Advanced Bird Species Classification using Self-Attention and GAN-based Data Augmentation on BIRDS 525 Dataset

Accurately categorizing bird species in fine-grained picture collections remains a difficult challenge due to considerable visual similarity across species and modest intra-class variances. This research presents a sophisticated bird species categorization system based on Vision Transformers (ViTs) and Generative Adversarial Networks (GANs), which was evaluated on the BIRDS 525 dataset. Vision Transformers are used to replace typical convolutional neural networks (CNNs), utilizing their superior self-attention processes to record intricate visual features including feather textures, color gradients, and shape variances. To address the limits imposed by the short dataset size, StyleGAN2 is employed to produce high-quality synthetic images, improving the model’s capacity to generalize across different species. Experiments show that the ViT based model has an improved performance with respect to classification over CNN based approaches when combining GAN generated pictures and audio data. Our study shows the potential of Vision Transformers in fine-grained classification tasks with advanced data augmentation strategies.