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Traffic Sign Detection and Recognition Read chapter Read first chapter

2017 | OriginalPaper | Chapter

4. Caffe Library

Authors : Hamed Habibi Aghdam, Elnaz Jahani Heravi

Published in: Guide to Convolutional Neural Networks

Publisher: Springer International Publishing

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Abstract

There are various powerful libraries such as Theano, Lasagne, Keras, mxnet, Torch, and TensorFlow that can be used for designing and training neural networks including convolutional neural networks. Among them, Caffe is a library that can be used for both doing research and developing real-world applications. In this chapter, we explained how to design and train neural networks using the Caffe library. Moreover, the Python interface of Caffe was discussed using real examples. Then, we mentioned how to develop new layers in Python and use them in neural networks.

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previous chapter Convolutional Neural Networks
next chapter Classification of Traffic Signs
Footnotes
2
There are some branches of Caffe that use OpenCL for communicating with GPU.
 
3
Implementations of the methods in this chapter are available at github.com/pcnn/.
 
4
All the explanations for the Caffe library in this chapter are valid for the commit number 5a201dd.
 
5
This definition may change in next versions.
 
6
It is possible to load and scale images using functions in caffe.io module. However, it should be noted that the imread function from OpenCV loads color images in BGR order rather than RGB. This is similar to the way the ImageData layer loads images using OpenCV. In the case of using caffe.io.load_image function, we must swap R and B channel before feeding them to the network.
 
Literature
Metadata
Title
Caffe Library
Authors
Hamed Habibi Aghdam
Elnaz Jahani Heravi
Copyright Year
2017
Book
Guide to Convolutional Neural Networks

Print ISBN: 978-3-319-57549-0

Electronic ISBN: 978-3-319-57550-6

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-57550-6_4

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