/ResNeXt.pytorch

Reproduces ResNet-V3 with pytorch

Primary LanguagePythonMIT LicenseMIT

ResNeXt.pytorch

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

  • Tried on pytorch 1.6
  • Trains on Cifar10 and Cifar100
  • Upload Cifar Training Curves
  • Upload Cifar Trained Models
  • Pytorch 0.4.0
  • Train Imagenet

Download

git clone https://github.com/prlz77/resnext.pytorch
cd resnext.pytorch
# git checkout R4.0 or R3.0 for backwards compatibility (not recommended).

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:

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

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}
}