Nikolaj Buhl
February 6, 2023

9 Best Image Annotation Tools for Computer Vision [2023 Review]

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Discover the 9 most popular free and paid image annotation tools that you need to know about heading into 2023. Compare their features and pricing, and choose the best image labelling tool for your use case.

It’s 2023.

And 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 annotation tools out there.

Whether you are:

A computer vision team building unmanned drones with your own in-house annotation tool...

A data science team working on a large-scale defect inspection model looking for labelling services...

Or a data operations team looking for the right platform for your radiologists to accurately label CT scans —

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

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

We’ll be updating this review monthly with notable feature releases and the latest annotation tools coming to market. So let's get to it!

The 9 most popular image annotation tools: 

  1. Encord Annotate
  2. Scale
  3. CVAT
  4. Labelbox
  5. Playment
  6. Appen
  7. Dataloop
  8. V7 Labs
  9. Hive

Encord Annotate

Encord Annotate is an automated annotation platform for AI-assisted image annotation, video annotation, and dataset management. It's the best option for teams that are:

  • Looking for automated, semi-automated or AI-assisted image and video annotation.
  • Annotating all modalities (DICOM and NIfTI, SAR, ultra-high-resolution, and more).
  • Wanting one place to easily manage annotators, track performance, and create QA/QC workflows.


Benefits & Key features:

  • Use-case centric annotations — from native DICOM & NIfTI annotations for medical imaging, to SAR-specific features for geospatial data.
  • Support for all annotation types — bounding boxes, polygons, polylines, image segmentation, keypoint, custom object primitives (rotating bounding boxes, 3D cuboids) and more.
  • Integrated data labeling services.
  • Integrated MLOps workflow for computer vision and machine learning teams — to detect edge cases and gaps in your training data, and generate augmented data to improve label quality.
  • Easy collaboration, annotator management and QA workflows — to track annotator performance and increase label quality.
  • Robust security functionality — label audit trails, encryption, FDA, CE Compliance, and HIPAA compliance.
  • Advanced Python SDK and API access (+ easy export into JSON and COCO formats).

Best for teams who:

  • Are graduating from an in-house solution or open-source tool and need a robust, secure, and collaborative platform to scale their annotation workflows.
  • Haven't found an annotation platform that can actually support their use case as well as they'd like (such as building complex nested ontologies, or rendering DICOM formats natively).

Pricing: Free trial model, and simple per-user pricing after that.

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One platform for creating better training data and debugging models.

Further reading:


Scale AI, now Scale, is a data and labelling services platform that supports image, audio, text and video annotations. As of 2023, Scale has also expanded into exciting new use cases, like user experience optimisation, large language models and synthetic data.

Annotating images with Scale

Benefits & Key features:

  • Leader in workforce management.
  • Support for multiple data modalities (image, video, document processing, audio and more).
  • Nucleus dataset management.

Best for: Workforce management

Pricing: On a per-image basis.

💡 More insights on data labelling with Scale:

Teams looking for annotation tools for Autonomous Vehicle vision should know… Scale is one of the earliest platforms on the market to support 3D Sensor Fusion annotation for RADAR and LiDAR use cases, and their capabilities are some of the most widely adopted.

Teams looking for medical imaging annotation tools should know… Platforms like Scale will usually not support DICOM or NIfTI data types, nor allow companies to work with their own annotators on the platform. So if you’re looking to annotate medical images, like CT, X-Ray or MRI scans, you’ll want to look for a medical purpose-built platform (built for radiologist or physician annotators, and specialist formats like DICOM and NIfTI) like Annotate.

Teams looking for labeling services should know… Scale is a very popular option for data labeling services. Other alternatives (discussed later in this article) are Appen and Playment.


CVAT (Computer Vision Annotation Tool) is a free, open-source, web-based annotation toolkit built by Intel. For image labelling - 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.

Annotating images with CVAT

Benefits & Key features:

  • Easy (& free) to get started labelling images with.
  • Great for manual data annotation — supports also semi-assisted labeling.
  • Robust ground-level annotation capabilities (including classification and object detection) for a broad set of computer vision use cases.

Best for: Students, researchers and academics testing the waters with image annotation (perhaps with a few images, or a small dataset).

