PyTorch-CIFAR10-Playground
I have built and trained various popular networks for classification task on CIFAR10 datasets. Details and results are as below.
Requirments
- python 3.6.10
- pytorch 1.1.0
- torchvision 0.3.0
- cuda 9.0
How to run
Run the script as below after cloning this repository.
python main.py --model xx --gpu x --epoch xxx
Implementation Details
- learning rate:
- epoch: [0, 100) 0.1
- epoch: [100, 200) 0.01
- epoch: [200, 300) 0.001
- weight decay: 0.0001
- Momentum: 0.9
- Optimizer: SGD
- Epoch: 300
- GPU: Nvidia GeForce GTX 1080 Ti
Performance
| model | accuracy(×100%) | epoch |
|---|---|---|
| ResNet18 | 0.9461 | 200 |
| ResNet34 | 0.9507 | 200 |
| ResNet50 | 0.9418 | 200 |
| ResNet101 | 0.9471 | 200 |
| ResNet152 | 0.95 | 200 |
| VGG11 | 0.9157 | 200 |
| VGG13 | 0.9032 | 200 |
| VGG16 | 0.9359 | 300 |
| VGG19 | 0.9347 | 300 |
| AlexNet | 0.8195 | 300 |
| LeNet | 0.7155 | 300 |
| GoogLeNet | 0.9477 | 300 |
| MobileNetV1 | 0.9185 | 300 |
| ShuffleNetV1_g1 | 0.9145 | 300 |
| ShuffleNetV1_g2 | 0.9123 | 300 |
| ShuffleNetV1_g3 | 0.9205 | 300 |
| ShuffleNetV1_g4 | 0.9175 | 300 |
| ShuffleNetV1_g8 | 0.916 | 300 |
| MobileNetV2 | 0.9434 | 300 |
| ShuffleNetV2_Z05 | 0.9009 | 300 |
| ShuffleNetV2_Z1 | 0.926 | 300 |
| ShuffleNetV2_Z15 | 0.9338 | 300 |
| ShuffleNetV2_Z2 | 0.9382 | 300 |
| DenseNet121 | 0.9476 | 300 |
| DenseNet169 | 0.9486 | 300 |
| DenseNet201 | 0.9476 | 300 |
| DenseNet264 | 0.9502 | 300 |
| PreActResNet18 | 0.94 | 300 |
| PreActResNet34 | 0.9474 | 300 |
| PreActResNet50 | 0.9525 | 300 |
| WRN_16_4 | 0.8189 | 300 |
| WRN_40_8 | 0.9119 | 300 |
| WRN_28_10 | 0.912 | 300 |
| ResNeXt50_8x14d | 0.9536 | 300 |
| ResNeXt50_1x64d | 0.9447 | 300 |
| ResNeXt50_32x4d, | 0.9548 | 300 |
| ResNeXt50_2x40d | 0.9505 | 300 |
| ResNeXt50_4x24d | 0.9533 | 300 |
| SEResNet | 0.9437 | 300 |
| SqueezeNet | 0.9297 | 300 |
| EfficientB0 | 0.948 | 300 |
| DPN92 | 0.9521 | 300 |
Since all networks use the same training method, this may not be the optimal performance of some networks.