FlagEvalMM: A Flexible Framework for Comprehensive Multimodal Model Evaluation

TL;DR

FlagEvalMM is an open-source, flexible evaluation framework that comprehensively assesses multimodal models across various vision-language tasks, improving efficiency and integration capabilities for research advancement.

Contribution

It introduces a decoupled, resource-efficient evaluation framework with advanced acceleration tools, enabling seamless integration of new tasks and models for multimodal research.

Findings

Provides accurate assessment of model strengths and limitations.

Significantly enhances evaluation efficiency with advanced inference acceleration.

Facilitates comprehensive multimodal model benchmarking.

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.