MR-IQA-2 / README.md
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metadata
license: apache-2.0
language:
  - en
tags:
  - image-quality-assessment
  - vision-language
  - reinforcement-learning
  - image-editing

MR-IQA-2 logoMR-IQA-2

MR-IQA-2 couples a multimodal Actor, a frozen FLUX.2-klein-4B Editor, and a frozen E5 Judge. Masked credit assigns reasoning and rating rewards only to their eligible completion tokens.

MR-IQA-2 masked-credit training overview

Quick start: one image, one GPU

Download the runnable code bundle, create the two pinned inference environments, and provide one image:

python -m pip install 'huggingface-hub==0.34.4'
huggingface-cli download RobinY99/MR-IQA-2 \
  --include 'code/**' \
  --local-dir mr-iqa-2-hf
cd mr-iqa-2-hf/code
bash scripts/setup_envs.sh --profile inference
python examples/quick_start.py /absolute/path/to/input.jpg --gpu 0

Actor, Editor, and Judge run sequentially in separate processes on the selected GPU. The Actor solution is forwarded verbatim to the Editor; the Judge reports J0, J1, and J1-J0. No HTTP service is started.

On hosts with multiple CUDA toolkits, select the toolkit used for runtime extension compilation:

python examples/quick_start.py /absolute/path/to/input.jpg \
  --gpu 0 \
  --cuda-home /usr/local/cuda

Outputs are written to outputs/quick_start/: actor_raw.txt, assessment.json, edited.png, evaluation.json, and result.json. The release path was smoke-tested end to end on one NVIDIA A6000 (48 GB), with J0=3.42, J1=4.12, and J1-J0=+0.70.

PLCC/SRCC performance

Actor-only rating performance on six generalization datasets. Each entry is PLCC / SRCC; Average is the unweighted macro mean.

Model KonIQ-10K SPAQ LIVE-W AGIQA-3K KADID-10K CSIQ Average
MR-IQA 0.949 / 0.931 0.892 / 0.897 0.899 / 0.883 0.804 / 0.732 0.672 / 0.683 0.767 / 0.732 0.831 / 0.810
MR-IQA-2 0.937 / 0.917 0.900 / 0.899 0.893 / 0.863 0.809 / 0.739 0.667 / 0.669 0.824 / 0.785 0.838 / 0.812

MR-IQA values are from the released Qwen3-VL-2B result in the MR-IQA paper. MR-IQA-2 uses the released masked-credit E5 Actor at step 1,455. Exact coefficients and valid-row counts are in the checkpoint results.

Released folders

  • actor/: masked-credit E5 Actor, step 1,455;
  • judge/: frozen E5 Judge, step 725;
  • editor/: FLUX.2-klein-4B;
  • code/: runnable single-image inference bundle.

Actor and Judge load with AutoModelForImageTextToText.from_pretrained using subfolder="actor" or subfolder="judge". The Editor loads from editor/ with Flux2KleinPipeline.from_pretrained.

A real Actor → Editor sample, including the exact completion and provenance, is available in examples/actor_editor/sample_0001.json. Training, evaluation, and deployment code is available on RobinY99/MR-IQA-2.