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

Installing PyTorch

PyTorch provides multiple functionalities that aid in building a neural network – abstracting the various components using high-level methods and also providing us with tensor objects that leverage GPUs to train a neural network faster.

Before installing PyTorch, we first need to install Python, as follows:

  1. To install Python, we'll use the platform to fetch an installer that will install Python as well as important deep learning-specific libraries for us automatically:

Choose the graphical installer of the latest Python version 3.xx (3.7, as of the time of writing this book) and let it download.

  1. Install it using the downloaded installer:
Choose the Add Anaconda to my PATH environment variable option during installation as this will make it easy to invoke Anaconda's version of Python when we type python in Command Prompt/Terminal.

Next, we'll install PyTorch, which is equally simple.

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