Semantic Segmentation Tutorial

An Introduction to Semantic Segmentation

Semantic segmentation is an image processing technique used in computer vision applications to identify and segment objects in an image. It uses a deep learning algorithm to assign a label to each pixel in a snap. It can also detect semantic boundaries between objects, such as roads or walls.

Introduction to U-Net: U-Net is a deep learning architecture for semantic and a decoder. The encoder is used to extract features from an image, and the decoder is used to generate a segmentation mask. The U-Net architecture can capture both global and local features in an image.

Demo on Semantic Segmentation: A demo of semantic segmentation can be found online. This demo will show how semantic segmentation can segment an image into multiple classes, such as people, cars, and trees. It will also show how semantics can detect boundaries between objects, such as roads and walls.

A Free Course on Semantic Segmentation by Great Learning: 

Great Learning offers a free course on semantic segmentation. It covers data pre-processing, feature extraction, and model evaluation. Students will learn to use the U-Net architecture to segment images into multiple classes and detect boundaries between objects.

The course covers the fundamentals of semantic segmentation and how to use the U-Net architecture for image segmentation. It also covers data pre-processing, feature extraction, and model evaluation. Students will learn how to use the U-Net architecture for image segmentation and detect boundaries between objects.

Upon completing the course, students will receive a certificate of completion.