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Instantiates the EfficientNetB5 architecture.

Source: R/applications.R
application_efficientnet_b5.Rd

Instantiates the EfficientNetB5 architecture.

Usage

application_efficientnet_b5(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000L,
  classifier_activation = "softmax",
  name = "efficientnetb5",
  ...
)

Arguments

include_top

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_shape

Optional shape tuple, only to be specified if include_top is FALSE. It should have exactly 3 inputs channels.

pooling

Optional pooling mode for feature extraction when include_top is FALSE. Defaults to NULL.

  • NULL means that the output of the model will be the 4D tensor output of the last convolutional layer.

  • avg means that global average pooling will be applied to the output of the last convolutional layer, 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. 1000 is how many ImageNet classes there are. 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. Defaults to 'softmax'. When loading pretrained weights, classifier_activation can only be NULL or "softmax".

name

The name of the model (string).

...

For forward/backward compatability.

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 EfficientNet, input preprocessing is included as part of the model (as a Rescaling layer), and thus application_preprocess_inputs() is actually a pass-through function. EfficientNet models expect their inputs to be float tensors of pixels with values in the [0-255] range.

See also