Swin Transformer: Empirical Evaluation on Small Fine-Grained Data

A controlled four-family architecture comparison (Swin T/S/B, RegNetY CNNs, EfficientNet B3-B7, ViT-B/16) on the Oxford-IIIT Pet Dataset under RTX 4090 constraints. Three findings: Swin's hierarchical attention transfers cleanly to small datasets (93.8-96.35%), EfficientNet's compound scaling breaks (B3 beats B7 by 8.66 points), and ViT catastrophically fails (7.17%: barely above the 2.7% random baseline).