
 
BEiT
	
Overview
	
The BEiT model was proposed in BEiT: BERT Pre-Training of Image Transformers by
Hangbo Bao, Li Dong and Furu Wei. Inspired by BERT, BEiT is the first paper that makes self-supervised pre-training of
Vision Transformers (ViTs) outperform supervised pre-training. Rather than pre-training the model to predict the class
of an image (as done in the original ViT paper), BEiT models are pre-trained to
predict visual tokens from the codebook of OpenAI’s DALL-E model given masked
patches.
The abstract from the paper is the following:
We introduce a self-supervised vision representation model BEiT, which stands for Bidirectional Encoder representation
from Image Transformers. Following BERT developed in the natural language processing area, we propose a masked image
modeling task to pretrain vision Transformers. Specifically, each image has two views in our pre-training, i.e, image
patches (such as 16x16 pixels), and visual tokens (i.e., discrete tokens). We first “tokenize” the original image into
visual tokens. Then we randomly mask some image patches and fed them into the backbone Transformer. The pre-training
objective is to recover the original visual tokens based on the corrupted image patches. After pre-training BEiT, we
directly fine-tune the model parameters on downstream tasks by appending task layers upon the pretrained encoder.
Experimental results on image classification and semantic segmentation show that our model achieves competitive results
with previous pre-training methods. For example, base-size BEiT achieves 83.2% top-1 accuracy on ImageNet-1K,
significantly outperforming from-scratch DeiT training (81.8%) with the same setup. Moreover, large-size BEiT obtains
86.3% only using ImageNet-1K, even outperforming ViT-L with supervised pre-training on ImageNet-22K (85.2%).
Tips:
BEiT models are regular Vision Transformers, but pre-trained in a self-supervised way rather than supervised. They
outperform both the original model (ViT) as well as Data-efficient Image Transformers (DeiT) when fine-tuned on ImageNet-1K and CIFAR-100. You can check out demo notebooks regarding inference as well as
fine-tuning on custom data here (you can just replace
ViTFeatureExtractor by BeitImageProcessor and
ViTForImageClassification by BeitForImageClassification).
There’s also a demo notebook available which showcases how to combine DALL-E’s image tokenizer with BEiT for
performing masked image modeling. You can find it here.
As the BEiT models expect each image to be of the same size (resolution), one can use
BeitImageProcessor to resize (or rescale) and normalize images for the model.
Both the patch resolution and image resolution used during pre-training or fine-tuning are reflected in the name of
each checkpoint. For example, microsoft/beit-base-patch16-224 refers to a base-sized architecture with patch
resolution of 16x16 and fine-tuning resolution of 224x224. All checkpoints can be found on the hub.
The available checkpoints are either (1) pre-trained on ImageNet-22k (a collection of
14 million images and 22k classes) only, (2) also fine-tuned on ImageNet-22k or (3) also fine-tuned on ImageNet-1k (also referred to as ILSVRC 2012, a collection of 1.3 million
images and 1,000 classes).
BEiT uses relative position embeddings, inspired by the T5 model. During pre-training, the authors shared the
relative position bias among the several self-attention layers. During fine-tuning, each layer’s relative position
bias is initialized with the shared relative position bias obtained after pre-training. Note that, if one wants to
pre-train a model from scratch, one needs to either set the use_relative_position_bias or the
use_relative_position_bias attribute of BeitConfig to True in order to add
position embeddings.
BEiT pre-training. Taken from the original paper.
This model was contributed by nielsr. The JAX/FLAX version of this model was
contributed by kamalkraj. The original code can be found here.
Resources
	
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BEiT.
Image Classification
BeitForImageClassification is supported by this example script and notebook.
See also: Image classification task guide
Semantic segmentation
Semantic segmentation task guide
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.
BEiT specific outputs
	
