List of manual image annotation tools

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Manual image annotation is the process of manually defining regions in an image and creating a textual description of those regions. Such annotations can for instance be used to train machine learning algorithms for computer vision applications.

This is a list of computer software which can be used for manual annotation of images.

SoftwareDescriptionPlatformLicenseReferences
Computer Vision Annotation Tool (CVAT)Computer Vision Annotation Tool (CVAT) is a free, open source, web-based annotation tool which helps to label video and images for computer vision algorithms. CVAT has many powerful features: interpolation of bounding boxes between key frames, automatic annotation using TensorFlow OD API and deep learning models in Intel OpenVINO IR format, shortcuts for most of critical actions, dashboard with a list of annotation tasks, LDAP and basic authorizations, etc. It was created for and used by a professional data annotation team. UX and UI were optimized especially for computer vision annotation tasks.JavaScript, HTML, CSS, Python, Django MIT License [1] [2] [3]
LabelMe Online annotation tool to build image databases for computer vision research.Perl, JavaScript, HTML, CSS [4] MIT License
Encord Encord is an automated annotation platform for AI-assisted image annotation, video annotation, and dataset management.
  • 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).
Python, JavaScript, HTML, CSS [5] Apache-2.0 License
TagLab Desktop open source interactive software system for facilitating the precise annotation of benthic species in orthophoto of the bottom of the sea.Python [6] GPL [7] [8]
VoTT (Visual Object Tagging Tool)Free and open source electron app for image annotation and labeling developed by Microsoft. TypeScript/Electron (Windows, Linux, macOS) MIT License [9] [10] [11] [12] [13] [14]

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Computer Vision Annotation Tool (CVAT) is a free, open source, web-based image and video annotation tool which is used for labeling data for computer vision algorithms. Originally developed by Intel, CVAT is designed for use by a professional data annotation team, with a user interface optimized for computer vision annotation tasks.

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VoTT is a free and open source Electron app for image annotation and labeling developed by Microsoft. The software is written in the TypeScript programming language and used for building end-to-end object detection models from image and videos assets for computer vision algorithms.

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References

  1. ↑ "Intel open-sources CVAT, a toolkit for data labeling". VentureBeat. 2019-03-05. Retrieved 2019-03-09.
  2. ↑ "Computer Vision Annotation Tool: A Universal Approach to Data Annotation". software.intel.com. 2019-03-01. Retrieved 2019-03-09.
  3. ↑ "Computer Vision Annotation Tool (CVAT) source code on github". GitHub . Retrieved 3 March 2019.
  4. ↑ "LabelMe Source". GitHub . Retrieved 26 January 2017.
  5. ↑ "Encord Source". Documentation . Retrieved 26 January 2017.
  6. ↑ "TagLab Source". GitHub . Retrieved 5 July 2023.
  7. ↑ Pavoni, Gaia; Corsini, Massimiliano; Ponchio, Federico; Muntoni, Alessandro; Edwards, Clinton; Pedersen, Nicole; Sandin, Stuart; Cignoni, Paolo (2022). "TagLab: AI-assisted annotation for the fast and accurate semantic segmentation of coral reef orthoimages". Journal of Field Robotics. 39 (3): 246–262. doi:10.1002/rob.22049. S2CID   244648241.
  8. ↑ Costa, Bryan; Sweeney, Edward; Mendez, Arnold (October 2022). "Leveraging Artificial Intelligence to Annotate Marine Benthic Species and Habitats". Noaa Technical Memorandum Nos Nccos. 306. doi:10.25923/7kgv-ba52.
  9. ↑ Tung, Liam. "Free AI developer app: IBM's new tool can label objects in videos for you". ZDNet.
  10. ↑ Bornstein, Aaron (Ari) (February 4, 2019). "Using Object Detection for Complex Image Classification Scenarios Part 4". Medium.
  11. ↑ Solawetz, Jacob (July 27, 2020). "Getting Started with VoTT Annotation Tool for Computer Vision". Roboflow Blog.
  12. ↑ "Best Open Source Annotation Tools for Computer Vision". www.sicara.ai.
  13. ↑ "Beyond Sentiment Analysis: Object Detection with ML.NET". September 20, 2020.
  14. ↑ "GitHub - microsoft/VoTT: Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos". November 15, 2020 – via GitHub.
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