google/mediapipe
Mediapipe
MediaPipe is a cross-platform machine learning framework designed for building and deploying pipelines that process live and streaming media. It provides a system for connecting processing components into custom machine learning chains to analyze real-time audio and video streams.
The framework includes a suite of pre-trained models for tasks such as hand, face, and pose tracking, along with tools for retraining and customizing these models with specific datasets. It also features a dedicated benchmarker for measuring the execution speed and accuracy of machine learning models directly within a web browser.
The system supports on-device deployment across Android, iOS, and web environments. Its capabilities cover machine learning pipeline orchestration, the integration of pre-trained assets, and performance benchmarking for end-user devices.
Features
- Cross-Platform ML Deployment - Provides a unified framework for building and deploying machine learning pipelines across Android, iOS, and web environments.
- Real-Time Object Detection - Analyzes live video and audio streams using machine learning for instant object and pattern detection.
- Cross-Platform Inference Frameworks - Functions as a cross-platform framework for deploying ML pipelines across multiple operating systems and hardware architectures.
- Media Processing Pipelines - Allows the creation of specialized media processing pipelines by connecting series of processing steps for real-time analysis.
- Real-Time AI Pipelines - Connects processing components into custom machine learning chains for real-time audio and video streams.
- Pre-made Models - Integrates ready-to-run pre-trained models into applications via standardized interfaces.
- Pre-trained Model Zoos - Offers a collection of ready-to-use pre-trained models for tasks such as hand, face, and pose tracking.
- On-Device Models - Enables the integration of pre-trained models into mobile and web apps for local execution without cloud servers.
- Quantized Inference Runtimes - Integrates a lightweight runtime to execute quantized machine learning models across diverse mobile and web hardware.
- Packet-Based Stream Processors - Uses typed data packets to communicate between pipeline components, ensuring synchronized media frames and timestamps.
- Machine Learning Pipelines - Orchestrates preprocessing, inference, and postprocessing steps through custom machine learning chains for media.
- Execution Graphs - Processes streaming data by passing packets through a directed graph of modular calculators.
- Hardware Abstraction Layers - Provides a hardware abstraction layer that maps high-level logic to platform-specific GPU and CPU kernels.
- Pipeline Component Modularization - Implements a modular design where processing logic is encapsulated into discrete calculators that can be reconfigured into different pipelines.
- Model Performance Benchmarking - Ships tools to measure the execution speed and accuracy of machine learning models directly within a browser.
- Browser-Based Model Benchmarking - Provides a dedicated benchmarker for measuring model execution speed and accuracy within the browser.
- Model Customization - Allows for the adaptation and retraining of existing models using custom datasets to improve specific use-case accuracy.
- Model Adaptation Tools - Provides tools for customizing and retraining existing machine learning models with specific datasets.
- Type Safety - Employs a centralized specification to define strict data structures for inputs and outputs to prevent runtime errors.
- Computer Vision Libraries - Framework for building multimodal ML solutions for mobile and web.
- Computer Vision Models - Cross-platform framework for building multimodal machine learning pipelines.
- Computer Vision Tools - A cross-platform framework for building multimodal machine learning pipelines.
- Frameworks and SDKs - Cross-platform AI pipeline for mobile and desktop.
- Streaming Libraries - Cross-platform ML solutions for live and streaming media.
- Object detection 3d - Listed in the “Object detection 3d” section of the Ailia Models awesome list.
Star history
How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
