README.md

---
license: apache-2.0
task_categories:
- multiple-choice
- question-answering
- visual-question-answering
language:
- en
size_categories:
- 100B<n<1T
---
* **`2024.08.20`** 🌟 We are proud to open-source MME-Unify, a comprehensive evaluation framework designed to systematically assess U-MLLMs. Our Benchmark covers 10 tasks with 30 subtasks, ensuring consistent and fair comparisons across studies.


Paper: https://arxiv.org/abs/2504.03641

Code: https://github.com/MME-Benchmarks/MME-Unify

Project page: https://mme-unify.github.io/



![](leaderboard.png)


## How to use?

You can download images in this repository and the final structure should look like this:

```
MME-Unify
├── CommonSense_Questions
├── Conditional_Image_to_Video_Generation
├── Fine-Grained_Image_Reconstruction
├── Math_Reasoning
├── Multiple_Images_and_Text_Interlaced
├── Single_Image_Perception_and_Understanding
├── Spot_Diff
├── Text-Image_Editing
├── Text-Image_Generation
├── Text-to-Video_Generation
├── Video_Perception_and_Understanding
└── Visual_CoT
```

## Dataset details


We present MME-Unify, a comprehensive evaluation framework designed to assess U-MLLMs systematically. Our benchmark includes:

1. **Standardized Traditional Task Evaluation** We sample from 12 datasets, covering 10 tasks with 30 subtasks, ensuring consistent and fair comparisons across studies.

2. **Unified Task Assessment** We introduce five novel tasks testing multimodal reasoning, including image editing, commonsense QA with image generation, and geometric reasoning.

3. **Comprehensive Model Benchmarking** We evaluate 12 leading U-MLLMs, such as Janus-Pro, EMU3, and VILA-U, alongside specialized understanding (e.g., Claude-3.5) and generation models (e.g., DALL-E-3).

Our findings reveal substantial performance gaps in existing U-MLLMs, highlighting the need for more robust models capable of handling mixed-modality tasks effectively.


![](Bin.png)