pimm export (config.json)
Browse files- README.md +8 -34
- config.json +133 -0
README.md
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---
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license: mit
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datasets:
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- DeepLearnPhysics/PILArNet-M
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library_name: pimm
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tags:
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- particle
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- lartpc
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- panda
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- point-transformer-v3
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---
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# Panda — particle panoptic detector
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instances in LArTPC images with per-particle PID, over a PTv3 backbone with a
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masked cross-attention decoder.
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- **pimm type:** `detector-v4`
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- **queries:** 32 · **classes:** 6 (PID) · **stuff classes:** [5]
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- **decoder depth:** 3 · **hidden:** 256 · **heads:** 16 · MLP heads.
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## Loading
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model = pimm.from_pretrained("hf://deeplearnphysics/panda-particle")
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```
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checkpoint.
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## Input
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A pimm `Point` dict for LArTPC (PILArNet) point clouds: `coord` (xyz) + `energy`,
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collected as `feat_keys=("coord", "energy")` (4 input channels). Coordinates are
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normalized in the model forward.
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## Notes
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- Config sets `enable_flash=True`. On CPU or non-FlashAttention setups, override
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it (e.g. pass `enable_flash=False` to `from_pretrained`, or for detectors set it
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on the `backbone`).
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## Provenance
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Repackaged from the original [`
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[`pimm`](https://github.com/youngsm/particle-imaging-models) export format. Inherits
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the license of the source repository.
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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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# Panda — particle panoptic detector
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Query-based panoptic detector segmenting individual particle instances with per-particle PID over a PTv3 backbone.
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- **pimm type:** `detector-v4` · 32 queries · 6-class PID
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## Loading
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model = pimm.from_pretrained("hf://deeplearnphysics/panda-particle")
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```
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Architecture + hyper-parameters travel in `config.json`, so no config file is
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needed. Weights are bitwise-identical to the original checkpoint.
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## Provenance
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Repackaged from the original [`panda`](https://github.com/DeepLearnPhysics/panda) checkpoints into the [`pimm`](https://github.com/youngsm/particle-imaging-models) export format. Inherits the source repo's license.
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config.json
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{
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"model": {
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"type": "detector-v4",
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"labels": [
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"particle"
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],
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"num_queries": 32,
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"num_classes": 6,
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"use_stuff_head": true,
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"stuff_classes": [
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5
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],
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"mlp_point_proj": true,
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"full_in_channels": 1232,
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"hidden_channels": 256,
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"num_heads": 16,
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"depth": 3,
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"mlp_ratio": 4.0,
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"qkv_bias": true,
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"qk_scale": null,
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"layer_scale": null,
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"pre_norm": true,
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"query_type": "learned",
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"pos_emb": true,
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"supervise_attn_mask": true,
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"enable_flash": false,
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"upcast_attention": false,
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"upcast_softmax": false,
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"backbone": {
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"type": "PT-v3m2",
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"in_channels": 4,
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"order": [
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"hilbert",
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"hilbert-trans",
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"z",
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"z-trans"
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],
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"stride": [
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2,
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2,
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2,
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2
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],
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"enc_depths": [
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3,
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3,
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3,
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9,
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3
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],
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"enc_channels": [
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48,
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96,
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192,
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384,
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512
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],
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"enc_num_head": [
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3,
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6,
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12,
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24,
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32
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],
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"enc_patch_size": [
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256,
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256,
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256,
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256,
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256
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],
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"dec_depths": [
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2,
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2,
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2,
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2
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],
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"dec_channels": [
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64,
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96,
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192,
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384
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],
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"dec_num_head": [
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4,
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6,
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12,
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24
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],
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"dec_patch_size": [
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256,
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256,
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256,
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256
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],
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"mlp_ratio": 4,
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"qkv_bias": true,
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"qk_scale": null,
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"layer_scale": 1e-05,
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"attn_drop": 0.0,
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"proj_drop": 0.0,
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"drop_path": 0.3,
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"shuffle_orders": true,
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"pre_norm": true,
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"enable_rpe": false,
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"enable_flash": true,
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"upcast_attention": false,
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"upcast_softmax": false,
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"traceable": false,
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"mask_token": false,
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"enc_mode": true,
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"freeze_encoder": false
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},
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"criteria": [
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{
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"type": "FastUnifiedInstanceLoss",
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"cost_mask": 1.0,
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"cost_dice": 1.0,
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"cost_class": 1.0,
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"loss_weight_focal": 2.0,
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"loss_weight_dice": 5.0,
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"cls_weight_matched": 2.0,
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"cls_weight_noobj": 0.5,
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"focal_alpha": 0.25,
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"focal_gamma": 2.0,
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"aux_loss_weight": 1.0,
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"num_points": 100000,
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"truth_label": "instance",
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"noobj_mask_loss_weight": 0.0
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}
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]
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}
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}
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