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 9 - Image Segmentation

  1. How does up-scaling help in U-Net architecture?
    Upscaling helps the feature map to increase in size so that the final output is the same size as the input size.
  2. Why do we need to have a fully convolutional network in U-Net?
    Because the outputs are also images, and it is difficult to predict an image shaped tensor using the Linear layer.
  3. How does RoI Align improve over RoI pooling in Mask R-CNN?
    RoI Align takes offsets of predicted proposals to fine-align the feature map.
  4. What is the major difference between U-Net and Mask R-CNN for segmentation?
    U-Net is fully convolutional and with a single end2end network, whereas Mask R-CNN uses mini networks such as Backbone, RPN, etc to do different tasks. Mask R-CNN is capable of identifying and separating several objects of the same type, but U-Net can only identify (but not separate them into individual instances).
  5. What is instance segmentation?
    If there are different objects of the same class in the same image...