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# Model Card for mlpf-clic-clusters-v1.6
This model reconstructs particles in a detector, based on the tracks and calorimeter clusters recorded by the detector.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Joosep Pata, Eric Wulff, Farouk Mokhtar, Mengke Zhang, David Southwick, Maria Girone, David Southwick, Javier Duarte
- **Model type:** graph neural network with learnable structure in locality-sensitive hashing bins
- **License:** Apache License
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/jpata/particleflow/releases/tag/v1.6
- **Paper:** https://doi.org/10.48550/arXiv.2309.06782
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
This model may be used to study the physics and computational performance on ML-based reconstruction in simulation.
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
This model is not intended for physics measurements on real data.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The model has only been trained on simulation data and has not been validated against real data.
## How to Get Started with the Model
Use the code below to get started with the model.
```
git clone https://github.com/jpata/particleflow/releases/tag/v1.6
cd particleflow
#Download the software image
wget https://hep.kbfi.ee/~joosep/tf-2.14.0.simg
#Download the checkpoint
wget https://huggingface.co/jpata/particleflow/resolve/clic_clusters_v1.6/weights-96-5.346523.hdf5
wget https://huggingface.co/jpata/particleflow/resolve/clic_clusters_v1.6/opt-96-5.346523.pkl
#Launch a shell in the software image
apptainer shell --nv tf-2.14.0.simg
#Continue the training from a checkpoint
python3 mlpf/pipeline.py train --config parameters/clic.yaml --weights weights-96-5.346523.hdf5 --batch-multiplier 0.5
#Run the evaluation for a given training directory, loading the best weight file in the directory
python3 mlpf/pipeline.py evaluate --train-dir experiments/clic-REPLACEME
```
## Training Details
### Training Data
Trained on the following dataset:
Pata, J., Wulff, E., Duarte, J., Mokhtar, F., Zhang, M., Girone, M., & Southwick, D. (2023). Simulated datasets for detector and particle flow reconstruction: CLIC detector, machine learning format (v1.5.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8409592
### Training Procedure
```
python3 mlpf/pipeline.py train --config parameters/clic.yaml
```
## Evaluation
```
python3 mlpf/pipeline.py evaluate --train-dir experiments/clic-REPLACEME
```
## Citation
**BibTeX:**
```
@misc{pata2023scalable,
title={Scalable neural network models and terascale datasets for particle-flow reconstruction},
author={Joosep Pata and Eric Wulff and Farouk Mokhtar and David Southwick and Mengke Zhang and Maria Girone and Javier Duarte},
year={2023},
eprint={2309.06782},
archivePrefix={arXiv},
primaryClass={physics.data-an}
}
```
## Glossary
PF - particle flow reconstruction
## Model Card Contact
Joosep Pata, [email protected]
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