Top 10 image annotation platforms for AI / Computer Vision projects [2025]
💡 In this article, discover the 10 best image annotation platforms for your AI projects
Introduction: Understanding the importance of image annotation for Computer Vision projects
As we allknow, AI developments rely on significant amounts of data. The foundations of the most innovative products, such as ChatGPT or the AIs embedded in advanced autonomous vehicles, rely on massive data sets to achieve exceptional (although still imperfect) levels of performance. These advances would not be possible without access to quality and large-scale data, which highlights the importance of advanced image annotation platforms, whose recent advances make it possible to industrialize AI development practices.
In this article, we dive into the landscape of image annotation platforms to present you with the best options to meet your specific needs. Whether you are working on ”Computer Vision“ models, such as object detection models or other tasks related to Machine Learning / Deep Learning, the principles of data augmentation, image segmentation or annotation with “Bounding Boxes” are probably not unfamiliar to you.
As an independent player and user of this type of platform, we have listed our top 10 AI annotation platforms on the market to help you make informed decisions and produce high-quality data for your models. Below are the platforms we are going to cover in this article:
#1 - Encord: ideal for the most complex data and cases
One of the newest and most powerful tools on the AI data processing market. Encord is a comprehensive platform designed to unleash the potential of data-driven AI. It enables the development, testing and deployment of large-scale predictive and generative AI systems. Encord offers tools for creating high-quality training data, using active learning pipelines, assessing model quality and much more. The platform is particularly effective for annotating various visual modalities, managing large-scale annotation teams with customizable workflows and integrated quality control tools.
What you'll love about Encord:
- Task management and automation: Encord simplifies and optimizes complex workflows, increasing efficiency and productivity. However, we regret the absence of dashboards to manage the activities (working time, number of labels completed, etc.) of Data Labeler team.
- Well documented APIs: Integrating Encord into existing pipelines is easy, and customer support is responsive and efficient
- Advanced annotation features and stability: Encord stands out for its remarkable speed and stability when processing videos, as well as for its support for DICOM files, useful for radiological studies. Encord is especially appreciated in the medical community. Be careful about hosting though, as some locations of the underlying infrastructure can lead to connection issues if your Data Labeling team is offshore.
- Workflow and team management: In its latest updates, Encord has features to manage teams of Data Labelers working on the same dataset. It is also possible to extract data on the time spent on certain tasks or the number of labels completed.
- Collaboration and quality control: Encord makes it easy for engineers and annotators to collaborate, eliminating the need for additional communication platforms.
What you might not like:
- Performance issues (relatively minor): Users have reported occasional issues with latency and slowness, sometimes related to the location of annotator teams. These issues are offset by highly responsive support.
- User interface sometimes confusing: Some users feel that the interface could be improved.
- Initial complexity: There is an initial learning curve, in part due to gaps in documentation (but it's getting better!).
#2 - V7 Labs, a comprehensive annotation tool, one of the market leaders
V7 (Darwin) is an AI training data platform that allows annotating images, videos, documents, and medical imaging files. It is designed to quickly provide high-quality annotated data for training Computer Vision models. V7 offers features for self-labeling, MLOps workflow management, and the ability to use the services of professional annotators. Recently, we have seen V7 active in the AI agents space more than in the Data Labeling space... Are they pivoting? Truth is... V7 Darwin remains an excellent choice for labeling at scale.
What you'll love about V7:
- Automatic annotation features: V7 integrates tools like SAM (Segment Anything) for auto-annotation, making it easy to annotate complex data and pre-labelling for “human-in-the-loop” strategies.
- Intuitive user interface: The interface is organized intuitively and the features are easy to use, even for the most complex tasks.
- Workflow and team management: The platform makes it easy to manage teams and work processes / workflows. Note that the Data Labelers interface for monitoring working time and productivity is among the most complete on the market, thought reliabily is sometimes an issue.
- Options for exporting data: V7 offers several options for exporting annotations, including integration with AWS S3 to automatically break down videos into images.
What you might not like:
- Lack of flexibility to handle files: Unlike services like Google Drive, V7 does not allow to manipulate files/folders on its platform (no Drag & Drop for example).
- Filtering issues: It can be difficult to filter or sort documents across large datasets.
- Protection of your data and intellectual property: some contractual clauses, especially in the trial version, are quite vague as to the ability of V7 to use and own your data as soon as you deposit it in the platform.
- Occasional slowness: The platform can sometimes be slow when working with large data ets.
#3 - Labelbox: expert in collaborative annotation. A versatile solution for large-scale AI projects
Labelbox is an annotation platform appreciated for its ease of use, efficiency and versatility. It stands out for its intuitive interface and fast learning curve.
