InPeerReview/E2A-IRSTD

🛠️ Requirements

Environment

• Python 3.8+
• PyTorch 1.13.0+
• CUDA 11.6+
• Ubuntu 18.04 or higher / Windows 10

Installation

# Create conda environment
conda create -n dccs python=3.8 -y
conda activate dccs

# Install PyTorch
pip install torch==1.13.0 torchvision==0.14.0 torchaudio==0.13.0

# Install dependencies
pip install packaging
pip install timm==0.4.12
pip install pytest chardet yacs termcolor
pip install submitit tensorboardX
pip install triton==2.0.0
pip install causal_conv1d==1.0.0
pip install mamba_ssm==1.0.1

# Or simply run
pip install -r requirements.txt

📁 Dataset Preparation

We evaluate our method on three public datasets: IRSTD-1K, NUAA-SIRST, and SIRST-Aug.

Dataset	Link
IRSTD-1K	Download
NUAA-SIRST	Download
SIRST-Aug	Download

Please organize the datasets as follows:

├── dataset/
│    ├── IRSTD-1K/
│    │    ├── images/
│    │    │    ├── XDU514png
│    │    │    ├── XDU646.png
│    │    │    └── ...
│    │    ├── masks/
│    │    │    ├── XDU514.png
│    │    │    ├── XDU646.png
│    │    │    └── ...
│    │    └── trainval.txt
│    │    └── test.txt
│    ├── NUAA-SIRST/
│    │    └── ...
│    └── SIRST-Aug/
│         └── ...

🚀 Training

python main.py --dataset-dir '/path/to/dataset' \
               --batch-size 4 \
               --epochs 400 \
               --lr 0.05 \
               --mode 'train'

Example:

python main.py --dataset-dir './dataset/IRSTD-1K' --batch-size 4 --epochs 400 --lr 0.05 --mode 'train'

📊 Testing

python main.py --dataset-dir '/path/to/dataset' \
               --batch-size 4 \
               --mode 'test' \
               --weight-path '/path/to/weight.tar'

Example:

python main.py --dataset-dir './dataset/IRSTD-1K' --batch-size 4 --mode 'test' --weight-path './weight/irstd1k_weight.pkl'

📈 Results

Quantitative Results

Dataset	IoU (×10⁻²)	Pd (×10⁻²)	Fa (×10⁻⁶)	Weights
IRSTD-1K	69.64	95.58	10.48	Download
NUAA-SIRST	78.65	78.65	2.48	Download
SIRST-Aug	75.57	98.90	33.46	Download

📂 Project Structure

DCCS/
├── dataset/          # Dataset loading and preprocessing
├── model/            # Network architecture
├── utils/            # Utility functions
├── weight/           # Pretrained weights
├── main.py           # Main entry point
├── requirements.txt  # Dependencies
└── README.md

🙏 Acknowledgement

We sincerely thank the following works for their contributions:

• BasicIRSTD - A comprehensive toolbox
• MSHNet - Scale and Location Sensitive Loss