Top 8 Video Annotation Tools for Computer Vision

Are you looking for a video annotation tool for your computer vision project? Look no further! We've compiled a list of the top eight best video annotation tools, complete with their use cases, benefits, key features, and pricing.

Deciding on the right video annotation toolkit for your needs depends on several factors, including whether you have vast amounts of unlabeled data and whether manual annotation is too time-consuming and expensive.

With a powerful video annotation tool, you can automate and accelerate the process. Our list is designed for data ops teams looking to manage in-house or outsourced annotators, CTOs hoping to reduce the cost of manual annotation, and data scientists and ML engineers in search of a solution to automate annotations and labeling while identifying potential edge cases and outliers.

Top 8 Video Annotation Tools for Computer Vision

1. Encord
2. LabelMe
3. CVAT
4. SuperAnnotate
5. Dataloop
6. Supervisely
7. Scale
8. Img Lab 

Let’s dive in ...

Encord

Encord's collaborative video annotation platform helps you label video training data more quickly, build active learning pipelines, create better-quality datasets and accelerate the development of your computer vision models.

Encord's suite of features and toolkits includes an automated video annotation platform that will help you 6x the speed and efficiency of model development.

Encord is a powerful solution for teams that: 

• Need a native-enabled video annotation platform with features that make it easy to automate the end-to-end management of data labeling, QA workflows, and automated AI-powered annotation
• Want to accelerate their computer vision model development, making video annotation 6x faster than manual labeling.

Benefits & key features: 

• Encord is a state-of-the-art AI-assisted labeling and workflow tooling platform powered by micro-models, ideal for video annotation, labeling, QA workflows, and training computer vision models
• Built for computer vision, with native support for numerous annotation types, such as bounding box, polygon, polyline, instance segmentation, keypoints, classification, and much more 
• As a computer vision toolkit, it supports a wide-range of native and visual modalities for video annotation and labeling, including native video file format support (e.g., full-length videos, and numerous file formats, including MP4 and WebM)
• Automated, AI-powered object tracking means your annotation teams can annotate videos 6x faster than manual processes
• Assess and rank the quality of your video-based datasets and labels against pre-defined or custom metrics, including brightness, annotation duplicates, occlusions in video or image sequences, frame object density, and numerous others 
• Evaluate training datasets more effectively using a trained model and imported model predictions with acquisition functions such as entropy, least confidence, margin, and variance with pre-built implementations
• Manage annotators collaboratively and at scale with customizable annotator and data management dashboards

Best for: 

• ML, data ops, and annotation teams looking for a video annotation tool that will accelerate model development.
• Data science and operations teams that need a solution for collaborative end-to-end management of outsourced video annotation work.

Pricing: 

Start with a free trial or contact sales for enterprise plans.

Further reading: 

• The Complete Guide to Image Annotation for Computer Vision
• 4 Ways to Debug Computer Vision Models [Step By Step Explainer]
• Closing the AI Production Gap with Encord Active
• Active Learning in Machine Learning: A Comprehensive Guide

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LabelMe

LabelMe is an open-source online annotation tool developed by the MIT Computer Science and Artificial Intelligence Laboratory. It includes the downloadable source code, a toolbox, an open-source version for 3D images, and image datasets you can train computer vision models on.

LabelMe

Benefits & key features: 

• LabelMe includes a dataset you can use to train models on, and you can use the LabelMe Matlab toolbox to annotate and label them (here’s the Github repository for this)
• It also comes with a 3D database with thousands of images of everyday scenes and object categories 
• You can also outsource annotation using Amazon Mechanical Turk, and LabelMe encourages this here.

Best for: 

ML and annotation teams. Although, given the open-source nature of LabelM and the database, it may be more effective and useful for academic rather than commercial computer vision projects. 

Pricing: 

Free, open-source. 

CVAT

CVAT (Computer Vision Annotation Tool) started life as an Intel application that they made open-source, thanks to an MIT license. Now it operates as an independent company and foundation, with Intel’s continued support under the OpenCV umbrella. 

CVAT.org has moved to its new home, at CVAT.ai. 

CVAT

Benefits & key features: 

• CVAT is now part of an extensive OpenCV ecosystem that includes a feauture-rich open-source annotation tool
• With CVAT, you can annotate images and videos by creating classifications, segmentations, 3D cuboids, and skeleton templates
• Over 1 million people have downloaded it since CVAT launched, and under OpenCV, there’s an even larger community of users to ask for guidance and support. 

