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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

Training Faster R-CNN on a custom dataset

In the following code, we will train the Faster R-CNN algorithm to detect the bounding boxes around objects present in images. For this, we will work on the same truck versus bus detection exercise from the previous chapter:

Find the following code in the Training_Faster_RCNN.ipynb file in the Chapter08 folder on GitHub at https://bit.ly/mcvp-2e.

  1. Download the dataset:
    !pip install -qU torch_snippets
    import os
    %%writefile kaggle.json
     {"username":"XXX", "key":"XXX"}
     !mkdir -p ~/.kaggle
     !cp kaggle.json ~/.kaggle/
     !chmod 600 /root/.kaggle/kaggle.json
     !kaggle datasets download -d sixhky/open-images-bus-trucks/
     !unzip -qq open-images-bus-trucks.zip
     !rm open-images-bus-trucks.zip
    
  2. Read the DataFrame containing metadata of information about images and their bounding box, and classes:
    from torch_snippets import *
    from PIL import Image
    IMAGE_ROOT ...
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