Label Studio

Label Studio is a free and open-source data annotation tool launched by Human Signal (formerly Heartex). This project has nearly 14,000 stars on GitHub and can help developers fine-tune large language models, prepare training data, or validate AI models.

The main features of Label Studio

• Support marking various types of data, including images, audio, text, time series, multi-domain, videos, etc.
• Flexible and configurable. Configurable layouts and templates can be combined with your own datasets and workflows.
• Machine learning-assisted marking. Predictions are used to assist the marking process through ML backend integration, thus saving time.
• Multiple projects and users. Support multiple projects, use cases, and data types on one platform.
• Integrate with your ML/AI pipeline. Identity authentication, project creation, task import, model prediction management, etc. can be performed using Webhooks, Python SDK, and API.

How to start using Label Studio?

• First, make sure that the libq-dev and python3-dev dependencies are installed on your computer.
• Then, install Label Studio using the command pip install label-studio.
• Start Label Studio in the terminal/command line by using the command label-studio start.
• Open the Label Studio UI at http://localhost:8080.
• Register using your own created email address and password.
• Click on Create to create a project and start labeling data.
• Name the project, you can enter a project description and select a color.
• Click on Data Import and upload the data file you want to use. If you want to use data from a local directory, cloud storage, or a database, you can skip this step for now.
• Click on Labeling Setup, set up and select a template, and customize the annotation names according to your use case.
• Click on Save to save your project.