## 3 Experiments and Results
### Experiments with BEE2
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **BEE Acc.** & **NO-BEE Acc.** & **Overall Acc.** \\ \hline ConvNet 1 & 94.34\% & 93.24\% & 94.08\% \\ ConvNet 3 & 88.44\% & 88.44\% & 88.84\% \\ VGG16 & 78.09\% & 95.12\% & 86.60\% \\ ConvNetGS-4 & 84.86\% & 91.03\% & 86.36\% \\ ConvNetGS-3 & 82.18\% & 90.73\% & 84.26\% \\ ConvNet 7 & 81.64\% & 88.85\% & 83.39\% \\ ConvNet 6 & 72.02\% & 86.45\% & 75.53\% \\ ConvNet 2 & 67.99\% & 93.66\% & 74.82\% \\ RF (100) & 50.27\% & 98.31\% & 74.29\% \\ RF (60) & 50.07\% & 98.34\% & 74.21\% \\ RF (80) & 49.90\% & 97.86\% & 73.88\% \\ RF (40) & 49.53\% & 97.82\% & 73.67\% \\ ConvNet 4 & 66.48\% & 95.87\% & 73.44\% \\ RF (20) & 46.62\% & 97.26\% & 71.94\% \\ SVM & 87.66\% & 51.06\% & 69.36\% \\ ResNet-32 & 38.11\% & 99.70\% & 68.90\% \\ ConvNet 9 & 60.49\% & 89.42\% & 67.53\% \\ ConvNet 5 & 0\% & 100\% & 24.31\% \\ ConvNet 8 & 0\% & 100\% & 24.31\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Model performance on BEE2_1S validation dataset; all hand-designed ConvNets, ResNet32, and VGG16 were trained for 100 epochs each. The two top performing auto-designed ConvNets (ConvNetGS-4 and ConvNetGS-3) are included for comparison. The overall accuracy is sorted from highest to lowest. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **BEE Acc.** & **NO-BEE Acc.** & **Overall Acc.** \\ \hline ConvNet 3 & 75.59\% & 81.31\% & 78.90\% \\ ConvNet 1 & 69.98\% & 94.84\% & 78.17\% \\ ConvNet 4 & 65.73\% & 84.36\% & 76.51\% \\ ConvNetGS-5 & 74.52\% & 77.42\% & 76.20\% \\ ConvNet 2 & 64.34\% & 90.51\% & 75.92\% \\ ConvNetGS-4 & 64.82\% & 83.39\% & 75.56\% \\ ConvNet 5 & 57.37\% & 90.06\% & 73.77\% \\ ResNet32 & 64.10\% & 83.37\% & 73.73\% \\ ConvNet 9 & 52.04\% & 87.49\% & 72.55\% \\ VGG16 & 60.78\% & 82.54\% & 71.66\% \\ ConvNet 6 & 51.77\% & 86.04\% & 68.90\% \\ SVM & 87.10\% & 41.95\% & 64.53\% \\ RF (60) & 39.05\% & 88.99\% & 64.02\% \\ RF (80) & 37.84\% & 88.34\% & 63.09\% \\ RF (100) & 37.71\% & 88.43\% & 63.07\% \\ RF (40) & 37.34\% & 87.83\% & 62.58\% \\ RF (20) & 35.36\% & 89.89\% & 62.62\% \\ ConvNet 7 & 0\% & 100\% & 57.86\% \\ ConvNet 8 & 0\% & 100\% & 57.86\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Model performance on BEE2_2S validation dataset; all ConvNets, ResNet32, and VGG16 were trained for 100 epochs each. The two top performing auto-designed ConvNets (ConvNetGS-5 and ConvNetGS-4) are included for comparison. The overall accuracy is sorted from highest to lowest. As shown in Table 6, on BEE2_1S, the two hand-designed ConvNets (ConvNets 1, 3) outperformed all other models, including ResNet32 and VGG16. The two top performing auto-designed ConvNets (ConvNetGS-4 and ConvNetGS-3) ranked fourth and fifth, respectively. The performance of the auto-designed ConvNets was better than the performance of the other seven hand-designed ConvNets, ResNet32, the RFs and the SVMs. The performance of the random forests improved on BEE2_1S. In particular, the RF with 100 trees outperformed four hand-designed ConvNets, ResNet32, and the best linear kernel SVM by significant margins. As shown in Table 7, on BEE2_2S, hand-designed ConvNets 3, 1, and 4 performed better than the two auto-designed ConvNets and took the Top 3 slots. The top performing auto-designed ConvNets, ConvNetGS-5 and ConvNetGS-4, ranked fourth and seventh, respectively. It is interesting to note that the auto-designed ConvNets outperformed both ResNet32 and VGG16. The best linear SVM model slightly outperformed the best RF with 60 trees and ranked much higher than on BEE1 or BEE2_1S.