## 3 Results  
Some of the typical defect classification results are given in Figure 12.  
Figure 11: Inception module in GoogleNet. CL is the convolution layer and MPL is the maximum pooling layer. Dimensionality reduction layers are depicted in yellow, the maximum pooling layer is depicted in red, and the layers in the convolution operations are shown in blue. The classification accuracy using the pre-trained AlexNet, VGG16, BNInception, and ResNet152 neural network models trained with different network parameters (batch size and learning speed) is summarized in Figures 13-16. Figure 12: Examples of wood veneer defect classification using faster R-CNN. Branches (B) are labeled in green. Scratches (S) are labeled in blue. Cores (C) are labeled in violet. Stains (T) are labeled in yellow. Figure 13: Classification accuracy using AlexNet neural network trained with different network parameters (batch size and learning speed). The best combinations of parameter values are indicated in red. Figure 14: Classification accuracy using VGG16 neural network trained with different network parameters (batch size and learning speed). The best combinations of parameter values are indicated in red. Figure 15: Classification accuracy using the BNInception neural network trained with different network parameters (batch size and learning speed). The best combinations of parameter values are indicated in red. The best results while using the AlexNet neural network were achieved using 32 batch size and 0.2 learning speed for stain class, 64 batch size and 0.2 learning speed for core class, 128 batch size and 0.1 learning speed for branch class, 256 batch size and 0.1 learning speed for scratch class, and 256 batch size and 0.1 learning speed for background class. The results with respect to different sliding window multipliers are presented in Table 2. The best accuracy, 76.6%, was achieved using the sliding window sizes of [4; 8; 12]. The best results while using the VGG16 neural network were achieved using 32 batch size and 0.01 learning speed for branch class, 32 batch size and 0.1 learning speed for scratch and background classes, 64 batch size and 0.2 learning speed for core class, and 100 batch size and 0.2 learning speed for background class. The best results while using the BNInception neural network were achieved using 64 batch size and 0.1 learning speed for core class, 128 batch size and 0.1 learning speed for branch and background class. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Sliding Window Size**} & \multicolumn{5}{c}{**Accuracy**} & \multirow{2}{*}{**Average Accuracy**} \\ \cline{2-2} \cline{4-6}  & **Branch** & **Scratch** & **Stain** & **Core** & \\ \hline
**[1; 2; 4]** & 88.2\% & 38.7\% & 25.0\% & 69.5\% & 70.5\% \\ \hline
**[2; 4; 8]** & 84.8\% & **40.0\%** & 20.0\% & 56.5\% & 67.8\% \\ \hline
**[2; 8; 16]** & 85.3\% & 33.3\% & 25.0\% & 63.6\% & 71.9\% \\ \hline
**[2; 8; 32]** & 86.2\% & 27.7\% & 27.2\% & 72.0\% & 65.4\% \\ \hline
**[2; 16; 32]** & 84.3\% & 35.4\% & 17.2\% & 72.0\% & 61.5\% \\ \hline
**[4; 8; 12]** & **90.5\%** & 39.2\% & **30.7\%** & 76.1\% & **76.6\%** \\ \hline
**[4; 8; 16]** & 86.2\% & 35.4\% & 25.0\% & 65.2\% & 71.4\% \\ \hline
**[8; 16; 32]** & 90.1\% & 33.3\% & 21.4\% & **78.2\%** & 71.8\% \\ \hline \end{tabular}
\end{table} 
Table 2: Classification results with different sliding window size with AlexNet neural network. The best results are shown in bold. Figure 16: Classification accuracy using ResNet152 neural network trained with different network parameters (batch size and learning speed). The best combinations of parameter values are indicated in red. classes, 128 batch size and 0.2 learning speed for stain class, and 256 batch size and 0.2 learning speed for scratch class.