![Label Studio by Heartex Labs](https://github.com/heartexlabs/label-studio/raw/master/images/heartex_icon_opossum_green@2x.png?raw=true&raw=true)
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
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.
- Classify text for sentiment
- Named entities recognition
- Transcribe audio
- Classify audio
- Conversational modeling & chatbots
- Image object detection
- Audio regions
- Image KeyPoints
- Image Polygons
- HTML DocumentsNew
Coming Soon:
- Time series
- Video
npm install label-studio
Check documentation about frontend integration.
Check documentation about backend + frontend integration.
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
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:
- Clone pyheartex, and start serving:
git clone https://github.com/heartexlabs/pyheartex.git cd pyheartex/examples/docker docker-compose up -d
- Specify running server in your label config:
"ml_backend": { "url": "http://localhost:9090", "model_name": "my_super_model" }
- 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)
Detailed changes for each release are documented in the release notes.
Please make sure to read the
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.
This software is licensed under the Apache 2.0 LICENSE © Heartex.