|
|
--- |
|
|
library_name: pytorch |
|
|
tags: |
|
|
- vision |
|
|
- image-classification |
|
|
- pytorch |
|
|
license: mit |
|
|
base_model: timm/maxvit_tiny_rw_224.sw_in1k |
|
|
--- |
|
|
|
|
|
# BTSbot |
|
|
|
|
|
This is a maxvit fine-tuned for classifying images from the |
|
|
Zwicky Transient Facility (ZTF) observatory. |
|
|
[Rehemtulla et al. 2024](https://arxiv.org/abs/2401.15167) originally introduced |
|
|
`BTSbot` and its classification task, and |
|
|
[Rehemtulla et al. 2025](https://arxiv.org/abs/2512.11957) performed |
|
|
architecture and pre-training benchmarking on this `BTSbot` image classification task. |
|
|
|
|
|
**Base Model**: |
|
|
[timm/maxvit_tiny_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_tiny_rw_224.sw_in1k) |
|
|
|
|
|
## Usage |
|
|
|
|
|
Easily install the btsbot package and load this model with: |
|
|
|
|
|
```python |
|
|
git clone https://github.com/nabeelre/BTSbot.git |
|
|
cd BTSbot |
|
|
pip install -e . |
|
|
|
|
|
import btsbot |
|
|
model = btsbot.load_HF_model( |
|
|
architecture="maxvit", multi_modal=False, pretrain="randinit" |
|
|
) |
|
|
``` |
|
|
|
|
|
Also see |
|
|
[`BTSbot/btsbot/inference_example.py`](https://github.com/nabeelre/BTSbot/blob/main/btsbot/inference_example.py). |
|
|
|
|
|
## Citation |
|
|
|
|
|
If you use this model, please cite: |
|
|
|
|
|
```bibtex |
|
|
@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} |
|
|
} |
|
|
``` |
|
|
|
|
|
## License |
|
|
|
|
|
This model is released under the MIT License. |
|
|
|
|
|
## Repository |
|
|
|
|
|
For more information, see the [BTSbot GitHub repository](https://github.com/nabeelre/BTSbot). |
|
|
|