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---
language: en
license: lgpl-3.0
tags:
- pytorch
- torchscript
- image-segmentation
- particle-physics
- neutrino-detectors
- liquid-argon
datasets:
- custom
library_name: pytorch
pipeline_tag: image-segmentation
model-index:
- name: DNN ROI Models
results:
- task:
type: image-segmentation
name: Region of Interest Detection
metrics:
- type: iou
value: N/A
name: Intersection over Union
- type: dice
value: N/A
name: Dice Coefficient
models:
- icarus/moon-2025-08-25
- pdhd/dikshant/mobileunet
- pdhd/hokyeong/mobilenetv3
- pdsp/unet
- pdsp/nestedunet
- sbnd/sbnd_data-v01_34_00
---
# DNN ROI Models
A collection of deep neural network models for region of interest (ROI) detection in LArTPC experiments, including ICARUS, ProtoDUNE-HD, ProtoDUNE-SP, and SBND.
```bash
├── icarus
│ └── moon-2025-08-25
│ ├── plane0_rand.ts
│ └── plane1_rand.ts
├── pdhd
│ ├── dikshant
│ │ ├── mobileunet_largedataset_fullimage.ts
│ │ ├── mobileunet_largedataset_rebin4.ts
│ │ ├── unet_largedataset_fullimage.ts
│ │ └── unet_largedataset_rebin4.ts
│ └── hokyeong
│ └── CP49_mobilenetv3.ts
├── pdsp
│ ├── pth-model # models in pytorch pickle format
│ │ ├── nestedunet-l23-cosmic500-e50.pth # input: loose, MP2, MP3
│ │ ├── unet-l23-cosmic500-e50.pth # input: loose, MP2, MP3
│ │ └── unet-lt-cosmic500-e50.pth # input: loose, tight
│ ├── ts-model-1.3 # TorchScript model saved using PyTorch 1.3
│ │ ├── nestedunet-l23-cosmic500-e50.ts
│ │ ├── unet-l23-cosmic500-e50.ts
│ │ └── unet-lt-cosmic500-e50.ts
│ └── ts-model-2.3 # TorchScript model saved using PyTorch 2.3
│ ├── nestedunet-l23-cosmic500-e50.ts
│ ├── unet-l23-cosmic500-e50.ts
│ └── unet-lt-cosmic500-e50.ts
├── README.md
└── sbnd
└── sbnd_data-v01_34_00
├── plane0.ts
└── plane1.ts
``` |