
 
MobileViT
	
Overview
	
The MobileViT model was proposed in MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer by Sachin Mehta and Mohammad Rastegari. MobileViT introduces a new layer that replaces local processing in convolutions with global processing using transformers.
The abstract from the paper is the following:
Light-weight convolutional neural networks (CNNs) are the de-facto for mobile vision tasks. Their spatial inductive biases allow them to learn representations with fewer parameters across different vision tasks. However, these networks are spatially local. To learn global representations, self-attention-based vision trans-formers (ViTs) have been adopted. Unlike CNNs, ViTs are heavy-weight. In this paper, we ask the following question: is it possible to combine the strengths of CNNs and ViTs to build a light-weight and low latency network for mobile vision tasks? Towards this end, we introduce MobileViT, a light-weight and general-purpose vision transformer for mobile devices. MobileViT presents a different perspective for the global processing of information with transformers, i.e., transformers as convolutions. Our results show that MobileViT significantly outperforms CNN- and ViT-based networks across different tasks and datasets. On the ImageNet-1k dataset, MobileViT achieves top-1 accuracy of 78.4% with about 6 million parameters, which is 3.2% and 6.2% more accurate than MobileNetv3 (CNN-based) and DeIT (ViT-based) for a similar number of parameters. On the MS-COCO object detection task, MobileViT is 5.7% more accurate than MobileNetv3 for a similar number of parameters.
Tips:
MobileViT is more like a CNN than a Transformer model. It does not work on sequence data but on batches of images. Unlike ViT, there are no embeddings. The backbone model outputs a feature map. You can follow this tutorial for a lightweight introduction.
One can use MobileViTImageProcessor to prepare images for the model. Note that if you do your own preprocessing, the pretrained checkpoints expect images to be in BGR pixel order (not RGB).
The available image classification checkpoints are pre-trained on ImageNet-1k (also referred to as ILSVRC 2012, a collection of 1.3 million images and 1,000 classes).
The segmentation model uses a DeepLabV3 head. The available semantic segmentation checkpoints are pre-trained on PASCAL VOC.
As the name suggests MobileViT was designed to be performant and efficient on mobile phones. The TensorFlow versions of the MobileViT models are fully compatible with TensorFlow Lite.
You can use the following code to convert a MobileViT checkpoint (be it image classification or semantic segmentation) to generate a
TensorFlow Lite model:
	Copied
from transformers import TFMobileViTForImageClassification
import tensorflow as tf
model_ckpt = "apple/mobilevit-xx-small"
model = TFMobileViTForImageClassification.from_pretrained(model_ckpt)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [
    tf.lite.OpsSet.TFLITE_BUILTINS,
    tf.lite.OpsSet.SELECT_TF_OPS,
]
tflite_model = converter.convert()
tflite_filename = model_ckpt.split("/")[-1] + ".tflite"
with open(tflite_filename, "wb") as f:
    f.write(tflite_model)
The resulting model will be just about an MB making it a good fit for mobile applications where resources and network
bandwidth can be constrained.
This model was contributed by matthijs. The TensorFlow version of the model was contributed by sayakpaul. The original code and weights can be found here.
Resources
	
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with MobileViT.
Image Classification
MobileViTForImageClassification 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.
MobileViTConfig
	
