token ensuring that the student learns from the teacher through attention. We show the interest of this token-based
distillation, especially when using a convnet as a teacher. This leads us to report results competitive with convnets
for both Imagenet (where we obtain up to 85.2% accuracy) and when transferring to other tasks. We share our code and
models.
Tips:
Compared to ViT, DeiT models use a so-called distillation token to effectively learn from a teacher (which, in the
  DeiT paper, is a ResNet like-model). The distillation token is learned through backpropagation, by interacting with
  the class ([CLS]) and patch tokens through the self-attention layers.
There are 2 ways to fine-tune distilled models, either (1) in a classic way, by only placing a prediction head on top
  of the final hidden state of the class token and not using the distillation signal, or (2) by placing both a
