| Architecture | Convolution, 1x1 Convolution, Dense Connections, Depthwise Separable Convolution, ReLU, Global Average Pooling, Max Pooling, Softmax, Residual Connection |
|---|---|
| ID | gluon_xception65 |
| SHOW MORE |
Xception is a convolutional neural network architecture that relies solely on depthwise separable convolution layers. The weights from this model were ported from Gluon.
To load a pretrained model:
import timm
m = timm.create_model('gluon_xception65', pretrained=True)
m.eval()
Replace the model name with the variant you want to use, e.g. gluon_xception65. 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.
@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
| BENCHMARK | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK |
|---|---|---|---|---|
| ImageNet | gluon_xception65 | Top 1 Accuracy | 79.7% | # 109 |
| Top 5 Accuracy | 94.87% | # 109 |
