This implementation is for cifar10/100 fast training base on (https://github.com/davidcpage/cifar10-fast)
Please check demo.ipynb.
You can run 70 epochs rather than 350 (https://github.com/kuangliu/pytorch-cifar) or 200 (https://github.com/weiaicunzai/pytorch-cifar100) epochs.
pytorch
apex (https://nvidia.github.io/apex/)
numpy
Those models from https://github.com/weiaicunzai/pytorch-cifar100/tree/master/models and https://github.com/kuangliu/pytorch-cifar/tree/master/models
| Model | CIFAR-10 accuracy | CIFAR-100 accuracy |
|---|---|---|
| ResNet18 | 95.32 | 76.75 |
| ResNet34 | 95.57 | 77.48 |
| ResNet50 | 95.62 | 77.66 |
| MobileNet | 92.25 | 60.35 |
| MobileNetV2 | 93.69 | 62.56 |
| DenseNet-cifar | 94.7 | 72.61 |
| DenseNet121 | 95.1 | 76.97 |
| DenseNet201 | 94.89 | 77.28 |
| Wide-ResNet40 | 95.13 | 74.79 |
| Wide-ResNet16 | 95.4 | 78.52 |
| Wide-ResNet28 | 96.21 | 79.86 |
| VGG11 | 92.41 | 70.07 |
| VGG16 | 94.27 | 72.68 |
| VGG19 | 94.22 | 71.11 |
| GoogleNet | 95.35 | 79.24 |
| InceptionV3 | 95.55 | 79.29 |