You're reading from Modern Computer Vision with PyTorch A practical roadmap from deep learning fundamentals to advanced applications and Generative AI

Product type Paperback

Published in Jun 2024

Publisher Packt

ISBN-13 9781803231334

Length 746 pages

Edition 2nd Edition

Languages

Python

Tools

PyTorch

Concepts

Computer Vision

Authors (2):

V Kishore Ayyadevara

Yeshwanth Reddy

View More author details

Table of Contents (26) Chapters

Preface

1. Section 1: Fundamentals of Deep Learning for Computer Vision

2. Artificial Neural Network Fundamentals FREE CHAPTER

3. PyTorch Fundamentals

4. Building a Deep Neural Network with PyTorch

5. Section 2: Object Classification and Detection

6. Introducing Convolutional Neural Networks

7. Transfer Learning for Image Classification

8. Practical Aspects of Image Classification

9. Basics of Object Detection

10. Advanced Object Detection

11. Image Segmentation

12. Applications of Object Detection and Segmentation

13. Section 3: Image Manipulation

14. Autoencoders and Image Manipulation

15. Image Generation Using GANs

16. Advanced GANs to Manipulate Images

17. Section 4: Combining Computer Vision with Other Techniques

18. Combining Computer Vision and Reinforcement Learning

19. Combining Computer Vision and NLP Techniques

20. Foundation Models in Computer Vision

21. Applications of Stable Diffusion

22. Moving a Model to Production

23. Other Books You May Enjoy

24. Index

Appendix

Summary

In this chapter, we learned about leveraging two different neural networks to generate new images of handwritten digits using GANs. Next, we generated realistic faces using DCGANs. Finally, we learned about conditional GANs, which help us in generating images of a certain class. Having generated images using different techniques, we could still see that the generated images were not sufficiently realistic. Furthermore, while we generated images by specifying the class of images we want to generate in conditional GANs, we are still not in a position to perform image translation, where we ask to replace one object in the image with another one, with everything else left as is. In addition, we are yet to have an image generation mechanism where the number of classes (styles) to generate is more unsupervised.

In the next chapter, we will learn about generating images that are more realistic using some of the latest variants of GANs. In addition, we will learn about generating...