What you'll love about Labelbox:
- Monitoring of working hours: Users appreciate the ease of tracking work time and the total number of labels by Data Labeler or team.
- Simple interface: Labelbox has a straightforward and easy to understand interface, both for managers of labeling projects and for annotators.
- A “user-friendly” tool: The platform is considered to be very user-friendly and easy to use for beginners.
- Ergonomic shortcuts: Users like the annotation tool's shortcuts, which are ergonomic and well-designed for practitioners.
- Python API support: The platform offers easy integration into pipelines via its Python API.
- Efficient workflows: Labelbox facilitates the work of filtering labels or tags, thus improving efficiency on projects.
What you might not like:
- Lack of personalization: Some users regret the lack of customization options, such as changing the background.
- Multi-channel image management: Labelbox has difficulties with multi-channel images, which are common in scientific imaging but rare in Computer Vision.
- Performance issues: Bugs and sluggishness have been reported, especially during updates or with high-resolution images.
- Limits of the segmentation tool: Precise segmentation can be difficult, especially for small portions of images.
#4 - Scale Rapid: fast, flexible, but not for all budgets
Scale Rapid is a platform recognized for its speed, ease of implementation and efficiency in processing large datasets. It is distinguished by its ability to provide high quality training data, and its ability to deliver data (with an embedded crowdsourcing service) in a very short time.
UPDATE (2024-2025): To our knowledge, Scale Rapid is no longer maintained by Scale AI.
What you'll love about Scale Rapid:
- Easy to set up and use: Users love the ease with which they can set up Scale Rapid in their environment and use it to produce image labels in a matter of hours.
- Numerous data formats supported: The platform supports various data formats like CSV files, text files, images, and videos.
- Fast labelling: Scale Rapid is known for its speed in processing data, offering real-time feedback on labeling instructions.
- Flexible payment, “on demand” model: The possibility of paying as you go is particularly appreciated, especially in environments with budgetary constraints.
What you might not like:
- User interface: Many users feel that the user interface could be improved to be more user-friendly and interactive.
- Complexity of the pricing model: Scale Rapid's pricing structure is considered complex and sometimes expensive, which can be a barrier for some users.
- Limitations in some use cases: While the platform is effective for many projects, it does not always provide the most accurate or effective results for all types of projects.
- Lack of precision: In some cases, annotated data is not as accurate as expected, requiring additional improvements and review cycles (for a fee).
#5 - CVAT.ai: a pioneer, known and recognized by Data Scientists... moving upmarket
CVAT (Computer Vision Annotation Tool) is an annotation tool designed for professional teams, focused on labeling data for Computer Vision algorithms. It is open-source, developed under the MIT license and can be installed on-premise or in the Cloud. More recently, a “CVAT.ai” web offer was created, with a very simple pricing model, which eliminates long installation or maintenance processes.
What you'll love about CVAT.ai:
- Open-Source and professional: CVAT is a professional open-source code, allowing easy adaptation and on-premise or Cloud use. If you want to keep data in your work environment, nothing could be easier: just install CVAT on one of your servers.
- A web-based version: The tool is now available online, making it easy to access and use without downloading and maintaining. Its pricing model is very simple (transparent price per user).
- Versatility: CVAT.ai is adaptable and adjustable for a variety of annotation scenarios, making it useful in various contexts (sports, retail, automotive, etc.).
- Support for a variety of data extraction formats: The main strength of CVAT, which supports more download formats than most of its competitors, offering quite unprecedented flexibility.
What you might not like:
- Performance issues: On the web version, if the CVAT.ai server goes down, there may be data loss, as the annotators cannot save their work. For larger volumes of data, it is recommended to set up an additional backup process. If you are working with a team of more than 3 Data Labelers, managing competing accesses can also be problematic.
- Limited data import features: The data import functionalities are very unergonomic, and performance problems have been noted by many users.
- Lack of features: Although the pricing model is simple, it lacks features to track hours worked, the number of image tags made, etc.
- Outdated interface : The user interface is considered to be a bit dated and could benefit from an update to be in line with other labeling platforms on the market. Saving annotations manually (clicking on a floppy disk), feels like we're back in the 90s!
#6 - Label Studio: Open Source Doesn't Mean Incomplete
Label Studio is a versatile tool for data annotation, often described as a Swiss Army knife in this field. It is a professional tool for machine learning, which is essential for building successful AI-based products.
What you'll love about Label Studio:
- Open-Source and professional: Label Studio is an open-source code developed under the MIT license, guaranteeing high accessibility and adaptability. The tool is powerful and can be installed on your own environments without difficulty, including on your Cloud (AWS, Google Cloud, etc.). The tool is updated regularly and covers a wide range of use cases. It is accessible via a web interface. It's regularly updated and past 2 years have seen a series of great additions to the UI (such as better audio annotation templates, Dark mode, etc.).
