# DeiT

## 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.*

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

## Usage 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.

## Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DeiT.

- [`DeiTForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).

- See also: [Image classification task guide](../tasks/image_classification)

Besides that:

- [`DeiTForMaskedImageModeling`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining).

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

## 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