Get started with Label Studio

What is Label Studio?

Label Studio is an open source data labeling tool for labeling and exploring multiple types of data. You can perform many different types of labeling for many different data formats.

You can also integrate Label Studio with machine learning models to supply predictions for labels (pre-labels), or perform continuous active learning. See Set up machine learning with your labeling process.

Quick start

1. Install Label Studio:

   pip install label-studio

2. Start Label Studio

   label-studio start

3. Open the Label Studio UI at http://localhost:8080.

4. Sign up with an email address and password that you create.

5. Click Create to create a project and start labeling data.

6. Name the project, and if you want, type a description and select a color.

7. Click Data Import and upload the data files that you want to use. If you want to use data from a cloud storage bucket or database, skip this step for now.

8. Click Labeling Setup and choose a template and customize the label names for your use case.

9. Click Save to save your project.

You’re ready to start labeling and annotating your data!

Labeling workflow with Label Studio

All the steps required to start and finish a labeling project with Label Studio:

1. Install Label Studio.
2. Start Label Studio.
3. Create accounts for Label Studio. Create an account to manage and set up labeling projects.
4. Set up the labeling project. Define the type of labeling to perform on the dataset, and add the labels that you want annotators to apply.
5. Import data as labeling tasks.
6. Label and annotate the data.
7. Export the labeled data or the annotations.

Label Studio terminology

When you upload data to Label Studio, each item in the dataset becomes a labeling task. The following table describes some terms you might encounter as you use Label Studio.

Term	Description
Dataset	What you upload to Label Studio, comprised of individual items.
Task	What Label Studio transforms your individual dataset items into.
Labels	What you add to each dataset item while performing a labeling task in Label Studio.
Region	The portion of the dataset item that has a label assigned to it.
Relation	A defined relationship between two labeled regions.
Pre-labeling	What machine learning models perform in Label Studio or separate from Label Studio. The result of predicting labels for items in a dataset are predicted labels, or pre-labels.
Annotations	The output of a labeling task. Previously called “completions”.
Templates	Example labeling configurations that you can use to specify the type of labeling that you’re performing with your dataset. See all available templates
Tags	Configuration options to customize the labeling interface. See more about tags.

Components and architecture

You can use any of the Label Studio components in your own tools, or customize them to suit your needs.

The component parts of Label Studio are available as modular extensible packages that you can integrate into your existing machine learning processes and tools.

Module	Technology	Description
Label Studio Backend	Python and Django	Use to perform data labeling.
Label Studio Frontend	JavaScript web app using React and MST	Perform data labeling in a user interface.
Data Manager	JavaScript web app using React	Manage data and tasks for labeling.
Machine Learning Backends	Python	Predict data labels at various parts of the labeling process.

Information collected by Label Studio

Label Studio collects anonymous usage statistics about the number of page visits and data types being used in labeling configurations that you set up. No sensitive information is included in the information we collect. The information we collect helps us improve the experience of labeling data in Label Studio and helps us plan future data types and labeling configurations to support.