Label Studio

Annotating data using Label Studio.

Label Studio is one of the leading open-source annotation platforms available to data scientists and ML practitioners. It is used to create or edit datasets that you can then use as part of training or validation workflows. It supports a broad range of annotation types, including:

Computer Vision (image classification, object detection, semantic segmentation)

Audio & Speech (classification, speaker diarization, emotion recognition, audio transcription)

Text / NLP (classification, NER, question answering, sentiment analysis)

Time Series (classification, segmentation, event recognition)

Multi-Modal / Domain (dialogue processing, OCR, time series with reference)

When would you want to use it?

If you need to label data as part of your ML workflow, that is the point at which you could consider adding the optional annotator stack component as part of your ZenML stack.

We currently support the use of annotation at the various stages described in the main annotators docs page, and also offer custom utility functions to generate Label Studio label config files for image classification and object detection. (More will follow in due course.)

The Label Studio integration currently is built to support workflows using the following three cloud artifact stores: AWS S3, GCP/GCS, and Azure Blob Storage. Purely local stacks will currently not work if you want to do add the annotation stack component as part of your stack.

How to deploy it?

The Label Studio Annotator flavor is provided by the Label Studio ZenML integration, you need to install it, to be able to register it as an Annotator and add it to your stack:

zenml integration install label_studio