Improve model card: Add pipeline tag, library name, abstract, and updated links

This PR significantly enhances the model card for the SimCIS model on the Hugging Face Hub by:

* **Adding `pipeline_tag: image-segmentation`** to the metadata, ensuring the model appears under relevant filters on the Hugging Face Models page.
* **Adding `library_name: detectron2`** to the metadata, correctly associating the model with its primary framework for better categorization and searchability.
* **Updating the paper link** to `https://huggingface.co/papers/2507.07831` (the official Hugging Face Papers page), replacing the local PDF link and improving discoverability. The badge and other mentions of the paper link in the content have also been updated.
* **Including the paper's abstract** under a new "

README.md

@@ -1,19 +1,137 @@
 ---
-language:
-  - en  
-license: mit 
+language: en
+license: mit
+pipeline_tag: image-segmentation
+library_name: detectron2
 ---
 
 # SimCIS
 
 **Rethinking Query-based Transformer for Continual Image Segmentation.** *(CVPR2025)*
 
-[![License](https://img.shields.io/badge/License-MIT-blue.svg?style=flat-square)](LICENSE)[![Paper](https://badgen.net/badge/icon/arXiv?icon=awesome&label&color=red&style=flat-square)](./assets/CVPR_2025_SimCIS.pdf)
-![SimCLS](assets/framework.svg)
+[![License](https://img.shields.io/badge/License-MIT-blue.svg?style=flat-square)](LICENSE)[![Paper](https://img.shields.io/badge/Paper-2507.07831-red?style=flat-square)](https://huggingface.co/papers/2507.07831)[![GitHub](https://img.shields.io/badge/GitHub-Code-black.svg?logo=github&style=flat-square)](https://github.com/SooLab/SimCIS)
 
