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Experiments with custom conv layers that summarize, propagate, and leverage information about the spatial geometry of features.

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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

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Experiments with custom conv layers that summarize, propagate, and leverage information about the spatial geometry of features.

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