Deep Learning for Computer Vision is a book intended for readers who want to learn deep-learning-based computer vision techniques for various applications. This book will give the reader tools and techniques to develop computer-vision-based products. There are plenty of practical examples covered in the book to follow the theory.
The reader wants to know how to apply deep learning to computer vision problems such as classification, detection, retrieval, segmentation, generation, captioning, and video classification. The reader also wants to understand how to achieve good accuracy under various constraints such as less data, imbalanced classes, and noise. Then the reader also wants to know how to deploy trained models on various platforms (AWS, Google Cloud, Raspberry Pi, and mobile phones). After completing this book, the reader should be able to develop code for problems of person detection, face recognition, product search, medical image segmentation, image generation, image captioning, video classification, and so on.
Chapter 1, Getting Started, introduces the basics of deep learning and makes the readers familiar with the vocabulary. The readers will install the software packages necessary to follow the rest of the chapters.
Chapter 2, Image Classification, talks about the image classification problem, which is labeling an image as a whole. The readers will learn about image classification techniques and train a deep learning model for pet classification. They will also learn methods to improve accuracy and dive deep into variously advanced architectures.
Chapter 3, Image Retrieval, covers deep features and image retrieval. The reader will learn about various methods of obtaining model visualization, visual features, inference using TensorFlow, and serving and using visual features for product retrieval.
Chapter 4, Object Detection, talks about detecting objects in images. The reader will learn about various techniques of object detection and apply them for pedestrian detection. The TensorFlow API for object detection will be utilized in this chapter.
Chapter 5, Semantic Segmentation, covers segmenting of images pixel-wise. The readers will earn about segmentation techniques and train a model for segmentation of medical images.
Chapter 6, Similarity Learning, talks about similarity learning. The readers will learn about similarity matching and how to train models for face recognition. A model to train facial landmark is illustrated.
Chapter 7, Image Captioning, is about generating or selecting captions for images. The readers will learn natural language processing techniques and how to generate captions for images using those techniques.
Chapter 8, Generative Models, talks about generating synthetic images for various purposes. The readers will learn what generative models are and use them for image generation applications, such as style transfer, training data, and so on.
Chapter 9, Video Classification, covers computer vision techniques for video data. The readers will understand the key differences between solving video versus image problems and implement video classification techniques.
Chapter 10, Deployment, talks about the deployment steps for deep learning models. The reader will learn how to deploy trained models and optimize for speed on various platforms.
The examples covered in this book can be run with Windows, Ubuntu, or Mac. All the installation instructions are covered. Basic knowledge of Python and machine learning is required. It's preferable that the reader has GPU hardware but it's not necessary.
You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.
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The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Deep-Learning-for-Computer-Vision. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
There are a number of text conventions used throughout this book.
CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Note that the graph is written once with the summary_writer."
A block of code is set as follows:
merged_summary_operation = tf.summary.merge_all()
train_summary_writer = tf.summary.FileWriter('/tmp/train', session.graph)
test_summary_writer = tf.summary.FileWriter('/tmp/test')Any command-line input or output is written as follows:
wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz
Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Once you are done, terminate the instance by clicking Actions|Instance State|Terminat."
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