# CF-CNN: Coarse-to-Fine Convolutional Neural Network
###### Abstract
Figure 1: Network structure and hierarchical structure label illustrations. (**a**) SplitNet [22], (**b**) HD-CNN [19], (**c**) B-CNN [20], (**d**) our CF-CNN, and (**e**) hierarchical structure label. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**CF-VGG-16**} \\ \hline \multirow{2}{*}{**Layer Name**} & \multirow{2}{*}{**Output Size**} & \multirow{2}{*}{**Block Structure**} & \multicolumn{4}{c}{**Block Numbers 164-Layer (326-Layer)**} \\ \cline{3-6}  & & & **Baseline** & **Main** & **Coarse1** & **Coarse2** \\ \hline Conv1 & \(32\times 32\) & \(3\times 3\), 64 & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{1} \\ \cline{3-6}  & & & \(3\times 3\), 64 & & & \\ \hline Maxpool & \(16\times 16\) & \(2\times 2\) & 1 & 1 & \\ \hline Conv2 & \(16\times 16\) & \(3\times 3\), 128 & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{1} \\ \cline{3-6}  & & \(3\times 3\), 128 & & & \\ \hline Maxpool & \(8\times 8\) & \(2\times 2\) & 1 & 1 & \\ \hline Conv3 & \(8\times 8\) & \(3\times 3\), 256 & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{1} \\ \cline{3-6}  & & \(3\times 3\), 256 & & & \\ \hline Maxpool & \(4\times 4\) & \(2\times 2\) & 1 & 1 & \\ \hline Conv4 & \(4\times 4\) & \(3\times 3\), 512 & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{1} \\ \cline{3-6}  & & \(3\times 3\), 512 & & & \\ \hline Maxpool & \(2\times 2\) & \(2\times 2\) & 1 & 1 & 1 & \\ \hline Conv5 & \(2\times 2\) & \(3\times 3\), 512 & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{1} \\ \cline{3-6}  & & \(3\times 3\), 512 & & & \\ \hline Refine layer & \(2\times 2\) & \(3\times 3\), 512 & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{1} \\ \cline{3-6}  & & \(3\times 3\), 512 & & & \\ \hline fc1 & 4096 & & 1 & 1 & & \\ \hline fc2 & 4096 & & 1 & 1 & 1 & 1 \\ \hline fc3 & Classes & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{1} \\ \cline{3-6}  & number & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Structure of the CF-VGG-16 model for CIFAR 100 dataset. Since the CIFAR image is very small, the last max-pooling layer of VGG-16 was not used. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**CF-ResNet**} \\ \hline \multirow{2}{*}{**Layer Name**} & \multirow{2}{*}{**Output Size**} & \multirow{2}{*}{**Block Structure**} & \multicolumn{4}{c}{**Block Numbers 164-Layer (326-Layer)**} \\ \cline{3-6}  & & & **Baseline** & **Main** & **Coarse1** & **Coarse2** \\ \hline Conv1 & \(32\times 32\) & \(3\times 3\), 16 & \(1\) (1) & \(1\) (1) & \\ \hline Conv2 & \(32\times 32\) & \(1\times 1\), 16 & \multirow{2}{*}{18 (36)} & \multirow{2}{*}{18 (36)} & \multirow{2}{*}{18 (36)} & \multirow{2}{*}{6 (6)} \\  & & \(1\times 1\), 64 & & & \\ \hline Conv3 & \(16\times 16\) & \(1\times 1\), 32 & \multirow{2}{*}{18 (36)} & \multirow{2}{*}{18 (36)} & \multirow{2}{*}{18 (36)} & \multirow{2}{*}{6 (6)} \\  & & \(3\times 3\), 32 & & & \\ \hline Conv4 & \(8\times 8\) & \(1\times 1\), 64 & \multirow{2}{*}{18 (36)} & \multirow{2}{*}{18 (36)} & \multirow{2}{*}{6 (6)} & \multirow{2}{*}{6 (6)} \\  & & \(3\times 3\), 64 & & & \\ \hline \(1\times 1\), 256 & \multirow{2}{*}{18 (36)} & \multirow{2}{*}{18 (36)} & \multirow{2}{*}{6 (6)} & \multirow{2}{*}{6 (6)} & \multirow{2}{*}{6 (6)} \\  & & \(1\times 1\), 256 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Structure of the CF-ResNet model for CIFAR-10 and CIFAR-100 datasets.