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Table 2 Brief overview of CNN architectures

From: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Model

Main finding

Depth

Dataset

Error rate

Input size

Year

AlexNet

Utilizes Dropout and ReLU

8

ImageNet

16.4

\(227 \times 227 \times 3\)

2012

NIN

New layer, called ‘mlpconv’, utilizes GAP

3

CIFAR-10, CIFAR-100, MNIST

10.41, 35.68, 0.45

\(32 \times 32 \times 3\)

2013

ZfNet

Visualization idea of middle layers

8

ImageNet

11.7

\(224 \times 224 \times 3\)

2014

VGG

Increased depth, small filter size

16, 19

ImageNet

7.3

\(224 \times 224 \times 3\)

2014

GoogLeNet

Increased depth,block concept, different filter size, concatenation concept

22

ImageNet

6.7

\(224 \times 224 \times 3\)

2015

Inception-V3

Utilizes small filtersize, better feature representation

48

ImageNet

3.5

\(229 \times 229 \times 3\)

2015

Highway

Presented the multipath concept

19, 32

CIFAR-10

7.76

\(32 \times 32 \times 3\)

2015

Inception-V4

Divided transform and integration concepts

70

ImageNet

3.08

\(229 \times 229 \times 3\)

2016

ResNet

Robust against overfitting due to symmetry mapping-based skip links

152

ImageNet

3.57

\(224 \times 224 \times 3\)

2016

Inception-ResNet-v2

Introduced the concept of residual links

164

ImageNet

3.52

\(229 \times 229 \times 3\)

2016

FractalNet

Introduced the concept of Drop-Path as regularization

40,80

CIFAR-10

4.60

\(32 \times 32 \times 3\)

2016

CIFAR-100

18.85

WideResNet

Decreased the depth and increased the width

28

CIFAR-10

3.89

\(32 \times 32 \times 3\)

2016

CIFAR-100

18.85

Xception

A depthwise convolutionfollowed by a pointwise convolution

71

ImageNet

0.055

\(229 \times 229 \times 3\)

2017

Residual attention neural network

Presented the attention technique

452

CIFAR-10, CIFAR-100

3.90, 20.4

\(40 \times 40\times 3\)

2017

Squeeze-and-excitation networks

Modeled interdependencies between channels

152

ImageNet

2.25

\(229 \times 229 \times 3\)

2017

\(224 \times 224 \times 3\)

\(320 \times 320 \times 3\)

DenseNet

Blocks of layers; layers connected to each other

201

CIFAR-10, CIFAR-100,ImageNet

3.46, 17.18, 5.54

\(224 \times 224 \times 3\)

2017

Competitive squeeze and excitation network

Both residual and identity mappings utilized to rescale the channel

152

CIFAR-10

3.58

\(32 \times 32 \times 3\)

2018

CIFAR-100

18.47

MobileNet-v2

Inverted residual structure

53

ImageNet

–

\(224 \times 224 \times 3\)

2018

CapsuleNet

Pays attention to special relationships between features

3

MNIST

0.00855

\(28 \times 28 \times 1\)

2018

HRNetV2

High-resolution representations

–

ImageNet

5.4

\(224 \times 224 \times 3\)

2020