Let’s face it—
It's easy to get confused when choosing the best image annotation tool for your specific use case.
Even more so, with a new data training platform entering the market every couple of months and promising innovative features, faster labeling, or higher accuracy.
But optimizing your data annotation process is crucial to ensure your model's high performance and reliability. So, picking the right tool for your computer vision projects should not be taken lightly.
To help you out, we put together a list of the most popular image annotation tools out there, along with their key features and pricing info.
Here’s what we’ll cover:
Note: Positions 1 through 8 are paid platforms, while 9 through 13 are free image annotation tools.
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Feeling short on time or too busy to go through all of the options?
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That's why we've prepared this handy graphic with Essential Features, Supported Formats, and Security Certificates that allows you to skip the nitty-gritty parts, and easily compare all the tools in seconds.
Let's explore each of these more in-depth. First, we’ll have a look at paid image annotations tools.
Let me start by saying that we won’t be outright telling you that V7 is the best image annotation tool out there.
We won’t be promoting ourselves as the “top training data platform” or brag about people naming V7 the most versatile and advanced tool for image and video annotation.
None of that.
Our own article isn’t the right place to make such bold statements.
We’d like to encourage you to try out V7 for yourself and see whether we deserve all the positive reviews we’ve got ;-)
V7 is an automated annotation platform combining dataset management, image annotation, Video annotation, and autoML model training to automatically complete labeling tasks.
V7 enables teams to store, manage, annotate, and automate their data annotation workflows in:
- DICOM medical data
- Microscopy images
- PDF and document processing
- 3D volumetric data
Key features include:
Price: From USD 0 (Education Plan), more details on the V7 pricing page
Labelbox is a training data platform built from three core layers that facilitate the entire process from labeling and collaboration to iteration. It was created in 2018 and has quickly become one of the most popular data labeling tools.
Labelbox offers AI-enabled labeling tools, labeling automation, human workforce, data management, a powerful API for integration, and a Python SDK for extensibility.
It enables annotations with polygons, bounding boxes, lines, as well as more advanced labeling tools.
Price: Free 5000 images/Custom Pro and Enterprise plans.
Scale is a data platform that enables annotations of large volumes of 3D sensor, image, and video data.
Scale offers ML-powered pre-labeling, an automated quality assurance system, dataset management, document processing, and AI-assisted data annotation eschewed towards data processing for autonomous driving.
This data annotation tool can be used for a variety of computer vision tasks, including object detection, classification, and text recognition and it supports multiple data formats.
Here you can learn more about Scale AI alternatives.
Price: From $50,000.
Superannotate is an end-to-end image and Video annotation platform that streamlines and automates computer vision workflows.
SuperAnnotate allows you to create high-quality training datasets for various computer vision tasks, including object detection, instance, and semantic segmentation, keypoint annotation, cuboid annotation, and video tracking.
Available tools include vector annotations (boxes, polygons, lines, ellipses, keypoints, and cuboids) and pixel-wise annotation using a brush.
Price: Free 14-day trial / Custom Starter, Pro, Enterprise plans
The all-in-one cloud-based annotation platform with embedded tools and automation for producing high-quality datasets.
Dataloop accommodates the whole AI lifecycle including annotation, model evaluation, and model improvement by utilizing the human feedback in the loop.
It offers tools for basic computer vision tasks like detection, classification, key points, and segmentation. Dataloop supports both image and video data.
Price: Free trial / Custom Enterprise plans
Playment is a fully managed data labeling platform generating training data for computer vision models founded in 2015.
Playment supports image and video data, and offers a wide range of basic annotation tools including bounding boxes, cuboids, polygons or landmarks.
It works on the principle of microwork, breaking down large problems into micro-tasks and distributing them among its large community of trained annotators.
Supervise.ly is a web-based image and Video annotation platform where individual researchers and large teams can annotate and experiment with datasets and neural networks.
Apart from basic annotation tools such as boxes, lines, dots, polygons, or bitmap brush, Supervise.ly offers a Data Transformation Language tool and enables 3D Point Cloud.
Price: Free 100 images in community edition.
Hive Data is a fully managed data annotation solution to source and label training data for AI / ML Models.
Hive Data supports image, video, text, 3D Point Cloud annotation and data sourcing. Apart from basic annotation types, Hive Data offers multi-frame object tracking, contours, and 3D panoptic segmentation.
Now, let’s have a look at several open-source, free image and video annotation tools that you can use for your computer vision and data science projects.
CVAT (Computer Vision Annotation Tool) is an open-source, web-based image and video annotation tool for labeling data for computer vision, supported and maintained by Intel.
CVAT supports the primary tasks of supervised machine learning: object detection, image classification, and image segmentation. It offers four basic types of annotation: boxes, polygons, polylines, and points.
LabelMe is an online annotation tool created by the MIT Computer Science and Artificial Intelligence Laboratory. It provides a dataset of digital images with annotations.
The dataset is free and open to external contributions.
Labelme supports six different annotation types such as polygon, rectangle, circle, line, point, and line strip. One of the limitations is that files can only be saved and exported in JSON format.
A graphical image annotation tool to label objects using bounding boxes in images. It is written in Python. You can export your annotations as XML files in PASCAL VOC format.
In its default version, Labelimg only offers one annotation type—a bounding box or rectangle shape. However, one can also add another shape with a code using a GitHub page.
VoTT (Visual Object Tagging Tool) is a free and open-source image annotation and labeling tool developed by Microsoft.
VoTT provides end-to-end support for generating datasets and validating object detection models from video and image assets.
ImgLab is an open-source and web-based image annotation tool.
ImgLab provides multiple label types such as points, circles, boundary boxes, polygons. It also supports various formats including dlib, XML, Pascal VOC and COCO.
There you have it—the list of 13 best free and paid image Annotation Tools.
Whether you are labeling small sets of data or building complex AI models, picking the right tool for your specific use case will directly impact both—the annotation process and your results.
Make sure to carefully consider all of your options to avoid wasting your time and money on a tool that doesn’t cater to all of your needs.
Finally, here's a visual comparison of all paid tools we've reviewed in this article.
And if you still cannot decide on the best image annotation solution—send us an email or book a call with our sales team to discuss your options.
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