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ResNeXt.pytorch

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
  • Train Imagenet

Usage

To train on Cifar-10 using 2 gpu:

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

Configurations

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:

CIFAR-10 CIFAR-100

Other frameworks

Cite

@article{xie2016aggregated,
  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},
  year={2016}
}

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Reproduces ResNet-V3 with pytorch

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