Label Studio
Open-source data labeling tool for audio, text, images, video, and time series.
About
Label Studio is an open-source data labeling tool (originally from Heartex, now HumanSignal) that lets teams label audio, text, images, video, and time series data through a simple web UI, then export annotations in formats consumable by ML libraries. It is used both to prepare raw datasets and to improve existing training data for more accurate models.
It ships with labeling templates, a customizable interface, ML-assisted pre-labeling that connects models in the loop, and integrations with storage backends, webhooks, and APIs. Licensed under Apache 2.0, it remains actively maintained and is one of the most widely self-hosted annotation tools in the ML ecosystem.
What it can do
- Label audio, text, images, video, and time series in one UI
- ML-assisted pre-labeling and active learning loops
- Export annotations to common ML library formats
- Customize UI templates, webhooks, and storage integrations
Setup
pip install label-studio, then run label-studio; or deploy via Docker. Community edition is free.
Prerequisites
Python 3.8+, a dataset to annotate; optional cloud storage credentials for bulk imports.
Use cases
Works with
Trust score — 8/10
Scored 8/10 (verified tier, metrics-v1). Evidence: Verified via vendor site/docs/GitHub and reputable coverage, wave 73; Trending score 78/100 from researcher assessment.
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.