Using CNN, LeNet, AlexNet as backbone network and apply mixup to improve and compare.
- Training set : Testing set = 4:1, separated from
train.csv. - MNIST dataset come from Kaggle competition.
- Alpha=0 means not using mixup
- For LeNet, changing alpha from 0 to 0.1, accuracy improved 0.9%!
| Alpha | CNN | LeNet | Alexnet |
|---|---|---|---|
| 0 | 98.631 | 97.631 | 98.821 |
| 0.1 | 99.024 | 98.524 | 98.440 |
| 0.2 | 98.964 | 98.262 | 98.774 |
| 0.4 | 99.060 | 98.464 | 99.000 |
| 0.8 | 98.857 | 98.476 | 98.952 |
Just run the notebook, require Pytorch.
You can add alpha=0 to the mixup notebooks so you don't need to run the original ones.
I still put the original ones on for someone who don't want to use mixup.
- You may choose the GPU version
CNN_GPU