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https://github.com/ggsDing/SAM-CD
Pytorch code of the SAM-CD
https://github.com/ggsDing/SAM-CD
Last synced: 3 months ago
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Pytorch code of the SAM-CD
- Host: GitHub
- URL: https://github.com/ggsDing/SAM-CD
- Owner: ggsDing
- Created: 2023-08-30T08:17:38.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-30T03:41:10.000Z (6 months ago)
- Last Synced: 2024-07-23T02:41:11.625Z (4 months ago)
- Language: Python
- Size: 425 KB
- Stars: 131
- Watchers: 7
- Forks: 11
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-remote-sensing-change-detection - Ding L, Zhu K, Peng D, et al. Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images
README
# SAM-CD
Pytorch codes of **Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images** [[paper](https://ieeexplore.ieee.org/document/10443350)]
The SAM-CD adopts [FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM) as the visual encoder with some modifications.
## 2024-4-30 Update:
SAM-CD now supports access to [efficientSAM](https://github.com/yformer/EfficientSAM). Check the updated model at ```models/effSAM_CD.py``` (prior installation of efficientSAM at the project folder is required). However, direct integration of efficientSAM may cause an accuracy drop, so there is space to further improve the SAM-CD architecture.
## How to Use
1. Installation
* Install [FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM) following the instructions.
* Modify the Ultralytics source files following the instructions at: ['SAM-CD/models/FastSAM/README.md'](https://github.com/ggsDing/SAM-CD/blob/main/models/FastSAM/README.md).
2. Dataset preparation.
* Please split the data into training, validation and test sets and organize them as follows:
```
YOUR_DATA_DIR
├── ...
├── train
│ ├── A
│ ├── B
│ ├── label
├── val
│ ├── A
│ ├── B
│ ├── label
├── test
│ ├── A
│ ├── B
│ ├── label
```
* Find change line 13 in [SAM-CD/datasets/Levir_CD.py](https://github.com/ggsDing/SAM-CD/blob/main/datasets/Levir_CD.py) (or other data-loading .py files), change `/YOUR_DATA_ROOT/` to your local dataset directory.
3. Training
classic CD training:
`python train_CD.py`
training CD with the proposed task-agnostic semantic learning:
`python train_SAM_CD.py`
line 16-45 are the major training args, which can be changed to load different datasets, models and adjust the training settings.
5. Inference and evaluation
inference on test sets: set the chkpt_path and run
`python pred_CD.py`
evaluation of accuracy: set the prediction dir and GT dir, and run
`python eval_CD.py`
(More details to be added...)
## Dataset Download
In the following, we summarize links to some frequently used CD datasets:
* [LEVIR-CD](https://justchenhao.github.io/LEVIR/)
* [WHU-CD](https://study.rsgis.whu.edu.cn/pages/download/) [(baidu)](https://pan.baidu.com/s/1A0_xbV4ZktWCbL3j94CInA?pwd=WHCD )
* [CLCD (Baidu)](https://pan.baidu.com/s/1iZtAq-2_vdqoz1RnRtivng?pwd=CLCD)
* [S2Looking](https://github.com/S2Looking/Dataset)
* [SYSU-CD](https://github.com/liumency/SYSU-CD)
## Pretrained Models
For readers to easily evaluate the accuracy, we provide the trained weights of the SAM-CD.
[Drive](https://drive.google.com/drive/folders/14tNtID43o-LHs8VaMK5jai1Uf8NqMDAW?usp=sharing)
[Baidu](https://pan.baidu.com/s/1V25TFGL5V05ZB5ttFXFSEA?pwd=SMCD) (pswd: SMCD)
## Cite SAM-CD
If you find this work useful or interesting, please consider citing the following BibTeX entry.
```
@article{ding2024adapting,
title={Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images},
author={Ding, Lei and Zhu, Kun and Peng, Daifeng and Tang, Hao and Yang, Kuiwu and Bruzzone, Lorenzo},
journal={IEEE Transactions on Geoscience and Remote Sensing},
year={2024},
volume={62},
pages={1-11},
doi={10.1109/TGRS.2024.3368168}
}
```