ACmix
This repo contains the official PyTorch code and pre-trained models for ACmix.
Introduction
We explore a closer relationship between convolution and self-attention in the sense of sharing the
same computation overhead (1×1 convolutions), and combining with the remaining lightweight aggregation operations.
Results
- Top-1 accuracy on ImageNet v.s. Multiply-Adds
Pretrained Models
| Backbone Models | Params | FLOPs | Top-1 Acc | Links |
|---|---|---|---|---|
| SAN-10 | 12.1M | 1.9G | 77.6 (+0.5) | In process |
| SAN-15 | 16.6M | 2.7G | 78.4 (+0.4) | In process |
| SAN-19 | 21.2M | 3.4G | 78.7 (+0.5) | In process |
| PVT-T | 13M | 2.0G | 78.0 (+2.9) | In process |
| PVT-S | 25M | 3.9G | 81.7 (+1.9) | In process |
| Swin-T | 30M | 4.6G | 81.9 (+0.6) | Tsinghua Cloud / Google Drive |
| Swin-S | 51M | 9.0G | 83.5 (+0.5) | Tsinghua Cloud / Google Drive |
Get Started
Please go to the folder Swin-Transformer for specific docs.
Contact
If you have any question, please feel free to contact the authors. Xuran Pan: [email protected].
Acknowledgment
Our code is based on SAN, PVT, and Swin Transformer.
Citation
If you find our work is useful in your research, please consider citing:
@misc{pan2021integration,
title={On the Integration of Self-Attention and Convolution},
author={Xuran Pan and Chunjiang Ge and Rui Lu and Shiji Song and Guanfu Chen and Zeyi Huang and Gao Huang},
year={2021},
eprint={2111.14556},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
