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README.md

OSLNet: Deep Small-Sample Classification with an Orthogonal Softmax Layer

Code release for OSLNet: Deep Small-Sample Classification with an Orthogonal Softmax Layer (TIP2020) DOI

Changelog

  • 2020/04/21 upload the code.

Dataset

CIFAR-100

Requirements

  • python 3.6
  • PyTorch 1.2.0
  • torchvision

Training

  • Download datasets
  • Train: python OS-CNN.py or python CNN.py
  • Description : PyTorch CIFAR-100 Training with OSNet or PyTorch CIFAR-100 Training with Vanilla Model.

Accuracy and Cross-entropy loss

AccuracyandCross-entropyloss

Citation

If you find this paper useful in your research, please consider citing:

@ARTICLE{9088302,

  author={X. {Li} and D. {Chang} and Z. {Ma} and Z. {Tan} and J. {Xue} and J. {Cao} and J. {Yu} and J. {Guo}},
  journal={IEEE Transactions on Image Processing}, 
  title={OSLNet: Deep Small-Sample Classification with an Orthogonal Softmax Layer}, 
  year={2020},
  volume={},
  number={},
  pages={1-1},
}

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Code release for OSLNet: Deep Small-Sample Classification with an Orthogonal Softmax Layer (TIP2020)

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