# MediaPipe — Cross-Platform ML Solutions by Google

> A framework for building multimodal applied ML pipelines, providing ready-to-use solutions for face detection, hand tracking, pose estimation, object detection, and text classification across mobile, web, and desktop.

## Install

Save as a script file and run:

# MediaPipe — Cross-Platform ML Solutions by Google

## Quick Use
```bash
pip install mediapipe
python -c "import mediapipe as mp; print(mp.__version__)"
```

## Introduction
MediaPipe is Google's framework for building perception pipelines that process video, audio, and sensor data. It provides production-ready ML solutions for common tasks like face detection, hand tracking, and pose estimation, optimized to run in real-time on mobile devices, web browsers, and desktops.

## What MediaPipe Does
- Detects faces, hands, and full-body poses in real-time video streams
- Classifies images, objects, and text with pretrained on-device models
- Segments images into foreground and background or semantic categories
- Generates face mesh landmarks and hand gesture recognition
- Runs ML inference on-device without requiring a server or internet connection

## Architecture Overview
MediaPipe uses a graph-based pipeline where processing nodes (calculators) are connected in a directed acyclic graph. Each calculator performs one operation such as image preprocessing, model inference, or post-processing. The framework handles scheduling, synchronization, and memory management across graph nodes. The Solutions API provides high-level wrappers that hide graph complexity for common tasks.

## Self-Hosting & Configuration
- Install Python package: `pip install mediapipe` for CPU inference
- Use the Solutions API for quick integration: `mp.solutions.hands`, `mp.solutions.face_mesh`, etc.
- Configure detection confidence thresholds and model complexity per solution
- Deploy on Android via the MediaPipe AAR or on iOS via the framework package
- Run in web browsers using the MediaPipe JavaScript or WASM packages

## Key Features
- Real-time performance on mobile and edge devices without GPU requirements
- 15+ pretrained solutions covering vision, text, and audio tasks
- Model Maker tool for fine-tuning models on custom datasets with transfer learning
- Cross-platform support: Python, Android, iOS, web (JavaScript), and C++
- On-device inference with no network dependency for privacy-sensitive applications

## Comparison with Similar Tools
- **OpenCV** — General-purpose CV library; MediaPipe provides higher-level ML solutions
- **TensorFlow Lite** — Lower-level inference runtime; MediaPipe adds pipeline orchestration
- **Core ML (Apple)** — Apple-only; MediaPipe runs cross-platform
- **ONNX Runtime** — Model inference without pipeline management or prebuilt solutions
- **Ultralytics YOLO** — Focused on detection; MediaPipe covers pose, hands, face, and more

## FAQ
**Q: Does MediaPipe require a GPU?**
A: No. MediaPipe solutions are optimized for CPU inference on mobile and desktop. GPU acceleration is optional and platform-dependent.

**Q: Can I train custom models with MediaPipe?**
A: Yes. MediaPipe Model Maker supports fine-tuning classification, detection, and text models on your own labeled data.

**Q: Does MediaPipe work offline?**
A: Yes. All inference runs locally on-device with bundled model weights and no network calls.

**Q: Which platforms are supported?**
A: Python (Linux, macOS, Windows), Android, iOS, and web browsers via JavaScript and WebAssembly.

## Sources
- https://github.com/google/mediapipe
- https://ai.google.dev/edge/mediapipe/solutions/guide

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Source: https://tokrepo.com/en/workflows/b379a90f-3c92-11f1-9bc6-00163e2b0d79
Author: Script Depot