Best CIFAR-10, CIFAR-100 results with wide-residual networks using PyTorch
Pytorch Implementation of Sergey Zagoruyko's Wide Residual Networks
For Torch implementations, see here.
Requirements
See the installation instruction for a step-by-step installation guide. See the server instruction for server settup.
- Install cuda-8.0
- Install cudnn v5.1
- Download Pytorch 2.7 and clone the repository.
pip install http://download.pytorch.org/whl/cu80/torch-0.1.12.post2-cp27-none-linux_x86_64.whl
pip install torchvision
git clone https://github.com/meliketoy/wide-resnet.pytorchHow to run
After you have cloned the repository, you can train each dataset of either cifar10, cifar100 by running the script below.
python main --lr 0.1 resume false --net_type [lenet/vggnet/resnet/wide-resnet] --depth 28 --widen_factor 10 --dropout_rate 0.3 --dataset [cifar10/cifar100] Implementation Details
| epoch | learning rate | weight decay | Optimizer | Momentum | Nesterov |
|---|---|---|---|---|---|
| 0 ~ 60 | 0.1 | 0.0005 | Momentum | 0.9 | true |
| 61 ~ 120 | 0.02 | 0.0005 | Momentum | 0.9 | true |
| 121 ~ 160 | 0.004 | 0.0005 | Momentum | 0.9 | true |
| 161 ~ 200 | 0.0008 | 0.0005 | Momentum | 0.9 | true |
CIFAR-10 Results
Below is the result of the test set accuracy for CIFAR-10 dataset training.
Accuracy is the average of 5 runs
| network | dropout | preprocess | GPU:0 | GPU:1 | per epoch | accuracy(%) |
|---|---|---|---|---|---|---|
| wide-resnet 28x10 | 0 | ZCA | 5.90G | - | 2 min 03 sec | 95.83 |
| wide-resnet 28x10 | 0 | meanstd | 5.90G | - | 2 min 03 sec | 96.21 |
| wide-resnet 28x10 | 0.3 | meanstd | 5.90G | - | 2 min 03 sec | 96.27 |
| wide-resnet 28x20 | 0.3 | meanstd | 8.13G | 6.93G | 4 min 10 sec | 96.55 |
| wide-resnet 40x10 | 0.3 | meanstd | 8.08G | - | 3 min 13 sec | 96.31 |
| wide-resnet 40x14 | 0.3 | meanstd | 7.37G | 6.46G | 3 min 23 sec | 96.34 |
CIFAR-100 Results
Below is the result of the test set accuracy for CIFAR-100 dataset training.
Accuracy is the average of 5 runs
| network | dropout | preprocess | GPU:0 | GPU:1 | per epoch | Top1 acc(%) | Top5 acc(%) |
|---|---|---|---|---|---|---|---|
| wide-resnet 28x10 | 0 | ZCA | 5.90G | - | 2 min 03 sec | 80.07 | 95.02 |
| wide-resnet 28x10 | 0 | meanstd | 5.90G | - | 2 min 03 sec | 81.02 | 95.41 |
| wide-resnet 28x10 | 0.3 | meanstd | 5.90G | - | 2 min 03 sec | 81.49 | 95.62 |
| wide-resnet 28x20 | 0.3 | meanstd | 8.13G | 6.93G | 4 min 05 sec | 82.45 | 96.11 |
| wide-resnet 40x10 | 0.3 | meanstd | 8.93G | - | 3 min 06 sec | 81.42 | 95.63 |
| wide-resnet 40x14 | 0.3 | meanstd | 7.39G | 6.46G | 3 min 23 sec | 81.87 | 95.51 |