Multi-Class Animal Image Classification Using Transfer Learning with VGG16 and InceptionV3

In this paper, two of the most common deep learning architectures, VGG16 and InceptionV3, will be compared in their performance for multi-class animal image classification. VGG16 is a deep convolutional neural network with an elegant stacked convolution and pooling structure that enables the successful extraction of strong hierarchical features from images. However, being of a large number of parameters means that more computation must be required and it will converge slightly slower. In contrast, InceptionV3 is an advanced architecture designed to achieve efficiency and gain accuracy with factorized convolutions, parallel processing branches, and multiscale feature extraction. This makes it capture finer visual details while maintaining faster training and better stability for convergence. When comparing both models, VGG16 showed a very good performance and proved that transfer learning can be super effective even when the available dataset is not enormous. It was able to achieve high classification accuracy but showed a relatively higher risk of overfitting and slower progress in learning. InceptionV3 performed better overall: faster convergence, it produced high validation accuracy, and minimal overfitting due to its architectural enhancements. Experimentally, InceptionV3 yielded a better accuracy with a stronger generalization capability than VGG16, thus supporting this method in dealing with complex and diverse image datasets. In general, VGG16 serves as a strong and stable baseline, while InceptionV3 improves the performance, stability, and confidence of the predictions. Further work may be related to investigating the performance of more recent architectures like EfficientNet or Vision Transformers, while further exploring lightweight deployment techniques so that these models could be applied effectively in real-time wildlife monitoring scenarios and other real-world applications.

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