
 
Big Transfer (BiT)
	
Overview
	
The BiT model was proposed in Big Transfer (BiT): General Visual Representation Learning by Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby.
BiT is a simple recipe for scaling up pre-training of ResNet-like architectures (specifically, ResNetv2). The method results in significant improvements for transfer learning.
The abstract from the paper is the following:
Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. We revisit the paradigm of pre-training on large supervised datasets and fine-tuning the model on a target task. We scale up pre-training, and propose a simple recipe that we call Big Transfer (BiT). By combining a few carefully selected components, and transferring using a simple heuristic, we achieve strong performance on over 20 datasets. BiT performs well across a surprisingly wide range of data regimes — from 1 example per class to 1M total examples. BiT achieves 87.5% top-1 accuracy on ILSVRC-2012, 99.4% on CIFAR-10, and 76.3% on the 19 task Visual Task Adaptation Benchmark (VTAB). On small datasets, BiT attains 76.8% on ILSVRC-2012 with 10 examples per class, and 97.0% on CIFAR-10 with 10 examples per class. We conduct detailed analysis of the main components that lead to high transfer performance.
Tips:
BiT models are equivalent to ResNetv2 in terms of architecture, except that: 1) all batch normalization layers are replaced by group normalization,
2) weight standardization is used for convolutional layers. The authors show that the combination of both is useful for training with large batch sizes, and has a significant
impact on transfer learning.
This model was contributed by nielsr.
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 BiT.
Image Classification
BitForImageClassification is supported by this example script and notebook.
See also: Image classification 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.
BitConfig
	
class transformers.BitConfig
<
source
>
(
num_channels = 3
embedding_size = 64
hidden_sizes = [256, 512, 1024, 2048]
depths = [3, 4, 6, 3]
layer_type = 'preactivation'
hidden_act = 'relu'
global_padding = None
num_groups = 32
drop_path_rate = 0.0
embedding_dynamic_padding = False
output_stride = 32
width_factor = 1
out_features = None
out_indices = None
**kwargs
)
Parameters 
num_channels (int, optional, defaults to 3) —
The number of input channels.
embedding_size (int, optional, defaults to 64) —
Dimensionality (hidden size) for the embedding layer.
hidden_sizes (List[int], optional, defaults to [256, 512, 1024, 2048]) —
Dimensionality (hidden size) at each stage.
depths (List[int], optional, defaults to [3, 4, 6, 3]) —
Depth (number of layers) for each stage.
layer_type (str, optional, defaults to "preactivation") —
The layer to use, it can be either "preactivation" or "bottleneck".
hidden_act (str, optional, defaults to "relu") —
The non-linear activation function in each block. If string, "gelu", "relu", "selu" and "gelu_new"
are supported.
global_padding (str, optional) —
Padding strategy to use for the convolutional layers. Can be either "valid", "same", or None.
num_groups (int, optional, defaults to 32) —
Number of groups used for the BitGroupNormActivation layers.
drop_path_rate (float, optional, defaults to 0.0) —
The drop path rate for the stochastic depth.
embedding_dynamic_padding (bool, optional, defaults to False) —
Whether or not to make use of dynamic padding for the embedding layer.
output_stride (int, optional, defaults to 32) —
The output stride of the model.
width_factor (int, optional, defaults to 1) —
The width factor for the model.
out_features (List[str], optional) —
If used as backbone, list of features to output. Can be any of "stem", "stage1", "stage2", etc.
(depending on how many stages the model has). If unset and out_indices is set, will default to the
corresponding stages. If unset and out_indices is unset, will default to the last stage.
out_indices (List[int], optional) —
If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
many stages the model has). If unset and out_features is set, will default to the corresponding stages.
If unset and out_features is unset, will default to the last stage.
This is the configuration class to store the configuration of a BitModel. It is used to instantiate an BiT
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 BiT
google/bit-50 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 BitConfig, BitModel
 # Initializing a BiT bit-50 style configuration
 configuration = BitConfig()
 # Initializing a model (with random weights) from the bit-50 style configuration
 model = BitModel(configuration)
 # Accessing the model configuration
 configuration = model.config
BitImageProcessor
	
