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https://github.com/CV-ShuchangLyu/SAM-JOANet
https://github.com/CV-ShuchangLyu/SAM-JOANet
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- Host: GitHub
- URL: https://github.com/CV-ShuchangLyu/SAM-JOANet
- Owner: CV-ShuchangLyu
- License: apache-2.0
- Created: 2024-08-11T14:20:36.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-11-06T04:07:26.000Z (2 months ago)
- Last Synced: 2024-11-06T05:18:14.919Z (2 months ago)
- Language: Python
- Size: 7.57 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
- Awesome-Segment-Anything - [code
README
# SAM-JOANet
This repo is the implementation of "Joint-Optimized Unsupervised Adversarial Domain Adaptation in Remote Sensing Segmentation with Prompted Foundation Model". We refer to [mmsegmentation](https://github.com/open-mmlab/mmsegmentation) and [mmagic](https://github.com/open-mmlab/mmagic). Many thanks to SenseTime and their two excellent repos.
## Dataset Preparation
We select ISPRS (Postsdam/Vaihingen) and CITY-OSM (Paris/Chicago) as benchmark datasets.
**We follow [ST-DASegNet](https://github.com/cv516Buaa/ST-DASegNet) for detailed dataset preparation.**
## SAM-JOANet
### Install
1. requirements:
python >= 3.7
pytorch >= 1.11
cuda >= 11.7
**This version depends on mmengine and mmcv (2.0.1)**
3. prerequisites: Please refer to [MMSegmentation PREREQUISITES](https://mmsegmentation.readthedocs.io/en/latest/get_started.html).
```
cd SAM-JOANet
pip install -e .
chmod 777 ./tools/dist_train.sh
chmod 777 ./tools/dist_test.sh
```
### Training
1. ISPRS UDA-RSSeg task:
```
cd SAM-JOANet
./tools/dist_train.sh ./experiments/SAM_UDA_Sb5PromptSTAdv_bit-b16_upernet.py 2
```
2. CITY-OSM UDA_RSSeg task:
```
cd SAM-JOANet
./tools/dist_train.sh ./experiments/SAM_UDA_Sb5PromptSTAdv_bit-b16_upernet_P2C.py 2
```
### Testing
Trained with the above commands, you can get your trained model to test the performance of your model.
1. ISPRS UDA-RSSeg task:
```
cd SAM-JOANet
./tools/dist_test.sh ./experiments/SAM_UDA_Sb5PromptSTAdv_bit-b16_upernet.py ./experiments/SAM_UDA_Sb5PromptSTAdv_bit-b16_upernet_results/iter_11000_P2V_66.86.pth
```
2. CITY-OSM UDA_RSSeg task:
```
cd SAM-JOANet
CUDA_VISIBLE_DEVICES=1 python ./tools/test.py ./experiments/SAM_UDA_Sb5PromptSTAdv_bit-b16_upernet_P2C.py ./experiments/iter_35000_P2C_56.96.pth --show-dir ./P2C_results
```
The ArXiv version of this paper will be release soon.
If you have any question, please discuss with me by sending email to [email protected].
# References
Many thanks to their excellent works
* [mmsegmentation](https://github.com/open-mmlab/mmsegmentation)
* [mmagic](https://github.com/open-mmlab/mmagic)