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164 lines
4.5 KiB
164 lines
4.5 KiB
# Xception
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**Xception** is a convolutional neural network architecture that relies solely on [depthwise separable convolution layers](https://paperswithcode.com/method/depthwise-separable-convolution).
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The weights from this model were ported from [Tensorflow/Models](https://github.com/tensorflow/models).
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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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@article{DBLP:journals/corr/ZagoruykoK16,
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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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Type: model-index
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Collections:
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- Name: 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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Models:
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- Name: xception
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In Collection: Xception
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Metadata:
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FLOPs: 10600506792
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Parameters: 22860000
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File Size: 91675053
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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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Tasks:
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- Image Classification
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Training Data:
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- ImageNet
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ID: xception
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Crop Pct: '0.897'
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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/xception.py#L229
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/xception-43020ad28.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 79.05%
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Top 5 Accuracy: 94.4%
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- Name: xception41
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In Collection: Xception
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Metadata:
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FLOPs: 11681983232
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Parameters: 26970000
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File Size: 108422028
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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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Tasks:
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- Image Classification
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Training Data:
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- ImageNet
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ID: xception41
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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/xception_aligned.py#L181
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_xception_41-e6439c97.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 78.54%
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Top 5 Accuracy: 94.28%
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- Name: xception65
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In Collection: Xception
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Metadata:
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FLOPs: 17585702144
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Parameters: 39920000
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File Size: 160536780
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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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Tasks:
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- Image Classification
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Training Data:
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- ImageNet
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ID: 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/xception_aligned.py#L200
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_xception_65-c9ae96e8.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 79.55%
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Top 5 Accuracy: 94.66%
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- Name: xception71
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In Collection: Xception
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Metadata:
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FLOPs: 22817346560
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Parameters: 42340000
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File Size: 170295556
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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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Tasks:
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- Image Classification
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Training Data:
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- ImageNet
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ID: xception71
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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/xception_aligned.py#L219
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_xception_71-8eec7df1.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 79.88%
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Top 5 Accuracy: 94.93%
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-->
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