{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/4"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/3"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/2"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/5"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"MediaPipe provides pre-built machine learning models and cross-platform APIs for vision, text, and audio tasks that run on-device without sending input data to external servers.","skillfed_tags":["on-device-inference","computer-vision","edge-ml"],"use_cases":["Build real-time pose or hand tracking in mobile apps or desktop applications without cloud inference.","Add object detection or face recognition to video processing pipelines on edge devices.","Classify text or audio input on-device for privacy-sensitive applications.","Prototype ML features quickly using pre-trained models before customizing with MediaPipe Model Maker.","Deploy ML inference in IoT or embedded systems where latency and privacy are critical."],"what_it_does":"MediaPipe is Google's framework for deploying machine learning models on-device across mobile, web, desktop, and edge platforms. It bundles pre-trained models for common tasks\u2014object detection, pose estimation, hand tracking, face recognition, text classification, and audio analysis\u2014alongside cross-platform APIs to integrate them into applications. All input data (images, video, text, audio) stays on the device; only performance metrics are sent to Google.\n\nThe package targets developers and researchers who need to add ML capabilities without building models from scratch or managing cloud inference. It depends on numpy, opencv-contrib-python, matplotlib, sounddevice, flatbuffers, absl-py, and certifi. Installation requires a compatible wheel for your platform (arm64 or x86_64 Linux, macOS 11+, or Windows), and you must supply or download pre-trained model files separately. The framework is actively maintained and in alpha status, with documentation and setup guides available for Python, Android, web, and iOS.","worth_installing":"Yes, if you need on-device ML inference for vision, text, or audio tasks and accept alpha-stage stability. The permissive Apache 2.0 license, active maintenance, and zero known vulnerabilities make it low-risk. Medium install friction (compiled wheels, multiple dependencies) is typical for ML packages. Verify that pre-trained models suit your use case and that you can manage the privacy implications of metrics collection to Google."},"id":"mediapipe","links":{"html":"https://skillfed.io/packages/mediapipe","md":"https://skillfed.io/packages/mediapipe.md","pypi":"https://pypi.org/project/mediapipe/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":null,"license_treatment":"permissive","name":"mediapipe","python_support":"unspecified","summary":"MediaPipe is the simplest way for researchers and developers to build world-class ML solutions and applications for mobile, edge, cloud and the web. See the privacy notice at https://goo.gle/mediapipe-privacy."},"popularity":{"monthly_downloads":2648995,"position":2961,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.0.1"}