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
+---
+* **`2024.08.20`** 🌟 We are proud to open-source MME-Unify, a comprehensive evaluation framework designed to systematically assess U-MLLMs. Our Benchmark covering 10 tasks with 30 subtasks, ensuring consistent and fair comparisons across studies.
+
+
+Paper: arxiv.org/abs/2408.13257
+
+Code: https://github.com/aba122/MME-Unify
+
+Project page: https://aba122.github.io/MME-Unify.github.io/
+
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/623d8ca4c29adf5ef6175615/ZnczJh10NHm0u03p7kjm_.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
+
+# Function to process each set of split files
+process_files() {
+    local part="$1"
+    
+    # Extract the base name of the file
+    local base_name=$(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"
+    
+    # Remove the individual split files
+    rm -rf "${base_name}".tar.gz.part_*
+
+    rm -rf "${base_name}.tar.gz"
+}
+
+export -f process_files
+
+# Find all .tar.gz.part_aa files and process them in parallel
+find . -name '*.tar.gz.part_aa' | parallel process_files
+
+# Wait for all background jobs to finish
+wait
+
+# nohup  bash unzip_file.sh >> unfold.log 2>&1 &
+
+
+```
+
+# 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 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.
+
+We present MME-RealWord, a benchmark meticulously designed to address real-world applications with practical relevance. Featuring 13,366 high-resolution images averaging 2,000 × 1,500 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. **Data Scale**: with the efforts of a total of 32 volunteers, we have manually annotated 29,429 QA pairs focused on real-world scenarios, making this the largest fully human-annotated benchmark known to date.
+
+2. **Data Quality**: 1) Resolution: Many image details, such as a scoreboard in a sports event, carry critical information. These details can only be properly interpreted with high- resolution images, which are essential for providing meaningful assistance to humans. To the best of our knowledge, MME-RealWorld features the highest average image resolution among existing competitors. 2) Annotation: All annotations are manually completed, with a professional team cross-checking the results to ensure data quality.
+
+3. **Task Difficulty and Real-World Utility.**: We can see that even the most advanced models have not surpassed 60% accuracy. Additionally, many real-world tasks are significantly more difficult than those in traditional benchmarks. For example, in video monitoring, a model needs to count the presence of 133 vehicles, or in remote sensing, it must identify and count small objects on a map with an average resolution exceeding 5000×5000.
+
+4. **MME-RealWord-CN.**: Existing Chinese benchmark is usually translated from its English version. This has two limitations: 1) Question-image mismatch. The image may relate to an English scenario, which is not intuitively connected to a Chinese question. 2) Translation mismatch [58]. The machine translation is not always precise and perfect enough. We collect additional images that focus on Chinese scenarios, asking Chinese volunteers for annotation. This results in 5,917 QA pairs.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/623d8ca4c29adf5ef6175615/Do69D0sNlG9eqr9cyE7bm.png)
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