metadata
license: apache-2.0
size_categories:
- 1K<n<10K
Captions randomly sampled from Conceptual Captions, and the diffusion model PIXART-Ξ± 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
@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}
}