Our top 10 video annotation tools for powerful models [2025]
Video annotation plays a fundamental role in the development and training of artificial intelligence models. It makes it possible to transform video sequences into usable data by identifying, Labeling and analysing the key elements in each image. This guide explores the solutions available on the market, including the best annotation tools, whether free or paid, to meet the needs of AI professionals and ambitious projects.
Annotations (or metadata), often carried out on a large scale, are essential for various applications such as object recognition, motion detection or even behavioral analysis. Faced with the rise of these technologies, the choice of a suitable annotation tool has become essential to guarantee the quality and effectiveness of AI models. It's also important to know how to annotate a video to improve the clarity and engagement of visual content.
What is a video annotation tool?
A video annotation tool is a software or application that allows you to add information, comments, or annotations to a video. These tools are designed to improve the understanding and analysis of videos, especially in the fields of education, training and research... and in particular to train artificial intelligence models! The features offered by these tools may vary, but generally include the ability to add subtitles, comments, labels, or visual annotations at specific points in the video.
For example, a teacher might use a video annotation tool to add explanatory captions to an educational video, while a researcher might mark specific segments of a video for further analysis. Annotations can take many forms, such as bounding boxes to identify objects, polygons to define areas of interest, or key points to mark specific positions.
1 - CVAT: the essential open-source tool for video annotation
CVAT (Computer Vision Annotation Tool) is an open-source platform designed to meet data annotation needs in artificial intelligence and computer vision projects. Known for its power and flexibility, CVAT is widely adopted by AI specialists looking to produce accurate and detailed annotations on videos and images.
Effective management of large volumes of data
CVAT excels at managing large annotation projects. Thanks to its advanced tools, it allows you to create complex annotations such as enclosing boxes, polygons, key points, and segmentation masks. The use of progress bars also makes it easier to manage ambitious projects and large volumes of data.
In addition, CVAT supports tracking objects across multiple frames (or ”Object Tracking“), making it an ideal choice for applications that require temporal coherence, such as the recognition of moving objects or behavioral analysis.
A customizable and intuitive interface
The user interface of CVAT is designed to be intuitive while allowing for complete customization. Users can configure tools according to the specific requirements of their projects, whether it's defining object classes or creating workflows optimized annotation. Its compatibility with various file formats, including MP4 and AVI, makes it a versatile and adaptable solution.
A tool designed for collaboration
CVAT facilitates teamwork by allowing tasks to be divided between several annotators. Built-in review capabilities ensure the quality of annotations throughout the process. Administrators can monitor progress and adjust workflows to ensure that projects are progressing effectively.
Open-source and highly flexible
As an open-source product, CVAT offers total transparency and the possibility of customizing the code according to specific needs. This flexibility is particularly appreciated in academic and industrial environments, where projects often require tailor-made solutions.
-hand-innv.png)
2 - LabelBox: a complete platform for collaborative video annotation
LabelBox is a platform dedicated to data annotation, designed to create high-quality data sets for training artificial intelligence models. It is distinguished by its advanced collaborative features, allowing internal and external teams to work together in real time on video annotation projects.
A range of tools for accurate video annotation
The video editor of LabelBox offers a variety of tools to annotate videos accurately. Users can apply bounding boxes, polylines, points, or segmentation masks to identify and label specific objects or concepts.
Video segmentation at the pixel level ensures optimal accuracy in identifying items of interest, which is essential for complex applications.
Automation with the”Model-Assisted Labeling“
To increase efficiency, LabelBox integrates automation tools like the Model-Assisted Labeling, which uses predictive models to pre-label data and therefore generate text content.
This allows annotators to focus on correcting and perfecting annotations, significantly reducing processing time. This feature can increase overall efficiency by up to 80%.
Strengthened collaboration
Collaboration is at the heart of LabelBox. The platform offers customizable workflows adapted to complex projects. Features like problem reporting, sharing, feedback, and notes promote clear communication between teams.
These tools make coordination smooth, even for geographically distributed teams.
