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Instantiates the MobileNetV2 architecture.

Source: R/applications.R
application_mobilenet_v2.Rd

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.

Usage

application_mobilenet_v2(
  input_shape = NULL,
  alpha = 1,
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  pooling = NULL,
  classes = 1000L,
  classifier_activation = "softmax",
  name = NULL
)

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 NULL. 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 NULL (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 keras_input()) to use as image input for the model. input_tensor is useful for sharing inputs between multiple different networks. Defaults to NULL.

pooling

Optional pooling mode for feature extraction when include_top is FALSE.

  • NULL (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=NULL to return the logits of the "top" layer. When loading pretrained weights, classifier_activation can only be NULL or "softmax".

name

The name of the model (string).

Value

A model instance.

Reference

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 application_preprocess_inputs() on your inputs before passing them to the model. application_preprocess_inputs() will scale input pixels between -1 and 1.

See also