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This video on "Semantic Segmentation Tutorial" will help you to master all the concepts of semantic segmentation. Semantic segmentation is very crucial in self-driving cars and robotics because it is important for the models to understand the context in the environment in which they're operating.

So far you have seen image classification, where the task of the network is to assign a label or class to an input image. However, suppose you want to know where an object is located in the image, the shape of that object, which pixel belongs to which object, etc. In this case, you will want to segment the image, i.e., each pixel of the image is given a label.
Thus, the task of image segmentation is to train a neural network to output a pixel-wise mask of the image. This helps in understanding the image at a much lower level, i.e., the pixel level. Image segmentation has many applications in medical imaging, self-driving cars, and satellite imaging to name a few

Semantic segmentation algorithms are super powerful and have many use cases, including self-driving cars — and in today’s video, we will be covering the certain application of Semantic Segmentation along with the Introduction to U-Net. At the end of the video, we will also be showing a demo on Semantic Segmentation.


0:00 Introduction
3:15 - Introduction to U-Net
9:05 - Introduction to Semantic Segmentation
29:15 - Demo on Semantic Segmentation
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