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

Preparing our data for image classification

Given that we are covering multiple scenarios in this chapter, in order for us to see the advantage of one scenario over the other, we will work on a single dataset throughout this chapter the Fashion MNIST dataset. Let's prepare this dataset:

The following code is available as Preparing_our_data.ipynb in the Chapter03 folder of this book's GitHub repository -
  1. Start by downloading the dataset and importing the relevant packages. The torchvision package contains various datasets one of which is the FashionMNIST dataset, which we will be working on in this chapter:
from torchvision import datasets
import torch
data_folder = '~/data/FMNIST' # This can be any directory
# you want to download FMNIST to
fmnist = datasets.FashionMNIST(data_folder, download=True, \

In the preceding code, we are specifying the folder (data_folder) where we...