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Reproduces ResNet-V3 with pytorch
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cifar10 Upload training curves, trained models Apr 14, 2017
cifar100 Upload training curves, trained models Apr 14, 2017
models disentangle base_width from widen_factor fix Nov 21, 2017
.gitignore Upload training curves, trained models Apr 14, 2017
LICENSE Initial commit Mar 30, 2017 Update May 10, 2018 Corrected description of File May 17, 2018


Reproduces ResNet-V3 (Aggregated Residual Transformations for Deep Neural Networks) with pytorch.

  • Trains on Cifar10 and Cifar100
  • Upload Cifar Training Curves
  • Upload Cifar Trained Models
  • Pytorch 4.0
  • Train Imagenet


git clone
cd resnext.pytorch
git checkout R4.0 # R3.0 for backwards compatibility.


To train on Cifar-10 using 2 gpu:

python ~/DATASETS/cifar.python cifar10 -s ./snapshots --log ./logs --ngpu 2 --learning_rate 0.05 -b 128

It should reach ~3.65% on Cifar-10, and ~17.77% on Cifar-100.

After train phase, you can check saved model.

Thanks to @AppleHolic we have now a test script:

To test on Cifar-10 using 2 gpu:

python ~/DATASETS/cifar.python cifar10 --ngpu 2 --load ./snapshots/model.pytorch --test_bs 128 


From the original paper:

cardinality base_width parameters Error cifar10 error cifar100 default
8 64 34.4M 3.65 17.77 x
16 64 68.1M 3.58 17.31

Update: widen_factor has been disentangled from base_width because it was confusing. Now widen factor is set to consant 4, and base_width is the same as in the original paper.

Trained models and curves

Link to trained models corresponding to the following curves:

Update: several commits have been pushed after training the models in Mega, so it is recommended to revert to e10c37d8cf7a958048bc0f58cd86c3e8ac4e707d


Other frameworks


  title={Aggregated residual transformations for deep neural networks},
  author={Xie, Saining and Girshick, Ross and Doll{\'a}r, Piotr and Tu, Zhuowen and He, Kaiming},
  journal={arXiv preprint arXiv:1611.05431},
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