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188-blind-image-quality-assessment-via-vision-language-correspondence-a-multitask-learning-perspect

https://github.com/szu-advtech-2024/188-blind-image-quality-assessment-via-vision-language-correspondence-a-multitask-learning-perspect

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
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    DOI references
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    Academic publication links
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    Academic email domains
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    Institutional organization owner
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (2.4%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: SZU-AdvTech-2024
  • Default Branch: main
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Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Citation

https://github.com/SZU-AdvTech-2024/188-Blind-Image-Quality-Assessment-via-Vision-Language-Correspondence-A-Multitask-Learning-Perspect/blob/main/ 

# Requirement

torch 1.8+

torchvision

Python 3

pip install ftfy regex tqdm

pip install git+https://github.com/openai/CLIP.git

# Pre-trained weights

Google Drive: 

https://drive.google.com/file/d/1GoKwUKNR-rvX11QbKRN8MuBZw2hXKHGh/view?usp=sharing

 

: https://pan.baidu.com/s/1KHjj7T8y2H_eKE6w7HnWJA : 2b8v 

LIQE.ptcheckpoints

# Demo

demo.pyimg1img2

```bash
python demo.py
```

# Training

```bash
python train_unique_clip_weight.py
```

# Evaluation

```bash
python BIQA_benchmark.py
```

Owner

  • Name: SZU-AdvTech-2024
  • Login: SZU-AdvTech-2024
  • Kind: organization

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