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Best Image Annotation Tools for Computer Vision [Updated 2024]

March 26, 2024
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10 mins
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Guide to the most popular image annotation tools that you need to know about in 2024. Compare the features and pricing, and choose the best image annotation tool for your use case.

It’s 2024—annotating images is still one of the most time-consuming steps in bringing a computer vision project to market. To help you out, we put together a list of the most popular image labeling tools out there.

Whether you are:

  • A computer vision team building unmanned drones with your own in-house annotation tool.
  • A team of data scientists working on an autonomous driving project looking for large-scale labeling services.
  • Or a data operations team working in healthcare looking for the right platform for your radiologists to accurately label CT scans.

This guide will help you compare the top AI annotation tools and find the right one for you.

We will compare each based on key factors - including image annotation service, support for different data types and use cases, QA/QC capabilities, security and data privacy, integration with the machine learning pipeline, and customer support.

But first, let's explore the process of selecting an image annotation tool from the available providers.

Choosing the right image annotation tool is a critical decision that can significantly impact the quality and efficiency of the annotation process. To make an informed choice, it's essential to consider several factors and evaluate the suitability of an image annotation tool for specific needs.

Evaluating Image Annotation Tools for Computer Vision Projects

Selecting the perfect image annotation tool is like choosing the perfect brush for your painting.

Different projects require specific annotation needs that dictate how downstream components. When evaluating an annotation tool that fits your project specifications, there are a few key factors you have to consider. In this section, we will explore those key factors and practical considerations to help you navigate the selection process and find the most fitting AI annotation tool for your computer vision applications.

  • Annotation Types: An effective labeling tool should support various annotation types, such as bounding boxes (ideal for object localization), polygons (useful for detailed object outlines), keypoints (for pose estimation), and semantic segmentation (for scene understanding). The tool must be adaptable to different annotation requirements, allowing users to annotate images with precision and specificity based on the task at hand.
  • User Interface (UI) and User Experience (UX): The user interface plays a crucial role in the efficiency and accuracy of the annotation process. A good annotation tool should have an intuitive interface that is easy to navigate, reducing the learning curve for users. Clear instructions, user-friendly controls, and efficient workflows contribute to a smoother annotation experience.
  • Scalability: Consider the tool's ability to scale with the growing volume of data. A tool that efficiently handles large datasets and multiple annotators is crucial for projects with evolving requirements.
  • Automation and AI Integration: Look for image labeling tools that offer automation features, such as automatic annotation tools or features, to accelerate the annotation process. Integrating an AI photo editor into the annotation process can significantly refine the accuracy of annotations, especially in complex imaging scenarios, thereby enhancing both the speed and quality of data labeling. Integration with artificial intelligence (AI) algorithms can further enhance efficiency by automating repetitive tasks, reducing manual effort, and improving annotation accuracy.
  • Collaboration and Workflow Management: Assess the data annotation tool's collaboration features, including version control, user roles, and workflow management. Collaboration tools are essential for teams working on complex annotation projects.
  • Data Security and Privacy: Ensure that the tool adheres to data security and privacy standards like GDPR. Evaluate encryption methods, access controls, and policies regarding the handling of sensitive data.
  • Pricing: Consider various pricing models, such as per-user, per-project, or subscription models. Also factor in scalability costs, and potential additional fees, ensuring transparency in the pricing structure.

Once you've identified which factors are most important for you to evaluate image annotating tools, the next step is understanding how to assess their suitability for your specific use case. 

Most Popular Image Annotation Tools

Let's compare the features offered by the best image annotation companies such as Encord, Scale AI, Label Studio, SuperAnnotate, CVAT, and Amazon SageMaker Ground Truth, and understand how they assist in annotating images.

This article discusses the top 17 image annotation tools in 2024 to help you choose the right image annotation software for your use case.

