|

ResNeXt.pytorch

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

• [x] Trains on Cifar10 and Cifar100

• [x] Upload Cifar Training Curves

• [x] Upload Cifar Trained Models

• [x] Pytorch 4.0

• [ ] Train Imagenet

Download

git clone https://github.com/prlz77/resnext.pytorchcd resnext.pytorch
git checkout R4.0 # R3.0 for backwards compatibility.

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

Other frameworks

• torch (@facebookresearch). (Original) Cifar and Imagenet

• caffe (@terrychenism). Imagenet

• MXNet (@dmlc). Imagenet

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}
}