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https://github.com/dyzy41/ChangeCLIP
https://github.com/dyzy41/ChangeCLIP
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/dyzy41/ChangeCLIP
- Owner: dyzy41
- License: apache-2.0
- Created: 2024-01-19T05:22:07.000Z (8 months ago)
- Default Branch: release
- Last Pushed: 2024-07-20T16:01:36.000Z (2 months ago)
- Last Synced: 2024-07-21T07:54:10.514Z (2 months ago)
- Language: Python
- Size: 5.63 MB
- Stars: 54
- Watchers: 3
- Forks: 1
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
- awesome-remote-sensing-change-detection - Dong S, Wang L, Du B, et al. ChangeCLIP: Remote sensing change detection with multimodal vision-language representation learning
README
# ChangeCLIP: Remote sensing change detection with multimodal vision-language representation learning
https://www.sciencedirect.com/science/article/pii/S0924271624000042
English | [简体中文](README_zh-CN.md)
## 1. In order to facilitate the use of relative paths, CDPATH is set in the ~/.bashrc file. Here is how to add this setting in the ~/.bashrc。
After adding CDPATH as mentioned above, you can quickly navigate to the respective data path in the following way:
```
import os
data_root = os.path.join(os.environ.get("CDPATH"), 'SYSU-CD')
```
## 2. I will use the SYSU-CD dataset as an example to introduce the usage of the code. First, use tools/general/write_path.py to generate a txt file for the dataset path. The format is as follows (for details, please refer to the code):
```
/home/user/dsj_files/CDdata/SYSU-CD/test/time1/03414.png /home/user/dsj_files/CDdata/SYSU-CD/test/time2/03414.png /home/user/dsj_files/CDdata/SYSU-CD/test/label/03414.png
/home/user/dsj_files/CDdata/SYSU-CD/test/time1/00708.png /home/user/dsj_files/CDdata/SYSU-CD/test/time2/00708.png /home/user/dsj_files/CDdata/SYSU-CD/test/label/00708.png
/home/user/dsj_files/CDdata/SYSU-CD/test/time1/03907.png /home/user/dsj_files/CDdata/SYSU-CD/test/time2/03907.png /home/user/dsj_files/CDdata/SYSU-CD/test/label/03907.png
/home/user/dsj_files/CDdata/SYSU-CD/test/time1/03107.png /home/user/dsj_files/CDdata/SYSU-CD/test/time2/03107.png /home/user/dsj_files/CDdata/SYSU-CD/test/label/03107.png
/home/user/dsj_files/CDdata/SYSU-CD/test/time1/02776.png /home/user/dsj_files/CDdata/SYSU-CD/test/time2/02776.png /home/user/dsj_files/CDdata/SYSU-CD/test/label/02776.png
/home/user/dsj_files/CDdata/SYSU-CD/test/time1/01468.png /home/user/dsj_files/CDdata/SYSU-CD/test/time2/01468.png /home/user/dsj_files/CDdata/SYSU-CD/test/label/01468.png
/home/user/dsj_files/CDdata/SYSU-CD/test/time1/00026.png /home/user/dsj_files/CDdata/SYSU-CD/test/time2/00026.png /home/user/dsj_files/CDdata/SYSU-CD/test/label/00026.png
/home/user/dsj_files/CDdata/SYSU-CD/test/time1/02498.png /home/user/dsj_files/CDdata/SYSU-CD/test/time2/02498.png /home/user/dsj_files/CDdata/SYSU-CD/test/label/02498.png
/home/user/dsj_files/CDdata/SYSU-CD/test/time1/02439.png /home/user/dsj_files/CDdata/SYSU-CD/test/time2/02439.png /home/user/dsj_files/CDdata/SYSU-CD/test/label/02439.png
/home/user/dsj_files/CDdata/SYSU-CD/test/time1/01057.png /home/user/dsj_files/CDdata/SYSU-CD/test/time2/01057.png /home/user/dsj_files/CDdata/SYSU-CD/test/label/01057.png
```
## 3.Use the CLIP model to perform inference on the SYSU-CD dataset. https://github.com/openai/CLIP, Generate a confidence JSON file.
3.1 First, it is necessary to install the CLIP project. Run the following command:
```
conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0
pip install ftfy regex tqdm
pip install git+https://github.com/openai/CLIP.git
```
3.2 Then run the following command:
```
cd tools
bash clip_infer_sysu.sh
```
3.3 After running the command, the following files will be generated:
```
/home/user/dsj_files/CDdata/SYSU-CD/train/time1_clipcls_56_vit16.json
/home/user/dsj_files/CDdata/SYSU-CD/train/time2_clipcls_56_vit16.json
/home/user/dsj_files/CDdata/SYSU-CD/val/time1_clipcls_56_vit16.json
/home/user/dsj_files/CDdata/SYSU-CD/val/time2_clipcls_56_vit16.json
/home/user/dsj_files/CDdata/SYSU-CD/test/time1_clipcls_56_vit16.json
/home/user/dsj_files/CDdata/SYSU-CD/test/time2_clipcls_56_vit16.json
```
## 4.For training and testing, You can view the contents of the tools/train.sh file and set the training plan yourself.
## 5.We have made the weights and log files of the training process public. If you cannot download the files of Baidu Netdisk, you can send me an email and I will reply in time and provide download links from other sources.
[ChangeCLIP_best_weights](https://pan.baidu.com/s/1MpmSofjJOBNx8ZCn2BWugQ), passwd：2f0p
google drive: https://drive.google.com/file/d/1WEkyyxezXU3bQSjUbffGu5TkmTax8tL2/view?usp=drive_link
## 6. The following is the comparison between ChangeCLIP and advanced algorithms.
# Acknowledgements
This repo benefits from awesome works of [mmsegmentation](https://github.com/open-mmlab/mmsegmentation), [DenseCLIP](https://github.com/raoyongming/DenseCLIP),
[CLIP](https://github.com/openai/CLIP). Please also consider citing them.
# Cite
```bibtex
@article{DONG202453,
title = {ChangeCLIP: Remote sensing change detection with multimodal vision-language representation learning},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {208},
pages = {53-69},
year = {2024},
issn = {0924-2716},
doi = {https://doi.org/10.1016/j.isprsjprs.2024.01.004},
url = {https://www.sciencedirect.com/science/article/pii/S0924271624000042},
author = {Sijun Dong and Libo Wang and Bo Du and Xiaoliang Meng}
}
```