# tf.keras.applications.convnext.ConvNeXtSmall

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.13.1/keras/applications/convnext.py#L607-L637) |

Instantiates the ConvNeXtSmall architecture.

#### View aliases


**Main aliases**

[`tf.keras.applications.ConvNeXtSmall`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/convnext/ConvNeXtSmall)
**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

\`tf.compat.v1.keras.applications.ConvNeXtSmall\`, \`tf.compat.v1.keras.applications.convnext.ConvNeXtSmall\`

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    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'
    )

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| ## References ||
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| <br /> - [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) (CVPR 2022) ||

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For image classification use cases, see
[this page for detailed examples](https://keras.io/api/applications/#usage-examples-for-image-classification-models).
For transfer learning use cases, make sure to read the
[guide to transfer learning \& fine-tuning](https://keras.io/guides/transfer_learning/).

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](https://github.com/facebookresearch/ConvNeXt). To get a
sense of how these parameters were converted to Keras compatible parameters,
please refer to
[this repository](https://github.com/sayakpaul/keras-convnext-conversion).

> [!NOTE]
> **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.

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| ## Args ||
|---|---|
| `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()`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/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 None. <br /> - `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"`. |

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| ## Returns ||
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| A [`keras.Model`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/Model) instance. ||

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