# Label Studio

Label Studio is a multi-type data labeling and annotation tool with standardized output format

Repository: https://github.com/HumanSignal/label-studio
Canonical: https://ross.abutalabs.com/products/label-studio
Homepage: https://labelstud.io
Language: TypeScript
License: Apache-2.0
License Family: permissive
Topics: computer-vision, deep-learning, image-annotation, annotation-tool, annotation, labeling, labeling-tool, image-labeling, image-labelling-tool, boundingbox, image-classification, annotations, semantic-segmentation, dataset, datasets, label-studio, data-labeling, text-annotation, yolo, mlops
Last push: 2026-08-26T21:21:10+00:00
Link (homepage): https://labelstud.io
Link (site_page): https://labelstud.io/guide/install.html
Link (site_page): https://labelstud.io/guide
Link (site_page): https://api.labelstud.io/api-reference/introduction/getting-started
Link (site_page): https://labelstud.io/integrations

## Health v2 (maintenance only)
Score: 86/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 62, longevity 100
- inputs: {"age_days": 2633, "days_push": 7, "days_rel": 173, "gap_med": 44.5, "n_releases_24m": 11}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 28150, forks 3678 (observed 2026-08-28T04:11:48.139357+00:00)

## What it is
Label Studio is an open-source data labeling and annotation platform supporting images, audio, text, video, and time series with a web UI and standardized export formats. It includes an ML backend for model-assisted pre-labeling, a Python SDK, API, and integrations with cloud storage and foundation models.

## Use cases
- label images for object detection training
- annotate text for named entity recognition
- create semantic segmentation datasets
- label audio for speech recognition
- collect human preference data for RLHF
- evaluate LLM outputs with human review
- pre-label images with YOLO or SAM models

## When to choose
- you need a self-hosted multi-modality annotation tool
- you want ML-assisted pre-labeling in a human-in-the-loop workflow
- you need standardized export formats for training pipelines
- you want API/SDK automation of labeling projects

## When to avoid
- you need only tiny one-off labeling done in a spreadsheet
- you require the enterprise features like advanced review queues without a paid plan
- you want a fully offline desktop tool with no server

## Facets
- artifact type: application
- maturity: active
- function: machine-learning, data-science, image-processing, nlp, audio-processing, video-processing, sdk, self-hosted
- domain: machine-learning, computer-vision, data-science, artificial-intelligence, developer-tools
- platform: python, self-hosted, cross-platform, cloud
- tags: data-labeling, annotation, human-in-the-loop, mlops, dataset-preparation, computer-vision, bounding-boxes, semantic-segmentation, rlhf, llm-evaluation, natural-language-processing, web-server, docker, kubernetes

## Member repositories
- HumanSignal/label-studio (main) score 86
- HumanSignal/label-studio-ml-backend (sdk) score 68

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:48.139357+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:54:26.850631+00:00, confidence not recorded.
  - readme: https://github.com/HumanSignal/label-studio (fetched 2026-08-28T04:11:48.139357+00:00, sha fdb6638b5857)
  - homepage: https://labelstud.io (fetched 2026-08-29T07:51:34.173184+00:00, sha 1d5b182be299)
  - site_page: https://labelstud.io/guide/install.html (fetched 2026-08-29T07:51:34.185497+00:00, sha b2134b5a9b4d)
  - site_page: https://labelstud.io/guide (fetched 2026-08-29T07:51:34.183125+00:00, sha d4a766948173)
  - site_page: https://api.labelstud.io/api-reference/introduction/getting-started (fetched 2026-08-29T07:51:34.187621+00:00, sha ba13c4dca5a7)
  - site_page: https://labelstud.io/integrations (fetched 2026-08-29T07:51:34.189643+00:00, sha a93bcfe6556e)
- Data as of 2026-08-30T08:39:29.467469+00:00.