# Image-Based Arabian Camel Breed Classification Using Transfer Learning on CNNs
## 4 Experimentation
### Experimental Results  
We evaluated and compared the performance of seven different CNN models that served as alternative base models in our project and were pre-trained on ImageNet and fine-tuned on our Arabian camel breed dataset. The seven base models are InceptionV3, NASNet-Large, PNASNet-5-Large, MobileNetV3-Large, and EfficientNetV2 (small, medium, and large variants). We evaluated these alternative base models in terms of accuracy, F1 score, and AUC on the test dataset, as well as the training and inference time. Table 3 shows the evaluation results of the models on the test dataset. We can see that NASNet-Large achieved the highest accuracy (85.80%) and F1 score (86%) on the test dataset, followed by EfficientNetV2-L with an accuracy of 83.95% and an F1 score of 84%. MobileNetV3-Large and InceptionV3 performed the worst on the test dataset, with accuracies of 77.78% and 79.63% and F1 scores of 77% and 79%, respectively. This experimental result suggests that NASNet-Large and EfficientNetV2-L are more suitable for camel breed classification than the other investigated models. To further analyze the performance of the models, we plotted the training accuracy and loss curves for all models on both training and validation datasets. Figure 4 shows the accuracy and loss progress for each model over the epochs. These results are consistent with the confusion metrics shown in Figure 5. The results of the confusion matrices indicate that all the models are able to accurately distinguish between the Majaheem and Waddeh classes, which can be attributed to their distinctive colors. However, the majority of the models exhibit a higher error rate when distinguishing between the Shageh, Sofor, Homor, and Shaele classes, likely due to their similarities in color. NASNet-Large's superior performance may be attributed to its larger number of parameters and depth compared to the other models. This allows it to learn more complex and discriminative features from the images. Additionally, NASNet-Large was generated by a neural architecture search algorithm and optimized for both accuracy and computational efficiency, making it more suitable for image classification tasks. On the other hand, InceptionV3 and MobileNetV3-Large's inferior performance may be due to their smaller number of parameters and depths compared to the other models, which limits their capacity to learn from the images. Furthermore, InceptionV3 and MobileNetV3-Large were designed for different purposes than image classification, such as reducing computational cost and latency, which may compromise their accuracy. Table 4 shows the comparison of the models in terms of the number of epochs, training time, and inference time. We can see that EfficientNetV2-L took the longest time to train followed by NASNet-Large. MobileNetV3-Large has the shortest training time followed by InceptionV3. In terms of the inference time, MobileNetV3-Large is the fastest, followed by PNASNet-5-Large. InceptionV3 is the slowest followed by EfficientNetV2-L.  
Figure 4: The training accuracy and loss curves for all models on both training and validation datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy** & **F1** & **AUC** \\ \hline InceptionV3 & 79.63 & 79 & 0.95 \\ NASNet-Large & 85.80 & 86 & 0.98 \\ PNASNet-5-Large & 81.48 & 81 & 0.95 \\ MobileNetV3-Large & 77.78 & 77 & 0.94 \\ EfficientNetV2-S & 82.10 & 81 & 0.95 \\ EfficientNetV2-M & 83.34 & 83 & 0.96 \\ EfficientNetV2-L & 83.95 & 84 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of performance metrics of the CNN models on the test dataset. Figure 5: Confusion Matrix of All Models Using the Test Dataset. Among the six models, NASNet-Large has the best trade-off between accuracy and inference time. However, it also requires more computational resources and time to train than the other models. Therefore, depending on the application scenario and resource constraints, other models such as EfficientNetV2-M or MobileNetV3-Large might be preferred.