## 4 Results
### BCCD Dataset Results  
Table 9 shows the tenth-epoch validation accuracy and loss (VA and VL) values of the seven pre-trained ImageNet ILSVRC models versus the Google ViT. The models were fitted \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Pre-Trained Models**} & \multicolumn{4}{c}{**VA (Epoch = 10)**} & \multicolumn{4}{c}{**VL (Epoch = 10)**} \\ \cline{2-7}  & **DS-1** & **DS-2** & **DS-3** & **DS-1** & **DS-2** & **DS-3** \\ \hline \multicolumn{7}{c}{ImageNet Models} \\ \hline DenseNet-121 & 96.00 & 95.50 & 100 & 100 & 0.113 & 0.186 & 0.000 \\ DenseNet-169 & 99.00 & 96.00 & 100 & 100 & 0.033 & 0.227 & 0.000 \\ DenseNet-201 & 99.00 & 96.50 & 99.20 & 99.20 & 0.030 & 0.162 & 0.034 \\ Inception V3 & 98.00 & 92.00 & 100 & 100 & 0.087 & 0.327 & 0.000 \\ Inception-ResNet V2 & 99.00 & 94.50 & 100 & 100 & 0.030 & 0.221 & 0.000 \\ ResNet-0V2 & 94.00 & 92.00 & 100 & 100 & 0.271 & 0.320 & 0.000 \\ ResNet-101V2 & 96.00 & 91.50 & 100 & 100 & 0.153 & 0.393 & 0.000 \\ ResNet-152 V2 & 97.00 & 92.50 & 100 & 100 & 0.141 & 0.297 & 0.000 \\ VGG-16 & 97.00 & 91.00 & 100 & 100 & 0.097 & 0.219 & 0.007 \\ VGG-19 & 98.00 & 94.00 & 100 & 100 & 0.117 & 0.249 & 0.013 \\ Xception & 96.00 & 92.00 & 99.40 & 99.40 & 0.224 & 0.404 & 0.013 \\ \hline \multicolumn{7}{c}{Vision Transformer (ViT)} \\ \hline Google ViT & 100 & 100 & 100 & 100 & 100 & 0.005 & 0.003 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: PBC dataset: Tenth-epoch VA/VL values of Google ViT versus ILSVRC models. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Pre-Trained Models**} & \multicolumn{4}{c}{**AD Values (Epoch = 10)**} \\ \cline{2-4}  & **DS-1** & **DS-2** & **DS-3** \\ \hline \multicolumn{4}{c}{ImageNet Models} \\ \hline DenseNet-121 & +4\% & +5\% & 0\% \\ DenseNet-169 & +1\% & +4\% & 0\% \\ DenseNet-201 & +1\% & +35\% & +0.1\% \\ Inception V3 & +2\% & +8\% & 0\% \\ Inception-ResNet V2 & +1\% & +5.5\% & 0\% \\ ResNet-50 V2 & +6\% & +8\% & 0\% \\ ResNet-101V2 & +4\% & +8.5\% & 0\% \\ ResNet-152 V2 & +3\% & +7.5\% & 0\% \\ VGG-16 & +3\% & +9\% & 0\% \\ VGG-19 & +2\% & +6\% & 0\% \\ Xception & +4\% & +8\% & +2.2\% \\ \hline \multicolumn{4}{c}{Vision Transformer (ViT)} \\ \hline Google ViT & 0\% & 0\% & 0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: PBC dataset: Tenth-epoch AD values of Google ViT versus ILSVRC models. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Pre-Trained Models**} & \multicolumn{4}{c}{**LD Values (Epoch = 10)**} \\ \cline{2-4}  & **DS-1** & **DS-2** & **DS-3** \\ \hline \multicolumn{4}{c}{ImageNet Models} \\ \hline DenseNet-121 & 0.111 & 0.185 & 0.000 \\ DenseNet-169 & 0.033 & 0.227 & 0.000 \\ DenseNet-201 & 0.029 & 0.161 & 0.003 \\ Inception V3 & 0.087 & 0.327 & 0.000 \\ Inception-ResNet V2 & 0.029 & 0.220 & 0.000 \\ ResNet-50 V2 & 0.270 & 0.320 & 0.000 \\ ResNet-101V2 & 0.153 & 0.392 & 0.000 \\ ResNet-152 V2 & 0.141 & 0.297 & 0.000 \\ VGG-16 & 0.063 & 0.200 & 0.000 \\ VGG-19 & 0.063 & 0.225 & 0.001 \\ Xception & 0.223 & 0.404 & 0.271 \\with the three BCCD datasets (DS-4, DS-5, and DS-6). The Google ViT demonstrated better performance than all ImageNet ILSVRC models. The Google ViT reached an 88.6% validation accuracy and a validation loss close to one when fitted with the BCCD DS-6 dataset. By comparison, when fitted with the DS-6 dataset, all ImageNet ILSVRC models displayed poor optimization learning and performance. This was due to the great amount of noise caused by the overuse of data augmentation and the poor quality of the original BCCD dataset images. As shown in Table 10, the Google ViT fitted with the DS-6 dataset again outperformed the other models, reaching a +11.64% accuracy difference (AD) value, which was far better than any AD achieved by the ILSVRC models. Table 11 clearly demonstrates that the Google ViT fitted with the DS-6 dataset again outperformed the other models, achieving a loss difference (LD) of around 1%, which was lower than any LD attained by the ILSVRC models. \begin{table}