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Computer Vision

Label Studio

Open-source labeling for vision and multimodal data

Open sourceRuns on your device

GitHub stars

28.7K

28,655 stars

Community forks

3.8K

Open collaboration

License

Apache-2.0

Read the full license

Platforms

macOS - Windows - Linux

Also Web

Last activity

Nov 9

Repository push date

Best for

Label images for detection and segmentation

Configure boxes, polygons, and keypoints in one interface and export to COCO or YOLO when you are done.

Pricing checked Sep 2026

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About Label Studio

Label Studio is the open-source standard for data annotation, covering images, video, audio, text, and time series under one configurable interface. Labeling configs, written in a compact XML-like syntax, define exactly what annotators see and produce, from bounding boxes and polygons to named entities and audio regions.

An ML backend hooks your own model into the loop for pre-labeling, so humans correct suggestions instead of starting from scratch. Exports cover standard formats including COCO, YOLO, and plain JSON, and a Python SDK plus REST API drive programmatic project management.

The Community edition is free to self-host under an open license, which is why teams with sensitive data or custom pipelines standardize on it. The interface is less polished than commercial tools, enterprise features such as SSO, RBAC, and analytics are paid, and self-hosting means you own upgrades and scaling.

  • Free to self-host; data never leaves your infrastructure
  • Covers image, video, audio, and text in one tool
  • ML-assisted pre-labeling cuts annotation time sharply
  • Exports to COCO, YOLO, and other standard formats
  • UI polish trails commercial annotation tools
  • SSO, analytics, and RBAC sit in the paid Enterprise edition
  • Self-hosting means owning upgrades, backups, and scaling
Install via pipLocal / CLI
$pip install label-studio

Open source data labeling and annotation tool for computer vision, audio, text, and time series.

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