cifar.torch

Newer version of this code is included in https://github.com/szagoruyko/wide-residual-networks

The code achieves 92.45% accuracy on CIFAR-10 just with horizontal reflections.

Corresponding blog post: http://torch.ch/blog/2015/07/30/cifar.html

Accuracies:

| No flips | Flips --- | --- | --- VGG+BN+Dropout | 91.3% | 92.45% NIN+BN+Dropout | 90.4% | 91.9%

Would be nice to add other architectures, PRs are welcome!

Data preprocessing:

OMP_NUM_THREADS=2 th -i provider.lua

provider = Provider()
provider:normalize()
torch.save('provider.t7',provider)

Takes about 30 seconds and saves 1400 Mb file.

Training:

CUDA_VISIBLE_DEVICES=0 th train.lua --model vgg_bn_drop -s logs/vgg