Flexibility for various types of data
Labelbox supports various types of data including videos, images, subtitles, PDF documents, geospatial data and audio. This flexibility makes it possible to manage a variety of projects within a single platform, avoiding dispersion between several tools.
Centralized management and access to experts
When it comes to management, LabelBox offers streamlined capabilities with workflows optimized to speed up the creation of datasets. Users can also collaborate with annotators experts for specific projects, thus contributing to the creation and training of ever more efficient AI models.
3 - Dataloop: the all-in-one solution for annotation and data management
Dataloop is a comprehensive platform designed to simplify and accelerate annotation processes while centralizing project management. Developed specifically for teams working on computer vision and artificial intelligence projects, it combines advanced annotation tools, automation features, and optimized management of workflows.
Extensive data format support
Dataloop is compatible with a wide range of formats, including videos, images, documents, and 3D files. For video annotation, the platform offers powerful tools such as enclosing boxes, semantic segmentation, polylines, and object tracking through frames. These features make it possible to process complex sequences while maintaining maximum consistency and precision.
Automation to save time and improve accuracy
One of Dataloop's strengths is its automation capabilities. By integrating machine learning models directly into the workflow, the platform allows for effective data pre-labeling. This automation significantly reduces the time required for manual annotation, while giving users the ability to refine and correct results to ensure optimal quality.
Project management and real-time monitoring
Dataloop includes robust project management tools. Administrators can structure the workflows, assign tasks, and monitor progress in real time via detailed dashboards. These tools also make it possible to assess the quality of annotations and monitor the performance of annotators, facilitating strategic adjustments to improve project efficiency.
Collaboration strengthened by the cloud
Thanks to its cloud infrastructure, Dataloop facilitates collaboration between teams, even when they are geographically dispersed. Data and annotations are accessible in real time, allowing teams to work simultaneously on the same project. Adjustments and revisions are simplified, reducing time frames and improving coordination.
4 - V7: the AI-assisted video annotation platform
V7 (or V7 Labs, or otherwise known by the name of its product, “Darwin”) is a cutting-edge platform dedicated to data annotation for training artificial intelligence models. It offers powerful tools for processing images and videos, while integrating advanced functionalities for automating and managing collaborative projects.
Precise and effective video annotation
V7 takes care of a wide range of annotation tasks, including object detection, semantic segmentation and pose estimate. These tools allow video sequences to be annotated quickly and accurately, guaranteeing the consistency of the annotations across all frames. Thanks to its object tracking system, V7 greatly simplifies complex projects that require time annotation.
Automation through artificial intelligence
One of the strengths of V7 is its integration of machine learning models into the annotation process. These models allow data to be pre-labeled, reducing the time spent on manual annotation. Users can adjust and refine AI-generated results to ensure high-quality annotations, while optimizing production timelines.
An intuitive and customizable interface
V7 offers a modern and intuitive user interface, facilitating navigation and collaborative work. Annotators can customize tools to suit their specific needs, whether it's creating object classes or adjusting task settings. This flexibility makes V7 suitable for a variety of sectors, including healthcare, robotics, and automotive.
Real-time collaboration and centralized management
With its collaborative functionalities, V7 allows several annotators to work simultaneously on the same project. The platform includes options for tracking progress, assigning tasks, and ensuring quality control in real time. These tools are ideal for dispersed teams or projects that require rigorous management of workflows.
5 - SuperAnnotate: a collaborative solution for advanced video annotation
SuperAnnotate is a comprehensive platform dedicated to the annotation of visual data, offering advanced tools for the annotation of images and videos. Designed to facilitate collaboration and improve the efficiency of artificial intelligence projects, it is aimed at teams looking to produce high-quality data sets for model training.
Advanced video annotation tools
SuperAnnotate supports a variety of video annotation tasks including:
· Object tracking and location : Use of surrounding boxes to detect and track the movement and position of objects through consecutive frames.
