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--- |
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license: mit |
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--- |
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## 🛠️ Requirements |
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### Environment |
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- **Python** 3.10+ |
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- **PyTorch** 1.13.0+ |
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- **CUDA** 11.6+ |
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- **Ubuntu** 18.04 or higher / Windows 10 |
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### Installation |
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```bash |
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# Create conda environment |
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conda create -n dccs python=3.8 -y |
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conda activate dccs |
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# Install PyTorch |
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pip install torch==1.13.0 torchvision==0.14.0 torchaudio==0.13.0 |
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# Install dependencies |
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pip install packaging |
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pip install timm==0.4.12 |
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pip install pytest chardet yacs termcolor |
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pip install submitit tensorboardX |
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pip install triton==2.0.0 |
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pip install causal_conv1d==1.0.0 |
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pip install mamba_ssm==1.0.1 |
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# Or simply run |
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pip install -r requirements.txt |
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``` |
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## 📁 Dataset Preparation |
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We evaluate our method on three public datasets: **IRSTD-1K**, **NUAA-SIRST**, and **SIRST-Aug**. |
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| Dataset | Link | |
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|---------|------| |
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| IRSTD-1K | [Download](https://github.com/RuiZhang97/ISNet) | |
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| NUAA-SIRST | [Download](https://github.com/YimianDai/sirst) | |
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| SIRST-Aug | [Download](https://github.com/Tianfang-Zhang/AGPCNet) | |
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Please organize the datasets as follows: |
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``` |
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├── dataset/ |
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│ ├── IRSTD-1K/ |
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│ │ ├── images/ |
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│ │ │ ├── XDU514png |
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│ │ │ ├── XDU646.png |
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│ │ │ └── ... |
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│ │ ├── masks/ |
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│ │ │ ├── XDU514.png |
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│ │ │ ├── XDU646.png |
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│ │ │ └── ... |
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│ │ └── trainval.txt |
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│ │ └── test.txt |
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│ ├── NUAA-SIRST/ |
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│ │ └── ... |
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│ └── SIRST-Aug/ |
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│ └── ... |
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``` |
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## 🚀 Training |
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```bash |
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python main.py --dataset-dir '/path/to/dataset' \ |
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--batch-size 4 \ |
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--epochs 400 \ |
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--lr 0.05 \ |
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--mode 'train' |
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``` |
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**Example:** |
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```bash |
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python main.py --dataset-dir './dataset/IRSTD-1K' --batch-size 4 --epochs 400 --lr 0.05 --mode 'train' |
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``` |
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## 📊 Testing |
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```bash |
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python main.py --dataset-dir '/path/to/dataset' \ |
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--batch-size 4 \ |
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--mode 'test' \ |
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--weight-path '/path/to/weight.tar' |
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``` |
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**Example:** |
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```bash |
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python main.py --dataset-dir './dataset/IRSTD-1K' --batch-size 4 --mode 'test' --weight-path './weight/irstd1k_weight.pkl' |
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``` |
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## 📈 Results |
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### Quantitative Results |
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| Dataset | IoU (×10⁻²) | Pd (×10⁻²) | Fa (×10⁻⁶) | Weights | |
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|:-------:|:------------:|:----------:|:----------:|:-------:| |
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| IRSTD-1K | 69.64 | 95.58 | 10.48 | [Download](https://drive.google.com/file/d/1KqlOVWIktfrBrntzr53z1eGnrzjWCWSe/view?usp=sharing) | |
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| NUAA-SIRST | 78.65 | 78.65 | 2.48 | [Download](https://drive.google.com/file/d/13JQ3V5xhXUcvy6h3opKs15gseuaoKrSQ/view?usp=sharing) | |
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| SIRST-Aug | 75.57 | 98.90 | 33.46 | [Download](https://drive.google.com/file/d/1lcmTgft0LStM7ABWDIMRHTkcOv95p9LO/view?usp=sharing) | |
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## 📂 Project Structure |
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``` |
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DCCS/ |
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├── dataset/ # Dataset loading and preprocessing |
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├── model/ # Network architecture |
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├── utils/ # Utility functions |
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├── weight/ # Pretrained weights |
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├── main.py # Main entry point |
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├── requirements.txt # Dependencies |
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└── README.md |
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``` |
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## 🙏 Acknowledgement |
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We sincerely thank the following works for their contributions: |
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- [BasicIRSTD](https://github.com/XinyiYing/BasicIRSTD) - A comprehensive toolbox |
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- [MSHNet](https://github.com/ying-fu/MSHNet) - Scale and Location Sensitive Loss |
