implementation for 'GETAM: Gradient-weighted Element-wise Transformer Attention Map for Weakly-supervised Semantic segmentation'
https://arxiv.org/abs/2112.02841
- clone this repo
git clone https://github.com/weixuansun/GETAM.git
- optionally create a new environment python>=3.6
- install requirements.txt
pip install -r requirements.txt
cd wrapper/bilateralfilter
python setup.py install
More details please see here
Training requires one GPU, you can change GPU setting accordingly.
CUDA_VISIBLE_DEVICES=7 python train_from_init.py --session_name getam_001 -n 1 -g 1 -nr 0 --max_epoches 20 --lr 0.04 --cls_step 0.5 --seg_lr_scale 0.1 --sal_loss True --backbone vitb_hybrid --address 1234 --voc12_root {path to pascal voc dataset} --saliencypath {path to saliency maps}
python test.py --weights {path to weight} --val True --backbone {backbone}
coming soon
| Dataset | Backbone | mIoU(val) | mIoU(test) | Checkpoint |
|---|---|---|---|---|
| PASCAL VOC 2012 | Vit_hybrid | 71.7 | 72.3 | Download |
| PASCAL VOC 2012 | Vit | 68.1 | 68.8 | Download |
| PASCAL VOC 2012 | deit | 66.0 | 68.9 | Download |
| PASCAL VOC 2012 | deit_distilled | 70.7 | 71.1 | Download |
| COCO | vit_hybrid | 36.4 | Download |
- Thanks for the saliency object detection code provided by UCNet
- Thanks for codebase provided by DPT
- Thanks for codebase provided by RRM
if you use this paper, please kindly cite:
@article{sun2021getam,
title={GETAM: Gradient-weighted Element-wise Transformer Attention Map for Weakly-supervised Semantic segmentation},
author={Sun, Weixuan and Zhang, Jing and Liu, Zheyuan and Zhong, Yiran and Barnes, Nick},
journal={arXiv preprint arXiv:2112.02841},
year={2021}
}