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Image classification in Galaxy with fruit 360 dataset

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Authors: AvatarKaivan Kamali

Questions

Objectives

Requirements

last_modification Last modification: Dec 1, 2021

What is a convolutional neural network (CNN)?

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What is a convolutional neural network (CNN)?


Convolutional Neural Network (CNN)


Inspiration for CNN


Inspiration for CNN


Architecture of CNN


Input layer


Convolution layer


3 by 3 Filter

A 3 by 3 filter applied to a 4 by 4 image, resulting in a 2 by 2 image


Convolution operator parameters


Filter size


Padding


3 by 3 filter with padding of 1

A 3 by 3 filter applied to a 5 by 5 image, with padding of 1, resulting in a 5 by 5 image


Stride


3 by 3 filter with stride of 2

A 3 by 3 filter applied to a 5 by 5 image, with stride of 2, resulting in a 2 by 2 image


Dilation


3 by 3 filter with dilation of 2

A 3 by 3 filter applied to a 7 by 7 image, with dilation of 2, resulting in a 3 by 3 image


Activation function


Relu activation function

Two matrices representing filter output before and after ReLU activation function is applied


Single channel 2D convolution

One matrix representing an input vector and another matrix representing a filter, along with calculation for single input channel two dimensional convolution operation


Triple channel 2D convolution

Three matrices representing an input vector and another three matrices representing a filter, along with calculation for multiple input channel two dimensional convolution operation


Triple channel 2D convolution in 3D

Multiple cubes representing input vector, filter, and output in a 3 channel 2 dimensional convolution operation


Change channel size


Pooling layer


Fully connected layer


An example CNN

A convolutional neural network with 3 convolution layers followed by 3 pooling layers


An example CNN


Fruit 360 dataset

Utilities for creating a subset of fruit 360 dataset

Classification of fruit/vegetable images with CNN

For references, please see tutorial’s References section


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This material is the result of a collaborative work. Thanks to the Galaxy Training Network and all the contributors! Galaxy Training Network This material is licensed under the Creative Commons Attribution 4.0 International License.