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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="convnext", multi_modal=True, pretrain="randinit"
6)BTSbot/btsbot/inference_example.py.1@ARTICLE{Rehemtulla+2025,
2 author = {{Rehemtulla}, Nabeel and {Miller}, Adam A. and {Walmsley}, Mike
3 and {Shah}, Ved G. and {Jegou du Laz}, Theophile and
4 {Coughlin}, Michael W. and {Sasli}, Argyro and
5 {Bloom}, Joshua and {Fremling}, Christoffer and
6 {Graham}, Matthew J. and {Groom}, Steven L. and {Hale}, David and
7 {Mahabal}, Ashish A. and {Perley}, Daniel A. and
8 {Purdum}, Josiah and {Rusholme}, Ben and {Sollerman}, Jesper and
9 {Kasliwal}, Mansi M.},
10 title = "{Pre-training vision models for the classification of alerts from
11 wide-field time-domain surveys}",
12 journal = {arXiv e-prints},
13 keywords = {Instrumentation and Methods for Astrophysics,
14 Computer Vision and Pattern Recognition},
15 year = 2025,
16 month = dec,
17 eid = {arXiv:2512.11957},
18 pages = {arXiv:2512.11957},
19 doi = {10.48550/arXiv.2512.11957},
20archivePrefix = {arXiv},
21 eprint = {2512.11957},
22 primaryClass = {astro-ph.IM},
23 adsurl = {https://ui.adsabs.harvard.edu/abs/2025arXiv251211957R},
24 adsnote = {Provided by the SAO/NASA Astrophysics Data System}
25}