Tianrun Chen, Lanyun Zhu, Chaotao Ding, Runlong Cao, Yan Wang, Zejian Li, Lingyun Sun, Papa Mao, Ying Zang
KOKONI, Moxin Technology (Huzhou) Co., LTD , Zhejiang University, Singapore University of Technology and Design, Huzhou University, Beihang University.
Update on 28 April: We tested the performance of polyp segmentation to show our approach can also work on medical datasets.
Update on 22 April: We report our SOTA result based on ViT-H version of SAM (use demo.yaml). We have also uploaded the yaml config for ViT-L and ViT-B version of SAM, suitable GPU with smaller memory (e.g. NVIDIA Tesla V-100), although they may compromise on accuracy.
This code was implemented with Python 3.8 and PyTorch 1.13.0. You can install all the requirements via:
pip install -r requirements.txt
- Download the dataset and put it in ./load.
- Download the pre-trained SAM(Segment Anything) and put it in ./pretrained.
- Training:
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nnodes 1 --nproc_per_node 4 loadddptrain.py --config configs/demo.yaml
!Please note that the SAM model consume much memory. We use 4 x A100 graphics card for training. If you encounter the memory issue, please try to use graphics cards with larger memory!
- Evaluation:
python test.py --config [CONFIG_PATH] --model [MODEL_PATH]
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch train.py --nnodes 1 --nproc_per_node 4 --config [CONFIG_PATH]
python test.py --config [CONFIG_PATH] --model [MODEL_PATH]
To be uploaded
If you find our work useful in your research, please consider citing:
@misc{chen2023sam,
title={SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, and More},
author={Tianrun Chen and Lanyun Zhu and Chaotao Ding and Runlong Cao and Shangzhan Zhang and Yan Wang and Zejian Li and Lingyun Sun and Papa Mao and Ying Zang},
year={2023},
eprint={2304.09148},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
The part of the code is derived from Explicit Visual Prompt by
Weihuang Liu, Xi Shen, Chi-Man Pun, and Xiaodong Cun by University of Macau and Tencent AI Lab.