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MobileNet V2

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This model was released on 2018-01-13 and added to Hugging Face Transformers on 2022-11-14.

PyTorch

MobileNet V2

MobileNet V2 improves performance on mobile devices with a more efficient architecture. It uses inverted residual blocks and linear bottlenecks to start with a smaller representation of the data, expands it for processing, and shrinks it again to reduce the number of computations. The model also removes non-linearities to maintain accuracy despite its simplified design. Like MobileNet V1, it uses depthwise separable convolutions for efficiency.

You can all the original MobileNet checkpoints under the Google organization.

Click on the MobileNet V2 models in the right sidebar for more examples of how to apply MobileNet to different vision tasks.

The examples below demonstrate how to classify an image with Pipeline or the AutoModel class.

Pipeline
AutoModel 
import torch
from transformers import pipeline

pipeline = pipeline(
    task="image-classification",
    model="google/mobilenet_v2_1.4_224",
    dtype=torch.float16,
    device=0
)
pipeline("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg")

Notes

  • Classification checkpoint names follow the pattern mobilenet_v2_{depth_multiplier}_{resolution}, like mobilenet_v2_1.4_224. 1.4 is the depth multiplier and 224 is the image resolution. Segmentation checkpoint names follow the pattern deeplabv3_mobilenet_v2_{depth_multiplier}_{resolution}.

  • While trained on images of a specific sizes, the model architecture works with images of different sizes (minimum 32x32). The MobileNetV2ImageProcessor handles the necessary preprocessing.

  • MobileNet is pretrained on ImageNet-1k, a dataset with 1000 classes. However, the model actually predicts 1001 classes. The additional class is an extra “background” class (index 0).

  • The segmentation models use a DeepLabV3+ head which is often pretrained on datasets like PASCAL VOC.

  • The original TensorFlow checkpoints determines the padding amount at inference because it depends on the input image size. To use the native PyTorch padding behavior, set tf_padding=False in MobileNetV2Config.

    from transformers import MobileNetV2Config

config = MobileNetV2Config.from_pretrained("google/mobilenet_v2_1.4_224", tf_padding=True)

  • The Transformers implementation does not support the following features.

    • Uses global average pooling instead of the optional 7x7 average pooling with stride 2. For larger inputs, this gives a pooled output that is larger than a 1x1 pixel.
    • output_hidden_states=True returns all intermediate hidden states. It is not possible to extract the output from specific layers for other downstream purposes.
    • Does not include the quantized models from the original checkpoints because they include “FakeQuantization” operations to unquantize the weights.
    • For segmentation models, the final convolution layer of the backbone is computed even though the DeepLabV3+ head doesn’t use it.

MobileNetV2Config

class transformers.MobileNetV2Config

< >

( output_hidden_states: bool | None = False return_dict: bool | None = True dtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = None chunk_size_feed_forward: int = 0 is_encoder_decoder: bool = False id2label: dict[int, str] | dict[str, str] | None = None label2id: dict[str, int] | dict[str, str] | None = None problem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = None tokenizer_class: str | transformers.tokenization_utils_base.PreTrainedTokenizerBase | None = None num_channels: int = 3 image_size: int | list[int] | tuple[int, int] = 224 depth_multiplier: float = 1.0 depth_divisible_by: int = 8 min_depth: int = 8 expand_ratio: float | int = 6.0 output_stride: int = 32 first_layer_is_expansion: bool = True finegrained_output: bool = True hidden_act: str = 'relu6' tf_padding: bool = True classifier_dropout_prob: float = 0.8 initializer_range: float = 0.02 layer_norm_eps: float = 0.001 semantic_loss_ignore_index: int = 255 )

