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BTSbot and its classification task, and
Rehemtulla et al. 2025 performed
architecture and pre-training benchmarking on this BTSbot image classification task.1pip install btsbot
2
3import btsbot
4model = btsbot.load_HF_model(
5 architecture="maxvit", multi_modal=False, pretrain="imagenet"
6)BTSbot/btsbot/inference_example.py.1@ARTICLE{Rehemtulla+2025,
2 author = {{Rehemtulla}, Nabeel and {Miller}, Adam A. and {Walmsley}, Mike and {Shah}, Ved G. and {Jegou du Laz}, Theophile and {Coughlin}, Michael W. and {Sasli}, Argyro and {Bloom}, Joshua and {Fremling}, Christoffer and {Graham}, Matthew J. and {Groom}, Steven L. and {Hale}, David and {Mahabal}, Ashish A. and {Perley}, Daniel A. and {Purdum}, Josiah and {Rusholme}, Ben and {Sollerman}, Jesper and {Kasliwal}, Mansi M.},
3 title = "{Pre-training vision models for the classification of alerts from wide-field time-domain surveys}",
4 journal = {arXiv e-prints},
5 keywords = {Instrumentation and Methods for Astrophysics, Computer Vision and Pattern Recognition},
6 year = 2025,
7 month = dec,
8 eid = {arXiv:2512.11957},
9 pages = {arXiv:2512.11957},
10 doi = {10.48550/arXiv.2512.11957},
11archivePrefix = {arXiv},
12 eprint = {2512.11957},
13 primaryClass = {astro-ph.IM},
14 adsurl = {https://ui.adsabs.harvard.edu/abs/2025arXiv251211957R},
15 adsnote = {Provided by the SAO/NASA Astrophysics Data System}
16}