SeaFormer: Squeeze-enhanced Axial Transformer for Mobile Semantic Segmentation,
Qiang Wan, Zilong Huang, Jiachen Lu, Gang Yu, Li Zhang
ICLR 2023
This repository contains the official implementation of SeaFormer.
Classification configs & weights see >>>here<<<.
- SeaFormer on ImageNet-1K
| Model | Size | Acc@1 | #Params (M) | FLOPs (G) |
|---|---|---|---|---|
| SeaFormer-Tiny | 224 | 68.1 | 1.8 | 0.1 |
| SeaFormer-Small | 224 | 73.4 | 4.1 | 0.2 |
| SeaFormer-Base | 224 | 76.4 | 8.7 | 0.3 |
| SeaFormer-Large | 224 | 79.9 | 14.0 | 1.2 |
Segmentation configs & weights see >>>here<<<.
- SeaFormer on ADE20K
| Method | Backbone | Pretrain | Iters | mIoU(ss) |
|---|---|---|---|---|
| Light Head | SeaFormer-Tiny | ImageNet-1K | 160K | 36.5 |
| Light Head | SeaFormer-Small | ImageNet-1K | 160K | 39.4 |
| Light Head | SeaFormer-Base | ImageNet-1K | 160K | 41.9 |
| Light Head | SeaFormer-Large | ImageNet-1K | 160K | 43.8 |
- SeaFormer on Cityscapes
| Method | Backbone | FLOPs | mIoU |
|---|---|---|---|
| Light Head(h) | SeaFormer-Small | 2.0G | 71.1 |
| Light Head(f) | SeaFormer-Small | 8.0G | 76.4 |
| Light Head(h) | SeaFormer-Base | 3.4G | 72.2 |
| Light Head(f) | SeaFormer-Base | 13.7G | 77.7 |
@inproceedings{wan2023seaformer,
title = {SeaFormer: Squeeze-enhanced Axial Transformer for Mobile Semantic Segmentation},
author = {Wan, Qiang and Huang, Zilong and Lu, Jiachen and Yu, Gang and Zhang, Li},
booktitle = {International Conference on Learning Representations (ICLR)},
year = {2023}
}
Thanks to previous open-sourced repo:
TopFormer
mmsegmentation
pytorch-image-models
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent