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Label Studio overview

docs/source/guide/get_started.md

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What is Label Studio?

Label Studio is an open source data labeling tool that supports multiple projects, users, and data types in one platform. It allows you to do the following:

  • Perform different types of labeling with many data formats.

  • Integrate Label Studio with machine learning models to supply predictions for labels (pre-labels), or perform continuous active learning. See Set up machine learning with your labeling process.

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Label Studio is also available an Enterprise cloud service with enhanced security (SSO, RBAC, SOC2), team management features, data discovery, analytics and reporting, and support SLAs. A free trial is available to get started quickly and explore the enterprise cloud product.

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Interface

<div class="opensource-only"> <center><i>Project List Screenshot</i></center> <center><i>Data Manager Screenshot</i></center> <center><i>Quick View Screenshot</i></center> </div> <div class="enterprise-only"> <center><i>Project List Screenshot</i></center> <center><i>Data Manager Screenshot</i></center> <center><i>Quick View Screenshot</i></center> </div>

Labeling workflow

Start and finish a labeling project with Label Studio by following these steps:

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  1. Install Label Studio.
  2. Start Label Studio.
  3. Create accounts for Label Studio. Create an account to manage and set up labeling projects.
  4. Set up the labeling project. Define the type of labeling to perform on the dataset and configure project settings.
  5. Set up the labeling interface. Add the labels that you want annotators to apply and customize the labeling interface.
  6. Import data as labeling tasks.
  7. Label and annotate the data.
  8. Export the labeled data or the annotations.
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  1. Create accounts for Label Studio. Create an account to manage and set up labeling projects.
  2. Restrict access to the project. Set up role-based access control. Only available in Label Studio Enterprise Edition.
  3. Set up the labeling project. Define the type of labeling to perform on the dataset and configure project settings.
  4. Set up the labeling interface. Add the labels that you want annotators to apply and customize the labeling interface.
  5. Import data as labeling tasks.
  6. Label and annotate the data.
  7. Review the annotated tasks. Only available in Label Studio Enterprise Edition.
  8. Export the labeled data or the annotations.
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Label Studio citations

If you would like to cite Label Studio, you can add the following information to your references section:

@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},
}

Architecture

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!!! error Enterprise You can use any of the Label Studio components in your own tools, or customize them to suit your needs. Before customizing Label Studio extensively, you might want to review Label Studio Enterprise Edition to see if it already contains the relevant functionality you want to build. See Label Studio Features for more.

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The component parts of Label Studio are available as modular extensible packages that you can integrate into your existing machine learning processes and tools.

ModuleTechnologyDescription
Label Studio main appPython and DjangoThe main app with most of the backend code for Label Studio; used to perform data labeling.
Label Studio frontendJavaScript web app using React and MSTLocated within the main app repo. web/apps/labelstudio acts as the central integration point for all frontend elements. web/libs/editor is the frontend library.
Data ManagerJavaScript web app using ReactManage data and tasks for labeling. Located under web/libs/datamanager in the main app repo.
Machine Learning BackendsPythonPredict data labels at various parts of the labeling process.
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