Semantic Segmentation Tutorial
Learn the basics of semantic segmentation in this free tutorial course which covers everything from fundamental concepts to advanced techniques. Sign up for free course & take the first step towards mastering semantic segmentation
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About this course
This course 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. You may be familiar with image classification- the network assigns a label or class to an input image, objects’ shape, sorting pixels with respect to objects etc. In this scenario, you will want to attain image segmentation that includes labeling each pixel of the image. Therefore, in simple terms, image segmentation involves training the neural network to output a pixel-wise mask of the image. This allows you to understand images at a pixel level - at a much lower level. You will find many image segmentation applications like 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 of Semantic Segmentation.
Course Outline
This module introduces you to U-Net, a convolutional neural network for image segmentation. You will thoroughly understand it with the help of the given examples.
In this module, you will learn semantic segmentation with the help of an image example. You will also comprehend instance segmentation, U-net, and standard convolutions.
This module contains a detailed hands-on demo on semantic segmentation using Python programming language.
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68% of learners found all the desired skills & tools
Frequently Asked Questions
Will I get a certificate after completing this Semantic Segmentation free course?
Yes, you will get a certificate of completion for Semantic Segmentation after completing all the modules and cracking the assessment. The assessment tests your knowledge of the subject and badges your skills.
How much does this Semantic Segmentation course cost?
It is an entirely free course from Great Learning Academy. Anyone interested in learning the basics of Semantic Segmentation can get started with this course.
Is there any limit on how many times I can take this free course?
Once you enroll in the Semantic Segmentation course, you have lifetime access to it. So, you can log in anytime and learn it for free online.
Can I sign up for multiple courses from Great Learning Academy at the same time?
Yes, you can enroll in as many courses as you want from Great Learning Academy. There is no limit to the number of courses you can enroll in at once, but since the courses offered by Great Learning Academy are free, we suggest you learn one by one to get the best out of the subject.
Why choose Great Learning Academy for this free Semantic Segmentation course?
Great Learning Academy provides this Semantic Segmentation course for free online. The course is self-paced and helps you understand various topics that fall under the subject with solved problems and demonstrated examples. The course is carefully designed, keeping in mind to cater to both beginners and professionals, and is delivered by subject experts.
Great Learning is a global ed-tech platform dedicated to developing competent professionals. Great Learning Academy is an initiative by Great Learning that offers in-demand free online courses to help people advance in their jobs. More than 5 million learners from 140 countries have benefited from Great Learning Academy's free online courses with certificates. It is a one-stop place for all of a learner's goals.
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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.