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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

Advanced Object Detection

In the previous chapter, we learned about the R-CNN and Fast R-CNN techniques, which leverage region proposals to generate predictions of the locations of objects in an image along with the classes corresponding to objects in the image. Furthermore, we learned about the bottleneck of the speed of inference, which happens due to having two different models – one for region proposal generation and another for object detection. In this chapter, we will learn about different modern techniques, such as Faster R-CNN, YOLO, and single-shot detector (SSD), that overcome slow inference time by employing a single model to make predictions for both the class of the object and the bounding box in a single shot. We will start by learning about anchor boxes and then proceed to learn how each of the techniques works and how to implement them to detect objects in an image.

We will cover the following topics in this chapter:

  • Components of modern object...
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