moonlightlane / pytorch-cifar

95.16% on CIFAR10 with PyTorch

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Train CIFAR10 with PyTorch

I'm playing with PyTorch on the CIFAR10 dataset.

Pros & cons

Pros:

  • Built-in data loading and augmentation, very nice!
  • Training is fast, maybe even a little bit faster.
  • Very memory efficient!

Cons:

  • No progress bar, sad :(
  • No built-in log.

Accuracy

Model Acc.
VGG16 92.64%
ResNet18 93.02%
ResNet50 93.62%
ResNet101 93.75%
MobileNetV2 94.43%
ResNeXt29(32x4d) 94.73%
ResNeXt29(2x64d) 94.82%
DenseNet121 95.04%
PreActResNet18 95.11%
DPN92 95.16%

Learning rate adjustment

I manually change the lr during training:

  • 0.1 for epoch [0,150)
  • 0.01 for epoch [150,250)
  • 0.001 for epoch [250,350)

Resume the training with python main.py --resume --lr=0.01

About

95.16% on CIFAR10 with PyTorch

License:MIT License


Languages

Language:Python 100.0%