/label-studio

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

Primary LanguageJavaScriptApache License 2.0Apache-2.0


Label Studio is an open-source, configurable data annotation tool.

Its purpose is to help you label different types of data using a simple interface with a standardized output format. It's mobile-friendly and fast.

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Label Studio

Features ✨

Simple: Crafted with minimal UI design. A simple design is the best design.

Configurable: Using high-level jsx tags config, you can fully customize the interface for your data.

Embeddable: It's an NPM package too. You can include it into your projects.

Quick Labeling Guides

Coming Soon:

  • Time series
  • Video

Usage

Frontend package

npm install label-studio

Check documentation about frontend integration.

Backend and frontend

Check documentation about backend + frontend integration.

Docker

docker run -p 8200:8200 -t -i heartexlabs/label-studio -c config.json -l ../examples/chatbot_analysis/config.xml -i ../examples/chatbot_analysis/tasks.json -o output

Machine learning integration

You can easily connect your favorite machine learning framework with Label Studio by using Heartex SDK.

That gives you the opportunities to:

  • use model predictions as prelabeling
  • simultaneously update (retrain) your model while new annotations are coming
  • perform labeling in active learning mode
  • instantly create running production-ready prediction service

There is a quick example tutorial how to do that with simple image classification:

  1. Clone pyheartex, and start serving:
    git clone https://github.com/heartexlabs/pyheartex.git
    cd pyheartex/examples/docker
    docker-compose up -d
  2. Specify running server in your label config:
    "ml_backend": {
      "url": "http://localhost:9090",
      "model_name": "my_super_model"
    }
  3. Launch Label Studio with image classification config:
    python server.py -l ../examples/image_classification/config.xml

Once you're satisfied with prelabeling results, you can imediately send prediction requests via REST API:

curl -X POST -H 'Content-Type: application/json' -d '{"image_url": "https://go.heartex.net/static/samples/kittens.jpg"}' http://localhost:8200/predict

Feel free to play around any other models & frameworks apart from image classifiers! (see instructions here)

Changelog

Detailed changes for each release are documented in the release notes.

Stay In Touch

Contributing

Please make sure to read the

Label Studio for Teams, Startups, and Enterprises

Label Studio for Teams is our enterprise edition (cloud & on-prem), that includes a data manager, high-quality baseline models, active learning, collaborators support, and more. Please visit the website to learn more.

License

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

Happy Labeling!