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Best Open-Source Image Annotation Tools in 2024

Computer vision has become an integral part of various industries, from autonomous vehicles to medical imaging. To train robust and accurate computer vision models, high-quality labeled datasets are essential; The open-source image annotation tools have emerged as powerful solutions to address this need. Such tools not only offer cost-effectiveness but also provide collaborative platforms for data labeling. In this article, we will explore the best open-source image annotation tools in 2024.

Computer Vision Annotation Tool (CVAT)

CVAT Annotation Interface

Computer Vision Annotation Tool ( is an open-source video and image annotation tool, well-regarded in the computer vision community. It supports key supervised machine learning tasks like object detection (supporting also 3D point cloud data), image classification, and image segmentation. CVAT is celebrated for its user-friendliness, comprehensive manual and automatic annotation features, collaborative capabilities, strong community support. CVAT also has a huge number of learning materials to dig into the tool on YouTube, and official documentation

Additionally, CVAT enables users to try its features online via the Cloud platform, allowing access without local installation. The online platform facilitates all the features available at open source and even more powerful capabilities to enhance the annotation process in a web-based environment, offering a convenient, accessible way to explore CVAT and assess its fit for specific annotation projects.

To sum it up: CVAT distinguishes itself as a highly comprehensive and user-friendly open-source annotation tool, making it a preferred choice for individual researchers and organizations. developers


  • Advanced annotation capabilities to label tags, rectangles, polygons, polylines, ellipses, points, binary masks, skeletons and 3D cuboids, including many automated features to accelerate the process.
  • Enables collaborative, role-based work with multiple users.
  • Features built-in annotation review mechanisms and automatic quality control features based on ground truth annotations
  • Supports integration with popular data storages, like AWS S3, Microsoft Azure, Google Cloud
  • Provides a lot of learning materials and documentation
  • Offers extensive support for 24+ popular annotation formats. 
  • Benefits from regular updates and strong community support.
  • Cloud platform does not require any technical knowledge for set up, it is ready to use. 


  • Self-hosted solution requires a relatively high level of technical expertise for setup and configuration.
  • Processing of the large datasets might require additional time.


LabelMe Annotation Interface

LabelMe is an open-source annotation tool for digital images, developed by the MIT Computer Science and Artificial Intelligence Laboratory in 2008. This freely accessible platform allows users to annotate images and contribute to its expanding dataset library.

It's designed to support various computer vision research and development projects, offering a collaborative environment for image labeling and dataset creation. LabelMe is recognized for its user-friendly interface and its significant contribution to the computer vision community, facilitating accessible data for research and application development.


  • Features a simple, intuitive user interface.
  • Supports different annotation primitives including polygon, rectangle, and point.
  • Offers an easy installation and setup process.
  • Provides the ability to export annotations in multiple formats.


  • Manual installation and setup are required.
  • The potential lack of frequent updates and maintenance might result in compatibility issues with newer technologies.


How to install LabelImg in Windows with Anaconda? | by Sang Huynh | Medium
LabelImg Annotation Interface

LabelImg is a graphical image annotation tool designed for drawing bounding boxes around objects in images. 

LabelImg is developed using Python and Qt, making it versatile and accessible across multiple operating systems including Windows, Linux, and macOS. This tool is useful for tasks in machine learning and computer vision that require precise object localization within images. Its compatibility with various platforms and ease of use for bounding box annotations make LabelImg a popular choice in the image annotation community.


  • Lightweight and straightforward for deployment.
  • Supports both bounding box and polygon annotations.
  • Efficiently integrates with popular deep learning frameworks.
  • Compatible with multiple platforms, including Windows, Linux, and macOS.


  • Annotation capabilities are more limited compared to other tools.
  • Lacks advanced features such as collaborative options and support for various annotation types.

Label Studio

Label Studio stands out as a comprehensive and adaptable open-source tool for data labeling. It caters to a variety of projects and users, handling diverse data types seamlessly on a single platform. The tool excels in offering a range of labeling options across different data formats and integrates smoothly with machine learning models. This integration enhances the efficiency and accuracy of the labeling process by providing predictive labeling and supporting ongoing active learning. Its modular design allows for easy integration into existing machine learning workflows, offering versatility for various labeling requirements. For more details, Label Studio's website provides extensive information.


  • Supports a variety of projects, users, and data types on a single platform.
  • Enables diverse types of labeling across numerous data formats.
  • Integrates with machine learning models for label predictions and active learning.
  • Offers an enterprise cloud service with advanced security, team management, data analytics, reporting, and SLA support.


  • Requires technical knowledge for setup and usage.
  • May not be ideal for smaller-scale projects.
  • Might not be the easiest option for those seeking minimal setup and ease of use.


Imagetagger Annotation Interface

Imagetagger is an open-source image annotation tool that allows users to label images for object detection and image segmentation. It is written in JavaScript and is available for Windows, Linux, and macOS.


  • User-friendly interface for quick annotation.
  • Supports polygon and bounding box annotations.
  • Easy integration with existing workflows.
  • Export annotations in popular formats.


  • Limited documentation and support resources.
  • May have performance issues with large datasets.


Deeplabel Annotation Interface

Deeplabel is an open-source image annotation tool that allows users to label images for object detection and image segmentation. It is written in Python and is available for Windows, Linux, and macOS.


  • Supports various annotation types, including bounding boxes, polygons, and keypoints.
  • Customizable interface and workflow.
  • Integration with popular deep learning frameworks.
  • Active development and community support.


  • Requires a certain level of technical expertise to use effectively.
  • Lack of a graphical user interface may be less user-friendly for some users.

Image annotation comparative table

Image annotation comparative table

In conclusion, the landscape of open-source image annotation tools in 2024 offers a diverse range of options tailored to different needs in the field of computer vision. From CVAT's advanced capabilities and robust community support to LabelImg's simplicity and multi-platform compatibility, each tool presents unique features and advantages. The choice of the right tool ultimately hinges on the specific requirements of your project, the scale of operations, and the desired ease of use. Whether you're an individual researcher or part of a larger organization, these tools provide cost-effective, flexible solutions to effectively label data, a critical step in developing accurate and efficient computer vision models. This array of tools underscores the dynamic nature of technology in the realm of AI and machine learning, offering promising avenues for innovation and progress.

Stay abreast of the latest tools and techniques in the fast-evolving field of computer vision.

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January 21, 2024
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