OpenGVLab/GenExam
GenExam: A Multidisciplinary Text-to-Image Exam
This project offers a specialized benchmark for evaluating text-to-image models on their ability to generate precise images based on complex, multidisciplinary instructions. It takes detailed text prompts across subjects like science, humanities, and arts as input, and outputs generated images which are then scored for semantic accuracy and visual plausibility. This is useful for AI researchers and developers working on or evaluating advanced image generation models.
Use this if you are developing or comparing text-to-image models and need a rigorous way to assess their precision in generating specific, exam-style visual content across various academic and professional domains.
Not ideal if you are looking for a tool to generate images for creative projects or general use, as this is a benchmark for evaluating model performance.
Stars
62
Forks
4
Language
Python
License
MIT
Category
Last pushed
Feb 27, 2026
Commits (30d)
0
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