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szagoruyko/wide-residual-networks

This code was used for experiments with Wide Residual Networks (BMVC 2016) http://arxiv.org/abs/1605.07146 by Sergey Zagoruyko and Nikos Komodakis.

of improved accuracy costs nearly doubling the number of layers, and so training

you're comparing your method against WRN, please report correct preprocessing numbers because they give substantially different results.

ImageNet WRN-50-2-bottleneck (ResNet-50 with wider inner bottleneck 3x3 convolution) is significantly faster than ResNet-152 and has better accuracy;

on COCO wide ResNet with 34 layers outperforms even Inception-v4-based Fast-RCNN model in single model performance.

Test error (%, flip/translation augmentation, meanstd normalization, median of 5 runs) on CIFAR: Single-time runs (meanstd normalization): See http://arxiv.org/abs/1605.07146 for details.

Follow instructions here and run: For visualizing training curves we used ipython notebook with pandas and bokeh.

We provide the following: To whiten CIFAR-10 and CIFAR-100 we used the following scripts https://github.com/lisa-lab/pylearn2/blob/master/pylearn2/scripts/datasets/make_cifar10_gcn_whitened.py and then converted to torch using https://gist.github.com/szagoruyko/ad2977e4b8dceb64c68ea07f6abf397b and npy to torch converter https://github.com/htwaijry/npy4th.

is saved to logs/wide-resnet_$RANDOM$RANDOM folder with json entries for each epoch and can be visualized with itorch/ipython later.

To reduce memory usage we use @fmassa's optimize-net, which automatically shares output and gradient tensors between modules.

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