Best for: 

Data ops and annotation teams that need access to an open-source tool and ecosystem of ML engineers and annotators. 

Pricing: 

Free, open-source. 

SuperAnnotate

SuperAnnotate is a commercial platform and toolkit for creating annotations and labels, managing automated annotation workflows, and even generating images and datasets for computer vision projects.

SuperAnnotate

Benefits & key features: 

• SuperAnnotate includes a full-service Data Studio, including access to a marketplace of 400+ outsourced annotation teams and service providers 
• It also comes with an ML Studio to manage computer vision and AI-based workflows, including AI data management and curation, MLOps and automation, and quality assurance (QA)
• It’s designed for numerous use cases, including healthcare, insurance, sports, autonomous driving, and several others. 

Best for: 

ML engineers, data scientists, annotation teams, and MLOps professionals in academia, businesses, and enterprise organizations. 

Pricing:

Free for early-stage startups and academic researchers. You would need a demo or contact sales for the Pro and Enterprise plans.

Dataloop

Dataloop is a "data engine for AI" that includes automated annotation for video datasets, full lifecycle dataset management, and AI-powered model training tools.

Dataloop

Benefits & key features: 

• Multiple data types supported, including numerous video file formats
• Automated and AI-powered data labeling
• End-to-end annotation and QA workflow managment and dashboards for collaborative working

Best for: 

ML, data ops, enterprise AI teams, and managing video annotation workflows with outsourced teams.

Pricing: 

From $85/mo for 150 annotation tool hours.

Supervisely

Supervisely is a "Unified OS enterprise-grade platform for computer vision" that includes video annotation tools and features.

Supervisely

Benefits & key features: 

• Native video file support, so that you don't need to cut them into segments or images
• Automated multi-track timelines within videos
• Built-in object tracking and segments tagging tools, and numerous other features for video annotation, QA, collaborative working, and computer vision model development

Best for: 

ML, data ops, and AI teams in Fortune 500 companies and computer vision research teams.

Pricing: 

30-day free trial, with custom plans after signing-up for a demo.

Scale

Scale is positioned as the AI data labeling and project/workflow management platform for “generative AI companies, US government agencies, enterprise organizations, and startups.” 

Building the best AI, ML, and CV models means accessing the “best data,” and for that reason, it comes with tools and solutions such as the Scale Data Engine and Generative AI Platform. 

Scale, an enterprise-grade data engine and generative AI platform

Benefits & key features: 

• A Data Engine to unlock data organizations already have or can tap into vast public and open-source datasets 
• Tools to create synthetic data (e.g., generative AI features)
• A full-stack Generative AI platform for AI companies and US government agencies 
• An extensive developers platform for Large Language Model (LLM) applications. 

Best for: 

Data scientists and ML engineers in generative AI companies, US government agencies, enterprise organizations, and startups. 

Pricing: 

There are two core offerings: Label My Data (priced per-label), and an Enterprise plan that requires a demo to secure a price.

Img Lab

Img Lab is an open-source image annotation tool to “simplify image labeling/ annotation process with multiple supported formats.” 

Img Lab

Benefits & key features: 

Img Lab isn’t as feature-rich as most of the tools and platforms on this list. It would need to be integrated with other tools and applications to ensure it could be used effectively for large-scale image annotation projects. 

Best for: 

Img Lab seems best equipped for annotators and those who need a quick and easy-to-use open-source annotation tool. 

Pricing: 

Free, open-source.  

How To Pick the Best Video Annotation Tool for Computer Vision Projects?

And there we go, the best video annotation tools for computer vision!

In this post, we covered Encord, LabelMe, CVAT, SuperAnnotate, Dataloop, Supervisely, Scale, and Img Lab. 

Each tool and suite of features that are included are applicable to a wide-range of use cases, data types, and project scales.

Making the right choice depends on what your computer vision project needs, such as supporting various data modalities and annotation types, active learning strategies, and pricing. 

When you’ve selected the best annotation tool for your project or AI application will accelerate model development, enhance the quality of your training data, and optimize your data labeling and annotation process. 

From scaling to enhancing your model development with data-driven insights

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