Pre-trained off-the-shelf BTSbots
Collection
ConvNeXt and MaxViT models with various pre-training regimens fine-tuned on the BTSbot task.
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14 items
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Updated
This is a maxvit fine-tuned for classifying images from the
Zwicky Transient Facility (ZTF) observatory.
Rehemtulla et al. 2024 originally introduced
BTSbot and its classification task, and
Rehemtulla et al. 2025 performed
architecture and pre-training benchmarking on this BTSbot image classification task.
Base Model: timm/maxvit_tiny_rw_224.sw_in1k
Easily install the btsbot package and load this model with:
git clone https://github.com/nabeelre/BTSbot.git
cd BTSbot
pip install -e .
import btsbot
model = btsbot.load_HF_model(
architecture="maxvit", multi_modal=True, pretrain="randinit"
)
Also see
BTSbot/btsbot/inference_example.py.
If you use this model, please cite:
@ARTICLE{Rehemtulla+2025,
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.},
title = "{Pre-training vision models for the classification of alerts from
wide-field time-domain surveys}",
journal = {arXiv e-prints},
keywords = {Instrumentation and Methods for Astrophysics,
Computer Vision and Pattern Recognition},
year = 2025,
month = dec,
eid = {arXiv:2512.11957},
pages = {arXiv:2512.11957},
doi = {10.48550/arXiv.2512.11957},
archivePrefix = {arXiv},
eprint = {2512.11957},
primaryClass = {astro-ph.IM},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025arXiv251211957R},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
This model is released under the MIT License.
For more information, see the BTSbot GitHub repository.
Base model
timm/maxvit_tiny_rw_224.sw_in1k