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# SwiftFormer # SwiftFormer
### **SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications** ### **SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications**
[Abdelrahman Shaker](https://scholar.google.com/citations?hl=en&user=eEz4Wu4AAAAJ), ![](https://i.imgur.com/waxVImv.png)
[Muhammad Maaz](https://scholar.google.com/citations?user=vTy9Te8AAAAJ&hl=en&authuser=1&oi=sra), [Abdelrahman Shaker](https://scholar.google.com/citations?hl=en&user=eEz4Wu4AAAAJ)<sup>*1</sup>, [Muhammad Maaz](https://scholar.google.com/citations?user=vTy9Te8AAAAJ&hl=en&authuser=1&oi=sra)<sup>1</sup>, [Hanoona Rasheed](https://scholar.google.com/citations?user=yhDdEuEAAAAJ&hl=en&authuser=1&oi=sra)<sup>1</sup>, [Salman Khan](https://salman-h-khan.github.io/)<sup>1</sup>, [Ming-Hsuan Yang](https://scholar.google.com/citations?user=p9-ohHsAAAAJ&hl=en)<sup>2,3</sup> and [Fahad Shahbaz Khan](https://scholar.google.es/citations?user=zvaeYnUAAAAJ&hl=en)<sup>1,4</sup>
[Hanoona Rasheed](https://scholar.google.com/citations?user=yhDdEuEAAAAJ&hl=en&authuser=1&oi=sra),
[Salman Khan](https://salman-h-khan.github.io),
[Ming-Hsuan Yang](https://scholar.google.com/citations?user=p9-ohHsAAAAJ&hl=en),
and [Fahad Shahbaz Khan](https://scholar.google.es/citations?user=zvaeYnUAAAAJ&hl=en)
Mohamed Bin Zayed University of Artificial Intelligence<sup>1</sup>, University of California Merced<sup>2</sup>, Google Research<sup>3</sup>, Linkoping University<sup>4</sup>
<!-- [![Website](https://img.shields.io/badge/Project-Website-87CEEB)](site_url) --> <!-- [![Website](https://img.shields.io/badge/Project-Website-87CEEB)](site_url) -->
[![paper](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2303.15446) [![paper](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2303.15446)
<!-- [![video](https://img.shields.io/badge/Video-Presentation-F9D371)](youtube_link) --> <!-- [![video](https://img.shields.io/badge/Video-Presentation-F9D371)](youtube_link) -->
<!-- [![slides](https://img.shields.io/badge/Presentation-Slides-B762C1)](presentation) --> <!-- [![slides](https://img.shields.io/badge/Presentation-Slides-B762C1)](presentation) -->
## :rocket: News ## :rocket: News
* **(Jul 14, 2023):** SwiftFormer has been accepted at ICCV 2023. :fire::fire:
* **(Mar 27, 2023):** Classification training and evaluation codes along with pre-trained models are released. * **(Mar 27, 2023):** Classification training and evaluation codes along with pre-trained models are released.
<hr /> <hr />
@@ -99,7 +97,7 @@ To train SwiftFormer models on an 8-GPU machine:
sh dist_train.sh /path/to/imagenet 8 sh dist_train.sh /path/to/imagenet 8
``` ```
Note: specify which model command you want to run in the script. To reproduce the results of the paper, use 16-GPU machine with batch-size of 128 or 8-GPU machine with batch size of 256. Auto Augmentation, CutMix, MixUp are disabled for SwiftFormer-XS only. Note: specify which model command you want to run in the script. To reproduce the results of the paper, use 16-GPU machine with batch-size of 128 or 8-GPU machine with batch size of 256. Auto Augmentation, CutMix, MixUp are disabled for SwiftFormer-XS, and CutMix, MixUp are disabled for SwiftFormer-S.
### Multi-node training ### Multi-node training