Book Image

Modern Computer Vision with PyTorch

By : V Kishore Ayyadevara, Yeshwanth Reddy
5 (2)
Book Image

Modern Computer Vision with PyTorch

5 (2)
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

Chapter 3 - Building a Deep Neural Network with PyTorch

  1. What is the issue if the input values are not scaled in the input dataset?
    It takes longer to adjust weights to optimal value because input values vary so widely when they are unscaled
  2. What could be the issue if the background has a white pixel color while the content has a black pixel color when training a neural network?
    The neural network has to learn to ignore a majority of the not so useful content that is white in color
  3. What is the impact of batch size on the model's training time, accuracy over a given number of epochs?
    The larger the batch size more is the time taken to converge and more iterations required to attain a high accuracy
  4. What is the impact of the input value range on weight distribution at the end of the training?
    If the input value is not scaled to a certain range, certain weights can aid in over-fitting
  5. How does batch normalization help in improving accuracy?
    Just like how it is important that we scale...