How to annotate images with CVAT: a detailed guide [2025]
This detailed guide will guide you through the CVAT interface, demonstrating its features designed to make the annotation process both accurate and efficient in terms of time and efficiency (i.e. number of annotated images per hour).
Whether you are a seasoned Data Scientist or just starting out, understanding how to use CVAT effectively can dramatically improve your project results and open up new possibilities in the field of Computer Vision. Get ready to discover how to unlock the full potential of your visual data with this guide.
What is CVAT? How to use it?
CVAT stands for Computer Vision Annotation Tool, is an open-source platform designed to facilitate image annotation task and video annotation for artificial intelligence projects, in particular the projects of Computer Vision. CVAT was originally developed by Intel, to meet the demand for a fast and accurate method of labeling visual data.
CVAT has evolved significantly thanks to numerous updates based on feedback from its developer community. CVAT.ai, the company that publishes CVAT, now operates independently. The platform offers improved features and a better user experience. Robust and proven by teams of all sizes, for data of all types and sizes, CVAT is extremely popular in the community of Data Scientists and AI researchers.
With CVAT, users can effectively annotate their datasets by drawing bounding boxes, polygons, lines and points on images, or by tagging time intervals on videos. CVAT also supports a wide range of annotation formats, making it flexible for different Computer Vision tasks and compatible with various machine learning frameworks.
CVAT exists in two versions: CVAT Cloud, which you can use online, and a self-hosted option, which you can install on your computer or server. Being open-source, CVAT is free to use, and everyone is welcome to suggest improvements or add new features.
💡 Whether it is academic research, commercial applications or projects carried out in his free time, CVAT allows Data Scientists, aux developers and for the various AI teams to take advantage of the full potential of their visual data, thus accelerating the development of Computer Vision models.
How do I annotate images with CVAT? Step by step
As we're discussing annotation with CVAT, here's a step-by-step instruction to help you understand the process. Follow the steps and opt for video annotation or image annotation according to your preferences!
Step 1: Start by visiting the CVAT website
CVAT is a free and open-source image annotation tool designed for beginners and professionals working in the field of Co. To find out more, access the CVAT platform by going to its official website.
Step 2: Create an account or sign in
If you are new to CVAT, you will need to create an account. All you need to do is follow the instructions on the screen. If you already have an account, simply sign in to start annotating.
Step 3: Download your data set
Once connected, you can download the images or videos you want to annotate. CVAT allows you to import data in a variety of file formats, making it easy to work with your existing datasets.
Step 4: Select an annotation task
Choose the type of computer vision annotation task you need to perform. CVAT is versatile, taking on tasks such as object detection, image segmentation, and classification.
Whether you are working on training a deep learning model or conducting academic research, choose the task that best fits the needs of your project.
Step 5: Annotate your images
Use CVAT's intuitive interface to annotate your images. You can draw bounding boxes, polygons, lines, and points, or tag time intervals on videos.
CVAT is designed to make the process both accurate and efficient, even offering features like the automatic object tracking for video frame annotation tasks.
Step 6: Review and adjust your annotations
After annotating your images or videos, take time to review and refine your work. Accuracy at this stage is critical to the quality of your Computer Vision model.
Step 7: Export your annotated dataset
Bonus tip
Remember, quality annotation is the basis for successful machine learning and artificial intelligence applications.
💡 By following these steps and using CVAT's features, you are well on your way to prepare quality datasets and create accurate models for your Computer Vision projects.
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Advantages and disadvantages of CVAT for image annotation
Benefits
User friendly interface
CVAT is designed with a simple interface, making it easy to annotate images and videos for beginners and professionals.
Support for various annotation tasks
Whether it's object detection, image segmentation or classifying, CVAT meets a wide range of annotation needs for Computer Vision, offering versatility for various projects.
Fair pricing
CVAT offers a fair and transparent pricing model, with a license cost per user displayed on its website.
Open Source
As an open-source tool, CVAT allows for continuous improvements and updates from its community, keeping the platform up to date with the latest advances.
Integration with machine learning frameworks
CVAT supports a variety of annotation formats, making it easy to export data and integrate it with multiple machine learning frameworks, promoting a smoother workflow for developing AI models.
Rich documentation and community support
There are an abundance of resources, including detailed documentation and tutorials, such as the CVAT YouTube channel, to help users get started and improve their annotation skills.
Disadvantages
Learning curve for advanced features
While CVAT is user-friendly for basic annotation tasks, mastering some of its more advanced features may require time to get started and trained.
Limited to Computer Vision projects
CVAT is specialized for Computer Vision applications, so those who want to annotate data for unrelated tasks (for example, text annotation tasks to train LLMs) may find it less useful.
Dependency on the Internet for cloud-based features
For users who rely on the cloud-hosted version of CVAT, a stable internet connection is essential for uninterrupted access to the platform and its features.
CVAT stands out as one of the most popular and effective data annotation tools for Computer Vision projects, offering a balance of ease of use, flexibility, and powerful features.
👉 Whether you are part of a data annotation team, whether you are an artificial intelligence researcher, or a developer working on deep learning models, CVAT can dramatically streamline the annotation process. However, it is important to weigh its benefits against potential limitations based on the specific requirements of your project.
Main uses of CVAT
Object detection
Object detection is a key application of CVAT, where this platform excels in allowing annotators to identify and label various objects in an image or video frame. This task is important for the development of Computer Vision models that require precise location of objects, such as in surveillance systems, autonomous vehicles, and facial recognition technologies.
CVAT simplifies this process by allowing users to draw bounding boxes around objects of interest, making it accessible for projects of any size.
Image classification
Image classification is another primary use case for CVAT, where it helps categorize images into predefined classes. This function is fundamental in many AI applications, including tagging photos on social media, analyzing medical images, and categorizing retail products.
By using the CVAT interface, data annotation teams can effectively label images, providing the essential tagged data needed to train accurate and robust image classification models.
Semantic and instance segmentation
Semantic and instance segmentation are advanced Computer Vision tasks that CVAT takes care of effectively. While semantic segmentation involves labeling specific parts of an image with a class, instance segmentation goes further by differentiating between individual instances of the same class.
These tasks are vital in applications such as autonomous driving, where distinguishing between different vehicles and pedestrians is critical, or in medical imaging, where accurate segmentation can aid in the diagnosis of diseases.
In addition, CVAT's ability to manage polygons and masks makes it ideal for these complex annotation requirements, making it easy to create high-quality training data for deep learning models.
By taking advantage of CVAT, users from different industries can improve their Computer Vision projects, benefiting from its ease of use, flexibility, and rich set of features it offers. This open-source platform not only speeds up the annotation process, but also ensures the development of accurate and efficient AI models.
Top Alternatives to CVAT
When it comes to improving your data annotation tasks for your AI projects, CVAT stands out for its robust features and its interface. However, exploring alternatives can provide different sets of features that could be better suited or complementary for your specific needs.
Here are some of the best alternatives to CVAT for annotating images and videos.
LabelImg
LabelImg is a great open-source tool for object detection tasks, similar to CVAT. He is particularly known for his simplicity and his efficiency in drawing encompassing boxes around objects.
This Python-based tool is widely adopted for projects looking for a lightweight solution to quickly annotate large sets of image data. Its integration with TensorFlow makes it an attractive option for teams working on deep learning projects.
Labelbox
Labelbox is an advanced data annotation platform that offers a wide range of types of data annotation tools, including image, video, and text annotation.
Its versatility and cloud-based infrastructure make it ideal for teams looking for a comprehensive solution that covers various Computer Vision tasks.
Labelbox is distinguished by its custom workflow and AI-assisted annotation features, which significantly reduce the time and effort of Data Labeler teams needed to prepare training data for artificial intelligence models.
VIA (VGG Image Annotator)
VIA is another open-source tool that is easy to use for basic image annotation tasks.
Designed by the Visual Geometry Group at the University of Oxford, it supports annotations in the form of rectangles, circles, ellipses, polygons, and points, making it ideal for a wide range of computer vision tasks.
VIA works entirely in a browser (Google Chrome, Firefox, Safari, etc.), without requiring software installation, making it incredibly accessible to beginners and professionals alike.
MakeSense.ai
MakeSense.ai offers a web-based platform that is free to use and requires no configuration or installation. It supports various forms of annotation, such as polygons, lines, and key points, which are essential for object detection, segmentation, and other complex computer vision or professional data annotation tasks.
One of the characteristics of MakeSense.ai is its simplicity and its ability to handle various annotation formats, making it a versatile tool for quickly annotating data in a variety of projects.
Each of these tools has its own unique strengths, and the choice depends largely on the specific requirements of your data annotation project.
💡 Whether you need a simple interface for quick annotation of enclosing boxes or a comprehensive platform with AI-assisted annotation capabilities, taking into account the scale, complexity, and budget of your project will guide you in using the appropriate tool.
Conclusion
In conclusion, CVAT stands as a beacon for those venturing into the complex world of image annotation, offering a blend of simplicity, flexibility, and sophistication.
Whether it's the precision required in object detection, the categorization required by image classification, or the accuracy requirements required for segmentation tasks, CVAT provides a comprehensive toolkit that allows users to achieve their goals effectively.
As we reach the end of our article, we are curious to hear your perspective. Have you used CVAT before? How does this discussion take place? Would you like to test CVAT or its alternatives for your next project? Your perspective is invaluable, and we invite you to share your thoughts and experiences, as they are at the heart of innovation in the ever-evolving field of artificial intelligence.
Resources
- Article from CVAT.ai introducing the tool: 🔗 https://www.cvat.ai/post/introduction-to-cvat-ai-best-image-annotation-tool-explained-in-simple-terms
- GitHub from CVAT, to request features or report bugs: 🔗 https://github.com/cvat-ai/cvat/issues
- CVAT YouTube channel, including numerous tutorials: 🔗 https://www.youtube.com/@cvat-ai
