## 4 Experimental Results and Discussion
### Discussion of Results  
The results of our transfer learning experiments can be seen in Tables 2 and 3. In both of the proposed approaches, Inception-ResNet-V2 obtained the highest F1-score and accuracy. Using the logistic regression classifier, this architecture obtained a 98.32 F1-score and 97.33 accuracy, with a variance of 0.70 and 1.12, respectively. On the SVM classifier, Inception-ResNet-V2 obtained slightly lower results, with a 98.13 F1-score and 97.03 accuracy. VGG16 and ResNet50 obtained the lowest F1-score, with 87.67 in both approaches. ResNet152v2 obtained a 10% F1-score over ResNet50, but with a computational time three times higher, highlighting the enhancements of the batch  
The MobileNet architecture obtained the second-best performance. On the logistic approach, V1 obtained scores of 97.58 and 96.15 against the 97.55 and 96.14 obtained by V2. When using the SVM classifier, MobileNet-V2 yielded slightly better results, with a 97.92 F1-score and 96.73 accuracy compared to the 97.56 and 96.14 results reported by V1. Despite these slight differences, MobileNet-V1 always achieved faster CPU and GPU times than V2, which included less variance in the time required to process the images. When comparing the logistic regression approach against the SVM classifier, we saw small improvements in both the F1-score and accuracy in Xception, InceptionV3, NasNet-Large, ResNet152V2, and MobileNet-V2. However, both MobileNet-V1 and Inception-ResNet-V2 obtained slightly lower F1 results, decreasing to 97.56 and 98.13, respectively. Lastly, ResNet50 and VGG16 remained with the same scores performance-wise. As for performance variance, ResNet50 and VGG16 obtained the least variance in both approaches for F1-score and accuracy, with 0.35 and 0.55, respectively. In our logistic classifier, InceptionV3 obtained the largest variance of F1-score and accuracy with 1.50 and 2.40, respectively, followed by NasNetLarge with 1.29 and 1.99. On our SVM approach, the Xception architecture surpassed the variance of these two models, with 1.51 F1-score and 2.83 accuracy variances. Regarding the computational cost, VGG16 and MobileNet-V1 were the best-performing architectures in both types of transfer learning. MobileNet-V1 was faster on the CPU, with 0.06 s against the 0.16 obtained by VGG16, but was surpassed on the GPU by 0.03 s.  
On average, the SVM classifier took slightly more time than the logistic regression approach, with the highest average increase seen in the NasNetLarge architecture from 0.49 to 0.54 on the CPU and 0.16 to 0.27 on the GPU. While the differences in most architectures \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Architecture** & **F1-Score (\%)** & **Accuracy (\%)** & **CPU (s)** & **GPU (s)** \\ \hline ResNet50 & 87.67 (\(+/-0.35\)) & 78.04 (\(+/-0.55\)) & 0.10 (\(+/-0.10\)) & 0.05 (\(+/-0.18\)) \\ VGG16 & 87.67 (\(+/-0.35\)) & 78.04 (\(+/-0.55\)) & 0.16 (\(+/-0.03\)) & **0.03 (\(+/-0.07\))** \\ Xception & 89.46 (\(+/-0.67\)) & 81.60 (\(+/-1.24\)) & 0.14 (\(+/-0.07\)) & 0.05 (\(+/-0.15\)) \\ Inception-V3 & 97.02 (\(+/-1.50\)) & 95.25 (\(+/-2.40\)) & 0.10 (\(+/-0.16\)) & 0.07 (\(+/-0.26\)) \\ Mobilenet-V1 & 97.58 (\(+/-0.70\)) & 96.15 (\(+/-1.17\)) & **0.06 (\(+/-0.05\))** & 0.04 (\(+/-0.12\)) \\ Mobilenet-V2 & 97.55 (\(+/-0.49\)) & 96.14 (\(+/-0.73\)) & 0.08 (\(+/-0.09\)) & 0.05 (\(+/-0.17\)) \\ NasNetLarge & 96.44 (\(+/-1.29\)) & 94.36 (\(+/-1.99\)) & 0.49 (\(+/-0.65\)) & 0.16 (\(+/-0.96\)) \\ Inception-ResNet-V2 & **98.32** (\(+/-0.70\)) & **97.33** (\(+/-1.12\)) & 0.21 (\(+/-0.41\)) & 0.12 (\(+/-0.63\)) \\ ResNet152v2 & 97.41 (\(+/-0.90\)) & 95.85 (\(+/-1.46\)) & 0.26 (\(+/-0.32\)) & 0.10 (\(+/-0.46\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: F1-score and accuracy results of our transfer learning strategy using a logistic regression classifier on critical infrastructure classification dataset (CRINF-300) with five-fold cross-validation. The time indicates the average computational cost per individual image. Bold highlights the best results.