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 working details of modern object detection algorithms: Faster R-CNN, YOLO, and SSD. We learned how they overcome the limitation of having two separate models – one for fetching region proposals and the other for fetching class and bounding box offsets on region proposals. Furthermore, we implemented Faster R-CNN using PyTorch, YOLO using darknet, and SSD from scratch.

In the next chapter, we will learn about image segmentation, which goes one step beyond object localization by identifying the pixels that correspond to an object.

Furthermore, in Chapter 15, Combining Computer Vision and NLP Techniques, we will learn about DETR, a transformer-based object detection algorithm, and in Chapter 10, Applications of Object Detection, and Segmentation, we will learn about the Detectron2 framework, which helps in not only detecting objects but also segmenting them in a single shot.