## 4 Results
### Baseline, Intra-Set Tests  
Baseline performance of the underwater data sets (AL0, AL4, AL8, AL12) and free-space sets (TB5, TB10, TB15) is established in this section. In establishing baseline performance, the focus is placed on training an architecture with one data set and testing it the corresponding holdout set. Later, an architecture will be trained with one data set (TB5 for example) and then the holdout sets from TB10 and TB15 (inter-set testing) will be used to explore how well the architecture is able to generalize on data outside the training set. Percent accuracy is used at the metric for comparing relative performance of the different architectures. Figure 10: Accuracy training curves for AL4 underwater and TB5 free-space OAM sets. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture** & **Underwater** & **Free-Space** \\ \hline AlexNet & \(1.2\times 10\)\({}^{-5}\) & \(1.1\times 10\)\({}^{-5}\) \\ DenseNet & \(4.2\times 10\)\({}^{-5}\) & \(4.2\times 10\)\({}^{-5}\) \\ MobileNet & \(1.2\times 10\)\({}^{-4}\) & \(7.4\times 10\)\({}^{-4}\) \\ ResNet & \(3.2\times 10\)\({}^{-5}\) & \(3.2\times 10\)\({}^{-5}\) \\ ResNeXt & \(2.4\times 10\)\({}^{-5}\) & \(2.4\times 10\)\({}^{-5}\) \\ ShuffleNet & \(4.2\times 10\)\({}^{-4}\) & \(3.1\times 10\)\({}^{-4}\) \\ SqueezeNet & \(1.5\times 10\)\({}^{-5}\) & \(5.5\times 10\)\({}^{-5}\) \\ WideResNet & \(1.4\times 10\)\({}^{-5}\) & \(1.4\times 10\)\({}^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Final learning rates for architectures in underwater and free-space data sets. Table 4 shows the baseline results for architectures trained with the underwater attenuated sets. As a whole, the architectures perform very well. Most accuracies achieved are 100%, or close to it. The only outlier is SqueezeNet on the AL12 set. Table 5 shows the baseline results for architectures trained with the free-space turbulent sets. This table includes a column that averages the results to help with sorting the architectures. The results in these tables are sorted by accuracy and show that the complexity imposed by turbulence, effects accuracy more than attenuation does. This table shows that AlexNet and SqueezeNet struggled the most. DenseNet appears to provide the best performance with the free-space data sets. Table 6 shows an example of the amount of time it took to train each architecture. The table shows the number of training epochs as well as the amount of time it took to train for the specified number of epochs. The training loop had a maximum number of training epochs, but was allowed to terminate early when a specific level of accuracy (99.9%) had been achieved on the validation set. Most of the architectures trained quickly in terms of epochs and overall time. AlexNet and SqueezeNet took the longest amount of time to train while yielding the poorest results. DenseNet and the ResNet family required the fewest training epochs and took a comparable amount of time to train. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture** & **Epochs** & **Time (sec)** \\ \hline AlexNet & 80 & 636 \\ DenseNet & 5 & 96 \\ MobileNet & 20 & 130 \\ ResNet & 6 & 110 \\ ResNeXt & 7 & 78 \\ ShuffleNet & 21 & 80 \\ SqueezeNet & 80 & 344 \\ Wide ResNet & 6 & 90 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Training epochs and time for the TB5 free-space set. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **AL0** & **AL4** & **AL8** & **AL12** \\ \hline SqueezeNet & 99.5 & 99.4 & 99.4 & 98.8 \\ AlexNet & 99.4 & 100.0 & 99.4 & 99.4 \\ ShuffleNet & 100.0 & 99.4 & 100.0 & 100.0 \\ DenseNet & 100.0 & 100.0 & 100.0 & 99.4 \\ ResNeXt & 100.0 & 100.0 & 100.0 & 99.4 \\ MobileNet & 100.0 & 100.0 & 100.0 & 100.0 \\ ResNet & 100.0 & 100.0 & 100.0 & 100.0 \\ Wide ResNet & 100.0 & 100.0 & 100.0 & 100.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Architecture baseline performance with underwater sets.