- Customized labelling interfaces: Label Studio is flexible and can be configured quickly, with a large number of templates and a configurable interface with XML code. Label Studio can be used for various types of data, audio, video, and text.
- Machine learning integration: The tool is designed to be easily integrated with a multitude of frameworks on the market.
- Accessibility: Even if there is a paid “Pro” offer, the vast majority of Label Studio's features are free. So you can install Label Studio on your servers and start working, without worrying about license fees, number of users, etc. For those who don't know, it's also quite convenient (and cheap) to install Label Studio on one of your spaces in Hugging Face!
What you might not like:
- Maintainability: Except if you're a Pro user, you need to install Label Studio on your environments. Practical from a data protection point of view, however, this implies that you take into account the costs associated with the use and maintenance of your servers (on-premise or Cloud).
- Limited collaboration features: Label Studio has user access management functionalities in its "Pro" version, not in "Community". It's very limited, to the point you can't delete an account. Additionally, the platform could benefit from an improvement in the monitoring of working hours and the number of labels produced by the teams. In addition, collaborative work sometimes poses performance problems: with poorly managed concurrent accesses, your annotators can hardly work on common datasets. The solution is to duplicate your initial dataset into a multitude of sub-datasets, which involves significant curation work prior to the labelling work.
- Data import and export: While Label Studio has made progress in this area, over the last two years, data import and export functionalities are still quite unstable and limited, and can sometimes generate errors. Note that there are well-documented APIs to integrate Label Studio into your ML setup, however users have reported performance issues and instability related to the use of these APIs. To be noted: recent updates have improved integrations with Cloud infrastructure (AWS, GCP and others).
#7 - Kili: a platform full of promises
Kili Technology is a comprehensive annotation tool designed to quickly label training data, identify and correct problems in datasets, and simplify annotation operations. This software dramatically accelerates the construction of reliable AIs and offers customizable interfaces for various types of data, including images, videos, texts, PDF documents, satellite imagery (quite good in this space, while you need to hire expert annotators on the side), and conversations.
What you'll love about Kili:
- Stability: Kili offers essential stability for working with large volumes of data and tight deadlines. It is easily adaptable to a variety of use cases including Computer Vision and NLP
- Collaboration features: The platform allows effective collaboration between team members and offers detailed analytical monitoring, which is necessary when working with a large number of collaborators.
- Workflow: Kili makes it possible to control the labeling queue and to set up review processes to ensure the consistency and quality of the annotations.
- Automatic annotation features: The platform offers automation tools such as interactive segmentation and tracking to speed up labeling without compromising quality.
What you might not like:
- Limited features for video annotation: Although improvements are planned, the current features for video nnotation are limited. Kili is not suitable for video use cases, especially when it comes to processing medical videos. Many users note instability issues for the video processing. We still believe there are better solutions in the video annotation space.
- Lack of advanced analytics features: Some users would like more advanced analytics capabilities to monitor the productivity of their team.
- API complexity: For those who don't have technical knowledge, using the API can be complex.
- Time to set up the environment: The initial implementation of projects on the platform can take time, especially for new users who are getting used to the interface.
#8 - SuperAnnotate: a complete and user-friendly solution
SuperAnnotate is a data labeling platform recognized for its efficiency and versatility in managing image and video annotation tasks... and more recently used for fine-tuning LLM. It is widely recognized for its ease of use and its effectiveness in accelerating the annotation process.
What you'll love about SuperAnnotate:
- Ease of use: Users find the platform extremely user-friendly, even for beginners, with a concise and intuitive user interface.
- Time-saving features: The platform is equipped with automatic labeling features such as “Magic Select,” dramatically reducing the time and effort required for the most time-consuming tasks.
- Effective data management: SuperAnnotate offers excellent Data Management services, facilitating the simultaneous management of different projects.
- Great customer support: The platform is known for its excellent, responsive, and useful customer service, which is especially beneficial for small businesses or new users to the platform.
- Collaboration and accessibility: SuperAnnotate allows collaborative work without requiring extensive programming knowledge, making it accessible to a wider range of users.
What you might not like:
- Low precision of the auto-segmentation feature: Some users have experienced issues with the accuracy of auto-segmentation, especially with low resolution images.
- Limitations of the selection tool: The inability of the Magic Select function to concatenate different parts of selections into a single object can be a limitation for some specific tasks.
- Financial commitment: The platform prefers upfront financial commitments, which may not be feasible for all users, especially when the number of labels required is uncertain.
- Charging time: Users have noted delays in loading the next frame when they pass a frame, which can slow down the workflow.
