pimm export (config.json)
Browse files- README.md +26 -1
- config.json +47 -0
- model.safetensors +3 -0
README.md
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---
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-
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---
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---
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library_name: pimm
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tags:
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- particle-physics
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- lartpc
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- point-cloud
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---
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# PoLAr-MAE — semantic segmentation
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[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.
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- **pimm type:** `PoLArMAE-SemSeg` · 4 classes
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> 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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## Loading
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```python
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import pimm
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model = pimm.from_pretrained("hf://deeplearnphysics/polar-mae-semantic")
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```
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Architecture + hyper-parameters travel in `config.json`; weights are bitwise-identical to the original checkpoint.
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## Provenance
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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.
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config.json
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{
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"model": {
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"type": "PoLArMAE-SemSeg",
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"num_classes": 4,
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"arch": "vit_small",
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"voxel_size": 5.0,
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"num_channels": 4,
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"seg_head_fetch_layers": [
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3,
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7,
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11
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],
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"seg_head_combination_method": "mean",
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"seg_head_dim": 512,
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"seg_head_dropout": 0.5,
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"freeze_encoder": false,
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"apply_encoder_postnorm": false,
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"condition_global_features": true,
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"upsampling_k": 5,
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"center": [
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384.0,
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384.0,
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384.0
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],
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"scale": 0.0015035163260146505,
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"transformer_kwargs": {
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"postnorm": false,
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"add_pos_at_every_layer": true,
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"drop_rate": 0.0,
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"attn_drop_rate": 0.05,
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"drop_path_rate": 0.25
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},
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"criteria": [
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{
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"type": "CrossEntropyLoss",
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"loss_weight": 1.0,
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"ignore_index": -1
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},
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{
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"type": "LovaszLoss",
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"mode": "multiclass",
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"loss_weight": 0.05,
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"ignore_index": -1
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}
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]
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}
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:b2de651fffcf83e22d388a56f315e031836b86195b022d7c869b4c3993fbd90f
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size 91470268
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