  [BeitFeatureExtractor] 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, microsoft/beit-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.
The available checkpoints are either (1) pre-trained on ImageNet-22k (a collection of
  14 million images and 22k classes) only, (2) also fine-tuned on ImageNet-22k or (3) also fine-tuned on ImageNet-1k (also referred to as ILSVRC 2012, a collection of 1.3 million
  images and 1,000 classes).
BEiT uses relative position embeddings, inspired by the T5 model. During pre-training, the authors shared the
