Introduction

Labelo is an open-source data annotation platform designed to streamline the labeling process for machine learning and data science projects. It supports multiple data types and allows teams to collaborate on labeling tasks with ease, all within a single, intuitive platform. Labelo enhances project efficiency by offering customizable workflows and integrations with other tools commonly used in the data science and AI ecosystem.

Key Features of Labelo

• Multi-Data Type Support: Label images, text, video, audio, time series, and more, making it versatile for various use cases.
• Collaboration: Manage projects, assign tasks, and collaborate in real-time with your team.
• Customization: Tailor the labeling interface to meet the specific needs of your project, improving the speed and accuracy of annotations.
• Open-Source: Modify, extend, or contribute to Labelo’s codebase, supported by an active community of developers.

Workflow

To start and complete a labeling project with Labelo, follow these steps:

1. Install Labelo: Set up Labelo on your machine or server using pip, Docker, or other installation methods.
2. Create a New Project: Launch Labelo and create a project within a workspace.
3. Import Data: Upload your dataset, whether it consists of images, text, video, audio, or other supported formats.
4. Configure Labeling Interface: Customize the interface and tools based on the specific requirements of your annotation task.
5. Label Your Data: Use Labelo’s annotation tools to label your data accurately.
6. Review Annotations: Verify the accuracy and consistency of the labeled data using the built-in review system.
7. Export Labeled Data: Export the annotated data in the required format to integrate it into your machine learning pipeline or data analysis project.

TIP

Take full advantage of Labelo’s customization features to streamline the labeling process and ensure that the tool is perfectly suited to your data types and project needs.

Integration

Labelo can be integrated with other machine learning and data science tools to seamlessly fit into your existing workflows. With its RESTful API, you can automate processes or fetch labeled data for further processing.

Open Source Community

Being open source, Labelo is freely available for use, modification, and redistribution. This fosters a strong community of contributors who continually improve the tool, offering new features, plugins, and enhancements.

Architecture Overview

Labelo’s architecture combines modern web technologies with a robust backend to ensure high performance and scalability across various data annotation use cases.

Module	Technology	Description
Frontend	React	The user interface is built with React, offering a dynamic and responsive UI.
Backend	Django	The backend is powered by Django, handling data processing and request routing.
API	RESTful API	Manages communication between the frontend and backend for real-time updates.
Database	PostgreSQL, SQLite	Relational databases used to store project configurations, user data, and metadata.
Machine Learning	Python	Enables integration with machine learning models to predict labels automatically.

Labelo’s architecture is built to scale, supporting large datasets and complex labeling workflows, making it a reliable choice for teams working on diverse data annotation tasks.