Software Alternatives, Accelerators & Startups

Accord.NET Framework VS Computer Vision Annotation Tool (CVAT)

Compare Accord.NET Framework VS Computer Vision Annotation Tool (CVAT) and see what are their differences

Accord.NET Framework logo Accord.NET Framework

Machine learning, computer vision and statistics framework for .NET

Computer Vision Annotation Tool (CVAT) logo Computer Vision Annotation Tool (CVAT)

Powerful and efficient Computer Vision Annotation Tool (CVAT) - opencv/cvat
  • Accord.NET Framework Landing page
    Landing page //
    2019-08-30
  • Computer Vision Annotation Tool (CVAT) Landing page
    Landing page //
    2023-08-26

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Computer Vision Annotation Tool (CVAT) videos

Computer Vision Annotation Tool (CVAT): annotation mode

Category Popularity

0-100% (relative to Accord.NET Framework and Computer Vision Annotation Tool (CVAT))
Data Science Tools
100 100%
0% 0
Data Science And Machine Learning
AI
0 0%
100% 100
OCR
100 100%
0% 0

User comments

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Social recommendations and mentions

Based on our record, Computer Vision Annotation Tool (CVAT) should be more popular than Accord.NET Framework. It has been mentiond 14 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Accord.NET Framework mentions (2)

  • Does Accord.NET work on ARM devices such as Android phones or iOS phones?
    Http://accord-framework.net/. Is there a better alternative? Source: 9 months ago
  • ML/Image Classification/Face recognition on M1 Mac?
    Have you tried http://accord-framework.net/? Source: almost 3 years ago

Computer Vision Annotation Tool (CVAT) mentions (14)

  • Exploring Open-Source Alternatives to Landing AI for Robust MLOps
    Another powerful resource is CVAT, the Computer Vision Annotation Tool which supports both image and video annotations with advanced capabilities such as interpolation of shapes between frames, making it highly suitable for computer vision. - Source: dev.to / 6 months ago
  • Need help identifying a good open source data annotation tool
    CVAT has an open source repo under MIT license: https://github.com/opencv/cvat I've not worked with it directly but it might be a good place to start. Source: 6 months ago
  • Way to label yolov7 images fast
    An open source annotation tool that integrates object detectors is CVAT https://github.com/opencv/cvat however, using your own detector might require some coding. There is an integration for yolov5, but without modification it only loads the pretrained models. Source: about 1 year ago
  • Segment Anything Model is now available in the open-source CVAT
    This integration is currently available in the open-source version of Computer Vision Annotation Tool (http://github.com/opencv/cvat)! Please use it for your computer vision projects to segment images faster. - Source: Hacker News / about 1 year ago
  • How to build computer vision dataset labeling team in-house
    You can download the CVAT docker from a github (Link) and install it yourself, keeping all data local. And here are two options - locally on your personal computer (or company server) or in your own cloud (there are instructions on how to do this with AWS). - Source: dev.to / about 1 year ago
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What are some alternatives?

When comparing Accord.NET Framework and Computer Vision Annotation Tool (CVAT), you can also consider the following products

FastCV Computer Vision - FastCV will enable you to add new user experiences into your camera-based apps like:

AWS SageMaker Ground Truth - Build highly accurate training datasets using machine learning and reduce data labeling costs by up to 70%.

OpenCV - OpenCV is the world's biggest computer vision library

Supervisely - Supervisely helps people with and without machine learning expertise to create state-of-the-art...

BoofCV - BoofCV is an open source library written from scratch for real-time computer vision.

Labelbox - Build computer vision products for the real world