# tf.keras.applications.regnet.RegNetX006

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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/regnet.py#L1080-L1109) |

Instantiates the RegNetX006 architecture.

#### View aliases


**Main aliases**

[`tf.keras.applications.RegNetX006`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/regnet/RegNetX006)
**Compat aliases for migration**

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

\`tf.compat.v1.keras.applications.RegNetX006\`, \`tf.compat.v1.keras.applications.regnet.RegNetX006\`

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    tf.keras.applications.regnet.RegNetX006(
        model_name='regnetx006',
        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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| ## Reference ||
|---|---|
| <br /> - [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) (CVPR 2020) ||

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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/).

> [!NOTE]
> **Note:** Each Keras Application expects a specific kind of input preprocessing. For Regnets, preprocessing is included in the model using a `Rescaling` layer. RegNet models expect their inputs to be float or uint8 tensors of pixels with values in the \[0-255\] range.

The naming of models is as follows: `RegNet<block_type><flops>` where
`block_type` is one of `(X, Y)` and `flops` signifies hundred million
floating point operations. For example RegNetY064 corresponds to RegNet with
Y block and 6.4 giga flops (64 hundred million flops).

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

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