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---
license: apache-2.0
language:
- en
datasets:
- ILSVRC/imagenet-1k
---
# Model Card for Model ID
VIT-MAE-r is a fine-tuned version of MAE for image reconstuction. We release a version fine-tuned from [MAE-Large](https://huggingface.co/facebook/vit-mae-large)
## Model Details
VIT-MAE-r is already converted to hf format and should be able to be used directly by `from_pretrained` method.
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** [LM4LV](https://github.com/bytetriper/LM4LV)
- **Paper:** [LM4LV: A Frozen Large Language Model for Low-level Vision Tasks](https://arxiv.org/abs/2405.15734v1)
- **source model**: [MAE-Large](https://huggingface.co/facebook/vit-mae-large)
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import AutoImageProcessor, AutoModelForPreTraining
model = AutoModelForPreTraining.from_pretrained("bytetriper/vit-mae-r")
```
## Evaluation
This model achieves a rFID on ImageNet val set of 1.24, evaluated using the standard tensorflow tool provided by [Guided-Diffusion](https://github.com/openai/guided-diffusion/tree/main/evaluations)
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
@article{zheng2024lm4lv,
title={LM4LV: A Frozen Large Language Model for Low-level Vision Tasks},
author={Zheng, Boyang and Gu, Jinjin and Li, Shijun and Dong, Chao},
journal={arXiv preprint arXiv:2405.15734},
year={2024}
}
## Model Card Authors
Boyang Zheng
## Model Card Contact
bytetriper@gmail.com |