
 
DeiT
	
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.
Overview
	
The DeiT model was proposed in Training data-efficient image transformers & distillation through attention by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre
Sablayrolles, Hervé Jégou. The Vision Transformer (ViT) introduced in Dosovitskiy et al., 2020 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. The TensorFlow version of this model was added by amyeroberts.
Resources
	
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DeiT.
Image Classification
DeiTForImageClassification is supported by this example script and notebook.
See also: Image classification task guide
Besides that:
DeiTForMaskedImageModeling is supported by this example script.
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
	
class transformers.DeiTConfig
<
source
>
(
hidden_size = 768
num_hidden_layers = 12
num_attention_heads = 12
intermediate_size = 3072
hidden_act = 'gelu'
hidden_dropout_prob = 0.0
attention_probs_dropout_prob = 0.0
initializer_range = 0.02
layer_norm_eps = 1e-12
image_size = 224
patch_size = 16
num_channels = 3
qkv_bias = True
encoder_stride = 16
**kwargs
)
Parameters 
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.
This is the configuration class to store the configuration of a 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
architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
Example:
	Copied
 from transformers import DeiTConfig, DeiTModel
 # Initializing a DeiT deit-base-distilled-patch16-224 style configuration
 configuration = DeiTConfig()
 # Initializing a model (with random weights) from the deit-base-distilled-patch16-224 style configuration
 model = DeiTModel(configuration)
 # Accessing the model configuration
 configuration = model.config
DeiTFeatureExtractor
	
class transformers.DeiTFeatureExtractor
<
source
>
(
*args
**kwargs
)
__call__
<
source
>
(
images
**kwargs
)
Preprocess an image or a batch of images.
DeiTImageProcessor
	
