Video Annotation Tools
Video annotation platforms provide tools for labeling or tagging video clips for machine learning and computer vision tasks. +Show More
Products | Position | Customer satisfaction | |||
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Leader
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Satisfactory
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V7 is a powerful AI training data platform that allows you to annotate images, videos, and volumetric series. It is the fastest way to get high-quality annotated data for your computer vision models. You can use auto-labeling features, train models in the cloud, or even hire professional annotators to help you out. - Pixel-perfect automatic data labeling - Intuitive dataset management - A one-click model training With advanced auto-labeling and team collaboration tools, V7 will reduce your data annotation time by 90% and help you manage your workflows. Annotate any dataset with ease and seamlessly integrate V7 into your training data management and model development pipeline. - Create automatic pixel-level labels for instance segmentation - Add automatic label interpolations between video frames - Detect and track objects in videos with instance IDs - Use auto-segmentation for medical imaging and volumetric data - Annotate medical file formats such as DICOM and NIfTI - Run your data through public models or train your own AI from scratch - Scan text, classify files, and extract relevant information with AI - Add key points and skeletons for human and animal pose estimation - Assign user roles, access permissions, and annotation tasks V7 is an intuitive annotation and machine learning platform that makes AI easy. Use bounding boxes, polygons, key points or auto-segmentation tools powered by ML models. Supervise and manage your data labeling projects with an easy-to-use visual interface. You can use the platform for designing custom data annotation workflows with review stages and model-assisted labeling. Import and export annotations in JSON, COCO, CVAT, Pascal Voc, PNG semantic masks, and other file formats. Get full control over your models, tasks, and datasets via the open API and Python CLI. Try out the ultimate data labeling tool for computer vision with our free plan: https://v7labs.com/get-started
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
50-100 employees
Company's social media followers
20k-30k followers
Total funding
$10-50m
# of funding rounds
3
Latest funding date
November 28, 2022
Last funding amount
$10-50m
Company
Type of company
private
Founding year
2018
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Leader
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Satisfactory
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Labeling Platform for High-Quality Training Data: One tool to label, find and fix issues, simplify DataOps, and dramatically accelerate the build of reliable AI.
At Kili Technology, we believe the foundation of better AI is excellent data.
Kili Technology's complete training data platform empowers all businesses to transform unstructured data into high quality data to train their AI and deliver successful AI projects. By using Kili Technology to build training datasets, teams will improve their productivity, accelerate go-to-production cycles of their AI projects and deliver quality AI.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
50-100 employees
Company's social media followers
5k-10k followers
Total funding
$10-50m
# of funding rounds
2
Latest funding date
July 27, 2021
Last funding amount
$10-50m
Company
Type of company
private
Founding year
2018
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Leader
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Satisfactory
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Labelbox’s training data platform is engineered to help you improve your training data iteration loop. It is designed around three core pillars: the ability to Annotate data, Diagnose model performance, and Prioritize based on your results. With Labelbox, you can: - Decrease annotation costs by 50-80% by leveraging the latest in labeling automation, model-error analysis and active learning - Iterate 3x faster on your AI data to build more performant models - Collaborate more efficiently between data scientists, labelers and domain experts
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Number of case studies
20-30 case studies
Company's number of employees
100-200 employees
Company's social media followers
20k-30k followers
Total funding
$100-250m
# of funding rounds
6
Latest funding date
January 6, 2022
Last funding amount
$100-250m
Company
Type of company
private
Founding year
2018
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Leader
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Satisfactory
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Dataloop’s end-to-end platform covers the entire AI lifecycle, from development to production. Their data-centric technology stack includes a data management and annotation platform to streamline the process of generating data for deep learning, while our automation pipelines accelerate computer vision projects to production, reducing costs and saving extensive engineering efforts on complex tools. Our vision is to make machine learning-based systems accessible, affordable and scalable for all.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
50-100 employees
Company's social media followers
5k-10k followers
Total funding
$10-50m
# of funding rounds
5
Latest funding date
November 3, 2022
Last funding amount
$10-50m
Company
Type of company
private
Founding year
2017
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Leader
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Satisfactory
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Cogito shoulders AI enterprises and their business initiatives by deploying a proficient workforce for a wide variety of Training Data Services such as Data Annotation, Labeling, Data Refinement & Enrichment. With 10+ years of experience & 500+ projects of capturing and enriching a wide variety of data types including speech, text, image and video, we have continuously been a reliable partner for leading Fortune500 Companies, AI Start-Ups, Government, Academia & Research Institutions.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
400-1k employees
Company's social media followers
5k-10k followers
Company
Type of company
private
Founding year
2011
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Challenger
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Satisfactory
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Playment offers a fully-managed data labeling solution to build highly accurate training datasets for computer vision models
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Number of case studies
5-10 case studies
Company's number of employees
3k-4k employees
Company's social media followers
100k-1m followers
Total funding
$1-5m
# of funding rounds
4
Latest funding date
November 21, 2017
Last funding amount
$1-5m
Company
Type of company
private
Founding year
2015
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Challenger
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Satisfactory
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Innotescus’ collaborative SaaS platform streamlines the Computer Vision development process via smart data handling, labeling, and annotation capabilities. Additionally, its data visualization and cross-functional collaboration features identify data bias early, improve data accuracy, and enable faster, cost-efficient deployment of high-performance AI.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
1-5 employees
Company's social media followers
400-1k followers
Company
Type of company
private
Founding year
2018
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Challenger
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Satisfactory
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Supervisely Enterprise is fully self-hosted and cloud frendly: install it on your servers or in the cloud, keep everything private. We provide API, SDK and backend source codes. So it is highly customizable and can be integrated into any technology stack.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
10-20 employees
Company's social media followers
400-1k followers
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Challenger
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N/A
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Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfactionMarket presence
Company's number of employees
4k-5k employees
Company's social media followers
100k-1m followers
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“-”: AIMultiple team has not yet verified that vendor provides the specified feature. AIMultiple team focuses on feature verification for top 10 vendors.
Sources
AIMultiple uses these data sources for ranking solutions and awarding badges in video annotation tools:
Video Annotation Leaders
According to the weighted combination of 4 metrics





