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Modern Computer Vision with PyTorch

You're reading from  Modern Computer Vision with PyTorch

Product type Book
Published in Nov 2020
Publisher Packt
ISBN-13 9781839213472
Pages 824 pages
Edition 1st Edition
Languages
Authors (2):
V Kishore Ayyadevara V Kishore Ayyadevara
Profile icon V Kishore Ayyadevara
Yeshwanth Reddy Yeshwanth Reddy
Profile icon Yeshwanth Reddy
View More author details

Table of Contents (25) Chapters

Preface Section 1 - Fundamentals of Deep Learning for Computer Vision
Artificial Neural Network Fundamentals PyTorch Fundamentals Building a Deep Neural Network with PyTorch Section 2 - Object Classification and Detection
Introducing Convolutional Neural Networks Transfer Learning for Image Classification Practical Aspects of Image Classification Basics of Object Detection Advanced Object Detection Image Segmentation Applications of Object Detection and Segmentation Section 3 - Image Manipulation
Autoencoders and Image Manipulation Image Generation Using GANs Advanced GANs to Manipulate Images Section 4 - Combining Computer Vision with Other Techniques
Training with Minimal Data Points Combining Computer Vision and NLP Techniques Combining Computer Vision and Reinforcement Learning Moving a Model to Production Using OpenCV Utilities for Image Analysis Other Books You May Enjoy Appendix

Chapter 8 - Advanced Object Detection

  1. Why is Faster R-CNN faster when compared to Fast R-CNN?
    We do not need to feed a lot of unnecessary proposals every time using the selectivesearch technique. Instead, Faster R-CNN automatically finds them using the region proposal network.
  2. How are YOLO and SSD faster when compared to Faster R-CNN?
    We don't need to rely on a new proposal network. The network directly finds the proposals in a single go.
  3. What makes YOLO and SSD single shot detector algorithms?
    Networks predict all the proposals and predictions in one shot
  4. What is the difference between the objectness score and class score?
    Objectness identifies if an object exists or not. But class score predicts what is the class for an anchor box whose objectness is non zero

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