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

Using a sequential method to build a neural network

So far, we have built a neural network by defining a class where we define the layers and how the layers are connected with each other. In this section, we will learn about a simplified way of defining the neural network architecture using the Sequential class. We will perform the same steps as we have done in the previous sections, except that the class that was used to define the neural network architecture manually will be substituted with a Sequential class for creating a neural network architecture.

Let's code up the network for the same toy data that we have worked on in this chapter:

The following code is available as Sequential_method_to_build_a_neural_network.ipynb in the Chapter02 folder of this book's GitHub repository -
  1. Define the toy dataset:
x = [[1,2],[3,4],[5,6],[7,8]]
y = [[3],[7],[11],[15]]
  1. Import the relevant packages and define the device we will work on:
import torch...