Book Image

Modern Computer Vision with PyTorch

By : V Kishore Ayyadevara, Yeshwanth Reddy
Book Image

Modern Computer Vision with PyTorch

By: V Kishore Ayyadevara, Yeshwanth Reddy

Overview of this book

Deep learning is the driving force behind many recent advances in various computer vision (CV) applications. This book takes a hands-on approach to help you to solve over 50 CV problems using PyTorch1.x on real-world datasets. You’ll start by building a neural network (NN) from scratch using NumPy and PyTorch and discover best practices for tweaking its hyperparameters. You’ll then perform image classification using convolutional neural networks and transfer learning and understand how they work. As you progress, you’ll implement multiple use cases of 2D and 3D multi-object detection, segmentation, human-pose-estimation by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 platform. The book will also guide you in performing facial expression swapping, generating new faces, and manipulating facial expressions as you explore autoencoders and modern generative adversarial networks. You’ll learn how to combine CV with NLP techniques, such as LSTM and transformer, and RL techniques, such as Deep Q-learning, to implement OCR, image captioning, object detection, and a self-driving car agent. Finally, you'll move your NN model to production on the AWS Cloud. By the end of this book, you’ll be able to leverage modern NN architectures to solve over 50 real-world CV problems confidently.
Table of Contents (25 chapters)
Section 1 - Fundamentals of Deep Learning for Computer Vision
Section 2 - Object Classification and Detection
Section 3 - Image Manipulation
Section 4 - Combining Computer Vision with Other Techniques

Introducing the torch_snippets library

As you may have noticed, we are using the same functions in almost all the sections. It is a waste of our time to write the same lines of functions again and again. For convenience, the authors of this book have written a Python library by the name of torch_snippets so that our code looks short and clean.

Utilities such as reading an image, showing an image, and the entire training loop are quite repetitive. We want to circumvent writing the same functions over and over by wrapping them in code that is preferably a single function call. For example, to read a color image, we need not write cv2.imread(...) followed by cv2.cvtColor(...) every time. Instead, we can simply call read(...). Similarly, for plt.imshow(...), there are numerous hassles, including the fact that the size of the image should be optimal, and that the channel dimension should be last (remember PyTorch has them first). These will always be taken care of by the single function, show...