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MIT

Train CIFAR10 with PyTorch

I'm playing with PyTorch on the CIFAR10 dataset.

Prerequisites

  • Python 3.6+
  • PyTorch 1.0+

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

MIT License Copyright (c) 2017 liukuang Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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