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---
license: mit
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data_files:
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path: test_simple_10k/train-*
- config_name: valid
data_files:
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path: valid/train-*
tags:
- chemistry
---
# MolParser-7M
**Anonymous Open Source now**
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 under review)
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-Pretrain**: More than 7.7M synthetic training data in `pretrain_synthetic_7M` subset;
* **MolParser-SFT [WIP]**: 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;
As the paper is still **under review**, this data is provided **anonymously**. More information will be provided after the paper has been accepted.
In the future, we will continue to re-clean this dataset, open-source more data, update model checkpoints, refresh the benchmark results, and release the training code.
[**Anonymous Demo: Click Here**](http://101.126.35.171:50008/) |