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# DeiT

<Tip>

This is a recently introduced model so the API hasn't been tested extensively. There may be some bugs or slight
breaking changes to fix it in the future. If you see something strange, file a [Github Issue](https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title).

</Tip>

## Overview

The DeiT model was proposed in [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre
Sablayrolles, Hervé Jégou. The [Vision Transformer (ViT)](vit) introduced in [Dosovitskiy et al., 2020](https://arxiv.org/abs/2010.11929) has shown that one can match or even outperform existing convolutional neural
networks using a Transformer encoder (BERT-like). However, the ViT models introduced in that paper required training on
expensive infrastructure for multiple weeks, using external data. DeiT (data-efficient image transformers) are more
efficiently trained transformers for image classification, requiring far less data and far less computing resources
compared to the original ViT models.

The abstract from the paper is the following:

*Recently, neural networks purely based on attention were shown to address image understanding tasks such as image
classification. However, these visual transformers are pre-trained with hundreds of millions of images using an
expensive infrastructure, thereby limiting their adoption. In this work, we produce a competitive convolution-free
transformer by training on Imagenet only. We train them on a single computer in less than 3 days. Our reference vision
transformer (86M parameters) achieves top-1 accuracy of 83.1% (single-crop evaluation) on ImageNet with no external
data. More importantly, we introduce a teacher-student strategy specific to transformers. It relies on a distillation
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
  prediction head on top of the class token and on top of the distillation token. In that case, the [CLS] prediction
  head is trained using regular cross-entropy between the prediction of the head and the ground-truth label, while the
  distillation prediction head is trained using hard distillation (cross-entropy between the prediction of the
  distillation head and the label predicted by the teacher). At inference time, one takes the average prediction
  between both heads as final prediction. (2) is also called "fine-tuning with distillation", because one relies on a
  teacher that has already been fine-tuned on the downstream dataset. In terms of models, (1) corresponds to
  [`DeiTForImageClassification`] and (2) corresponds to
  [`DeiTForImageClassificationWithTeacher`].
- Note that the authors also did try soft distillation for (2) (in which case the distillation prediction head is
  trained using KL divergence to match the softmax output of the teacher), but hard distillation gave the best results.
- All released checkpoints were pre-trained and fine-tuned on ImageNet-1k only. No external data was used. This is in
  contrast with the original ViT model, which used external data like the JFT-300M dataset/Imagenet-21k for
  pre-training.
- The authors of DeiT also released more efficiently trained ViT models, which you can directly plug into
  [`ViTModel`] or [`ViTForImageClassification`]. Techniques like data
  augmentation, optimization, and regularization were used in order to simulate training on a much larger dataset
  (while only using ImageNet-1k for pre-training). There are 4 variants available (in 3 different sizes):
  *facebook/deit-tiny-patch16-224*, *facebook/deit-small-patch16-224*, *facebook/deit-base-patch16-224* and
  *facebook/deit-base-patch16-384*. Note that one should use [`DeiTImageProcessor`] in order to
  prepare images for the model.

This model was contributed by [nielsr](https://huggingface.co/nielsr). The TensorFlow version of this model was added by [amyeroberts](https://huggingface.co/amyeroberts).


## DeiTConfig

[[autodoc]] DeiTConfig

## DeiTFeatureExtractor

[[autodoc]] DeiTFeatureExtractor
    - __call__

## DeiTImageProcessor

[[autodoc]] DeiTImageProcessor
    - preprocess

## DeiTModel

[[autodoc]] DeiTModel
    - forward

## DeiTForMaskedImageModeling

[[autodoc]] DeiTForMaskedImageModeling
    - forward

## DeiTForImageClassification

[[autodoc]] DeiTForImageClassification
    - forward

## DeiTForImageClassificationWithTeacher

[[autodoc]] DeiTForImageClassificationWithTeacher
    - forward

## TFDeiTModel

[[autodoc]] TFDeiTModel
    - call

## TFDeiTForMaskedImageModeling

[[autodoc]] TFDeiTForMaskedImageModeling
    - call

## TFDeiTForImageClassification

[[autodoc]] TFDeiTForImageClassification
    - call

## TFDeiTForImageClassificationWithTeacher

[[autodoc]] TFDeiTForImageClassificationWithTeacher
    - call
