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.
- Try out Label Studio
- What you get from Label Studio
- Included templates for labeling data in Label Studio
- Set up machine learning models with Label Studio
- Integrate Label Studio with your existing tools
Have a custom dataset? You can customize Label Studio to fit your needs. Read an introductory blog post to learn more.
You can run the latest Label Studio version locally without installing the package with pip.
# Install all package dependencies
pip install -e .
# 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
- 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
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:
Connect your favorite machine learning model using the Label Studio Machine Learning SDK. Follow these steps:
- Start your own machine learning backend server. See more detailed instructions.
- 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.
You can use Label Studio as an independent part of your machine learning workflow or integrate the frontend or backend into your existing tools.
- Use the Label Studio Frontend as a separate React library. See more in the Frontend Library documentation.
| Project | Description |
|---|---|
| label-studio | Server, distributed as a pip package |
| label-studio-frontend | React and JavaScript frontend and can run standalone in a web browser or be embedded into your application. |
| data-manager | React and JavaScript frontend for managing data. Includes the Label Studio Frontend. Relies on the label-studio server or a custom backend with the expected API methods. |
| 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 |
Want to use The Coolest Feature X but Label Studio doesn't support it? Check out our public roadmap!
@misc{Label Studio,
title={{Label Studio}: Data labeling software},
url={https://github.com/heartexlabs/label-studio},
note={Open source software available from https://github.com/heartexlabs/label-studio},
author={
Maxim Tkachenko and
Mikhail Malyuk and
Andrey Holmanyuk and
Nikolai Liubimov},
year={2020-2022},
}This software is licensed under the Apache 2.0 LICENSE © Heartex. 2020-2022