  1. Encord
  2. Scale
  3. CVAT
  4. Label Studio
  5. Labelbox
  6. Playment
  7. Appen
  8. Dataloop
  9. SuperAnnotate
  10. V7 Labs
  11. Hive
  12. COCO Annotator
  13. Make Sense
  14. VGG Image Annotator
  15. LabelMe
  16. Amazon SageMaker Ground Truth
  17. VOTT

Encord

Encord is an automated annotation platform for AI-assisted image annotation, video annotation, and dataset management

Encord image annotation platform

Key Features

  • Data Management: Compile your raw data into curated datasets, organize datasets into folders, and send datasets for labeling. 
  • AI-assisted Labeling: Automate 97% of your annotations with 99% accuracy using auto-annotation features powered by Meta's Segment Anything Model or GPT-4’s LLaVA.
  • Collaboration: Integrate human-in-the-loop seamlessly with customized Workflows - create workflows with the no-code drag and drop builder to fit your data ops & ML pipelines.
  • Quality Assurance: Robust annotator management & QA workflows to track annotator performance and increase label quality. 
  • Integrated Data Labeling Services for all Industries: outsource your labeling tasks to an expert workforce of vetted, trained and specialized annotators to help you scale.
  • Video Labeling Tool: provides the same support for video annotation. One of the leading video annotation tools with positive customer reviews, providing automated video annotations without frame rate errors.
  • Robust Security Functionality: label audit trails, encryption, FDA, CE Compliance, and HIPAA compliance.
  • Integrations: Advanced Python SDK and API access (+ easy export into JSON and COCO formats).

Best for

  • Commercial teams: Teams translating from an in-house solution or open-source tool that require a scalable annotation workflow with a robust, secure, and collaborative enterprise-grade platform.
  • Complex or unique use case: For teams that require advanced annotation tool and functionality. It includes, complex nested ontologies or rendering native DICOM formats.

Pricing

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Scale

Scale AI, now Scale, is a data and labeling services platform that supports computer vision use cases but specializes in RLHF, user experience optimization, large language models, and synthetic data.

Scale AI's Image Annotation Tool.

Key Features

  • Customizable Workflows: Offers customizable labeling workflows tailored to specific project requirements and use cases.
  • Data labeling services: Provides high-quality data labeling services for various data types, including images, text, audio, and video.
  • Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs.

Best for

  • Teams Looking for a Labeling Tool: Scale is a very popular option for data labeling services.
  • Teams Looking for Annotation Tools for Autonomous Vehicle Vision: Scale is one of the earliest platforms on the market to support 3D Sensor Fusion annotation for RADAR and LiDAR use cases.
  • Teams Looking for Medical Imaging Annotation Tools: Platforms like Scale will usually not support DICOM or NIfTI data types nor allow companies to work with their data annotators on the platform.

Pricing

  • On a per-image basis

CVAT (Computer Vision Annotation Tool)

CVAT is an open source image annotation tool that is a web-based annotation toolkit, built by Intel. For image labeling, CVAT supports four types of annotations: points, polygons, bounding boxes, and polylines, as well as a subset of computer vision tasks: image segmentation, object detection, and image classification. In 2022, CVAT’s data, content, and GitHub repository were migrated over to OpenCV, where CVAT continues to be open-source. Furthermore, CVAT can also be utilized to annotate QR codes within images, facilitating the integration of QR code recognition into computer vision pipelines and applications.

CVAT Label Editor.

Key Features

  • Open-source: Easy and free to get started labeling images.
  • Manual Annotation Tools: Supports a wide range of annotation types including bounding boxes, polygons, polylines, points, and cuboids, catering to diverse annotation needs.
  • Multi-platform Compatibility: Works on various operating systems such as Windows, Linux, and macOS, providing flexibility for users.
  • Export Formats: CVAT offers support for various data formats including JSON, COCO, and XML-based like Pascal VOC, ensuring annotation compatibility with diverse tools and platforms.

Best for

  • Students, researchers, and academics testing the waters with image annotation (perhaps with a few images or a small dataset).
  • Not preferable for commercial teams as it lacks scalability, collaborative features, and robust security.

