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
ImageNet
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
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: