Skip to content
master
Go to file
Code

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 

README.md

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

About

92.45% on CIFAR-10 in Torch

Resources

License

Releases

No releases published

Packages

No packages published

Languages

You can’t perform that action at this time.