EvoQuality

About

What EvoQuality is

EvoQuality 1. Model Overview - Model Name: EvoQuality (Self-Evolving VLM for Image Quality Assessment) - Task: No-Reference Image Quality Assessment (NR-IQA), supporting both single-image quality scoring and pairwise quality comparison (ranking) - Core Idea: Without relying on any human-annotated quality scores or distortion-type labels, EvoQuality generates pseudo-ranking labels via pairwise majority voting, and converts them into an optimizable reward signal through GRPO to iteratively self-evolve its quality perception capability - Paper: Self-Evolving Vision-Language Models for Image Quality Assessment via Voting and Ranking (ICLR 2026, arXiv:2509.25787) 2. Model and Framework Details - Backbone Model (paper setting): Qwen2.5-VL-7B (used as the baseline policy) - Training Paradigm: Two-stage cycle, supports multi-round iteration (T=2 in the paper) - Offline Stage (Pseudo-label): Perform K comparisons on randomly sampled image pairs, then derive pseudo-preferences p(xi, xj) via majority voting - Online Stage (RL): Convert pseudo-preferences into a fidelity reward and update the policy via Group Relative Policy Optimization (GRPO) (full fine-tuning of the VLM) 3. Prompts - Offline Comparison ccompare: - <image<image You are performing an image quality assessment task. Compare the two images and decide which one has better perceptual quality. Answer strictly with the index of the better image: 0 if the first image is better, or 1 if the second image is better. - Online Scoring cscore: - <image You are doing the image quality assessment task. Here is the question: What is your overall rating on the quality of this picture? The rating should be a float between 1 and 5, rounded to two decimal places, with 1 representing very poor quality and 5 representing excellent quality. - Reasoning Suffix (for self-consistency sampling): - You FIRST think about the reasoning process as an internal monologue and then provide the final answer. The reasoning process MUST BE enclosed within <think </think tags. The final answer MUST BE put in boxed{}. 4. Training - Number of Iterations: T = 1 (the open-sourced model weights are the result of the first round of self-evolution) - T

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

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Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys evoquality for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (evoquality below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"evoquality","messages":[{"role":"user","content":"Hello"}]}'

Details

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Papers

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Open weights

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