ID	legacy_seresnext101_32x4d
LR	0.6
Epochs	100
Layers	101
Dropout	0.2
Crop Pct	0.875
Momentum	0.9
Batch Size	1024
Image Size	224
Interpolation	bilinear

ID	legacy_seresnext26_32x4d
LR	0.6
Epochs	100
Layers	26
Dropout	0.2
Crop Pct	0.875
Momentum	0.9
Batch Size	1024
Image Size	224
Interpolation	bicubic

ID	legacy_seresnext50_32x4d
LR	0.6
Epochs	100
Layers	50
Dropout	0.2
Crop Pct	0.875
Momentum	0.9
Batch Size	1024
Image Size	224
Interpolation	bilinear

Legacy SE ResNeXt

rwightman / pytorch-image-models

Last updated on Feb 14, 2021

Parameters 49 Million

FLOPs 10 Billion

File Size 187.37 MB

Training Data ImageNet

Training Resources 8x NVIDIA Titan X GPUs

Training Time

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	legacy_seresnext101_32x4d
LR	0.6
Epochs	100
Layers	101
Dropout	0.2
Crop Pct	0.875
Momentum	0.9
Batch Size	1024
Image Size	224
Interpolation	bilinear
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Parameters 17 Million

FLOPs 3 Billion

File Size 64.23 MB

Training Data ImageNet

Training Resources 8x NVIDIA Titan X GPUs

Training Time

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	legacy_seresnext26_32x4d
LR	0.6
Epochs	100
Layers	26
Dropout	0.2
Crop Pct	0.875
Momentum	0.9
Batch Size	1024
Image Size	224
Interpolation	bicubic
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Parameters 28 Million

FLOPs 5 Billion

File Size 105.44 MB

Training Data ImageNet

Training Resources 8x NVIDIA Titan X GPUs

Training Time

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	legacy_seresnext50_32x4d
LR	0.6
Epochs	100
Layers	50
Dropout	0.2
Crop Pct	0.875
Momentum	0.9
Batch Size	1024
Image Size	224
Interpolation	bilinear
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README.md

Summary

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.

How do I load this model?

To load a pretrained model:

import timm
m = timm.create_model('legacy_seresnet152', pretrained=True)
m.eval()

Replace the model name with the variant you want to use, e.g. legacy_seresnet152. You can find the IDs in the model summaries at the top of this page.

How do I train this model?

You can follow the timm recipe scripts for training a new model afresh.

Citation

@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}
}

Results

Image Classification on ImageNet

Image Classification

BENCHMARK	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
ImageNet	legacy_seresnext101_32x4d	Top 1 Accuracy	80.23%	# 93
		Top 5 Accuracy	95.02%	# 93
ImageNet	legacy_seresnext50_32x4d	Top 1 Accuracy	79.08%	# 133
		Top 5 Accuracy	94.43%	# 133
ImageNet	legacy_seresnext26_32x4d	Top 1 Accuracy	77.11%	# 193
		Top 5 Accuracy	93.31%	# 193