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hari-krishnan-u/
og_girafe_unet_crop

Name: og_girafe_unet_crop
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OpenGlottal: GIRAFE-trained U-Net (Crop Mode)

This repository contains the pre-trained U-Net weights (og_girafe_unet_crop.pt) for glottal area segmentation, as presented in the paper: A Detection-Gated Pipeline for Robust Glottal Area Waveform Extraction and Clinical Pathology Assessment.

This model is part of the OpenGlottal toolkit, an open-source framework designed for automated glottal area segmentation from high-speed videoendoscopy (HSV). This specific checkpoint was trained on the GIRAFE dataset using the "YOLO-Crop+UNet" strategy, where a detector localizes the glottis to provide a tight crop for the U-Net, ensuring higher effective resolution.

Paper: arXiv:2603.02087
Code: GitHub - openglottal

How to use

You can download the U-Net weights and run displacement extraction using the openglottal toolkit.

Download Weights

python

from huggingface_hub import hf_hub_download
unet_path = hf_hub_download(repo_id="hari-krishnan-u/og_girafe_unet_crop", filename="og_girafe_unet_crop.pt")

Run Inference (CLI)

To run displacement extraction in Left/Right (LR) mode:

bash

openglottal displacement /path/to/video.avi \
  --unet-weights "$unet_path" \
  --start 0 --end 500 \
  --mode lr \
  --lr-position 0.5 \
  --output results/

Citation

If you use this model or the OpenGlottal toolkit in your research, please cite:

bibtex

@misc{unnikrishnan2026openglottal,
  title         = {A Detection-Gated Pipeline for Robust Glottal Area
                   Waveform Extraction and Clinical Pathology Assessment},
  author        = {Unnikrishnan, Harikrishnan},
  year          = {2026},
  eprint        = {2603.02087},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2603.02087}
}