MediaPipe: A Framework for Building Perception Pipelines
Summary
This paper introduces MediaPipe, a framework by Google for building cross-platform perception pipelines that simplifies development of machine learning applications by providing tools for combining components, prototyping, and measuring performance.
View Cached Full Text
Cached at: 07/27/26, 01:44 AM
Paper page - MediaPipe: A Framework for Building Perception Pipelines
Source: https://huggingface.co/papers/1906.08172 Published on Jun 14, 2019
Authors:
,
,
,
,
,
,
,
,
,
,
,
,
Abstract
MediaPipe framework facilitates the development of perception applications by providing tools for combining components, prototyping, and measuring performance across platforms.
Building applications that perceive the world around them is challenging. A developer needs to (a) select and develop corresponding machine learning algorithms and models, (b) build a series ofprototypesand demos, (c) balanceresource consumptionagainst the quality of the solutions, and finally (d) identify and mitigate problematic cases. The MediaPipe framework addresses all of these challenges. A developer can use MediaPipe to buildprototypesby combining existingperception components, to advance them to polishedcross-platform applicationsand measuresystem performanceand resource consumption on target platforms. We show that these features enable a developer to focus on the algorithm or model development and use MediaPipe as an environment for iteratively improving their application with results reproducible across different devices and platforms. MediaPipe will be open-sourced at https://github.com/google/mediapipe.
View arXiv pageView PDFGitHub36.3kautoAdd to collection
Get this paper in your agent:
hf papers read 1906\.08172
Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash
Models citing this paper0
No model linking this paper
Cite arxiv.org/abs/1906.08172 in a model README.md to link it from this page.
Datasets citing this paper0
No dataset linking this paper
Cite arxiv.org/abs/1906.08172 in a dataset README.md to link it from this page.
Spaces citing this paper1
Collections including this paper0
No Collection including this paper
Add this paper to acollectionto link it from this page.
Similar Articles
buildpipe
Buildpipe is a tool for composing, running, and automating multi-step AI developer workflows.
Open-source agent that uses MediaPipe to read your face and adapt its voice in real time
Vision Agents is an open-source Python framework for building multimodal AI agents that process video and audio in real time. It enables conversational agents to adapt their voice based on facial expressions and gaze using MediaPipe.
MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models
MedPMC is an automated framework that transforms medical literature into high-fidelity multimodal data for foundation models, achieving significant improvements across multiple benchmarks and clinical settings.
Introducing Modular Diffusers - Composable Building Blocks for Diffusion Pipelines
Hugging Face introduces Modular Diffusers, a new framework for building diffusion pipelines using composable, reusable building blocks instead of monolithic pipeline implementations. The system allows flexible mixing and matching of components for image generation workflows, with integration support for visual workflow tools like Mellon.
PiPMRE: A Pipeline Based on Language Model for Medical Relation Extraction
PiPMRE is a novel pipeline framework for medical relation extraction that uses a relation generator and filter to enhance performance, surpassing previous state-of-the-art methods on public datasets.