This course "Semantic Segmentation: Tutorial" will assist you in learning to understand all the principles that are involved in semantic segmentation. Semantic segmentation is a crucial element for self-driving vehicles and robotics since it's crucial for models to comprehend the context in the environment that they operate in. You're probably familiar with image classification - it assigns a label, or class to an image input or object's shape and shape, then sorting pixels in relation to objects, etc. In this case you'll need to achieve image segmentation which includes labeling every one of the pixels in the image. In simple terms, image segmentation is creating a neural network that is trained to produce a pixel-wise image of an image. This allows you to comprehend images on a pixel level but at a lower level. There are a variety of images segmentation applications, such as medical imaging, autonomous vehicles and satellite imaging to mention a few. Semantic segmentation algorithms are extremely efficient and can be used in a variety of scenarios, such as self-driving vehicles and, in this video, we'll be discussing the specific application of Semantic Segmentation as well as the introduction to U-Net. In the final part of the video, we'll show a demonstration using Semantic Segmentation.
