Discover powerful ways to explore deep learning algorithms and solve real-world computer vision problems using Python
Key Features
• Solve the trickiest of problems in CV by combining the power of deep learning and neural networks
• Get the most out of PyTorch 1.x capabilities to perform image classification, object detection, and much more
• Train and deploy enterprise-grade, deep learning models for computer vision applications
Book Description
Developers can gain a high-level understanding of digital images and videos using computer vision techniques. With this book, you'll learn how to solve the trickiest of problems in computer vision (CV) using the power of deep learning algorithms, and leverage the latest features of PyTorch 1.x to perform a variety of computer vision tasks.
Starting with a quick overview of the PyTorch library and key deep learning concepts, the book covers common and not-so-common challenges faced while performing image recognition, image segmentation, captioning, image generation, and many other tasks. You'll implement these tasks using various deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long-short term memory (LSTM), and generative adversarial networks (GANs). Using a problem-solution approach, you'll solve any issue you might face while fine-tuning the performance of the model or integrating the model into your application. Additionally, you'll even get to grips with scaling the model to handle larger workloads and implement best practices for training models efficiently.
By the end of this book, you'll be able to solve any problem relating to training effective computer vision models.
What you will learn
• Implement a multi-class image classification network using PyTorch
• Understand how to fine-tune and change hyperparameters to train deep learning algorithms
• Perform various CV tasks such as classification, detection, and segmentation
• Implement a neural-style transfer network based on CNN and pre-trained models
• Generate new images using generative adversarial networks
• Implement video classification models based on RNN and LSTM
• Discover best practices for training and deploying deep learning algorithms for CV applications
Who This Book Is For
Computer vision professionals, data scientists, deep learning engineers, and AI developers looking for quick solutions for various computer vision problems will find this book useful. Intermediate knowledge of computer vision concepts along with Python programming experience is required.
Author(s): Michael Avendi
Edition: 1
Publisher: Packt Publishing
Year: 2020
Language: English
Commentary: True PDF
Pages: 340
City: Birmingham, UK
Tags: Computer Vision; Video; Python; Generative Adversarial Networks; Classification; Transfer Learning; Cookbook; Best Practices; PyTorch; Image Segmentation; Object Detection
Cover
Title Page
Copyright and Credits
About Packt
Contributors
Table of Contents
Preface
Chapter 1: Getting Started with PyTorch for Deep Learning
Technical requirements
Installing software tools and packages
How to do it...
Installing Anaconda
Installing PyTorch
Verifying the installation
Installing other packages
How it works...
Working with PyTorch tensors
How to do it...
Defining the tensor data type
Changing the tensor's data type
Converting tensors into NumPy arrays
Converting NumPy arrays into tensors
Moving tensors between devices
How it works...
See also
Loading and processing data
How to do it...
Loading a dataset
Data transformation
Wrapping tensors into a dataset
Creating data loaders
How it works...
Building models
How to do it...
Defining a linear layer
Defining models using nn.Sequential
Defining models using nn.Module
Moving the model to a CUDA device
Printing the model summary
How it works...
Defining the loss function and optimizer
How to do it...
Defining the loss function
Defining the optimizer
How it works...
See also
Training and evaluation
How to do it...
Storing and loading models
Deploying the model
How it works...
There's more...
Chapter 2: Binary Image Classification
Exploring the dataset
Getting ready
How to do it...
How it works...
Creating a custom dataset
How to do it...
How it works...
Splitting the dataset
How to do it...
How it works...
Transforming the data
How to do it...
How it works...
Creating dataloaders
How to do it...
How it works...
Building the classification model
How to do it...
How it works...
See also
Defining the loss function
How to do it...
How it works...
See also
Defining the optimizer
How to do it...
How it works...
See also
Training and evaluation of the model
How to do it...
How it works...
There's more...
Deploying the model
How to do it...
How it works...
Model inference on test data
Getting ready
How to do it...
How it works...
See also
Chapter 3: Multi-Class Image Classification
Loading and processing data
How to do it...
How it works...
There's more...
See also
Building the model
How to do it...
How it works...
There's more...
See also
Defining the loss function
How to do it...
How it works...
See also
Defining the optimizer
How to do it...
How it works...
See also
Training and transfer learning
How to do it...
How it works...
See also
Deploying the model
How to do it...
How it works...
Chapter 4: Single-Object Detection
Exploratory data analysis
Getting ready
How to do it...
How it works...
Data transformation for object detection
How to do it...
How it works...
There's more...
See also
Creating custom datasets
How to do it...
How it works...
Creating the model
How to do it...
How it works...
Defining the loss, optimizer, and IOU metric
How to do it...
How it works...
Training and evaluation of the model
How to do it...
How it works...
Deploying the model
How to do it...
How it works...
Chapter 5: Multi-Object Detection
Creating datasets
Getting ready
How to do it...
Creating a custom COCO dataset
Transforming data
Defining the Dataloaders
How it works...
Creating a YOLO-v3 model
How to do it...
Parsing the configuration file
Creating PyTorch modules
Defining the Darknet model
How it works...
Defining the loss function
How to do it...
How it works...
Training the model
How to do it...
How it works...
Deploying the model
How to do it...
How it works...
See also
Chapter 6: Single-Object Segmentation
Creating custom datasets
Getting ready
How to do it...
Data exploration
Data augmentation
Creating the datasets
How it works...
Defining the model
How to do it...
How it works...
Defining the loss function and optimizer
How to do it...
How it works...
Training the model
How to do it...
How it works...
Deploying the model
How to do it...
How it works...
Chapter 7: Multi-Object Segmentation
Creating custom datasets
How to do it...
How it works...
Defining and deploying a model
How to do it...
How it works...
See also
Defining the loss function and optimizer
How to do it...
How it works...
Training the model
How to do it...
How it works...
Chapter 8: Neural Style Transfer with PyTorch
Loading the data
Getting ready
How to do it...
How it works...
Implementing neural style transfer
How to do it...
Loading the pretrained model
Defining loss functions
Defining the optimizer
Running the algorithm
How it works...
See also
Chapter 9: GANs and Adversarial Examples
Creating the dataset
How to do it...
How it works...
Defining the generator and discriminator
How to do it...
How it works...
Defining the loss and optimizer
How to do it...
How it works...
Training the models
How to do it...
How it works...
See also
Deploying the generator
How to do it...
How it works...
Attacking models with adversarial examples
Getting ready
How to do it...
Loading the dataset
Loading the pre-trained model
Implementing the attack
How it works...
There's more...
Chapter 10: Video Processing with PyTorch
Creating the dataset
Getting ready
How to do it...
Preparing the data
Splitting the data
Defining the PyTorch datasets
Defining the data loaders
How it works...
Defining the model
How to do it...
How it works...
Training the model
How to do it...
How it works...
Deploying the video classification model
How to do it...
How it works...
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