hephaex / label-studio

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

Label Studio

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.

Website • Docs • Twitter • Join Slack Community

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

• Classify text for sentiment
• Named entities recognition
• Transcribe audio
• Classify audio
• Conversational modeling & chatbots
• Image object detection
• Audio regions
• Image KeyPoints
• Image Polygons
• HTML Documents^New

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

• Slack
• Twitter
• Email

Contributing

Please make sure to read the

• Contributing Guideline
• Code Of Conduct

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!