jschuel / migYOLO

Data processing and analysis tools for using YOLOv8 with CMOS camera images from the MIGDAL TPC

migYOLO v1.0.0

Update (June 24th, 2024): Data is available (see Installation instructions in the official documentation)

migYOLO is a package containing tools for using the YOLOv8-based processing and rare event search analysis pipeline for CMOS camera data from the MIGDAL experiment (paper preprint here). This package has GPU support through PyTorch. For installation and usage instructions, please consult the official documentation.

How to Cite

If you have found this software useful for your work, please consider citing both our paper and this software:

Paper Citation

@article{MIGDAL:2024alc,
    author = "Schueler, J. and others",
    collaboration = "MIGDAL",
    title = "{Transforming a rare event search into a not-so-rare event search in real-time with deep learning-based object detection}",
    eprint = "2406.07538",
    archivePrefix = "arXiv",
    primaryClass = "hep-ex",
    month = "6",
    year = "2024"
}

Software Citation

@software{schueler_2024_12628437,
  author       = {Schueler, Jeffrey},
  title        = {{migYOLO - An end-to-end YOLOv8-based object 
                   detection pipeline for CMOS camera images recorded
                   by the MIGDAL experiment}},
  month        = jul,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {v1.0.0},
  doi          = {10.5281/zenodo.12628437},
  url          = {https://doi.org/10.5281/zenodo.12628437}
}