# tf.keras.applications.resnet.ResNet101

<br />

|---|
| [![](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/resnet.py#L536-L568) |

Instantiates the ResNet101 architecture.

#### View aliases


**Main aliases**

[`tf.keras.applications.ResNet101`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet/ResNet101)
**Compat aliases for migration**

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

\`tf.compat.v1.keras.applications.ResNet101\`, \`tf.compat.v1.keras.applications.resnet.ResNet101\`

<br />

    tf.keras.applications.resnet.ResNet101(
        include_top=True,
        weights='imagenet',
        input_tensor=None,
        input_shape=None,
        pooling=None,
        classes=1000,
        **kwargs
    )

#### Reference:

- [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) (CVPR 2015)

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 ResNet, call [`tf.keras.applications.resnet.preprocess_input`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/applications/resnet50/preprocess_input) on your inputs before passing them to the model. [`resnet.preprocess_input`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/applications/resnet50/preprocess_input) will convert the input images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling.

<br />

<br />

<br />

| ## Args ||
|---|---|
| `include_top` | whether to include the fully-connected layer at the top of the network. |
| `weights` | one of `None` (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. |
| `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 (otherwise the input shape has to be `(224, 224, 3)` (with `'channels_last'` data format) or `(3, 224, 224)` (with `'channels_first'` data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. `(200, 200, 3)` would be one valid value. |
| `pooling` | Optional pooling mode for feature extraction when `include_top` is `False`. <br /> - `None` means that the output of the model will be the 4D tensor output of the last convolutional block. - `avg` means that global average pooling will be applied to the output of the last convolutional block, 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. |
| `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. When loading pretrained weights, `classifier_activation` can only be `None` or `"softmax"`. |

<br />

<br />

<br />

| ## Returns ||
|---|---|
| A Keras model instance. ||

<br />