## 5 Experimental Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline
**EfficientNetB0** & **CH** & **HE** & **LO** & **TR** & **RN** & **TB** & **LY** & **MA** & **LG** & **LA** & **CO** & **BG** & **LAR** & **RT** & **HP** & **Avg** \\ \hline ReLU & 94.46 & 91.28 & 94.80 & 92.18 & 68.50 & 62.58 & 88.80 & 92.50 & 97.33 & 96.76 & 95.04 & 90.67 & 87.35 & 71.21 & 52.27 & 85.05 \\
15 Reit & 96.00 & 92.09 & 95.40 & 93.82 & 74.00 & 65.98 & 89.07 & 93.33 & 97.00 & 98.29 & 95.60 & 90.00 & 88.94 & 71.61 & 61.36 & 86.83 \\ \hline
**MobileNetV2** & **CH** & **HE** & **LO** & **TR** & **RN** & **TB** & **LY** & **MA** & **LG** & **LA** & **CO** & **BG** & **LAR** & **RT** & **HP** & **Avg** \\ \hline ReLU & 98.15 & 92.91 & 97.40 & 92.91 & 69.00 & 64.54 & 76.00 & 91.67 & 96.67 & 96.76 & 94.54 & 89.00 & 90.23 & 69.53 & 50.57 & 84.65 \\
15 ReLU & 99.08 & 95.23 & 98.00 & 95.64 & 75.00 & 70.41 & 80.27 & 95.42 & 98.00 & 97.71 & 95.46 & 90.67 & 91.52 & 69.24 & 55.11 & 87.17 \\ Stoc\_4 & 99.08 & 95.35 & 99.20 & 98.36 & 84.00 & 76.91 & 87.20 & 94.58 & 100 & 99.62 & 95.50 & 94.00 & 95.08 & 77.02 & 63.64 & 90.63 \\ \hline
**DarkNet53** & **CH** & **HE** & **LO** & **TR** & **RN** & **TB** & **LY** & **MA** & **LG** & **LA** & **CO** & **BG** & **LAR** & **RT** & **HP** & **Avg** \\ \hline ReLU & 98.77 & 93.60 & 98.00 & 95.82 & 71.00 & 67.84 & 81.33 & 71.25 & 98.00 & 96.95 & 92.02 & 91.67 & 91.44 & 67.12 & 53.98 & 84.58 \\
15 Leaky & 99.69 & 95.12 & 99.20 & 99.45 & 89.00 & 77.94 & 91.73 & 89.17 & 100 & 99.81 & 95.56 & 93.00 & 93.56 & 76.02 & 61.93 & 90.74 \\ Stoc\_4 & 99.69 & 95.93 & 98.80 & 88.80 & 88.00 & 77.73 & 96.00 & 88.33 & 100 & 99.81 & 95.28 & 91.00 & 92.12 & 74.33 & 67.05 & 90.86 \\ \hline
**ResNet50** & **CH** & **HE** & **LO** & **TR** & **RN** & **TB** & **LY** & **MA** & **LG** & **LG** & **LG** & **LAR** & **RT** & **HP** & **Avg** \\ \hline ReLU & 97.54 & 94.19 & 98.40 & 95.82 & 74.50 & 65.15 & 80.00 & 92.08 & 98.00 & 96.76 & 96.26 & 89.67 & 91.44 & 77.21 & 55.68 & 86.84 \\
15 ReLU & 99.08 & 95.70 & 99.20 & 97.27 & 79.00 & 69.38 & 84.27 & 95.42 & 97.33 & 98.10 & 97.00 & 91.00 & 93.79 & 77.15 & 59.66 & 88.89 \\ Stoc\_4 & 99.69 & 95.47 & 99.20 & 98.00 & 85.00 & 75.26 & 91.47 & 95.00 & 99.00 & 99.62 & 97.02 & 93.00 & 94.85 & 75.18 & 62.50 & 90.68 \\ \hline
**DenseNet201** & **CH** & **HE** & **LO** & **TR** & **RN** & **TB** & **LY** & **MA** & **LG** & **LA** & **CO** & **BG** & **LAR** & **RT** & **HP** & **Avg** \\ \hline ReLU & 98.73 & 95.29 & 98.37 & 96.92 & 71.40 & 66.80 & 82.20 & 91.31 & 98.22 & 98.12 & 95.88 & 91.69 & 93.96 & 49.92 & 54.70 & 85.56 \\
15 ReLU & 99.38 & 96.40 & 98.40 & 98.55 & 79.00 & 71.24 & 86.40 & 94.58 & 99.67 & 99.24 & 97.84 & 95.33 & 96.14 & 77.57 & 61.36 & 90.07 \\ Stoc\_4 & 99.69 & 94.88 & 99.20 & 99.27 & 84.00 & 76.29 & 93.87 & 96.67 & 100 & 100 & 97.84 & 93.00 & 95.38 & 77.67 & 69.89 & 91.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ensemble performance (accuracy) on a set of different topologies (due to the high computational time for CO we have run only 4 Sto_4 Densenet201). Figure 6: Average rank (lower is better) obtained by different AFs and ensembles coupled with VGG16 (light blue represents stand-alone methods and dark blue, ensembles). The tested CNNs are the following:  
* EfficientNetB0 [81]: this CNN does not have ReLU layers, so we only compare the stand-alone CNN with the ensemble labeled 15 Reit (15 reiterations of the training). * MobileNetV2 [82]. * DarkNet53, [83]: this deep network uses LeakyReLU with no ReLU layers; the fusion of 15 standard DarkNet53 models is labeled 15 Leaky. * DenseNet201 [84]. * ResNet50. As in the previous tests, training images were augmented with random reflections on both axes and two independent random rescales of both axes by two factors uniformly sampled in [1,2] (using MATLAB data augmentation procedures). The objective was to rescale both the vertical and horizontal proportions of the new image.