| --- |
| pretty_name: CommonSketch |
| license: cc-by-4.0 |
| task_categories: |
| - image-classification |
| - image-to-text |
| - visual-question-answering |
| language: |
| - en |
| tags: |
| - image |
| - sketch |
| - captions |
| - visual-question-answering |
| - commonsense |
| - abstraction |
| - computer-vision |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # CommonSketch |
|
|
| ## Dataset Summary |
|
|
| CommonSketch is a semantically annotated sketch dataset introduced in the paper [SEA: Evaluating Sketch Abstraction Efficiency via Element-level Commonsense Visual Question Answering](https://arxiv.org/abs/2603.28363). The dataset contains 23,100 human-drawn sketches across 300 object classes. Each sketch is paired with a fine-grained caption and element-level commonsense annotations for evaluating sketch abstraction and semantic recognizability. |
|
|
| ## Dataset Structure |
|
|
| ```text |
| CommonSketch/ |
| data/ |
| train-00000-of-00012.parquet |
| ... |
| metadata.csv |
| captions.csv |
| vqa_element_annotation.json |
| metadata/ |
| classes.csv |
| classes.txt |
| commonsense_elements.json |
| ``` |
|
|
| ## Data Fields |
|
|
| The main row-level metadata is provided in `metadata.csv`. |
|
|
| | Field | Description | |
| | --- | --- | |
| | `file_name` | Relative path to the sketch image. | |
| | `caption` | Fine-grained caption describing the sketch. | |
| | `class_name` | Object class name. | |
| | `class_id` | Class identifier from `001` to `300`. | |
| | `category` | High-level category for the class. | |
|
|
| The Parquet files under `data/` provide the dataset examples for direct loading with the Hugging Face `datasets` library. Each row contains the following fields: |
|
|
| | Field | Description | |
| | --- | --- | |
| | `image` | Sketch image embedded in the dataset. | |
| | `file_name` | Original relative image path. | |
| | `caption` | Fine-grained caption describing the sketch. | |
| | `class_name` | Object class name. | |
| | `class_id` | Class identifier from `001` to `300`. | |
| | `category` | High-level category for the class. | |
| | `element_annotation` | JSON-serialized binary element annotation for the sketch. | |
|
|
| The `captions.csv` file provides the image-caption pairs with the following fields. |
|
|
| | Field | Description | |
| | --- | --- | |
| | `image` | Image file name. | |
| | `caption` | Fine-grained caption describing the sketch. | |
|
|
| ## Annotation Files |
|
|
| `vqa_element_annotation.json` contains image-level binary element annotations. Each annotation indicates whether a class-specific commonsense element is present in the sketch. |
|
|
| `metadata/commonsense_elements.json` defines the class-level commonsense element schema used by the VQA annotations. Each class entry includes its `class_id`, `total_elements`, and the list of element definitions. |
|
|
| `metadata/classes.csv` provides the 300 class names, class IDs, high-level categories, and the number of commonsense elements per class. `metadata/classes.txt` provides the class list only. |
|
|
| ## Category Statistics |
|
|
| | Category | # Classes | |
| | --- | ---: | |
| | animal | 61 | |
| | body part | 7 | |
| | clothing | 8 | |
| | container | 9 | |
| | electronic device | 22 | |
| | food | 28 | |
| | furniture | 25 | |
| | icon | 13 | |
| | musical instrument | 11 | |
| | nature | 14 | |
| | sports equipment | 14 | |
| | structure | 28 | |
| | tool | 37 | |
| | vehicle | 23 | |
|
|
| ## Usage |
|
|
| After upload to the Hugging Face Hub, the dataset can be loaded with: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("ziiio/CommonSketch") |
| ``` |
|
|
| The `image` field contains the sketch image, and `element_annotation` contains the image-level element annotation as a JSON string. |
|
|
| ## License |
|
|
| CommonSketch is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. If you use CommonSketch, please cite the SEA paper. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{park2026sea, |
| title={SEA: Evaluating Sketch Abstraction Efficiency via Element-level Commonsense Visual Question Answering}, |
| author={Park, Jiho and Choi, Sieun and Seo, Jaeyoon and Sohn, Minho and Kim, Yeana and Kim, Jihie}, |
| journal={arXiv preprint arXiv:2603.28363}, |
| year={2026} |
| } |
| ``` |
|
|
| ## Links |
|
|
| - Paper: https://arxiv.org/abs/2603.28363 |
| - PDF: https://arxiv.org/pdf/2603.28363 |
| - Code: https://github.com/zihos/SEA |
| - Project page: https://zihos.github.io/SEA |
|
|
| ## Note |
|
|
| CommonSketch includes a subset of the SketchDUO dataset. For more information about SketchDUO, please refer to https://huggingface.co/datasets/ziiio/SketchDUO. |
|
|