Vision Transformer (ViT) — Attention-Based Image Classification

I will train a Vision Transformer (ViT or DeiT) for your image classification task — covering patch embedding configuration, position encoding, attention head design, training with appropriate data augmentation for transformer-based image models, and comparison against a CNN baseline to confirm the transformer approach is justified by your data scale. Vision Transformers outperform CNNs on image classification at large data scales but underperform them at small data scales — the right choice depends on your dataset size, and a baseline comparison prevents adopting the architecturally fashionable choice rather than the empirically better one for your specific dataset.

The training covers patch size and embedding configuration, data augmentation appropriate for ViT training (mixup, cutmix, rand augment), training with warmup and cosine annealing, per-class evaluation, CNN baseline comparison, and model export.