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Instantiates the ConvNeXtLarge architecture.

Source: R/applications.R
application_convnext_large.Rd

Instantiates the ConvNeXtLarge architecture.

Usage

application_convnext_large(
  include_top = TRUE,
  include_preprocessing = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000L,
  classifier_activation = "softmax",
  name = "convnext_large"
)

Arguments

include_top

Whether to include the fully-connected layer at the top of the network. Defaults to TRUE.

include_preprocessing

Boolean, whether to include the preprocessing layer at the bottom of the network.

weights

One of NULL (random initialization), "imagenet" (pre-training on ImageNet-1k), 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 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).

Value

A model instance.

References

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.

The base, large, and xlarge models were first pre-trained on the ImageNet-21k dataset and then fine-tuned on the ImageNet-1k dataset. The pre-trained parameters of the models were assembled from the official repository. To get a sense of how these parameters were converted to Keras compatible parameters, please refer to this repository.

Note

Each Keras Application expects a specific kind of input preprocessing. For ConvNeXt, preprocessing is included in the model using a Normalization layer. ConvNeXt models expect their inputs to be float or uint8 tensors of pixels with values in the [0-255] range.

When calling the summary() method after instantiating a ConvNeXt model, prefer setting the expand_nested argument summary() to TRUE to better investigate the instantiated model.

See also