Distilled Data efficient Image Transformer (base sized model) Distilled data efficient Image Transformer (DeiT) model pre trained and fine tuned on ImageNet 1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Training data efficient image transformers & distillation through attention by Touvron et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman. Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team. Model description This model is a distilled Vision Transformer (ViT). It uses a distillation token, besides the class token, to effectively learn from a teacher (CNN) during both pre training and fine tuning. The distillation token is learned through backpropagation, by interacting with the class ([CLS]) and patch tokens through the self attention layers. Images are presented to the model as a sequence of fixed size patches (resolution 16x16), which are linearly embedded. Intended uses & limitations You can use the raw model for image classification. See the model hub to look for fine tu…
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