label-studio: a multi-modal open-source data labeling tool with ML-assisted pre-labeling and active learning

What it solves

Label Studio solves the challenge of preparing high-quality training data for machine learning models. It provides a centralized tool to label raw data across various formats, allowing teams to create datasets from scratch or refine existing annotations to improve model accuracy.

How it works

Label Studio provides a customizable user interface where users can annotate data imported from local files or cloud storage (AWS S3, Google Cloud Storage). It supports a wide range of data types and offers configurable label formats. The tool can be integrated into larger data pipelines via a REST API and can connect to external machine learning backends via an SDK to enable pre-labeling, online learning, and active learning.

Who it’s for

It is designed for data scientists, ML engineers, and annotation teams who need a flexible, multi-user environment to label text, audio, images, video, and time-series data.

Highlights

• Multi-modal support: Labels audio, text, images, videos, and time series.
• ML Integration: Connects to models for pre-labeling and active learning to reduce manual effort.
• Coutomizable UI: Uses a specific configuration language to create custom labeling interfaces.
• Flexible Import/Export: Supports cloud storage and various file formats (JSON, CSV, TSV, etc.) and exports to multiple model formats.