
The most flexible open-source data labeling platform for AI and ML
Label Studio is an open-source, multi-modal data labeling platform developed by HumanSignal. It supports annotation of text, images, audio, video, and time series data, making it a versatile tool for training ML models, fine-tuning LLMs, and evaluating AI systems. With configurable labeling interfaces, ML-assisted annotation, and cloud storage integration, it serves both individual practitioners and enterprise teams.
Annotate text, images, audio, video, and time series data from a single platform with task-specific labeling interfaces
Customize labeling layouts with a template-based system and XML-like configuration for any annotation workflow
Integrate custom ML backends to generate pre-annotations and predictions, accelerating the labeling process
Connect to AWS S3, Google Cloud Storage, Azure Blob, Databricks, and Redis for seamless data import and export
Programmatic access for data upload, annotation retrieval, project management, and integration into ML pipelines
Multi-user annotation with task assignment, role-based access control, and quality review workflows
Trigger external actions on annotation events for automated pipeline orchestration and real-time notifications
Create labeled datasets across text, image, and audio for training custom machine learning models including NER, object detection, and classification
Annotate instruction-response pairs, evaluate model outputs, and build custom benchmarks for fine-tuning and assessing large language models
Label images and video with bounding boxes, polygons, and semantic segmentation for autonomous vehicles, medical imaging, and manufacturing QA
Annotate PDFs and scanned documents with layout-aware labeling for building intelligent document processing pipelines
Built-in PDF and OCR interface for labeling complex documents with layout-aware annotation tools

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