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Last Updated: 07-13-2026 04:51 PM (UTC)

Label Studio

Images, text, audio, video, HTML, PDFs, and time series, labeled in one tool with a standardized output format: Label Studio is the open-source data labeling platform for building training datasets. Computer vision tasks cover classification, object detection (boxes, polygons, ellipses, keypoints), and semantic segmentation; audio work spans transcription, speaker diarization, and emotion recognition; NLP handles named entity recognition and document classification with taxonomies up to 10,000 classes; and GenAI workflows support LLM fine-tuning data and RLHF response ranking. Labeling interfaces are fully configurable with an XML-like templating language, so the UI matches the task instead of the reverse. The ML backend SDK turns any model into a connected web server for pre-annotation (model predicts, humans verify), interactive labeling (real-time predictions as annotators draw regions or highlight text), and model evaluation - cutting annotation time dramatically on large datasets. Data imports from S3, GCS, or file uploads; the Data Manager filters and explores tasks; exports convert to the format your ML library expects via label-studio-converter. Multi-user accounts tie every annotation to its author, and webhooks, a Python SDK, and REST API embed labeling into any pipeline. Self-hosting keeps proprietary training data - often a company's most sensitive asset - entirely on your infrastructure.

Label Studio
Label Studio

Benefits

  • Training Data Never Leaves
  • Proprietary datasets - often the crown jewels of an ML effort - stay on your infrastructure instead of a labeling vendor's cloud.
  • Models Label, Humans Verify
  • ML backend pre-annotation turns labeling from creation into review, multiplying annotator throughput on large datasets.
  • One Tool, Every Modality
  • Images, text, audio, video, and time series share one platform, one output format, and one workflow instead of five separate tools.
  • Interfaces Shaped to the Task
  • Configurable templates build exactly the labeling UI each project needs, from bounding boxes to RLHF response ranking.

Features

  • Multi-Type Annotation
  • Images, text, audio, video, HTML, PDF, and time series with a standardized JSON output.
  • ML Backend Integration
  • SDK wraps any model for pre-annotation, interactive labeling, and evaluation loops.
  • Configurable Templates
  • XML-like configs define custom labeling interfaces per task, up to 10,000-class taxonomies.
  • Cloud Storage Sync
  • Label data directly from S3 and GCS, or import JSON, CSV, and archives.
  • Data Manager
  • Advanced filtering and exploration to prepare, slice, and assign datasets.
  • Pipeline APIs
  • REST API, Python SDK, webhooks, and format converters embed labeling in any ML workflow.