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

Introducing transfer learning

Transfer learning is a technique where knowledge gained from one task is leveraged to solve another similar task.

Imagine a model that is trained on millions of images that span thousands of object classes (not just cats and dogs). The various filters (kernels) of the model would activate for a wide variety of shapes, colors, and textures within the images. Those filters can then be reused to learn features on a new set of images. Post learning the features, they can be connected to a hidden layer prior to the final classification layer for customizing on the new data.

ImageNet (http://www.image-net.org/) is a competition hosted to classify approximately 14 million images into 1,000 different classes. It has a variety of classes in the dataset, including Indian elephant, lionfish, hard disk, hair spray, and jeep.

The deep neural network architectures that we will go through in this chapter have been trained on the ImageNet dataset. Furthermore...