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

Moving a Model to Production

Moving a model to production is a step toward enabling the consumption of our model by an external party. We should expose our model to the world and start rendering predictions on real, unseen input.

It is not sufficient to have a trained PyTorch model for deployment. We need additional server components to set up communication channels from the real world to the PyTorch model and back to the real world. It is important that we know how to create an API (through which a user can interact with the model), wrap it as a self-contained application (so that it can be deployed on any computer), and ship it to the cloud – so that anybody with the required URL and credentials can interact with the model. To successfully move a model to production, all these steps are necessary.

In addition, we will have to deal with constraints around the latency of predictions (the time taken to get a prediction) and the size of the model (when deploying on edge...