Squeeze-enhanced axial Transformer
Paper
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.
SeaFormer achieves superior trade-off between performance and latency
The overall architecture of Seaformer
The schematic illustration of the SeaFormer layer
Model Zoo
Image Classification
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 |
Semantic Segmentation
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 |
Citation
@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}
}
Acknowledgment
Thanks to previous open-sourced repo:
TopFormer
mmsegmentation
pytorch-image-models