Update and rename readme to README.md

Author: startracker0

README.md

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+# PhysAug: A Physical-guided and Frequency-based Data Augmentation for Single-Domain Generalized Object Detection
+
+This repository contains the official implementation of our AAAI 2025 accepted paper:
+
+**"PhysAug: A Physical-guided and Frequency-based Data Augmentation for Single-Domain Generalized Object Detection"**
+
+## 🎯 Abstract
+
+PhysAug is a novel data augmentation technique designed for single-domain generalized object detection. By leveraging physical priors and frequency-based operations, PhysAug enhances the robustness of detection models under various challenging conditions, such as low-light or motion blur, while maintaining computational efficiency. Extensive experiments demonstrate the superior performance of PhysAug over existing methods, particularly in adverse real-world scenarios.
+
+## 📜 Highlights
+
+- **Physical-guided Augmentation**: Simulates real-world conditions using physical priors.
+- **Frequency-based Feature Simulation**: Operates in the frequency domain for precise and computationally efficient augmentation.
+- **Improved Robustness**: Enhances model performance in challenging conditions like diverse weather.
+- **Single-Domain Generalization**: Outperforms traditional methods without requiring domain adaptation techniques.
+
+
+## 🚀 Installation
+```bash
+git clone https://github.com/startracker0/PhysAug.git
+cd PhysAug
+
+conda create -n physaug python=3.8 -y
+pip install torch==1.13.0+cu116 torchvision==0.14.0+cu116 torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cu116
+
+pip install -U openmim
+mim install mmengine
+mim install "mmcv==2.1.0"
+pip install -v -e .
+
+pip install einops==0.3.2
+pip install opt-einsum==3.3.0
+pip install tensorboard==2.13.0
+```
+To ensure reproducibility, the detailed environment dependencies are provided in requirements.txt and environment.yaml
+
+## 📊 Reproducing Results
+
+Follow the steps below to reproduce the results reported in our AAAI 2025 paper.
+
+### 1. Prepare the Dataset
+Download and prepare the dataset required for the experiments. Update the dataset path in the configuration file.
+
+#### DWD Dataset
+You can download the DWD dataset from the following link:
+[Download DWD Dataset](https://drive.google.com/drive/folders/1IIUnUrJrvFgPzU8D6KtV0CXa8k1eBV9B)
+
+#### Cityscapes-C Dataset
+The Cityscapes dataset can be downloaded from the official website:
+[Download Cityscapes Dataset](https://www.cityscapes-dataset.com/)
+
+We generate the Cityscapes-C validation set based on the cityscapes/leftImg8bit/val portion of the dataset.
+You can create this dataset using the [imagecorruptions](https://github.com/bethgelab/imagecorruptions) library, which provides various corruption functions to simulate adverse conditions such as noise, blur, weather, and digital artifacts.
+
+```bash
+git clone https://github.com/bethgelab/imagecorruptions.git
+cd imagecorruptions
+pip install -v -e .
+python gen_cityscapes_c.py
+```
+
+The datasets should be organized as follows:
+```bash
+datasets/
+├── DWD/
+│   ├── daytime_clear/
+│   ├── daytime_foggy/
+│   ├── dusk_rainy/
+│   ├── night_rainy/
+│   └── night_sunny/
+├── Cityscapes-c/
+│   ├── brightness/
+│   ├── contrast/
+│   ├── defocus_blur/
+........
+│   └── zoom_blur/
+```
+
+### 2. Training the Model
+
+To train the model using PhysAug, follow these steps:
+
+1. Ensure the dataset paths are correctly configured in `configs/_base_/datasets/dwd.py` and `configs/_base_/datasets/cityscapes_detection.py`.
+2. Run the following command to start training:
+
+```bash
+bash train_dwd.sh
+bash train_cityscapes_c.sh
+```
+
+### 3. Evaluating the Model
+
+To evaluate the trained model, follow these steps:
+
+1. Specify the dataset to evaluate (e.g., DWD, Cityscapes, or Cityscapes-C).
+2. Run the evaluation script with the following command:
+
+```bash
+bash test.sh
+```
+
+### 4. Pre-trained Models
+
+You can download the pre-trained models including Physaug_DWD and Physaug_Cityscapes from [here](https://pan.baidu.com/s/1bSoP0b2Ce4W4_14wwTyxcQ?pwd=6ske)
+
+If the links are no longer accessible, please feel free to contact me.