class transformers.models.beit.modeling_beit.BeitModelOutputWithPooling
<
source
>
(
last_hidden_state: FloatTensor = None
pooler_output: FloatTensor = None
hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None
attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None
)
Parameters 
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)) —
Average of the last layer hidden states of the patch tokens (excluding the [CLS] token) if
config.use_mean_pooling is set to True. If set to False, then the final hidden state of the [CLS] token
will be returned.
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.
Class for outputs of BeitModel.
class transformers.models.beit.modeling_flax_beit.FlaxBeitModelOutputWithPooling
<
source
>
(
last_hidden_state: Array = None
pooler_output: Array = None
hidden_states: typing.Optional[typing.Tuple[jax.Array]] = None
attentions: typing.Optional[typing.Tuple[jax.Array]] = None
)
Parameters 
last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size)) —
Sequence of hidden-states at the output of the last layer of the model.
pooler_output (jnp.ndarray of shape (batch_size, hidden_size)) —
Average of the last layer hidden states of the patch tokens (excluding the [CLS] token) if
config.use_mean_pooling is set to True. If set to False, then the final hidden state of the [CLS] token
will be returned.
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of jnp.ndarray (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(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of jnp.ndarray (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.
Class for outputs of FlaxBeitModel.
BeitConfig
	
class transformers.BeitConfig
<
source
>
(
vocab_size = 8192
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
use_mask_token = False
use_absolute_position_embeddings = False
use_relative_position_bias = False
use_shared_relative_position_bias = False
layer_scale_init_value = 0.1
drop_path_rate = 0.1
use_mean_pooling = True
out_indices = [3, 5, 7, 11]
pool_scales = [1, 2, 3, 6]
use_auxiliary_head = True
auxiliary_loss_weight = 0.4
auxiliary_channels = 256
auxiliary_num_convs = 1
auxiliary_concat_input = False
semantic_loss_ignore_index = 255
**kwargs
)
Parameters 
vocab_size (int, optional, defaults to 8092) —
Vocabulary size of the BEiT model. Defines the number of different image tokens that can be used during
pre-training.
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 0.0) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (float, optional, defaults to 0.0) —
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.
use_mask_token (bool, optional, defaults to False) —
Whether to use a mask token for masked image modeling.
use_absolute_position_embeddings (bool, optional, defaults to False) —
Whether to use BERT-style absolute position embeddings.
use_relative_position_bias (bool, optional, defaults to False) —
Whether to use T5-style relative position embeddings in the self-attention layers.
use_shared_relative_position_bias (bool, optional, defaults to False) —
Whether to use the same relative position embeddings across all self-attention layers of the Transformer.
layer_scale_init_value (float, optional, defaults to 0.1) —
Scale to use in the self-attention layers. 0.1 for base, 1e-5 for large. Set 0 to disable layer scale.
drop_path_rate (float, optional, defaults to 0.1) —
Stochastic depth rate per sample (when applied in the main path of residual layers).
use_mean_pooling (bool, optional, defaults to True) —
Whether to mean pool the final hidden states of the patches instead of using the final hidden state of the
CLS token, before applying the classification head.
out_indices (List[int], optional, defaults to [3, 5, 7, 11]) —
Indices of the feature maps to use for semantic segmentation.
pool_scales (Tuple[int], optional, defaults to [1, 2, 3, 6]) —
Pooling scales used in Pooling Pyramid Module applied on the last feature map.
use_auxiliary_head (bool, optional, defaults to True) —
Whether to use an auxiliary head during training.
auxiliary_loss_weight (float, optional, defaults to 0.4) —
Weight of the cross-entropy loss of the auxiliary head.
auxiliary_channels (int, optional, defaults to 256) —
Number of channels to use in the auxiliary head.
auxiliary_num_convs (int, optional, defaults to 1) —
Number of convolutional layers to use in the auxiliary head.
auxiliary_concat_input (bool, optional, defaults to False) —
Whether to concatenate the output of the auxiliary head with the input before the classification layer.
semantic_loss_ignore_index (int, optional, defaults to 255) —
The index that is ignored by the loss function of the semantic segmentation model.
This is the configuration class to store the configuration of a BeitModel. It is used to instantiate an BEiT
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 BEiT
microsoft/beit-base-patch16-224-pt22k architecture.
Example:
	Copied
 from transformers import BeitConfig, BeitModel
 # Initializing a BEiT beit-base-patch16-224-pt22k style configuration
 configuration = BeitConfig()
 # Initializing a model (with random weights) from the beit-base-patch16-224-pt22k style configuration
 model = BeitModel(configuration)
 # Accessing the model configuration
 configuration = model.config
BeitFeatureExtractor
	
