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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

Understanding IoU

Imagine a scenario where we came up with a prediction of a bounding box for an object. How do we measure the accuracy of our prediction? The concept IoU comes in handy in such a scenario.

The word intersection within the term intersection over union refers to measuring how much the predicted and actual bounding boxes overlap, while union refers to measuring the overall space possible for overlap. IoU is the ratio of the overlapping region between the two bounding boxes over the combined region of both bounding boxes.

This can be represented in a diagram, as follows:

Figure 7.6: Visualizing IoU

In the preceding diagram of two bounding boxes (rectangles), let’s consider the left bounding box as the ground truth and the right bounding box as the predicted location of the object. IoU as a metric is the ratio of the overlapping region over the combined region between the two bounding boxes. In the following diagram, you can observe the variation...

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