Training Techniques | SGD with Momentum, Weight Decay, Label Smoothing |
---|---|
Architecture | 1x1 Convolution, Squeeze-and-Excitation Block, Batch Normalization, Convolution, Grouped Convolution, Global Average Pooling, ResNeXt Block, Residual Connection, ReLU, Max Pooling, Softmax |
ID | seresnext26d_32x4d |
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Training Techniques | SGD with Momentum, Weight Decay, Label Smoothing |
---|---|
Architecture | 1x1 Convolution, Squeeze-and-Excitation Block, Batch Normalization, Convolution, Grouped Convolution, Global Average Pooling, ResNeXt Block, Residual Connection, ReLU, Max Pooling, Softmax |
ID | seresnext26t_32x4d |
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Training Techniques | SGD with Momentum, Weight Decay, Label Smoothing |
---|---|
Architecture | 1x1 Convolution, Squeeze-and-Excitation Block, Batch Normalization, Convolution, Grouped Convolution, Global Average Pooling, ResNeXt Block, Residual Connection, ReLU, Max Pooling, Softmax |
ID | seresnext50_32x4d |
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SE ResNeXt is a variant of a ResNext that employs squeeze-and-excitation blocks to enable the network to perform dynamic channel-wise feature recalibration.
To load a pretrained model:
import timm
m = timm.create_model('seresnext26d_32x4d', pretrained=True)
m.eval()
Replace the model name with the variant you want to use, e.g. seresnext26d_32x4d
. 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{hu2019squeezeandexcitation,
title={Squeeze-and-Excitation Networks},
author={Jie Hu and Li Shen and Samuel Albanie and Gang Sun and Enhua Wu},
year={2019},
eprint={1709.01507},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
BENCHMARK | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK |
---|---|---|---|---|
ImageNet | seresnext50_32x4d | Top 1 Accuracy | 81.27% | # 63 |
Top 5 Accuracy | 95.62% | # 63 | ||
ImageNet | seresnext26t_32x4d | Top 1 Accuracy | 77.99% | # 168 |
Top 5 Accuracy | 93.73% | # 168 | ||
ImageNet | seresnext26d_32x4d | Top 1 Accuracy | 77.59% | # 178 |
Top 5 Accuracy | 93.61% | # 178 |