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Index
Title Page Copyright and Credits
PyTorch Computer Vision Cookbook
About Packt
Why subscribe?
Contributors
About the author About the reviewers Packt is searching for authors like you
Preface
Who this book is for What this book covers To get the most out of this book
Download the example code files Download the color images Conventions used
Sections
Getting ready How to do it… How it works… There's more… See also
Get in touch
Reviews
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...
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
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...
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...
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
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...
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...
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
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...
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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