---
license: apache-2.0
size_categories:
- 1K<n<10K
---
<div align="center">

## 🚀 LR0.FM (ICLR-25 🎉)<br> [webpage](https://ucf-crcv.github.io/lr0.fm/) | [paper](https://arxiv.org/abs/2502.03950) | [video](https://recorder-v3.slideslive.com/#/share?share=99927&s=b52e48b7-e501-45c7-b7c9-b1d415e77f1e) | [results]() | [weights]()<br><br> <p align="left"></p>
</div>


Captions randomly sampled from [Conceptual Captions](https://github.com/google-research-datasets/conceptual-captions), and the diffusion model [PIXART-α](https://github.com/PixArt-alpha/PixArt-alpha) generates synthetic dataset for it.
7,000 randomly sampled captions. 

 
 
```
import torch 
from diffusers import PixArtAlphaPipeline
pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", torch_dtype=torch.float16)
pipe = pipe.to('cuda')


line = line.strip() ## caption line from either `caption_2k.txt' or `caption_5k.txt'

offset = 0 
for fold in range(7):
    images =pipe(line, num_images_per_prompt=10,  ).images
    [img.save(f"{ROOT}/{offset + k}.png") for k,img in enumerate(images)]
    offset += 10
```


---
```bibtex 
@inproceedings{
    pathak2025lrfm,
    title={{ LR0.FM: Low-Res Benchmark and Improving robustness for Zero-Shot Classification in Foundation Models} },
    author={Priyank Pathak and Shyam Marjit and Shruti Vyas and Yogesh S Rawat},
    booktitle={The Thirteenth International Conference on Learning Representations},
    year={2025},
    url={https://openreview.net/forum?id=AsFxRSLtqR}
}

@article{pathak2025lr0,
  title={LR0. FM: Low-Resolution Zero-shot Classification Benchmark For Foundation Models},
  author={Pathak, Priyank and Marjit, Shyam and Vyas, Shruti and Rawat, Yogesh S},
  journal={arXiv preprint arXiv:2502.03950},
  year={2025}
}
```
---


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license: cc
---