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.
