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Original author(s) | Commercial Software Engineering (CSE) group at Microsoft in Israel |
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Developer(s) | Microsoft and community |
Initial release | 2018 |
Stable release | v2.2.0
/ June 3, 2020 |
Repository | github |
Written in | TypeScript |
Operating system | Windows, Linux, macOS |
Platform | Cross-platform |
Type | Image annotation tool |
License | MIT License |
Website | vott |
VoTT (Visual Object Tagging Tool) is a free and open source Electron app for image annotation and labeling developed by Microsoft.[1] 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.
Overview
[edit]VoTT is a React+Redux web application that requires Node.js and npm.[2] It is available as a stand-alone web application and can be used in any modern web browser.[3]
Notable features include the ability to label images or video frames, support for importing data from local or cloud storage providers,[2] and support for exporting labeled data to local or cloud storage providers.
Labeled assets can be exported into the following formats:
- Comma-separated values (CSV)
- Microsoft Azure Custom Vision Service
- Microsoft Cognitive Toolkit (CNTK)
- TensorFlow (Pascal VOC and TFRecords)
- VoTT (generic JSON schema)[4]
The VoTT source code is licensed under MIT License and available on GitHub.[5]
See also
[edit]- List of manual image annotation tools
- Computer Vision Annotation Tool
- LabelMe
- Supervised learning
- Image segmentation
References
[edit]- ^ Tung, Liam. "Free AI developer app: IBM's new tool can label objects in videos for you". ZDNet.
- ^ a b Solawetz, Jacob (July 27, 2020). "Getting Started with VoTT Annotation Tool for Computer Vision". Roboflow Blog.
- ^ "Best Open Source Annotation Tools for Computer Vision". www.sicara.ai.
- ^ "Beyond Sentiment Analysis: Object Detection with ML.NET". September 20, 2020.
- ^ "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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