Classification on CIFAR-10/100 and ImageNet with PyTorch.
copy from https://github.com/bearpaw/pytorch-classification.git
- Unified interface for different network architectures
- Multi-GPU support
- Training progress bar with rich info
- Training log and training curve visualization code (see
./utils/logger.py)
Activation: Relu,Selu,Swish,Mish
Optimizer: SGD,Adam,Radam,adamW(+warm_up)
Init: Kaiming
Other: cutout,lookahead,mix_up,FRN
python3 cifar.py
Top1 error rate on the CIFAR-10/100 benchmarks are reported. You may get different results when training your models with different random seed. Note that the number of parameters are computed on the CIFAR-10 dataset.
| Model | Params (M) | CIFAR-10 (%) | CIFAR-100 (%) |
|---|---|---|---|
| alexnet | 2.47 | 22.78 | 56.13 |
| vgg19_bn | 20.04 | 6.66 | 28.05 |
| ResNet-110 | 1.70 | 6.11 | 28.86 |
| PreResNet-110 | 1.70 | 4.94 | 23.65 |
| WRN-28-10 (drop 0.3) | 36.48 | 3.79 | 18.14 |
| ResNeXt-29, 8x64 | 34.43 | 3.69 | 17.38 |
| ResNeXt-29, 16x64 | 68.16 | 3.53 | 17.30 |
| DenseNet-BC (L=100, k=12) | 0.77 | 4.54 | 22.88 |
| DenseNet-BC (L=190, k=40) | 25.62 | 3.32 | 17.17 |