Animal Image Classification Using the Vision Transformer Model

Automatic animal identification through image analysis holds immense value across various fields. This study evaluates the effectiveness of the Vision Transformer (ViT) model for this task. We trained a ViT model on a dataset of animal images and assessed its performance. The model achieved an accuracy of 89%, demonstrating robust classification capabilities for most animal classes. Notably, several species were classified perfectly, highlighting ViT's potential for accurate animal identification. However, the performance varied across classes, with some requiring further exploration for improvement. These findings suggest that ViT is a promising approach for animal image classification, offering a powerful tool for various applications in ecology, conservation, and beyond.