Huggingface cloth segmentation using U2NET

This repo contains inference code and gradio demo script using pre-trained U2NET model for Cloths Parsing from human portrait.
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

• 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

• Run python app.py
• Navigate to local or public url provided by app on successfull execution.

OR

• Inference in colab from here

Huggingface Demo

• Check gradio demo on Huggingface space from here huggingface-cloth-segmentation.

Output samples

This model works well with any background and almost all poses.

Acknowledgements

• U2net model is from original u2net repo. Thanks to Xuebin Qin for amazing repo.
• Most of the code is taken and modified from levindabhi/cloth-segmentation