|
--- |
|
license: apache-2.0 |
|
task_categories: |
|
- multiple-choice |
|
- question-answering |
|
- visual-question-answering |
|
language: |
|
- en |
|
size_categories: |
|
- 100B<n<1T |
|
--- |
|
|
|
|
|
|
|
|
|
|
|
# MME-RealWorld Data Card |
|
|
|
## Dataset details |
|
|
|
Existing Multimodal Large Language Model 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 leading to large performance variance; |
|
2) reliance on model-based annotations, resulting in significant model bias; |
|
3) restricted data sources, often overlapping with existing benchmarks and posing a risk of data leakage; |
|
4) insufficient task difficulty and discrimination, especially the limited image resolution. |
|
|
|
|
|
We present MME-RealWord, a benchmark meticulously designed to address real-world applications with practical relevance. Featuring 13,366 high-resolution images averaging 1,734 × 1,734 pixels, MME-RealWord poses substantial recognition challenges. Our dataset encompasses 29,429 annotations across 43 tasks, all expertly curated by a team of 25 crowdsource workers and 7 MLLM experts. The main advantages of MME-RealWorld compared to existing MLLM benchmarks as follows: |
|
|
|
1. **Scale, Diversity, and Real-World Utility**: MME-RealWord is the largest fully human-annotated MLLM benchmark, covering 6 domains and 14 sub-classes, closely tied to real-world tasks. |
|
|
|
2. **Quality**: The dataset features high-resolution images with crucial details and manual annotations verified by experts to ensure top-notch data quality. |
|
|
|
3. **Safety**: MME-RealWord avoids data overlap with other benchmarks and relies solely on human annotations, eliminating model biases and personal bias. |
|
|
|
4. **Difficulty and Distinguishability**: The dataset poses significant challenges, with models struggling to achieve even 55% accuracy in basic tasks, clearly distinguishing between different MLLMs. |
|
|
|
5. **MME-RealWord-CN**: The dataset includes a specialized Chinese benchmark with images and questions tailored to Chinese contexts, overcoming issues in existing translated benchmarks. |
|
|
|
|
|
 |
|
|
|
|
|
## How to use? |
|
|
|
Since the image files are large and have been split into multiple compressed parts, please first merge the compressed files with the same name and then extract them together. |
|
|
|
``` |
|
#!/bin/bash |
|
|
|
# Navigate to the directory containing the split files |
|
cd TARFILES |
|
|
|
# Loop through each set of split files |
|
for part in *.tar.gz.part_aa; do |
|
# Extract the base name of the file |
|
base_name=$(basename "$part" .tar.gz.part_aa) |
|
|
|
# Merge the split files into a single archive |
|
cat "${base_name}".tar.gz.part_* > "${base_name}.tar.gz" |
|
|
|
# Extract the merged archive |
|
tar -xzf "${base_name}.tar.gz" |
|
|
|
# Optional: Remove the temporary merged archive |
|
rm "${base_name}.tar.gz" |
|
done |
|
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
