Og Bagls Unet Full

--- language: - en license: mit pipeline_tag: image-segmentation tags: - bagls - unet - medical - image-segmentation --- # OpenGlottal: U-Net (BAGLS Full-frame) This repository contains the pre-trained U-Net weights (`og_bagls_unet_full.pt`) for glottal area segmentation, as presented in the paper: [A Detection-Gated Pipeline for Robust Glottal Area Waveform Extraction and Clinical Pathology Assessment](https://huggingface.co/papers/2603.02087). This model is part of the **OpenGlottal** toolkit, an open-source framework for automated glottal area segmentation from high-speed videoendoscopy (HSV). - **Code:** [GitHub Repository](https://github.com/hari-krishnan/openglottal) - **Paper:** [arXiv:2603.02087](https://arxiv.org/abs/2603.02087) ## Model Description The model is a U-Net pixel-level segmenter trained on the [BAGLS dataset](https://zenodo.org/records/3762320) (N=55,750). It is designed to extract glottal area waveforms (GAW) and left/right (L/R) displacement waveforms along a medial axis to enable clinical assessment of vocal fold function and pathology. ## How to use ### Installation ```bash pip install openglottal ``` ### Python API You can load the weights and run inference using the following snippet: ```python import torch from openglottal import UNet from huggingface_hub import hf_hub_download device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Download weights from Hugging Face unet_path = hf_hub_download(repo_id="hari-krishnan-u/og_bagls_unet_full", filename="og_bagls_unet_full.pt") # Initialize and load model model = UNet(1, 1, (32, 64, 128, 256)).to(device) model.load_state_dict(torch.load(unet_path, map_location=device)) model.eval() ``` ### CLI Run displacement extraction in LR mode: ```bash openglottal displacement /path/to/video.avi \ --unet-weights "og_bagls_unet_full.pt" \ --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} } ```

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