nielsr HF Staff commited on
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1 Parent(s): 5a9b35c

Correct pipeline tag and add library name

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This PR corrects the `pipeline_tag` to `image-to-text` which more accurately reflects the model's functionality (image input, text output). It also adds the `library_name` as `transformers` since the provided code snippets utilize the transformers library.

Files changed (1) hide show
  1. README.md +20 -25
README.md CHANGED
@@ -1,19 +1,20 @@
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- ---
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- license: mit
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- language:
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- - en
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- base_model:
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- - Qwen/Qwen2.5-VL-7B-Instruct
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- pipeline_tag: reinforcement-learning
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- tags:
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- - IQA
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- - Reasoning
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- - VLM
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- - Pytorch
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- - R1
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- - GRPO
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- - RL2R
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- ---
 
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  # VisualQuality-R1-7B
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  This is the latest version of VisualQuality-R1, trained on a diverse combination of synthetic and realistic datasets.<br>
@@ -22,10 +23,8 @@ Code link: [github](https://github.com/TianheWu/VisualQuality-R1)
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  > The first NR-IQA model enhanced by RL2R, capable of both quality description and rating through reasoning.
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-
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  <img src="https://cdn-uploads.huggingface.co/production/uploads/655de51982afda0fc479fb91/JZgVeMtAVASCCNYO5VCyn.png" width="600"/>
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-
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  ## Quick Start
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  This section includes the usages of **VisualQuality-R1**.
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@@ -241,7 +240,8 @@ path_score_dict = score_batch_image(
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  file_name = "output.txt"
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  with open(file_name, "w") as file:
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  for key, value in path_score_dict.items():
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- file.write(f"{key} {value}\n")
 
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  print("Done!")
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  ```
@@ -325,17 +325,13 @@ print(answer)
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  ```
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  </details>
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-
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-
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  ## Related Projects
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  - [ECCV 2024] [A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessment](https://arxiv.org/abs/2403.10854v2)
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  - [CVPR 2025] [Toward Generalized Image Quality Assessment: Relaxing the Perfect Reference Quality Assumption](https://www.arxiv.org/abs/2503.11221)
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-
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  ## 📧 Contact
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  If you have any question, please email `wth22@mails.tsinghua.edu.cn` or `tianhewu@cityu.edu.hk`.
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-
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  ## BibTeX
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  ```
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  @article{wu2025visualquality,
@@ -343,5 +339,4 @@ If you have any question, please email `wth22@mails.tsinghua.edu.cn` or `tianhew
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  author={Wu, Tianhe and Zou, Jian and Liang, Jie and Zhang, Lei and Ma, Kede},
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  journal={arXiv preprint arXiv:2505.14460},
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  year={2025}
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- }
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- ```
 
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+ ---
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+ base_model:
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+ - Qwen/Qwen2.5-VL-7B-Instruct
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+ language:
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+ - en
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+ license: mit
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+ pipeline_tag: image-to-text
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+ library_name: transformers
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+ tags:
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+ - IQA
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+ - Reasoning
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+ - VLM
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+ - Pytorch
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+ - R1
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+ - GRPO
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+ - RL2R
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+ ---
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  # VisualQuality-R1-7B
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  This is the latest version of VisualQuality-R1, trained on a diverse combination of synthetic and realistic datasets.<br>
 
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  > The first NR-IQA model enhanced by RL2R, capable of both quality description and rating through reasoning.
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  <img src="https://cdn-uploads.huggingface.co/production/uploads/655de51982afda0fc479fb91/JZgVeMtAVASCCNYO5VCyn.png" width="600"/>
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  ## Quick Start
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  This section includes the usages of **VisualQuality-R1**.
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  file_name = "output.txt"
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  with open(file_name, "w") as file:
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  for key, value in path_score_dict.items():
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+ file.write(f"{key} {value}
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+ ")
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  print("Done!")
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  ```
 
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  ```
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  </details>
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  ## Related Projects
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  - [ECCV 2024] [A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessment](https://arxiv.org/abs/2403.10854v2)
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  - [CVPR 2025] [Toward Generalized Image Quality Assessment: Relaxing the Perfect Reference Quality Assumption](https://www.arxiv.org/abs/2503.11221)
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  ## 📧 Contact
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  If you have any question, please email `wth22@mails.tsinghua.edu.cn` or `tianhewu@cityu.edu.hk`.
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  ## BibTeX
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  ```
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  @article{wu2025visualquality,
 
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  author={Wu, Tianhe and Zou, Jian and Liang, Jie and Zhang, Lei and Ma, Kede},
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  journal={arXiv preprint arXiv:2505.14460},
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  year={2025}
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+ }