updated readme

README.md

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 license: openrail
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+
+# Huggingface cloth segmentation using U2NET
+
+![Python 3.8](https://img.shields.io/badge/python-3.8-green.svg)
+[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1LGgLiHiWcmpQalgazLgq4uQuVUm9ZM4M?usp=sharing)
+
+This repo contains inference code and gradio demo script using pre-trained U2NET model for Cloths Parsing from human portrait.</br>
+Here clothes are parsed into 3 category: Upper body(red), Lower body(green) and Full body(yellow). The provided script also generates alpha images for each class. 
+
+
+# Inference on local
+- clone the repo `git clone https://github.com/wildoctopus/huggingface-cloth-segmentation.git`.
+- Install dependencies `pip install -r requirements.txt`
+- Run `python process.py --image 'input/03615_00.jpg' . **Script will automatically download the pretrained model**. 
+- Outputs will be saved in `output` folder.
+- `output/alpha/..` contains alpha images corresponding to each class.
+- `output/cloth_seg` contains final segmentation.
+- 
+
+# Gradio Demo for colab/local
+- Run `python app.py'
+- Navigate to local or public url provided by app on successfull execution. 
+### OR 
+- Inference in colab from here [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1LGgLiHiWcmpQalgazLgq4uQuVUm9ZM4M?usp=sharing)
+
+# Huggingface Demo 
+- Check gradio demo on Huggingface space from here [huggingface-cloth-segmentation](https://huggingface.co/spaces/wildoctopus/cloth-segmentation).
+
+# Output samples
+![Sample 000](assets/1.png)
+![Sample 024](assets/2.png)
+
+
+This model works well with any background and almost all poses. 
+
+# Acknowledgements
+- U2net model is from original [u2net repo](https://github.com/xuebinqin/U-2-Net). Thanks to Xuebin Qin for amazing repo.
+- Most of the code is taken and modified from  [levindabhi/cloth-segmentation](https://github.com/levindabhi/cloth-segmentation)