DeepLearnPhysics/PoLAr-MAE-Semantic

PoLAr-MAE — semantic segmentation

PoLAr-MAE fine-tuned for 4-class LArTPC semantic segmentation (shower, track, Michel, delta); reproduces the paper mF1 ≈ 0.82.

• pimm type: PoLArMAE-SemSeg · 4 classes

Faithful eval needs PoLAr-MAE preprocessing: LogTransform(min_val=0.13), energy_threshold=0.13, remove_low_energy_scatters=True. Coordinate normalization is in-model.

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import pimm
model = pimm.from_pretrained("hf://deeplearnphysics/polar-mae-semantic")

Architecture + hyper-parameters travel in config.json; weights are bitwise-identical to the original checkpoint.

Provenance

Repackaged from the original PoLAr-MAE release checkpoints into the pimm export format. Inherits the source repo license.