Transformers documentation

MobileNet V1

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MobileNet V1

Overview

The MobileNet model was proposed in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.

The abstract from the paper is the following:

We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.

Tips:

• The checkpoints are named mobilenet_v1_depth_size, for example mobilenet_v1_1.0_224, where 1.0 is the depth multiplier (sometimes also referred to as “alpha” or the width multiplier) and 224 is the resolution of the input images the model was trained on.

• Even though the checkpoint is trained on images of specific size, the model will work on images of any size. The smallest supported image size is 32x32.

• One can use MobileNetV1ImageProcessor to prepare images for the model.

• 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). However, the model predicts 1001 classes: the 1000 classes from ImageNet plus an extra “background” class (index 0).

• The original TensorFlow checkpoints use different padding rules than PyTorch, requiring the model to determine the padding amount at inference time, since this depends on the input image size. To use native PyTorch padding behavior, create a MobileNetV1Config with tf_padding = False.

Unsupported features:

• The MobileNetV1Model outputs a globally pooled version of the last hidden state. In the original model it is possible to use a 7x7 average pooling layer with stride 2 instead of global pooling. For larger inputs, this gives a pooled output that is larger than 1x1 pixel. The HuggingFace implementation does not support this.

• It is currently not possible to specify an output_stride. For smaller output strides, the original model invokes dilated convolution to prevent the spatial resolution from being reduced further. The output stride of the HuggingFace model is always 32.

• The original TensorFlow checkpoints include quantized models. We do not support these models as they include additional “FakeQuantization” operations to unquantize the weights.

• It’s common to extract the output from the pointwise layers at indices 5, 11, 12, 13 for downstream purposes. Using output_hidden_states=True returns the output from all intermediate layers. There is currently no way to limit this to specific layers.

This model was contributed by matthijs. 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 MobileNetV1.

Image Classification

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.

MobileNetV1Config

class transformers.MobileNetV1Config

< >

( num_channels = 3 image_size = 224 depth_multiplier = 1.0 min_depth = 8 hidden_act = 'relu6' tf_padding = True classifier_dropout_prob = 0.999 initializer_range = 0.02 layer_norm_eps = 0.001 **kwargs )

Parameters

• num_channels (int, optional, defaults to 3) — The number of input channels.
• image_size (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. Default is 1.0, which starts the network with 32 channels. This is sometimes also called “alpha” or “width multiplier”.
• min_depth (int, optional, defaults to 8) — All layers will have at least this many channels.
• hidden_act (str or function, optional, defaults to "relu6") — The non-linear activation function (function or string) in the Transformer encoder and convolution layers.
• tf_padding (bool, optional, defaults to True) — Whether to use TensorFlow padding rules on the convolution layers.
• classifier_dropout_prob (float, optional, defaults to 0.999) — 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 0.001) — The epsilon used by the layer normalization layers.

This is the configuration class to store the configuration of a MobileNetV1Model. It is used to instantiate a MobileNetV1 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 MobileNetV1 google/mobilenet_v1_1.0_224 architecture.

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 MobileNetV1Config, MobileNetV1Model

>>> # Initializing a "mobilenet_v1_1.0_224" style configuration
>>> configuration = MobileNetV1Config()

>>> # Initializing a model from the "mobilenet_v1_1.0_224" style configuration
>>> model = MobileNetV1Model(configuration)

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

MobileNetV1FeatureExtractor

class transformers.MobileNetV1FeatureExtractor

< >

( *args **kwargs )

preprocess

< >

( 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: typing.Optional[bool] = None size: typing.Dict[str, int] = None resample: Resampling = None do_center_crop: bool = None crop_size: typing.Dict[str, int] = None do_rescale: typing.Optional[bool] = None rescale_factor: typing.Optional[float] = None do_normalize: typing.Optional[bool] = None image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None return_tensors: typing.Union[transformers.utils.generic.TensorType, str, NoneType] = None data_format: typing.Union[str, transformers.image_utils.ChannelDimension] = <ChannelDimension.FIRST: 'channels_first'> **kwargs )

Parameters

• images (ImageInput) — Image to preprocess.
• do_resize (bool, optional, defaults to self.do_resize) — Whether to resize the image.
• size (Dict[str, int], optional, defaults to self.size) — Size of the image after resizing. Shortest edge of the image is resized to size[“shortest_edge”], with the longest edge resized to keep the input aspect ratio.
• resample (PILImageResampling filter, optional, defaults to self.resample) — PILImageResampling filter to use if resizing the image e.g. PILImageResampling.BILINEAR. Only has an effect if do_resize is set to True.
• do_center_crop (bool, optional, defaults to self.do_center_crop) — Whether to center crop the image.
• crop_size (Dict[str, int], optional, defaults to self.crop_size) — Size of the center crop. Only has an effect if do_center_crop is set to True.
• do_rescale (bool, optional, defaults to self.do_rescale) — Whether to rescale the image values between [0 - 1].
• rescale_factor (float, optional, defaults to self.rescale_factor) — Rescale factor to rescale the image by if do_rescale is set to True.
• do_normalize (bool, optional, defaults to self.do_normalize) — Whether to normalize the image.
• image_mean (float or List[float], optional, defaults to self.image_mean) — Image mean to use if do_normalize is set to True.
• image_std (float or List[float], optional, defaults to self.image_std) — Image standard deviation to use if do_normalize is set to True.
• 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:
• "channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
• "channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
• Unset: Use the channel dimension format of the input image.

