## 3 Development and Results
### Training and Evaluation Phases
#### 3.1.1 Tests Performed Using Half of the Database  
Proposed Networks Based on VGG Net:  
Firstly, two configurations were proposed based on VGG Net. These configurations were the same in the convolutional layers, but they differed in the number of neurons used in the fully connected network (FC). These networks were characterized by the use of a large number of small convolutional filters in their layers. These configurations implemented a 1-pixel step in the convolutional filters and a 2-pixel step in the max-pooling layers. These two configurations are shown in Table 1. The results obtained with these configurations are shown in Table 2.  
Proposed Networks Based on ZF Net:  
Three architectures were proposed based on ZF Net. These networks were characterized by the use of large filters in their first convolutional layers, differing from the previous architectures based on  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Configurations**} \\ \hline C1 & C2 \\ \hline Conv 3 \(\times\) 3 - 64 & Conv 3 \(\times\) 3 - 64 \\ Max-Pooling 2 \(\times\) 2 \\ \hline Conv 3 \(\times\) 3 - 128 & Conv 3 \(\times\) 3 - 128 \\ Max-Pooling 2 \(\times\) 2 \\ \hline Conv 3 \(\times\) 3 - 256 & Conv 3 \(\times\) 3 - 256 \\ Max-Pooling 2 \(\times\) 2 \\ \hline Conv 3 \(\times\) 3 - 512 & Conv 3 \(\times\) 3 - 512 \\ Max-Pooling 2 \(\times\) 2 \\ \hline FC - 2048 & FC - 4096 \\ FC - 2048 & FC - 2 \\ SoftMax \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proposed networks based on VGG Net—Using half of the database. FC: fully connected.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Tests**} & \multirow{2}{*}{**Image**} & \multirow{2}{*}{**Steps**} & \multicolumn{3}{c}{**Training**} & \multicolumn{3}{c}{**Evaluation**} \\ \cline{4-8}  & & & & **Loss** & **Accuracy** & **Loss** & **Accuracy** \\ \hline C1 & T1 & RGB & 10,000 & 0.32 & 0.81 & 0.30 & 0.84 \\ \hline \multirow{4}{*}{C2} & T2 & RGB & 10,000 & 0.27 & 0.83 & 0.30 & 0.86 \\  & T3 & RGB & 12,500 & 0.20 & 0.84 & 0.25 & 0.89 \\ \cline{1-1}  & T4 & RGB & 16,000 & 0.16 & 0.87 & 0.22 & 0.90 \\ \cline{1-1}  & T5 & Gray & 16,000 & 0.20 & 0.85 & 0.26 & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results obtained with the networks based on VGG Net—Using half of the database.  
VGG Net that used \(3\times 3\) filters in all their layers. These configurations implemented a 2-pixel step in the first two convolutional layers and a 1-pixel step was used the following layers. In the max-pooling layers a 2-pixel step was used. These three configurations are shown in Table 3. The results obtained with these configurations are shown in Table 4.  
Configurations C2 in test T4 and C3 in test T1 yielded the best results in the evaluation phase. These results were compared, showing that test T4 had an improvement in accuracy of 3.33% and in loss of 21.4% compared with the results obtained in test T1. Moreover, with these results, it can be concluded that the new images that were created in the database augmentation phase provided new information that helped the training of the system by providing complementary information to the initial one.