What is Label Studio?
Label Studio is an open-source data labeling and AI evaluation platform designed for multi-modal annotation. It supports a wide range of data types including images, text, audio, video, time series, and documents, making it suitable for tasks such as computer vision, natural language processing, audio transcription, and document AI. The platform enables human-in-the-loop workflows by connecting observability tools, allowing teams to collect human preferences, corrections, and rankings for applications like RLHF and LLM evaluation. Users can create custom benchmarks and rubrics, perform side-by-side comparisons, evaluate retrieval relevance, and grade generated answers against source material.
Key features include advanced labeling interfaces for speed and precision at scale. For images, users can categorize, detect objects with bounding boxes, polygons, circles, and keypoints, and label objects frame-by-frame for video. Audio support includes partitioning streams by speaker identity and tagging emotions. Time series data can be categorized, and regions relevant to activity types can be identified. The platform also handles complex document annotation, allowing extraction of information into predefined categories, question answering, and sentiment analysis.
Label Studio offers extensive customization with custom layouts and templates that adapt to specific data, tasks, and evaluation criteria. It provides an API, Python SDK, and webhooks for real-time project creation, prediction streaming, and triggering training, active learning, and evaluation workflows. Data can be synced from any storage, and any model can be connected to power AI-assisted labeling and continuous model evaluation. The platform is backed by a vibrant global community and is used to build state-of-the-art AI systems, as evidenced by its adoption in research and industry.
Use Cases
Key Features
multi modal annotation image labeling audio transcription video frame labeling time series categorization document annotation human in the loop workflows LLM evaluation custom benchmarks side by side comparisonsWho is it for?
data scientists, machine learning engineers, AI researchers, computer vision teams, NLP teams, audio processing teams, document AI teams
