Developing your own AI model and looking for a reliable data annotation tool for successful project completion?
Don’t bounce away, as we have a big list of 10 best data annotation tools that can be suitable for different projects and data types.
I have also highlighted their main features, pros, and cons to help you make an informed decision for your projects.
Let’s navigate the list right here!
Introduction to Data Annotation and Why Does it Matter?
The process of labeling data to train AI models to identify features and provide predictions is known as data annotation. It is the foundation of computer vision models and an essential stage in training AI and machine learning models for better outcomes.
The lack of adequately annotated data can cause your AI model to work poorly with incomplete information, which means it won’t perform as expected.Â
Best Data Annotation Tools
1. Labelbox
Labelbox is a comprehensive data annotation platform designed for machine learning. It supports image, video, and text annotations, making it the best choice for various projects. Its intuitive interface allows users to manage and label data efficiently. The platform also provides automation tools to speed up the annotation process.
| Pros | Cons |
|---|---|
| User-friendly interface | Expensive |
| Scalable for large projects | Occasional glitches with large datasets |
| Integrates well with other machine learning tools | |
| Advanced automation features |
Pricing
Free: 500 LBUs per month
Starter: $0.10 per LBU
2. SuperAnnotate
SuperAnnotate is popular for its powerful annotation and management features, especially for complex projects. It supports a wide variety of annotation formats and offers tools to ensure high-quality annotations. The platform provides collaboration features, making it easier for teams to work together.
| Pros | Cons |
|---|---|
| High accuracy | Steeper learning curve |
| Supports numerous file formats | Initial setup can be time-consuming |
| Excellent customer support | |
| Strong collaboration tools |
Pricing
Free: Basic features
Pro: Contact Sales
Enterprise: Contact Sales
3. V7
V7 is one of the leading annotation tools that focuses on image and video data. It is equipped with AI-assisted labeling to speed up the annotation process. The platform offers a user-friendly interface and advanced collaboration features, making it suitable for teams.
| Pros | Cons |
|---|---|
| AI-assisted labelling | Limited support for non-image/video data |
| User-friendly | Higher cost for advanced features |
| Excellent collaboration features | May require training for new users |
| Supports a wide range of image and video formats. |
Pricing
Basic: Free
Pro: $249/ month
Enterprise: Contact Sales
4. Scale AI
Scale AI provides high-quality data annotation features, particularly for enterprise-level projects. The tool supports various data types and have a robust quality control system in place. The platform ensures that annotations are accurate and consistent.
| Pros | Cons |
|---|---|
| High-quality annotations | Expensive |
| Strong quality control | Good for enterprise use only |
| Scalable | |
| Offers a variety of annotation types |
Pricing
Book a demo for details and pricing
5. CVAT (Computer Vision Annotation Tool)
CVAT is an open-source tool with a range of annotation features for computer vision tasks. It is highly customizable and supports collaborative work, making it the best option for developers and data experts. CVAT could be the best data annotation tool for your next AI or ML model project.
| Pros | Cons |
|---|---|
| Free and open-source | Requires some technical knowledge to set up |
| Customizable | User interface can be a bit clunky |
| Supports collaborative annotations | May lack some advanced features |
| Integration with other tools |
Pricing
Free: 0
Solo: $33/ month
Team: $33/ user/month
6. Roboflow
Roboflow is a great data annotation tool and a popular choice for annotating image data. It offers a seamless workflow from annotation to model training. The platform supports a wide range of image formats and provides tools to improve annotation efficiency.
| Pros | Cons |
|---|---|
| Integrated workflow | Limited to image data |
| User-friendly interface | Some advanced features require a paid subscription |
| Better community support | May not be suitable for non-image annotation projects |
| Supports different image formats |
Pricing
Public: Free
Starter: $249/ month
Enterprise: Contact sales
7. Amazon SageMaker Ground Truth
Amazon SageMaker Ground Truth offers automated data labeling using machine learning, reducing the time and cost of annotation. It can be integrated easily with other AWS services, making it a powerful tool for those already using Amazon’s ecosystem for developing an AI model.
| Pros | Cons |
|---|---|
| Automated labelling | Can be expensive for small projects |
| Â Integrates well with AWS services | Requires AWS expertise |
| Scalable | |
| Supports multiple data types |
Pricing
Free Tier
On-demand pricing
8. Supervisely
The company’s internal tools gave rise to the Supervisely platform, which is intended to speed up machine learning research and development. It has an integrated data management system, an extensive collection of annotation tools, and capabilities for easy team collaboration.
| Pros | Cons |
|---|---|
| Highly customizable | Does not support non-visual data |
| Supports various annotation tasks | Require some technical knowledge for customization |
Pricing
Community: Free
Pro: 199 €/ month
Enterprise: Contact sales
9. Kili
Annotating text, image, and video data is supported by the Kili platform for labeling datasets utilized in generative AI, CV models, and LLMs. With its strong API and user-friendly interface, users can quickly annotate training data, identify and resolve problems with datasets, and expedite labeling processes.
| Pros | Cons |
|---|---|
| Supports multiple data formats | Limited features compared to other tools |
| Scalable | Not suitable for large-scale projects |
| Special reports generation |
Pricing
Request a demo for details
10. Segments.ai
With its focus on multi-sensor data tagging, the SegmentsAI platform provides accurate annotation tools and AI-assisted features to boost the labeling process. The platform’s design prioritizes user experience while offering capabilities for project management and collaboration.
| Pros | Cons |
|---|---|
| Supports multiple languages | Lacks some advanced features |
| Easy to use | Not suitable for different data types |
| Versatile for various annotation tasks. |
Pricing
Team: $9600/ year
Scale: Contact sales
Enterprise: Contact sales
Final Words
Modern data annotation tools come with more advanced features catering to various needs and project scales. Choosing the right data annotation tool depends on your specific needs, budget, and the nature of data you are working on. After reading the blog post, you now have a detailed overview of the top 10 data annotation tools, making it easier to select the one that best suits your needs.
