# Efficient Attention Mechanism for Dynamic Convolution in Lightweight Neural Network
## 4 Experimental Results
### Numerous Results
#### 4.2.1 Baseline Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Network**} & **CIFAR10** & **CIFAR100** & **ImageNet100** \\  & **Top-1 (\%)** & **Top-1 (\%)** & **Top-1 (\%)** \\ \hline MobilenetV1 \(\times\) 1.0 & 93.83 & 74.41 & 75.27 \\ DyNet MobilenetV1 \(\times\) 1.0 & 93.99 & 74.76 & 75.94 \\ EAM-DyNet MobilenetV1 \(\times\) 1.0 + RCR & 93.61 & 74.83 & **76.37** \\ EAM-DyNet MobilenetV1 \(\times\) 1.0 + CGR & **93.93** & **75.21** & 76.11 \\ \hline DyNet MobilenetV1 \(\times\) 0.75 & 93.50 & 73.54 & 74.14 \\ EAM-DyNet MobilenetV1 \(\times\) 0.75 + RCR & 93.49 & 73.88 & **74.67** \\ EAM-DyNet MobilenetV1 \(\times\) 0.75 + CGR & **93.55** & **74.04** & 74.51 \\ DyNet MobilenetV1 \(\times\) 0.5 & 93.15 & 72.31 & 72.27 \\ EAM-DyNet MobilenetV1 \(\times\) 0.5 + RCR & 92.70 & **72.85** & **72.93** \\ EAM-DyNet MobilenetV1 \(\times\) 0.5 + CGR & 92.78 & 72.42 & 72.15 \\ DyNet MobilenetV1 \(\times\) 0.25 & 91.14 & 68.30 & 66.76 \\ EAM-DyNet MobilenetV1 \(\times\) 0.25 + RCR & 91.01 & 68.61 & **68.50** \\ EAM-DyNet MobilenetV1 \(\times\) 0.25 + CGR & **91.12** & **68.71** & 67.25 \\ MobilenetV2 \(\times\) 1.0 & 93.60 & 74.90 & _77.66_ \\ DyNet MobilenetV2 \(\times\) 1.0 & 94.09 & 75.50 & 78.11 \\ EAM-DyNet MobilenetV2 \(\times\) 1.0 + RCR & 94.07 & 75.23 & **78.67** \\ EAM-DyNet MobilenetV2 \(\times\) 1.0 + CGR-MAX & **94.22** & **75.44** & 78.26 \\ DyNet MobilenetV3\_small \(\times\) 1.0 & 91.97 & 73.10 & 69.22 \\ EAM-DyNet MobilenetV3\_small \(\times\) 1.0 + RCR & 91.93 & 72.14 & 69.65 \\ EAM-DyNet MobilenetV3\_small \(\times\) 1.0 + CGR-MAX & **92.13** & **72.42** & **70.64** \\ MobilenetV3\_large \(\times\) 1.0 & 93.70 & 75.20 & 76.36 \\ DyNet MobilenetV3\_large \(\times\) 1.0 & 94.15 & 75.03 & 76.37 \\ EAM-DyNet MobilenetV3\_large \(\times\) 1.0 + RCR & 94.00 & 74.77 & **76.62** \\ EAM-DyNet MobilenetV3\_large \(\times\) 1.0 + CGR-MAX & **94.20** & **75.15** & 76.37 \\ \hline \hline \end{tabular}  
* DyNet is a standard dynamic network, and EAM-DyNet was our model. After the ‘+’ is the method used to select the channel. After the ’\(\times\)’ is the width of the network.  
\end{table} 
Table 2: The parameter value and FLOPs value of MobilenetV1 and MobilenetV2 in ImageNet100. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Network**} & **ImageNet100** & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**FLOPs**} \\  & **Top-1 (\%)** & & \\ \hline DyNet MobilenetV1 \(\times\) 1.0 & 75.94 & 14.27M & 589.70M \\ EAM-DyNet MobilenetV1 \(\times\) 1.0 + RCR & **76.37 (+0.43)** & **12.89M (\(\mathbf{-9.6\%}\))** & **583.55M** \\ EAM-DyNet MobilenetV1 \(\times\) 1.0 + CGR & 76.11 & **12.89M (\(\mathbf{-9.6\%}\))** & **583.55M** \\ DyNet MobilenetV2 \(\times\) 1.0 & 78.11 & 11.25M & 336.34M \\ EAM-DyNet MobilenetV2 \(\times\) 1.0 + RCR & **78.67 (+0.56)** & **8.98M (\(\mathbf{-20.17\%}\))** & **327.42M** \\ EAM-DyNet MobilenetV2 \(\times\) 1.0 + CGR & 78.26 & **8.98M (\(\mathbf{-20.17\%}\))** & **327.42M** \\ \hline \hline \end{tabular}  
* DyNet is a standard dynamic network, and EAM-DyNet was our model. After the ‘+’ is the method used to select the channel. After the ’\(\times\)’ is the width of the network.  
\end{table} 
Table 1: The accuracy of different architectures on CIFAR10, CIFAR100, and ImageNet100.