· Action detection : Recognition and detection of specific actions or activities, even those that cannot be defined by a simple encompassing box, such as rain or traffic.
· Installation estimate : Determination of the position and orientation of joints and body parts.
· Segmentation and instance tracking : Identification and monitoring of individual instances within a video sequence.
· Lane detection : Identification and tracking of lanes on a road in videos.
These features allow for accurate and detailed annotation, which is essential for the development of efficient AI models.
Automating and accelerating the annotation process
To optimize time and resources, SuperAnnotate integrates automation tools such as:
· Autotrack : Partial automation of the video annotation process by predicting the movement of one or more moving objects.
· Interpolation : Predicting the movement of an instance between two frames, thus speeding up the video annotation process.
These tools significantly reduce the time needed to annotate long video sequences, while maintaining high precision.
Real-time collaboration and quality management
SuperAnnotate makes it easy for team members to collaborate with features like:
· Feedback system : Enables stakeholders to participate in the process of reviewing the quality of annotations, making collaboration and task distribution smoother than ever. Additionally, collaboration with partners is critical to improving workflow and productivity.
· Status of items and projects : Overview of the status of all items and projects to monitor their progress.
· Detailed instructions : Providing clear guidelines to the team to ensure the successful execution of the project.
These tools guarantee effective communication and rigorous management of the quality of annotations.
Integration and flexibility
The platform is compatible with a variety of video data formats, making it adaptable to a multitude of use cases, such as autonomous driving, robotics, surveillance, and more. Additionally, SuperAnnotate offers integrations with machine learning models, allowing for increased automation and continuous improvement of annotation processes.
6 - Amazon SageMaker Ground Truth: video annotation powered by AWS
Amazon SageMaker Ground Truth is a service managed by AWS that makes it easy to create annotated data sets for training artificial intelligence models. It offers specific features for video annotation, thus optimizing the visual data preparation process.
Types of video annotation tasks
Ground Truth offers three types of built-in tasks for video annotation:
· Classification of video clips : Allows annotators to categorize entire videos into predefined categories, such as sports, comedy, or education.
· Detecting objects in video frames : Annotators identify and locate specific objects in images extracted from videos using bounding boxes, polylines, or polygons.
· Tracking objects in video frames : This task consists in following the movement of objects across several frames, ensuring the temporal coherence of the annotations.
Automation and efficiency
Ground Truth includes automation features that reduce the time and costs associated with manual annotation. By using machine learning models, the service can pre-label data, allowing human annotators to focus on validating and adjusting annotations, improving the overall efficiency of the process.
User interface and collaboration
The platform offers an intuitive user interface for annotators, with tools adapted to each type of task. However, unlike Google Docs, which has limitations when entering suggestions, our tool allows for smooth integration with advanced annotation features. Instructions for workers can be customized for each project, ensuring a clear understanding of the requirements. Additionally, Ground Truth allows collaboration between multiple annotators and centralized project management, facilitating progress monitoring and quality control.
Integrating with the AWS ecosystem
As an AWS service, Ground Truth integrates seamlessly with other AWS services, such as Amazon S3 for data storage and Amazon SageMaker for model deployment. This integration simplifies the workflow, from preparing data to training and deploying AI models.
7 - Encord: a tool specialized in video annotation and monitoring
Encord is a platform dedicated to video annotation and monitoring, designed to meet the needs of artificial intelligence projects requiring accurate and reliable data sets. It is distinguished by its advanced tools that simplify and speed up the annotation process, while ensuring high quality results.
AI-assisted annotation
Encord integrates AI-assisted annotation software, making it possible to tag videos of various formats (MP4, AVI, MOV, WebM) and of various lengths. This feature provides valuable flexibility for teams working on complex projects and makes it easy to integrate the platform into various workflows. AI pre-tagging dramatically reduces manual work, while improving accuracy and efficiency.
Advanced tools for follow-up and continuity
One of the strengths of Encord is its ability to manage the tracking and interpolation of objects between frames. This feature ensures consistent annotations throughout the video sequence, a key requirement for applications such as object detection or semantic segmentation. With these tools, users can annotate complex videos quickly and accurately.
