- Get started
- Create user accounts
Install and Upgrade
- Install and upgrade
- Set up database storage
- Get data
- Import pre-annotated data
- Sync data from cloud or Redis storage
Manage Data & Projects
- Set up your labeling project
- Label and annotate data
- Export results
Machine Learning Setup
- Set up machine learning with your labeling process
- Frontend library
- Frontend reference
- Backend API
Get started with 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.
Install Label Studio:
pip install label-studio
Start Label Studio
Open the Label Studio UI at http://localhost:8080.
Sign up with an email address and password that you create.
Click Create to create a project and start labeling data.
Name the project, and if you want, type a description and select a color.
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.
Click Labeling Setup and choose a template and customize the label names for your use case.
Click Save to save your project.
You’re ready to start labeling and annotating your data!
All the steps required to start and finish a labeling project with Label Studio:
- Install Label Studio.
- Start Label Studio.
- Create accounts for Label Studio. Create an account to manage and set up labeling projects.
- 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.
- Import data as labeling tasks.
- Label and annotate the data.
- Export the labeled data or the annotations.
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.
|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.|
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.
|Label Studio Backend||Python and Django||Use to perform data labeling.|
|Machine Learning Backends||Python||Predict data labels at various parts of the labeling process.|
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.