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README.md
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license: mit
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---
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license: mit
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---
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# Huggingface cloth segmentation using U2NET
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[](https://opensource.org/licenses/MIT)
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[](https://colab.research.google.com/drive/1LGgLiHiWcmpQalgazLgq4uQuVUm9ZM4M?usp=sharing)
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This repo contains inference code and gradio demo script using pre-trained U2NET model for Cloths Parsing from human portrait.</br>
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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.
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# Inference
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- clone the repo `git clone https://github.com/wildoctopus/huggingface-cloth-segmentation.git`.
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- Install dependencies `pip install -r requirements.txt`
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- Run `python process.py --image 'input/03615_00.jpg' . **Script will automatically download the pretrained model**.
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- Outputs will be saved in `output` folder.
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- `output/alpha/..` contains alpha images corresponding to each class.
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- `output/cloth_seg` contains final segmentation.
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# Gradio Demo
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- Run `python app.py'
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- Navigate to local or public url provided by app on successfull execution.
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### OR
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- Inference in colab from here [](https://colab.research.google.com/drive/1LGgLiHiWcmpQalgazLgq4uQuVUm9ZM4M?usp=sharing)
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# Huggingface Demo
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- Check gradio demo on Huggingface space from here [huggingface-cloth-segmentation](https://huggingface.co/spaces/wildoctopus/cloth-segmentation).
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# Output samples
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This model works well with any background and almost all poses.
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# Acknowledgements
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- U2net model is from original [u2net repo](https://github.com/xuebinqin/U-2-Net). Thanks to Xuebin Qin for amazing repo.
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- Most of the code is taken and modified from [levindabhi/cloth-segmentation](https://github.com/levindabhi/cloth-segmentation)
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