To feed images to the Transformer encoder, each image is split into a sequence of fixed-size non-overlapping patches,
  which are then linearly embedded. A [CLS] token is added to serve as representation of an entire image, which can be
  used for classification. The authors also add absolute position embeddings, and feed the resulting sequence of
  vectors to a standard Transformer encoder.
As the Vision Transformer expects each image to be of the same size (resolution), one can use
  [ViTFeatureExtractor] to resize (or rescale) and normalize images for the model.
Both the patch resolution and image resolution used during pre-training or fine-tuning are reflected in the name of
  each checkpoint. For example, google/vit-base-patch16-224 refers to a base-sized architecture with patch
  resolution of 16x16 and fine-tuning resolution of 224x224. All checkpoints can be found on the hub.
