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60 lines
1.6 KiB
60 lines
1.6 KiB
# Summary
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**Xception** is a convolutional neural network architecture that relies solely on [depthwise separable convolution](https://paperswithcode.com/method/depthwise-separable-convolution) layers.
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The weights from this model were ported from [Gluon](https://cv.gluon.ai/model_zoo/classification.html).
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{% include 'code_snippets.md' %}
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## How do I train this model?
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You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
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## Citation
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```BibTeX
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@misc{chollet2017xception,
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title={Xception: Deep Learning with Depthwise Separable Convolutions},
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author={François Chollet},
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year={2017},
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eprint={1610.02357},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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<!--
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Models:
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- Name: gluon_xception65
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Metadata:
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FLOPs: 17594889728
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Training Data:
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- ImageNet
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Architecture:
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- 1x1 Convolution
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- Convolution
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- Dense Connections
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- Depthwise Separable Convolution
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Connection
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- Softmax
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File Size: 160551306
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Tasks:
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- Image Classification
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ID: gluon_xception65
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Crop Pct: '0.903'
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Image Size: '299'
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_xception.py#L241
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In Collection: Gloun Xception
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Collections:
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- Name: Gloun Xception
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Paper:
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title: 'Xception: Deep Learning with Depthwise Separable Convolutions'
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url: https://paperswithcode.com//paper/xception-deep-learning-with-depthwise
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type: model-index
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Type: model-index
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-->
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