/SeaFormer

[ICLR 2023] SeaFormer: Squeeze-enhanced Axial Transformer for Mobile Semantic Segmentation

Primary LanguagePython

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