FcaNet

FcaNet: Frequency Channel Attention Networks

PyTorch implementation of the paper “FcaNet: Frequency Channel Attention Networks”.

Simplest usage

Models pretrained on ImageNet can be simply accessed by (without any configuration or installation):

model = torch.hub.load('cfzd/FcaNet', 'fca34' ,pretrained=True)
model = torch.hub.load('cfzd/FcaNet', 'fca50' ,pretrained=True)
model = torch.hub.load('cfzd/FcaNet', 'fca101' ,pretrained=True)
model = torch.hub.load('cfzd/FcaNet', 'fca152' ,pretrained=True)

Install

Please see INSTALL.md

Models

Classification models on ImageNet

Due to the conversion between FP16 training and the provided FP32 models, the evaluation results are slightly different(max -0.06%/+0.05%) compared with the reported results.

Detection and instance segmentation models on COCO

Training

Please see launch_training_classification.sh and launch_training_detection.sh for training on ImageNet and COCO, respectively.

Testing

Please see launch_eval_classification.sh and launch_eval_detection.sh for testing on ImageNet and COCO, respectively.

FAQ

Since the paper is uploaded to arxiv, many academic peers ask us: the proposed DCT basis can be viewed as a simple tensor, then how about learning the tensor directly? Why use DCT instead of learnable tensor? Learnable tensor can be better than DCT.

Our concrete answer is: the proposed DCT is better than the learnable way, although it is counter-intuitive.

To verify this results, one can select the cooresponding types of tensor in the L73-L83 in model/layer.py, uncomment it and train the whole network.

TODO

• Object detection models
• Instance segmentation models
• Fix the incorrect results of detection models
• Make the switching between configs more easier