Impact of Pre-trained Weights on MobileNet Architectures for Animal Classification

Abstract

This study aims to address the problem of improving animal image classification accuracy using different versions of MobileNet. Accurate animal classification plays a vital role in biodiversity protection, environmental monitoring, and agriculture. The research is significant because existing studies focus on specific models and datasets, leaving a gap in the comparative performance analysis of MobileNet versions. To address this issue, MobileNet V1, V2, and V3 models were utilized, both with and without ImageNet pre-trained weights. The models were trained on a dataset composed of 30,179 images from two sources, covering 13 animal categories. The experiment involved training the models over 10 epochs using a standard configuration of the TensorFlow framework, with accuracy serving as the primary evaluation metric.The results showed that MobileNet V3Large, with pre-trained weights, achieved the highest accuracy (97.43%), outperforming V1 and V2. Using pre-trained weights consistently enhanced performance, as models without pre-training exhibited lower accuracy and slower convergence. This study contributes by providing a comprehensive comparison of MobileNet versions in animal classification tasks, demonstrating the importance of pre-training and model architecture optimization for achieving high accuracy in image classification.