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Practical Convolutional Neural Networks

You're reading from   Practical Convolutional Neural Networks Implement advanced deep learning models using Python

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781788392303
Length 218 pages
Edition 1st Edition
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Authors (3):
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Mohit Sewak Mohit Sewak
Author Profile Icon Mohit Sewak
Mohit Sewak
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
Pradeep Pujari Pradeep Pujari
Author Profile Icon Pradeep Pujari
Pradeep Pujari
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Table of Contents (11) Chapters Close

Preface 1. Deep Neural Networks – Overview FREE CHAPTER 2. Introduction to Convolutional Neural Networks 3. Build Your First CNN and Performance Optimization 4. Popular CNN Model Architectures 5. Transfer Learning 6. Autoencoders for CNN 7. Object Detection and Instance Segmentation with CNN 8. GAN: Generating New Images with CNN 9. Attention Mechanism for CNN and Visual Models 10. Other Books You May Enjoy

The differences between object detection and image classification


Let's take another example. You are watching the movie 101 Dalmatians, and you want to know how many Dalmatians you can actually count in a given movie scene from that movie. Image Classification could, at best, tell you that there is at least one dog or one Dalmatian (depending upon which level you have trained your classifier for), but not exactly how many of them there are.

Another issue with classification-based models is that they do not tell you where the identified entity in the image is. Many times, this is very important. Say, for example, you saw your neighbor's dog playing with him (Person) and his cat. You took a snap of them and wanted to extract the image of the dog from there to search on the web for its breed or similar dogs like it. The only problem here is that searching the whole image might not work, and without identifying individual objects from the image, you have to do the cut-extract-search job manually...

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