PyTorch models trained on CIFAR-10 dataset
- I modified TorchVision official implementation of popular CNN models, and trained those on CIFAR-10 dataset.
- I changed number of class, filter size, stride, and padding in the the original code so that it works with CIFAR-10.
- I also share the weights of these models, so you can just load the weights and use them.
Statistics of supported models
| No. | Model | Val. Acc. | No. Params | Size |
|---|---|---|---|---|
| 1 | vgg11_bn | 92.61% | 128.813 M | 491 MB |
| 2 | vgg13_bn | 94.27% | 128.998 M | 492 MB |
| 3 | vgg16_bn | 94.07% | 134.310 M | 512 MB |
| 4 | vgg19_bn | 94.25% | 139.622 M | 533 MB |
| 5 | resnet18 | 93.48% | 11.174 M | 43 MB |
| 6 | resnet34 | 93.82% | 21.282 M | 81 MB |
| 7 | resnet50 | 94.38% | 23.521 M | 90 MB |
| 8 | densenet121 | 94.76% | 6.956 M | 27 MB |
| 9 | densenet161 | 94.96% | 26.483 M | 102 MB |
| 10 | densenet169 | 94.74% | 12.493 M | 48 MB |
| 11 | mobilenet_v2 | 93.85% | 2.237 M | 9 MB |
| 12 | googlenet | 95.08% | 5.491 M | 21 MB |
| 13 | inception_v3 | 95.41% | 21.640 M | 83 MB |
How To Use
Download the weights
Download weights from Google Drive Link, and put the weights in models/state_dicts/ folder.
from cifar10_models import *
# Untrained model
my_model = vgg11_bn()
# Pretrained model
my_model = vgg11_bn(pretrained=True)Remember to normalize data before feeding to model
transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])]Training Hyper-paramters
- Batch size: 256
- Number of epochs: 600
- Learning rate: 0.05, multiply by factor 0.1 every 200 epochs
- Weight decay: 0.001
- Nesterov SGD optimizer with momentum = 0.9