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---
base_model:
- MAGAer13/mplug-owl2-llama2-7b
language:
- en
license: mit
library_name: transformers
pipeline_tag: image-to-text
---
# DeQA-Score-LoRA-Mix3
DeQA-Score (
[project page](https://depictqa.github.io/deqa-score/) /
[codes](https://github.com/zhiyuanyou/DeQA-Score) /
[paper](https://arxiv.org/abs/2501.11561)
) model weights LoRA fine-tuned on KonIQ, SPAQ, and KADID datasets.
This work is under our [DepictQA project](https://depictqa.github.io/).
## Non-reference IQA Results (PLCC / SRCC)
| | Fine-tune | KonIQ | SPAQ | KADID | PIPAL | LIVE-Wild | AGIQA | TID2013 | CSIQ |
|--------------|-----------|-----------|----------|----------|----------|-----------|----------|----------|----------|
| Q-Align (Baseline) | Fully | 0.945 / 0.938 | 0.933 / 0.931 | 0.935 / 0.934 | 0.409 / 0.420 | 0.887 / 0.883 | 0.788 / 0.733 | 0.829 / 0.808 | 0.876 / 0.845 |
| DeQA-Score (Ours) | LoRA | **0.956 / 0.944** | **0.939 / 0.935** | **0.953 / 0.951** | **0.481 / 0.481** | **0.903 / 0.890** | **0.806 / 0.754** | **0.851 / 0.821** | **0.900 / 0.860** |
If you find our work useful for your research and applications, please cite using the BibTeX:
```bibtex
@inproceedings{deqa_score,
title={Teaching Large Language Models to Regress Accurate Image Quality Scores using Score Distribution},
author={You, Zhiyuan and Cai, Xin and Gu, Jinjin and Xue, Tianfan and Dong, Chao},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
year={2025},
}
``` |