Pricing: Free!

💡 More insights on image labelling with CVAT:

If your team is looking for a free annotation tool, you should know… 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 labelling 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.

Further reading:


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 labelling platform, as well as labelling services. Teams can annotate a wide range of data types (PDF, audio, images, videos and more).

Annotating images with Labelbox

Benefits & Key features:

  • Flexible QA workflows and annotator performance tracking.
  • Integrated 3rd party labeling services, through Labelbox Boost.
  • Model assisted annotations.
  • Robust support for multiple data types beyond images, especially text.

Best for: Teams looking for a powerful platform to quickly annotate documents and text.

Pricing: Varies based on volume of data, percent of the total volume needing to be labelled, number of seats, number of projects and percent of data used in model training.

💡 More insights on image labelling with Labelbox:

Teams looking for document processing annotation should know that… Labelbox has been heavily investing in its Document and Conversational AI products, released in 2023. Its document annotation capabilities are quickly gaining traction, particularly within the financial services industry. 

Teams carrying out annotation projects that are use-case specific should know that… 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 image annotation platforms will be a better fit.


Playment is a fully-managed data annotation platform. The workforce labeling company was acquired by Telus in 2021, and provides computer vision teams with high-quality training data for a variety of use cases, supported by manual labellers as well as a machine learning platform.

Annotating images with Playment

Benefits & Key features:

  • One of the largest global workforces of contractors and data labelers (+1M annotators).
  • Human-assisted 2D and 3D functionality for image annotations.
  • Speech recognition training platform (handles all data types across 500+ languages and dialects).

Best for: Teams looking for a fully managed solution to get labeled data from (just share data and label guidelines).

Pricing: Enterprise plan.


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.

Annotating images with Appen

Benefits & Key features:

  • Support for multiple annotation types (bounding boxes, polygons, and image segmentation).
  • Data sourcing (pre-labeled datasets), data preparation and real-world model evaluation.
  • Natural language processing and functionality for broader text-to-speech support available.

Best for: Teams looking for image data sourcing and collection alongide annotation services.

Pricing: Enterprise plan.


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.

Annotating images with Dataloop

Benefits & Key features:

  • Features for image annotation tasks, including classification, detection and semantic segmentation.
  • Support for video annotations.
  • Intuitive and easy to use user interface.

Best for: Teams looking for a powerful platform to annotate wide variety of data types.

Pricing: Free trial and enterprise plan.

💡 More insights on image labelling with Dataloop:

Teams carrying out specific image and video annotation projects that are use-case specific should know that… 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). 

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.

Annotating images with V7 Labs

Benefits & Key features:

  • Robust project management and automation workflow functionality, with real-time collaboration and tagging.
  • Integrated labeling services.
  • 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).

Pricing: Various options including academic, business and pro.


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’s platform allows engineers to also bring their own pre-trained ML models into the platform, and leverage them for content moderation (including detection of AI-generated data).

Annotating images with Hive

Benefits & Key features:

  • End-to-end image annotation tool.
  • Support for unique image annotation use cases (ad targeting, semi-automated logo detection).
  • Flexible access to model predictions with a single API call.
  • Bring your own AI model (BYOM).

Best for: Teams labelling images and other data types for the purpose of content moderation. Hive is especially popular with social media platforms, like Reddit, BeReal, and Quora.

Pricing: Enterprise plan.


There you have it! The 9 Best Image Annotation Tools for computer vision in 2023. 

For further reading, you might also want to check out a few 2023 honorable mentions, both paid and free annotation tools:

  • Labelstudio - user-friendly open source tool, praised for its manual annotation process capabilities.
  • Supervisely - commercial data labeling platform, praised for its quality control functionality and basic interpolation feature.
  • VoTT - open source tool, praised for its tags and assets export to Tensorflow (PascalVOC) and YOLO format.

Unlabeled image or video dataset? You're in the right place.

"We're ready - we want to automatically or semi-automatically label our dataset."
You can get started with Encord Annotate here, and generate labels with our AI-assisted tools ⚡️

"We're looking for labelling services." - Here you go 🤝

"Just reading around.. Any more to know about automatic annotation?" - Yes and yes! More 📚 here

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