application_efficientnet_v2b1: Instantiates the EfficientNetV2B1 architecture.
In keras3: R Interface to 'Keras'

application_efficientnet_v2b1

R Documentation

Instantiates the EfficientNetV2B1 architecture.

Description

Instantiates the EfficientNetV2B1 architecture.

Usage

application_efficientnet_v2b1(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000L,
  classifier_activation = "softmax",
  include_preprocessing = TRUE,
  name = "efficientnetv2-b1"
)

Arguments

`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_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. Defaults to 1000 (number of ImageNet classes).
`classifier_activation`	A string 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"`.
`include_preprocessing`	Boolean, whether to include the preprocessing layer at the bottom of the network.
`name`	The name of the model (string).

Value

A model instance.

Reference

EfficientNetV2: Smaller Models and Faster Training (ICML 2021)

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 EfficientNetV2, by default input preprocessing is included as a part of the model (as a Rescaling layer), and thus application_preprocess_inputs() is actually a pass-through function. In this use case, EfficientNetV2 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 EfficientNetV2 models expect their inputs to be float tensors of pixels with values in the ⁠[-1, 1]⁠ range.