FlagEvalMM: A Flexible Framework for Comprehensive Multimodal Model Evaluation

• URL: http://arxiv.org/abs/2506.09081v3
• Date: Mon, 28 Jul 2025 19:31:25 GMT
• Title: FlagEvalMM: A Flexible Framework for Comprehensive Multimodal Model Evaluation
• Authors: Zheqi He, Yesheng Liu, Jing-shu Zheng, Xuejing Li, Jin-Ge Yao, Bowen Qin, Richeng Xuan, Xi Yang,
• Abstract summary: We present FlagEvalMM, an open-source evaluation framework designed to assess multimodal models.<n>We decouple model inference from evaluation through an independent evaluation service.<n>FlagEvalMM offers accurate and efficient insights into model strengths and limitations.
• Score: 9.18997651928914
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present FlagEvalMM, an open-source evaluation framework designed to comprehensively assess multimodal models across a diverse range of vision-language understanding and generation tasks, such as visual question answering, text-to-image/video generation, and image-text retrieval. We decouple model inference from evaluation through an independent evaluation service, thus enabling flexible resource allocation and seamless integration of new tasks and models. Moreover, FlagEvalMM utilizes advanced inference acceleration tools (e.g., vLLM, SGLang) and asynchronous data loading to significantly enhance evaluation efficiency. Extensive experiments show that FlagEvalMM offers accurate and efficient insights into model strengths and limitations, making it a valuable tool for advancing multimodal research. The framework is publicly accessible at https://github.com/flageval-baai/FlagEvalMM.

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