pkumivision / FFC

This is an official pytorch implementation of Fast Fourier Convolution.

Fast Fourier Convolution (FFC) for Image Classification

This is the official code of Fast Fourier Convolution for image classification on ImageNet.

Main Results

Results on ImageNet

Method	GFLOPs	#Params	Top-1 Acc
ResNet-50	4.1	25.6	76.3
FFC-ResNet-50	4.2	26.1	77.6
FFC-ResNet-50 (+LFU)	4.3	26.7	77.8

Quick starts

Requirements

• pip install -r requirements.txt

Data preparation

You can follow the Pytorch implementation: https://github.com/pytorch/examples/tree/master/imagenet

Training

To train a model, run main.py with the desired model architecture and other super-paremeters:

python main.py -a ffc_resnet50 --lfu [imagenet-folder with train and val folders]

We use "lfu" to control whether to use Local Fourier Unit (LFU). Default: False.

Testing

python main.py -a ffc_resnet50 --lfu --resume PATH/TO/CHECKPOINT [imagenet-folder with train and val folders]

Citation

If you find this work or code is helpful in your research, please cite:

@InProceedings{Chi_2020_FFC,
  author = {Chi, Lu and Jiang, Borui and Mu, Yadong},
  title = {Fast Fourier Convolution},
  booktitle = {Advances in Neural Information Processing Systems},
  year = {2020}
}