## 4 Experimental Results and Discussion
### Discussion of Results
\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 (\(+/-0.11\)) & 0.06 (\(+/-0.18\)) \\ VGG16 & 87.67 (\(+/-0.35\)) & 78.04 (\(+/-0.55\)) & 0.14 (\(+/-0.03\)) & **0.03 (\(+/-0.07\))** \\ Xception & 90.08 (\(+/-1.51\)) & 82.77 (\(+/-2.83\)) & 0.14 (\(+/-0.07\)) & 0.05 (\(+/-0.14\)) \\ Inception-V3 & 97.20 (\(+/-1.31\)) & 95.54 (\(+/-2.12\)) & 0.14 (\(+/-0.16\)) & 0.12 (\(+/-0.26\)) \\ Mobilenet-V1 & 97.56 (\(+/-0.75\)) & 96.14 (\(+/-1.19\)) & **0.08** (\(+/-0.05\)) & 0.06 (\(+/-0.11\)) \\ Mobilenet-V2 & 97.92 (\(+/-0.74\)) & 96.73 (\(+/-1.12\)) & 0.10 (\(+/-0.09\)) & 0.07 (\(+/-0.16\)) \\ NasNetLarge & 96.78 (\(+/-1.04\)) & 94.96 (\(+/-1.52\)) & 0.54 (\(+/-0.66\)) & 0.27 (\(+/-0.81\)) \\ Inception-ResNet-V2 & **98.13** (\(+/-0.84\)) & **97.03** (\(+/-1.33\)) & 0.27 (\(+/-0.40\)) & 0.15 (\(+/-0.55\)) \\ ResNet152v2 & 97.59 (\(+/-0.94\)) & 96.14 (\(+/-1.52\)) & 0.33 (\(+/-0.32\)) & 0.17 (\(+/-0.42\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: F1-score and accuracy results of our transfer learning strategy using a support vector machine (SVM) classifier on CRINF-300 with five-fold cross-validation. The time indicates the average computational cost per individual image. Bold highlights the best results. between the two proposed transfer learning approaches are minimal, they can be critical in real-time systems. Our fine-tuned results are presented in Table 4. VGG16 obtained the best results with an F1-score of 93.73 and an accuracy of 95.59, which improved significantly from the transfer learning results of 87.67 F1-score and 78.04 accuracy. ResNet50 was the second-best-performing architecture, with a 92.16 F1-score and 94.12 accuracy. While ResNet50 and VGG16 obtained significant improvements in both F1-score and accuracy, they did not surpass the best results yielded by the transfer learning approach. Furthermore, they increased the computational time required by up to three times when compared to the transfer-based models. However, VGG16 remained as the architecture with the least time variance and fastest processing time, followed by MobileNet-V1 on the CPU and Xception on the GPU. These enhanced scores suggest that fine-tuning of the layers and parameters is more beneficial to these architectures than simply freezing the final layers, despite our low number of images. Although our fine-tuning additions perform well on top of ResNet50 and VGG16, they do not achieve good performance on the rest of the architectures, obtaining lower results than those of the transfer-learning-based models. In particular, MobileNet-V2 obtained an F1-score of 48.78 from the original 97.55. This drop in performance can also be attributed to our new head of the model and the low number of training images. Given the real-time processing requirement of monitoring ICSs and the particular focus on light deployment, we recommend the pipeline with the Mobilenet-V1 architecture in order to have the best speed-performance trade-off. Although VGG16 is the fastest architecture on the GPU for our fine-tuning approach, and with the least variance across all proposals, its performance is much lower than that of Mobilenet-V1 in transfer learning. Figures 3 and 4 graphically illustrate our results, highlighting the best-performing architectures across all three approaches.