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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

Shipping and running the Docker container on the cloud

We will rely on AWS for our cloud requirements. We will use two of AWS’s free offerings for our purpose:

  • Elastic Container Registry (ECR): Here, we will store our Docker image.
  • EC2: Here, we will create a Linux system to run our API Docker image.

In this section, we will focus only on ECR. Here is a high-level overview of the steps we will follow to push the Docker image to the cloud:

  1. Configure AWS on the local machine.
  2. Create a Docker repository on AWS ECR and push the sdd:latest image.
  3. Create an EC2 instance.
  4. Install dependencies on the EC2 instance.
  5. Create and run the pushed Docker image in step 2, on the EC2 instance.

The code in the following sections is also summarized as a video walkthrough here: https://tinyurl.com/MCVP-FastAPI2AWS.

Let’s implement the preceding steps, starting with configuring AWS in the next section.

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