If you’re diving into machine learning or AI, you’ll quickly realize that data labeling is a crucial step. Annotation tools help you label your images, videos, and other datasets, making them ready for training your models. While many annotation tools come with a hefty price tag, there are some great free options out there. These free tools can help you get the job done without breaking the bank, whether you’re working on a small project or need a scalable solution. Let’s take a look at the best free annotation tools available and how they can help streamline your data labeling process.
1. FlyPix AI
At FlyPix AI, we specialize in geospatial analysis by leveraging artificial intelligence to turn satellite and aerial imagery into actionable insights. The platform is designed to help us detect objects, monitor changes, and identify anomalies, which is crucial for industries like agriculture, urban planning, and environmental monitoring. Although FlyPix AI is a paid platform, it provides valuable features that can be useful as free annotation tools for smaller projects or teams just starting with geospatial data.
We value FlyPix AI for its no-code platform that allows us to easily create and train custom AI models. The interactive annotation tools help us annotate imagery and train models without needing coding skills, making it an accessible tool for both technical and non-technical users. Additionally, FlyPix AI’s real-time analytics and heatmap generation help us track changes over time, providing clear and actionable data insights.
Key Features:
- No-code platform for image annotation and model training
- Supports satellite, drone, and LiDAR data types
- Interactive tools for annotating objects and tracking changes
- Real-time analytics with heatmap generation
- Enterprise-grade support for large-scale projects
Services:
- Geospatial object detection and localization
- Detecting and tracking changes or anomalies in images
- Custom AI model development for your unique project needs
- Integration with GIS systems for a smoother workflow
- Visualizing data patterns with heatmaps
Best For:
- Teams working with satellite, drone, or LiDAR imagery
- Projects in agriculture, urban planning, and environmental monitoring
- Users looking for a no-code tool to quickly annotate and train models
- Anyone needing real-time change tracking and visual data representation
Contact and Social Media Information:
- Website: flypix.ai
- Address: Robert-Bosch-Str. 7, 64293 Darmstadt, Germany
- Phone: +49 6151 2776497
- Email: info@flypix.ai
- LinkedIn: www.linkedin.com/company/flypix-ai
2. CVAT
CVAT (Computer Vision Annotation Tool) is an open-source platform designed for annotating images and videos, mainly for computer vision tasks. It’s a popular choice among teams working on machine learning and AI projects that require object detection, segmentation, and tracking. The platform supports various annotation formats and provides a user-friendly interface, making it a go-to tool for those looking to label large datasets efficiently.
CVAT stands out with its focus on flexibility and collaboration. Teams can easily work together on annotation projects, and the platform integrates well with machine learning workflows, allowing users to export annotated data for further processing. Whether you’re working on a research project or need to label data for commercial applications, CVAT provides the necessary tools to handle complex annotation tasks.
Key Features:
- Open-source and customizable
- Supports object detection, segmentation, and tracking
- Real-time collaboration for team-based annotation
- Integration with machine learning pipelines
- Works with various image and video formats
Best For:
- AI and machine learning teams needing accurate data labeling
- Large-scale annotation projects requiring team collaboration
- Companies looking for a free, open-source tool
- Research teams focused on computer vision tasks
Contact and Social Media Information:
- Website: www.cvat.ai
- LinkedIn: www.linkedin.com/company/cvat-ai
- Facebook: www.facebook.com/cvat.corp
3. Label Studio
Label Studio is an open-source tool that provides flexible data labeling for images, text, audio, and video. It’s a great option for machine learning projects where you need to annotate diverse data types, including both structured and unstructured data. Label Studio is designed to be user-friendly while also offering the power of customization, making it a solid choice for teams with unique annotation requirements.
This tool supports a wide range of data types and formats, allowing teams to annotate data for projects involving computer vision, natural language processing, and other AI-related tasks. Its open-source nature means it’s cost-effective and customizable, so users can adapt it to meet their specific project needs without needing a hefty budget.
Key Features:
- Open-source and highly customizable
- Supports image, video, text, and audio annotation
- Real-time collaboration for team-based work
- Seamless integration with machine learning workflows
- Easy-to-use interface for efficient data labeling
Best For:
- Teams needing to label diverse data types for machine learning
- Companies looking for a free, open-source tool with customization options
- Projects involving computer vision, NLP, and AI
- Teams working on data labeling tasks that require real-time collaboration
Contact and Social Media Information:
- Website: labelstud.io
- LinkedIn: www.linkedin.com/company/heartex
- Twitter: x.com/labelstudiohq
4. LabelMe
LabelMe is a simple, open-source tool for annotating images. It’s particularly useful for teams that need to quickly label data for machine learning tasks like object detection and segmentation. LabelMe supports a variety of annotation formats, including bounding boxes and polygons, making it flexible enough for different types of projects.
Though it is a relatively basic tool, LabelMe’s simplicity and accessibility make it an ideal choice for small-scale projects or researchers working on a budget. It’s open-source, which means anyone can use it for free, and it’s easy to get started with, making it a practical solution for teams that don’t need the complexity of larger annotation tools.