What are video annotation
customer satisfaction leaders?
Taking into account the latest metrics outlined below, these are the current video annotation customer satisfaction leaders:





Which video annotation solution provides the most customer satisfaction?
AIMultiple uses product and service reviews from multiple review platforms in determining customer satisfaction.
While deciding a product's level of customer satisfaction, AIMultiple takes into account its number of reviews, how reviewers rate it and the recency of reviews.
- Number of reviews is important because it is easier to get a small number of high ratings than a high number of them.
- Recency is important as products are always evolving.
- Reviews older than 5 years are not taken into consideration
- older than 12 months have reduced impact in average ratings in line with their date of publishing.
What are video annotation
market leaders?
Taking into account the latest metrics outlined below, these are the current video annotation market leaders:





Which one has collected the most reviews?
AIMultiple uses multiple datapoints in identifying market leaders:
- Product line revenue (when available)
- Number of reviews
- Number of case studies
- Number and experience of employees
- Social media presence and engagement
What are the most mature video annotation tools?
Which one has the most employees?





Which video annotation companies have the most employees?
92 employees work for a typical company in this solution category which is 69 more than the number of employees for a typical company in the average solution category.
In most cases, companies need at least 10 employees to serve other businesses with a proven tech product or service. 8 companies with >10 employees are offering video annotation tools. Top 3 products are developed by companies with a total of 8k employees. The largest company in this domain is Scale with more than 4,000 employees. Scale provides the video annotation solution: Scale
Insights
What are the most common words describing video annotation tools?
This data is collected from customer reviews for all video annotation companies. The most positive word describing video annotation tools is “Easy to use” that is used in 5% of the reviews. The most negative one is “Difficult” with which is used in 1% of all the video annotation reviews.
What is the average customer size?
According to customer reviews, most common company size for video annotation customers is 1-50 Employees. Customers with 1-50 Employees make up 50% of video annotation customers. For an average Data solution, customers with 1-50 Employees make up 36% of total customers.
Customer Evaluation
These scores are the average scores collected from customer reviews for all video annotation tools. Video Annotation Tools are most positively evaluated in terms of "Customer Service" but falls behind in "Ease of Use".
Where are video annotation vendors' HQs located?
Trends
What is the level of interest in video annotation tools?
This category was searched on average for 725 times per month on search engines in 2024. This number has decreased to 0 in 2025. If we compare with other data solutions, a typical solution was searched 725 times in 2024 and this decreased to 0 in 2025.
Learn more about Video Annotation Tools
Video annotation is a method of teaching AI and ML systems to mimic the human eye and an integral part of this process is labeling objects in videos. Video annotation are types of data annotation methods that fall under the field of computer vision (CV), which is the broader field of artificial intelligence (AI).
Automatic video annotation refers to automatically labeling objects in the video clip through an annotation tool.
- Single frame annotation: Done by dividing or separating the video into individual frames or images.
- Multi-frame / streaming annotation: The annotator uses data annotations tools to label objects as the video streams.
Some of the many uses of video annotation are:
- Retail: Video annotation can be used to improve retail AI systems to monitor how customers are reacting to the products.
- Autonomous vehicles: Video annotation is widely used in autonomous vehicles to identify objects on the street and other vehicles around the car.
- Traffic surveillance: Traffic surveillance systems can monitor accidents and quickly alert authorities. Traffic congestion can also be analyzed through video annotation systems.
- Surgery: This technology is executed through augmented reality (AR) and enables remote surgery capabilities and the ability to share the surgery with clarity.
- Sign language translation: Video annotation and computer vision technologies are also used for translating sign language into text and speech.