MobileNet, MobileNetV2, and MobileNetV3

[source]

MobileNet function

tf_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",
    **kwargs
)

Instantiates the MobileNet architecture.

Reference

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

This function returns a TF-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 TF-Keras Application expects a specific kind of input preprocessing. For MobileNet, call tf.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 TF-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".
• **kwargs: For backwards compatibility only.

Returns

A keras.Model instance.

[source]

MobileNetV2 function

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

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 TF-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 TF-Keras Application expects a specific kind of input preprocessing. For MobileNetV2, call tf.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, 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 (224, 224, 3). 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: Float, larger than zero, controls the width of the network. This is known as the width multiplier in the MobileNetV2 paper, but the name is kept for consistency with applications.MobileNetV1 model in TF-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 = 1.0, default number of filters from the paper are used at each layer.
• 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 TF-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: Optional integer number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified.
• 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".
• **kwargs: For backwards compatibility only.

Returns

A keras.Model instance.

[source]

MobileNetV3Small function

tf_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,
)

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 TF-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 tf.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 (224, 224, 3). 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 TF-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 TF-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.

Call arguments

• inputs: A floating point numpy.array or a tf.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 keras.Model instance.

[source]

MobileNetV3Large function

tf_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,
)

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 TF-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 tf.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 (224, 224, 3). 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 TF-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 TF-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.

Call arguments

• inputs: A floating point numpy.array or a tf.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 keras.Model instance.