class transformers.DeiTImageProcessor
<
source
>
(
do_resize: bool = True
size: typing.Dict[str, int] = None
resample: Resampling = 3
do_center_crop: bool = True
crop_size: typing.Dict[str, int] = None
rescale_factor: typing.Union[int, float] = 0.00392156862745098
do_rescale: bool = True
do_normalize: bool = True
image_mean: typing.Union[float, typing.List[float], NoneType] = None
image_std: typing.Union[float, typing.List[float], NoneType] = None
**kwargs
)
Parameters 
do_resize (bool, optional, defaults to True) —
Whether to resize the image’s (height, width) dimensions to the specified size. Can be overridden by
do_resize in preprocess.
size (Dict[str, int] optional, defaults to {"height" -- 256, "width": 256}):
Size of the image after resize. Can be overridden by size in preprocess.
resample (PILImageResampling filter, optional, defaults to PILImageResampling.BICUBIC) —
Resampling filter to use if resizing the image. Can be overridden by resample in preprocess.
do_center_crop (bool, optional, defaults to True) —
Whether to center crop the image. If the input size is smaller than crop_size along any edge, the image
is padded with 0’s and then center cropped. Can be overridden by do_center_crop in preprocess.
crop_size (Dict[str, int], optional, defaults to {"height" -- 224, "width": 224}):
Desired output size when applying center-cropping. Can be overridden by crop_size in preprocess.
do_rescale (bool, optional, defaults to True) —
Whether to rescale the image by the specified scale rescale_factor. Can be overridden by the do_rescale
parameter in the preprocess method.
rescale_factor (int or float, optional, defaults to 1/255) —
Scale factor to use if rescaling the image. Can be overridden by the rescale_factor parameter in the
preprocess method.
do_normalize (bool, optional, defaults to True) —
Whether to normalize the image. Can be overridden by the do_normalize parameter in the preprocess
method.
image_mean (float or List[float], optional, defaults to IMAGENET_STANDARD_MEAN) —
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the image_mean parameter in the preprocess method.
image_std (float or List[float], optional, defaults to IMAGENET_STANDARD_STD) —
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the image_std parameter in the preprocess method.
Constructs a DeiT image processor.
preprocess
<
source
>
(
images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]]
do_resize: bool = None
size: typing.Dict[str, int] = None
resample = None
do_center_crop: bool = None
crop_size: typing.Dict[str, int] = None
do_rescale: bool = None
rescale_factor: float = None
do_normalize: bool = None
image_mean: typing.Union[float, typing.List[float], NoneType] = None
image_std: typing.Union[float, typing.List[float], NoneType] = None
return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None
data_format: ChannelDimension = <ChannelDimension.FIRST: 'channels_first'>
**kwargs
)
Parameters 
images (ImageInput) —
Image to preprocess.
do_resize (bool, optional, defaults to self.do_resize) —
Whether to resize the image.
size (Dict[str, int], optional, defaults to self.size) —
Size of the image after resize.
resample (PILImageResampling, optional, defaults to self.resample) —
PILImageResampling filter to use if resizing the image Only has an effect if do_resize is set to
True.
do_center_crop (bool, optional, defaults to self.do_center_crop) —
Whether to center crop the image.
crop_size (Dict[str, int], optional, defaults to self.crop_size) —
Size of the image after center crop. If one edge the image is smaller than crop_size, it will be
padded with zeros and then cropped
do_rescale (bool, optional, defaults to self.do_rescale) —
Whether to rescale the image values between [0 - 1].
rescale_factor (float, optional, defaults to self.rescale_factor) —
Rescale factor to rescale the image by if do_rescale is set to True.
do_normalize (bool, optional, defaults to self.do_normalize) —
Whether to normalize the image.
image_mean (float or List[float], optional, defaults to self.image_mean) —
Image mean.
image_std (float or List[float], optional, defaults to self.image_std) —
Image standard deviation.
return_tensors (str or TensorType, optional) —
The type of tensors to return. Can be one of:
None: Return a list of np.ndarray.
TensorType.TENSORFLOW or 'tf': Return a batch of type tf.Tensor.
TensorType.PYTORCH or 'pt': Return a batch of type torch.Tensor.
TensorType.NUMPY or 'np': Return a batch of type np.ndarray.
TensorType.JAX or 'jax': Return a batch of type jax.numpy.ndarray.
data_format (ChannelDimension or str, optional, defaults to ChannelDimension.FIRST) —
The channel dimension format for the output image. Can be one of:
ChannelDimension.FIRST: image in (num_channels, height, width) format.
ChannelDimension.LAST: image in (height, width, num_channels) format.
Preprocess an image or batch of images.
DeiTModel
	
class transformers.DeiTModel
<
source
>
(
config: DeiTConfig
add_pooling_layer: bool = True
use_mask_token: bool = False
)
Parameters 
config (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() method to load the model weights.
The bare DeiT Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch 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.
forward
<
source
>
(
pixel_values: typing.Optional[torch.Tensor] = None
bool_masked_pos: typing.Optional[torch.BoolTensor] = None
head_mask: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.BaseModelOutputWithPooling or tuple(torch.FloatTensor)
Parameters 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See
DeiTImageProcessor.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 instead of a plain tuple.
bool_masked_pos (torch.BoolTensor of shape (batch_size, num_patches), optional) —
Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).
Returns
transformers.modeling_outputs.BaseModelOutputWithPooling or tuple(torch.FloatTensor)
A 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) 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, if the model has an embedding layer, +
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 optional 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.
The DeiTModel forward method, overrides the __call__ special method.
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.
Example:
	Copied
 from transformers import AutoImageProcessor, DeiTModel
 import torch
 from datasets import load_dataset
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 image_processor = AutoImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
 model = DeiTModel.from_pretrained("facebook/deit-base-distilled-patch16-224")
 inputs = image_processor(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]
DeiTForMaskedImageModeling
	
