tf.keras.applications.convnext.ConvNeXtSmall

Instantiates the ConvNeXtSmall architecture.

tf.keras.applications.convnext.ConvNeXtSmall(
    model_name='convnext_small',
    include_top=True,
    include_preprocessing=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax'
)

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.

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.

include_top Whether to include the fully-connected layer at the top of the network. Defaults to True.
weights One of None (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 layers.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 None.

  • None 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=None to return the logits of the "top" layer. Defaults to "softmax". When loading pretrained weights, classifier_activation can only be None or "softmax".

A keras.Model instance.