Update README.md

README.md

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 ---
 license: mit
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-## 🛠️ Requirements
-
-### Environment
-- **Python** 3.10+
-- **PyTorch** 1.13.0+
-- **CUDA** 11.6+
-- **Ubuntu** 18.04 or higher / Windows 10
-
-### Installation
-
-```bash
-# 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](https://github.com/RuiZhang97/ISNet) |
-| NUAA-SIRST | [Download](https://github.com/YimianDai/sirst) |
-| SIRST-Aug | [Download](https://github.com/Tianfang-Zhang/AGPCNet) |
-
-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
-
-```bash
-python main.py --dataset-dir '/path/to/dataset' \
-               --batch-size 4 \
-               --epochs 400 \
-               --lr 0.05 \
-               --mode 'train'
-```
-
-**Example:**
-```bash
-python main.py --dataset-dir './dataset/IRSTD-1K' --batch-size 4 --epochs 400 --lr 0.05 --mode 'train'
-```
-
-## 📊 Testing
-
-```bash
-python main.py --dataset-dir '/path/to/dataset' \
-               --batch-size 4 \
-               --mode 'test' \
-               --weight-path '/path/to/weight.tar'
-```
-
-**Example:**
-```bash
-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](https://drive.google.com/file/d/1KqlOVWIktfrBrntzr53z1eGnrzjWCWSe/view?usp=sharing) |
-| NUAA-SIRST | 78.65 | 78.65 | 2.48 | [Download](https://drive.google.com/file/d/13JQ3V5xhXUcvy6h3opKs15gseuaoKrSQ/view?usp=sharing) |
-| SIRST-Aug | 75.57 | 98.90 | 33.46 | [Download](https://drive.google.com/file/d/1lcmTgft0LStM7ABWDIMRHTkcOv95p9LO/view?usp=sharing) |
-
-
-## 📂 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](https://github.com/XinyiYing/BasicIRSTD) - A comprehensive toolbox 
-- [MSHNet](https://github.com/ying-fu/MSHNet) - Scale and Location Sensitive Loss