MobileNetV3 in Python | Shuffle Attention for MobileNetV3 in Python

SA-MobileNetV3

Shuffle Attention for MobileNetV3 in python

Shuffle Attention for MobileNetV3

Reference

Experiments

on ImageNet

AttemptParametersMaddsTop1-accSample visualization
MobileNetV3-Large5.4 M448.69 M75.2% 
SA-MobileNetV3-Large3.9 M445.68 M76.8% 

on CIFAR-10

AttemptParametersMaddsTop1-accSample visualization
MobileNetV3-Large4.2 M446.16 M  
SA-MobileNetV3-Large2.7 M443.14 M  

on MNist

AttemptParametersMaddsTop1-accSample visualizationSample visualization
MobileNetV3-Large4.2 M446.16 M0.997%Shuffle Attention for MobileNetV3 in pythonShuffle Attention for MobileNetV3 in python
SA-MobileNetV3-Large2.7 M443.14 M0.998%Shuffle Attention for MobileNetV3 in pythonShuffle Attention for MobileNetV3 in python

Train

Run the following command for train model on your own dataset:

python train.py --dataset mnist 

Test

Run the following command for evaluation trained model on test dataset:

python test.py --dataset mnist

Predict

Run the following command for classification images:

python predict.py --input /path/to/image.jpg 

Citation

Please cite our paper if you find this repo useful in your research.

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Download Details: 
 

Author: SajjadAemmi

Official Website: https://github.com/SajjadAemmi/SA-MobileNetV3 

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