Parameters

  • output_hidden_states (bool, optional, defaults to False) — Whether or not the model should return all hidden-states.
  • return_dict (bool, optional, defaults to True) — Whether to return a ModelOutput (dataclass) instead of a plain tuple.
  • dtype (Union[str, torch.dtype], optional) — The chunk size of all feed forward layers in the residual attention blocks. A chunk size of 0 means that the feed forward layer is not chunked. A chunk size of n means that the feed forward layer processes n < sequence_length embeddings at a time. For more information on feed forward chunking, see How does Feed Forward Chunking work?.
  • chunk_size_feed_forward (int, optional, defaults to 0) — The dtype of the weights. This attribute can be used to initialize the model to a non-default dtype (which is normally float32) and thus allow for optimal storage allocation. For example, if the saved model is float16, ideally we want to load it back using the minimal amount of memory needed to load float16 weights.
  • is_encoder_decoder (bool, optional, defaults to False) — Whether the model is used as an encoder/decoder or not.
  • id2label (Union[dict[int, str], dict[str, str]], optional) — A map from index (for instance prediction index, or target index) to label.
  • label2id (Union[dict[str, int], dict[str, str]], optional) — A map from label to index for the model.
  • problem_type (Literal[regression, single_label_classification, multi_label_classification], optional) — Problem type for XxxForSequenceClassification models. Can be one of "regression", "single_label_classification" or "multi_label_classification".
  • tokenizer_class (Union[str, ~tokenization_utils_base.PreTrainedTokenizerBase], optional) — The class name of model’s tokenizer.
  • num_channels (int, optional, defaults to 3) — The number of input channels.
  • image_size (Union[int, list[int], tuple[int, int]], optional, defaults to 224) — The size (resolution) of each image.
  • depth_multiplier (float, optional, defaults to 1.0) — Shrinks or expands the number of channels in each layer. This is sometimes also called “alpha” or “width multiplier”.
  • depth_divisible_by (int, optional, defaults to 8) — The number of channels in each layer will always be a multiple of this number.
  • min_depth (int, optional, defaults to 8) — All layers will have at least this many channels.
  • expand_ratio (float, optional, defaults to 6.0) — The number of output channels of the first layer in each block is input channels times expansion ratio.
  • output_stride (int, optional, defaults to 32) — The ratio between the spatial resolution of the input and output feature maps. By default the model reduces the input dimensions by a factor of 32. If output_stride is 8 or 16, the model uses dilated convolutions on the depthwise layers instead of regular convolutions, so that the feature maps never become more than 8x or 16x smaller than the input image.
  • first_layer_is_expansion (bool, optional, defaults to True) — True if the very first convolution layer is also the expansion layer for the first expansion block.
  • finegrained_output (bool, optional, defaults to True) — If true, the number of output channels in the final convolution layer will stay large (1280) even if depth_multiplier is less than 1.
  • hidden_act (str, optional, defaults to relu6) — The non-linear activation function (function or string) in the decoder. For example, "gelu", "relu", "silu", etc.
  • tf_padding (bool, optional, defaults to True) — Whether to use TensorFlow padding rules on the convolution layers.
  • classifier_dropout_prob (float, optional, defaults to 0.8) — The dropout ratio for classifier.
  • 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 0.001) — The epsilon used by the layer normalization layers.
  • 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 MobileNetV2Model. It is used to instantiate a Mobilenet V2 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 google/mobilenet_v2_1.0_224

Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Read the documentation from PreTrainedConfig for more information.

Example:

>>> from transformers import MobileNetV2Config, MobileNetV2Model

>>> # Initializing a "mobilenet_v2_1.0_224" style configuration
>>> configuration = MobileNetV2Config()

>>> # Initializing a model from the "mobilenet_v2_1.0_224" style configuration
>>> model = MobileNetV2Model(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

MobileNetV2ImageProcessor

class transformers.MobileNetV2ImageProcessor

< >

( **kwargs: typing_extensions.Unpack[transformers.models.mobilenet_v2.image_processing_mobilenet_v2.MobileNetV2ImageProcessorKwargs] )

Parameters

  • do_reduce_labels (bool, kwargs, optional, defaults to self.do_reduce_labels) — Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.
  • **kwargs (ImagesKwargs, optional) — Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

Constructs a MobileNetV2ImageProcessor image processor.

preprocess

< >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']] segmentation_maps: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], NoneType] = None **kwargs: typing_extensions.Unpack[transformers.models.mobilenet_v2.image_processing_mobilenet_v2.MobileNetV2ImageProcessorKwargs] ) → ~image_processing_base.BatchFeature

Parameters

  • images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]) — Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set do_rescale=False.
  • segmentation_maps (ImageInput, optional) — The segmentation maps to preprocess.
  • do_reduce_labels (bool, kwargs, optional, defaults to self.do_reduce_labels) — Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.
  • return_tensors (str or TensorType, optional) — Returns stacked tensors if set to 'pt', otherwise returns a list of tensors.
  • **kwargs (ImagesKwargs, optional) — Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

Returns

~image_processing_base.BatchFeature

  • data (dict) — Dictionary of lists/arrays/tensors returned by the call method (‘pixel_values’, etc.).
  • tensor_type (Union[None, str, TensorType], optional) — You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization.

post_process_semantic_segmentation

< >

( outputs target_sizes: list[tuple] | None = None )

Converts the output of MobileNetV2ForSemanticSegmentation into semantic segmentation maps.

MobileNetV2ImageProcessorPil

class transformers.MobileNetV2ImageProcessorPil

< >

( **kwargs: typing_extensions.Unpack[transformers.models.mobilenet_v2.image_processing_mobilenet_v2.MobileNetV2ImageProcessorKwargs] )

Parameters

  • do_reduce_labels (bool, kwargs, optional, defaults to self.do_reduce_labels) — Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.
  • **kwargs (ImagesKwargs, optional) — Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

Constructs a MobileNetV2ImageProcessor image processor.

preprocess

< >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']] segmentation_maps: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], NoneType] = None **kwargs: typing_extensions.Unpack[transformers.models.mobilenet_v2.image_processing_mobilenet_v2.MobileNetV2ImageProcessorKwargs] ) → ~image_processing_base.BatchFeature