#9 - Dataloop, a Data Management platform for AI with a data annotation module
Dataloop is a comprehensive platform covering the entire life cycle of AI, from development to production. It includes a data management and annotation platform to facilitate the generation of data for supervised learning, as well as automation pipelines to accelerate Computer Vision projects.
What you'll love about Dataloop:
- Easy to use interface: Dataloop offers an intuitive interface, which makes it easy to use the tool, even for complex research projects such as text-to-image generation.
- Responsive and personalized customer support: The Dataloop support team is responsive and works closely with users to adapt the tool to their specific needs.
- Very intuitive Python SDK: The Python SDK is easy to use, allowing quick integration into projects.
What you might not like:
- Limited functionality for some use cases: For some atypical projects, such as the annotation of images with varied questions and a broad taxonomy, Dataloop may lack specific functionalities, although the support team can often find solutions to remedy them.
- Human-in-the-loop annotation API: The Dataloop API for manual annotation may not meet all needs.
- Complexity of the platform: Dataloop is a complete platform but can be complex to master, with some learning time required to fully use it.
- Notifications that are sometimes late: Delays in notifications about pipeline issues can lead to production delays.
#10 - Playment, a market veteran and a reliable solution
Playment is a veteran in the (young) market for AI solutions. Playment's GT Studio is a no-code data annotation platform designed to help ML teams create diverse, high-quality reference data sets in an efficient manner. GT Studio is a web labeling platform that eliminates inefficiencies for the annotator and project manager through ML-assisted annotation tools and easy-to-use workflow management software.
What you'll love about Playment:
- Flexible and used in many areas: Playment makes it possible to produce training data for multiple sectors, such as e-commerce, banking, document management, etc.
- Ease of use: The application is simple but powerful, ergonomic, with fairly few delays compared to many platforms on the market.
What you might not like:
- High price: Although Playment offers quality services, cost can be considered a barrier, especially due to a pricing model per annotated object.
- Lack of functionality to produce custom reportss: The Playment application reports are not fully customizable and may lack some fields required to effectively manage its labeling activities.
Bonus: Labellerr, the perfect choice for agile and collaborative annotation management
Labellerr is a modern and intuitive platform designed to meet AI data management needs with an approach that focuses on collaboration and agility. This solution makes it possible to develop, test, and deploy AI systems efficiently and quickly. Labellerr offers tools for automated data annotation, team and project management, and quality control. It supports various types of data such as images, videos, text, audio, and PDFs, making it extremely versatile for machine learning teams.
What you'll love about Labellerr:
- Task management and automation: Labellerr allows automated data annotation and facilitates exploratory data analysis (EDA). This greatly increases the efficiency and productivity of teams. The ability to manage multiple projects and users simplifies collaborative work.
- Intuitive user interface: Labellerr's interface is very user-friendly, which makes navigating through its features easy and fast. Users can focus more on their annotation tasks without worrying about the complexity of the tool.
- Precision and efficiency: Labellerr's advanced algorithms guarantee high precision in data annotation. The functionality of Smart Labelling and the integration of AI generation models save valuable time.
- Responsive customer support: Labellerr support team is always available to offer technical assistance and advice, which greatly improves the user experience and the quality of projects.
- Flexibility and diversity of data formats: Labellerr supports various data formats, making it suitable for a multitude of use cases, whether for image, text, or audio annotations.
What you might not like:
- Minor performance issues: Some users have reported latency issues when handling large amounts of data. However, the auto-save feature helps to minimize the impact of these slowdowns.
- Customizing shortcuts: Customizing keyboard shortcuts could improve annotation efficiency, although this feature is not yet available.
- Limited support for 3D models: Although Labellerr excels at annotating 2D data, support for 3D models is still under development, which could limit its use in some specific areas.
- Complexity of integration: Although Labellerr offers integration capabilities with frameworks popular in Machine Learning, this may require additional development efforts to integrate the tool into existing workflows.
Conclusion: choosing a tool adapted to your use case has a direct impact on your AI pipeline
Here we are: thanks for reading our list of the top 10 image annotation tools. Whether you're labeling small datasets or building complex AI models, we're confident that the right tool for your specific use case will have a direct impact on both the annotation process and your results.
Be sure to consider all of your options carefully to avoid wasting time and money on a tool that doesn't meet all of your needs.
Not sure where to start? Innovatiana is an independent player that can help you choose the image annotation platform that best suits your needs. We offer personalized advice to help you select the ideal tool, taking into account the specificities of your projects, your budget and your goals. If you have already made your choice, we can also provide you with our team of specialized and expert Data Labelers, ready to take care of your annotation projects with efficiency and precision. Do not hesitate to contact us!
