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Table 1 GoogleNet architecture modified with Batch Normalization

From: Going deeper in the automated identification of Herbarium specimens

Type Patch size / Stride Output size Depth Params Ops
Convolution 7 ×7/2 112 ×112×64 1 2.7K 34M
Max pool 3 ×3/2 56 ×56×64 0   
Batch norm   56 ×56×64 0   
LRN   56 ×56×64 0   
Convolution 3 ×3/1 56 ×56×192 2 112K 360M
Max pool 3 ×3/2 28 ×28×192 0   
Batch norm   28 ×28×192 0   
LRN   28 ×28×192 0   
Inception (3a)   28 ×28×256 2 159K 128M
Inception (3b)   28 ×28×480 2 380K 304M
Max pool 3 ×3/2 14 ×14×480 0   
Batch norm   14 ×14×480 0   
Inception (4a)   14 ×14×512 2 364K 73M
Inception (4b)   14 ×14×512 2 437K 88M
inception (4c)   14 ×14×512 2 463K 100M
Inception (4d)   14 ×14×528 2 580K 119M
Inception (4e)   14 ×14×832 2 840K 170M
Max pool 3 ×3/2 7 ×7×832 0   
Batch norm   7 ×7×832 0   
Inception (5a)   7 ×7×832 2 1072K 54M
Inception (5b)   7 ×7×1024 2 1388K 71M
Avg pool 7 ×7/1 1 ×1×1024 0   
Batch norm   1 ×1×1024 0   
Linear   1 ×1×10000 1 1000K 1M
Softmax   1 ×1×10000 0