---
license: mit
language:
- en
base_model:
- MAGAer13/mplug-owl2-llama2-7b
---

# DeQA-Score-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 fully fine-tuned on KonIQ, SPAQ, and KADID datasets. 

This work is under our [DepictQA project](https://depictqa.github.io/).

## Quick Start with AutoModel

For this image, ![](https://raw.githubusercontent.com/zhiyuanyou/DeQA-Score/main/fig/singapore_flyer.jpg) start an AutoModel scorer with `transformers==4.36.1`:

```python
import requests
import torch
from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
  "zhiyuanyou/DeQA-Score-Mix3",
  trust_remote_code=True,
  attn_implementation="eager", 
  torch_dtype=torch.float16,
  device_map="auto", 
)

from PIL import Image

# The inputs should be a list of multiple PIL images
score = model.score(
  [Image.open(requests.get(
    "https://raw.githubusercontent.com/zhiyuanyou/DeQA-Score/main/fig/singapore_flyer.jpg", stream=True
    ).raw)]
)
```

The "score" result should be 1.9404 (in range [1,5], higher is better).


## Non-reference IQA Results (PLCC / SRCC)

| Dataset      | KonIQ     | SPAQ     | KADID    | PIPAL    | LIVE-Wild | AGIQA    | TID2013  | CSIQ     |
|--------------|-----------|----------|----------|----------|-----------|----------|----------|----------|
| Q-Align (Baseline) | 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)  | **0.956 / 0.943** | **0.938 / 0.934** | **0.955 / 0.953** | **0.495 / 0.496** | **0.900 / 0.887** | **0.808 / 0.745** | **0.852 / 0.820** | **0.900 / 0.857** |


If you find our work useful for your research and applications, please cite using the BibTeX:

```bibtex
@article{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},
  journal={arXiv preprint arXiv:2501.11561},
  year={2025},
}
```