class transformers.BeitFeatureExtractor
<
source
>
(
*args
**kwargs
)
__call__
<
source
>
(
images
segmentation_maps = None
**kwargs
)
post_process_semantic_segmentation
<
source
>
(
outputs
target_sizes: typing.List[typing.Tuple] = None
)
→
semantic_segmentation
Parameters 
outputs (BeitForSemanticSegmentation) —
Raw outputs of the model.
target_sizes (List[Tuple] of length batch_size, optional) —
List of tuples corresponding to the requested final size (height, width) of each prediction. If left to
None, predictions will not be resized.
Returns
semantic_segmentation
List[torch.Tensor] of length batch_size, where each item is a semantic
segmentation map of shape (height, width) corresponding to the target_sizes entry (if target_sizes is
specified). Each entry of each torch.Tensor correspond to a semantic class id.
Converts the output of BeitForSemanticSegmentation into semantic segmentation maps. Only supports PyTorch.
BeitImageProcessor
	
class transformers.BeitImageProcessor
<
source
>
(
do_resize: bool = True
size: typing.Dict[str, int] = None
resample: Resampling = <Resampling.BICUBIC: 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
do_reduce_labels: bool = False
**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 the
do_resize parameter in the preprocess method.
size (Dict[str, int] optional, defaults to {"height" -- 256, "width": 256}):
Size of the output image after resizing. Can be overridden by the size parameter in the preprocess
method.
resample (PILImageResampling, optional, defaults to PILImageResampling.BICUBIC) —
Resampling filter to use if resizing the image. Can be overridden by the resample parameter in the
preprocess method.
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 the do_center_crop parameter in the
preprocess method.
crop_size (Dict[str, int], optional, defaults to {"height" -- 224, "width": 224}):
Desired output size when applying center-cropping. Only has an effect if do_center_crop is set to True.
Can be overridden by the crop_size parameter in the preprocess method.
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) —
The mean to use if normalizing the image. This is a float or list of floats of length of the number of
channels of 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) —
The standard deviation to use if normalizing the image. This is a float or list of floats of length of the
number of channels of the image. Can be overridden by the image_std parameter in the preprocess method.
do_reduce_labels (bool, optional, defaults to False) —
Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is
used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The
background label will be replaced by 255. Can be overridden by the do_reduce_labels parameter in the
preprocess method.
Constructs a BEiT 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')]]
segmentation_maps: 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')], NoneType] = None
do_resize: bool = None
size: typing.Dict[str, int] = None
resample: Resampling = 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
do_reduce_labels: typing.Optional[bool] = 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 resizing.
resample (int, optional, defaults to self.resample) —
Resampling filter to use if resizing the image. This can be one of the enum PILImageResampling, 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.
do_reduce_labels (bool, optional, defaults to self.do_reduce_labels) —
Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0
is used for background, and background itself is not included in all classes of a dataset (e.g.
ADE20k). The background label will be replaced by 255.
return_tensors (str or TensorType, optional) —
The type of tensors to return. Can be one of:
Unset: 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.
post_process_semantic_segmentation
<
source
>
(
outputs
target_sizes: typing.List[typing.Tuple] = None
)
→
semantic_segmentation
Parameters 
outputs (BeitForSemanticSegmentation) —
Raw outputs of the model.
target_sizes (List[Tuple] of length batch_size, optional) —
List of tuples corresponding to the requested final size (height, width) of each prediction. If left to
None, predictions will not be resized.
Returns
semantic_segmentation
List[torch.Tensor] of length batch_size, where each item is a semantic
segmentation map of shape (height, width) corresponding to the target_sizes entry (if target_sizes is
specified). Each entry of each torch.Tensor correspond to a semantic class id.
Converts the output of BeitForSemanticSegmentation into semantic segmentation maps. Only supports PyTorch.
BeitModel
	
