Create README.md

README.md

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+---
+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.
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/623d8ca4c29adf5ef6175615/Do69D0sNlG9eqr9cyE7bm.png)
+
+
+## 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
+
+```
+
+
+
+
+
+