25 lines
1.4 KiB
Markdown
25 lines
1.4 KiB
Markdown
# Squeeze-and-Excitation Networks
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## Introduction
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[BACKBONE]
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```latex
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@inproceedings{hu2018squeeze,
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title={Squeeze-and-excitation networks},
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author={Hu, Jie and Shen, Li and Sun, Gang},
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booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
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pages={7132--7141},
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year={2018}
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}
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```
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## Results and models
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### ImageNet
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| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
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|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:|
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| SE-ResNet-50 | 28.09 | 4.13 | 77.74 | 93.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet50/seresnet50_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth) | [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200708-657b3c36.log.json) |
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| SE-ResNet-101 | 49.33 | 7.86 | 78.26 | 94.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet101/seresnet101_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200804-ba5b51d4.pth) | [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200708-038a4d04.log.json) |
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