pytorch-image-models/docs/models/xception.md

# Summary

**Xception** is a convolutional neural network architecture that relies solely on [depthwise separable convolution layers](https://paperswithcode.com/method/depthwise-separable-convolution).

## How do I use this model on an image?
To load a pretrained model:

```python
import timm
model = timm.create_model('xception', pretrained=True)
model.eval()
```

To load and preprocess the image:
```python 
import urllib
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

config = resolve_data_config({}, model=model)
transform = create_transform(**config)

url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
urllib.request.urlretrieve(url, filename)
img = Image.open(filename).convert('RGB')
tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```

To get the model predictions:
```python
import torch
with torch.no_grad():
    out = model(tensor)
probabilities = torch.nn.functional.softmax(out[0], dim=0)
print(probabilities.shape)
# prints: torch.Size([1000])
```

To get the top-5 predictions class names:
```python
# Get imagenet class mappings
url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
urllib.request.urlretrieve(url, filename) 
with open("imagenet_classes.txt", "r") as f:
    categories = [s.strip() for s in f.readlines()]

# Print top categories per image
top5_prob, top5_catid = torch.topk(probabilities, 5)
for i in range(top5_prob.size(0)):
    print(categories[top5_catid[i]], top5_prob[i].item())
# prints class names and probabilities like:
# [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```

Replace the model name with the variant you want to use, e.g. `xception`. You can find the IDs in the model summaries at the top of this page.

To extract image features with this model, follow the [timm feature extraction examples](https://rwightman.github.io/pytorch-image-models/feature_extraction/), just change the name of the model you want to use.

## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```python
model = timm.create_model('xception', pretrained=True).reset_classifier(NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.

## How do I train this model?

You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.

## Citation

```BibTeX
@article{DBLP:journals/corr/ZagoruykoK16,
@misc{chollet2017xception,
      title={Xception: Deep Learning with Depthwise Separable Convolutions}, 
      author={François Chollet},
      year={2017},
      eprint={1610.02357},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```

<!--
Models:
- Name: xception
  Metadata:
    FLOPs: 10600506792
    Training Data:
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    Architecture:
    - 1x1 Convolution
    - Convolution
    - Dense Connections
    - Depthwise Separable Convolution
    - Global Average Pooling
    - Max Pooling
    - ReLU
    - Residual Connection
    - Softmax
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  In Collection: Xception
- Name: xception41
  Metadata:
    FLOPs: 11681983232
    Training Data:
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  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/xception_aligned.py#L181
  In Collection: Xception
- Name: xception65
  Metadata:
    FLOPs: 17585702144
    Training Data:
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    Architecture:
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  In Collection: Xception
- Name: xception71
  Metadata:
    FLOPs: 22817346560
    Training Data:
    - ImageNet
    Architecture:
    - 1x1 Convolution
    - Convolution
    - Dense Connections
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    - Max Pooling
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    - Residual Connection
    - Softmax
    File Size: 170295556
    Tasks:
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    ID: xception71
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  In Collection: Xception
Collections:
- Name: Xception
  Paper:
    title: 'Xception: Deep Learning with Depthwise Separable Convolutions'
    url: https://paperswithcode.com//paper/xception-deep-learning-with-depthwise
  type: model-index
Type: model-index
-->