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What is Label Studio?

Label Studio is an open source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can be used to prepare raw data or improve existing training data to get more accurate ML models.

Gif of Label Studio annotating different types of data

Have a custom dataset? You can customize Label Studio to fit your needs. Read an introductory blog post to learn more.

Try out Label Studio

Install Label Studio locally or deploy it in a cloud instance. Or sign up for a free trial of our Starter Cloud edition! You can learn more about what each edition offers here.

Install locally with Docker

Official Label Studio docker image is here and it can be downloaded with docker pull.
Run Label Studio in a Docker container and access it at http://localhost:8080.

docker pull heartexlabs/label-studio:latest
docker run -it -p 8080:8080 -v $(pwd)/mydata:/label-studio/data heartexlabs/label-studio:latest

You can find all the generated assets, including SQLite3 database storage label_studio.sqlite3 and uploaded files, in the ./mydata directory.

Override default Docker install

You can override the default launch command by appending the new arguments:
bash docker run -it -p 8080:8080 -v $(pwd)/mydata:/label-studio/data heartexlabs/label-studio:latest label-studio --log-level DEBUG

Build a local image with Docker

If you want to build a local image, run:
bash docker build -t heartexlabs/label-studio:latest .

Run with Docker Compose

Docker Compose script provides production-ready stack consisting of the following components:

  • Label Studio
  • Nginx - proxy web server used to load various static data, including uploaded audio, images, etc.
  • PostgreSQL - production-ready database that replaces less performant SQLite3.

To start using the app from http://localhost run this command:
bash docker-compose up

Run with Docker Compose + MinIO

You can also run it with an additional MinIO server for local S3 storage. This is particularly useful when you want to
test the behavior with S3 storage on your local system. To start Label Studio in this way, you need to run the following command:
bash # Add sudo on Linux if you are not a member of the docker group docker compose -f docker-compose.yml -f docker-compose.minio.yml up -d
If you do not have a static IP address, you must create an entry in your hosts file so that both Label Studio and your
browser can access the MinIO server. For more detailed instructions, please refer to our guide on storing data.

Install locally with pip

# Requires Python >=3.8
pip install label-studio

# Start the server at http://localhost:8080
label-studio

Install locally with poetry

### install poetry
pip install poetry

### set poetry environment
poetry new my-label-studio
cd my-label-studio
poetry add label-studio

### activate poetry environment
poetry shell

### Start the server at http://localhost:8080
label-studio

Install locally with Anaconda

conda create --name label-studio
conda activate label-studio
conda install psycopg2
pip install label-studio

Install for local development

You can run the latest Label Studio version locally without installing the package from pypi.

# Install all package dependencies
pip install poetry
poetry install
# Run database migrations
python label_studio/manage.py migrate
python label_studio/manage.py collectstatic
# Start the server in development mode at http://localhost:8080
python label_studio/manage.py runserver

Deploy in a cloud instance

You can deploy Label Studio with one click in Heroku, Microsoft Azure, or Google Cloud Platform:

Deploy

Apply frontend changes

For information about updating the frontend, see label-studio/web/README.md.

Install dependencies on Windows

To run Label Studio on Windows, download and install the following wheel packages from Gohlke builds to ensure you're using the correct version of Python:
- lxml

# Upgrade pip 
pip install -U pip

# If you're running Win64 with Python 3.8, install the packages downloaded from Gohlke:
pip install lxml‑4.5.0‑cp38‑cp38‑win_amd64.whl

# Install label studio
pip install label-studio

Run test suite

To add the tests' dependencies to your local install:

poetry install --with test

Alternatively, it is possible to run the unit tests from a Docker container in which the test dependencies are installed:

make build-testing-image
make docker-testing-shell

In either case, to run the unit tests:

cd label_studio

# sqlite3
DJANGO_DB=sqlite DJANGO_SETTINGS_MODULE=core.settings.label_studio pytest -vv

# postgres (assumes default postgres user,db,pass. Will not work in Docker
# testing container without additional configuration)
DJANGO_DB=default DJANGO_SETTINGS_MODULE=core.settings.label_studio pytest -vv

What you get from Label Studio

https://github.com/user-attachments/assets/525ad5ff-6904-4398-b507-7e8954268d69

  • Multi-user labeling sign up and login, when you create an annotation it's tied to your account.
  • Multiple projects to work on all your datasets in one instance.
  • Streamlined design helps you focus on your task, not how to use the software.
  • Configurable label formats let you customize the visual interface to meet your specific labeling needs.
  • Support for multiple data types including images, audio, text, HTML, time-series, and video.
  • Import from files or from cloud storage in Amazon AWS S3, Google Cloud Storage, or JSON, CSV, TSV, RAR, and ZIP archives.
  • Integration with machine learning models so that you can visualize and compare predictions from different models and perform pre-labeling.
  • Embed it in your data pipeline REST API makes it easy to make it a part of your pipeline

Included templates for labeling data in Label Studio

Label Studio includes a variety of templates to help you label your data, or you can create your own using specifically designed configuration language. The most common templates and use cases for labeling include the following cases:

Set up machine learning models with Label Studio

Connect your favorite machine learning model using the Label Studio Machine Learning SDK. Follow these steps:

  1. Start your own machine learning backend server. See more detailed instructions.
  2. Connect Label Studio to the server on the model page found in project settings.