Versatile subtitle support
Encord offers a variety of annotation types, including classification, object detection, segmentation, and even pose estimation. This diversity makes it possible to create rich data sets, adapted to a wide range of computer vision applications, such as image recognition or behavioral analysis.
Effective management of workflows
The platform offers customizable workflows that facilitate the management of annotation projects. Users can allocate tasks, track annotator performance, and set up review and quality control pipelines.
8 - Scale AI: high-quality video annotations for your AI models
Scale AI is a company specialized in high-quality annotated data for training artificial intelligence models. It offers a range of services adapted to the needs of companies developing AI-based applications, particularly in the fields of computer vision, autonomous driving and augmented reality.
Video annotation services
Scale AI offers advanced video annotation services, allowing for the accurate labeling of moving objects in video sequences. Thanks to a combination of artificial intelligence and human intervention, the company ensures fast and accurate annotation, which is essential for the effective training of AI models.
Pipeline optimized annotation
To manage the annotation of long videos, Scale AI uses a process called “video stitching.” This method involves breaking up long video clips into several smaller subtasks that can be processed in parallel. The results are then assembled to create a coherent annotation of the entire video, making annotating videos with more than 30 frames more operational.
Quality and precision
Scale AI's platform combines machine learning algorithms with improved statistical controls to ensure the quality of annotations. This hybrid approach makes it possible to process large amounts of data while maintaining a high level of precision, which is important for the development of reliable AI models.
Varied applications
Scale AI services are used by leading businesses in a variety of industries, such as autonomous vehicles, mapping, augmented reality/virtual reality, and robotics. By providing high-quality annotated data, Scale AI is helping these businesses accelerate the development of their AI-based applications.
9 - VGG Image Annotator (VIA): a lightweight open-source video annotation tool
The VGG Image Annotator (VIA) is an open-source annotation solution, developed by Visual Geometry Group from the University of Oxford. This software is characterized by its lightness, accessibility and ease of use, making it a preferred tool for researchers and developers working on projects that require quick and effective annotation.
No installation required for easy use
One of the main features of VIA is that it does not require any complex installation or configuration. It works directly in a web browser, eliminating compatibility issues with various operating systems.
In addition, annotations and data are saved locally, ensuring confidentiality and total control of the files. This cloud-free approach is particularly suited to environments where data security is paramount.
Varied features for annotating images and videos
In terms of features, VIA allows you to annotate both images and videos. Users can create bounding boxes, polygons, key points, or segments to precisely mark objects or areas of interest.
The tool also offers options for adding metadata to annotations, which is useful for enriching datasets with additional information.
Flexibility and customization
VIA is also known for its flexibility. Thanks to its minimalist interface, it can be customized to adapt to the specific needs of users. For example, developers can modify the source code to incorporate additional features or adjust the tool to specific data formats.
A powerful but limited tool for advanced projects
Although its simplicity is a major advantage, VIA may not be suitable for very complex projects that require advanced functionalities, such as tracking objects across multiple frames or integrating with collaborative workflows in real time. However, for basic to intermediate annotation needs, it is still a powerful, free, and easy to use tool.
10 - Supervise.ly: advanced features for accurate video annotation
Supervise.ly is an innovative platform specially designed for projects that require accurate video annotations. With a full range of advanced features, it is an essential tool for developers and teams working on complex artificial intelligence models.
An intelligent auto-tracking system
Supervise.ly stands out thanks to its auto-tracking system powered by predictive models. This technology makes it possible to automatically identify and track objects in a video, whether using surrounding boxes, skeletons, or segmentation masks.
Auto-tracking adjusts the annotations frame by frame and detects when an object leaves the scene, reducing the time needed to annotate long sequences. This feature can increase productivity by up to 500% depending on the publisher.
