## 4 System Evaluation and Results
### Accuracy of Retrained Model  
To evaluate the accuracy before integrating the model to our application, we provided the model with ten testing images for each class. Learning rate was set to 0.01 with batch size as 100. Table 2 shows the results we got for each testing image. For the dirt images, the accuracy differed nonetheless, the model gave correct prediction of the label. For the grass class and ocean, they gave the highest accuracy results compared to the rest of the classes. However, the ocean images with dark color--images that have been taken after setting the time to night--gave lower scores. For the fire images, even though we reached an accuracy score of 0.99 in a few testing images, most of them ranged between 0.7 and 0.6 and this, due to the difficulty in collecting data which had fire only from the game. Fire was usually found in images that had grass too. For images containing snow, the color of snow was similar to that of the ocean and sky in some weather conditions provided by the game, thus causing the accuracy to drop in many of the testing images. If more training images were fed to stimulate the model in all conditions, then the accuracy would be much higher. In fact, the increasing of training images in all classes would increase the overall accuracy of the whole model. The images that contained illustrations of zombies were associated with the scent of mildew to represent an unpleasant scent. In comparison to the other classes, the zombies had the lowest scores when it was tested on the application, mainly due to the difficulty in training the system on high resolution images that represent zombies alone. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Image** & & **Model Predication** \\ \hline Dirt & \(\bullet\) & 7 images were correctly predicted with score 0.8 \\  & \(\bullet\) & Dropped to 0.6 in the rest \\  & \(\bullet\) & 6 images were correctly classified with the score 0.99 \\ Grass & \(\bullet\) & 3 images were correctly classified with the score equal to 1 \\  & \(\bullet\) & 1 image was correctly classified with score equal to 0.26 \\ Ocean & \(\bullet\) & 3 image were classified as ocean with the score 0.99 \\  & \(\bullet\) & The rest of images were classified with the scores of 0.95, 0.90, 0.77, 0.73 and 0.64 \\ Snow & \(\bullet\) & 4 images were classified with the score 0.99 \\  & \(\bullet\) & The rest of the images had 0.93, 0.94, 0.72, 0.79 \\  & \(\bullet\) & 2 out of 10 testing images were correctly classified with score equal to 0.99 \\ Zombies (mildew) & \(\bullet\) & The other images had 0.98, 0.96, 0.95, 0.92, 0.91, 0.85, 0.45 and 0.18 scores \\ Fire & \(\bullet\) & 3 images gave a score of 0.99 \\  & \(\bullet\) & The other images had 0.97, 0.93, 0.89, 0.85, 0.77, 0.73, and 0. 64 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model predications for 10 testing images of single label. In some cases, we wanted two scents to be released, therefore; we had 3 cases for training models to recognize them. We selected these three cases with a motive of how frequent they were shown in the game. Table 3 shows the results of these images. The first case is that, if the image has grass and ocean, then we wanted both of these classes to have high accuracy; we provided the model with 10 testing images similar to the previous cases. The model had higher accuracy for both classes of ocean and grass. However, in this data for the images that contained both grasses and ocean, we noticed that the grass class gave higher scores than the ocean's class. This is similar with the image that contains either grass or ocean; the grass was correctly classified with high scores compared to the ocean class. In the second case, if we want both scents to be released when the image contains both grass and fire; only one image gave an accuracy of 0.99 for both fire and grass. Similar to the grass and ocean images, we got a higher accuracy score for the grass compared to the accuracy of the fire. This can be well seen if we compare the accuracy of the fire and grass alone.