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

Training YOLO on a custom dataset

Building on top of others' work is very important to becoming a successful practitioner in deep learning. For this implementation, we will use the official YOLO-v4 implementation to identify the location of buses and trucks in images. We will clone the repository of the authors' own implementation of YOLO and customize it to our needs in the following code.

The following code is available as Training_YOLO.ipynb in the Chapter08 folder of this book's GitHub repository -

Installing Darknet

First, pull the darknet repository from GitHub and compile it in the environment. The model is written in a separate language called Darknet, which is different from PyTorch. We will do so using the following code:

  1. Pull the Git repo:
!git clone
%cd darknet
  1. Reconfigure the Makefile file:
!sed -i 's/OPENCV=0/OPENCV=1/' Makefile
# In case you dont have a GPU, make sure to comment...