An intuitive interface for effective management
The interface of Supervise.ly is designed to simplify workflows. The definitions panel allows a quick search and an instant selection of object classes to annotate, avoiding constantly navigating between tools. This ergonomics considerably improves the user experience, making complex projects more fluid to manage.
A system of Tagging pliable
Supervise.ly offers a simplified tagging method, allowing users to add global or specific tags to each image or video. This system is particularly useful for enriching annotations with detailed metadata, thus facilitating database analysis and use.
A combination of automation and human control
One of the great strengths of Supervise.ly is its ability to balance automation and human control. Repetitive tasks can be taken care of by artificial intelligence, while users maintain the ability to manually refine annotations to achieve optimal accuracy.
How does outsourcing video annotation work?
Outsourcing video annotation involves outsourcing the task of annotating videos to external service providers. This approach is particularly useful for businesses or organizations that need to process a large volume of videos but lack the internal resources or skills to do so effectively.
Video annotation service providers use specialized video annotation tools to add annotations and comments to videos. These annotations can include subtitles, labels, bounding boxes, polygons, and other forms of visual annotations. Once the work is complete, the annotated videos are returned to the client company or organization.
Outsourcing has several advantages, including the possibility of managing large volumes of data in a short period of time and benefiting from the expertise of professionals who specialize in video annotation. However, it can also pose challenges, such as the need to ensure the quality and consistency of annotations, as well as the management of data privacy and security.
The challenges of using video annotation tools
Using video annotation tools can present several challenges, including:
- The quality of the annotations : Ensuring that annotations are accurate and consistent is crucial to avoid mistakes or misunderstandings. Poor quality annotation can lead to incorrect results when training artificial intelligence models, compromising the effectiveness of projects.
- Data management : Video annotation tools can generate a large volume of data, requiring an effective data management system to store, organize, and analyze this information. Inadequate data management can cause data loss or difficulty retrieving specific annotations.
- The safety : Annotated videos can contain sensitive information, so taking security measures to protect that information is critical. This includes using robust security protocols for storing and transferring data, as well as implementing strict access controls to limit access to sensitive data.
To overcome these challenges, it is recommended to adopt best practices such as training annotators, using advanced data management systems, and implementing robust security measures.
How do you choose the best video annotation tool for your needs?
To choose the best video annotation tool for your needs, it's important to consider the following factors:
- The functionalities : What types of annotations and comments do you want to add to your videos? Make sure the tool has the features you need to meet your specific needs, whether it's subtitles, bounding boxes, polygons, or other forms of annotations.
- The quality of the annotations : What are the quality standards you want to achieve for your annotations? Look for tools that offer quality control features, such as peer review or automatic verification algorithms.
- Data management : How are you going to manage and analyze the data generated by video annotation tools? Choose tools that offer robust data management solutions, including options for secure storage, search, and annotation retrieval.
- The safety : What are the security risks associated with using video annotation tools and how will you mitigate them? Choose tools that focus on data security, with encryption protocols and strict access controls.
💡 By considering these factors, you will be able to choose the video annotation tool that best suits your needs and goals, which should in turn ensure the success of your video annotation projects.
Conclusion
Video annotation is a fundamental step in creating powerful artificial intelligence models, and choosing the right tool can greatly influence the quality and effectiveness of your projects. Whether you are looking for an open-source solution like CVAT or a complete and collaborative platform like Labelbox, each option presented in this top 10 meets specific needs, whether related to precision, automation or the management of workflows.
These tools offer a variety of features, from simple annotations to advanced solutions that incorporate machine learning models to speed up the process. They are aimed at a wide audience, from researchers to professionals to businesses looking to optimize their data for complex AI applications.
Choosing the ideal solution will depend on your specific requirements: volume of data, type of project, level of collaboration or even available budget. By exploring these options, you can find the tool that integrates seamlessly into your workflows and allows you to maximize your results.
With constantly evolving technologies, these platforms continue to have a new regular update, making video annotation more accessible, fast, and accurate. By adopting one of these tools, you are giving yourself the means to take your artificial intelligence projects to the next level.