Key Features:
- Open-source and free to use
- Supports bounding boxes, polygons, and other annotations
- Simple interface for quick image labeling
- Great for object detection and segmentation tasks
- Customizable for specific annotation needs
Best For:
- Small-scale image annotation projects
- Researchers or teams on a budget needing a quick solution
- Projects involving object detection and segmentation
- Users looking for an open-source, no-cost annotation tool
Contact and Social Media Information:
5. Scalabel
Scalabel is a flexible, open-source image annotation tool that’s designed for large-scale projects. It supports a variety of annotation types like bounding boxes, keypoints, and segmentation, and it’s ideal for machine learning tasks that require precise, high-quality labels. Scalabel is built to scale, so it works well for both small and large datasets.
One of the features that sets Scalabel apart is its collaboration capabilities, which allow teams to work together on annotation tasks in real time. Whether you’re working with a small team or need a tool that can handle enterprise-level datasets, Scalabel provides a reliable solution for efficient data labeling.
Key Features:
- Open-source and scalable for large datasets
- Supports bounding boxes, keypoints, and segmentation
- Real-time collaboration for team-based annotation
- Customizable for specific project requirements
- Integrates with machine learning workflows
Best For:
- Teams working with large, complex datasets
- Projects requiring high-quality image annotation
- Machine learning teams focused on object detection and segmentation
- Collaborative projects that need team-based annotation features
Contact and Social Media Information:
- Website: www.scalabel.ai
6. VGG Image Annotator (VIA)
VGG Image Annotator (VIA) is a simple, open-source tool developed by the Visual Geometry Group at the University of Oxford. It’s designed for labeling images and videos, making it a solid choice for teams working on machine learning and AI tasks like object detection and segmentation. VIA runs directly in the browser, so there’s no need for installation, making it incredibly easy to access and start using right away.
What sets VIA apart is its simplicity and accessibility. It’s not overloaded with unnecessary features, which makes it perfect for smaller projects or teams that just need a straightforward tool for annotation. While it may lack the advanced features of some other tools, it provides the essential functionalities needed for quick, efficient labeling, especially for researchers and developers who need a no-frills tool.
Key Features:
- Open-source and browser-based
- Supports object detection and segmentation tasks
- Simple, user-friendly interface
- No installation required
- Ideal for small-scale annotation projects
Best For:
- Small-scale image annotation projects
- Researchers or developers needing a simple, accessible tool
- Teams looking for an open-source, no-cost solution
- Users who prefer a browser-based tool with no installation
Contact and Social Media Information:
- Website: www.robots.ox.ac.uk/~vgg/software/via
- E‑mail: vgg-webmasters@robots.ox.ac.takethisout.uk
- Twitter: x.com/Oxford_VGG
7. MONAI Label
MONAI Label is an open-source image annotation tool built for medical imaging tasks. It’s part of the larger MONAI framework, which focuses on AI in healthcare. This tool helps researchers and healthcare teams annotate medical images for projects like segmentation, classification, and detection. MONAI Label integrates seamlessly with medical imaging software, which makes it especially useful for teams working in healthcare or medical research.
MONAI Label’s strength lies in its focus on healthcare. The platform is built specifically for medical image annotation, allowing professionals to annotate medical scans or radiology images with ease. With its open-source nature, MONAI Label provides a flexible, cost-effective solution for teams needing precise labeling for medical AI models.
Key Features:
- Open-source tool specifically for medical image annotation
- Supports segmentation, classification, and detection tasks
- Integrates with popular medical imaging software
- Focused on healthcare and medical research applications
- Customizable for specific medical use cases
Best For:
- Healthcare and medical research teams working with medical imaging
- Projects requiring precise annotation of medical images
- Teams needing an open-source, customizable annotation tool for healthcareOrganizations integrating annotation into their medical imaging workflows
Contact and Social Media Information:
- Website: monai.io
- LinkedIn: www.linkedin.com/company/projectmonai
- Twitter: x.com/ProjectMONAI
8. Annotely
Annotely is an image annotation software that allows teams to label images for machine learning and AI projects. It offers a simple, intuitive interface for tasks like object detection and image segmentation. Annotely stands out for its straightforwardness, making it easy for users to jump straight into annotating images without dealing with complicated setups or steep learning curves.
This tool is particularly suited for small to medium-scale projects, and it’s a great option for businesses or individuals who need a reliable, no-cost solution for data labeling. Annotely provides all the basic features needed for image annotation, and its flexibility makes it adaptable for different types of AI and machine learning applications.
Key Features:
- Simple and intuitive user interface
- Supports bounding box and polygon annotation
- Ideal for object detection and segmentation tasks
- Customizable for different annotation needs
- Free and accessible for small to medium-scale projects
Best For:
- Small to medium teams needing a quick and easy annotation tool
- Projects involving object detection and segmentation
- Users who want an accessible, no-cost tool for labeling images
- Businesses needing a reliable image annotation software
Contact and Social Media Information:
- Website: annotely.com
- Twitter: x.com/@annotely
Conclusion
Choosing the right free annotation tools is crucial for streamlining data labeling in machine learning and AI projects. These tools simplify the process of preparing your datasets, whether for object detection, image segmentation, or other AI tasks. From basic open-source solutions to more feature-rich platforms, there are options available for every need and project size.
The best tools not only speed up annotation but also ensure the accuracy of the data, which is essential for building reliable AI models. With many tools offering collaborative features and integration capabilities, teams can efficiently work together on large datasets. No matter your project’s scale or complexity, the right annotation tool helps save time and ensures that the data is ready for training your models.