Update README.md

README.md

@@ -34,104 +34,7 @@ Class-incremental/Continual image segmentation (CIS) aims to train an image segm
 - [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 [email protected]:SooLab/SimCIS.git
-cd SimCIS
-
-git clone [email protected]: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
-```
-
+## PLEASE FOLLOW this [Github Repo](https://github.com/SooLab/SimCIS) to use the weights!!!
 
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