---
local: deit
sections:
- local: overview
  title: Overview
- local: transformers.DeiTConfig
  title: DeiTConfig
- local: transformers.DeiTFeatureExtractor
  title: DeiTFeatureExtractor
- local: transformers.DeiTModel
  title: DeiTModel
- local: transformers.DeiTForMaskedImageModeling
  title: DeiTForMaskedImageModeling
- local: transformers.DeiTForImageClassification
  title: DeiTForImageClassification
- local: transformers.DeiTForImageClassificationWithTeacher
  title: DeiTForImageClassificationWithTeacher
title: DeiT
---
<script>
import Tip from "./Tip.svelte";
import Youtube from "./Youtube.svelte";
import Docstring from "./Docstring.svelte";
import CodeBlock from "./CodeBlock.svelte";
import CodeBlockFw from "./CodeBlockFw.svelte";
import DocNotebookDropdown from "./DocNotebookDropdown.svelte";
import IconCopyLink from "./IconCopyLink.svelte";
export let fw: "pt" | "tf"
</script>
<!--Copyright 2021 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->

<h1 id="deit">DeiT</h1>

<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>

<h2 id="overview">Overview</h2>

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](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTForImageClassification) and (2) corresponds to
  [DeiTForImageClassificationWithTeacher](/docs/transformers/pr_15592/en/model_doc/deit#transformers.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](/docs/transformers/pr_15592/en/model_doc/vit#transformers.ViTModel) or [ViTForImageClassification](/docs/transformers/pr_15592/en/model_doc/vit#transformers.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 [DeiTFeatureExtractor](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTFeatureExtractor) in order to
  prepare images for the model.

This model was contributed by [nielsr](https://huggingface.co/nielsr).


<h2 id="transformers.DeiTConfig">DeiTConfig</h2>

<div class="docstring">

<docstring><name>class transformers.DeiTConfig</name><anchor>transformers.DeiTConfig</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/deit/configuration_deit.py#L29</source><parameters>[{"name": "hidden_size", "val": " = 768"}, {"name": "num_hidden_layers", "val": " = 12"}, {"name": "num_attention_heads", "val": " = 12"}, {"name": "intermediate_size", "val": " = 3072"}, {"name": "hidden_act", "val": " = 'gelu'"}, {"name": "hidden_dropout_prob", "val": " = 0.0"}, {"name": "attention_probs_dropout_prob", "val": " = 0.0"}, {"name": "initializer_range", "val": " = 0.02"}, {"name": "layer_norm_eps", "val": " = 1e-12"}, {"name": "is_encoder_decoder", "val": " = False"}, {"name": "image_size", "val": " = 224"}, {"name": "patch_size", "val": " = 16"}, {"name": "num_channels", "val": " = 3"}, {"name": "qkv_bias", "val": " = True"}, {"name": "encoder_stride", "val": " = 16"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **hidden_size** (`int`, *optional*, defaults to 768) --
  Dimensionality of the encoder layers and the pooler layer.
- **num_hidden_layers** (`int`, *optional*, defaults to 12) --
  Number of hidden layers in the Transformer encoder.
- **num_attention_heads** (`int`, *optional*, defaults to 12) --
  Number of attention heads for each attention layer in the Transformer encoder.
- **intermediate_size** (`int`, *optional*, defaults to 3072) --
  Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
- **hidden_act** (`str` or `function`, *optional*, defaults to `"gelu"`) --
  The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
  `"relu"`, `"selu"` and `"gelu_new"` are supported.
- **hidden_dropout_prob** (`float`, *optional*, defaults to 0.1) --
  The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
- **attention_probs_dropout_prob** (`float`, *optional*, defaults to 0.1) --
  The dropout ratio for the attention probabilities.
- **initializer_range** (`float`, *optional*, defaults to 0.02) --
  The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- **layer_norm_eps** (`float`, *optional*, defaults to 1e-12) --
  The epsilon used by the layer normalization layers.
- **image_size** (`int`, *optional*, defaults to `224`) --
  The size (resolution) of each image.
- **patch_size** (`int`, *optional*, defaults to `16`) --
  The size (resolution) of each patch.
- **num_channels** (`int`, *optional*, defaults to `3`) --
  The number of input channels.
- **qkv_bias** (`bool`, *optional*, defaults to `True`) --
  Whether to add a bias to the queries, keys and values.
- **encoder_stride** (`int`, `optional`, defaults to 16) --
  Factor to increase the spatial resolution by in the decoder head for masked image modeling.</paramsdesc><paramgroups>0</paramgroups></docstring>

This is the configuration class to store the configuration of a [DeiTModel](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTModel). It is used to instantiate an DeiT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the DeiT
[facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224)
architecture.

