The automated attribution of historical paintings to their school of origin is a long-standing challenge at the intersection of computer vision and art history. This challenge is especially acute for non-Western art traditions, such as Persian miniature painting, for which digitized and labelled image corpora remain comparatively small. This paper addresses the classification of Persian miniature paintings into five historical schools including, Herat, Qajar, Shiraz-e Avval, Tabriz-e Avval, and Tabriz-e Dovvom, using a Vision Transformer (ViT). Transfer learning is used to adapt a ViT-B/16 model, pre-trained on ImageNet-21k, to this task. Because the available dataset contains only 890 images, five complementary augmentation operations i.e. random-resized cropping, horizontal flipping, rotation, color jitter, and random erasing, are applied during training, together with Mixup as an additional sample-mixing regularizer. A progressive layer-freezing strategy keeps the first six Transformer encoder blocks fixed and fine-tunes only the last six blocks. To stabilize training on the small fine-tuning set, this strategy utilizes a classification head combined with label smoothing and a low, cosine-annealing learning rate. On a held-out test set of 178 images, the fine-tuned model reaches an overall accuracy of 92.13% and a weighted F1-score of 0.92, with three of the five schools classified at an F1-score above 0.94. These results indicate that a Vision Transformer, can reach competitive accuracy on a small, specialized cultural-heritage dataset when transfer learning and augmentation are used simultaneously.