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)
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

Chapter 7 - Basics of Object Detection

  1. How does the region proposal technique generate proposals?
    It identifies regions that are similar in color, texture, size, and shape.
  2. How is IoU calculated if there are multiple objects in an image?
    IoU is calculated for each object with the ground truth, using Intersection Over Union metric
  3. Why does R-CNN take a long time to generate predictions?
    Because we create as many forward propagations as there are proposals
  4. Why is Fast R-CNN faster when compared to R-CNN?
    For all proposals, extracting the feature map from the VGG backbone is common. This reduces almost 90% of the computations as compared to Fast RCNN
  1. How does RoI Pooling work?
    All the selectivesearch crops are passed through adaptive pooling kernel so that the final output is of the same size
  2. What is the impact of not having multiple layers, post obtaining feature map, when predicting the bounding box corrections?
    You might not notice that the model did not learn to predict the bounding...