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 started with learning about creating a training dataset for the process of object localization and detection. Next, we learned about SelectiveSearch, a region proposal technique that recommends regions based on the similarity of pixels in proximity. We next learned about calculating the IoU metric to understand the goodness of the predicted bounding box around the objects present in the image. We next learned about performing non-max suppression to fetch one bounding box per object within an image before learning about building R-CNN and Fast R-CNN models from scratch. In addition, we learned about the reason why R-CNN is slow and how Fast R-CNN leverages RoI pooling and fetches region proposals from feature maps to make inference faster. Finally, we understood that having region proposals coming from a separate model is resulting in the higher time taken to predict on new images.

In the next chapter, we will learn about some of the modern object detection...