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--- |
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language: |
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- en |
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license: apache-2.0 |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- image-text-to-text |
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pretty_name: ColorBench |
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dataset_info: |
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features: |
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- name: idx |
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dtype: int64 |
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- name: id |
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dtype: int64 |
|
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- name: type |
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dtype: string |
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|
- name: task |
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dtype: string |
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- name: filename |
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dtype: string |
|
|
- name: image |
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dtype: image |
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- name: prompt |
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dtype: string |
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- name: question |
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dtype: string |
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- name: choices |
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sequence: string |
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- name: answer |
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dtype: string |
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- name: image_url |
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dtype: string |
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splits: |
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- name: test |
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num_bytes: 1808612125.49 |
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num_examples: 5814 |
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download_size: 580531082 |
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dataset_size: 1808612125.49 |
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configs: |
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- config_name: default |
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data_files: |
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- split: test |
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path: data/test-* |
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--- |
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# 🎨 ColorBench |
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[**📖 Paper**](https://huggingface.co/papers/2504.10514) | [**💻 GitHub**](https://github.com/tianyi-lab/ColorBench) |
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ColorBench is a **multimodal dataset** to comprehensively assess capabilities of VLMs in color understanding, including color perception, reasoning, and robustness, introduced in ["ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness"](todo). |
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It provides: |
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- **More than 5,800 image-text questions** covering diverse application scenarios and practical challenges for VLMs evaluation. |
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- **3 categories and 11 tasks** for various color-centric capabilities evaluation including Perception (Color Recognition, Color Extraction and Object Recognition), Reasoning (Color Proportion, Color Comparison, Color Counting, and more.) and Robustness. |
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## 📃 Instruction |
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The data/test*.parquet files contain the dataset annotations and images pre-loaded for processing with HF Datasets. |
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```bash |
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from datasets import load_dataset |
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colro_bench = load_dataset("umd-zhou-lab/ColorBench") |
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``` |
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## 📂 Dataset Description |
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The dataset contains the following fields: |
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| Field Name | Description | |
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|------------|-----------------------------------------------------------------------------| |
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| idx | Global index of the sample in the dataset | |
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| id | Index of the sample in each task | |
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| type | Type of category: Perception, Reasoning, or Robustness | |
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| task | Type of task: Colo Recognition, Color Extraction, Color Counting , and more | |
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| filename | Path to the image | |
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| image_url | Source of the image | |
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| prompt | Prompt with question and choices pre-formatted | |
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| question | Question asked about the image | |
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| choices | Answer choices for the question | |
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| answer | Correct answer to the question | |
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| image | Image object (PIL.Image) | |
