Label Studio

Label Studio is an open-source multimodal data labeling and management platform designed to create, manage, and annotate training data for machine learning and AI projects.

It supports annotation for text, images, audio, video, time series, and HTML documents.

Typically via pip (pip install label-studio), and it also supports Docker or Kubernetes for containerized deployment. After starting, access the web interface via a local port (e.g., 8080).

Yes, it includes built-in team collaboration features for multiple users to work on the same project, assign tasks, manage permissions, and review labeling quality.

Label Studio offers a free open-source version (OSS) that you can download and use. The company also provides cloud services and enterprise-grade solutions; some advanced features or services may be subject to commercial terms.

Labeling results can be exported to multiple mainstream formats, including JSON, CSV, COCO, YOLO, etc., to fit different ML frameworks and downstream tasks.

Yes, it is commonly used to prepare and annotate data for fine-tuning, evaluating LLMs, and retrieval-augmented generation (RAG) systems, including instructions, responses, and preference rankings.

As open-source software, users are responsible for securing their own deployment environment. The platform's documentation provides guidance on persistence and security settings; enterprise services may include more comprehensive security guarantees.

Yes, it has an active open-source community on GitHub, with detailed documentation, tutorials, forums, and a Slack channel where users can connect, get help, and submit feedback.