Preprocess an image or batch of images.

MobileNetV1ImageProcessor

class transformers.MobileNetV1ImageProcessor

< >

( do_resize: bool = True size: typing.Union[typing.Dict[str, int], NoneType] = None resample: Resampling = <Resampling.BILINEAR: 2> do_center_crop: bool = True crop_size: typing.Dict[str, int] = None do_rescale: bool = True rescale_factor: typing.Union[int, float] = 0.00392156862745098 do_normalize: bool = True image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None **kwargs )

Parameters

• do_resize (bool, optional, defaults to True) — Whether to resize the image’s (height, width) dimensions to the specified size. Can be overridden by do_resize in the preprocess method.
• size (Dict[str, int] optional, defaults to {"shortest_edge" -- 256}): Size of the image after resizing. The shortest edge of the image is resized to size[“shortest_edge”], with the longest edge resized to keep the input aspect ratio. Can be overridden by size in the preprocess method.
• resample (PILImageResampling, optional, defaults to PILImageResampling.BILINEAR) — Resampling filter to use if resizing the image. Can be overridden by the resample parameter in the preprocess method.
• do_center_crop (bool, optional, defaults to True) — Whether to center crop the image. If the input size is smaller than crop_size along any edge, the image is padded with 0’s and then center cropped. Can be overridden by the do_center_crop parameter in the preprocess method.
• crop_size (Dict[str, int], optional, defaults to {"height" -- 224, "width": 224}): Desired output size when applying center-cropping. Only has an effect if do_center_crop is set to True. Can be overridden by the crop_size parameter in the preprocess method.
• do_rescale (bool, optional, defaults to True) — Whether to rescale the image by the specified scale rescale_factor. Can be overridden by the do_rescale parameter in the preprocess method.
• rescale_factor (int or float, optional, defaults to 1/255) — Scale factor to use if rescaling the image. Can be overridden by the rescale_factor parameter in the preprocess method. do_normalize — Whether to normalize the image. Can be overridden by the do_normalize parameter in the preprocess method.
• image_mean (float or List[float], optional, defaults to IMAGENET_STANDARD_MEAN) — Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the image_mean parameter in the preprocess method.
• image_std (float or List[float], optional, defaults to IMAGENET_STANDARD_STD) — Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the image_std parameter in the preprocess method.

Constructs a MobileNetV1 image processor.

preprocess

< >

( 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: typing.Optional[bool] = None size: typing.Dict[str, int] = None resample: Resampling = None do_center_crop: bool = None crop_size: typing.Dict[str, int] = None do_rescale: typing.Optional[bool] = None rescale_factor: typing.Optional[float] = None do_normalize: typing.Optional[bool] = None image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None return_tensors: typing.Union[transformers.utils.generic.TensorType, str, NoneType] = None data_format: typing.Union[str, transformers.image_utils.ChannelDimension] = <ChannelDimension.FIRST: 'channels_first'> **kwargs )

Parameters

• images (ImageInput) — Image to preprocess.
• do_resize (bool, optional, defaults to self.do_resize) — Whether to resize the image.
• size (Dict[str, int], optional, defaults to self.size) — Size of the image after resizing. Shortest edge of the image is resized to size[“shortest_edge”], with the longest edge resized to keep the input aspect ratio.
• resample (PILImageResampling filter, optional, defaults to self.resample) — PILImageResampling filter to use if resizing the image e.g. PILImageResampling.BILINEAR. Only has an effect if do_resize is set to True.
• do_center_crop (bool, optional, defaults to self.do_center_crop) — Whether to center crop the image.
• crop_size (Dict[str, int], optional, defaults to self.crop_size) — Size of the center crop. Only has an effect if do_center_crop is set to True.
• do_rescale (bool, optional, defaults to self.do_rescale) — Whether to rescale the image values between [0 - 1].
• rescale_factor (float, optional, defaults to self.rescale_factor) — Rescale factor to rescale the image by if do_rescale is set to True.
• do_normalize (bool, optional, defaults to self.do_normalize) — Whether to normalize the image.
• image_mean (float or List[float], optional, defaults to self.image_mean) — Image mean to use if do_normalize is set to True.
• image_std (float or List[float], optional, defaults to self.image_std) — Image standard deviation to use if do_normalize is set to True.
• 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:
• "channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
• "channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
• Unset: Use the channel dimension format of the input image.

Preprocess an image or batch of images.

MobileNetV1Model

class transformers.MobileNetV1Model

< >

( config: MobileNetV1Config add_pooling_layer: bool = True )

Parameters

• config (MobileNetV1Config) — 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 MobileNetV1 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

< >

( 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 MobileNetV1ImageProcessor.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 (MobileNetV1Config) 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 MobileNetV1Model 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, MobileNetV1Model
>>> import torch

>>> image = dataset["test"]["image"][0]

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

...     outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 1024, 7, 7]

MobileNetV1ForImageClassification

class transformers.MobileNetV1ForImageClassification

< >

( config: MobileNetV1Config )

Parameters

• config (MobileNetV1Config) — 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.

MobileNetV1 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

< >

( 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 MobileNetV1ImageProcessor.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 (MobileNetV1Config) 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 MobileNetV1ForImageClassification 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, MobileNetV1ForImageClassification
>>> import torch

>>> image = dataset["test"]["image"][0]

tabby, tabby cat