Book Image

Modern Computer Vision with PyTorch

By : V Kishore Ayyadevara, Yeshwanth Reddy
Book Image

Modern Computer Vision with PyTorch

By: V Kishore Ayyadevara, Yeshwanth Reddy

Overview of this book

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.
Table of Contents (25 chapters)
Section 1 - Fundamentals of Deep Learning for Computer Vision
Section 2 - Object Classification and Detection
Section 3 - Image Manipulation
Section 4 - Combining Computer Vision with Other Techniques

Understanding the basics of an API

By now, we know how to create a deep learning model for various tasks. It accepts/returns tensors as input/output. But an outsider such as a client/end user would talk only in terms of images and classes. Furthermore, they would expect to send and receive input/output over channels that might have nothing to do with Python. The internet is the easiest channel to communicate on. Hence, for a client, the best-case deployment scenario would be if we can set up a publically available URL and ask them to upload their images there. One such paradigm is called an Application Programming Interface (API), which has standard protocols that accept input and post output over the internet while abstracting the user away from how the input is processed or the output is generated.

Some common protocols in APIs are POST, GET, PUT, and DELETE, which are sent as requests by the client to the host server along with relevant data. Based on the request and data, the server...