This lets you:

  • Pre-label your data using model predictions.
  • Do online learning and retrain your model while new annotations are being created.
  • Do active learning by labeling only the most complex examples in your data.

Integrate Label Studio with your existing tools

You can use Label Studio as an independent part of your machine learning workflow or integrate the frontend or backend into your existing tools.

Ecosystem

Project Description
label-studio Server, distributed as a pip package
Frontend library The Label Studio frontend library. This uses React to build the UI and mobx-state-tree for state management.
Data Manager library A library for the Data Manager, our data exploration tool.
label-studio-converter Encode labels in the format of your favorite machine learning library
label-studio-transformers Transformers library connected and configured for use with Label Studio

Citation

Include a citation for Label Studio in the References section of your articles:

@misc{Label Studio,
  title={{Label Studio}: Data labeling software},
  url={https://github.com/HumanSignal/label-studio},
  note={Open source software available from https://github.com/HumanSignal/label-studio},
  author={
    Maxim Tkachenko and
    Mikhail Malyuk and
    Andrey Holmanyuk and
    Nikolai Liubimov},
  year={2020-2025},
}

License

This software is licensed under the Apache 2.0 LICENSE © Heartex. 2020-2025

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Label Studio Contributor’s Guide

Are you looking for ways to start contributing to Label Studio? This guide will help you understand how to contribute to the project, giving you an understanding of the types of contributions you can make, the standards for each type of contribution, the overall organization of the Label Studio project, and the contribution process.

Types of Contributions

While one of the most common ways to contribute to open source software is through code, there are many other ways to participate in the community development and maintenance of Label Studio. In addition to code pull requests, you can contribute through bug reports, documentation fixes, feature requests, labeling templates, storage backends, and machine learning examples. You can also participate in the Label Studio community Slack by engaging with the rest of the community and answering questions. No contribution is too small!

Docs Update

One of the easiest ways to contribute to Label Studio is through documentation updates. Documentation is one of the first ways that new users will engage with Label Studio, and should help to guide users throughout their journey with Label Studio. Helping to craft clear and correct documentation can have a lasting impact on the experience of the entire user community.

In addition to the change itself, docs updates should include a description of the documentation problem in the pull request, and how the pull request addresses the issue.

Use the Docs Update template for your pull request, and prefix your pull request title with docs:.

Bug Report

Bug reports help identify issues the development team may have missed in testing, or edge cases that diminish the user experience. A good bug report not only alerts the development team to an issue, but also provides the conditions to reproduce, verify, and fix the bug.

When filling out a bug report, please include as much of the following information as possible. If the development team can't reproduce your bug, they can’t take the necessary steps to fix it.

A bug report can enter several different states, including:

  • verified: The bug report has been verified and is in the development pipeline to be fixed
  • not a bug: The report does not describe a bug, which might be the result of expected behavior, or misconfiguration of the platform
  • needs information: The development team couldn’t verify the bug, and needs additional information before action can be taken
  • fixed: The bug report describes a bug that has been fixed in the latest version of Label Studio

When a bug report enters the “fixed” or “not a bug” states, the issue will be closed.

Use the Bug Report template for your issue.

Bug Fix

Bug fixes build upon bug reports, and provide code that addresses the issue. Before submitting a bug fix, please submit a bug report to provide the necessary context for the development team. Bug fixes should follow the coding standards for Label Studio and include tests. Unit tests are necessary to demonstrate the bug has been fixed, and to also provide a safeguard against future regressions. In addition to unit tests, you should provide acceptance criteria that the QA team can use to verify the application's behavior. Bug fixes must reference the original bug report.

Use the Bug Fix template for your pull request, and prefix your pull request title with fix:.

Labeling Templates

One of the most powerful features of Label Studio is its flexible and configurable annotation interface. Label Studio ships with several example annotation interfaces covering many use cases. If you have a workflow not covered by the default templates, you can submit a new interface for inclusion.

Use the Annotation Interface template for your pull request, and prefix your pull request title with feat:

ML Backend Example

The Label Studio ML Backend repository includes a number of different machine learning examples that users can build their own machine learning systems on. Please be sure that your example follows the repository conventions, and has complete documentation in a [README.md](http://README.md) that includes hardware requirements, installation instructions, and usage examples. Prefix your pull request title with feat:.

