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	test	50,000	Real
ACS	test	331	Real
WildMol-10K	test	9,889	Real
Indigo	test	5,719	Synthetic
ChemDraw	test	5,719	Synthetic

Schema

Each sample has three fields:

Field	Type	Description
image_id	string	Original identifier for the sample
image	Image	PNG image of the chemical structure diagram
SMILES	string	Ground-truth SMILES string

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:

Dataset	Source
USPTO, CLEF, JPO, UOB, Staker	Rajan et al., 2020, Xiong et al., 2023
Indigo, ChemDraw, ACS, Staker	Qian et al., 2023
USPTO-10K	Morin et al., 2023
WildMol-10K	Fang et al., 2025

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}
}