Pricing

  • Free

💡 More insights on image labeling with CVAT:

For a team looking for free image annotation tools, CVAT is one of the most popular open-source tools in the space, with over 1 million downloads since 2021. Other popular free image annotation alternatives to CVAT are 3D Slicer, Labelimg, VoTT (Visual Object Tagging Tool - developed by Microsoft), VIA (VGG Image Annotator), LabelMe, and Label Studio.

If data security is a requirement for your annotation project… Commercial labeling tools will most likely be a better fit — key security features like audit trails, encryption, SSO, and generally-required vendor certifications (like SOC2, HIPAA, FDA, and GDPR) are usually not available in open-source tools.

  

Label Studio

Label Studio is another popular open source data labeling platform. It provides a versatile platform for annotating various data types, including images, text, audio, and video. Label Studio supports collaborative labeling, custom labeling interfaces, and integration with machine learning pipelines for data annotation tasks.

Label Studio Image Annotation Tool.

Key Features

  • Customizable Labeling Interfaces: Flexible configuration for tailored annotation interfaces to specific tasks.
  • Collaboration Tools: Real-time annotation and project sharing capabilities for seamless collaboration among annotators.
  • Extensible: Easily connect to cloud object storage and label data there directly
  • Export Formats: Label Studio supports multiple data formats including JSON, CSV, TSV, and VOC XML like Pascal VOC, facilitating integration and annotation from diverse sources for machine learning tasks.

Best for

  • Data scientists, machine learning engineers, and researchers or teams requiring versatile data labeling for images. 
  • Not suitable for teams with limited technical expertise or resources for managing an open source tool

Price

  • Free with enterprise plan available

Labelbox

Labelbox is a US-based data annotation platform founded in 2017. Like most of the other platforms mentioned in this guide, Labelbox offers both an image labeling platform, as well as labeling services.

Labelbox Image Editor

Key Features

  • Data Management: QA workflows and data annotator performance tracking.
  • Customizable Labeling Interface: 3rd party labeling services through Labelbox Boost.
  • Automation: Integration with AI models for automatic data labeling to accelerate the annotation process.
  • Annotation Type: Support for multiple data types beyond images, especially text.

Best for

  • Teams looking for a platform to quickly annotate documents and text.
  • Teams carrying out annotation projects that are use-case specific.
    As generalist tools, platforms like Labelbox are great at handling a broad variety of data types. If you’re working on a unique use-case-specific annotation project (like scans in DICOM formats or high-resolution images that require pixel-perfect annotations), other commercial AI labeling tools will be a better fit: check out our blog exploring Best DICOM Labeling Tools.

Pricing

  • Varies based on the volume of data, percent of the total volume needing to be labeled, number of seats, number of projects, and percent of data used in model training.
  • For larger commercial teams, this pricing may get expensive as your project scales.

Playment

Playment is a fully-managed data annotation platform. The workforce labeling company was acquired by Telus in 2021 and provides computer vision teams with training data for various use cases, supported by manual labelers and a machine learning platform.

Playment Image Annotation Tool

Key Features

  • Data Labeling Services: Provides high-quality data labeling services for various data types including images, videos, text, and sensor data.
  • Support: Global workforces of contractors and data labelers.
  • Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs.
  • Audio Labeling Tool: Speech recognition training platform (handles all data types across 500+ languages and dialects).

Best for

  • Teams looking for a fully managed solution who do not need visibility into the process.

Pricing

  • Enterprise plan

Appen

Appen is a data labeling services platform founded in 1996, making it one of the first and oldest solutions in the market. The company offers data labeling services for a wide range of industries and in 2019, acquired Figure Eight to build out its software capabilities and help businesses also train and improve their computer vision models.

Appen Image Annotation Tool

Key Features

  • Data Labeling Services: Support for multiple annotation types (bounding boxes, polygons, and image segmentation).
  • Data Collection: Data sourcing (pre-labeled datasets), data preparation, and real-world model evaluation.
  • Natural Language Processing:  Supports natural language processing tasks such as sentiment analysis, entity recognition, and text classification.
  • Image and Video Analysis: Analyzes images and videos for tasks such as object detection, image classification, and video segmentation.

