xenopus-maskunet-pytorch

KapoorLabs/xenopus-maskunet-pytorch

xenopus-maskunet-pytorch is a machine learning model from KapoorLabs. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as bsd-3-clause.

PyTorch-Lightning checkpoint trained with kapoorlabsvollseg (modelsmaskunetpytorch). Flat layout — the folder ships:

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Updated May 19, 2026

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65.7 MB

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.ckpt65.7 MB · 100%

At a glance

License: bsd-3-clause
Access: Public
Created: May 19, 2026
Updated: May 19, 2026
SHA: b9d2d43b

Model card

From the Hugging Face model README

xenopus-maskunet-pytorch

PyTorch-Lightning checkpoint trained with kapoorlabs_vollseg (models_maskunet_pytorch). Flat layout — the folder ships:

last.ckpt (and optionally per-epoch <model_name>-epoch=NNN.ckpt)
training_config.json — Hydra parameters block, what kapoorlabs_vollseg reads first to rebuild the architecture
<model_name>.json — legacy CareInception fallback config
rays.npy (StarDist only) — the rays array used at training time

Loading

# StarDist
from kapoorlabs_vollseg import StarDistSegmenter, ensure_model
folder = ensure_model("./local_models", "models_maskunet_pytorch",
                      repo_id="KapoorLabs/xenopus-maskunet-pytorch")
star = StarDistSegmenter.from_folder(folder)
labels = star.predict(volume).labels

# U-Net
from kapoorlabs_vollseg import UNetSegmenter, ensure_model
folder = ensure_model("./local_models", "models_maskunet_pytorch",
                      repo_id="KapoorLabs/xenopus-maskunet-pytorch")
unet = UNetSegmenter.from_folder(folder)
labels = unet.predict(volume).labels

See https://github.com/Kapoorlabs-CAPED/KapoorLabs-VollSeg for the full segmentation pipeline.

License: bsd-3-clause
Created: May 19, 2026
Updated: May 19, 2026