapipe-1.1.0-py3-none-win_amd64.whl
Size 22.4 MB
Tags Python 3 Windows x86-64
SHA-256 checksum
How to use checksums
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BLAKE2b-256 checksum
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No
Uploaded via twine/7.0.0 CPython/3.13.15

Release files / mediapipe-1.1.0-py3-none-manylinux_2_28_x86_64.whl

Download URL mediapipe-1.1.0-py3-none-manylinux_2_28_x86_64.whl
Size 40.6 MB
Tags Linux glibc 2.28+ x86-64 Python 3
SHA-256 checksum
How to use checksums
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BLAKE2b-256 checksum
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101dae070817ebc1b9500cec3f83faeeed1a405dcb764c23738a87726d96432c
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No
Uploaded via twine/7.0.0 CPython/3.13.15

Release files / mediapipe-1.1.0-py3-none-manylinux_2_28_aarch64.whl

Download URL mediapipe-1.1.0-py3-none-manylinux_2_28_aarch64.whl
Size 38.5 MB
Tags Linux glibc 2.28+ ARM64 Python 3
SHA-256 checksum
How to use checksums
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No
Uploaded via twine/7.0.0 CPython/3.13.15

Release files / mediapipe-1.1.0-py3-none-macosx_11_0_arm64.whl

Download URL mediapipe-1.1.0-py3-none-macosx_11_0_arm64.whl
Size 42.6 MB
Tags Python 3 macOS 11.0+ ARM64
SHA-256 checksum
How to use checksums
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e07ce5e1b0fd0a43a8f71db9062c94731a3de196186d392ce0c4417d7923a1a0
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Uploaded via twine/7.0.0 CPython/3.13.15
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