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

Autoencoders and Image Manipulation

In previous chapters, we learned about classifying images, detecting objects in an image, and segmenting the pixels corresponding to objects in images. In this chapter, we will learn about representing an image in a lower dimension using autoencoders and then leveraging the lower-dimensional representation of an image to generate new images by using variational autoencoders. Learning how to represent images in a lower number of dimensions helps us manipulate (modify) the images to a considerable degree. We will also learn about generating novel images that are based on the content and style of two different images. We will then explore how to modify images in such a way that the image is visually unaltered; however, the class corresponding to the image is changed from one to another when the image is passed through an image classification model. Finally, we will learn about generating deepfakes: given a source image of person A, we generate a target...