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Table 4 The configurations of AlexNET, ZF-NET, GoogLeNET, VGG-NET and ResNET models

From: Deep convolutional neural networks for mammography: advances, challenges and applications

  AlexNet [16] ZF-Net [77] GoogLeNet [78] VGG-Net [79] ResNet [80]
Year 2012 2013 2014 2014 2015
Image Resolution 227 ×227 227 ×227 224 ×224 224 ×224 2244 ×224
Number of layers 8 8 22 19 152
Number of Conv-Pool layers 5 5 21 16 151
Number of FC layers 3 3 1 3 1
Full connected layer size 4096,4096,1000 4096,4096,1000 1000 4096,4096,1000 1000
Filter Sizes 3, 5, 11 3, 5, 11 1,3,5,7 3 1,3,7
Number of Filters 96 - 384 96 - 384 64 - 384 64 - 512 64 - 2048
Strides 1, 4 1, 4 1, 2 1 1, 2
Data Augmentation + + + + +
Dropout + + + + +
Batch Normalization - - - - +
Number of GPU 2 GTX 1 GTX A few high-end 4 Nvidia  
580 GPUs 580 GPUs GPUs Titan Black GPUs Titan Black GPUs 8 GPUs
Training Time 5:6 days 12 days 1 week 2:3 weeks 2:3 weeks
Top-5 error 16.40% 11.2% 6.70% 7.30% 3.57%