【深度學習】alexnet、vgg19_bn、ResNet-110、PreResNet-110、ResNeXt-29, 8x64等模型效能對比
阿新 • • 發佈:2018-12-21
alexnet、vgg19_bn、ResNet-110、PreResNet-110、WRN-28-10 (drop 0.3)、ResNeXt-29, 8x64、ResNeXt-29, 16x64、DenseNet-BC (L=100, k=12)、DenseNet-BC (L=190, k=40)等模型效能對比:
CIFAR
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 |