Explore five open-source image annotation tools for Vision AI projects. Compare key features, ease of use, and compatibility to pick the right one.
The core of any computer vision solution is a Vision AI model, like Ultralytics YOLO11, that can analyze and understand visual data such as images and videos. More specifically, it’s a model trained on a high-quality, well-annotated dataset or a collection of images or videos labeled with relevant information like object names, locations, or actions. These annotations teach the model how to recognize patterns and make accurate predictions.
In fact, the reliability of a model is often directly tied to this data. If the labels are clear and accurate, the model learns better. However, if the data is messy or inconsistent, the model can get confused and make mistakes. This makes data annotation an important step in building computer vision solutions, especially when they are created for very specific applications.
Even though pre-trained, out-of-the-box computer vision models today are advanced and perform well on many tasks, they’re not always the right fit for every project. Pre-trained models are great for general use, but for more specific requirements, a custom-trained model generally performs better. For example, a model trained to detect everyday objects might struggle to identify specialized items like medical instruments or industrial components without additional training.
Often, training such a custom model also means creating your own dataset, tailored to the unique needs of your application. Image annotation tools can be used to label images and create such datasets.
Thanks to the collaborative nature of the AI community, there are many open-source image annotation tools available to choose from. The best tool for your project depends on the solution you are creating and factors like the type of labels, how much data you are labeling, and how data labeling fits into your workflow.
In this article, we’ll explore five open-source image annotation tools that are easy to get started with.
Open-source tools, models, and software are very common in the AI space nowadays. When we refer to something as open-source, it means the code behind it is publicly available. Anyone can look at it, use it, make changes, and even share their own version. Most open-source projects are built to encourage collaboration and constant improvement.
This makes open-source annotation tools a great choice for many people working on computer vision projects. Since they’re free, they’re especially useful for students, researchers, startups, or anyone working with a limited budget. But it’s not just about the cost - these tools are also flexible. You can customize them to fit your specific needs, host them locally to keep your data private, and even add new features if needed.
Another big advantage is the sense of community that comes with these tools. Open-source tools are often supported by active groups of users and developers who help fix bugs, add new features, and keep things up to date. Most tools are user-friendly, making them accessible even to beginners.
With that in mind, let’s take a closer look at five open-source image annotation tools that stand out today.
CVAT, initially developed by Intel, is a popular open-source tool used for labeling images and videos. You can use it online or install it and use it locally on your computer. It provides various options for labeling, such as bounding boxes, masks, polygons, and key points, making it useful for different Vision AI projects.
CVAT also supports popular annotation formats like YOLO (a simple text-based format used by the YOLO models), Pascal VOC (an annotation standard used in early object detection datasets), and MS COCO (a large-scale dataset developed by Microsoft that includes images labeled for object detection, segmentation, and captioning, using a standardized JSON format - a lightweight, human-readable data format), so your labeled data can be easily integrated with different models.
One of CVAT’s key features is that two users can work together using this tool, making it great for team projects. While it offers great features, setting up CVAT locally can be tricky since it requires Docker, a platform that lets you package and run software in a self-contained environment, which can be a bit challenging for beginners.
However, you can also use CVAT online by creating an account, uploading your images or videos, and labeling them using the built-in tools. Once you're done, you can export your data and review the results.
Similarly, LabelImg is an open-source tool for labeling images using bounding boxes. Created in 2015, it’s written in Python and uses QT (a cross-platform application development framework) for its graphical interface. It's the perfect tool for small projects and people working on a computer vision project for the first time.
LabelImg saves annotations as XML files (a structured text format used to store data) in the PASCAL VOC format. While this format is easy to read and is still used in some projects, it’s not directly compatible with many modern object detection models without conversion. However, LabelImg also supports the YOLO and CreateML (used by Apple’s CreateML tool for training machine learning models on macOS) formats.
Using LabelImg requires some basic technical knowledge, like working with Python and using the terminal (a text-based tool for entering commands on your computer). The easiest way to install it is with Python’s package manager, pip. Or, you can run it directly by cloning the LabelImg GitHub repository, without installing anything system-wide.
LabelMe supports a wide range of annotation types, including polygons, rectangles, circles, lines, and points. It can be used to create datasets for various computer vision tasks like object detection, image classification, semantic segmentation, and instance segmentation.
By default, it saves annotations in JSON format, which can be converted into other formats like Pascal VOC and COCO (a widely used dataset format that stores annotations such as object bounding boxes and segmentation masks in JSON).
However, it doesn’t natively support YOLO or OpenImages (a dataset format developed by Google that includes annotations like bounding boxes, segmentation masks, and point labels across millions of images). While reliable, it lacks features like team collaboration, image editing, or built-in dataset management, so it’s better suited for smaller or academic projects.
Using LabelMe is pretty straightforward, but it does require some basic technical knowledge, like using Python and the terminal. The most common way to install it is with Python’s package manager, pip. Also, if you’re using Linux, you might also be able to install it through your system’s package manager, depending on your distribution.
VGG Image Annotator (VIA) is a simple, open-source tool for labeling images, videos, and audio. It was developed by the Visual Geometry Group at the University of Oxford and runs directly in your web browser without any installation needed.
It supports a wide range of annotation types, including bounding boxes, polygons, points, circles, lines, and even temporal segments for video and audio.
Since it works offline and is built as a single HTML file (a standard file format used to create and display web pages), it’s great for researchers or anyone looking for a lightweight, portable annotation tool.
To try it out, you can download the HTML file from the official website or the GitHub repository and open it in your browser.
ImgLab is an open-source, browser-based image annotation tool designed primarily for creating bounding box and landmark annotations, especially for projects using Dlib, a popular C++ toolkit for face detection, object tracking, and other computer vision tasks.
One of ImgLab’s key features is its lightweight and platform-independent design. It runs directly in the browser, requires no installation, and uses minimal system resources, making it ideal for quick labeling tasks. It supports multiple export formats, including Dlib XML, Pascal VOC, and COCO.
Since it runs in a browser, most users can start labeling images right away. For local use or customization, you can also download the project and run it locally from the ImgLab GitHub repository.
Picking the right annotation tool depends on your specific AI project needs. First, start by deciding what kind of labels you need. Ask yourself questions like, are bounding boxes enough, or do you need more detailed shapes like polygons and masks? Some tools offer a variety of label types, while others do not.
Next, consider the scale of the project. If you’re working alone, a tool like LabelImg is a good option. But if you're working with a team, a tool like CVAT with multi-user support is better. Image dataset size also matters. For small projects, simpler tools work fine (LabelImg, LabelMe, VIA, ImgLab), but if you're labeling thousands of images, you'll need a tool like CVAT that can handle large datasets better.
Finally, consider where the tool runs. If you need something web-based for easy access, select an online tool (which may lack privacy). If you prefer more privacy and control, choose one that runs offline, like VIA. Some tools, like CVAT, require Docker for setup, so understanding the technical requirements beforehand is a good idea.
The performance of AI models often depends on the quality of your data, and the right annotation tool can make a big difference. Whether you're working on a small personal project or a large-scale enterprise solution, the best tool really comes down to your specific needs.
Many popular annotation tools are open source, giving you the flexibility to customize them. Taking the time to choose the right tool can help you work more efficiently and build a more reliable computer vision model.
Want to learn more about Vision AI? Explore our GitHub repository, connect with our community, and check out our licensing options to jumpstart your computer vision project. If you're exploring innovations like AI in manufacturing or computer vision in the automotive industry, visit our solutions pages to discover more.
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