Best for

  • Teams looking for image data sourcing and collection alongside annotation services.

Pricing

  • Enterprise plan

Dataloop

Dataloop is an Israel-based data labeling platform that provides a comprehensive solution for data Dataloop is an Israel-based data labeling platform that provides a comprehensive solution for data management and annotation projects. The tool offers data labeling capabilities across images, text, audio, and video annotation, helping businesses train and improve their machine learning models.

Dataloop Image Annotation Tool

Key Features

  • Data Annotation: Features for image annotation tasks, including classification, detection, and semantic segmentation.
  • Video Annotation Tool: Support for video annotations.
  • Collaboration Tool: Features for real-time collaboration among annotators, project sharing, and version control for efficient teamwork.
  • Data Management: Offers data management capabilities including data versioning, tracking, and organization for streamlined workflows.

Best for

  • Teams looking for a generalist annotation tool for various data annotation needs.
  • Teams carrying out specific image and video annotation projects that are use-case specific.
  • As generalist tools, platforms like Dataloop are built to support a wide variety of simple use cases, so other commercial platforms are a better fit if you’re trying to label use-case-specific annotation projects (like high-resolution images that require pixel-perfect annotations in satellite imaging or DICOM files for medical teams).

Pricing

  • Free trial and an enterprise plan.

SuperAnnotate

SuperAnnotate provides enterprise solutions for image and video annotation, catering primarily to the needs of the computer vision community. It provides powerful annotation tools and features tailored for machine learning and AI applications, offering efficient labeling solutions to enhance model training and accuracy.

SuperAnnotate - Image Annotation Tool

Key Features

  • Multi-Data Type Support: Versatile annotation tool for image, video, text, and audio.
  • AI Assistance: Integrates AI-assisted annotation to accelerate the annotation process and improve efficiency.
  • Customization: Provides customizable annotation interfaces and workflows to tailor annotation tasks according to specific project requirements.
  • Integration: Seamlessly integrates with machine learning pipelines and workflows for efficient model training and deployment.
  • Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs.
  • Export Formats: SuperAnnotate supports multiple data formats, including popular ones like JSON, COCO, and Pascal VOC.

Best for

  • Larger teams working on various machine learning solutions looking for a versatile annotation tool.

Pricing

  • Free for early stage startups and academics for team size up to 3.
  • Enterprise plan

V7 Labs

V7 is a UK-based data annotation platform founded in 2018. The company enables teams to annotate training data, support the human-in-the-loop processes, and also connect with annotation services. V7 offers annotation of a wide range of data types alongside image annotation tooling, including documents and videos.

V7 Labs Image Annotation Tool

Key Features

  • Collaboration Capabilities: Project management and automation workflow functionality, with real-time collaboration and tagging.
  • Data Labeling Services: Provides labeling services for images and videos.
  • AI Assistance: Model-assisted annotation of multiple annotation types (segmentation, detection, and more).

Best for

  • Students or teams looking for a generalist platform to easily annotate different data types in one place (like documents, images, and short videos).
  • Limited functionalities for use-case specific annotations.

Pricing

  • Various options, including academic, business, and pro.

Hive

Hive was founded in 2013 and provides cloud-based AI solutions for companies wanting to label content across a wide range of data types, including images, video, audio, text, and more.

Hive Image Annotation Tool

Key Features

  • Image Annotation Tool: Offers annotation tools and workflows for labeling images along with support for unique image annotation use cases (ad targeting, semi-automated logo detection).
  • Ease of Access: Flexible access to model predictions with a single API call.
  • Integration: Seamlessly integrates with machine learning pipelines and workflows for AI model training and deployment.

Best for

  • Teams labeling images and other data types for the purpose of content moderation.

