og_bagls_yolo

OpenGlottal: YOLO-BAGLS Glottis Detector

This repository contains a YOLOv8n glottis localizer trained on the BAGLS dataset. It was introduced in the paper A Detection-Gated Pipeline for Robust Glottal Area Waveform Extraction and Clinical Pathology Assessment.

This model is a core component of the OpenGlottal toolkit, an open-source framework for automated glottal area segmentation and clinical kinematic feature extraction from high-speed videoendoscopy (HSV).

• Code: https://github.com/hari-krishnan/openglottal
• Author: Harikrishnan Unnikrishnan

Model Description

The model serves as a detection gate in the OpenGlottal pipeline. It defines a tight crop to ensure a consistent field of view and gates the output to reduce spurious segmentations during glottal closure or when the endoscope is not centered on the glottis.

How to use

Installation

First, install the OpenGlottal toolkit:

git clone https://github.com/hari-krishnan/openglottal.git
cd openglottal
pip install -e .

Python API

You can download the weights and initialize the detector using the following snippet:

from huggingface_hub import hf_hub_download
from openglottal import TemporalDetector

# Download the weight
yolo_path = hf_hub_download(repo_id="hari-krishnan-u/og_bagls_yolo", filename="og_bagls_yolo.pt")

# Initialize the detector
detector = TemporalDetector(yolo_path)

CLI

Alternatively, run the vft or guided-vft pipelines directly via the command line:

openglottal run /path/to/video.avi \
  --pipeline vft \
  --yolo-weights "og_bagls_yolo.pt" \
  --output results/

Citation

If you use this model, please cite:

@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}
}