MobileNet, MobileNetV2, and MobileNetV3

[source]

MobileNet function

keras.applications.MobileNet(
    input_shape=None,
    alpha=1.0,
    depth_multiplier=1,
    dropout=0.001,
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
    name=None,
)

Instantiates the MobileNet architecture.

Reference

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.

For image classification use cases, see this page for detailed examples.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Note: each Keras Application expects a specific kind of input preprocessing. For MobileNet, call keras.applications.mobilenet.preprocess_input on your inputs before passing them to the model. mobilenet.preprocess_input will scale input pixels between -1 and 1.

Arguments

• input_shape: Optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (224, 224, 3) (with "channels_last" data format) or (3, 224, 224) (with "channels_first" data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (200, 200, 3) would be one valid value. Defaults to None. input_shape will be ignored if the input_tensor is provided.
• alpha: Controls the width of the network. This is known as the width multiplier in the MobileNet paper.
  • If alpha < 1.0, proportionally decreases the number of filters in each layer.
  • If alpha > 1.0, proportionally increases the number of filters in each layer.
  • If alpha == 1, default number of filters from the paper are used at each layer. Defaults to 1.0.
• depth_multiplier: Depth multiplier for depthwise convolution. This is called the resolution multiplier in the MobileNet paper. Defaults to 1.0.
• dropout: Dropout rate. Defaults to 0.001.
• include_top: Boolean, whether to include the fully-connected layer at the top of the network. Defaults to True.
• weights: One of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to "imagenet".
• input_tensor: Optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model. input_tensor is useful for sharing inputs between multiple different networks. Defaults to None.
• pooling: Optional pooling mode for feature extraction when include_top is False.
  • None (default) means that the output of the model will be the 4D tensor output of the last convolutional block.
  • avg means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor.
  • max means that global max pooling will be applied.
• classes: Optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified. Defaults to 1000.
• classifier_activation: A str or callable. The activation function to use on the "top" layer. Ignored unless include_top=True. Set classifier_activation=None to return the logits of the "top" layer. When loading pretrained weights, classifier_activation can only be None or "softmax".
• name: String, the name of the model.

Returns

A model instance.

[source]

MobileNetV2 function

keras.applications.MobileNetV2(
    input_shape=None,
    alpha=1.0,
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
    name=None,
)

Instantiates the MobileNetV2 architecture.

MobileNetV2 is very similar to the original MobileNet, except that it uses inverted residual blocks with bottlenecking features. It has a drastically lower parameter count than the original MobileNet. MobileNets support any input size greater than 32 x 32, with larger image sizes offering better performance.

Reference

• MobileNetV2: Inverted Residuals and Linear Bottlenecks (CVPR 2018)

This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.

For image classification use cases, see this page for detailed examples.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Note: each Keras Application expects a specific kind of input preprocessing. For MobileNetV2, call keras.applications.mobilenet_v2.preprocess_input on your inputs before passing them to the model. mobilenet_v2.preprocess_input will scale input pixels between -1 and 1.

Arguments

• input_shape: Optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (224, 224, 3) (with "channels_last" data format) or (3, 224, 224) (with "channels_first" data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (200, 200, 3) would be one valid value. Defaults to None. input_shape will be ignored if the input_tensor is provided.
• alpha: Controls the width of the network. This is known as the width multiplier in the MobileNet paper.
  • If alpha < 1.0, proportionally decreases the number of filters in each layer.
  • If alpha > 1.0, proportionally increases the number of filters in each layer.
  • If alpha == 1, default number of filters from the paper are used at each layer. Defaults to 1.0.
• include_top: Boolean, whether to include the fully-connected layer at the top of the network. Defaults to True.
• weights: One of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to "imagenet".
• input_tensor: Optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model. input_tensor is useful for sharing inputs between multiple different networks. Defaults to None.
• pooling: Optional pooling mode for feature extraction when include_top is False.
  • None (default) means that the output of the model will be the 4D tensor output of the last convolutional block.
  • avg means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor.
  • max means that global max pooling will be applied.
• classes: Optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified. Defaults to 1000.
• classifier_activation: A str or callable. The activation function to use on the "top" layer. Ignored unless include_top=True. Set classifier_activation=None to return the logits of the "top" layer. When loading pretrained weights, classifier_activation can only be None or "softmax".
• name: String, the name of the model.

Returns

A model instance.

[source]

MobileNetV3Small function

keras.applications.MobileNetV3Small(
    input_shape=None,
    alpha=1.0,
    minimalistic=False,
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    classes=1000,
    pooling=None,
    dropout_rate=0.2,
    classifier_activation="softmax",
    include_preprocessing=True,
    name="MobileNetV3Small",
)

Instantiates the MobileNetV3Small architecture.

Reference

• Searching for MobileNetV3 (ICCV 2019)

The following table describes the performance of MobileNets v3:

MACs stands for Multiply Adds

For image classification use cases, see this page for detailed examples.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Note: each Keras Application expects a specific kind of input preprocessing. For MobileNetV3, by default input preprocessing is included as a part of the model (as a Rescaling layer), and thus keras.applications.mobilenet_v3.preprocess_input is actually a pass-through function. In this use case, MobileNetV3 models expect their inputs to be float tensors of pixels with values in the [0-255] range. At the same time, preprocessing as a part of the model (i.e. Rescaling layer) can be disabled by setting include_preprocessing argument to False. With preprocessing disabled MobileNetV3 models expect their inputs to be float tensors of pixels with values in the [-1, 1] range.

