Stepwise Feature Fusion: Local Guides Global
This is the official implementation for Stepwise Feature Fusion: Local Guides Global
packages
- Please see requirements.txt
Dataset
- The dataset we used can be download from here
Result and Checkpoint
ssformer-S
dataset |
meanDic |
meanIou |
wFm |
Sm |
meanEm |
mae |
CVC-300 |
0.887 |
0.821 |
0.869 |
0.929 |
0.000 |
0.007 |
CVC-ClinicDB |
0.916 |
0.873 |
0.924 |
0.937 |
0.000 |
0.007 |
Kvasir |
0.925 |
0.878 |
0.921 |
0.931 |
0.000 |
0.017 |
CVC-ColonDB |
0.772 |
0.697 |
0.766 |
0.844 |
0.000 |
0.036 |
ETIS |
0.767 |
0.698 |
0.736 |
0.863 |
0.000 |
0.016 |
ssformer-L
dataset |
meanDic |
meanIou |
wFm |
Sm |
meanEm |
mae |
CVC-300 |
0.895 |
0.827 |
0.881 |
0.933 |
0.000 |
0.007 |
CVC-ClinicDB |
0.906 |
0.855 |
0.913 |
0.929 |
0.000 |
0.008 |
Kvasir |
0.917 |
0.864 |
0.916 |
0.922 |
0.000 |
0.022 |
CVC-ColonDB |
0.802 |
0.721 |
0.798 |
0.860 |
0.000 |
0.031 |
ETIS |
0.796 |
0.720 |
0.771 |
0.873 |
0.000 |
0.014 |
Checkpoints
- The checkpoint for ssformer-S can be downloaded from here
- The checkpoint for ssformer-L can be downloaded from here
Usage
Test
- modified
configs/ssformer-S.yaml
dataset
set to your data path
test.checkpoint_save_path
: path to your downloaded checkpoint
- run
python test.py configs/ssformer-S.yaml
Train
- modified
configs/train.yaml
model.pretrained_path
: mit pre-trained checkpoint path
other
: path to save your training checkpoint and log file
- run
python train.py configs/train.yaml
Citation
@article{wang2022stepwise,
title={Stepwise Feature Fusion: Local Guides Global},
author={Wang, Jinfeng and Huang, Qiming and Tang, Feilong and Meng, Jia and Su, Jionglong and Song, Sifan},
journal={arXiv preprint arXiv:2203.03635},
year={2022}
}