hercunet-v0

HercUNet v0 — iterative surface (sheet) refiner for Herculaneum scrolls

HercUNet is a self-refining U-Net that detects the medial surfaces (sheets) of rolled papyrus in carbonised Herculaneum scrolls from X-ray µCT. This is v0 (the run301 model): the iterative AffinityMalis + online-DAgger refiner D(CT, prev) → surface, warm-started from the ScrollPrize m7 surface detector and trained on our stage-1 ∇φ pseudo-labels plus a mined m7 rehearsal corpus.

• 📦 Code + docs: https://github.com/jimmylomro/hercUNet
• 🗂️ Training corpus: https://huggingface.co/datasets/jimmylomro/hercunet-corpus

Files (a standard nnU-Net trained-model folder)

Use it

Install HercUNet (pip install -e ".[train]" from the repo), then warm-start / re-train / fine-tune from this model — the checkpoint is pulled from this repo automatically:

hercunet train fit --dataset <NNN> --pretrained-hf jimmylomro/hercunet-v0

--pretrained-hf downloads checkpoint_best.pth and loads it (expanding the input stem as needed). See docs/training.md.

⚠️ Inference is not nnUNetv2_predict

HercUNet's network is not a plain ResEncUNet: the trainer builds an 8-channel stem [CT, prev, orientation×6] + an affinity head, and inference is iterative (feed the previous pass's softmax back in, Jacobi-blend). plans.json describes only the base geometry — the stem, the head, and the iteration live in the hercunet code. So this checkpoint is loadable by anyone, but running it requires hercunet inference, not stock nnUNetv2_predict (which would silently build the wrong network).

Recipe & provenance

Trainer nnUNetTrainer_AffinityMalis_IterDagger_500epochs, K=4 candidate channels. The exact hyperparameters are the shipped recipe hercunet/recipes/hercunet.yml (run301). Methodology: the write-up in the repo (submission/writeup/).

License: see the GitHub repository.