Parameters

  • images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]) — Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set do_rescale=False.
  • segmentation_maps (ImageInput, optional) — The segmentation maps to preprocess.
  • do_reduce_labels (bool, kwargs, optional, defaults to self.do_reduce_labels) — Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.
  • return_tensors (str or TensorType, optional) — Returns stacked tensors if set to 'pt', otherwise returns a list of tensors.
  • **kwargs (ImagesKwargs, optional) — Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

Returns

~image_processing_base.BatchFeature

  • data (dict) — Dictionary of lists/arrays/tensors returned by the call method (‘pixel_values’, etc.).
  • tensor_type (Union[None, str, TensorType], optional) — You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization.

post_process_semantic_segmentation

< >

( outputs target_sizes: list[tuple] | None = None )

Converts the output of MobileNetV2ForSemanticSegmentation into semantic segmentation maps.

MobileNetV2Model

class transformers.MobileNetV2Model

< >

( config: MobileNetV2Config add_pooling_layer: bool = True )

Parameters

  • config (MobileNetV2Config) — 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.
  • add_pooling_layer (bool, optional, defaults to True) — Whether to add a pooling layer

The bare Mobilenet V2 Model outputting raw hidden-states without any specific head on top.

This model inherits from PreTrainedModel. 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 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

< >

( pixel_values: torch.Tensor | None = None output_hidden_states: bool | None = None return_dict: bool | None = None **kwargs ) → BaseModelOutputWithPoolingAndNoAttention or tuple(torch.FloatTensor)

Parameters

  • pixel_values (torch.Tensor of shape (batch_size, num_channels, image_size, image_size), optional) — The tensors corresponding to the input images. Pixel values can be obtained using MobileNetV2ImageProcessor. See MobileNetV2ImageProcessor.__call__() for details (processor_class uses MobileNetV2ImageProcessor for processing images).
  • 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

BaseModelOutputWithPoolingAndNoAttention or tuple(torch.FloatTensor)

A 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 (MobileNetV2Config) and inputs.

The MobileNetV2Model 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.

  • 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.

Example:

MobileNetV2ForImageClassification

class transformers.MobileNetV2ForImageClassification

< >

( config: MobileNetV2Config )

Parameters

  • config (MobileNetV2Config) — 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.

MobileNetV2 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 PreTrainedModel. 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 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

< >

( pixel_values: torch.Tensor | None = None output_hidden_states: bool | None = None labels: torch.Tensor | None = None return_dict: bool | None = None **kwargs ) → ImageClassifierOutputWithNoAttention or tuple(torch.FloatTensor)

Parameters

  • pixel_values (torch.Tensor of shape (batch_size, num_channels, image_size, image_size), optional) — The tensors corresponding to the input images. Pixel values can be obtained using MobileNetV2ImageProcessor. See MobileNetV2ImageProcessor.__call__() for details (processor_class uses MobileNetV2ImageProcessor for processing images).
  • 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.
  • 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).
  • return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

ImageClassifierOutputWithNoAttention or tuple(torch.FloatTensor)

A 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 (MobileNetV2Config) and inputs.

The MobileNetV2ForImageClassification 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.

  • 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.

Example:

>>> from transformers import AutoImageProcessor, MobileNetV2ForImageClassification
>>> import torch
>>> from datasets import load_dataset

>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]

>>> image_processor = AutoImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224")
>>> model = MobileNetV2ForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224")

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

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

>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
...

MobileNetV2ForSemanticSegmentation

class transformers.MobileNetV2ForSemanticSegmentation

< >

( config: MobileNetV2Config )

Parameters

  • config (MobileNetV2Config) — 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.

MobileNetV2 model with a semantic segmentation head on top, e.g. for Pascal VOC.

This model inherits from PreTrainedModel. 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 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

< >

( pixel_values: torch.Tensor | None = None labels: torch.Tensor | None = None output_hidden_states: bool | None = None return_dict: bool | None = None **kwargs ) → SemanticSegmenterOutput or tuple(torch.FloatTensor)

Parameters

  • pixel_values (torch.Tensor of shape (batch_size, num_channels, image_size, image_size), optional) — The tensors corresponding to the input images. Pixel values can be obtained using MobileNetV2ImageProcessor. See MobileNetV2ImageProcessor.__call__() for details (processor_class uses MobileNetV2ImageProcessor for processing images).
  • 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).
  • 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

SemanticSegmenterOutput or tuple(torch.FloatTensor)

A 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 (MobileNetV2Config) and inputs.

The MobileNetV2ForSemanticSegmentation 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.

  • 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.

Examples:

>>> from transformers import AutoImageProcessor, MobileNetV2ForSemanticSegmentation
>>> from PIL import Image
>>> import httpx
>>> from io import BytesIO

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> with httpx.stream("GET", url) as response:
...     image = Image.open(BytesIO(response.read()))

>>> image_processor = AutoImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513")
>>> model = MobileNetV2ForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513")

>>> 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
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