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 4 - Introducing Convolutional Neural Networks

  1. Why is the prediction on a translated image low when using traditional neural networks?
    All images were centered in the original dataset, so the ANN learned the task for only centered images.
  2. How is Convolution done?
    Convolution is a multiplication between two matrices.
  3. How are optimal weight values in a filter identified?
    Through backpropagation.
  4. How does the combination of convolution and pooling help in addressing the issue of image translation?
    While convolution gives important image features, pooling takes the most prominent features in a patch of the image. This makes pooling a robust operation over the vicinity, i.e., even if something is translated by a few pixels, pooling will still return the expected output.
  5. What do the filters in layers closer to the input layer learn?
    Low-level features like edges.
  6. What functionality does pooling do that helps in building a model?
    It reduces input size by reducing feature map size and...