Update dataset card for parquet release
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README.md
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license: other
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size_categories:
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- 1K<n<10K
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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dataset_info:
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features:
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- name: index
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dtype: int64
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- name: real_image_path
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dtype: string
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- name: image
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dtype: image
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- name: question
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dtype: string
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- name: question_type
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dtype: string
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- name: answer
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dtype: string
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- name: answer_type
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dtype: string
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- name: category
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dtype: string
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- name: split
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dtype: string
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- name: origin_dataset
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dtype: string
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splits:
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- name: train
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num_bytes: 2041995103
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num_examples: 8657
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download_size: 1991106566
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dataset_size: 2041995103
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---
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# HVSBench
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HVSBench is a benchmark for evaluating how well multimodal large language models align with human perceptual behavior. It covers human visual system tasks across prominence, subitizing, prioritizing, free-viewing, and searching.
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This Hugging Face release packages the cleaned
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Paper: **Do MLLMs Exhibit Human-like Perceptual Behaviors? HVSBench: A Benchmark for MLLM Alignment with Human Perceptual Behavior**
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## Dataset Structure
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The dataset is uploaded
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Columns:
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- `image`: raw RGB image
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- `index`: source row index
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- `real_image_path`: original relative path under `raw_datasets`
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- `question`: benchmark prompt
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license: other
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size_categories:
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- 1K<n<10K
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---
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# HVSBench
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HVSBench is a benchmark for evaluating how well multimodal large language models align with human perceptual behavior. It covers human visual system tasks across prominence, subitizing, prioritizing, free-viewing, and searching.
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This Hugging Face release packages the cleaned subset provided with the repository as parquet shards with embedded image bytes. It contains 8,657 question-answer examples and 7,507 unique raw images referenced through the `image` column. The corresponding paper describes the full HVSBench benchmark with 85,147 multimodal QA pairs across 13 question types and 5 fields.
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Paper: **Do MLLMs Exhibit Human-like Perceptual Behaviors? HVSBench: A Benchmark for MLLM Alignment with Human Perceptual Behavior**
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## Dataset Structure
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The dataset is uploaded as parquet shards. The `image` feature contains the image data embedded in parquet, and `real_image_path` preserves the original relative source path under `raw_datasets`.
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Columns:
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- `image`: raw RGB image embedded in parquet
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- `index`: source row index
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- `real_image_path`: original relative path under `raw_datasets`
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- `question`: benchmark prompt
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