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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
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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

Object Detection and Instance Segmentation with CNN

Until now, in this book, we have been mostly using convolutional neural networks (CNNs) for classification. Classification classifies the whole image into one of the classes with respect to the entity having the maximum probability of detection in the image. But what if there is not one, but multiple entities of interest and we want to have the image associated with all of them? One way to do this is to use tags instead of classes, where these tags are all classes of the penultimate Softmax classification layer with probability above a given threshold. However, the probability of detection here varies widely by size and placement of entity, and from the following image, we can actually say, How confident is the model that the identified entity is the one that is claimed? What if we are very confident that there is an...

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