Export Format Additions or Improvements

The Label Studio Converter repository helps you to encode labels into the format of your favorite machine learning library. It can run conversation from the command line, or directly from within Label Studio. When submitting new codecs, prefix your pull request title with feat:.

Feature Request

You may find that Label Studio is missing a feature that would reduce the friction in your annotation workflow. The development team wants to hear your feedback, and feature requests help to prioritize future work on the Label Studio platform.

It helps the development team and product team to use concrete examples of how the feature works through user stories. A user story follows a standard format: As a [user], I want [an outcome] so that [a benefit/value].

Feature Implementation

A feature implementation is a much larger endeavor that will require coordination with the development team. A feature implementation requires two major parts: a Product Requirements Document (PRD) and the feature pull request.

The PRD should be filed as an issue with the prefix prd:. It should contain the following sections:

  • Problem: A description of the problem you are attempting to address with this feature.
  • Proof of Concept (PoC) Notes: If you have working code, a link to a branch with highlights on how the PoC is working.
  • User Stories: Concrete examples of how the feature works, following a format similar to: As a [user], I want [an action] so that [a benefit/value].

The core development and product team will review the feature request and provide feedback. If the feature is accepted for inclusion, it should be followed by a pull request. The feature pull request should include:

  • The code necessary to implement the feature.
  • Unit and integration tests to prove that the feature works.
  • Documentation that describes how to use the feature.
  • Acceptance criteria in the commit message that describes how QA should test the feature to guarantee it works as intended.
  • A link to the PRD issue in the commit message.

Prefix your pull request title with feat:

Review expectations

We may request that some changes (especially larger ones, or changes to performance-sensitive aspects of the backend infrastructure) be guarded behind a feature flag, to ensure that they can be safely rolled out. In these cases, we will either provide the name of a feature flag to check using flag_set in the code, or add the feature flag directly to the PR ourselves.

We may also add references to JIRA tickets created on our side for the purpose of tracking which changes are included in our different releases.

Code Organization

Label Studio

The primary repository for Label Studio, and contains the majority of the logic for how labels are managed. Three areas where you may want to contribute include:

  • label_studio--This is the main app, containing most of the backend code.
  • web/apps/labelstudio -- This acts as the central integration point for all frontend elements.
  • web/libs/editor--This is the frontend library. It uses React to build the UI and mobx-state-tree for state management.
  • web/libs/datamanager--This is the frontend interface for the Label Studio Data Manager, our data exploration tool.

Label Studio SDK

Python SDK to build advanced automation and integrations against the Label Studio API.

Label Studio Converter

Library for converting between Label Studio format and different machine learning formats. It can be run standalone or as an extension to Label Studio

Label Studio ML Backend

Machine learning backend interface and server code for building machine learning integrations with Label Studio. Includes several example backends.

Coding Standards

When submitting pull requests for code changes, please use the following standards.

  • Keep your pull request small and target only a single feature or a single bug.
  • Use single quotes for strings.
  • Use comments to describe code blocks.
  • Use semantic variable naming.
  • Prefer functions that do a limited number of things.
  • Prefer loose coupling.
  • Follow all linting standards enforced by QA. We use [ruff](https://beta.ruff.rs/docs/) for linting, [blue](https://github.com/grantjenks/blue) for styling, and are in the process of adding [mypy](https://github.com/python/mypy) for static typing. All of these will eventually be enforced on CI. Please type hint all new code!
  • Prefer smaller patches. As a rough guideline, limit the lines changed to around 400. Open multiple PRs for larger changes.

Testing

  • Include unit tests when you contribute bug fixes and new features. Unit tests help prove that your code works correctly and protects against future breaking changes.
  • Use tavern for testing API endpoints whenever possible.
  • Please describe acceptance criteria with your bug fixes and new features. Acceptance criteria give the QA team clear guidance on how the chance should behave. Acceptance criteria follow the form, “When a user does , then .”
  • After submitting your pull request, verify that the code coverage tests and automatic testing for pull requests pass.
  • For documentation pull requests, verify that the change is correctly rendered in the automatically generated preview.
  • Label Studio backend code should be compatible with sqlite and postgresql databases. Our automated tests will run against both sqlite and postgresql-equipped environments, but please use database backend specific features with care.

Additional questions

If you have any questions that aren't answered in these guidelines, please find us in the #contributor channel of the Label Studio Slack Community.

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Label Studio (TM)
Copyright (c) 2019-2021  Heartex, Inc. All Rights Reserved.
Source code in this repository is licensed under the Apache License
Version 2.0, Please see LICENSE for more information.
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