Object Segmentation with VGG Image Annotator: A Tutorial on Using the U-NET Data Annotation Tool

In this video tutorial, we will be introducing the VGG Image Annotator, a powerful data annotation tool for object segmentation using the U-NET architecture. This tool allows you to easily label and segment objects in images, making it an essential tool for creating high-quality training datasets for machine learning models. We will be walking you through the process of using the VGG Image Annotator, from setting up your project to exporting your labeled data. Additionally, we will be discussing some best practices for creating accurate and consistent annotations. Whether you're a beginner or an experienced user, this tutorial will provide you with all the information you need to effectively use the VGG Image Annotator for object segmentation with U-NET.
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[@cedrickiplimo2991]

This is a well-explained tutorial. At first, I was scared by its length, but I found myself following undistracted all through to the end. Thumbs up!

[@harunwagura4727]

Thanks @Augmented Startups. Now I know how VGG works.

[@Augmented_AI]

I'm glad we can help :)

[@nishantkshirsagar13]

this was a great tutorial thank you very much

[@hakansaglam7205]

thanks

[@shaker8984]

What about if I have for example more than one class in an image, and I work with 10 classes? How to do it then?

[@supriyoghosh2003]

Have you found any solution or any reference? Please help me out then

[@shaker8984]

@@supriyoghosh2003 Look up the structure of json file, i was just changing names according to classes since i didnt need other information. You can add them also thoguh

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