class transformers.DeiTForMaskedImageModeling
<
source
>
(
config: DeiTConfig
)
Parameters 
config (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() method to load the model weights.
DeiT Model with a decoder on top for masked image modeling, as proposed in SimMIM.
Note that we provide a script to pre-train this model on custom data in our examples
directory.
This model is a PyTorch 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.
forward
<
source
>
(
pixel_values: typing.Optional[torch.Tensor] = None
bool_masked_pos: typing.Optional[torch.BoolTensor] = None
head_mask: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.MaskedImageModelingOutput or tuple(torch.FloatTensor)
Parameters 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See
DeiTImageProcessor.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 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).
Returns
transformers.modeling_outputs.MaskedImageModelingOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.MaskedImageModelingOutput 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) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when bool_masked_pos is provided) — Reconstruction loss.
reconstruction (torch.FloatTensor of shape (batch_size, num_channels, height, width)) — Reconstructed / completed images.
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, if the model has an embedding layer, +
one for the output of each stage) of shape (batch_size, sequence_length, hidden_size). Hidden-states
(also called feature maps) of the model at the output of each stage.
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, patch_size, sequence_length). Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
The DeiTForMaskedImageModeling forward method, overrides the __call__ special method.
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.
Examples:
	Copied
 from transformers import AutoImageProcessor, DeiTForMaskedImageModeling
 import torch
 from PIL import Image
 import requests
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 image_processor = AutoImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
 model = DeiTForMaskedImageModeling.from_pretrained("facebook/deit-base-distilled-patch16-224")
 num_patches = (model.config.image_size // model.config.patch_size) ** 2
 pixel_values = image_processor(images=image, return_tensors="pt").pixel_values
 # create random boolean mask of shape (batch_size, num_patches)
 bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool()
 outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
 loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction
 list(reconstructed_pixel_values.shape)
[1, 3, 224, 224]
DeiTForImageClassification
	
class transformers.DeiTForImageClassification
<
source
>
(
config: DeiTConfig
)
Parameters 
config (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() method to load the model weights.
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 subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
forward
<
source
>
(
pixel_values: typing.Optional[torch.Tensor] = None
head_mask: typing.Optional[torch.Tensor] = None
labels: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.ImageClassifierOutput or tuple(torch.FloatTensor)
Parameters 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See
DeiTImageProcessor.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 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).
Returns
transformers.modeling_outputs.ImageClassifierOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.ImageClassifierOutput 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) 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, if the model has an embedding layer, +
one for the output of each stage) of shape (batch_size, sequence_length, hidden_size). Hidden-states
(also called feature maps) of the model at the output of each stage.
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, patch_size, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The DeiTForImageClassification forward method, overrides the __call__ special method.
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.
Examples:
	Copied
 from transformers import AutoImageProcessor, DeiTForImageClassification
 import torch
 from PIL import Image
 import requests
 torch.manual_seed(3)
 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
 image_processor = AutoImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
 model = DeiTForImageClassification.from_pretrained("facebook/deit-base-distilled-patch16-224")
 inputs = image_processor(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])
Predicted class: magpie
DeiTForImageClassificationWithTeacher
	
class transformers.DeiTForImageClassificationWithTeacher
<
source
>
(
config: DeiTConfig
)
Parameters 
config (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() method to load the model weights.
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 subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
forward
<
source
>
(
pixel_values: typing.Optional[torch.Tensor] = None
head_mask: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.models.deit.modeling_deit.DeiTForImageClassificationWithTeacherOutput or tuple(torch.FloatTensor)
Parameters 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See
DeiTImageProcessor.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 instead of a plain tuple.
Returns
transformers.models.deit.modeling_deit.DeiTForImageClassificationWithTeacherOutput or tuple(torch.FloatTensor)
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) 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.
The DeiTForImageClassificationWithTeacher forward method, overrides the __call__ special method.
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.
Example:
	Copied
 from transformers import AutoImageProcessor, DeiTForImageClassificationWithTeacher
 import torch
 from datasets import load_dataset
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 image_processor = AutoImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
 model = DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224")
 inputs = image_processor(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
TFDeiTModel
	
