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

Multi-object instance segmentation

In previous chapters, we learned about various object detection algorithms. In this section, we will learn about the Detectron2 platform (https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/) before we implement multi-object instance segmentation using the Google Open Images dataset. Detectron2 is a platform built by the Facebook team. Detectron2 includes high-quality implementations of state-of-the-art object detection algorithms, including DensePose of the Mask R-CNN model family. The original Detectron framework was written in Caffe2, while the Detectron2 framework is written using PyTorch.

Detectron2 supports a range of tasks related to object detection. Like the original Detectron, it supports object detection with boxes and instance segmentation masks, as well as human pose prediction. Beyond that, Detectron2 adds support for semantic segmentation and panoptic segmentation (a task that combines both...