MME-RealWorld
A comprehensive evaluation benchmark for Multimodal Large Language Models featuring over 13,366 high-resolution images and 29,429 question-answer pairs across 43 subtasks and 5 real-world scenarios. The largest manually annotated multimodal benchmark to date, designed to test MLLMs on challenging high-resolution real-world scenarios.
Qwen2.5-Omni-7B from Alibaba Cloud / Qwen Team currently leads the MME-RealWorld leaderboard with a score of 0.616 across 1 evaluated AI models.
What MME-RealWorld measures
MME-RealWorld is a multimodal benchmark that evaluates large language models on general, multimodal, and vision tasks. LLM Stats tracks 1 model on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.6, with the leader reaching 0.6.
Compare leaders on the best AI for general, best AI for multimodal and best AI for vision leaderboards.
Publication
- Paper
- MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?
- Authors
- Yi-Fan Zhang, Huanyu Zhang, Haochen Tian, Chaoyou Fu, and 9 others
- Published
- arXiv
- 2408.13257
Abstract
Comprehensive evaluation of Multimodal Large Language Models (MLLMs) has recently garnered widespread attention in the research community. However, we observe that existing benchmarks present several common barriers that make it difficult to measure the significant challenges that models face in the real world, including: 1) small data scale leads to a large performance variance; 2) reliance on model-based annotations results in restricted data quality; 3) insufficient task difficulty, especially caused by the limited image resolution. To tackle these issues, we introduce MME-RealWorld. Specifically, we collect more than $300$K images from public datasets and the Internet, filtering $13,366$ high-quality images for annotation. This involves the efforts of professional $25$ annotators and $7$ experts in MLLMs, contributing to $29,429$ question-answer pairs that cover $43$ subtasks across $5$ real-world scenarios, extremely challenging even for humans. As far as we know, MME-RealWorld is the largest manually annotated benchmark to date, featuring the highest resolution and a targeted focus on real-world applications. We further conduct a thorough evaluation involving $28$ prominent MLLMs, such as GPT-4o, Gemini 1.5 Pro, and Claude 3.5 Sonnet. Our results show that even the most advanced models struggle with our benchmarks, where none of them reach $60\%$ accuracy. The challenges of perceiving high-resolution images and understanding complex real-world scenarios remain urgent issues to be addressed. The data and evaluation code are released at https://mme-realworld.github.io/ .
Qwen2.5-Omni-7B leads with 61.6%.
Progress Over Time
Interactive timeline showing model performance evolution on MME-RealWorld
MME-RealWorld Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  Alibaba Cloud / Qwen Team | 7B | — | — |
FAQ
Common questions about MME-RealWorld.
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