FlagEvalMM: A Flexible Framework for Comprehensive Multimodal Model Evaluation

2025-06-10

AI Summary

Existing evaluation frameworks struggle to flexibly and efficiently support unified assessment of multimodal models across diverse tasks—including visual question answering, text-to-image/video generation, and image-text retrieval. To address this, we propose the first open-source multimodal evaluation framework that decouples inference from evaluation. Our framework adopts a microservice-based architecture, enabling asynchronous data loading and dynamic integration of multiple backend inference engines (e.g., vLLM, SGLang). It features modular task interfaces and a fine-grained, extensible, cross-task unified evaluation protocol. Empirical evaluation on mainstream benchmarks demonstrates significant improvements in throughput and resource utilization, while maintaining high assessment accuracy and robustness. The framework is publicly released and has already been adopted by multiple research teams.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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 athttps://github.com/flageval-baai/FlagEvalMM.

Problem

• Assessing multimodal models across diverse vision-language tasks
• Decoupling model inference from evaluation for flexibility
• Enhancing evaluation efficiency with advanced acceleration tools

Innovation

• Decouples model inference from evaluation service
• Uses advanced inference acceleration tools
• Implements asynchronous data loading