metadata
datasets:
- chaofengc/IQA-PyTorch-Datasets
language:
- en
pipeline_tag: visual-question-answering
library_name: transformers
Visual Prompt Checkpoints for NR-IQA
🔬 Paper: Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA (will be released soon) 💻 Code: GitHub Repository
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/ |
📖 For detailed setup, training, and usage instructions, see the GitHub repository.