README.md

metadata

license: mit
dataset_info:
  - config_name: pretrain_synthetic_7M
    features:
      - name: image
        dtype: image
      - name: SMILES
        dtype: string
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      - name: train
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        num_examples: 7720468
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  - config_name: sft_real
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      - name: image
        dtype: image
      - name: SMILES
        dtype: string
    splits:
      - name: train
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  - config_name: test_markush_10k
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      - name: image
        dtype: image
      - name: SMILES
        dtype: string
    splits:
      - name: train
        num_bytes: 228019568
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  - config_name: test_simple_10k
    features:
      - name: image
        dtype: image
      - name: SMILES
        dtype: string
    splits:
      - name: train
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        num_examples: 10000
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  - config_name: valid
    features:
      - name: image
        dtype: image
      - name: SMILES
        dtype: string
    splits:
      - name: train
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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 | Paper

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: Nearly 400k samples 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;

📖 Citation

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

@article{fang2024molparser,
  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},
  journal={arXiv preprint arXiv:2411.11098},
  year={2024}
}