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--- |
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datasets: |
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- chaofengc/IQA-PyTorch-Datasets |
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language: |
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- en |
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pipeline_tag: visual-question-answering |
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library_name: transformers |
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--- |
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# Visual Prompt Checkpoints for NR-IQA |
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🔬 **Paper**: [Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA](https://arxiv.org/abs/xxxx.xxxxx) (will be released soon) |
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💻 **Code**: [GitHub Repository](https://github.com/yahya-ben/mplug2-vp-for-nriqa) |
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## Overview |
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Pre-trained visual prompt checkpoints for **No-Reference Image Quality Assessment (NR-IQA)** using mPLUG-Owl2-7B. Achieves competitive performance with only **~600K parameters** vs 7B+ for full fine-tuning. |
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## Available Checkpoints |
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**Download**: `visual_prompt_ckpt_trained_on_mplug2.zip` |
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| Dataset | SROCC | Experiment Folder | |
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|---------|-------|-------------------| |
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| KADID-10k | 0.932 | `SGD_mplug2_exp_04_kadid_padding_30px_add/` | |
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| KonIQ-10k | 0.852 | `SGD_mplug2_exp_05_koniq_padding_30px_add/` | |
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| AGIQA-3k | 0.810 | `SGD_mplug2_exp_06_agiqa_padding_30px_add/` | |
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**📖 For detailed setup, training, and usage instructions, see the [GitHub repository](https://github.com/your-username/visual-prompt-nr-iqa).** |
