Update README.md

README.md

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-<p align="center">
-<img src="https://github.com/lime-j/RDNet/blob/main/figures/Title.png?raw=true" width=95%>
-<p>
-
-# Reversible Decoupling Network for Single Image Reflection Removal
-
-<div align="center">
-
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/reversible-decoupling-network-for-single/reflection-removal-on-sir-2-objects)](https://paperswithcode.com/sota/reflection-removal-on-sir-2-objects?p=reversible-decoupling-network-for-single)
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/reversible-decoupling-network-for-single/reflection-removal-on-sir-2-wild)](https://paperswithcode.com/sota/reflection-removal-on-sir-2-wild?p=reversible-decoupling-network-for-single)
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/reversible-decoupling-network-for-single/reflection-removal-on-sir-2-postcard)](https://paperswithcode.com/sota/reflection-removal-on-sir-2-postcard?p=reversible-decoupling-network-for-single)
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/reversible-decoupling-network-for-single/reflection-removal-on-nature)](https://paperswithcode.com/sota/reflection-removal-on-nature?p=reversible-decoupling-network-for-single)
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/reversible-decoupling-network-for-single/reflection-removal-on-real20)](https://paperswithcode.com/sota/reflection-removal-on-real20?p=reversible-decoupling-network-for-single)
-
-</div>
-<p align="center" style="font-size: larger;">
-  <a href="https://arxiv.org/abs/2410.08063"> Reversible Decoupling Network for Single Image Reflection Removal</a>
-</p>
-<p align="center">
-  <a href="https://github.com/WHiTEWoLFJ"> Hao Zhao</a> ⚔️,
-  <a href="https://github.com/lime-j"> Mingjia Li</a> ⚔️,
-  <a href="https://github.com/mingcv"> Qiming Hu</a>,
-  <a href="https://sites.google.com/view/xjguo"> Xiaojie Guo</a> 🦅,
-  <p align="center">(⚔️: equal contribution, 🦅 : corresponding author)</p>
-</p>
-
-<p align="center">
-<img src="https://github.com/lime-j/RDNet/blob/main/figures/net.png?raw=true" width=95%>
-<p>
-**Our work is accepted by CVPR 2025! See you at the conference!**
-<details>
-  <summary>Click for the Abstract of RDNet</summary>
-  We present a Reversible Decoupling Network (RDNet), which employs a reversible encoder to secure valuable information while flexibly decoupling transmission-and-reflection-relevant features during the forward pass. Furthermore, we customize a transmission-rate-aware prompt generator to dynamically calibrate features, further boosting performance. Extensive experiments demonstrate the superiority of RDNet over existing SOTA methods on five widely-adopted benchmark datasets.
-</details>
-
-## 🚀Todo
-
-- [ ] Release the Training code of RDNet.
-
-## 🌠 Gallery
-
-
-<table class="center">
-    <tr>
-      <td><p style="text-align: center">Class Room</p></td>
-      <td><p style="text-align: center">White Wall Chamber</p></td>
-    </tr>
-    <tr>
-      <td>
-        <div style="width: 100%; max-width: 600px; position: relative;">
-          <img src="https://github.com/lime-j/RDNet/blob/main/figures/Input_class.png?raw=true" style="width: 100%; height: 300px; display: block;">
-          <img src="https://github.com/lime-j/RDNet/blob/main/figures/Ours_class.png?raw=true" style="width: 100%; height: 300px; display: block; position: absolute; top: 0; left: 0; opacity: 0; transition: opacity 0.5s;" onmouseover="this.style.opacity=1;" onmouseout="this.style.opacity=0;">
-        </div>
-      </td>
-      <td>
-        <div style="width: 100%; max-width: 600px; position: relative;">
-          <img src="https://github.com/lime-j/RDNet/blob/main/figures/input_white.jpg?raw=true" style="width: 100%; height: 300px; display: block;">
-          <img src="https://github.com/lime-j/RDNet/blob/main/figures/Ours_white.png?raw=true" style="width: 100%; height: 300px; display: block; position: absolute; top: 0; left: 0; opacity: 0; transition: opacity 0.5s;" onmouseover="this.style.opacity=1;" onmouseout="this.style.opacity=0;">
-        </div>
-      </td>
-    </tr>
-    <tr>
-      <td><p style="text-align: center">Car Window</p></td>
-      <td><p style="text-align: center">Very Green Office</p></td>
-    </tr>
-    <tr>
-      <td>
-        <div style="width: 100%; max-width: 600px; position: relative;">
-          <img src="https://github.com/lime-j/RDNet/blob/main/figures/Input_car.jpg?raw=true" style="width: 100%; height: 300px; display: block;">
-          <img src="https://github.com/lime-j/RDNet/blob/main/figures/Ours_car.png?raw=true" style="width: 100%; height: 300px; display: block; position: absolute; top: 0; left: 0; opacity: 0; transition: opacity 0.5s;" onmouseover="this.style.opacity=1;" onmouseout="this.style.opacity=0;">
-        </div>
-      </td>
-      <td>
-        <div style="width: 100%; max-width: 600px; position: relative;">
-          <img src="https://github.com/lime-j/RDNet/blob/main/figures/Input_green.png?raw=true" style="width: 100%; height: 300px; display: block;">
-          <img src="https://github.com/lime-j/RDNet/blob/main/figures/Ours_green.png?raw=true" style="width: 100%; height: 300px; display: block; position: absolute; top: 0; left: 0; opacity: 0; transition: opacity 0.5s;" onmouseover="this.style.opacity=1;" onmouseout="this.style.opacity=0;">
-        </div>
-      </td>
-    </tr>
-</table>
-
-## Requirements
-We recommend torch 2.x for our code, but it should works fine with most of the modern versions.
-
-```
-pip install torch>=2.0 torchvision
-pip install einops ema-pytorch fsspec fvcore huggingface-hub matplotlib numpy opencv-python omegaconf pytorch-msssim scikit-image scikit-learn scipy tensorboard tensorboardx wandb timm
-```
-
-# Testing 
-The checkpoint for the main network is available at https://checkpoints.mingjia.li/rdnet.pth ; while the model for cls_model is at https://checkpoints.mingjia.li/cls_model.pth . Please put the cls_model.pth under "pretrained" folder.
-
-```python
-python3 test_sirs.py --icnn_path <path to the main checkpoint> --resume
-```
-# Training 
-
-Training script will be released in a few days. 
-
+title: RDNet
+emoji: 💻
+colorFrom: indigo
+colorTo: blue
+sdk: gradio
+sdk_version: 4.43.0
+app_file: app.py
+pinned: false