---
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.