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The Regularization Cookbook

You're reading from   The Regularization Cookbook Explore practical recipes to improve the functionality of your ML models

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Product type Paperback
Published in Jul 2023
Publisher Packt
ISBN-13 9781837634088
Length 424 pages
Edition 1st Edition
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Author (1):
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Vincent Vandenbussche Vincent Vandenbussche
Author Profile Icon Vincent Vandenbussche
Vincent Vandenbussche
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Table of Contents (14) Chapters Close

Preface 1. Chapter 1: An Overview of Regularization 2. Chapter 2: Machine Learning Refresher FREE CHAPTER 3. Chapter 3: Regularization with Linear Models 4. Chapter 4: Regularization with Tree-Based Models 5. Chapter 5: Regularization with Data 6. Chapter 6: Deep Learning Reminders 7. Chapter 7: Deep Learning Regularization 8. Chapter 8: Regularization with Recurrent Neural Networks 9. Chapter 9: Advanced Regularization in Natural Language Processing 10. Chapter 10: Regularization in Computer Vision 11. Chapter 11: Regularization in Computer Vision – Synthetic Image Generation 12. Index 13. Other Books You May Enjoy

Semantic segmentation using transfer learning

In this recipe, we will take advantage of transfer learning and the fine-tuning of pretrained models to undertake a specific task of computer vision – the semantic segmentation of drone images.

Object detection and instance segmentation are about detecting objects in an image – an object is delimited by a bounding box, as well as a polygon in the case of instance segmentation. Alternatively, semantic segmentation is about classifying all the pixels of an image in a class.

As we can see in Figure 10.22, all pixels have a given color so that each one is attributed a class.

Figure 10.22 – An example of annotation of semantic segmentation. On the left is the original image, and on the right is the labeled image – there is one class of object per color, and each pixel is assigned to a given class

Figure 10.22 – An example of annotation of semantic segmentation. On the left is the original image, and on the right is the labeled image – there is one class of object per color, and each pixel is assigned to a given class

Even if it may look similar to instance segmentation, we will see in this recipe that...

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