<![CDATA[This study compares the performance of three Convolutional Neural Network (CNN) models—EfficientNet, VGG16, and ResNet50—in motor vehicle classification tasks using the "Car vs Bike" dataset. Transfer learning was applied using pretrained weights from ImageNet. The results indicate that VGG16 achieved the best performance with 95% accuracy, precision of 0.95, recall of 0.96, and an F1-score of 0.95, demonstrating high balance in recognizing both classes. ResNet50 attained 87% accuracy on the test dataset with a precision of 0.89, recall of 0.84, and an F1-score of 0.87, offering a trade-off between accuracy and computational efficiency. Conversely, EfficientNet exhibited the lowest performance with 50% accuracy, failing to recognize the "Car" class effectively, as evidenced by precision and recall values of 0.00. Factors such as architectural complexity, dataset bias, and computational efficiency influenced these outcomes. This study reinforces previous findings on the strengths and weaknesses of CNN models in motor vehicle classification applications. Furthermore, it highlights the importance of balanced data management and model selection tailored to specific application requirements. However, the dataset's limitation of only two classes and reliance on transfer learning remain areas for future improvement. These findings provide valuable insights for developing intelligent transportation systems requiring high accuracy and efficiency.]]>
Copyrights © 2024
