Awesome
SAM-HTJONet
This repo is the implementation of "Hybrid Training Driven Joint-Optimized Unsupervised Domain Adaptation in Remote Sensing Segmentation with Prompted Foundation Model". We refer to mmsegmentation and mmagic. Many thanks to SenseTime and their two excellent repos.
<table> <tr> <td><img src="PaperFigs\Fig1.png" width = "100%" alt="SAM-JOANet"/></td> </tr> </table>Dataset Preparation
We select ISPRS (Postsdam/Vaihingen) and CITY-OSM (Paris/Chicago) as benchmark datasets.
We follow ST-DASegNet for detailed dataset preparation.
<table> <tr> <td><img src="PaperFigs\tree_data.png" width = "100%" alt="tree-data"/></td> </tr> </table>SAM-HTJONet
Install
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requirements:
python >= 3.7
pytorch >= 1.11
cuda >= 11.7
This version depends on mmengine and mmcv (2.0.1)
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prerequisites: Please refer to MMSegmentation PREREQUISITES.
cd SAM-HTJONet pip install -e . chmod 777 ./tools/dist_train.sh chmod 777 ./tools/dist_test.sh
Training
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ISPRS UDA-RSSeg task:
cd SAM-HTJONet ./tools/dist_train.sh ./experiments/SAM_UDA_Sb5PromptSTAdv_bit-b16_upernet.py 2
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CITY-OSM UDA_RSSeg task:
cd SAM-HTJONet ./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.
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ISPRS UDA-RSSeg task:
cd SAM-HTJONet ./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
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CITY-OSM UDA_RSSeg task:
cd SAM-HTJONet 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
[ArXiv version of this paper] (https://arxiv.org/abs/2411.05878).
If you have any question, please discuss with me by sending email to lyushuchang@buaa.edu.cn.
References
Many thanks to their excellent works