# Deep Learning Based Semantic Image Segmentation Methods for Classification of Web Page Imagery
## 4 Results and Discussion
### Analysis of UNet and FCN-8 Model Results  
Utilizing L2 norm and a momentum of 0.9, the stochastic gradient descent optimizers SGD, Adam, and Adagrad were able to avoid over-fitting. The whole network is trained with 500 epochs. A 50% dropout rate is utilized. The approach with this hyperparameter yielded 95% mean average accuracy and a 20% miss detection rate. The average mean accuracy is just a fraction of the true positive rates. Using L2 norm and a momentum of 0.9, the stochastic gradient descent optimizers SGD, Adam, and Adagrad were able to avoid over-fitting. The whole network undergoes 500 iterations of training. We used a 50% dropout rate. The approach using this hyperparameter achieved an average mean accuracy of 95%. To further improve the performance of segmentation models, training is done utilizing the data set. The above graph depicts the training accuracy and training loss curves for the segmentation models used in this study. Using cross-entropy and loss functions, as mentioned above, a result is produced (Table 2). It demonstrates that the UNet accuracy curves are marginally superior to those of the FCN model. Similarly, the UNet loss curves were reduced, indicating that it converges more readily. \begin{table}
\begin{tabular}{l l} \hline Learning Rate & 0.0001 Adam \\ \hline Momentum & 0.9 \\ \hline Regularization & L2 norm \\ \hline Cost/Loss & Cross-entropy \\ \hline Batch size & 5 \\ \hline Dropout & 50\% \\ \hline Epoch & 500 epochs \\ \hline Decay Rate & 0.9 \\ \hline \end{tabular}
\end{table} 
Table 1: Hyperparameters used for Seg-UNet. The performance of trained models is improved or increased in comparison to the performance of the pre-trained models, as demonstrated in the metrics (Figure 3(a),b). The validation loss follows the training loss despite the noise, indicating that the model can learn the dataset. This is followed by the epoch accuracy, dice, and loss of the trained models. According to the results, the UNet model shows an accuracy of 95%, respectively, with different epoch range counts (Figure 4(a),b). The proposed model has been optimized in order to compare three different optimizers with different epoch range counts. (1) Epoch count--300, Optimizer--[PERSON]  
From Figure 6, we can see that the training curve of the model gradually rises from epoch 10, though the given epoch count is in the 500 range. The accuracy of 95% is achieved by the 300 epoch count. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **IoU** & **mIoU** \\ \hline UNet & 75\% & 93\% & 82\% & 85\% & 83\% \\ \hline FCN-8 & 63\% & 91\% & 77\% & 84\% & 81\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation of the results. Figure 4: ROC graph of the (a) UNet model and (b) FCN-8 model. Figure 5: Epoch accuracy graph (a) and loss graph (b) of UNet (red line) and FCN-8 (blue line). (2) Epoch count--50, Optimizer--Adagrad  
From Figure 7, we can see that the training curve of the model starts to get flat from the 30 range, similar to the loss curve. (3) Epoch count--50, Optimizer--SGD  
From Figure 8, the SGD optimizer shows that there are no gradual but only inconsistent levels in the dice and loss scalar graphs. FCN-8 has shown worse results compared to UNet. We had to run for 500 epochs to achieve 88% accuracy in the training of the model for the desired output. The training curves show that there is a gradual increase from 100 epochs, with minimal loss (Figure 9). Figure 8: Dice score (**a**) and epoch loss (**b**) of the SGD optimizer for the epoch count of 50. Figure 6: (**a**) Epoch accuracy (**a**) and loss (**b**) of the Adam optimizer with 300 epoch count. The training curves explain that the SGD optimizer has continuous loss and gain in terms of training the model with the data (Figure 10). The training curves explain that the Adagrad optimizer had a flat accuracy gain from the epoch 300 to 500 range, similar to the loss (Figure 11). The masked image output of the proposed model with the Adam optimizer for 500 epochs is shown.