class transformers.TFDeiTModel
<
source
>
(
*args
**kwargs
)
Parameters 
config (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() method to load the model weights.
The bare DeiT Model transformer outputting raw hidden-states without any specific head on top.
This model is a TensorFlow
tf.keras.layers.Layer. Use it as a regular
TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and behavior.
call
<
source
>
(
pixel_values: tf.Tensor | None = None
bool_masked_pos: tf.Tensor | None = None
head_mask: tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
training: bool = False
)
→
transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling or tuple(tf.Tensor)
Parameters 
pixel_values (tf.Tensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See
DeiTImageProcessor.call() for details.
head_mask (tf.Tensor 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 instead of a plain tuple.
Returns
transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (DeiTConfig) and inputs.
last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
pooler_output (tf.Tensor of shape (batch_size, hidden_size)) — Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.
This output is usually not a good summary of the semantic content of the input, you’re often better with
averaging or pooling the sequence of hidden-states for the whole input sequence.
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (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(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (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.
The TFDeiTModel forward method, overrides the __call__ special method.
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.
Example:
	Copied
 from transformers import AutoImageProcessor, TFDeiTModel
 from datasets import load_dataset
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 image_processor = AutoImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
 model = TFDeiTModel.from_pretrained("facebook/deit-base-distilled-patch16-224")
 inputs = image_processor(image, return_tensors="tf")
 outputs = model(**inputs)
 last_hidden_states = outputs.last_hidden_state
 list(last_hidden_states.shape)
[1, 198, 768]
TFDeiTForMaskedImageModeling
	
class transformers.TFDeiTForMaskedImageModeling
<
source
>
(
*args
**kwargs
)
Parameters 
config (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() method to load the model weights.
DeiT Model with a decoder on top for masked image modeling, as proposed in SimMIM.
This model is a TensorFlow
tf.keras.layers.Layer. Use it as a regular
TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and behavior.
call
<
source
>
(
pixel_values: tf.Tensor | None = None
bool_masked_pos: tf.Tensor | None = None
head_mask: tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
training: bool = False
)
→
transformers.modeling_tf_outputs.TFMaskedImageModelingOutput or tuple(tf.Tensor)
Parameters 
pixel_values (tf.Tensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See
DeiTImageProcessor.call() for details.
head_mask (tf.Tensor 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 instead of a plain tuple.
bool_masked_pos (tf.Tensor of type bool and shape (batch_size, num_patches)) —
Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).
Returns
transformers.modeling_tf_outputs.TFMaskedImageModelingOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFMaskedImageModelingOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (DeiTConfig) and inputs.
loss (tf.Tensor of shape (1,), optional, returned when bool_masked_pos is provided) — Reconstruction loss.
reconstruction (tf.Tensor of shape (batch_size, num_channels, height, width)) — Reconstructed / completed images.
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when
config.output_hidden_states=True):
Tuple of tf.Tensor (one for the output of the embeddings, if the model has an embedding layer, + one for
the output of each stage) of shape (batch_size, sequence_length, hidden_size). Hidden-states (also called
feature maps) of the model at the output of each stage.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when
config.output_attentions=True):
Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, patch_size, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The TFDeiTForMaskedImageModeling forward method, overrides the __call__ special method.
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.
Examples:
	Copied
 from transformers import AutoImageProcessor, TFDeiTForMaskedImageModeling
 import tensorflow as tf
 from PIL import Image
 import requests
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 image_processor = AutoImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
 model = TFDeiTForMaskedImageModeling.from_pretrained("facebook/deit-base-distilled-patch16-224")
 num_patches = (model.config.image_size // model.config.patch_size) ** 2
 pixel_values = image_processor(images=image, return_tensors="tf").pixel_values
 # create random boolean mask of shape (batch_size, num_patches)
 bool_masked_pos = tf.cast(tf.random.uniform((1, num_patches), minval=0, maxval=2, dtype=tf.int32), tf.bool)
 outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
 loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction
 list(reconstructed_pixel_values.shape)
[1, 3, 224, 224]
TFDeiTForImageClassification
	
