| --- |
| library_name: pimm |
| datasets: |
| - DeepLearnPhysics/PILArNet-M |
| tags: |
| - particle-physics |
| - lartpc |
| - point-cloud |
| --- |
| |
| # PoLAr-MAE — semantic segmentation |
|
|
| [PoLAr-MAE](https://arxiv.org/abs/2502.02558) fine-tuned for 4-class LArTPC semantic segmentation (shower, track, Michel, delta); reproduces the paper mF1 ≈ 0.82. |
|
|
| - **pimm type:** `PoLArMAE-SemSeg` · 4 classes |
|
|
| ## Loading |
|
|
| ```python |
| import pimm |
| model = pimm.from_pretrained("hf://deeplearnphysics/polar-mae-semantic") |
| ``` |
|
|
| ## Provenance |
|
|
| Repackaged from the original [PoLAr-MAE](https://github.com/DeepLearnPhysics/PoLAr-MAE) release checkpoints into the [pimm](https://github.com/youngsm/particle-imaging-models) export format. Inherits the source repo license. |
|
|
