SA-MobileNetV3
Shuffle Attention for MobileNetV3
Reference
- SA-NET: Shuffle Attention for deep convolutional neural networks: https://github.com/wofmanaf/SA-Net
Experiments
on ImageNet
| Attempt | Parameters | Madds | Top1-acc | Sample visualization |
|---|---|---|---|---|
| MobileNetV3-Large | 5.4 M | 448.69 M | 75.2% | |
| SA-MobileNetV3-Large | 3.9 M | 445.68 M | 76.8% |
on CIFAR-10
| Attempt | Parameters | Madds | Top1-acc | Sample visualization |
|---|---|---|---|---|
| MobileNetV3-Large | 4.2 M | 446.16 M | ||
| SA-MobileNetV3-Large | 2.7 M | 443.14 M |
on MNist
| Attempt | Parameters | Madds | Top1-acc | Sample visualization | Sample visualization |
|---|---|---|---|---|---|
| MobileNetV3-Large | 4.2 M | 446.16 M | 0.997% |  |
 |
| SA-MobileNetV3-Large | 2.7 M | 443.14 M | 0.998% |  |
 |
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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