class transformers.TFDeiTForImageClassification
<
source
>
(
*args
**kwargs
)
Parameters 
config (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() method to load the model weights.
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 TensorFlow
tf.keras.layers.Layer. Use it as a regular
TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and behavior.
call
<
source
>
(
pixel_values: tf.Tensor | None = None
head_mask: tf.Tensor | None = None
labels: tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
training: bool = False
)
→
transformers.modeling_tf_outputs.TFImageClassifierOutput or tuple(tf.Tensor)
Parameters 
pixel_values (tf.Tensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See
DeiTImageProcessor.call() for details.
head_mask (tf.Tensor 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 instead of a plain tuple.
labels (tf.Tensor 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).
Returns
transformers.modeling_tf_outputs.TFImageClassifierOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFImageClassifierOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (DeiTConfig) and inputs.
loss (tf.Tensor of shape (1,), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.
logits (tf.Tensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings, if the model has an embedding layer, + one for
the output of each stage) of shape (batch_size, sequence_length, hidden_size). Hidden-states (also called
feature maps) of the model at the output of each stage.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, patch_size, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The TFDeiTForImageClassification forward method, overrides the __call__ special method.
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.
Examples:
	Copied
 from transformers import AutoImageProcessor, TFDeiTForImageClassification
 import tensorflow as tf
 from PIL import Image
 import requests
 tf.keras.utils.set_random_seed(3)
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 # note: we are loading a TFDeiTForImageClassificationWithTeacher from the hub here,
 # so the head will be randomly initialized, hence the predictions will be random
 image_processor = AutoImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
 model = TFDeiTForImageClassification.from_pretrained("facebook/deit-base-distilled-patch16-224")
 inputs = image_processor(images=image, return_tensors="tf")
 outputs = model(**inputs)
 logits = outputs.logits
 # model predicts one of the 1000 ImageNet classes
 predicted_class_idx = tf.math.argmax(logits, axis=-1)[0]
 print("Predicted class:", model.config.id2label[int(predicted_class_idx)])
Predicted class: little blue heron, Egretta caerulea
TFDeiTForImageClassificationWithTeacher
	
class transformers.TFDeiTForImageClassificationWithTeacher
<
source
>
(
*args
**kwargs
)
Parameters 
config (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() method to load the model weights.
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 TensorFlow
tf.keras.layers.Layer. Use it as a regular
TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and behavior.
call
<
source
>
(
pixel_values: tf.Tensor | None = None
head_mask: tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
training: bool = False
)
→
transformers.models.deit.modeling_tf_deit.TFDeiTForImageClassificationWithTeacherOutput or tuple(tf.Tensor)
Parameters 
pixel_values (tf.Tensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See
DeiTImageProcessor.call() for details.
head_mask (tf.Tensor 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 instead of a plain tuple.
Returns
transformers.models.deit.modeling_tf_deit.TFDeiTForImageClassificationWithTeacherOutput or tuple(tf.Tensor)
A transformers.models.deit.modeling_tf_deit.TFDeiTForImageClassificationWithTeacherOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (DeiTConfig) and inputs.
logits (tf.Tensor of shape (batch_size, config.num_labels)) — Prediction scores as the average of the cls_logits and distillation logits.
cls_logits (tf.Tensor 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 (tf.Tensor 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(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (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(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (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.
The TFDeiTForImageClassificationWithTeacher forward method, overrides the __call__ special method.
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.
Example:
	Copied
 from transformers import AutoImageProcessor, TFDeiTForImageClassificationWithTeacher
 import tensorflow as tf
 from datasets import load_dataset
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 image_processor = AutoImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
 model = TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224")
 inputs = image_processor(image, return_tensors="tf")
 logits = model(**inputs).logits
 # model predicts one of the 1000 ImageNet classes
 predicted_label = int(tf.math.argmax(logits, axis=-1))
 print(model.config.id2label[predicted_label])
tabby, tabby cat
←Deformable DETR
DETA→
DeiT
Overview
Resources
DeiTConfig
DeiTFeatureExtractor
DeiTImageProcessor
DeiTModel
DeiTForMaskedImageModeling
DeiTForImageClassification
DeiTForImageClassificationWithTeacher
TFDeiTModel
TFDeiTForMaskedImageModeling
TFDeiTForImageClassification
TFDeiTForImageClassificationWithTeacher
