VoTT
| Original author(s) | Commercial Software Engineering (CSE) group at Microsoft in Israel |
|---|---|
| Developer(s) | Microsoft and community |
| Initial release | 2018; 7 years ago (2018) |
| Stable release | v2.2.0
/ June 3, 2020; 4 years ago (2020年06月03日) |
| 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. It is available as a stand-alone web application and can be used in any modern web browser.[2]
Notable features include the ability to label images or video frames, support for importing data from local or cloud storage providers, 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)[3]
The VoTT source code is licensed under MIT License and available on GitHub.[4]
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.
- ^ "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.
