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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

Applications of Object Detection and Segmentation

In previous chapters, we learned about various object detection techniques, such as the R-CNN family of algorithms, YOLO, SSD, and the U-Net and Mask R-CNN image segmentation algorithms. In this chapter, we will take our learning a step further – we will work on more realistic scenarios and learn about frameworks/architectures that are more optimized to solve detection and segmentation problems.

We will start by leveraging the Detectron2 framework to train and detect custom objects present in an image. We will also predict the pose of humans present in an image using a pre-trained model. Furthermore, we will learn how to count the number of people in a crowd in an image and then learn about leveraging segmentation techniques to perform image colorization. Next, we will learn about a modified version of YOLO to predict 3D bounding boxes around objects by using point clouds obtained from a LIDAR sensor. Finally, we will learn...

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