MobileViT

## [](#overview)Overview

The MobileViT model was proposed in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) 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](https://keras.io/examples/vision/mobilevit) for a lightweight introduction.
    
-   One can use [MobileViTImageProcessor](/docs/transformers/v4.30.0/en/model_doc/mobilevit#transformers.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](https://huggingface.co/datasets/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](https://arxiv.org/abs/1706.05587) head. The available semantic segmentation checkpoints are pre-trained on [PASCAL VOC](http://host.robots.ox.ac.uk/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](https://www.tensorflow.org/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:
    

```
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](https://huggingface.co/Matthijs). The TensorFlow version of the model was contributed by [sayakpaul](https://huggingface.co/sayakpaul). The original code and weights can be found [here](https://github.com/apple/ml-cvnets).

## [](#resources)Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with MobileViT.

-   [MobileViTForImageClassification](/docs/transformers/v4.30.0/en/model_doc/mobilevit#transformers.MobileViTForImageClassification) is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
-   See also: [Image classification task guide](../tasks/image_classification)

**Semantic segmentation**

-   [Semantic segmentation task guide](../tasks/semantic_segmentation)

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.

## [](#transformers.MobileViTConfig)MobileViTConfig

### class transformers.MobileViTConfig

[](#transformers.MobileViTConfig)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/mobilevit/configuration_mobilevit.py#L46)

( num\_channels = 3image\_size = 256patch\_size = 2hidden\_sizes = \[144, 192, 240\]neck\_hidden\_sizes = \[16, 32, 64, 96, 128, 160, 640\]num\_attention\_heads = 4mlp\_ratio = 2.0expand\_ratio = 4.0hidden\_act = 'silu'conv\_kernel\_size = 3output\_stride = 32hidden\_dropout\_prob = 0.1attention\_probs\_dropout\_prob = 0.0classifier\_dropout\_prob = 0.1initializer\_range = 0.02layer\_norm\_eps = 1e-05qkv\_bias = Trueaspp\_out\_channels = 256atrous\_rates = \[6, 12, 18\]aspp\_dropout\_prob = 0.1semantic\_loss\_ignore\_index = 255\*\*kwargs )

This is the configuration class to store the configuration of a [MobileViTModel](/docs/transformers/v4.30.0/en/model_doc/mobilevit#transformers.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](https://huggingface.co/apple/mobilevit-small) architecture.

Configuration objects inherit from [PretrainedConfig](/docs/transformers/v4.30.0/en/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the documentation from [PretrainedConfig](/docs/transformers/v4.30.0/en/main_classes/configuration#transformers.PretrainedConfig) for more information.

Example:

```
>>> from transformers import MobileViTConfig, MobileViTModel

>>> 
>>> configuration = MobileViTConfig()

>>> 
>>> model = MobileViTModel(configuration)

>>> 
>>> configuration = model.config```

## [](#transformers.MobileViTFeatureExtractor)MobileViTFeatureExtractor

Preprocess an image or a batch of images.

( outputstarget\_sizes: typing.List\[typing.Tuple\] = None ) → `List[torch.Tensor]`

Parameters

-   [](#transformers.MobileViTFeatureExtractor.post_process_semantic_segmentation.outputs)**outputs** ([MobileViTForSemanticSegmentation](/docs/transformers/v4.30.0/en/model_doc/mobilevit#transformers.MobileViTForSemanticSegmentation)) — Raw outputs of the model.
-   [](#transformers.MobileViTFeatureExtractor.post_process_semantic_segmentation.target_sizes)**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.

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](/docs/transformers/v4.30.0/en/model_doc/mobilevit#transformers.MobileViTForSemanticSegmentation) into semantic segmentation maps. Only supports PyTorch.

