Modern Computer Vision with PyTorch

Explore deep learning concepts and implement over 50 real-world image applications

V Kishore Ayyadevara , Yeshwanth Reddy 지음

Packt(GCO Science) 2020년 11월 27일 출간

작품소개

책소개

Get to grips with deep learning techniques for building image processing applications using PyTorch with the help of code notebooks and test questions

▶Book Description
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.

▶What You Will Learn
-Train a NN from scratch with NumPy and PyTorch
-Implement 2D and 3D multi-object detection and segmentation
-Generate digits and DeepFakes with autoencoders and advanced GANs
-Manipulate images using CycleGAN, Pix2PixGAN, StyleGAN2, and SRGAN
-Combine CV with NLP to perform OCR, image captioning, and object detection
-Combine CV with reinforcement learning to build agents that play pong and self-drive a car
-Deploy a deep learning model on the AWS server using FastAPI and Docker
-Implement over 35 NN architectures and common OpenCV utilities

▶Key Features
-Implement solutions to 50 real-world computer vision applications using PyTorch
-Understand the theory and working mechanisms of neural network architectures and their implementation
-Discover best practices using a custom library created especially for this book

▶Who This Book Is For
This book is for beginners to PyTorch and intermediate-level machine learning practitioners who are looking to get well-versed with computer vision techniques using deep learning and PyTorch. If you are just getting started with neural networks, you'll find the use cases accompanied by notebooks in GitHub present in this book useful. Basic knowledge of the Python programming language and machine learning is all you need to get started with this book.

목차

▶TABLE of CONTENTS
▷Section 1 - Fundamentals of Deep Learning for Computer Vision
-Chapter 1: Artificial Neural Network Fundamentals
-Chapter 2: PyTorch Fundamentals
-Chapter 3: Building a Deep Neural Network with PyTorch

▷Section 2 - Object Classification and Detection
-Chapter 4: Introducing Convolutional Neural Networks
-Chapter 5: Transfer Learning for Image Classification
-Chapter 6: Practical Aspects of Image Classification
-Chapter 7: Basics of Object Detection
-Chapter 8: Advanced Object Detection
-Chapter 9: Image Segmentation
-Chapter 10: Applications of Object Detection and Segmentation

▷Section 3 - Image Manipulation
-Chapter 11: Autoencoders and Image Manipulation
-Chapter 12: Image Generation Using GANs
-Chapter 13: Advanced GANs to Manipulate Images

▷Section 4 - Combining Computer Vision with Other Techniques
-Chapter 14: Training with Minimal Data Points
-Chapter 15: Combining Computer Vision and NLP Techniques
-Chapter 16: Combining Computer Vision and Reinforcement Learning
-Chapter 17: Moving a Model to Production
-Chapter 18: Using OpenCV Utilities for Image Analysis

책 속으로

▶What this book covers
- Chapter 1, Artificial Neural Network Fundamentals, gives you the complete details of how a neural network works. You will start by learning the key terminology associated with neural networks. Next, you will understand the working details of the building blocks and build a neural network from scratch on a toy dataset. By the end of this chapter, you will be confident about how a neural network works.

- Chapter 2, PyTorch Fundamentals, introduces you to working with PyTorch. You will learn about the ways of creating and manipulating tensor objects before learning about the different ways of building a neural network model using PyTorch. You will still work with a toy dataset so that you understand the specifics of working with PyTorch.

- Chapter 3, Building a Deep Neural Network with PyTorch, combines all that has been covered in the previous chapters to understand the impact of various neural network hyperparameters on model accuracy. By the end of this chapter, you will be confident about working with neural networks on a realistic dataset.

- Chapter 4, Introducing Convolutional Neural Networks, details the challenges of using a vanilla neural network and you will be exposed to the reason why convolutional neural networks overcome the various limitations of traditional neural networks. You will dive deep into the working details of CNN and understand the various components in it. Next, you will learn the best practices of working with images. In this chapter, you will start working with real-world images and learn the intricacies
of how CNNs help in image classification.

- Chapter 5, Transfer Learning for Image Classification, exposes you to solving image classification problems in real-world. You will learn about multiple transfer learning architectures and also understand how it helps in significantly improving the image classification accuracy. Next, you will leverage transfer learning to implement the use cases of facial keypoint detection and age, gender estimation.

