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From Wikipedia, the free encyclopedia

VoTT
Original author(s)Commercial Software Engineering (CSE) group at Microsoft in Israel
Developer(s)Microsoft and community
Initial release2018; 5 years ago (2018)
Stable release
v2.2.0 / June 3, 2020; 3 years ago (2020-06-03)
Repositorygithub.com/microsoft/VoTT
Written inTypeScript
Operating systemWindows, Linux, macOS
PlatformCross-platform
TypeImage annotation tool
LicenseMIT License
Websitevott.z22.web.core.windows.net

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.[2]

YouTube Encyclopedic

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  • How to Label Images for Object Detection with CVAT

Transcription

Overview

VoTT is a React+Redux web application that requires Node.js and npm.[3] It is available as a stand-alone web application and can be used in any modern web browser.[4]

Notable features include the ability to label images or video frames,[2] support for importing data from local or cloud storage providers,[3] and support for exporting labeled data to local or cloud storage providers.

Labeled assets can be exported into the following formats:

The VoTT source code is licensed under MIT License and available on GitHub.[6]

See also

References

  1. ^ Tung, Liam. "Free AI developer app: IBM's new tool can label objects in videos for you". ZDNet.
  2. ^ a b c Bornstein, Aaron (Ari) (February 4, 2019). "Using Object Detection for Complex Image Classification Scenarios Part 4". Medium.
  3. ^ a b Solawetz, Jacob (July 27, 2020). "Getting Started with VoTT Annotation Tool for Computer Vision". Roboflow Blog.
  4. ^ "Best Open Source Annotation Tools for Computer Vision". www.sicara.ai.
  5. ^ "Beyond Sentiment Analysis: Object Detection with ML.NET". September 20, 2020.
  6. ^ "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.

External links

This page was last edited on 23 December 2023, at 00:35
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