# Texture and Materials Image Classification Based on Wavelet Pooling Layer in CNN
## 5 Experimental Results
### Model Training Results and Analysis
#### 5.1.3 Image Classification with Materials FMD  
The third dataset we used is FMD with ten classes of different materials. Moreover, it is a small dataset since it only has 100 images per class. Likewise, we performed two experiments: the first with the SGD optimizer and the second with the Adam optimizer. Table 6 shows that our proposed DWTP method using its DWTdP configuration outperforms all methods. In addition, we retain a similitude in the three sets: training (16.87%), validation (18.67%), and test (14.00%). The DWTdP model obtained with this configuration is shown in Figure A6 of Appendix B, which shows the correlation of each class with its actual and predicted label.  
The change of the optimizer, in this case, was beneficial for AveP learning. Table 7 shows that our proposed DWTdP method and AveP exhibit the best classification performance on all three datasets. Figure 5 shows the learning curves of the pooling methods for FMD. In this case, MaxP shows a smooth learning descent and similar behavior between the two sets, but after epoch 22, it does not resist overfitting. AveP achieves the best performance at epoch 17, avoiding overfitting in the following epochs. DWTP and DWTaP maintain a consistent learning progression, and their validation sets progress at a similar rate but does not resist overfitting. DWTdP shows a slow learning trend in the early epochs, but after epoch 15, the learning rate improves, and the sets evolve at a similar rate, achieving good accuracy performance.  
The DWTdP and AveP learning models obtained with this configuration are shown in Figure A7 of Appendix B, which summarizes the level of success of the classification model predictions. Moreover, the classification reports obtained with both configurations (SGD and Adam) for FMD are shown in Tables A4 and A5 of Appendix C.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{1}{l}{**Trainable**} & \multirow{2}{*}{**Loss**} & \multirow{2}{*}{**Acc**} & \multirow{2}{*}{**Val\_Loss**} & \multirow{2}{*}{**Val\_Acc**} & \multirow{2}{*}{**Test\_Loss**} & \multirow{2}{*}{**Test\_Acc**} \\  & **Params** & & & & & & \\ \hline MaxP & 12,341,686 & 2.6234 & 0.2239 & 2.4013 & 0.1667 & 2.4426 & 0.1333 \\ AveP & 12,341,686 & 1.7555 & 0.4369 & 2.2773 & 0.2067 & 2.3420 & 0.2467 \\ DWTP & 48,744,886 & 1.5113 & 0.4896 & 2.0208 & 0.3533 & 2.1488 & 0.2867 \\ DWTaP & 12,341,686 & 1.3802 & 0.5101 & 2.1916 & 0.3267 & 2.3301 & 0.3066 \\ DWTdP & 36,610,486 & 3.0464 & 0.1687 & 2.3172 & 0.1867 & 2.4176 & 0.1400 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of pooling methods on FMD—SGD optimizer.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{1}{l}{**Trainable**} & \multirow{2}{*}{**Loss**} & \multirow{2}{*}{**Acc**} & \multirow{2}{*}{**Val\_Loss**} & \multirow{2}{*}{**Val\_Acc**} & \multirow{2}{*}{**Test\_Loss**} & \multirow{2}{*}{**Test\_Acc**} \\  & **Params** & & & & & & \\ \hline MaxP & 12,341,686 & 1.4713 & 0.4821 & 2.0068 & 0.2867 & 2.1222 & 0.2867 \\ AveP & 12,341,686 & 1.9043 & 0.3594 & 2.2249 & 0.3267 & 2.0981 & 0.3000 \\ DWTP & 48,744,886 & 1.3116 & 0.5493 & 2.0832 & 0.3200 & 2.0667 & 0.3133 \\ DWTaP & 12,341,686 & 1.4319 & 0.5108 & 2.0566 & 0.3667 & 2.0717 & 0.2866 \\ DWTdP & 36,610,486 & 2.1660 & 0.2239 & 2.1728 & 0.2600 & 2.2071 & 0.2199 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of pooling methods on FMD—Adam optimizer.