dataset_info:
features:
- name: image
dtype: image
- name: E-SMILES
dtype: string
splits:
- name: train
num_bytes: 195826797222
num_examples: 10327534
download_size: 192734235604
dataset_size: 195826797222
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
MolGallery: A 10 Million-Scale Real-World Dataset for Optical Chemical Structure Recognition
Dataset Summary
MolGallery is a large-scale dataset containing 10 million chemical structure images sourced from real-world documents, including patents, scientific literature, books and website.
Unlike synthetic datasets, MolGallery captures the complex noise, diverse rendering styles, and scanning artifacts found in genuine chemical documents. The labels are provided in E-SMILES format, following the notation standards defined in the MolParser paper.
🏷️ Pseudo-Labeling Pipeline
We use inference consistency across two different weights of MolParser 1.0 together with predictions from the MolScribe model as a confidence criterion. Molecules with consistent predictions across these models are selected as high-confidence samples and used as pseudo-labels.
⚠️ Notice
Pseudo-labels may contain a small proportion of errors. Therefore, they are primarily recommended for pretraining.
For later training stages or the final training iterations, it is advisable to use higher-quality human annotation data to ensure optimal model performance.
📜 License
This dataset is provided for non-commercial use only.
For commercial use, please contact: fangxi@dp.tech or add a discussion in HuggingFace.
📖 Citation
MolGallery Dataset Report Comming Soon!
If you use this datasets in your work, please cite:
@article{fang2025uniparser,
title={Uni-Parser Technical Report},
author={Fang, Xi and Tao, Haoyi and Yang, Shuwen and Huang, Chaozheng and Zhong, Suyang and Lu, Haocheng and Lyu, Han and Li, Xinyu and Zhang, Linfeng and Ke, Guolin},
journal={arXiv preprint arXiv:2512.15098},
year={2025}
}
@inproceedings{fang2025molparser,
title={Molparser: End-to-end visual recognition of molecule structures in the wild},
author={Fang, Xi and Wang, Jiankun and Cai, Xiaochen and Chen, Shangqian and Yang, Shuwen and Tao, Haoyi and Wang, Nan and Yao, Lin and Zhang, Linfeng and Ke, Guolin},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={24528--24538},
year={2025}
}