Awesome
<h2 align="center">Multi-LoRA Fine-Tuned Segment Anything Model for Extraction of Urban Man-Made Objects </h2> <h5 align="center"> <a href="https://scholar.google.com/citations?user=MDA37NMAAAAJ&hl=zh-CN">Xiaoyan LU</a> and <a>Qihao WENG</a></h5>[Paper
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Multi-LoRA Fine-Tuned SAM Framework
<div align="center"> <img src="./img/SAM_LoRA.png?raw=true"> </div>The training dataset
- <b>DeepGlobe Road Training Dataset </b>: 4696 samples
- <b>SpaceNet Building AOI2 and AOI4 Dataset </b>: 8429 samples
The validation set
- <b>DeepGlobe Road Test Dataset </b>: 1530 samples
- <b>SpaceNet Building AOI3 and AOI5 Dataset </b>: 1148 (Paris) and 1101 (Khartoum) samples
- <b>The WHU building (Christchurch) dataset</b>: 2416 samples
- The trained weights of SAM_Adapter, SAM_LoRA (r=96), and SAM_MLoRA (r=32,n=3) are released at <b>Baidu Drive</b>, Code: MODE
Road Extraction
SAM_Adapter
python train_sam_adapter.py --name='b_adapter_sam'
SAM_LoRA (r=96)
python train_sam_adapter.py --name='b_adapter_sam_lora96_96'
SAM_MLoRA (r=32,n=3)
python train_sam_adapter.py --name='b_adapter_sam_multi_lora'
Building Extraction
SAM_Adapter
python train_sam_adapter_build.py --name='b_adapter_sam_sp24'
SAM_LoRA (r=96)
python train_sam_adapter_build.py --name='b_adapter_sam_lora96_96_sp24'
SAM_MLoRA (r=32,n=3)
python train_sam_adapter_build.py --name='b_adapter_sam_multi_lora32_sp24'
Citation
If this code or dataset contributes to your research, please kindly consider citing our paper :)
@article{Lu2024MLoRA,
title = {Multi-LoRA Fine-Tuned Segment Anything Model for Urban Man-Made Object Extraction},
author = {Xiaoyan LU and Qihao Weng},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = {62},
pages = {1-19},
year = {2024},
doi = {https://doi.org/10.1109/TGRS.2024.3435745}
}