FlagEvalMM: A Flexible Framework for Comprehensive Multimodal Model Evaluation
This provides a flexible evaluation tool for multimodal AI researchers, though it is incremental as it builds on existing evaluation paradigms with efficiency improvements.
The authors tackled the problem of evaluating multimodal models by introducing FlagEvalMM, an open-source framework that comprehensively assesses vision-language tasks like visual question answering and text-to-image generation, resulting in a tool that offers accurate and efficient insights into model performance.
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.