| Architecture | Convolution, 1x1 Convolution, Dense Connections, Depthwise Separable Convolution, ReLU, Global Average Pooling, Max Pooling, Softmax, Residual Connection |
|---|---|
| ID | xception |
| SHOW MORE |
| Architecture | Convolution, 1x1 Convolution, Dense Connections, Depthwise Separable Convolution, ReLU, Global Average Pooling, Max Pooling, Softmax, Residual Connection |
|---|---|
| ID | xception41 |
| SHOW MORE |
| Architecture | Convolution, 1x1 Convolution, Dense Connections, Depthwise Separable Convolution, ReLU, Global Average Pooling, Max Pooling, Softmax, Residual Connection |
|---|---|
| ID | xception65 |
| SHOW MORE |
| Architecture | Convolution, 1x1 Convolution, Dense Connections, Depthwise Separable Convolution, ReLU, Global Average Pooling, Max Pooling, Softmax, Residual Connection |
|---|---|
| ID | xception71 |
| SHOW MORE |
Xception is a convolutional neural network architecture that relies solely on depthwise separable convolution layers.
To load a pretrained model:
import timm
m = timm.create_model('xception', pretrained=True)
m.eval()
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.
You can follow the timm recipe scripts for training a new model afresh.
@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}
}
Image Classification on ImageNet
| MODEL | TOP 1 ACCURACY | TOP 5 ACCURACY |
|---|---|---|
| xception71 | 79.88% | 94.93% |
| xception65 | 79.55% | 94.66% |
| xception | 79.05% | 94.4% |
| xception41 | 78.54% | 94.28% |
