Update README.md

README.md

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-# Edge Weight Prediction For Category-Agnostic Pose Estimation
-<a href="https://orhir.github.io/edge_cape/"><img src="https://img.shields.io/static/v1?label=Project&message=Website&color=blue"></a>
-<a href="https://arxiv.org/pdf/2411.16665"><img src="https://img.shields.io/badge/arXiv-311.17891-b31b1b.svg"></a>
-<a href="https://www.apache.org/licenses/LICENSE-2.0.txt">
-<img src="https://img.shields.io/badge/License-Apache-yellow"></a>
-
-
-By [Or Hirschorn](https://scholar.google.co.il/citations?user=GgFuT_QAAAAJ&hl=iw&oi=ao) and [Shai Avidan](https://scholar.google.co.il/citations?hl=iw&user=hpItE1QAAAAJ)
-
-This repo is the official implementation of "[Edge Weight Prediction For Category-Agnostic Pose Estimation
-](https://arxiv.org/abs/2411.16665)".
-
-# Hugging Face Demo Coming Soon!
-### Stay tuned for the upcoming demo release!
-
-
-## 🔔 News
-- **`25 November 2024`** Initial Code Release
-
-
-## Introduction
-Given only one example image and skeleton,  our method refines the skeleton to enhance pose estimation on unseen categories.
-
-Using our method, given a support image and skeleton we can refine the structure for better pose estimation on images from unseen categories.
-
-## Citation
-Please consider citing our paper and GraphCape if you found our work useful:
-```bibtex
-@misc{hirschorn2024edgeweightpredictioncategoryagnostic,
-      title={Edge Weight Prediction For Category-Agnostic Pose Estimation}, 
-      author={Or Hirschorn and Shai Avidan},
-      year={2024},
-      eprint={2411.16665},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2411.16665}, 
-}
-
-@misc{hirschorn2023pose,
-      title={A Graph-Based Approach for Category-Agnostic Pose Estimation}, 
-      author={Or Hirschorn and Shai Avidan},
-      year={2024},
-      eprint={2311.17891},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2311.17891}, 
-}
-```
-
-## Getting Started
-
-### Docker [Recommended]
-We provide a docker image for easy use.
-You can simply pull the docker image from docker hub, containing all the required libraries and packages:
-
-```
-docker pull orhir/edgecape
-docker run --name edgecape -v {DATA_DIR}:/workspace/EdgeCape/EdgeCape/data/mp100 -it orhir/edgecape /bin/bash
-```
-### Conda Environment
-We train and evaluate our model on Python 3.8 and Pytorch 2.0.1 with CUDA 12.1. 
-
-Please first install pytorch and torchvision following official documentation Pytorch. 
-Then, follow [MMPose](https://mmpose.readthedocs.io/en/latest/installation.html) to install the following packages:
-```
-mmcv-full=1.7.2
-mmpose=0.29.0
-```
-Having installed these packages, run:
-```
-python setup.py develop
-```
-
-## MP-100 Dataset
-Please follow the [official guide](https://github.com/orhir/PoseAnything) to prepare the MP-100 dataset for training and evaluation, and organize the data structure properly.
-
-## Training
-
-### Training
-To train the model, run:
-```
-python run.py --config [path_to_config_file]  --work_dir [path_to_work_dir]
-```
-
-## Evaluation and Pretrained Models
-
-### Evaluation
-The evaluation on a single GPU will take approximately 30 min. 
-
-To evaluate the pretrained model, run:
-```
-python test.py [path_to_config_file] [path_to_pretrained_ckpt]
-```
-
-### Pretrained Models
-
-You can download the pretrained models from following [link](https://drive.google.com/drive/folders/1gbeeVQ-Y8Dj2FrsDatf5ZLWpzv5u8HyL?usp=sharing).
-
-## Acknowledgement
-
-Our code is based on code from:
- - [MMPose](https://github.com/open-mmlab/mmpose)
- - [PoseAnything](https://github.com/orhir/PoseAnything)
-
-
-## License
-This project is released under the Apache 2.0 license.
+title: EdgeCape
+emoji: 🏢
+colorFrom: red
+colorTo: red
+sdk: gradio
+sdk_version: 5.7.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+startup_duration_timeout: 1h