Upload hugginface_README.md

hugginface_README.md

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+# Visual Prompt Checkpoints for NR-IQA
+
+🔬 **Paper**: [Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA](https://arxiv.org/abs/xxxx.xxxxx)  
+💻 **Code**: [GitHub Repository](https://github.com/your-username/visual-prompt-nr-iqa)
+
+## Overview
+
+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.
+
+## Available Checkpoints
+
+**Download**: `visual_prompt_ckpt_trained_on_mplug2.zip`
+
+| Dataset | SROCC | Experiment Folder |
+|---------|-------|-------------------|
+| KADID-10k | 0.932 | `SGD_mplug2_exp_04_kadid_padding_30px_add/` |
+| KonIQ-10k | 0.852 | `SGD_mplug2_exp_05_koniq_padding_30px_add/` |
+| AGIQA-3k | 0.810 | `SGD_mplug2_exp_06_agiqa_padding_30px_add/` |
+
+## Citation
+
+```bibtex
+@article{benmahane2024parameter,
+  title={Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA},
+  author={Benmahane, Yahya and El Hassouni, Mohammed},
+  journal={arXiv preprint},
+  year={2024}
+}
+```
+
+**📖 For detailed setup, training, and usage instructions, see the [GitHub repository](https://github.com/your-username/visual-prompt-nr-iqa).**