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

Creating an API and making predictions on a local server

In this section, we will learn about making predictions on a local server (that has nothing to do with the cloud). At a high level, this involves the following steps:

  1. Installing FastAPI
  2. Creating a route to accept incoming requests
  3. Saving an incoming request on disk
  4. Loading the requested image, then preprocessing and predicting with the trained model
  5. Postprocessing the results and sending back the predictions as a response to the same incoming request
All of the steps in this section are summarized as a video walk-through here:

Let's begin by installing FastAPI in the following subsection.

Installing the API module and dependencies

Since FastAPI is a Python module, we can use pip for installation, and be ready to code an API. We will now open a new terminal and run the following command:

$pip install fastapi uvicorn aiofiles jinja2 

We have installed a couple more dependencies that...