R: Instantiates the MobileNetV2 architecture.

application_mobilenet_v2 {keras3}

R Documentation

Instantiates the MobileNetV2 architecture.

Description

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

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