Measure CNN,VGG,Resnet,WideResnet models' accuracy on dataset CIFAR10,CIFAR100 using pytorch-lightning.
- setup/requirements.txt
torch 1.5.1
torchvision 0.6.1
pytorch-lightning 0.9.0rc5
tqdm
argparse
pytablewriter
seaborn
enum34
scipy
cffi
sklearn- install requirements using pip
pip3 install -r setup/requirements.txtAfter you have cloned the repository, you can train each models with datasets cifar10, cifar100. Trainable models are VGG, Resnet, WideResnet, Densenet-BC, Densenet.
python train.py- CIFAR10
| epoch | learning rate | weight decay | Optimizer | Momentum | Nesterov |
|---|---|---|---|---|---|
| 0 ~ 20 | 0.1 | 0.0005 | SGD | 0.9 | False |
| 21 ~ 40 | 0.01 | 0.0005 | SGD | 0.9 | False |
| 41 ~ 60 | 0.001 | 0.0005 | SGD | 0.9 | False |
- CIFAR100
| epoch | learning rate | weight decay | Optimizer | Momentum | Nesterov |
|---|---|---|---|---|---|
| 0 ~ 60 | 0.1 | 0.0005 | SGD | 0.9 | False |
| 61 ~ 120 | 0.01 | 0.0005 | SGD | 0.9 | False |
| 121 ~ 180 | 0.001 | 0.0005 | SGD | 0.9 | False |
Below is the result of the test set accuracy for CIFAR-10, CIFAR-100 dataset training
Accuracy of models trained on CIFAR10
| network | dropout | preprocess | parameters | accuracy(%) |
|---|---|---|---|---|
| VGG16 | 0 | meanstd | 14M | 91.09 |
| Resnet-50 | 0 | meanstd | 23M | 92.11 |
| WideResnet 28x10 | 0.3 | meanstd | 36M | 93.61 |
| Densenet-BC | 0 | meanstd | 769K | 92.85 |
| Densenet | 0 | meanstd | 769K | 93.06 |
Accuracy of models trained on CIFAR100
| network | dropout | preprocess | parameters | accuracy(%) |
|---|---|---|---|---|
| VGG16 | 0 | meanstd | 14M | 72.79 |
| Resnet-50 | 0 | meanstd | 23M | 75.80 |
| WideResnet 28x20 | 0.3 | meanstd | 145M | 75.46 |
| Densenet-BC | 0 | meanstd | 800K | 72.23 |
| Densenet | 0 | meanstd | 800K | 75.58 |
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