class transformers.MobileViTConfig
<
source
>
(
num_channels = 3
image_size = 256
patch_size = 2
hidden_sizes = [144, 192, 240]
neck_hidden_sizes = [16, 32, 64, 96, 128, 160, 640]
num_attention_heads = 4
mlp_ratio = 2.0
expand_ratio = 4.0
hidden_act = 'silu'
conv_kernel_size = 3
output_stride = 32
hidden_dropout_prob = 0.1
attention_probs_dropout_prob = 0.0
classifier_dropout_prob = 0.1
initializer_range = 0.02
layer_norm_eps = 1e-05
qkv_bias = True
aspp_out_channels = 256
atrous_rates = [6, 12, 18]
aspp_dropout_prob = 0.1
semantic_loss_ignore_index = 255
**kwargs
)
Parameters 
num_channels (int, optional, defaults to 3) —
The number of input channels.
image_size (int, optional, defaults to 256) —
The size (resolution) of each image.
patch_size (int, optional, defaults to 2) —
The size (resolution) of each patch.
hidden_sizes (List[int], optional, defaults to [144, 192, 240]) —
Dimensionality (hidden size) of the Transformer encoders at each stage.
neck_hidden_sizes (List[int], optional, defaults to [16, 32, 64, 96, 128, 160, 640]) —
The number of channels for the feature maps of the backbone.
num_attention_heads (int, optional, defaults to 4) —
Number of attention heads for each attention layer in the Transformer encoder.
mlp_ratio (float, optional, defaults to 2.0) —
The ratio of the number of channels in the output of the MLP to the number of channels in the input.
expand_ratio (float, optional, defaults to 4.0) —
Expansion factor for the MobileNetv2 layers.
hidden_act (str or function, optional, defaults to "silu") —
The non-linear activation function (function or string) in the Transformer encoder and convolution layers.
conv_kernel_size (int, optional, defaults to 3) —
The size of the convolutional kernel in the MobileViT layer.
output_stride (int, optional, defaults to 32) —
The ratio of the spatial resolution of the output to the resolution of the input image.
hidden_dropout_prob (float, optional, defaults to 0.1) —
The dropout probabilitiy for all fully connected layers in the Transformer encoder.
attention_probs_dropout_prob (float, optional, defaults to 0.0) —
The dropout ratio for the attention probabilities.
classifier_dropout_prob (float, optional, defaults to 0.1) —
The dropout ratio for attached classifiers.
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-5) —
The epsilon used by the layer normalization layers.
qkv_bias (bool, optional, defaults to True) —
Whether to add a bias to the queries, keys and values.
aspp_out_channels (int, optional, defaults to 256) —
Number of output channels used in the ASPP layer for semantic segmentation.
atrous_rates (List[int], optional, defaults to [6, 12, 18]) —
Dilation (atrous) factors used in the ASPP layer for semantic segmentation.
aspp_dropout_prob (float, optional, defaults to 0.1) —
The dropout ratio for the ASPP layer for semantic segmentation.
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 MobileViTModel. It is used to instantiate a
MobileViT 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 MobileViT
apple/mobilevit-small 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 MobileViTConfig, MobileViTModel
 # Initializing a mobilevit-small style configuration
 configuration = MobileViTConfig()
 # Initializing a model from the mobilevit-small style configuration
 model = MobileViTModel(configuration)
 # Accessing the model configuration
 configuration = model.config
MobileViTFeatureExtractor
	
class transformers.MobileViTFeatureExtractor
<
source
>
(
*args
**kwargs
)
__call__
<
source
>
(
images
**kwargs
)
Preprocess an image or a batch of images.
post_process_semantic_segmentation
<
source
>
(
outputs
target_sizes: typing.List[typing.Tuple] = None
)
→
List[torch.Tensor]
Parameters 
outputs (MobileViTForSemanticSegmentation) —
Raw outputs of the model.
target_sizes (List[Tuple], optional) —
A list of length batch_size, where each item is a Tuple[int, int] corresponding to the requested
final size (height, width) of each prediction. If left to None, predictions will not be resized.
Returns
List[torch.Tensor]
A list 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 MobileViTForSemanticSegmentation into semantic segmentation maps. Only supports
PyTorch.
MobileViTImageProcessor
	
