readme

README.md

@@ -1,48 +1,9 @@
-# CAT-Seg🐱: Cost Aggregation for Open-Vocabulary Semantic Segmentation
-
-This is our official implementation of CAT-Seg🐱!
-
-[[arXiv](#)] [[Project](#)]<br>
-by [Seokju Cho](https://seokju-cho.github.io/)\*, [Heeseong Shin](https://github.com/hsshin98)\*, [Sunghwan Hong](https://sunghwanhong.github.io), Seungjun An, Seungjun Lee, [Anurag Arnab](https://anuragarnab.github.io), [Paul Hongsuck Seo](https://phseo.github.io), [Seungryong Kim](https://cvlab.korea.ac.kr)
-
-
-## Introduction
-![](assets/fig1.png)
-We introduce cost aggregation to open-vocabulary semantic segmentation, which jointly aggregates both image and text modalities within the matching cost.
-
-## Installation
-Install required packages. 
-
-```bash
-conda create --name catseg python=3.8
-conda activate catseg
-conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge
-pip install -r requirements.txt
-```
-
-## Data Preparation
-
-
-## Training
-### Preparation
-you have to blah
-### Training script
-```bash
-python train.py --config configs/eval/{a847 | pc459 | a150 | pc59 | pas20 | pas20b}.yaml
-```
-
-## Evaluation
-```bash
-python eval.py --config configs/eval/{a847 | pc459 | a150 | pc59 | pas20 | pas20b}.yaml
-```
-
-## Citing CAT-Seg🐱 :pray:
-
-```BibTeX
-@article{liang2022open,
-  title={Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP},
-  author={Liang, Feng and Wu, Bichen and Dai, Xiaoliang and Li, Kunpeng and Zhao, Yinan and Zhang, Hang and Zhang, Peizhao and Vajda, Peter and Marculescu, Diana},
-  journal={arXiv preprint arXiv:2210.04150},
-  year={2022}
-}
-```
+---
+title: CAT-Seg Demo
+emoji: 🤗
+colorFrom: yellow
+colorTo: orange
+sdk: gradio
+app_file: app.py
+pinned: false
+---