PyTorch Computer Vision Cookbook

Discover powerful ways to use deep learning algorithms and solve real-world computer vision problems using Python

Key Features

• Solve the trickiest of problems in computer vision by combining the power of deep learning and neural networks
• Leverage PyTorch 1.x capabilities to perform image classification, object detection, and more
• Train and deploy enterprise-grade, deep learning models for computer vision applications

Book Description

Computer vision techniques play an integral role in helping developers gain a high-level understanding of digital images and videos. 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 CV tasks.

Starting with a quick overview of the PyTorch library and key deep learning concepts, the book then covers common and not-so-common challenges faced while performing image recognition, image segmentation, captioning, image generation, and other tasks. Next, you’ll understand how to 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 learn how to solve any issue you might face while fine-tuning the performance of a model or integrating it into your application. Later, you’ll get to grips with scaling your model to handle larger workloads, and even implementing best practices for training models efficiently.

By the end of this CV book, you’ll be proficient in confidently solving any problem relating to training CV models using PyTorch recipes.

What you will learn

• Employ 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 CNNs and pre-trained models
• Generate new images using GANs
• Implement video classification models based on RNNs 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-level knowledge of computer vision concepts, along with Python programming experience is required.