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Deep Learning for Computer Vision

You're reading from Deep Learning for Computer Vision Expert techniques to train advanced neural networks using TensorFlow and Keras

Product type Paperback

Published in Jan 2018

Publisher Packt

ISBN-13 9781788295628

Length 310 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Computer Vision

Author (1):

Shanmugamani

View More author details

Table of Contents (17) Chapters

Title Page

Copyright and Credits

Packt Upsell

Foreword

Contributors

Preface

1. Getting Started FREE CHAPTER

2. Image Classification

3. Image Retrieval

4. Object Detection

5. Semantic Segmentation

6. Similarity Learning

7. Image Captioning

8. Generative Models

9. Video Classification

10. Deployment

1. Other Books You May Enjoy

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Segmenting satellite images

In this section, we will use a dataset provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). The dataset contains satellite images of Potsdam, Germany with 5 cm resolution. These images come with an additional data of infrared and height contours of the images. There are six labels associated with the images, which are:

Building
Vegetation
Trees
Cabs
Clutter
Impervious

A total of 38 images are provided with 6,000 x 6,000 patches. Please go to the page, http://www2.isprs.org/commissions/comm3/wg4/data-request-form2.html and fill in the form. After that, select the following options on the form:

Post the form, an email will be sent to you, from which the data can be downloaded.

Modeling FCN for segmentation

Import the libraries and get the shape of the input. The number of labels is defined as 6:

from .resnet50 import ResNet50
nb_labels = 6

img_height, img_width, _ = input_shape
input_tensor = tf.keras.layers.Input(shape=input_shape)
weights...

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Authors (1)

Shanmugamani

Shanmugamani

Rajalingappaa Shanmugamani is currently working as an Engineering Manager for a Deep learning team at Kairos. Previously, he worked as a Senior Machine Learning Developer at SAP, Singapore and worked at various startups in developing machine learning products. He has a Masters from Indian Institute of TechnologyMadras. He has published articles in peer-reviewed journals and conferences and submitted applications for several patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.

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