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Modern Computer Vision with PyTorch

You're reading from   Modern Computer Vision with PyTorch Explore deep learning concepts and implement over 50 real-world image applications

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Product type Paperback
Published in Nov 2020
Publisher Packt
ISBN-13 9781839213472
Length 824 pages
Edition 1st Edition
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Authors (2):
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Yeshwanth Reddy Yeshwanth Reddy
Author Profile Icon Yeshwanth Reddy
Yeshwanth Reddy
V Kishore Ayyadevara V Kishore Ayyadevara
Author Profile Icon V Kishore Ayyadevara
V Kishore Ayyadevara
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Toc

Table of Contents (25) 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. Training with Minimal Data Points 19. Combining Computer Vision and NLP Techniques 20. Combining Computer Vision and Reinforcement Learning 21. Moving a Model to Production 22. Using OpenCV Utilities for Image Analysis 23. Other Books You May Enjoy Appendix

Mean average precision

So far, we have looked at getting an output that comprises a bounding box around each object within the image and the class corresponding to the object within the bounding box. Now comes the next question: How do we quantify the accuracy of the predictions coming from our model?

mAP comes to the rescue in such a scenario. Before we try to understand mAP, let's first understand precision, then average precision, and finally, mAP:

  • Precision: Typically, we calculate precision as:

A true positive refers to the bounding boxes that predicted the correct class of objects and that have an IoU with the ground truth that is greater than a certain threshold. A false positive refers to the bounding boxes that predicted the class incorrectly or have an overlap that is less than the defined threshold with the ground truth. Furthermore, if there are multiple bounding boxes that are identified for the same ground truth bounding box, only one box can get into a true positive...

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