| | --- |
| | license: cc-by-nc-sa-4.0 |
| | dataset_info: |
| | - config_name: pretrain_synthetic_7M |
| | features: |
| | - name: image |
| | dtype: image |
| | - name: SMILES |
| | dtype: string |
| | splits: |
| | - name: train |
| | num_bytes: 115375911760.028 |
| | num_examples: 7720468 |
| | download_size: 122046202421 |
| | dataset_size: 115375911760.028 |
| | - config_name: sft_real |
| | features: |
| | - name: image |
| | dtype: image |
| | - name: SMILES |
| | dtype: string |
| | splits: |
| | - name: train |
| | num_bytes: 2479379042.298 |
| | num_examples: 91166 |
| | download_size: 2416204649 |
| | dataset_size: 2479379042.298 |
| | - config_name: test_markush_10k |
| | features: |
| | - name: image |
| | dtype: image |
| | - name: SMILES |
| | dtype: string |
| | splits: |
| | - name: train |
| | num_bytes: 228019568 |
| | num_examples: 10000 |
| | download_size: 233407872 |
| | dataset_size: 228019568 |
| | - config_name: test_simple_10k |
| | features: |
| | - name: image |
| | dtype: image |
| | - name: SMILES |
| | dtype: string |
| | splits: |
| | - name: train |
| | num_bytes: 291640094 |
| | num_examples: 10000 |
| | download_size: 292074581 |
| | dataset_size: 291640094 |
| | - config_name: valid |
| | features: |
| | - name: image |
| | dtype: image |
| | - name: SMILES |
| | dtype: string |
| | splits: |
| | - name: train |
| | num_bytes: 13538058 |
| | num_examples: 403 |
| | download_size: 13451383 |
| | dataset_size: 13538058 |
| | 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} |
| | } |
| | ``` |
