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Computer Vision with RegNet

Image Classification using SuperGradients and RegNet

What you’ll learn

  • Define how to design a search space for network architectures
  • Describe the AnyNet architecture and how it informs the RegNet design space
  • Describe the analysis and findings of the RegNet paper
  • Perform image classification using RegNet

Course content

33 total mins


  • The target learners are students with a strong foundation in machine learning and a basic understanding of deep learning. These students need to learn about the history and current state of computer vision, as well as gain practical skills for developing and training deep neural networks for image classification tasks.


In this course, you are going to learn about RegNet architecture.

You’ll learn what it is, why it’s important, and the novelty that it introduced. With all of the free courses that I put on Udemy, I’m focused more on giving you the intuition and the kind of reasoning into the importance of this paper. If you’re looking for mathematical details, I’ll link to the paper and additional resources that you can go through. The thing about these free courses is that they’re limited to no more than an hour, which means I can only fit so much into this course.

You will learn a lot and you’ll walk away with a template for a project that you can use, cuz you will see RedNet inaction using the open-source training library called SuperGradients. It’ll provide a great foundation for you to just plug and play your own data sets and do a cool image classification project.

Here’s what the agenda is like for this course: Start off by talking about the motivation for RegNet: what it is, why is it that we’re embarking on this journey. And then from there you’ll learn about design spaces and then we’ll get into two specific design spaces, the AnyNet design space and the RegNet design space. I’ll link to resources that will give you a deeper understanding of these as well.

The course will wrap up with an overview of the analysis and findings that the researchers discovered in their experiments.

And then finally, we’ll see RegNet in action using the SuperGradients training library to perform image classification tasks.

Who this course is for:

  • To complete this course, learners should have a strong foundation in machine learning and a basic understanding of computer vision. This includes knowledge of supervised learning, neural networks, and image processing. Regarding skill level, learners should to be advanced beginners to intermediate. They have a solid understanding of the fundamental concepts and techniques of machine learning but may still be learning about more advanced topics such as computer vision. They have experience with Python, Pandas, scikit-learn and PyTorch.

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