Multiclass semantic segmentation using U-Net

This video shows the implementation of a computer vision technique known as semantic segmentation. The methodology employed in this code revolves around utilizing a neural network architecture called U-Net.

Semantic segmentation involves segmenting an image into various meaningful segments, where each segment corresponds to a particular class. In this specific code, the segmentation task is "multiclass," which means that the neural network is designed to differentiate and classify multiple classes or objects within an image simultaneously.

The dataset used in this video can be downloaded from the link below. This dataset can be used to train and test machine learning algorithms designed for multiclass semantic segmentation. Please read the Readme document for more information.

https://drive.google.com/file/d/1HWtBaSa-LTyAMgf2uaz1T9o1sTWDBajU/view

To annotate images and generate labels, you can use APEER (for free):
www.apeer.com