RxnLabelData

Annotated chemical reaction diagram dataset for the paper: RobustRDP: Advancing Reaction Diagram Parsing via Synthetic-to-Real Data Scaling and Robustness-Oriented Training.

Description

This dataset contains 3,500 annotated chemical reaction diagram images with bounding box and reaction relationship annotations. The annotations were created using the RxnLabel annotation platform.

Annotation Workflow

1. Object Detection: A YOLO model automatically detects molecules (Mol) and text (Txt) regions in reaction images.
2. Relationship Annotation: An interactive web-based tool is used to label relationships between detected objects, grouping them into reactants, conditions, and products.

Data Structure

dataset/
├── images.zip       # ZIP archive containing all PNG images (3,500)
├── labels.zip       # ZIP archive containing all JSON annotation files (3,500)
└── README.md

Quick Start

Download and extract the archives:

# Download from Hugging Face
# Option 1: Using git
git clone https://huggingface.co/datasets/Jingcz/RxnLabelData
cd RxnLabelData
unzip images.zip
unzip labels.zip

# Option 2: Using Python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="Jingcz/RxnLabelData", local_dir="./RxnLabelData")

After extraction, you will have:

RxnLabelData/
├── images/          # 3,500 PNG images (000000.png ~ 003499.png)
├── labels/          # 3,500 JSON annotation files (000000.json ~ 003499.json)
├── images.zip
├── labels.zip
└── README.md

Label Format

Each JSON file contains:

{
  "id": 0,
  "width": 1496,
  "height": 1370,
  "file_name": "000000.png",
  "bboxes": [
    {
      "id": 0,
      "bbox": [66.88, 183.59, 37.72, 51.03],
      "category_id": 3
    }
  ],
  "reactions": [
    {
      "reactants": [0],
      "conditions": [1, 2],
      "products": [3]
    }
  ]
}

Category IDs

ID	Label	Description
1	Mol	Molecule structure
2	Txt	Text / condition label
3	Idt	Identifier / arrow label

Usage Example

import json
from PIL import Image

# Load image
img = Image.open("images/000000.png")

# Load annotations
with open("labels/000000.json") as f:
    data = json.load(f)

# Access bounding boxes
for box in data["bboxes"]:
    print(f"ID: {box['id']}, Category: {box['category_id']}, Box: {box['bbox']}")

# Access reactions
for rxn in data["reactions"]:
    print(f"Reactants: {rxn['reactants']}")
    print(f"Conditions: {rxn['conditions']}")
    print(f"Products: {rxn['products']}")

Citation

If you use this dataset, please cite:

@article{robustrdp,
  title={RobustRDP: Advancing Reaction Diagram Parsing via Synthetic-to-Real Data Scaling and Robustness-Oriented Training},
  author={...},
  year={2025}
}

Related Resources

• Annotation Platform: RxnLabel - Web-based tool for annotating chemical reaction diagrams