[GoogLeNet]Inception-v4

发表于 2020-04-12 更新于 2021-07-09 分类于目标分类/object classification 阅读次数：

本文字数： 3.4k 阅读时长 ≈ 6 分钟

参考：Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning，解析Inception-v4架构

总体架构

各个模块输出如下：

\(Input = 299\times 299\times 3\)
\(Stem = 35\times 35\times 384\)
\(4\times Inception-A = 35\times 35\times 384\)
\(Reduction-A = 17\times 17\times 1024\)
\(7\times Inception-B = 17\times 17\times 1024\)
\(Reduction-B = 8\times 8\times 1536\)
\(3\times Inception-C = 8\times 8\times 1536\)
\(Average Pooling = 1\times 1\times 1536\)
\(Dropout(keep 0.8) = 1536\)
\(Softmax = 1000\)

All the convolutions not marked with “V” in the figures are same-padded meaning that their output grid matches the size of their input. Convolutions marked with “V” are valid padded, meaning that input patch of each unit is fully contained in the previous layer and the grid size of the output activation map is reduced accordingly

在下面各个模块的结构图中，没有加\(V\)符号的表示其输出数据体的空间尺寸和输入相同；加\(V\)符号的表示输出数据体空间尺寸进行了衰减

Stem

Stem模块实现了网络早期运算，推导如下：

\(Input = 299\times 299\times 3\)
\(Conv\)
- \(3\times 3, S=2, N=32\)
- \(Output = 149\times 149\times 32\)
\(Conv\)
- \(3\times 3, N=32\)
- \(Output = 147\times 147\times 32\)
\(Conv\)
- \(3\times 3, N=64, P=1\)
- \(Output = 147\times 147\times 64\)
\(Concat\)
- \(Max Pool\)
  - \(S=2\)
  - \(Output = 73\times 73\times 64\)
- \(Conv\)
  - \(3\times 3, S=2, N=96\)
  - \(Output=73\times 73\times 96\)
- \(Cat\)
  - \(Output = 73\times 73\times 160\)
\(Concat\)
- \(One\)
  - \(Conv\)
    - \(1\times 1, N=64\)
    - \(Output = 73\times 73\times 64\)
  - \(Conv\)
    - \(3\times 3, N=96\)
    - \(Output = 71\times 71\times 96\)
- \(Two\)
  - \(Conv\)
    - \(1\times 1, N=64\)
    - \(Output = 73\times 73\times 64\)
  - \(Conv\)
    - \(7\times 1, N=64, P=(3, 0)\)
    - \(Output = 73\times 73\times 64\)
  - \(Conv\)
    - \(1\times 7, N=64, P=(0, 3)\)
    - \(Output = 73\times 73\times 64\)
  - \(Conv\)
    - \(3\times 3, N=96\)
    - \(Output = 71\times 71\times 96\)
- \(Cat\)
  - \(Output = 71\times 71\times 192\)
\(Concat\)
- \(Conv\)
  - \(3\times 3, S=2, N=192\)
  - \(Output = 35\times 35\times 192\)
- \(Max Pool\)
  - \(S=2\)
  - \(Output = 35\times 35\times 192\)
- \(Cat\)
  - \(Output = 35\times 35\times 384\)

Inception-A

这是GoogLeNet_BN使用的Inception架构，推导如下：

\(Input = 35\times 35\times 384\)
\(1\times 1\)
- \(Conv\)
  - \(1\times 1, N=96\)
  - \(Output = 35\times 35\times 96\)
\(3\times 3\)
- \(Conv\)
  - \(1\times 1, N=64\)
  - \(Output = 35\times 35\times 64\)
- \(Conv\)
  - \(3\times 3, N=96, P=1\)
  - \(Output = 35\times 35\times 96\)
\(double 3\times 3\)
- \(Conv\)
  - \(1\times 1, N=64\)
  - \(Output = 35\times 35\times 64\)
- \(Conv\)
  - \(3\times 3, N=96, P=1\)
  - \(Output = 35\times 35\times 96\)
- \(Conv\)
  - \(3\times 3, N=96, P=1\)
  - \(Output = 35\times 35\times 96\)
\(Pool\)
- \(Avg Pool\)
  - \(3\times 3, P=1\)
  - \(Output = 35\times 35\times 384\)
- \(Conv\)
  - \(1\times 1, N=96\)
  - \(Output = 35\times 35\times 96\)
\(Concat\)
- \(Output = 35\times 35\times 384\)

Reduction-A

模块Reduction-A实现空间尺寸缩减功能，从\(35\times 35 \gg 17\times 17\)。其超参数\(k=192, l=224, m=256, n=384\)

\(Input = 35\times 35\times 384\)
\(3\times 3\)
- \(Conv\)
  - \(3\times 3, S=2, N=384\)
  - \(Output = 17\times 17\times 384\)
\(double 3\times 3\)
- \(Conv\)
  - \(1\times 1, N=192\)
  - \(Output = 35\times 35\times 192\)
- \(Conv\)
  - \(3\times 3, N=224, P=1\)
  - \(Output = 35\times 35\times 224\)
- \(Conv\)
  - \(3\times 3, S=2, N=256\)
  - \(17\times 17\times 256\)
\(Pool\)
- \(MaxPool\)
  - \(S=2\)
  - \(Output = 17\times 17\times 384\)
\(Concat\)
- \(Output = 17\times 17\times 1024\)