class transformers.BitImageProcessor
<
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
do_rescale: bool = True
rescale_factor: typing.Union[int, float] = 0.00392156862745098
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_convert_rgb: bool = True
**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 the preprocess method.
size (Dict[str, int] optional, defaults to {"shortest_edge" -- 224}):
Size of the image after resizing. The shortest edge of the image is resized to size[“shortest_edge”], with
the longest edge resized to keep the input aspect ratio. Can be overridden by size in the preprocess
method.
resample (PILImageResampling, optional, defaults to PILImageResampling.BICUBIC) —
Resampling filter to use if resizing the image. Can be overridden by resample in the preprocess method.
do_center_crop (bool, optional, defaults to True) —
Whether to center crop the image to the specified crop_size. Can be overridden by do_center_crop in the
preprocess method.
crop_size (Dict[str, int] optional, defaults to 224) —
Size of the output image after applying center_crop. Can be overridden by crop_size 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 do_rescale 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 rescale_factor in the preprocess
method.
do_normalize —
Whether to normalize the image. Can be overridden by do_normalize 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) —
Image standard deviation.
do_convert_rgb (bool, optional, defaults to True) —
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 BiT 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: Resampling = None
do_center_crop: bool = None
crop_size: 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_convert_rgb: bool = None
return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None
data_format: typing.Optional[transformers.image_utils.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. Shortest edge of the image is resized to size[“shortest_edge”], with
the longest edge resized to keep the input aspect ratio.
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 center crop. Only has an effect if do_center_crop is set to True.
do_rescale (bool, optional, defaults to self.do_rescale) —
Whether to rescale the image.
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 to use for normalization. Only has an effect if do_normalize is set to True.
image_std (float or List[float], optional, defaults to self.image_std) —
Image standard deviation to use for normalization. Only has an effect if do_normalize is set to
True.
do_convert_rgb (bool, optional, defaults to self.do_convert_rgb) —
Whether to convert the image to RGB.
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.
Unset: defaults to the channel dimension format of the input image.
Preprocess an image or batch of images.
BitModel
	
class transformers.BitModel
<
source
>
(
config
)
Parameters 
config (BitConfig) — 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 BiT model outputting raw features 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: Tensor
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention 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 BitImageProcessor.call()
for details.
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_outputs.BaseModelOutputWithPoolingAndNoAttention or tuple(torch.FloatTensor)
A transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention 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 (BitConfig) and inputs.
last_hidden_state (torch.FloatTensor of shape (batch_size, num_channels, height, width)) — 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 after a pooling operation on the spatial dimensions.
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, num_channels, height, width).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
The BitModel 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, BitModel
 import torch
 from datasets import load_dataset
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 image_processor = AutoImageProcessor.from_pretrained("google/bit-50")
 model = BitModel.from_pretrained("google/bit-50")
 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, 2048, 7, 7]
BitForImageClassification
	
class transformers.BitForImageClassification
<
source
>
(
config
)
Parameters 
config (BitConfig) — 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.
BiT Model with an image classification head on top (a linear layer on top of the pooled features), 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.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.ImageClassifierOutputWithNoAttention 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 BitImageProcessor.call()
for details.
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 classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_outputs.ImageClassifierOutputWithNoAttention or tuple(torch.FloatTensor)
A transformers.modeling_outputs.ImageClassifierOutputWithNoAttention 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 (BitConfig) 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, num_channels, height, width). Hidden-states (also
called feature maps) of the model at the output of each stage.
The BitForImageClassification 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, BitForImageClassification
 import torch
 from datasets import load_dataset
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 image_processor = AutoImageProcessor.from_pretrained("google/bit-50")
 model = BitForImageClassification.from_pretrained("google/bit-50")
 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])
tiger cat
←BEiT
Conditional DETR→
Big Transfer (BiT)
Overview
Resources
BitConfig
BitImageProcessor
BitModel
BitForImageClassification
