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Pretraining Wide Residual Network

This repository contains code for pretraining Wide Residual Network (WRN) [1] on downsampled [2] ImageNet 32x32, ImageNet 64x64, and ImageNet 224x224 using cross-entropy and triplet loss [3].

Environment setup

For creating conda environment, a yml file tf2.yml is provided for replicating setup.

conda env create -f tf2.yml
conda activate tf2

Data preparation

ImageNet full dataset can be downloaded from link. After downloading, set the path of base_dir in data.py.

ImageNet 32x32 and ImageNet 64x64 datasets can be generated either using scripts provided by Downsampled ImageNet or TensorFlow datasets package. The tensorflow_datasets package can be installed using pip:

pip install tensorflow_datasets

The current version of tensorflow_datasets=4.4.0 package has a broken link for downloading ImageNet 32x32 and ImageNet 64x64. The workaround is available at GitHub.

Pretraining

For pretraining from scratch using different setups, pretrain.py can be used. Details of self-explanatory commandline arguments can be seen by passing --help to it.

 python pretrain.py --help
 
       USAGE: pretrain.py [flags]
flags:

pretrain.py:
  --bs: batch_size
    (default: '128')
    (an integer)
  --d: <imagenet_resized/32x32|imagenet_resized/64x64|imagenet-full>: dataset
    (default: 'imagenet_resized/32x32')
  --e: number of epochs
    (default: '50')
    (an integer)
  --g: gpu id
    (default: '0')
  --lbl: <lda|knn>: Specify labelling method either LDA or KNN.
    (default: 'lda')
  --lr: learning_rate
    (default: '0.001')
    (a number)
  --lt: <cross-entropy|triplet>: loss_type  either cross-entropy  or triplet.
    (default: 'cross-entropy')
  --margin: margin for triplet loss
    (default: '1.0')
    (a number)
  --n: network
    (default: 'wrn-28-2')
  --[no]sw: save weights
    (default: 'false')

Try --helpfull to get a list of all flags.

Pretrained weights will be saved into weights/ directory. We also provide pretrained weights. They can be downloaded from releases and saved into weights/ directory. Path of downloaded weights can be set in wrn.py.

Example usage

For using pretrained weights, an example notebook is provided . For more details, see cifar_example.ipynb.

Citation

If you use the provided weights, kindly cite our paper.

@inproceedings{sahito2022better,
  title={Better self-training for image classification through self-supervision},
  author={Sahito, Attaullah and Frank, Eibe and Pfahringer, Bernhard},
  booktitle={Australasian Joint Conference on Artificial Intelligence},
  pages={645--657},
  year={2022},
  organization={Springer}
}

References

  1. Wide Residual Networks. Sergey Zagoruyko and Nikos Komodakis. In British Machine Vision Conference 2016. British Machine Vision Association, 2016.
  2. A downsampled variant of ImageNet as an alternative to the CIFAR datasets. Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter. arXiv preprint arXiv:1707.08819, 2017 .
  3. Distance metric learning for large margin nearest neighbour classification. Kilian Q Weinberger and Lawrence K Saul. Journal of Machine Learning Research, 10(2), 2009.

License

MIT