SE ResNet

#SE ResNet | 来源: 网络整理| 查看: 265

SE ResNet

rwightman / pytorch-image-models Last updated on Feb 14, 2021 seresnet152d

Parameters 67 Million FLOPs 20 Billion File Size 255.72 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, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax ID seresnet152d LR 0.6 Epochs 100 Layers 152 Dropout 0.2 Crop Pct 0.94 Momentum 0.9 Batch Size 1024 Image Size 256 Interpolation bicubic SHOW MORE SHOW LESS

seresnet50

Parameters 28 Million FLOPs 5 Billion File Size 107.40 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, Bottleneck Residual Block, Batch Normalization, Convolution, Global Average Pooling, Residual Block, Residual Connection, ReLU, Max Pooling, Softmax ID seresnet50 LR 0.6 Epochs 100 Layers 50 Dropout 0.2 Crop Pct 0.875 Momentum 0.9 Batch Size 1024 Image Size 224 Interpolation bicubic SHOW MORE SHOW LESS

README.md Summary

SE ResNet is a variant of a ResNet 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('seresnet50', pretrained=True) m.eval()

Replace the model name with the variant you want to use, e.g. seresnet50. 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 seresnet152d Top 1 Accuracy 83.74% # 29 Top 5 Accuracy 96.77% # 29 ImageNet seresnet50 Top 1 Accuracy 80.26% # 91 Top 5 Accuracy 95.07% # 91

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