This repository contains TFNet implementation by Pytorch.
To show the effectiveness of the proposed acon family, we provide an extreme simple toy funnel network (TFNet) made only by pointwise convolution and ACON-FReLU operators.
The simple TFNet without the SE modules can outperform the state-of-the art light-weight networks without the SE modules.
FLOPs | #Params. | top-1 err. | |
---|---|---|---|
MobileNetV2 0.17 | 42M | 1.4M | 52.6 |
ShuffleNetV2 0.5x | 41M | 1.4M | 39.4 |
TFNet 0.5 | 43M | 1.3M | 36.6 (+2.8) |
MobileNetV2 0.6 | 141M | 2.2M | 33.3 |
ShuffleNetV2 1.0x | 146M | 2.3M | 30.6 |
TFNet 1.0 | 135M | 1.9M | 29.7 (+0.9) |
MobileNetV2 1.0 | 300M | 3.4M | 28.0 |
ShuffleNetV2 1.5x | 299M | 3.5M | 27.4 |
TFNet 1.5 | 279M | 2.7M | 26.0 (+1.4) |
MobileNetV2 1.4 | 585M | 5.5M | 25.3 |
ShuffleNetV2 2.0x | 591M | 7.4M | 25.0 |
TFNet 2.0 | 474M | 3.8M | 24.3 (+0.7) |
Download the ImageNet dataset and move validation images to labeled subfolders. To do this, you can use the following script: https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh
Train:
python train.py --train-dir YOUR_TRAINDATASET_PATH --val-dir YOUR_VALDATASET_PATH
Eval:
python train.py --eval --eval-resume YOUR_WEIGHT_PATH --train-dir YOUR_TRAINDATASET_PATH --val-dir YOUR_VALDATASET_PATH
If you use these models in your research, please cite:
@inproceedings{ma2021activate,
title={Activate or Not: Learning Customized Activation},
author={Ma, Ningning and Zhang, Xiangyu and Liu, Ming and Sun, Jian},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
year={2021}
}