Configuration objects inherit from [PretrainedConfig](/docs/transformers/pr_15592/en/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the
documentation from [PretrainedConfig](/docs/transformers/pr_15592/en/main_classes/configuration#transformers.PretrainedConfig) for more information.




Example:

```python
>>> from transformers import DeiTModel, DeiTConfig

>>> # Initializing a DeiT deit-base-distilled-patch16-224 style configuration
>>> configuration = DeiTConfig()

>>> # Initializing a model from the deit-base-distilled-patch16-224 style configuration
>>> model = DeiTModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```

</div>

<h2 id="transformers.DeiTFeatureExtractor">DeiTFeatureExtractor</h2>

<div class="docstring">

<docstring><name>class transformers.DeiTFeatureExtractor</name><anchor>transformers.DeiTFeatureExtractor</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/deit/feature_extraction_deit.py#L37</source><parameters>[{"name": "do_resize", "val": " = True"}, {"name": "size", "val": " = 256"}, {"name": "resample", "val": " = 3"}, {"name": "do_center_crop", "val": " = True"}, {"name": "crop_size", "val": " = 224"}, {"name": "do_normalize", "val": " = True"}, {"name": "image_mean", "val": " = None"}, {"name": "image_std", "val": " = None"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **do_resize** (`bool`, *optional*, defaults to `True`) --
  Whether to resize the input to a certain `size`.
- **size** (`int` or `Tuple(int)`, *optional*, defaults to 256) --
  Resize the input to the given size. If a tuple is provided, it should be (width, height). If only an
  integer is provided, then the input will be resized to (size, size). Only has an effect if `do_resize` is
  set to `True`.
- **resample** (`int`, *optional*, defaults to `PIL.Image.BICUBIC`) --
  An optional resampling filter. This can be one of `PIL.Image.NEAREST`, `PIL.Image.BOX`,
  `PIL.Image.BILINEAR`, `PIL.Image.HAMMING`, `PIL.Image.BICUBIC` or `PIL.Image.LANCZOS`. Only has an effect
  if `do_resize` is set to `True`.
- **do_center_crop** (`bool`, *optional*, defaults to `True`) --
  Whether to crop the input at the center. If the input size is smaller than `crop_size` along any edge, the
  image is padded with 0's and then center cropped.
- **crop_size** (`int`, *optional*, defaults to 224) --
  Desired output size when applying center-cropping. Only has an effect if `do_center_crop` is set to `True`.
- **do_normalize** (`bool`, *optional*, defaults to `True`) --
  Whether or not to normalize the input with `image_mean` and `image_std`.
- **image_mean** (`List[int]`, defaults to `[0.485, 0.456, 0.406]`) --
  The sequence of means for each channel, to be used when normalizing images.
- **image_std** (`List[int]`, defaults to `[0.229, 0.224, 0.225]`) --
  The sequence of standard deviations for each channel, to be used when normalizing images.</paramsdesc><paramgroups>0</paramgroups></docstring>

Constructs a DeiT feature extractor.

This feature extractor inherits from [FeatureExtractionMixin](/docs/transformers/pr_15592/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin) which contains most of the main methods. Users
should refer to this superclass for more information regarding those methods.





<div class="docstring">
<docstring><name>\_\_call\_\_</name><anchor>transformers.DeiTFeatureExtractor.__call__</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/deit/feature_extraction_deit.py#L92</source><parameters>[{"name": "images", "val": ": typing.Union[PIL.Image.Image, numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]]"}, {"name": "return_tensors", "val": ": typing.Union[str, transformers.file_utils.TensorType, NoneType] = None"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **images** (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`) --
  The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
  tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
  number of channels, H and W are image height and width.

- **return_tensors** (`str` or [TensorType](/docs/transformers/pr_15592/en/internal/file_utils#transformers.TensorType), *optional*, defaults to `'np'`) --
  If set, will return tensors of a particular framework. Acceptable values are:

  - `'tf'`: Return TensorFlow `tf.constant` objects.
  - `'pt'`: Return PyTorch `torch.Tensor` objects.
  - `'np'`: Return NumPy `np.ndarray` objects.
  - `'jax'`: Return JAX `jnp.ndarray` objects.</paramsdesc><paramgroups>0</paramgroups><rettype>[BatchFeature](/docs/transformers/pr_15592/en/main_classes/feature_extractor#transformers.BatchFeature)</rettype><retdesc>A [BatchFeature](/docs/transformers/pr_15592/en/main_classes/feature_extractor#transformers.BatchFeature) with the following fields:

- **pixel_values** -- Pixel values to be fed to a model, of shape (batch_size, num_channels, height,
  width).</retdesc></docstring>

Main method to prepare for the model one or several image(s).

<Tip warning=&amp;lcub;true}>

NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass
PIL images.

</Tip>








</div></div>

<h2 id="transformers.DeiTModel">DeiTModel</h2>

<div class="docstring">

<docstring><name>class transformers.DeiTModel</name><anchor>transformers.DeiTModel</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/deit/modeling_deit.py#L453</source><parameters>[{"name": "config", "val": ""}, {"name": "add_pooling_layer", "val": " = True"}, {"name": "use_mask_token", "val": " = False"}]</parameters><paramsdesc>- **config** ([DeiTConfig](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTConfig)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>
The bare DeiT Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.DeiTModel.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/deit/modeling_deit.py#L478</source><parameters>[{"name": "pixel_values", "val": " = None"}, {"name": "bool_masked_pos", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}]</parameters><paramsdesc>- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) --
  Pixel values. Pixel values can be obtained using [DeiTFeatureExtractor](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTFeatureExtractor). See
  [DeiTFeatureExtractor.__call__()](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTFeatureExtractor.__call__) for details.

- **head_mask** (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) --
  Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.BaseModelOutputWithPooling](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.BaseModelOutputWithPooling](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([DeiTConfig](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTConfig)) and inputs.

- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
  through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
  the classification token after processing through a linear layer and a tanh activation function. The linear
  layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The [DeiTModel](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTModel) forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Example:

