--- title: UniMER_Dataset canonical_url: "https://www.modelscope.cn/datasets/Virgo-Internal/UniMER_Dataset" md_url: "https://www.modelscope.cn/datasets/Virgo-Internal/UniMER_Dataset.md" repository: Virgo-Internal/UniMER_Dataset last_updated: 2025-08-15 license: apache-2.0 storage_size: "2.0 GB" downloads: 655 stars: 2 --- # UniMER_Dataset > UniMER_Dataset - Virgo-Internal 在 ModelScope 开源的数据集。For detailed instructions on using the dataset, please refer to the project homepage: UniMERNet Homepage Virgo-Internal/UniMER_Dataset 是 ModelScope 魔搭社区上的数据集，存储大小 2.0 GB，采用 apache-2.0 许可。 - **Repository**: Virgo-Internal/UniMER_Dataset - **License**: apache-2.0 - **Storage size**: 2.0 GB - **Downloads**: 655 - **Stars**: 2 - **Last updated**: 2025-08-15 Source: https://www.modelscope.cn/datasets/Virgo-Internal/UniMER_Dataset --- # UniMER Dataset For detailed instructions on using the dataset, please refer to the project homepage: [UniMERNet Homepage](https://github.com/opendatalab/UniMERNet/tree/main) ## Introduction The UniMER dataset is a specialized collection curated to advance the field of Mathematical Expression Recognition (MER). It encompasses the comprehensive UniMER-1M training set, featuring over one million instances that represent a diverse and intricate range of mathematical expressions, coupled with the UniMER Test Set, meticulously designed to benchmark MER models against real-world scenarios. The dataset details are as follows: - **UniMER-1M Training Set:** - Total Samples: 1,061,791 Latex-Image pairs - Composition: A balanced mix of concise and complex, extended formula expressions - Aim: To train robust, high-accuracy MER models, enhancing recognition precision and generalization - **UniMER Test Set:** - Total Samples: 23,757, categorized into four types of expressions: - Simple Printed Expressions (SPE): 6,762 samples - Complex Printed Expressions (CPE): 5,921 samples - Screen Capture Expressions (SCE): 4,742 samples - Handwritten Expressions (HWE): 6,332 samples - Purpose: To provide a thorough evaluation of MER models across a spectrum of real-world conditions ## Visual Data Samples ![UniMER-Test](https://github.com/opendatalab/UniMERNet/assets/69186975/7301df68-e14c-4607-81bc-b6ee3ba1780b) ## Data Statistics | **Dataset** | **Sub** | **Source** | **Sample Size** | |:-----------:|:-------:|:-------------------------------------------:|:---------------:| | UniMER-1M | | Pix2tex Train | 158,303 | | | | Arxiv † | 820,152 | | | | CROHME Train | 8,834 | | | | HME100K Train ‡ | 74,502 | | UniMER-Test | SPE | Pix2tex Validation | 6,762 | | | CPE | Arxiv † | 5,921 | | | SCE | PDF Screenshot † | 4,742 | | | HWE | CROHME & HME100K | 6,332 | † Indicates data collected, processed, and annotated by our team. ‡ For copyright compliance, please manually download this dataset portion: [HME100K dataset](https://ai.100tal.com/dataset). ## Acknowledgements We would like to express our gratitude to the creators of the [Pix2tex](https://github.com/lukas-blecher/LaTeX-OCR), [CROHME](https://www.cs.rit.edu/~rlaz/files/CROHME+TFD%E2%80%932019.pdf), and [HME100K](https://github.com/tal-tech/SAN) datasets. Their foundational work has significantly contributed to the development of the UniMER dataset. A new metric for evaluating this dataset is presented in [CDM: A Reliable Metric for Fair and Accurate Formula Recognition Evaluation](https://huggingface.co/papers/2409.03643). ## Citations ```text @misc{wang2024unimernet, title={UniMERNet: A Universal Network for Real-World Mathematical Expression Recognition}, author={Bin Wang and Zhuangcheng Gu and Chao Xu and Bo Zhang and Botian Shi and Conghui He}, year={2024}, eprint={2404.15254}, archivePrefix={arXiv}, primaryClass={cs.CV} } @misc{conghui2022opendatalab, author={He, Conghui and Li, Wei and Jin, Zhenjiang and Wang, Bin and Xu, Chao and Lin, Dahua}, title={OpenDataLab: Empowering General Artificial Intelligence with Open Datasets}, howpublished = {\url{https://opendatalab.com}}, year={2022} } ``` --- # UniMER 数据集 数据集使用详细说明请参考项目主页：[UniMERNet 主页](https://github.com/opendatalab/UniMERNet/tree/main) ## 简介 UniMER数据集是专门为通用数学表达式识别（MER）发布的数据集。它包含了真实全面的UniMER-1M训练集，拥有超过一百万个代表广泛和复杂数学表达式的实例，以及精心设计的UniMER测试集，用于在真实世界场景中评估MER模型。数据集详情如下： - **UniMER-1M 训练集：** - 总样本数：1,061,791 - 组成：简洁与复杂、扩展公式表达式的平衡融合 - 目标：帮助训练鲁棒性强、高精度的MER模型，增强识别准确性和模型泛化能力 - **UniMER 测试集：** - 总样本数：23,757，分为四种表达式类型： - 简单印刷表达式（SPE）：6,762 个样本 - 复杂印刷表达式（CPE）：5,921 个样本 - 屏幕截图表达式（SCE）：4,742 个样本 - 手写表达式（HWE）：6,332 个样本 - 目的：为MER模型提供一个全面的评估平台，以准确评估真实场景下各类公式识别能力 ## 视觉数据样本 ![UniMER-测试集](https://github.com/opendatalab/UniMERNet/assets/69186975/7301df68-e14c-4607-81bc-b6ee3ba1780b) ## 数据统计 | **数据集** | **子集** | **来源** | **样本数量** | |:-----------:|:-------:|:-------------------------------------------:|:------------:| | UniMER-1M | | Pix2tex 训练集 | 158,303 | | | | Arxiv † | 820,152 | | | | CROHME 训练集 | 8,834 | | | | HME100K 训练集 ‡ | 74,502 | | UniMER-测试集 | SPE | Pix2tex 验证集 | 6,762 | | | CPE | Arxiv † | 5,921 | | | SCE | PDF 截图 † | 4,742 | | | HWE | CROHME & HME100K | 6,332 | † 表示由我们团队收集、处理和注释的数据。 ‡ 由于版权合规，请手动下载此部分数据集：[HME100K 数据集](https://ai.100tal.com/dataset)。 ## 致谢 我们对[Pix2tex](https://github.com/lukas-blecher/LaTeX-OCR), [CROHME](https://www.cs.rit.edu/~rlaz/files/CROHME+TFD%E2%80%932019.pdf)和[HME100K](https://github.com/tal-tech/SAN) 数据集的创建者表示感谢。他们的基础工作对 UniMER 数据集的构建及发布做出了重大贡献。 ## 引用 ```text @misc{wang2024unimernet, title={UniMERNet: A Universal Network for Real-World Mathematical Expression Recognition}, author={Bin Wang and Zhuangcheng Gu and Chao Xu and Bo Zhang and Botian Shi and Conghui He}, year={2024}, eprint={2404.15254}, archivePrefix={arXiv}, primaryClass={cs.CV} } @misc{conghui2022opendatalab, author={He, Conghui and Li, Wei, Jin, Zhenjiang and Wang, Bin and Xu, Chao and Lin, Dahua}, title={OpenDataLab: Empowering General Artificial Intelligence with Open Datasets}, howpublished = {\url{https://opendatalab.com}}, year={2022} } ```