restnet's Introduction

RestNet: Boosting Cross-Domain Few-Shot Segmentation with Residual Transformation Network

Datasets

PASCAL VOC2012:

Download PASCAL VOC2012 devkit (train/val data):
```
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
```
Download PASCAL VOC2012 SDS extended mask annotations from [Google Drive].
FSS-1000:

Home: https://github.com/HKUSTCV/FSS-1000

Direct: https://drive.google.com/file/d/16TgqOeI_0P41Eh3jWQlxlRXG9KIqtMgI/view
ISIC2018:

Home: http://challenge2018.isic-archive.com

Direct (must login): https://challenge.isic-archive.com/data#2018
Chest X-ray:

Home: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4256233/

Direct: https://www.kaggle.com/datasets/nikhilpandey360/chest-xray-masks-and-labels

Training

PASCAL VOC

python train.py --backbone {vgg16, resnet50} 
                --fold 4 
                --benchmark pascal
                --lr 1e-3
                --bsz 20
                --logpath "your_experiment_name"

Testing

FSS-1000

python test.py --backbone {vgg16, resnet50} 
               --benchmark fss 
               --nshot {1, 5} 
               --load "path_to_trained_model/best_model.pt"

ISIC

python test.py --backbone {vgg16, resnet50} 
               --fold {0, 1, 2, 3} 
               --benchmark isic 
               --nshot {1, 5} 
               --load "path_to_trained_model/best_model.pt"

Chest X-ray

python test.py --backbone {vgg16, resnet50} 
               --benchmark lung 
               --nshot {1, 5} 
               --load "path_to_trained_model/best_model.pt"

Acknowledgement

The implementation is highly based on HSNet and PATNet.
Thank them for their great work.

Citation

@misc{huang2023restnet,
      title={RestNet: Boosting Cross-Domain Few-Shot Segmentation with Residual Transformation Network}, 
      author={Xinyang Huang and Chuang Zhu and Wenkai Chen},
      year={2023},
      eprint={2308.13469},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}