## [](#transformers.MobileViTImageProcessor)MobileViTImageProcessor

### class transformers.MobileViTImageProcessor

[](#transformers.MobileViTImageProcessor)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/mobilevit/image_processing_mobilevit.py#L51)

( do\_resize: bool = Truesize: typing.Dict\[str, int\] = Noneresample: Resampling = <Resampling.BILINEAR: 2>do\_rescale: bool = Truerescale\_factor: typing.Union\[int, float\] = 0.00392156862745098do\_center\_crop: bool = Truecrop\_size: typing.Dict\[str, int\] = Nonedo\_flip\_channel\_order: bool = True\*\*kwargs )

Constructs a MobileViT image processor.

#### preprocess

[](#transformers.MobileViTImageProcessor.preprocess)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/mobilevit/image_processing_mobilevit.py#L198)

( 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 = Nonesize: typing.Dict\[str, int\] = Noneresample: Resampling = Nonedo\_rescale: bool = Nonerescale\_factor: float = Nonedo\_center\_crop: bool = Nonecrop\_size: typing.Dict\[str, int\] = Nonedo\_flip\_channel\_order: bool = Nonereturn\_tensors: typing.Union\[str, transformers.utils.generic.TensorType, NoneType\] = Nonedata\_format: ChannelDimension = <ChannelDimension.FIRST: 'channels\_first'>\*\*kwargs )

Preprocess an image or batch of images.

#### post\_process\_semantic\_segmentation

[](#transformers.MobileViTImageProcessor.post_process_semantic_segmentation)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/mobilevit/image_processing_mobilevit.py#L300)

( outputstarget\_sizes: typing.List\[typing.Tuple\] = None ) → `List[torch.Tensor]`

Parameters

-   [](#transformers.MobileViTImageProcessor.post_process_semantic_segmentation.outputs)**outputs** ([MobileViTForSemanticSegmentation](/docs/transformers/v4.30.0/en/model_doc/mobilevit#transformers.MobileViTForSemanticSegmentation)) — Raw outputs of the model.
-   [](#transformers.MobileViTImageProcessor.post_process_semantic_segmentation.target_sizes)**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](/docs/transformers/v4.30.0/en/model_doc/mobilevit#transformers.MobileViTForSemanticSegmentation) into semantic segmentation maps. Only supports PyTorch.

## [](#transformers.MobileViTModel)MobileViTModel

### class transformers.MobileViTModel

[](#transformers.MobileViTModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/mobilevit/modeling_mobilevit.py#L708)

( config: MobileViTConfigexpand\_output: bool = True )

Parameters

-   [](#transformers.MobileViTModel.config)**config** ([MobileViTConfig](/docs/transformers/v4.30.0/en/model_doc/mobilevit#transformers.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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.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](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

#### forward

[](#transformers.MobileViTModel.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/mobilevit/modeling_mobilevit.py#L745)

( pixel\_values: typing.Optional\[torch.Tensor\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → `transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention` or `tuple(torch.FloatTensor)`

The [MobileViTModel](/docs/transformers/v4.30.0/en/model_doc/mobilevit#transformers.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:

```
>>> 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]```

## [](#transformers.MobileViTForImageClassification)MobileViTForImageClassification

### class transformers.MobileViTForImageClassification

[](#transformers.MobileViTForImageClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/mobilevit/modeling_mobilevit.py#L802)

( config: MobileViTConfig )

Parameters

-   [](#transformers.MobileViTForImageClassification.config)**config** ([MobileViTConfig](/docs/transformers/v4.30.0/en/model_doc/mobilevit#transformers.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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.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](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

The [MobileViTForImageClassification](/docs/transformers/v4.30.0/en/model_doc/mobilevit#transformers.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:

```
>>> 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

>>> 
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
tabby, tabby cat```

## [](#transformers.MobileViTForSemanticSegmentation)MobileViTForSemanticSegmentation

### class transformers.MobileViTForSemanticSegmentation

[](#transformers.MobileViTForSemanticSegmentation)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/mobilevit/modeling_mobilevit.py#L997)

( config: MobileViTConfig )

Parameters

-   [](#transformers.MobileViTForSemanticSegmentation.config)**config** ([MobileViTConfig](/docs/transformers/v4.30.0/en/model_doc/mobilevit#transformers.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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.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](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