```python
>>> from transformers import DeiTFeatureExtractor, DeiTModel
>>> import torch
>>> from datasets import load_dataset

>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]

>>> feature_extractor = DeiTFeatureExtractor.from_pretrained("facebook/deit-base-distilled-patch16-224")
>>> model = DeiTModel.from_pretrained("facebook/deit-base-distilled-patch16-224")

>>> inputs = feature_extractor(image, return_tensors="pt")

>>> with torch.no_grad():
...     outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 198, 768]
```


</div></div>

<h2 id="transformers.DeiTForMaskedImageModeling">DeiTForMaskedImageModeling</h2>

<div class="docstring">

<docstring><name>class transformers.DeiTForMaskedImageModeling</name><anchor>transformers.DeiTForMaskedImageModeling</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/deit/modeling_deit.py#L556</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([DeiTConfig](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTConfig)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>
DeiT Model with a decoder on top for masked image modeling, as proposed in `SimMIM &amp;lt;https://arxiv.org/abs/2111.09886>`__.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.DeiTForMaskedImageModeling.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/deit/modeling_deit.py#L570</source><parameters>[{"name": "pixel_values", "val": " = None"}, {"name": "bool_masked_pos", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}]</parameters><paramsdesc>- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) --
  Pixel values. Pixel values can be obtained using [DeiTFeatureExtractor](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTFeatureExtractor). See
  [DeiTFeatureExtractor.__call__()](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTFeatureExtractor.__call__) for details.

- **head_mask** (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) --
  Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.

- **bool_masked_pos** (`torch.BoolTensor` of shape `(batch_size, num_patches)`) --
  Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.MaskedLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.MaskedLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([DeiTConfig](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTConfig)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Masked language modeling (MLM) loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The [DeiTForMaskedImageModeling](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTForMaskedImageModeling) forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Examples:
```python
>>> from transformers import DeiTFeatureExtractor, DeiTForMaskedImageModeling
>>> from PIL import Image
>>> import requests

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)

>>> feature_extractor = DeiTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
>>> model = DeiTForMaskedImageModeling.from_pretrained("google/vit-base-patch16-224-in21k")

>>> inputs = feature_extractor(images=image, return_tensors="pt")

