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FlagevalMM - A multimodal model evaluation framework from Zhiyuan Open Source

FlagEvalMM is an open-source multimodal model evaluation framework from the Beijing Academy of Artificial Intelligence. It comprehensively evaluates models handling multiple modalities, including text, images, and video, and supports various tasks and metrics. The framework employs a combination of evaluation and model...

What is FlagevalMM?

FlagEvalMM is an open-source multimodal model evaluation framework from the Beijing Academy of Artificial Intelligence. It can comprehensively evaluate models that process multiple modalities, including text, images, and videos, and supports various tasks and metrics. The framework adopts a decoupled design between evaluation and model inference, unifying the evaluation process for various models such as visual language models, text-to-image, text-to-video, and image-text retrieval, improving evaluation efficiency and facilitating rapid adaptation to new tasks and models.

FlagEvalMM's main functions

  • Multimodal model supportIt supports evaluating various types of multimodal models, including visual question answering (VQA), image retrieval, and text-to-image generation.
  • Comprehensive benchmarks and metricsSupports new and commonly used benchmarks and evaluation metrics to comprehensively measure model performance.
  • Model library integrationProvides a model library (model_zoo) that supports inference for various popular multimodal models, such as QWenVL and LLaVA, and integrates with API-based models such as GPT, Claude, and HuanYuan.
  • Multi-backend supportIt supports multiple backend engines for inference, such as VLLM and SGLang, to adapt to different models and needs.

The technical principle of FlagEvalMM

  • Decoupling Evaluation and Model InferenceFlagEvalMM separates the evaluation logic from the model inference logic, making the evaluation framework independent of model updates, thus improving the framework's flexibility and maintainability.
  • Unified evaluation frameworkIt uses a unified architecture to evaluate different types of multimodal models, reducing code duplication and improving code reusability.
  • Plug-in designThe framework adopts a plug-in design, allowing users to add new plug-ins to extend the supported models, tasks, and evaluation metrics.
  • Backend engine adaptationThe framework supports multiple backend engines and handles the interface differences between different backend engines based on the adaptation layer, allowing users to switch seamlessly between different engines.

FlagEvalMM's project address

Application scenarios of FlagEvalMM

  • academic researchResearchers evaluate and compare the performance of different multimodal models on tasks such as visual question answering and image retrieval, and publish academic papers.
  • Industrial applicationsEnterprises test and optimize their multimodal products, such as intelligent customer service systems, to improve user experience.
  • Model DevelopmentWhen developing new multimodal models, developers should evaluate the models to ensure that their performance in real-world applications meets expectations.
  • Education: To improve teaching effectiveness by evaluating multimodal interaction models in teaching support systems for educational institutions.
  • Content creationContent creators can evaluate and select models suitable for generating text and image content, thereby improving the efficiency and quality of content creation.
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