Pricing

  • Enterprise plan

COCO Annotator

COCO Annotator is a web-based image annotation tool, crafted by Justin Brooks under the MIT license. Specifically designed to streamline the process of labeling images for object detection, localization, and keypoints detection models, this tool offers a range of features that cater to the diverse needs of machine learning practitioners and researchers. 

COCO Annotator - Image Annotation Tool

Key Features

  • Image Annotation: Supports annotation of images for object detection, instance segmentation, keypoint detection, and captioning tasks.
  • Export Formats: To facilitate large-scale object detection, the tool exports and stores annotations in the COCO format. 
  • Automations: The tool makes annotating an image easier by incorporating semi-trained models. Additionally, it provides access to advanced selection tools, including the MaskRCNN, Magic Wand and DEXTR.

Best For

  • ML Research Teams: COCO Annotator is a good choice for ML researchers, preferable for image annotation for tasks like object detection and keypoints detection.

Price

  • Free

Make Sense

Make Sense AI is a user-friendly and open-source annotation tool, available under the GPLv3 license. Accessible through a web browser without the need for advanced installations, this tool simplifies the annotation process for various image types.

Alternative to Make Sense as an Image Annotation Tool is Encord | Image Annotation Tools

Make Sense - Image Annotation Tool

Key Features

  • Open Sourced: Make Sense AI stands out as an open-source tool, freely available under the GPLv3 license, fostering collaboration and community engagement for its ongoing development.
  • Accessibility: It ensures web-based accessibility, operating seamlessly in a web browser without complex installations, promoting ease of use across various devices.
  • Export Formats: It facilitates exporting annotations in multiple formats (YOLO, VOC XML like Pascal VOC, VGG JSON, and CSV), ensuring compatibility with diverse machine learning algorithms and seamless integration into various workflows.

Best For

  • Small teams seeking an efficient solution to annotate an image.

Price

  • Free

VGG Image Annotator

VGG Image Annotator (VIA) is a versatile open-source tool crafted by the Visual Geometry Group (VGG) for the manual annotation of both image and video data. Released under the permissive BSD-2 clause license, VIA serves the needs of both academic and commercial users, offering a lightweight and accessible solution for annotation tasks.

VGG Image Annotator - Image Annotation Tool

Key Features

  • Lightweight and User-Friendly: VIA is a lightweight, self-contained annotation tool, utilizing HTML, Javascript, and CSS without external libraries, enabling offline usage in modern web browsers without setup or installation.
  • Offline Capability: The tool is designed to be used offline, providing a full application experience within a single HTML file of size less than 200 KB. 
  • Multi-User Collaboration: Facilitates collaboration among multiple annotators with features such as project sharing, real-time annotation, and version control.

Best For

  • VGG Image Annotator (VIA) is ideal for individuals and small teams involved in projects for academic researchers.

Price

  • Free

LabelMe

LabelMe is an open-source web-based tool developed by the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) that allows users to label and annotate images for computer vision research. It provides a user-friendly interface for drawing bounding boxes, polygons, and semantic segmentation masks to label objects within images.

LabelMe is an Image Annotation Tool | Encord

LabelMe Image Annotation Tool

Key Features

  • Web-Based: Accessible through a web-based interface, allowing for annotation tasks to be performed in any modern web browser without requiring software installation.
  • Customizable Interface: Provides a customizable annotation interface with options to adjust settings, colors, and layout preferences to suit specific project requirements.

Best for

  • Academic and research purposes

Pricing

  • Free

Amazon SageMaker Ground Truth

Amazon SageMaker Ground Truth is a fully managed data labeling service provided by Amazon Web Services (AWS). It offers a platform for efficiently labeling large datasets to train machine learning models. Ground Truth supports various annotation tasks, including image classification, object detection, semantic segmentation, and more.

