Book Image

Modern Computer Vision with PyTorch

By : V Kishore Ayyadevara, Yeshwanth Reddy
5 (1)
Book Image

Modern Computer Vision with PyTorch

5 (1)
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)
1
Section 1 - Fundamentals of Deep Learning for Computer Vision
5
Section 2 - Object Classification and Detection
13
Section 3 - Image Manipulation
17
Section 4 - Combining Computer Vision with Other Techniques

Summarizing the training process of a neural network

Training a neural network is a process of coming up with optimal weights for a neural network architecture by repeating the two key steps, forward-propagation and backpropagation with a given learning rate.

In forward-propagation, we apply a set of weights to the input data, pass it through the defined hidden layers, perform the defined nonlinear activation on the hidden layers' output, and then connect the hidden layer to the output layer by multiplying the hidden-layer node values with another set of weights to estimate the output value. Then, we finally calculate the overall loss corresponding to the given set of weights. For the first forward-propagation, the values of the weights are initialized randomly.

In backpropagation, we decrease the loss value (error) by adjusting weights in a direction that reduces the overall loss. Further, the magnitude of the weight update is the gradient times the learning rate.

The process of feedforward propagation and backpropagation is repeated until we achieve as minimal a loss as possible. This implies that, at the end of the training, the neural network has adjusted its weights such that it predicts the output that we want it to predict. In the preceding toy example, after training, the updated network will predict a value of 0 as output when {1,1} is fed as input as it is trained to achieve that.