Label Studio

Overview

Label Studio is a free and open-source data annotation tool developed by HumanSignal, formerly known as Heartex. Being open source, it is freely available for both commercial and non-commercial use, with continuous improvements driven by a vibrant community. It provides developers with a comprehensive suite for creating high-quality labeled datasets, making it easier to prepare training data for machine learning projects.

With nearly 14,000 stars on GitHub, Label Studio has earned significant adoption within the developer community. It helps developers fine-tune large language models, validate AI models, and prepare training data for a wide range of AI applications. By streamlining the annotation process, it enables teams to build reliable datasets with greater efficiency, ultimately contributing to more accurate and robust AI models.

Core capabilities

Data labeling platform

Label text, images, audio, video and time-series data

Multiple task types

Support classification, object detection, segmentation and more

ML integration

Connect machine learning models for pre-labeling and active learning

Collaboration

Invite teams and manage annotation projects together

Customizable UI

Build custom labeling interfaces with a flexible template system

Self-hosted

Open-source and self-hostable for data privacy

Export formats

Export labeled data to common ML formats like COCO and JSON

Use cases

Labeling object bounding boxes in images when training object detection models

Annotating text classification and entity recognition datasets for NLP tasks

Collecting RLHF data to optimize chatbot conversation quality

Manually evaluating and scoring outputs from large language models

Annotating audio clips for speech recognition or speaker separation tasks

Creating training data for document AI, such as table recognition and information extraction

Labeling video frames for action recognition or behavior analysis models

Selecting optimal annotation samples with active learning strategies to reduce labeling costs