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


  1. What is an encoder in an autoencoder?
  2. What loss function does an autoencoder optimize for?
  3. How do autoencoders help in grouping similar images?
  4. When is a convolutional autoencoder useful?
  5. Why do we get non-intuitive images if we randomly sample from vector space of embeddings obtained from vanilla/convolutional autoencoders?
  6. What are the loss functions that VAEs optimize for?
  7. How do VAEs overcome the limitation of vanilla/convolutional autoencoders to generate new images?
  8. During an adversarial attack, why do we modify the input image pixels and not the weight values?
  1. In a neural style transfer, what are the losses that we optimize for?
  2. Why do we consider the activation of different layers and not the original image when calculating style and content loss?
  3. Why do we consider gram matrix loss and not the difference between images when calculating style loss?
  4. Why do we warp images while building a model to generate deep fakes?