Arguments

• input_shape: Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels. You can also omit this option if you would like to infer input_shape from an input_tensor. If you choose to include both input_tensor and input_shape then input_shape will be used if they match, if the shapes do not match then we will throw an error. E.g. (160, 160, 3) would be one valid value.
• alpha: controls the width of the network. This is known as the depth multiplier in the MobileNetV3 paper, but the name is kept for consistency with MobileNetV1 in Keras.
  • If alpha < 1.0, proportionally decreases the number of filters in each layer.
  • If alpha > 1.0, proportionally increases the number of filters in each layer.
  • If alpha == 1, default number of filters from the paper are used at each layer.
• minimalistic: In addition to large and small models this module also contains so-called minimalistic models, these models have the same per-layer dimensions characteristic as MobilenetV3 however, they don't utilize any of the advanced blocks (squeeze-and-excite units, hard-swish, and 5x5 convolutions). While these models are less efficient on CPU, they are much more performant on GPU/DSP.
• include_top: Boolean, whether to include the fully-connected layer at the top of the network. Defaults to True.
• weights: String, one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
• input_tensor: Optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
• pooling: String, optional pooling mode for feature extraction when include_top is False.
  • None means that the output of the model will be the 4D tensor output of the last convolutional block.
  • avg means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor.
  • max means that global max pooling will be applied.
• classes: Integer, optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified.
• dropout_rate: fraction of the input units to drop on the last layer.
• classifier_activation: A str or callable. The activation function to use on the "top" layer. Ignored unless include_top=True. Set classifier_activation=None to return the logits of the "top" layer. When loading pretrained weights, classifier_activation can only be None or "softmax".
• include_preprocessing: Boolean, whether to include the preprocessing layer (Rescaling) at the bottom of the network. Defaults to True.
• name: String, the name of the model.

Call arguments

• inputs: A floating point numpy.array or backend-native tensor, 4D with 3 color channels, with values in the range [0, 255] if include_preprocessing is True and in the range [-1, 1] otherwise.

Returns

A model instance.

[source]

MobileNetV3Large function

keras.applications.MobileNetV3Large(
    input_shape=None,
    alpha=1.0,
    minimalistic=False,
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    classes=1000,
    pooling=None,
    dropout_rate=0.2,
    classifier_activation="softmax",
    include_preprocessing=True,
    name="MobileNetV3Large",
)

Instantiates the MobileNetV3Large architecture.

Reference

• Searching for MobileNetV3 (ICCV 2019)

The following table describes the performance of MobileNets v3:

MACs stands for Multiply Adds

For image classification use cases, see this page for detailed examples.

For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

Note: each Keras Application expects a specific kind of input preprocessing. For MobileNetV3, by default input preprocessing is included as a part of the model (as a Rescaling layer), and thus keras.applications.mobilenet_v3.preprocess_input is actually a pass-through function. In this use case, MobileNetV3 models expect their inputs to be float tensors of pixels with values in the [0-255] range. At the same time, preprocessing as a part of the model (i.e. Rescaling layer) can be disabled by setting include_preprocessing argument to False. With preprocessing disabled MobileNetV3 models expect their inputs to be float tensors of pixels with values in the [-1, 1] range.

Arguments

• input_shape: Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels. You can also omit this option if you would like to infer input_shape from an input_tensor. If you choose to include both input_tensor and input_shape then input_shape will be used if they match, if the shapes do not match then we will throw an error. E.g. (160, 160, 3) would be one valid value.
• alpha: controls the width of the network. This is known as the depth multiplier in the MobileNetV3 paper, but the name is kept for consistency with MobileNetV1 in Keras.
  • If alpha < 1.0, proportionally decreases the number of filters in each layer.
  • If alpha > 1.0, proportionally increases the number of filters in each layer.
  • If alpha == 1, default number of filters from the paper are used at each layer.
• minimalistic: In addition to large and small models this module also contains so-called minimalistic models, these models have the same per-layer dimensions characteristic as MobilenetV3 however, they don't utilize any of the advanced blocks (squeeze-and-excite units, hard-swish, and 5x5 convolutions). While these models are less efficient on CPU, they are much more performant on GPU/DSP.
• include_top: Boolean, whether to include the fully-connected layer at the top of the network. Defaults to True.
• weights: String, one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
• input_tensor: Optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
• pooling: String, optional pooling mode for feature extraction when include_top is False.
  • None means that the output of the model will be the 4D tensor output of the last convolutional block.
  • avg means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor.
  • max means that global max pooling will be applied.
• classes: Integer, optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified.
• dropout_rate: fraction of the input units to drop on the last layer.
• classifier_activation: A str or callable. The activation function to use on the "top" layer. Ignored unless include_top=True. Set classifier_activation=None to return the logits of the "top" layer. When loading pretrained weights, classifier_activation can only be None or "softmax".
• include_preprocessing: Boolean, whether to include the preprocessing layer (Rescaling) at the bottom of the network. Defaults to True.
• name: String, the name of the model.

Call arguments

• inputs: A floating point numpy.array or backend-native tensor, 4D with 3 color channels, with values in the range [0, 255] if include_preprocessing is True and in the range [-1, 1] otherwise.

Returns

A model instance.