Keylab/OCSR-Benchmarks
OCSR Benchmarks A collection of ten benchmark datasets for Optical Chemical Structure Recognition (OCSR) — the task of converting chemical structure diagram images into machine-readable SMILES strings. These benchmarks were used to evaluate the COMO model (Closed-Loop Optical Molecule Recognition). Subsets Config Split Size Domain CLEF test 992 Real JPO test 449 Real UOB test 5,740 Real USPTO test 5719 Real USPTO-10K test 9,999 Real Staker… See the full description on the dataset page: https://huggingface.co/datasets/Keylab/OCSR-Benchmarks.
OCSR Benchmarks
A collection of ten benchmark datasets for Optical Chemical Structure Recognition (OCSR) — the task of converting chemical structure diagram images into machine-readable SMILES strings.
These benchmarks were used to evaluate the COMO model (Closed-Loop Optical Molecule Recognition).
Subsets
Schema
Each sample has three fields:
Usage
from datasets import load_dataset
# Load a single benchmark
ds = load_dataset("Keylab/OCSR-Benchmarks", name="USPTO", split="test")
sample = ds[0]
sample["image"].show() # PIL Image
print(sample["SMILES"])
# Iterate over all benchmarks
for config in ["CLEF", "JPO", "UOB", "USPTO", "USPTO-10K",
"Staker", "ACS", "WildMol-10K", "Indigo", "ChemDraw"]:
ds = load_dataset("Keylab/OCSR-Benchmarks", name=config, split="test")
print(f"{config}: {len(ds)} samples")Bulk Download
Pre-packaged .tar.gz archives (images + CSV) are also available in the COMO model repository for direct download without the datasets library.
License
These benchmarks are collected from existing public OCSR datasets. Please refer to the original sources for attribution and applicable terms:
Citation
If you use these benchmarks, please cite the COMO paper and the original benchmark sources:
@article{lyu2026closed,
title={COMO: Closed-Loop Optical Molecule Recognition with Minimum Risk Training},
author={Lyu, Zhuoqi and Ke, Qing},
journal={arXiv preprint arXiv:2604.23546},
year={2026}
}