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EvolvingLMMs-Lab/lmms-eval

⭐ 4443 Python repository created 2024-03-07

LMMs-Eval is an open-source evaluation toolkit designed to benchmark and analyze the capabilities of multimodal large language models (LMMs). It aims to address the fragmentation in the multimodal evaluation ecosystem by providing a reproducible, efficient, and trustworthy platform. Key features include a unified pipeline for deterministic results, async serving and adaptive batching for efficient evaluation, and robust statistical methods for trustworthy outcomes, such as confidence intervals and paired comparisons. The toolkit supports over 100 tasks across various modalities (text, image, video, audio) and integrates with more than 30 LMMs. Recent updates have focused on operational simplicity, pipeline maturity, and expanding task coverage, including agentic task evaluation, video I/O optimizations, and the introduction of evaluation as a service via a standalone HTTP evaluation server. It also provides detailed results and insights into model performance through comprehensive data sheets. LMMs-Eval is geared towards helping model developers and researchers accurately assess and compare LMMs, fostering improvements in model development.

https://github.com/EvolvingLMMs-Lab/lmms-eval

multimodal-evaluationllm-evaluationbenchmarkvideo-evaluationaudio-evaluationvision-language-modelsllmsevaluation-toolkitreproducibilityefficiency

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