## 4 Experiments and Results Discussion
### Results Discussion  
Two different sorts of experiments have been carried out. The first experiment improved the baseline classification set by the authors of the Grocery Store Dataset [3]. They obtained a test accuracy of 85% using a DenseNet-169 model. A ResNeXt-101 with cardinality 32, a bottleneck width of eight [4] and a 20% dropout layer has improved the result up to 91.60% test accuracy. As the model was confused between oranges and satsumas, another tiered model was trained and added to reduce this error. By staggering the two models, the test accuracy has increased to 92%. This cascade modeling technique allows improving the model performance when there is a certain error in the detection of some specific classes.  
Recently, a paper published by [PERSON] et al. [40] has surpassed these values by using a technique known as _Ensemble of networks_. Considered an ensemble of \(M\) models, this technique chooses the output according to a maximum number of occurrences among the \(M\) individual networks. As an example, the _Ensemble C_ uses the following models simultaneously: ResNet-50, ResNet-101, ResNet-152, EfficientNet-b1, DenseNet-121, DenseNet-161 and DenseNet-201. The problem with this approach is that it requires the training of a large group of models, not only increasing the time in training but also in prediction.  
The second experiment has consisted in integrating multiple classifiers into a Siamese network in order to show how this new method improves the current OSL techniques. The results of four Siamese models have been shown, one including a ResNeXt-101 in each sister, one using a LOMO descriptor, one including a ResNeXt-101 together with a LOMO descriptor and one similar to the previous model but using a L2-regularization.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**ResNeXt-101**} & \multirow{2}{*}{**LOMO**} & \multicolumn{2}{c}{**ResNeXt-101**} & \multicolumn{1}{c}{**ResNeXt-101**} \\  & & & **+ LOMO** & **(with L2-Regularization)** \\ \hline
**Test accuracy (\(k=5\))** & 84.5 & 78.0 & 86.5 & 88.2 \\
**Balanced Test accuracy (\(k=5\))** & 84.5 & 78.0 & 86.5 & 88.2 \\
**Precision (\(k=5\))** & 83.9 & 73.6 & 84.6 & 82.7 \\
**Recall (\(k=5\))** & 85.5 & 87.3 & 89.2 & 96.5 \\
**F1-score (\(k=5\))** & 84.7 & 79.9 & 86.8 & 89.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different proposed Siamese networks.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**ResNeXt-101**} & \multirow{2}{*}{**ResNeXt-101**} & \multirow{2}{*}{**LOMO**} & \multirow{2}{*}{**ResNeXt-101**} & \multicolumn{1}{c}{**ResNeXt-101**} \\  & & & & & **+ LOMO** & **(with L2-Regularization)** \\ \hline
**Test accuracy (\(k=5\))** & 84.5 & 78.0 & 86.5 & 88.2 \\
**Balanced Test accuracy (\(k=5\))** & 84.5 & 78.0 & 86.5 & 88.2 \\
**Precision (\(k=5\))** & 83.9 & 73.6 & 84.6 & 82.7 \\
**Recall (\(k=5\))** & 85.5 & 87.3 & 89.2 & 96.5 \\
**F1-score (\(k=5\))** & 84.7 & 79.9 & 86.8 & 89.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different proposed Siamese networks.  
The L2-regularization has been used to reduce the overfitting detected in the model with ResNeXt-101 and LOMO.  
The integration of a descriptor and a CNN into the Siamese improves the model initially defined by [PERSON] et al. (2018) and can be easily applied to other problems, such as medical image classification ([PERSON] et al., 2018) or people re-identification ([PERSON] et al., 2018). The F1-score obtained using a traditional Siamese with a deep CNN has been 84.5%, while the score obtained with the composite architecture has been 89.1%. This technique is open to using different descriptors or classifiers and even can integrate more than two within the same network.