>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
```

</div></div>

<h2 id="transformers.DeiTForImageClassification">DeiTForImageClassification</h2>

<div class="docstring">

<docstring><name>class transformers.DeiTForImageClassification</name><anchor>transformers.DeiTForImageClassification</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/deit/modeling_deit.py#L658</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([DeiTConfig](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTConfig)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>

DeiT Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
the [CLS] token) e.g. for ImageNet.

This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.DeiTForImageClassification.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/deit/modeling_deit.py#L671</source><parameters>[{"name": "pixel_values", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "labels", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}]</parameters><paramsdesc>- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) --
  Pixel values. Pixel values can be obtained using [DeiTFeatureExtractor](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTFeatureExtractor). See
  [DeiTFeatureExtractor.__call__()](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTFeatureExtractor.__call__) for details.

- **head_mask** (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) --
  Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.

- **labels** (`torch.LongTensor` of shape `(batch_size,)`, *optional*) --
  Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
  config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
  `config.num_labels > 1` a classification loss is computed (Cross-Entropy).</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.SequenceClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.SequenceClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([DeiTConfig](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTConfig)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Classification (or regression if config.num_labels==1) loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`) -- Classification (or regression if config.num_labels==1) scores (before SoftMax).
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The [DeiTForImageClassification](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTForImageClassification) forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Examples:

```python
>>> from transformers import DeiTFeatureExtractor, DeiTForImageClassification
>>> from PIL import Image
>>> import requests

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)

>>> # note: we are loading a DeiTForImageClassificationWithTeacher from the hub here,
>>> # so the head will be randomly initialized, hence the predictions will be random
>>> feature_extractor = DeiTFeatureExtractor.from_pretrained("facebook/deit-base-distilled-patch16-224")
>>> model = DeiTForImageClassification.from_pretrained("facebook/deit-base-distilled-patch16-224")

>>> inputs = feature_extractor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_class_idx = logits.argmax(-1).item()
>>> print("Predicted class:", model.config.id2label[predicted_class_idx])
```

</div></div>

<h2 id="transformers.DeiTForImageClassificationWithTeacher">DeiTForImageClassificationWithTeacher</h2>

<div class="docstring">

<docstring><name>class transformers.DeiTForImageClassificationWithTeacher</name><anchor>transformers.DeiTForImageClassificationWithTeacher</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/deit/modeling_deit.py#L792</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([DeiTConfig](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTConfig)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>

DeiT Model transformer with image classification heads on top (a linear layer on top of the final hidden state of
the [CLS] token and a linear layer on top of the final hidden state of the distillation token) e.g. for ImageNet.

.. warning::

This model supports inference-only. Fine-tuning with distillation (i.e. with a teacher) is not yet
supported.

This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.DeiTForImageClassificationWithTeacher.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/deit/modeling_deit.py#L810</source><parameters>[{"name": "pixel_values", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}]</parameters><paramsdesc>- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) --
  Pixel values. Pixel values can be obtained using [DeiTFeatureExtractor](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTFeatureExtractor). See
  [DeiTFeatureExtractor.__call__()](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTFeatureExtractor.__call__) for details.

- **head_mask** (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) --
  Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>`transformers.models.deit.modeling_deit.DeiTForImageClassificationWithTeacherOutput`or `tuple(torch.FloatTensor)`</rettype><retdesc>A `transformers.models.deit.modeling_deit.DeiTForImageClassificationWithTeacherOutput`or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([DeiTConfig](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTConfig)) and inputs.

- **logits** (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`) -- Prediction scores as the average of the cls_logits and distillation logits.
- **cls_logits** (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`) -- Prediction scores of the classification head (i.e. the linear layer on top of the final hidden state of the
  class token).
- **distillation_logits** (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`) -- Prediction scores of the distillation head (i.e. the linear layer on top of the final hidden state of the
  distillation token).
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
  plus the initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
  the self-attention heads.</retdesc></docstring>
The [DeiTForImageClassificationWithTeacher](/docs/transformers/pr_15592/en/model_doc/deit#transformers.DeiTForImageClassificationWithTeacher) forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Example:

```python
>>> from transformers import DeiTFeatureExtractor, DeiTForImageClassificationWithTeacher
>>> import torch
>>> from datasets import load_dataset

>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]

>>> feature_extractor = DeiTFeatureExtractor.from_pretrained("facebook/deit-base-distilled-patch16-224")
>>> model = DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224")

>>> inputs = feature_extractor(image, return_tensors="pt")

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
'tabby, tabby cat'
```


</div></div>
