| --- |
| library_name: pimm |
| datasets: |
| - DeepLearnPhysics/PILArNet-M |
| tags: |
| - particle-physics |
| - lartpc |
| - point-cloud |
| --- |
| |
| # PoLAr-MAE — pretrained (self-supervised) |
|
|
| Full [PoLAr-MAE](https://arxiv.org/abs/2502.02558) masked-autoencoder pretrained on LArTPC (PILArNet) point clouds (masked point reconstruction + energy infilling): ViT-S encoder, MAE decoder, reconstruction heads. Use the encoder as a backbone / warm-start. |
|
|
| - **pimm type:** `PoLAr-MAE` · arch `vit_small` |
|
|
| ## Loading |
|
|
| ```python |
| import pimm |
| model = pimm.from_pretrained("hf://deeplearnphysics/polar-mae-pretrain") |
| ``` |
|
|
|
|
| ## 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. |
|
|
