OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Data & Analytics

Label Studio — HumanSignal

Label Studio is an open-source application for labeling text, images, audio, video, HTML and time-series data. It provides configurable interfaces and templates, and exports annotations in formats used by machine-learning workflows.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Label Studio — HumanSignal — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Label Studio when a dataset needs human-readable, configurable annotations before or during machine-learning development, especially when the work spans multiple data types.

Who it is for

  • Teams preparing raw datasets or improving existing training data.
  • Practitioners who need annotation workflows for more than one data type.
  • Developers incorporating data labeling into an existing machine-learning pipeline.

Documented uses

  • Create labels for text, image, audio, video or time-series examples.
  • Configure annotation interfaces and templates for a dataset.
  • Import data from files or supported cloud storage.
  • Review model predictions and use them for pre-labeling through a connected machine-learning backend.
  • Export annotations for machine-learning workflows.
Before you choose

Requirements & limits

The repository documents local installation with Docker or pip and cloud-instance deployment options, as well as a free trial of the Starter Cloud edition. The project is open source under Apache-2.0.

  • Annotation configurations can be customized, and the project includes templates.
  • Data can be imported from files and from Amazon S3 or Google Cloud Storage; listed file options include JSON, CSV, TSV, RAR and ZIP.
  • Model-assisted pre-labeling requires a separately started machine-learning backend connected through project settings.
  • The documented Docker Compose stack uses Nginx and PostgreSQL; a separate configuration adds MinIO for local S3-storage testing.
  • The repository describes a REST API for embedding the application in a data pipeline.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Start Label Studio locally with Docker

Launch the application locally and reach its web interface.

Before you start

  • Docker
  • A local directory to mount for Label Studio data

Steps

  1. Pull the official Label Studio Docker image.

  2. Run the container with port 8080 mapped to the host and a local directory mounted at /label-studio/data.

  3. Open http://localhost:8080.

Expected outcome

A local Label Studio instance is available at http://localhost:8080, with generated assets and uploaded files stored in the mounted directory.

Guide limitations

  • This guide covers starting the application only; it does not configure a labeling project or import a dataset.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
13 October 2026
Original website title
HumanSignal/label-studio · Official homepage title · retained in its source language
How we review sourcesSuggest a correction