| --- |
| license: apache-2.0 |
| size_categories: |
| - 1K<n<10K |
| --- |
| <div align="center"> |
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| ## 🚀 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> |
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| 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. |
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| |
| |
| ``` |
| 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 |
| ``` |
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|
| --- |
| ```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 |
| --- |