The [MobileViTForSemanticSegmentation](/docs/transformers/v4.30.0/en/model_doc/mobilevit#transformers.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:

```
>>> from transformers import AutoImageProcessor, MobileViTForSemanticSegmentation
>>> 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 = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-small")

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

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

>>> 
>>> logits = outputs.logits```

## [](#transformers.TFMobileViTModel)TFMobileViTModel

### class transformers.TFMobileViTModel

[](#transformers.TFMobileViTModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/mobilevit/modeling_tf_mobilevit.py#L800)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFMobileViTModel.config)**config** ([MobileViTConfig](/docs/transformers/v4.30.0/en/model_doc/mobilevit#transformers.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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.TFPreTrainedModel.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](/docs/transformers/v4.30.0/en/main_classes/model#transformers.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](https://www.tensorflow.org/api_docs/python/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](https://keras.io/guides/making_new_layers_and_models_via_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!

The [TFMobileViTModel](/docs/transformers/v4.30.0/en/model_doc/mobilevit#transformers.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:

```
>>> 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]```

## [](#transformers.TFMobileViTForImageClassification)TFMobileViTForImageClassification

### class transformers.TFMobileViTForImageClassification

[](#transformers.TFMobileViTForImageClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/mobilevit/modeling_tf_mobilevit.py#L835)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFMobileViTForImageClassification.config)**config** ([MobileViTConfig](/docs/transformers/v4.30.0/en/model_doc/mobilevit#transformers.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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.TFPreTrainedModel.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](/docs/transformers/v4.30.0/en/main_classes/model#transformers.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](https://www.tensorflow.org/api_docs/python/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](https://keras.io/guides/making_new_layers_and_models_via_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

[](#transformers.TFMobileViTForImageClassification.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/mobilevit/modeling_tf_mobilevit.py#L848)

( pixel\_values: tf.Tensor | None = Noneoutput\_hidden\_states: Optional\[bool\] = Nonelabels: tf.Tensor | None = Nonereturn\_dict: Optional\[bool\] = Nonetraining: Optional\[bool\] = False ) → `transformers.modeling_tf_outputs.TFImageClassifierOutputWithNoAttention` or `tuple(tf.Tensor)`

The [TFMobileViTForImageClassification](/docs/transformers/v4.30.0/en/model_doc/mobilevit#transformers.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:

```
>>> 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

>>> 
>>> predicted_label = int(tf.math.argmax(logits, axis=-1))
>>> print(model.config.id2label[predicted_label])
tabby, tabby cat```

## [](#transformers.TFMobileViTForSemanticSegmentation)TFMobileViTForSemanticSegmentation

### class transformers.TFMobileViTForSemanticSegmentation

[](#transformers.TFMobileViTForSemanticSegmentation)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/mobilevit/modeling_tf_mobilevit.py#L1011)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFMobileViTForSemanticSegmentation.config)**config** ([MobileViTConfig](/docs/transformers/v4.30.0/en/model_doc/mobilevit#transformers.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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.TFPreTrainedModel.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](/docs/transformers/v4.30.0/en/main_classes/model#transformers.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](https://www.tensorflow.org/api_docs/python/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](https://keras.io/guides/making_new_layers_and_models_via_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

[](#transformers.TFMobileViTForSemanticSegmentation.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/mobilevit/modeling_tf_mobilevit.py#L1039)

( pixel\_values: tf.Tensor | None = Nonelabels: tf.Tensor | None = Noneoutput\_hidden\_states: Optional\[bool\] = Nonereturn\_dict: Optional\[bool\] = Nonetraining: bool = False ) → `transformers.modeling_tf_outputs.TFSemanticSegmenterOutputWithNoAttention` or `tuple(tf.Tensor)`

The [TFMobileViTForSemanticSegmentation](/docs/transformers/v4.30.0/en/model_doc/mobilevit#transformers.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:

```
>>> 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 = outputs.logits```