class transformers.BeitModel
<
source
>
(
config: BeitConfig
add_pooling_layer: bool = True
)
Parameters 
config (BeitConfig) — 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 Beit 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.models.beit.modeling_beit.BeitModelOutputWithPooling 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
BeitImageProcessor.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.models.beit.modeling_beit.BeitModelOutputWithPooling or tuple(torch.FloatTensor)
A transformers.models.beit.modeling_beit.BeitModelOutputWithPooling 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 (BeitConfig) 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)) — Average of the last layer hidden states of the patch tokens (excluding the [CLS] token) if
config.use_mean_pooling is set to True. If set to False, then the final hidden state of the [CLS] token
will be returned.
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 BeitModel 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, BeitModel
 import torch
 from datasets import load_dataset
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 image_processor = AutoImageProcessor.from_pretrained("microsoft/beit-base-patch16-224-pt22k")
 model = BeitModel.from_pretrained("microsoft/beit-base-patch16-224-pt22k")
 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, 197, 768]
BeitForMaskedImageModeling
	
class transformers.BeitForMaskedImageModeling
<
source
>
(
config: BeitConfig
)
Parameters 
config (BeitConfig) — 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.
Beit Model transformer with a ‘language’ modeling head on top. BEiT does masked image modeling by predicting
visual tokens of a Vector-Quantize Variational Autoencoder (VQ-VAE), whereas other vision models like ViT and DeiT
predict RGB pixel values. As a result, this class is incompatible with AutoModelForMaskedImageModeling, so you
will need to use BeitForMaskedImageModeling directly if you wish to do masked image modeling with BEiT.
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
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.MaskedLMOutput 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
BeitImageProcessor.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).
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.MaskedLMOutput or tuple(torch.FloatTensor)
A 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 (BeitConfig) 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, 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 BeitForMaskedImageModeling 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, BeitForMaskedImageModeling
 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("microsoft/beit-base-patch16-224-pt22k")
 model = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k")
 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, logits = outputs.loss, outputs.logits
 list(logits.shape)
[1, 196, 8192]
BeitForImageClassification
	
class transformers.BeitForImageClassification
<
source
>
(
config: BeitConfig
)
Parameters 
config (BeitConfig) — 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.
Beit Model transformer with an image classification head on top (a linear layer on top of the average of the final
hidden states of the patch tokens) 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
BeitImageProcessor.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 (BeitConfig) 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 BeitForImageClassification 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, BeitForImageClassification
 import torch
 from datasets import load_dataset
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 image_processor = AutoImageProcessor.from_pretrained("microsoft/beit-base-patch16-224")
 model = BeitForImageClassification.from_pretrained("microsoft/beit-base-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
BeitForSemanticSegmentation
	
class transformers.BeitForSemanticSegmentation
<
source
>
(
config: BeitConfig
)
Parameters 
config (BeitConfig) — 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.
Beit Model transformer with a semantic segmentation head on top e.g. for ADE20k, CityScapes.
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.SemanticSegmenterOutput 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
BeitImageProcessor.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, height, width), optional) —
Ground truth semantic segmentation maps for computing the loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels > 1, a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_outputs.SemanticSegmenterOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.SemanticSegmenterOutput 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 (BeitConfig) 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, logits_height, logits_width)) — Classification scores for each pixel.
The logits returned do not necessarily have the same size as the pixel_values passed as inputs. This is
to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the
original image size as post-processing. You should always check your logits shape and resize as needed.
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, patch_size, 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, patch_size, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The BeitForSemanticSegmentation 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, BeitForSemanticSegmentation
 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("microsoft/beit-base-finetuned-ade-640-640")
 model = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")
 inputs = image_processor(images=image, return_tensors="pt")
 outputs = model(**inputs)
 # logits are of shape (batch_size, num_labels, height, width)
 logits = outputs.logits
FlaxBeitModel
	
