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


In this chapter, we have learned about the different variants of autoencoders: vanilla, convolutional, and variational. We also learned about how the number of units in the bottleneck layer influences the reconstructed image. Next, we learned about identifying images that are similar to a given image using the t-SNE technique. We learned that when we sample vectors, we cannot get realistic images, and by using variational autoencoders, we learned about generating new images by using a combination of reconstruction loss and KL divergence loss. Next, we learned how to perform an adversarial attack on images to modify the class of an image while not changing the perceptive content of the image. Finally, we learned about leveraging the combination of content loss and gram matrix-based style loss to optimize for content and style loss of images to come up with an image that is a combination of two input images. Finally, we learned about tweaking an autoencoder to swap two faces without...