# Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network +
## 5 Experimental Results
### Exploring an Optimal Architecture--CarNet, by Making Experiments on PKLot Dataset
#### 5.2.1 Experimental Results on PKLot Dataset  
Training and validation accuracies on PKLot dataset are provided by Figure 12.  
As can be seen in the figure, in all three-line graphs deeper models show higher scores. The exact accuracy values are provided in Table 9. The lines for VGG16 and VGG19 describe the training accuracies of these models trained from the scratch while the patterns for VGG16 (fine-tuned) and VGG19 (fine-tuned) illustrate the scores of these models, which we fine-tuned pre-trained weights trained on ImageNet. A few conclusions can be obtained from the results shown in Table 9. The highest scores for both training and validation accuracy were achieved by AlexNet, 99.98% and 98.82% respectively, while the scores for CarNet are the lowest, 97.23% and 90.70%. However, AlexNet is not the deepest model, but rather has a smaller number of layers than the other models in the table. This proves that deeper models do not yield better solutions on this task and inspired our idea to use fewer layers. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of Method** & **Training Accuracy** & **Validation Accuracy** \\ \hline AlexNet [6] & **99.98\%** & **98.82\%** \\ CarNet & 97.23\% & 90.70\% \\ VGG16 [52] & 99.38\% & 93.72\% \\ VGG16 (fine-tuned) [52] & 99.94\% & 94.44\% \\ VGG19 [52] & 99.94\% & 95.25\% \\ VGG19 (fine-tuned) [52] & 99.96\% & 94.54\% \\ Xception [53] & 99.92\% & 98.53\% \\ Inception V3 [54] & 99.61\% & 97.73\% \\ ResNet50 [55] & 99.51\% & 98.02\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results related to PKLot [4] dataset. Comparison of training and validation accuracies for CarNet with well-known architectures. Figure 12: Experiment on PKLot [4] dataset. Comparison of our model with respect to other models. x-direction: number of epochs (500), y-direction: accuracy. **(a)** training accuracy; **(b)** validation accuracy. Thus, rather than conducting experiments on deeper networks, we decided to develop a model with fewer layers. Furthermore, that a model shows the lowest training and validation accuracy does not mean that it is the least effective model. The graphs indicate only the training phase accuracy. The performance shown by the other methods may also mean that they are overfitting. The experimental results shown in Figure 12, and prove that CarNet demonstrates a higher accuracy than all the other models. Finally, Table 10 shows the testing accuracies of nine experiments related to Figure 12. In the table, testing scores of the models AlexNet, CarNet, VGG16, VGG16 (fine-tuned), VGG19, VGG19 (fine-tuned), Xception, Inception V3, ResNet50 are given. An examination of these architectures in the table reveals that deep networks are very effective for solving this task. As mentioned previously, in previous studies the performances of the methods were compared with that of AlexNet. However, the results of our experiments indicate that, among these networks, except CarNet, Inception V3 and ResNet50 are the most robust. See the scores for Inception V3 and ResNet50 in Table 10. The reason why AlexNet was chosen as a comparative method is that it shows the best performance for images from the same dataset. We give examples to support this consideration and show performances in training/testing format from the scores for AlexNet in Table 9. The score for PUCPR/PUCPR (training/testing) is 98.6%, which is the second highest after that of CarNet. Next, the score for UFPR04/UFPR04 (training/testing) is 98.2%, which is the highest among all the architectures we used in our experiments. The score for UFPR05/UFPR05 (training/testing) is 98%, which is also higher than that for all the other networks. However, AlexNet shows poorer results when we trained with one subset of images and tested with a different subset. For instance, the score for UFPR04/PUCPR is 89.5% and the score for PUCPR/UFPR05 is 83.4%, as shown in the table.