## 5 Results  
In this study, transfer learning as functional application programming interface (API) was used to distinguish among 4 different barriers. In addition to having transfer learning models for performance comparison, a simple CNN model was used and compared. For transfer learning three pre-trained models were considered. Primarily, the transferred learning models were overfit, as can be seen from a huge gap between training and validation accuracy, see Figure 4(b) for instance. Thus, to address this issue a dropout layer is added before doing a prediction at a last dense layer. A drop out amount, a fraction of the input units to drop, was set as 0.5. After adding a drop out layer, no sign of overfitting was observed from model accuracy plots, see Figure 4(c). However, still a small overfitting could be observed for Densenet 121 even after adding dropout layer. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Kernel Size** & **\# of Filters** \\ \hline Conv1 & \(3\times 3\) & 32 \\ Max pooling & & Pool size 2 \(\times\) 2 & \\ Conv2 & \(3\times 3\) & 64 \\ Max pooling & & Pool size 2 \(\times\) 2 & \\ Conv3 & \(3\times 3\) & 128 \\ Max pooling & & Pool size 2 \(\times\) 2 & \\ Conv4 & \(3\times 3\) & 128 \\ Max pooling & & Pool size 2 \(\times\) 2 & \\ \hline \hline \multicolumn{3}{c}{Layer flatten + layer dropout (0.5) + layer dense (512) + layer dense (4)} \\ \hline \multicolumn{3}{c}{\#epochs = 30, steps per epochs: 12, validation steps: 10, lr = 1 \(\times\) 10\({}^{-3}\), \#batch = 32} \\ \end{tabular}
\end{table} 
Table 2: Design scheme of classification pathway, non-transfer learning. Based on Table 3, although the four models were performed on such a small dataset, VGG-19 transfer learning perform significantly better than all transfer learning and non-transfer learning. However, non-transfer learning still performs better than denseNet and inception V3 models. It should be noted that the main advantage of non-transfer learning was related to much lower cost compared to transfer learning, in terms of time spend for training. The models were implemented on unseen test dataset by evaluating generator function. Table 3 presents a summary of generated class prediction, confusion matrix of the 4 included categories over the test dataset. Also Figure 5 presents accuracy and loss plots of the incorporated model. As an example, Inception model with and without a drop out are presented, Figure 4(b),c.  
A can be seen from Figure 5, all training figures have higher accuracy and lower loss for training than the validation datasets. That is expected as the model is already familiar with the training dataset, while the validation data is a collection of new input to the model. The validation dataset, as a set of a sample data, would be used to provide an estimate of model skill during model's hyperparameters' tuning. The validation dataset would be used while the model is fitting on the training dataset for evaluation of the prediction power, qualify the model performance so it can adjust the performance while being trained. Figure 5: A plot of model accuracy on train and validation datasets, transfer learning (left) versus non-transfer learning (right). \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Metric (Accuracy)**} & \% \\ \hline \multirow{3}{*}{Transfer Learning} & Inception V3 & 78 \\ \cline{2-3}  & Densenet 121 & 65 \\ \cline{2-3}  & VGG 19 & 97 \\ \hline \multicolumn{2}{c}{Non- transfer learning} & 85 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of the model on test dataset. So, the model already turned its hyperparameters on the validation so it would not be a fair evaluation to present the validation accuracy. The confusion matrix for VGG 19 and non-transfer learning, as the best performed models, were included in Table 4 for the test dataset.