README.md

metadata

title: Structured Defect Grounding
emoji: 🔎
colorFrom: red
colorTo: yellow
sdk: gradio
sdk_version: 6.19.0
app_file: app.py
python_version: '3.12'
short_description: Ground localized defects in AI-generated images
startup_duration_timeout: 1h
pinned: false

Structured Defect Grounding for Text-to-Image Feedback

Interactive demo of the SDG detector from "Where, What, Why, and Importance: Structured Defect Grounding for Text-to-Image Feedback" (arXiv:2606.06113).

The detector (P1n3/sdg-detector-grpo) is a GRPO-tuned Qwen3-VL-4B model that turns text-to-image diagnosis into structured set prediction. For every localized defect it predicts:

• Where — a bounding box (normalized 0–1000).
• What — artifact (visual defect) or misalignment (caption/image mismatch).
• Why — a short natural-language description.
• Importance — a severity estimate when the model emits one.

Usage

Upload an AI-generated image and, optionally, the caption/prompt it was meant to depict (used for the misalignment checks). The app overlays the predicted defect boxes and renders a structured feedback table plus the model's reasoning.

Links

• Paper: https://arxiv.org/abs/2606.06113
• Code: https://github.com/nianbai006/SDG
• Dataset: https://huggingface.co/datasets/P1n3/SDG-30K