README.md

PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images

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📢 Latest Updates

• 20 Sep 2024: The arXiv version is released here. The code will be released soon.

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🎨 Overview

🎮 Getting Started

1.Install Environment

conda create --name pointsam python=3.10
conda activate pointsam

pip install torch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cu118
git clone https://github.com/Lans1ng/PointSAM.git
cd PointSAM
pip install -r requirements.txt

cd segment_anything_2
pip install -e .
cd ..

2.Prepare Dataset

WHU Building Dataset

• Dataset download address: WHU Building Dataset。

• For converting semantic label to instance label, you can refer to corresponding conversion script.

HRSID Dataset

• Dataset download address: HRSID Dataset.

NWPU VHR-10 Dataset

• Dataset download address: NWPU VHR-10 Dataset.

• Instance label download address: NWPU VHR-10 Instance Label.

For convenience, we have included all the JSON annotations in this repo, and you only need to download the corresponding images. Specifically, organize the dataset as follows:

data 
├── WHU
│    ├── annotations
│    │   ├── WHU_building_train.json
│    │   ├── WHU_building_test.json
│    │   └── WHU_building_val.json
│    └── images
│        ├── train
│        │    ├── image
│        │    └── label
│        ├── val
│        │    ├── image
│        │    └── label
│        └── test
│             ├── image
│             └── label
├── HRSID
│    ├── Annotations
│    │   ├── all
│    │   ├── inshore
│    │   │      ├── inshore_test.json
│    │   │      └── inshore_train.json       
│    │   └── offshore
│    └── Images
└── NWPU
     ├── Annotations
     │   ├── NWPU_instnaces_train.json
     │   └── NWPU_instnaces_val.json
     └── Images

💡 Acknowledgement

• wesam
• OWOD
• RSPrompter

🖊️ Citation

If you find this project useful in your research, please consider cite:

@article{liu2024pointsam,
  title={PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images},
  author={Liu, Nanqing and Xu, Xun and Su, Yongyi and Zhang, Haojie and Li, Heng-Chao},
  journal={arXiv preprint arXiv:2409.13401},
  year={2024}
}