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Classification on CIFAR-10/100 and ImageNet with PyTorch.


  • Unified interface for different network architectures
  • Multi-GPU support
  • Training progress bar with rich info
  • Training log and training curve visualization code (see ./utils/


  • Install PyTorch
  • Clone recursively
    git clone --recursive


Please see the Training recipes for how to train the models.



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



Single-crop (224x224) validation error rate is reported.

Model Params (M) Top-1 Error (%) Top-5 Error (%)
ResNet-18 11.69 30.09 10.78
ResNeXt-50 (32x4d) 25.03 22.6 6.29

Validation curve

Pretrained models

Our trained models and training logs are downloadable at OneDrive.

Supported Architectures

CIFAR-10 / CIFAR-100

Since the size of images in CIFAR dataset is 32x32, popular network structures for ImageNet need some modifications to adapt this input size. The modified models is in the package models.cifar:



Feel free to create a pull request if you find any bugs or you want to contribute (e.g., more datasets and more network structures).

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