Og Bagls Unet Crop

--- language: - en license: mit pipeline_tag: image-segmentation tags: - bagls - unet - crop - image-segmentation - medical --- # OpenGlottal: U-Net (BAGLS-Crop) This repository contains the pre-trained U-Net weights (`og_bagls_unet_crop.pt`) for the OpenGlottal toolkit. ## Model Description OpenGlottal is an open-source toolkit for automated glottal area segmentation from high-speed videoendoscopy (HSV). This specific model is a U-Net pixel-level segmenter trained on the [BAGLS dataset](https://zenodo.org/records/3762320) using a crop-based approach. In this pipeline, a YOLOv8 glottis localizer defines a tight crop which is then processed by this U-Net to extract the glottal area waveform. The model was presented in the paper [A Detection-Gated Pipeline for Robust Glottal Area Waveform Extraction and Clinical Pathology Assessment](https://huggingface.co/papers/2603.02087). - **Code:** [GitHub - hari-krishnan/openglottal](https://github.com/hari-krishnan/openglottal) - **Paper:** [arXiv:2603.02087](https://arxiv.org/abs/2603.02087) ## Installation To use this model with the official toolkit: ```bash git clone https://github.com/hari-krishnan/openglottal.git cd openglottal pip install -e . ``` ## How to use ### Python API ```python import torch from openglottal import TemporalDetector, UNet, extract_features_unet from huggingface_hub import hf_hub_download device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Download the weights unet_path = hf_hub_download(repo_id="hari-krishnan-u/og_bagls_unet_crop", filename="og_bagls_unet_crop.pt") # Initialize and load U-Net model = UNet(1, 1, (32, 64, 128, 256)).to(device) model.load_state_dict(torch.load(unet_path, map_location=device)) model.eval() # Note: The detection-gated pipeline also requires a detector (YOLO) # detector = TemporalDetector("path/to/yolo_weights.pt") # features = extract_features_unet("video.avi", detector, model, device) ``` ### CLI 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_bagls_unet_crop", filename="og_bagls_unet_crop.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/ ``` ## Citatio...

Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Repo hari-krishnan-u/og_bagls_unet_crop