Awesome
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.
- The code is highly re-producible and readable by using PyTorch-Lightning.
Statistics of supported models
| No. | Model | Val. Acc. | No. Params | Size |
|---|---|---|---|---|
| 1 | vgg11_bn | 92.39% | 28.150 M | 108 MB |
| 2 | vgg13_bn | 94.22% | 28.334 M | 109 MB |
| 3 | vgg16_bn | 94.00% | 33.647 M | 129 MB |
| 4 | vgg19_bn | 93.95% | 38.959 M | 149 MB |
| 5 | resnet18 | 93.07% | 11.174 M | 43 MB |
| 6 | resnet34 | 93.34% | 21.282 M | 82 MB |
| 7 | resnet50 | 93.65% | 23.521 M | 91 MB |
| 8 | densenet121 | 94.06% | 6.956 M | 28 MB |
| 9 | densenet161 | 94.07% | 26.483 M | 103 MB |
| 10 | densenet169 | 94.05% | 12.493 M | 49 MB |
| 11 | mobilenet_v2 | 93.91% | 2.237 M | 9 MB |
| 12 | googlenet | 92.85% | 5.491 M | 22 MB |
| 13 | inception_v3 | 93.74% | 21.640 M | 83 MB |
Details Report & Run Logs
Weight and Biases' details report for this project WandB Report
Weight and Biases' run logs for this project WandB Run Log. You can see each run hyper-parameters, training accuracy, validation accuracy, loss, time taken.
How To Cite
How to use pretrained models
Automatically download and extract the weights from Box (933 MB)
python train.py --download_weights 1
Or use Google Drive backup link (you have to download and extract manually)
Load model and run
from cifar10_models.vgg import vgg11_bn, vgg13_bn, vgg16_bn, vgg19_bn
# Untrained model
my_model = vgg11_bn()
# Pretrained model
my_model = vgg11_bn(pretrained=True)
my_model.eval() # for evaluation
If you use your own images, all models expect data to be in range [0, 1] then normalized by
mean = [0.4914, 0.4822, 0.4465]
std = [0.2471, 0.2435, 0.2616]
How to train models from scratch
Check the train.py to see all available hyper-parameter choices.
To reproduce the same accuracy use the default hyper-parameters
python train.py --classifier resnet18
How to test pretrained models
python train.py --test_phase 1 --pretrained 1 --classifier resnet18
Output
{'acc/test': tensor(93.0689, device='cuda:0')}
Requirements
Just to use pretrained models
- pytorch = 1.7.0
To train & test
- pytorch = 1.7.0
- torchvision = 0.7.0
- tensorboard = 2.2.1
- pytorch-lightning = 1.1.0