63
GitHub - leehomyc/mixup_pytorch: A PyTorch implementation of the paper Mixup: Be...
source link: https://github.com/leehomyc/mixup_pytorch
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.
Mixup: Beyond Empirical Risk Minimization in PyTorch
This is an unofficial PyTorch implementation of mixup: Beyond Empirical Risk Minimization. The code is adapted from PyTorch CIFAR.
The results:
I only tested using CIFAR 10 and CIFAR 100. The network we used is PreAct ResNet-18. For mixup, we set alpha to be default value 1, meaning we sample the weight uniformly between zero and one. I trained 200 epochs for each setting. The learning rate is 0.1 (iter 1-100), 0.01 (iter 101-150) and 0.001 (iter 151-200). The batch size is 128.
Dataset and Model | Acc. |
---|---|
CIFAR 10 no mixup | 94.97% |
CIFAR 10 mixup | 95.53% |
CIFAR 100 no mixup | 76.53% |
CIFAR 100 mixup | 77.83% |
CIFAR 10 test accuracy evolution
CIFAR 100 test accuracy evolution
Usage
# Train and test CIFAR 10 with mixup.
python main_cifar10.py --mixup --exp='cifar10_nomixup'
# Train and test CIFAR 10 without mixup.
python main_cifar10.py --exp='cifar10_nomixup'
# Train and test CIFAR 100 with mixup.
python main_cifar100.py --mixup --exp='cifar100_mixup'
# Train and test CIFAR 100 without mixup.
python main_cifar100.py --exp='cifar100_nomixup'
Recommend
About Joyk
Aggregate valuable and interesting links.
Joyk means Joy of geeK