Og Girafe Unet Full

Run displacement (LR mode):

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, 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}
}

--- license: mit language: - en base_model: - PyTorch pipeline_tag: image-segmentation tags: - girafe - unet - full - image-segmentation arxiv: 2603.02087 --- ## Model Pre-trained OpenGlottal checkpoint: `og_girafe_unet_full.pt`. This model is part of the OpenGlottal toolkit: https://github.com/hari-krishnan/openglottal ## How to use Download the U-Net weight and run displacement extraction. ```python from huggingface_hub import hf_hub_download unet_path = hf_hub_download(repo_id="hari-krishnan-u/og_girafe_unet_full", filename="og_girafe_unet_full.pt") ``` Run displacement (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, 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} } ```