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 transfer learning

Transfer learning is a technique where knowledge gained from one task is leveraged to solve another similar task.

Imagine a model that is trained on millions of images that span thousands of classes of objects (not just cats and dogs). The various filters (kernels) of the model would activate for a wide variety of shapes, colors, and textures within the images. Those filters can now be reused to learn features on a new set of images. Post learning the features, they can be connected to a hidden layer prior to the final classification layer for customizing on the new data.

ImageNet ( is a competition hosted to classify approximately 14 million images into 1,000 different classes. It has a variety of classes in the dataset, including Indian elephant, lionfish, hard disk, hair spray, and jeep.

The deep neural network architectures that we will go through in this chapter have been trained on the ImageNet dataset. Furthermore, given...