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Pytorch Baseline

Original : https://github.com/kuangliu/pytorch-cifar by kuangliu.

Prerequisites

  • Python 3.6+
  • PyTorch 1.0+

Log and checkpoint

Add logger and checkpoint. Log and checkpoint files will be saved into each directory. (./log/[cur_time] && ./checkpoint/[cur_time])

Learning rate adjustment

You can adjust learning rate with manual method or auto method. Train with auto scheduler with python3 main.py --scheduler or you can use a manual way with adjust_learning_rate().

With python3 main.py --help, you will get more information.

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%

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95.16% on CIFAR10 with PyTorch

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