|
|
--- |
|
|
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} |
|
|
} |
|
|
``` |