class transformers.MobileViTImageProcessor
<
source
>
(
do_resize: bool = True
size: typing.Dict[str, int] = None
resample: Resampling = <Resampling.BILINEAR: 2>
do_rescale: bool = True
rescale_factor: typing.Union[int, float] = 0.00392156862745098
do_center_crop: bool = True
crop_size: typing.Dict[str, int] = None
do_flip_channel_order: 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 the
do_resize parameter in the preprocess method.
size (Dict[str, int] optional, defaults to {"shortest_edge" -- 224}):
Controls the size of the output image after resizing. Can be overridden by the size parameter in the
preprocess method.
resample (PILImageResampling, optional, defaults to PILImageResampling.BILINEAR) —
Defines the resampling filter to use if resizing the image. Can be overridden by the resample 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_center_crop (bool, optional, defaults to True) —
Whether to crop the input at the center. If the input size is smaller than crop_size along any edge, the
image is padded with 0’s and then center cropped. Can be overridden by the do_center_crop parameter in
the preprocess method.
crop_size (Dict[str, int], optional, defaults to {"height" -- 256, "width": 256}):
Desired output size (size["height"], size["width"]) when applying center-cropping. Can be overridden by
the crop_size parameter in the preprocess method.
do_flip_channel_order (bool, optional, defaults to True) —
Whether to flip the color channels from RGB to BGR. Can be overridden by the do_flip_channel_order
parameter in the preprocess method.
Constructs a MobileViT 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_rescale: bool = None
rescale_factor: float = None
do_center_crop: bool = None
crop_size: typing.Dict[str, int] = None
do_flip_channel_order: 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_rescale (bool, optional, defaults to self.do_rescale) —
Whether to rescale the image by rescale factor.
rescale_factor (float, optional, defaults to self.rescale_factor) —
Rescale factor to rescale the image by if do_rescale 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 if do_center_crop is set to True.
do_flip_channel_order (bool, optional, defaults to self.do_flip_channel_order) —
Whether to flip the channel order of the image.
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
)
→
List[torch.Tensor]
Parameters 
outputs (MobileViTForSemanticSegmentation) —
Raw outputs of the model.
target_sizes (List[Tuple], optional) —
A list of length batch_size, where each item is a Tuple[int, int] corresponding to the requested
final size (height, width) of each prediction. If left to None, predictions will not be resized.
Returns
List[torch.Tensor]
A list 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 MobileViTForSemanticSegmentation into semantic segmentation maps. Only supports
PyTorch.
MobileViTModel
	
class transformers.MobileViTModel
<
source
>
(
config: MobileViTConfig
expand_output: bool = True
)
Parameters 
config (MobileViTConfig) — 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 MobileViT model 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
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
MobileViTImageProcessor.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 (MobileViTConfig) 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 MobileViTModel 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, MobileViTModel
 import torch
 from datasets import load_dataset
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 image_processor = AutoImageProcessor.from_pretrained("apple/mobilevit-small")
 model = MobileViTModel.from_pretrained("apple/mobilevit-small")
 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, 640, 8, 8]
MobileViTForImageClassification
	
class transformers.MobileViTForImageClassification
<
source
>
(
config: MobileViTConfig
)
Parameters 
config (MobileViTConfig) — 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.
MobileViT 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.Tensor] = None
output_hidden_states: typing.Optional[bool] = None
labels: typing.Optional[torch.Tensor] = 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
MobileViTImageProcessor.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 regression loss is computed (Mean-Square loss). 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 (MobileViTConfig) 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 MobileViTForImageClassification 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, MobileViTForImageClassification
 import torch
 from datasets import load_dataset
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 image_processor = AutoImageProcessor.from_pretrained("apple/mobilevit-small")
 model = MobileViTForImageClassification.from_pretrained("apple/mobilevit-small")
 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
MobileViTForSemanticSegmentation
	
class transformers.MobileViTForSemanticSegmentation
<
source
>
(
config: MobileViTConfig
)
Parameters 
config (MobileViTConfig) — 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.
MobileViT model with a semantic segmentation head on top, e.g. for Pascal VOC.
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
labels: typing.Optional[torch.Tensor] = 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
MobileViTImageProcessor.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, 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 (MobileViTConfig) 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 MobileViTForSemanticSegmentation 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
 import requests
 import torch
 from PIL import Image
 from transformers import AutoImageProcessor, MobileViTForSemanticSegmentation
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 image_processor = AutoImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-small")
 model = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-small")
 inputs = image_processor(images=image, return_tensors="pt")
 with torch.no_grad():
...     outputs = model(**inputs)
 # logits are of shape (batch_size, num_labels, height, width)
 logits = outputs.logits
TFMobileViTModel
	
