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LICENSE

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+MIT License
+
+Copyright (c) 2023 tianrun-chen
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+## SAM-adapter: Adapting SAM in Underperformed Scenes
+
+Tianrun Chen, Lanyun Zhu, Chaotao Ding, Runlong Cao, Yan Wang, Shangzhan Zhang, Zejian Li, Lingyun Sun, Papa Mao, Ying Zang
+
+<a href='https://www.kokoni3d.com/'> KOKONI, Moxin Technology (Huzhou) Co., LTD </a>, Zhejiang University, Singapore University of Technology and Design, Huzhou University, Beihang University.
+
+In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 3367-3375).
+
+  <a href='https://tianrun-chen.github.io/SAM-Adaptor/'><img src='https://img.shields.io/badge/Project-Page-Green'></a>
+## 
+
+<a href='https://arxiv.org/abs/2304.09148'><img src='https://img.shields.io/badge/ArXiv-2304.09148-red' /></a> 
+Update on 30 August: This paper will be prsented at ICCV 2023. 
+
+Update on 28 April: We tested the performance of polyp segmentation to show our approach can also work on medical datasets.
+<img src='https://tianrun-chen.github.io/SAM-Adaptor/static/images/polyp.jpg'>
+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.
+
+## Environment
+This code was implemented with Python 3.8 and PyTorch 1.13.0. You can install all the requirements via:
+```bash
+pip install -r requirements.txt
+```
+
+
+## Quick Start
+1. Download the dataset and put it in ./load.
+2. Download the pre-trained [SAM(Segment Anything)](https://github.com/facebookresearch/segment-anything) and put it in ./pretrained.
+3. Training:
+```bash
+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!
+
+
+4. Evaluation:
+```bash
+python test.py --config [CONFIG_PATH] --model [MODEL_PATH]
+```
+
+## Train
+```bash
+CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch train.py --nnodes 1 --nproc_per_node 4 --config [CONFIG_PATH]
+```
+Updates on 30 July. As mentioned by @YunyaGaoTree in issue #39
+You can also try to use the code below to gain (probably) faster training.
+```bash
+!torchrun train.py --config configs/demo.yaml
+CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nnodes 1 --nproc_per_node 4 loadddptrain.py --config configs/demo.yaml
+```
+
+## Test
+```bash
+python test.py --config [CONFIG_PATH] --model [MODEL_PATH]
+```
+
+## Pre-trained Models
+https://drive.google.com/file/d/1MMUytUHkAQvMRFNhcDyyDlEx_jWmXBkf/view?usp=sharing
+
+## Dataset
+
+### Camouflaged Object Detection
+- **[COD10K](https://github.com/DengPingFan/SINet/)**
+- **[CAMO](https://drive.google.com/open?id=1h-OqZdwkuPhBvGcVAwmh0f1NGqlH_4B6)**
+- **[CHAMELEON](https://www.polsl.pl/rau6/datasets/)**
+
+### Shadow Detection
+- **[ISTD](https://github.com/DeepInsight-PCALab/ST-CGAN)**
+
+### Polyp Segmentation - Medical Applications
+- **[Kvasir](https://datasets.simula.no/kvasir-seg/)**
+
+## Citation
+
+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}
+}
+```
+
+## Acknowledgements
+The part of the code is derived from Explicit Visual Prompt   <a href='https://nifangbaage.github.io/Explicit-Visual-Prompt/'><img src='https://img.shields.io/badge/Project-Page-Green'></a> by 
+Weihuang Liu, [Xi Shen](https://xishen0220.github.io/), [Chi-Man Pun](https://www.cis.um.edu.mo/~cmpun/), and [Xiaodong Cun](https://vinthony.github.io/) by University of Macau and Tencent AI Lab.
+

requirements.txt

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+imageio==2.9.0
+ipython~=8.11.0
+matplotlib~=3.1.2
+opencv-python~=4.7.0.72
+PyYAML~=6.0
+scikit-learn~=1.2.2
+scipy~=1.10.1
+
+tqdm~=4.51.0
+torch~=1.13.0+cu116
+numpy~=1.20.3
+typing~=3.7.4.3
+terminaltables~=3.1.10
+Pillow~=9.4.0
+torchvision~=0.14.0+cu116
+tensorboardX~=2.6
+onnxruntime~=1.14.1
+setuptools~=67.6.1
+timm~=0.3.2
+easydict~=1.10
+attr~=0.3.2
+thop~=0.1.1.post2209072238
+torchsummary~=1.5.1