## 3 Experiments and Results
### Results
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **k** & **Accuracy Min** & **Accuracy Max** & **Accuracy Avg** & **Accuracy Stdev** \\ \hline v1m1 & 5 & 0.784 & 0.857 & 0.829 & 0.0284 \\  & 10 & 0.551 & 0.918 & 0.833 & 0.103 \\ \hline v1m2 & 5 & 0.788 & 0.890 & 0.860 & 0.042 \\  & 10 & 0.714 & 0.892 & 0.825 & 0.058 \\ \hline v1m3 & 5 & 0.777 & 0.879 & 0.843 & 0.040 \\  & 10 & 0.699 & 0.909 & 0.812 & 0.077 \\ \hline v1m4 & 5 & 0.712 & 0.871 & 0.810 & 0.073 \\  & 10 & 0.808 & 0.877 & 0.852 & 0.021 \\ \hline v1m5 & 5 & 0.716 & 0.874 & 0.829 & 0.0657 \\  & 10 & 0.851 & 0.892 & 0.874 & 0.012 \\ \hline v1m6 & 5 & 0.788 & 0.877 & 0.845 & 0.036 \\  & 10 & 0.777 & 0.913 & 0.867 & 0.0362 \\ \hline v2m1 & **5** & **0.895** & **0.922** & **0.915** & **0.011** \\  & **10** & **0.843** & **0.931** & **0.907** & **0.033** \\ \hline v2m2 & **5** & **0.906** & **0.931** & **0.922** & **0.010** \\  & **10** & **0.910** & **0.938** & **0.927** & **0.007** \\ \hline v2m3 & **5** & **0.889** & **0.935** & **0.916** & **0.017** \\  & **10** & **0.900** & **0.937** & **0.925** & **0.012** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: K-fold test accuracy results for best designed networks (selected by validation accuracy) for the IGN-PNOA dataset. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Test Accuracy 95\% t Confidence** & **Test Accuracy 95\% Confidence** \\  & **Interval (k-fold k = 5)** & **Interval (k-fold k = 10)** \\ \hline v1m1 & 0.823 \(\pm\) 0.035 [0.793, 0.864] & 0.833 \(\pm\) 0.074 = [0.759, 0.907] \\ v1m2 & 0.860 \(\pm\) 0.052 [0.808, 0.912] & 0.826 \(\pm\) 0.042 = [0.784, 0.867] \\ v1m3 & 0.843 \(\pm\) 0.050 [0.793, 0.893] & 0.812 \(\pm\) 0.055 = [0.757, 0.867] \\ v1m4 & 0.810 \(\pm\) 0.091 [0.719, 0.901] & 0.852 \(\pm\) 0.015 = [0.836, 0.867] \\ v1m5 & 0.829 \(\pm\) 0.082 [0.747, 0.911] & 0.874 \(\pm\) 0.009 = [0.865, 0.882] \\ v1m6 & 0.845 \(\pm\) 0.045 [0.800, 0.890] & 0.867 \(\pm\) 0.026 = [0.842, 0.893] \\ v2m1 & **0.915 \(\pm\) 0.013 [0.901, 0.928]** & **0.907 \(\pm\) 0.024 = [0.884, 0.931]** \\ v2m2 & **0.922 \(\pm\) 0.012 [0.910, 0.934]** & **0.927 \(\pm\) 0.005 = [0.922, 0.932]** \\ v2m3 & **0.916 \(\pm\) 0.021 [0.894, 0.937]** & **0.925 \(\pm\) 0.008 = [0.917, 0.934]** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test accuracy (95% confidence intervals) for all models calculated from k-fold results for the IGN-PNOA dataset. For each model and k value, we show the calculated accuracy, expected interval mean, standard deviation and the interval itself. Finally, to give a point of common reference for other authors to be able to compare results, we ran preliminary tests (with no additional fine-tuning of any kind) on a typical NAS benchmark, the CIFAR-10 dataset (we set the maximum number of FC layers to 3 on this experiments). Here, the best model (selected by validation loss) designed by VGNet obtained test categorical accuracies that oscillate in the range shown in Table 5. The gap between this results and the current state-of-the-art for CIFAR-10 can be explained by the fact that the grammar, as well as other internal hyperparameters, have been designed for road-detection on the IGN-PNOA dataset (including as mentioned some parameters that are set due to our current hardware constraints). This suggests that application to other problem domains, such as the case of CIFAR-10 or other remote sensing tasks, may need further grammar modifications and fine-tuning to achieve optimal results.