---
library_name: keras-hub
---
### Model Overview
# Model Summary

Xception introduces a novel CNN architecture that reinterprets Inception modules as a step toward depthwise separable convolutions — operations consisting of a depthwise convolution followed by a pointwise convolution. By replacing Inception modules with these more efficient separable convolutions, Xception achieves superior performance, slightly surpassing Inception V3 on the ImageNet dataset and significantly outperforming it on a larger dataset with 350 million images and 17,000 classes. These improvements are achieved without increasing the number of model parameters, indicating a more effective utilization of the network's capacity.

 Weights are released under the [MIT License](https://www.mit.edu/~amini/LICENSE.md) . Keras model code is released under the [Apache 2 License](https://github.com/keras-team/keras-hub/blob/master/LICENSE).

## Links

* [Xception Quickstart Notebook](https://www.kaggle.com/code/laxmareddypatlolla/xception-quick-start-notebook)
* [Xception API Documentation](coming soon)
* [Xception Model Card](https://arxiv.org/abs/1610.02357)
* [KerasHub Beginner Guide](https://keras.io/guides/keras_hub/getting_started/)
* [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/)

## Installation

Keras and KerasHub can be installed with:

```
pip install -U -q keras-hub
pip install -U -q keras
```

Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the [Keras Getting Started](https://keras.io/getting_started/) page.

## Presets

The following model checkpoints are provided by the Keras team. Full code examples for each are available below.

| Preset name                            | Parameters | Description                                                                                                  |
|---------------------------------------|------------|--------------------------------------------------------------------------------------------------------------|
| xception_41_imagenet |  23 M | 41-layer Xception model with 23 million parameters trained on ImageNet |

## Example Usage
```python

# Pretrained Xception backbone.
model = keras_hub.models.XceptionBackbone.from_preset("xception_41_imagenet")
input_data = np.random.uniform(0, 1, size=(2, 299, 299, 3))
model(input_data)

# Randomly initialized Xception backbone with a custom config.
model = keras_hub.models.XceptionBackbone(
    stackwise_conv_filters=[[32, 64], [64, 128], [256, 256]],
    stackwise_pooling=[True, True, False],
)
model(input_data)

# Use Xception for image classification
model = keras_hub.models.ImageClassifier.from_preset("xception_41_imagenet")


```

## Example Usage with Hugging Face URI

```python

# Pretrained Xception backbone.
model = keras_hub.models.XceptionBackbone.from_preset("hf://keras/xception_41_imagenet")
input_data = np.random.uniform(0, 1, size=(2, 299, 299, 3))
model(input_data)

# Randomly initialized Xception backbone with a custom config.
model = keras_hub.models.XceptionBackbone(
    stackwise_conv_filters=[[32, 64], [64, 128], [256, 256]],
    stackwise_pooling=[True, True, False],
)
model(input_data)

# Use Xception for image classification
model = keras_hub.models.ImageClassifier.from_preset("hf://keras/xception_41_imagenet")


```