class transformers.FlaxBeitModel
<
source
>
(
config: BeitConfig
input_shape = None
seed: int = 0
dtype: dtype = <class 'jax.numpy.float32'>
_do_init: bool = True
**kwargs
)
Parameters 
config (BeitConfig) — 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.
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
The bare Beit Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from FlaxPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a Flax Linen flax.linen.Module
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.
Finally, this model supports inherent JAX features such as:
Just-In-Time (JIT) compilation
Automatic Differentiation
Vectorization
Parallelization
__call__
<
source
>
(
pixel_values
bool_masked_pos = None
params: dict = None
dropout_rng: PRNGKey = None
train: bool = False
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.models.beit.modeling_flax_beit.FlaxBeitModelOutputWithPooling or tuple(torch.FloatTensor)
Returns
transformers.models.beit.modeling_flax_beit.FlaxBeitModelOutputWithPooling or tuple(torch.FloatTensor)
A transformers.models.beit.modeling_flax_beit.FlaxBeitModelOutputWithPooling 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 (<class 'transformers.models.beit.configuration_beit.BeitConfig'>) and inputs.
last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
pooler_output (jnp.ndarray of shape (batch_size, hidden_size)) — Average of the last layer hidden states of the patch tokens (excluding the [CLS] token) if
config.use_mean_pooling is set to True. If set to False, then the final hidden state of the [CLS] token
will be returned.
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (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(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (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 FlaxBeitPreTrainedModel 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, FlaxBeitModel
 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("microsoft/beit-base-patch16-224-pt22k-ft22k")
 model = FlaxBeitModel.from_pretrained("microsoft/beit-base-patch16-224-pt22k-ft22k")
 inputs = image_processor(images=image, return_tensors="np")
 outputs = model(**inputs)
 last_hidden_states = outputs.last_hidden_state
FlaxBeitForMaskedImageModeling
	
class transformers.FlaxBeitForMaskedImageModeling
<
source
>
(
config: BeitConfig
input_shape = None
seed: int = 0
dtype: dtype = <class 'jax.numpy.float32'>
_do_init: bool = True
**kwargs
)
Parameters 
config (BeitConfig) — 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.
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
Beit Model transformer with a ‘language’ modeling head on top (to predict visual tokens).
This model inherits from FlaxPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a Flax Linen flax.linen.Module
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.
Finally, this model supports inherent JAX features such as:
Just-In-Time (JIT) compilation
Automatic Differentiation
Vectorization
Parallelization
__call__
<
source
>
(
pixel_values
bool_masked_pos = None
params: dict = None
dropout_rng: PRNGKey = None
train: bool = False
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_flax_outputs.FlaxMaskedLMOutput or tuple(torch.FloatTensor)
Returns
transformers.modeling_flax_outputs.FlaxMaskedLMOutput or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxMaskedLMOutput 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 (<class 'transformers.models.beit.configuration_beit.BeitConfig'>) and inputs.
logits (jnp.ndarray 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(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (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(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (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 FlaxBeitPreTrainedModel 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.
bool_masked_pos (numpy.ndarray of shape (batch_size, num_patches)):
Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).
Examples:
	Copied
 from transformers import AutoImageProcessor, BeitForMaskedImageModeling
 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("microsoft/beit-base-patch16-224-pt22k")
 model = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k")
 inputs = image_processor(images=image, return_tensors="np")
 outputs = model(**inputs)
 logits = outputs.logits
FlaxBeitForImageClassification
	
class transformers.FlaxBeitForImageClassification
<
source
>
(
config: BeitConfig
input_shape = None
seed: int = 0
dtype: dtype = <class 'jax.numpy.float32'>
_do_init: bool = True
**kwargs
)
Parameters 
config (BeitConfig) — 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.
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
Beit Model transformer with an image classification head on top (a linear layer on top of the average of the final
hidden states of the patch tokens) e.g. for ImageNet.
This model inherits from FlaxPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a Flax Linen flax.linen.Module
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.
Finally, this model supports inherent JAX features such as:
Just-In-Time (JIT) compilation
Automatic Differentiation
Vectorization
Parallelization
__call__
<
source
>
(
pixel_values
bool_masked_pos = None
params: dict = None
dropout_rng: PRNGKey = None
train: bool = False
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput or tuple(torch.FloatTensor)
Returns
transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput 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 (<class 'transformers.models.beit.configuration_beit.BeitConfig'>) and inputs.
logits (jnp.ndarray of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (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(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (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 FlaxBeitPreTrainedModel 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, FlaxBeitForImageClassification
 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("microsoft/beit-base-patch16-224")
 model = FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224")
 inputs = image_processor(images=image, return_tensors="np")
 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])
←YOSO
BiT→
BEiT
Overview
Resources
BEiT specific outputs
BeitConfig
BeitFeatureExtractor
BeitImageProcessor
BeitModel
BeitForMaskedImageModeling
BeitForImageClassification
BeitForSemanticSegmentation
FlaxBeitModel
FlaxBeitForMaskedImageModeling
FlaxBeitForImageClassification