- Chapter 6, Practical Aspects of Image Classification, provides insight into the practical aspects to take care of while building and deploying image classification models. You will practically see the advantages of leveraging data augmentation and batch normalization on real-world data. Further, you will learn about how class activation maps help in explaining the reason why CNN model predicted a certain outcome. By the end of this chapter, you can confidently tackle a majority of image classification problems and leverage the models discussed in the previous 3 chapters on your custom dataset.

- Chapter 7, Basics of Object Detection, lays the foundation for object detection where you will learn about the various techniques that are used to build an object detection model. Next, you will learn about region proposal-based object-detection techniques through a use case where you will implement a model to locate trucks and buses in an image.

- Chapter 8, Advanced Object Detection, exposes you to the limitations of the regionproposal based architectures. You will then learn about the working details of more advanced architectures that address the issues of region proposal-based architectures. You will implement all the architectures on the same dataset (trucks vs buses detection) so that you can contrast how each architecture works.

- Chapter 9, Image Segmentation, builds upon the learnings in previous chapters and will help you build models that pin-point the location of the objects of various classes as well as instances of objects in an image. You will implement the use cases on images of a road and also on images of common household. By the end of this chapter, you will confidently tackle any image classification, object detection/ segmentation problem and solve it by building a model using PyTorch.
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출판사 서평

▶ Preface
Artificial Intelligence (AI) is here, and has become a powerful force and is fuelling some of the modern applications that are used on a daily basis. Much like the discovery/invention of fire, wheel, oil, electricity, and electronics - Artificial Intelligence is reshaping our world in ways that we could only fantasize about. AI has been historically a niche computer science subject, offered by a handful of labs. But because of the explosion of excellent theory, increase in computing power, and availability of data, the field started growing exponentially since the 2000s and has shown no sign of slowing down anytime soon.

AI has proven again and again that given the right algorithm and enough amount of data, it can learn the task by itself with limited human intervention and produce results that rival human judgement and sometimes even surpass them. Whether you are a rookie learning the ropes or a veteran driving large organizations, there is every reason to understand how AI works. Neural networks are some of the most flexible classes of Artificial Intelligence algorithms that have been adapted to a vast range of applications including structured data, text, and vision domains.

This book starts with the basics of neural networks and covers over 50 applications of computer vision. First, you will build a neural network (NN) from scratch using both NumPy, PyTorch, and then learn the best practices of tweaking a NN's hyperparameters. As we progress, you will learn about CNNs, transfer-learning with a focus on classifying images. You will also learn about the practical aspects to take care of while building a NN model.

Next, you will learn about multi-object detection, segmentation, and implement them using R-CNN family, SSD, YOLO, U-Net, Mask-RCNN architectures. You will then learn to use the Detectron2 framework to simplify the process of building a NN for object detection and human-pose-estimation. Finally, you will implement 3-D object detection.

Subsequently, you will learn about auto-encoders and GANs with a strong focus on image manipulation and generation. Here, you will implement VAE, DCGAN, CGAN, Pix2Pix, CycleGan, StyleGAN2, SRGAN, Style-Transfer to manipulate images on a variety of tasks.

You will then learn to combine NLP and CV techniques while performing OCR, Image Captioning, object detection with transformers. Next, you will learn to combine RL with CV techniques to implement a self-driving car agent. Finally, you'll wrap up with moving a NN model to production and learn conventional CV techniques using the OpenCV library.

작가정보

저자(글) V Kishore Ayyadevara

V Kishore Ayyadevara leads a team focused on using AI to solve problems in the healthcare space. He has more than 10 years' experience in the field of data science with prominent technology companies. In his current role, he is responsible for developing a variety of cutting-edge analytical solutions that have an impact at scale while building strong technical teams.

저자(글) Yeshwanth Reddy

Yeshwanth Reddy is a senior data scientist with a strong focus on the research and implementation of cutting-edge technologies to solve problems in the health and computer vision domains. He has filed four patents in the field of OCR. He also has 2 years of teaching experience, where he delivered sessions to thousands of students in the fields of statistics, machine learning, AI, and natural language processing. He has completed his MTech and BTech at IIT Madras.