README.md

---
license: cc-by-nc-sa-4.0
dataset_info:
- config_name: pretrain_synthetic_7M
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  - name: image
    dtype: image
  - name: SMILES
    dtype: string
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- config_name: sft_real
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  - name: SMILES
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- config_name: test_markush_10k
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  - name: SMILES
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- config_name: test_simple_10k
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- config_name: valid
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  - name: SMILES
    dtype: string
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configs:
- config_name: pretrain_synthetic_7M
  data_files:
  - split: train
    path: pretrain_synthetic_7M/train-*
- config_name: sft_real
  data_files:
  - split: train
    path: sft_real/train-*
- config_name: test_markush_10k
  data_files:
  - split: train
    path: test_markush_10k/train-*
- config_name: test_simple_10k
  data_files:
  - split: train
    path: test_simple_10k/train-*
- config_name: valid
  data_files:
  - split: train
    path: valid/train-*
tags:
- chemistry
---

# MolParser-7M

[**Demo**](https://ocsr.dp.tech/) | [**Paper**](https://arxiv.org/abs/2411.11098)


This repo provides the training data and evaluation data for MolParser, proposed in paper *“MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild“* (ICCV2025 accept)

MolParser-7M contains nearly 8 million paired image-SMILES data. It should be noted that the caption of image is our extended-SMILES format, which suggested in our paper.

* **MolParser-7M (Pretrain)**: More than 7.7M synthetic training data in `pretrain_synthetic_7M` subset;

* **MolParser-SFT**: Human-labeled real molecule figures for fine-tuning stage in `sft_real` subset. (We are organizing an OCSR competition based on MolParser-7M, so we have reserved part of the MolParser-SFT data for the competition. Stay tuned!)

* **MolParser-Val**: A small validation set carefully selected in-the-wild in `valid` subset. It can be used to quickly valid the model ability during the training process;

* **WildMol Benchmark**: 20k molecule structure images cropped from real patents or paper, `test_simple_10k`（WildMol-10k）subset and `test_markush_10k`（WildMol-10k-M）subset;


## 📜 License

This dataset is provided for **non-commercial use only**. 

For commercial use, please contact: [fangxi@dp.tech](mailto:fangxi@dp.tech) or add a discussion in HuggingFace.


## 📖 Citation

If you use this datasets in your work, please cite:

```
@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}
}
```