StochNorm

Original implementation for NeurIPS 2020 paper Stochastic Normalization.

[News] 2022/04/27 Please refer to the Transfer Learning Library for a modular implementation.

Dependencies

python3
torch == 1.1.0 (with suitable CUDA and CuDNN version)
torchvision == 0.3.0
numpy
argparse
tqdm

Datasets

Dataset	Download Link
CUB-200-2011	http://www.vision.caltech.edu/visipedia/CUB-200-2011.html
Stanford Cars	http://ai.stanford.edu/~jkrause/cars/car_dataset.html
FGVC Aircraft	http://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/
NIH Chest X-ray	https://nihcc.app.box.com/v/ChestXray-NIHCC

Quick Start

python --gpu [gpu_num] --data_path /path/to/dataset --class_num [class_num] --p 0.5 train.py

Some Results

We re-trained our StochNorm with this code on full 15% train data of CUB-200-2011. Results are shown in the table below.

Sampling Rate	Top-1 Acc(%)
15%	50.41
30%	62.09
50%	72.05
100%	79.65

Citation

If you use this code for your research, please consider citing:

@article{kou2020stochastic,
  title={Stochastic Normalization},
  author={Kou, Zhi and You, Kaichao and Long, Mingsheng and Wang, Jianmin},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}

Contact

If you have any problem about our code, feel free to contact kz19@mails.tsinghua.edu.cn.

thuml/StochNorm