# Simplified Machine Learning Model as an Intelligent Support for Safe Urban Cycling
## 4 Results
### Performance and Generalization in the State-of-the-Art  
When classifying new data with state-of-the-art reference models, the accuracy tends to decrease due to the change in data distribution as they could demonstrate a study by [PERSON] et al. [31], where all the data have the same statistical distribution even if they come from different classified groups. Our Siamese network, being the basis of the SML model, used the ResNet-18 and EfficientNet-B0 networks as feature extractors; was trained with the MiniImageNet dataset; and was also validated with the CIFAR-100, CUB-200-2011, and DroNet datasets. For the state-of-the-art models used in the comparison, very similar networks and datasets and CNNs were used, thus allowing the results to be evaluated using their classification accuracies in the two modes, one-shot and five-shot, with a 95% trust. Below in Tables 5-8, we present the results about what we have described. Figure 13: EfficientNet-B0 Summary. Comparative summary of average accuracy with the different datasets. \begin{table}
\begin{tabular}{l l c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Feature Extractor**} & \multicolumn{2}{c}{**MiniImageNet**} \\ \cline{3-4}  & & **1-Shot** & **5-Shot** \\ \hline
[23] MatchNet & ResNet-12 & 63.08 & 75.99 \\
[41] Meta-SGD & ResNet-50 & 50.47 & 64.66 \\
[22] ProtoNet & ResNet-12 & 62.39 & 68.20 \\
[36] RelationNet & ResNet-34 & 57.02 & 71.07 \\
[40] RENet & ResNet-12 & 67.60 & 82.58 \\ \hline SML (Our model) & ResNet-18 & **67.05** & **81.40** \\ SML (Our model) & EfficientNet-B0 & **66.90** & **81.71** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with the state of the art using MiniImageNet. The accuracy between the state-of-the-art models and the SML proposed model is presented using the MiniImageNet dataset. Columns show the name of the model used, the feature extractor, the dataset, and the two k-shot learning modes used. The best results with our model are highlighted in bold. The above results allow us to conclude that our SML model, compared with the state-of-the-art models, using two of the datasets (CIFAR-100 and DroNet), has better performance and generalization in the one-shot and five-shot mode using both the ResNet-18 and EfficientNet-B0 feature extractors. However, for the MiniImageNet and CUB-200-2011 datasets, results close to the RENet model were obtained, which was the one that obtained the best results in terms of average accuracy. \begin{table}
\begin{tabular}{l l c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Feature Extractor**} & \multicolumn{2}{c}{**CUB-200–2011**} \\ \cline{3-4}  & & **1-Shot** & **5-Shot** \\ \hline
[23] MatchNet & ResNet-12 & 71.87 & 85.08 \\
[41] Meta-SGD & ResNet-50 & 53.34 & 67.59 \\
[22] ProtoNet & ResNet-12 & 66.09 & 82.50 \\
[36] RelationNet & ResNet-34 & 66.20 & 82.30 \\
[40] RENet & ResNet-12 & 79.49 & 91.11 \\ \hline SML (Our model) & ResNet-18 & **73.52** & **83.73** \\ SML (Our model) & EfficientNet-B0 & **74.34** & **84.42** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison with the state of the art using CUB-200–2011. The accuracy between the state-of-the-art models and the SML proposed model is presented using the MiniImageNet dataset. Columns show the name of the model used, the feature extractor, the dataset, and the two k-shot learning modes used. The best results with our model are highlighted in bold. \begin{table}
\begin{tabular}{l l c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Feature Extractor**} & \multicolumn{2}{c}{**CIFAR-100**} \\ \cline{3-4}  & & **1-Shot** & **5-Shot** \\ \hline
[42] MAML & ResNet-12 & 49.28 & 58.30 \\
[23] MatchNet & ResNet-12 & 50.53 & 60.30 \\
[41] Meta-SGD & ResNet-50 & 53.83 & 70.40 \\
[38] DEML+Meta-SGD & ResNet-50 & 61.62 & 77.94 \\
[35] Dual TriNet & ResNet-18 & 63.41 & 78.43 \\ \hline SML (Our model) & ResNet-18 & **67.14** & **80.17** \\ SML (Our model) & EfficientNet-B0 & **71.17** & **81.12** \\ \hline \hline \end{tabular}
\end{table}