Transfer Learning Performance and Efficiency of VGG16 and MobileNetV2 on Three-Class Animals-10 Classification
DOI:
https://doi.org/10.64366/ijids.v3i2.576Keywords:
Animal Image Classification; MobileNetV2; Transfer Learning; VGG16; Computational EfficiencyAbstract
Animal image classification remains challenging because variations in pose, illumination, object scale, viewing angle, and background can affect model predictions. In addition, high-capacity convolutional neural networks may impose substantial computational and storage requirements. This study compares the classification performance and computational efficiency of VGG16 and MobileNetV2 using transfer learning. Dog, Cat, and Sheep images were selected from the Animals-10 dataset to represent visually related companion animals and a visually distinct livestock category while allowing balanced sampling. Following image validation, duplicate checking, and class balancing, the final dataset contained 5,004 images, with 1,668 images per class. The dataset was divided using an identical stratified 80:20 split for both models, with 20% of the training portion used for validation. Both models used ImageNet weights, 224 × 224-pixel inputs, identical classification heads, data augmentation, and equivalent training configurations. MobileNetV2 achieved 97.80% accuracy, a 97.81% macro F1-score, and a 99.88% macro ROC-AUC, whereas VGG16 achieved 96.80%, 96.81%, and 99.67%, respectively. MobileNetV2 reduced training time by 66.03%, inference time by 59.56%, and weight size by 77.83%. However, the exact McNemar test indicated no statistically significant difference in classification performance (p = 0.064). Therefore, MobileNetV2 provided substantially greater computational efficiency while maintaining classification performance comparable to VGG16, making it a more practical option for resource-constrained animal image classification systems.
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