class transformers.TFMobileViTModel
<
source
>
(
*args
**kwargs
)
Parameters 
config (MobileViTConfig) — 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 MobileViT model outputting raw hidden-states without any specific head on top.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
TensorFlow models and layers in transformers accept two formats as input:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
a single Tensor with pixel_values only and nothing else: model(pixel_values)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([pixel_values, attention_mask]) or model([pixel_values, attention_mask, token_type_ids])
a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})
Note that when creating models and layers with
subclassing then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
call
<
source
>
(
pixel_values: tf.Tensor | None = 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 (np.ndarray, tf.Tensor, List[tf.Tensor], Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See
MobileViTImageProcessor.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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
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 (MobileViTConfig) 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 TFMobileViTModel 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, TFMobileViTModel
 from datasets import load_dataset
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 image_processor = AutoImageProcessor.from_pretrained("apple/mobilevit-small")
 model = TFMobileViTModel.from_pretrained("apple/mobilevit-small")
 inputs = image_processor(image, return_tensors="tf")
 outputs = model(**inputs)
 last_hidden_states = outputs.last_hidden_state
 list(last_hidden_states.shape)
[1, 640, 8, 8]
TFMobileViTForImageClassification
	
class transformers.TFMobileViTForImageClassification
<
source
>
(
*args
**kwargs
)
Parameters 
config (MobileViTConfig) — 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.
MobileViT model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
TensorFlow models and layers in transformers accept two formats as input:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
a single Tensor with pixel_values only and nothing else: model(pixel_values)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([pixel_values, attention_mask]) or model([pixel_values, attention_mask, token_type_ids])
a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})
Note that when creating models and layers with
subclassing then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
call
<
source
>
(
pixel_values: tf.Tensor | None = None
output_hidden_states: Optional[bool] = None
labels: tf.Tensor | None = None
return_dict: Optional[bool] = None
training: Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFImageClassifierOutputWithNoAttention or tuple(tf.Tensor)
Parameters 
pixel_values (np.ndarray, tf.Tensor, List[tf.Tensor], Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See
MobileViTImageProcessor.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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
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.TFImageClassifierOutputWithNoAttention or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFImageClassifierOutputWithNoAttention 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 (MobileViTConfig) 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, num_channels, height, width). Hidden-states (also called
feature maps) of the model at the output of each stage.
The TFMobileViTForImageClassification 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, TFMobileViTForImageClassification
 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("apple/mobilevit-small")
 model = TFMobileViTForImageClassification.from_pretrained("apple/mobilevit-small")
 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
TFMobileViTForSemanticSegmentation
	
class transformers.TFMobileViTForSemanticSegmentation
<
source
>
(
*args
**kwargs
)
Parameters 
config (MobileViTConfig) — 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.
MobileViT model with a semantic segmentation head on top, e.g. for Pascal VOC.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
TensorFlow models and layers in transformers accept two formats as input:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
a single Tensor with pixel_values only and nothing else: model(pixel_values)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([pixel_values, attention_mask]) or model([pixel_values, attention_mask, token_type_ids])
a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})
Note that when creating models and layers with
subclassing then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
call
<
source
>
(
pixel_values: tf.Tensor | None = None
labels: tf.Tensor | None = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
training: bool = False
)
→
transformers.modeling_tf_outputs.TFSemanticSegmenterOutputWithNoAttention or tuple(tf.Tensor)
Parameters 
pixel_values (np.ndarray, tf.Tensor, List[tf.Tensor], Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See
MobileViTImageProcessor.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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
labels (tf.Tensor 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_tf_outputs.TFSemanticSegmenterOutputWithNoAttention or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFSemanticSegmenterOutputWithNoAttention 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 (MobileViTConfig) 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, 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(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 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.
The TFMobileViTForSemanticSegmentation 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, TFMobileViTForSemanticSegmentation
 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("apple/deeplabv3-mobilevit-small")
 model = TFMobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-small")
 inputs = image_processor(images=image, return_tensors="tf")
 outputs = model(**inputs)
 # logits are of shape (batch_size, num_labels, height, width)
 logits = outputs.logits
←MobileNetV2
MobileViTV2→
MobileViT
Overview
Resources
MobileViTConfig
MobileViTFeatureExtractor
MobileViTImageProcessor
MobileViTModel
MobileViTForImageClassification
MobileViTForSemanticSegmentation
TFMobileViTModel
TFMobileViTForImageClassification
TFMobileViTForSemanticSegmentation
