What is FlagevalMM
FlagEvalMM is an open-source multimodal model evaluation framework from the Beijing Academy of Artificial Intelligence (BAAI). It comprehensively evaluates models that process multiple modalities such as text, images, and video, and supports a variety of tasks and metrics. The framework adopts a design that decouples evaluation from model inference, unifying the evaluation process for various models such as vision-language models, text-to-image, text-to-video, and image-text retrieval, improving evaluation efficiency and making it easy to quickly adapt to new tasks and models.
Main Features of FlagEvalMM
- Multimodal Model Support: Supports evaluating multiple types of multimodal models, including visual question answering (VQA), image retrieval, text-to-image generation, etc.
- Comprehensive Benchmarks and Metrics: Supports new and commonly used benchmarks and evaluation metrics to comprehensively measure model performance.
- Model Zoo Integration: Provides a model zoo (model_zoo) that supports inference for a variety of popular multimodal models, such as QWenVL and LLaVA, and integrates with API-based models such as GPT, Claude, HuanYuan, etc.
- Multi-Backend Support: Supports multiple backend engines for inference, such as VLLM, SGLang, etc., to accommodate different models and requirements.
Technical Principles of FlagEvalMM
- Decoupling of Evaluation and Model Inference: FlagEvalMM separates evaluation logic from model inference logic, making the evaluation framework independent of model updates and improving the framework's flexibility and maintainability.
- Unified Evaluation Architecture: Handles evaluation of different types of multimodal models based on a unified architecture, reducing duplicate code and improving code reusability.
- Plugin-Based Design: The framework adopts a plugin-based design, allowing users to add new plugins to extend the supported models, tasks, and evaluation metrics.
- Backend Engine Adaptation: The framework supports multiple backend engines and uses an adaptation layer to handle interface differences between different backend engines, allowing users to seamlessly switch between different engines.
FlagEvalMM Project Address
- GitHub Repository：https://github.com/flageval-baai/FlagEvalMM
Application Scenarios of FlagEvalMM
- Academic Research: Researchers evaluate and compare the performance of different multimodal models on tasks such as visual question answering and image retrieval, and publish academic papers.
- Industrial Applications: Enterprises test and optimize their own multimodal products, such as intelligent customer service systems, to improve user experience.
- Model Development: Developers evaluate models when developing new multimodal models to ensure that model performance in real applications meets expectations.
- Education: Educational institutions evaluate multimodal interaction models in teaching assistance systems to improve teaching effectiveness.
- Content Creation: Content creators evaluate and select models suitable for generating image-text content to improve the efficiency and quality of content creation.