-By [Yuchen Zhu*](https://github.com/ZhuYuChenNO1), [Cheng Shi*](https://chengshiest.github.io/),  Dingyou Wang, Jiajin Tang, Zhengxuan Wei, Yu Wu, Guanbin Li and [Sibei Yang†](https://faculty.sist.shanghaitech.edu.cn/yangsibei/)
+![SimCLS](https://huggingface.co/LightningNO1/SimCIS/resolve/main/assets/framework.svg)
+
+By [Yuchen Zhu*](https://github.com/ZhuYuChenNO1), [Cheng Shi*](https://chengshiest.github.io/), Dingyou Wang, Jiajin Tang, Zhengxuan Wei, Yu Wu, Guanbin Li and [Sibei Yang†](https://faculty.sist.shanghaitech.edu.cn/yangsibei/)
+
+*Equal contribution; †Corresponding Author
+
+## Abstract
+Class-incremental/Continual image segmentation (CIS) aims to train an image segmenter in stages, where the set of available categories differs at each stage. To leverage the built-in objectness of query-based transformers, which mitigates catastrophic forgetting of mask proposals, current methods often decouple mask generation from the continual learning process. This study, however, identifies two key issues with decoupled frameworks: loss of plasticity and heavy reliance on input data order. To address these, we conduct an in-depth investigation of the built-in objectness and find that highly aggregated image features provide a shortcut for queries to generate masks through simple feature alignment. Based on this, we propose SimCIS, a simple yet powerful baseline for CIS. Its core idea is to directly select image features for query assignment, ensuring "perfect alignment" to preserve objectness, while simultaneously allowing queries to select new classes to promote plasticity. To further combat catastrophic forgetting of categories, we introduce cross-stage consistency in selection and an innovative "visual query"-based replay mechanism. Experiments demonstrate that SimCIS consistently outperforms state-of-the-art methods across various segmentation tasks, settings, splits, and input data orders. All models and codes will be made publicly available at this https URL.
+
+## 📣 News
+
+* [2025.06.17] 🤗 🤗 🤗 We Release the weights on [huggingface](https://huggingface.co/LightningNO1/SimCIS).
+* [2025.06.09] 🤗 We fully release SimCIS, including both code and [paper](https://huggingface.co/papers/2507.07831)!
+* [2025.03.03] We are preparing the code and camera ready version of our paper!
+* [2025.02.27] Our paper is accepted by CVPR2025!
+
+
+## 📝 To-do list
+
+- [x] Release the code and paper.
+- [x] Release the weights in the next few days.
+- [x] More detailed instructions.
+
+
+## 💡 Quick Start
+### 1. Set up environments
+
+```bash
+conda create --name simcis python=3.8 -y
+conda activate simcis
+conda install pytorch==1.9.0 torchvision==0.10.0 cudatoolkit=11.1 -c pytorch -c nvidia
+pip install -U opencv-python
+
+git clone git@github.com:SooLab/SimCIS.git
+cd SimCIS
+
+git clone git@github.com:facebookresearch/detectron2.git
+cd detectron2
+pip install -e .
+pip install git+https://github.com/cocodataset/panopticapi.git
+pip install git+https://github.com/mcordts/cityscapesScripts.git
+cd ..
+pip install -r requirements.txt
+
+```
+
+#### CUDA kernel for MSDeformAttn
+After preparing the required environment, run the following command to compile CUDA kernel for MSDeformAttn:
+
+`CUDA_HOME` must be defined and points to the directory of the installed CUDA toolkit.
+
+```bash
+cd mask2former/modeling/pixel_decoder/ops
+sh make.sh
+```
+
+## 2. Data Preparation
+
+We follow the previous work [Balconpas](https://github.com/jinpeng0528/BalConpas/tree/master) to prepare the training data.
+
+Please download the ADE20K dataset and its instance annotation from [here](http://sceneparsing.csail.mit.edu/), then place the dataset in or create a symbolic link to the `./datasets` directory. The structure of data path should be organized as follows:
+```
+ADEChallengeData2016/
+  images/
+  annotations/
+  objectInfo150.txt
+  sceneCategories.txt
+  annotations_instance/
+  annotations_detectron2/
+  ade20k_panoptic_{train,val}.json
+  ade20k_panoptic_{train,val}/
+  ade20k_instance_{train,val}.json
+```
+The directory `annotations_detectron2` is generated by running `python datasets/prepare_ade20k_sem_seg.py`.
+Then, run `python datasets/prepare_ade20k_pan_seg.py` to combine semantic and instance annotations for panoptic annotations and run `python datasets/prepare_ade20k_ins_seg.py` to extract instance annotations in COCO format.
+
+To fit the requirements of continual segmentation tasks, run `python continual/prepare_datasets.py` to reorganize the annotations (reorganized annotations will be placed in `./json`).
+
+#### Example data preparation
+```bash
+# for Mask2Former
+cd datasets
+wget http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip
+unzip ADEChallengeData2016.zip
+cd ADEChallengeData2016
+wget http://sceneparsing.csail.mit.edu/data/ChallengeData2017/annotations_instance.tar
+tar -xvf annotations_instance.tar
+cd ../..
+python datasets/prepare_ade20k_sem_seg.py
+python datasets/prepare_ade20k_pan_seg.py
+python datasets/prepare_ade20k_ins_seg.py
+
+# for continual segmentation
+python continual/prepare_datasets.py
+```
+
+## 🔥 Training
+
+Download the weights of the base step(step1) from [huggingface](https://huggingface.co/LightningNO1/SimCIS).
+
+Please follow the [scripts](./scripts) to train SimCIS!
+
+For example:
+
+```bash
+bash scripts/pan_100-5.sh
+```
+
+## ⚡️ Evaluation
+
+Download the weights from [huggingface](https://huggingface.co/LightningNO1/SimCIS).
+
+Please follow the [scripts](./scripts) to evaluate SimCIS!
+
+For example:
+
+```bash
+# 11 means the 11th step(last step for 100-5 setting)
+bash scripts/panoptic_eval.sh 11
+```
 
-## SEE [Github](https://github.com/SooLab/SimCIS) to use the weights!!!
 
 ## 📖 Cite Us
 If you find this repository useful in your research, please consider giving a star ⭐ and a citation
@@ -30,6 +148,4 @@ If you find this repository useful in your research, please consider giving a st
 
 ## 👍 Acknowledgement and Related Work
 * This code is mainly based on [Mask2Former](https://github.com/facebookresearch/Mask2Former). We thank them for their excellent work.
-* Related work for continual image segmentation: [Balconpas](https://github.com/jinpeng0528/BalConpas/tree/master), [ECLIPSE](https://github.com/clovaai/ECLIPSE). We appreciate the contributions of these researchers.
-
-
+* Related work for continual image segmentation: [Balconpas](https://github.com/jinpeng0528/BalConpas/tree/master), [ECLIPSE](https://github.com/clovaai/ECLIPSE). We appreciate the contributions of these researchers.