  Data augmentation (DA) enhances model generalization in computer vision but
may introduce biases, impacting class accuracy unevenly. Our study extends this
inquiry, examining DA's class-specific bias across various datasets, including
those distinct from ImageNet, through random cropping. We evaluated this
phenomenon with ResNet50, EfficientNetV2S, and SWIN ViT, discovering that while
residual models showed similar bias effects, Vision Transformers exhibited
greater robustness or altered dynamics. This suggests a nuanced approach to
model selection, emphasizing bias mitigation. We also refined a "data
augmentation robustness scouting" method to manage DA-induced biases more
efficiently, reducing computational demands significantly (training 112 models
instead of 1860; a reduction of factor 16.2) while still capturing essential
bias trends.
