This is the code repository for PyTorch Computer Vision Cookbook, published by Packt.

Over 70 recipes to master the art of computer vision with deep learning and PyTorch 1.x

This book enables you to solve the trickiest of problems in computer vision using deep learning algorithms and techniques. You will learn to use several different algorithms for different CV problems such as classification, detection, segmentation, and more using Pytorch. Packed with best practices in training and deployment of CV applications.

This book covers the following exciting features:

• Develop, train and deploy deep learning algorithms using PyTorch 1.x
• Understand how to fine-tune and change hyperparameters to train deep learning algorithms
• Perform various CV tasks such as classification, detection, and segmentation
• Implement a neural style transfer network based on CNNs and pre-trained models
• Generate new images and implement adversarial attacks using GANs
• Implement video classification models based on RNN, LSTM, and 3D-CNN
• Discover best practices for training and deploying deep learning algorithms for CV applications

If you feel this book is for you, get your copy today!

All of the code is organized into folders.

The code will look like the following:

# define a tensor with specific data type
x = torch.ones(2, 2, dtype=torch.int8)
print(x)
print(x.dtype)
tensor([[1, 1],
 [1, 1]], dtype=torch.int8)
torch.int8

Following is what you need for this book: Computer vision professionals, data scientists, deep learning engineers, and AI developers looking for quick solutions for various computer vision problems will find this book useful. Intermediate-level knowledge of computer vision concepts, along with Python programming experience is required.

With the following software and hardware list you can run all code files present in the book (Chapter 1-10).

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

• Deep Learning with PyTorch 1.x - Second Edition [Packt] [Amazon]

• Hands-On Generative Adversarial Networks with PyTorch 1.x [Packt] [Amazon]

Michael Avendi is a principal data scientist with vast experience in deep learning, computer vision, and medical imaging analysis. He works on the research and development of data-driven algorithms for various imaging problems, including medical imaging applications. His research papers have been published in major medical journals, including the Medical Imaging Analysis journal. Michael Avendi is an active Kaggle participant and was awarded a top prize in a Kaggle competition in 2017.

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