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 are VGG and ResNet pre-trained architectures trained on?
  2. Why does VGG11 have an inferior accuracy to VGG16?
  3. What does the number 11 in VGG11 represent?
  4. What is residual in the residual network?
  5. What is the advantage of a residual network?
  6. What are the various popular pre-trained models?
  7. During transfer learning, why should images be normalized with the same mean and standard deviation as those that were used during the training of a pre-trained model?
  8. Why do we freeze certain parameters in a model?
  9. How do we know the various modules that are present in a pre-trained model?
  10. How do we train a model that predicts categorical and numerical values together?
  11. Why might age and gender prediction code not always work for an image of your own interest if we execute the same code as we wrote in the age and gender estimation section?
  12. How can we further improve the accuracy of the facial keypoint recognition model that we wrote about in the facial key points prediction section?