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Modern Computer Vision with PyTorch

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

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Product type Paperback
Published in Jun 2024
Publisher Packt
ISBN-13 9781803231334
Length 746 pages
Edition 2nd Edition
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Authors (2):
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V Kishore Ayyadevara V Kishore Ayyadevara
Author Profile Icon V Kishore Ayyadevara
V Kishore Ayyadevara
Yeshwanth Reddy Yeshwanth Reddy
Author Profile Icon Yeshwanth Reddy
Yeshwanth Reddy
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Toc

Table of Contents (26) Chapters Close

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

Image Segmentation

In the previous chapter, we learned about detecting objects present in images, along with the classes that correspond to the detected objects. In this chapter, we will go one step further by not only drawing a bounding box around an object but also by identifying the exact pixels that contain the object. In addition to that, by the end of this chapter, we will be able to single out instances/objects that belong to the same class.

We will also learn about semantic segmentation and instance segmentation by looking at the U-Net and Mask R-CNN architectures. Specifically, we will cover the following topics:

  • Exploring the U-Net architecture
  • Implementing semantic segmentation using U-Net to segment objects on a road
  • Exploring the Mask R-CNN architecture
  • Implementing instance segmentation using Mask R-CNN to identify multiple instances of a given class

A succinct illustration of what we are trying to achieve through image segmentation...

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