Amazon SageMaker Ground Truth - Image Annotation Tool

Key Features

  • Managed Service: Fully managed by AWS, eliminating the need for infrastructure setup and management.
  • Human-in-the-Loop Labeling: Harnesses the power of human feedback across the ML lifecycle to improve the accuracy and relevancy of models.
  • Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs.
  • Integration with Amazon SageMaker: Seamlessly integrates with Amazon SageMaker for model training and deployment, providing a streamlined end-to-end machine learning workflow.

Best for

  • Teams requiring large-scale data labeling.

Pricing

  • Varies based on labeling task and type of data.

VOTT

VOTT or Visual Object Tagging Tool is an open-source tool developed by Microsoft for annotating images and videos to create training datasets for computer vision models. VOTT provides an intuitive interface for drawing bounding boxes around objects of interest and labeling them with corresponding class names.

VOTT is one of the Image Annotation Tools apart from Encord

VOTT Image Annotation Tool

Key Features

  • Versatile Annotation Tool: Supports a wide range of annotation types including bounding boxes, polygons, polylines, points, and segmentation masks for precise labeling.
  • Video Annotation: Enables annotation of videos frame by frame, with support for object tracking and interpolation to streamline the annotation process.
  • Multi-Platform Compatibility: Works across various operating systems such as Windows, Linux, and macOS, ensuring flexibility for users.

Best for

  • Teams requiring lightweight and customizable annotation tool for object detection.

Pricing

  • Free

Image Annotation Tool: Key Takeaways

There you have it!

The 17 Best Image Annotation Tools for computer vision in 2024. 

For further reading, you might also want to check out a few 2024 honorable mentions, both paid and free annotation tools:

  • Supervisely - commercial data labeling platform praised for its quality control functionality and basic interpolation feature.
  • Labelimg - Labelimg is an open source multi-modal data annotation tool now part of Label Studio.
  • MarkUp - MarkUp image is a free web annotation tool to annotate an image or a PDF.

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Nikolaj Buhl

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Frequently asked questions
  • There are various options, including open-source, low-code or no-code, and active learning annotation solutions like Encord. Encord is the leading annotation tool to build better models, faster. Accelerate the creation of training data with pixel-perfect AI-assisted labeling to develop high quality training data and build product-ready models up to 10x faster.

  • Image annotation involves several stages. Image and image-based datasets need to be sourced (either bought or downloaded from open-source databases), cleaned, and uploaded into annotation tools and software.

  • Automatically annotate images with active learning annotation platforms, like Encord. Traditional computer vision models require extensive data for robustness and generalizability. You can leverage the power of the Segment Anything Model to complete single one-click annotations and in just minutes, train Encord’s micro-models starting from a small set of labels.

  • Automatically annotate images with active learning annotation platforms, like Encord. Traditional computer vision models require extensive data for robustness and generalizability. You can leverage the power of the Segment Anything Model to complete single one-click annotations and in just minutes, train Encord’s micro-models starting from a small set of labels.

  • You should look for versatility in annotation types (bounding boxes, polygons), user-friendly interfaces, collaboration support, scalability, automation features, and compatibility with various annotation formats for seamless integration into your workflow.

  • Model-assisted labeling involves using pre-trained models to assist in the annotation process, improving efficiency by automating certain tasks and reducing manual effort.

  • Encord offers a complimentary trial, followed by straightforward per-user pricing. Consider notable options like CVAT, MakeSense, and VGG Annotator, known for being open-source, web-based, supporting diverse annotation types, and offering export flexibility in various formats.

  • Choose your preferred annotator, upload the image, select the annotation type (bounding box, polygon), annotate, and export labels in your chosen format. Tools like Encord offer automated annotation features, streamlining the process for added convenience.

  • Auto annotation features, like the one by Encord Annotator, assist in the annotation process by leveraging semi-trained models to automatically suggest annotations, enhancing efficiency in various deep learning tasks like object detection, instance segmentation, object recognition, and localization.

  • Image annotation tools facilitate collaboration by enabling multiple users to annotate images simultaneously, fostering real-time communication and feedback. They streamline the process of labeling objects within images, enhancing efficiency and accuracy in object detection and classification tasks.