{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"Label Studio is a web-based data annotation platform that lets you label images, audio, text, video, and time-series data through a browser UI, then export annotations in formats ready for machine learning training.","skillfed_tags":["data-annotation","ml-data-prep","web-ui"],"use_cases":["Prepare raw image, text, or audio datasets for supervised learning by labeling examples through a team-accessible web interface.","Improve existing training datasets by reviewing and correcting model predictions displayed side-by-side with raw data.","Integrate data labeling into a ML pipeline via the REST API, triggering exports to training-ready formats after annotation.","Run a self-hosted annotation service for sensitive data that cannot be sent to third-party SaaS platforms.","Customize labeling workflows for domain-specific tasks using Label Studio's configuration language and template system."],"what_it_does":"Label Studio is a Django-based web application that provides a browser-accessible interface for annotating raw data across multiple media types\u2014images, audio, text, video, and time series. You run it as a standalone server (via the `label-studio` command or Docker), create projects, upload or connect to cloud storage (S3, GCS, Azure Blob), and invite team members to label data through a customizable UI. Annotations are stored in a database (SQLite by default, PostgreSQL for production) and can be exported in formats compatible with common ML frameworks.\n\nThe package is built on Django and Django REST Framework, with integrations for cloud storage (boto3, azure-storage-blob, google-cloud-logging) and optional machine learning model integration for pre-labeling. It is designed as a self-hosted or cloud-deployed service rather than a library you import into your own code; you install it, start the server, and interact via the web UI or REST API. The 58 runtime dependencies reflect this architecture\u2014most are Django middleware and utilities for authentication, CORS, CSP, file serving, and job queuing.","worth_installing":"Yes, if you need a self-hosted, open-source annotation platform. Label Studio is actively maintained with no known vulnerabilities and supports multiple data types and export formats. The Apache-2.0 license is permissive. Install friction is low, but deployment requires managing 58 dependencies and typically running it as a service (Docker or local server) rather than embedding it in a Python script. Best suited for teams annotating datasets at scale or organizations with data privacy constraints."},"id":"label-studio","links":{"html":"https://skillfed.io/packages/label-studio","md":"https://skillfed.io/packages/label-studio.md","pypi":"https://pypi.org/project/label-studio/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-13","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"label-studio","python_support":"supports_current","summary":"Label Studio annotation tool"},"popularity":{"monthly_downloads":113849,"position":12326,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.23.0"}