Update README.md

README.md

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 ## 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.
+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.
 
-2. **Quality**: The dataset features high-resolution images with crucial details and manual annotations verified by experts to ensure top-notch data quality.
+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:
 
-3. **Safety**: MME-RealWord avoids data overlap with other benchmarks and relies solely on human annotations, eliminating model biases and personal bias.
+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.
 
-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.
+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.
 
-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.
+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)