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🧪 DECIMER Image Transformer 🖼️

Deep Learning for Chemical Image Recognition using Efficient-Net V2 + Transformer

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📚 Table of Contents


📝 Abstract

The DECIMER 2.2 project tackles the OCSR (Optical Chemical Structure Recognition) challenge using cutting-edge computational intelligence methods. Our goal? To provide an automated, open-source software solution for chemical image recognition.

We've supercharged DECIMER with Google's TPU (Tensor Processing Unit) to handle datasets of over 1 million images with lightning speed!


💡 Method and Model Changes

🖼️ Image Feature Extraction

Now utilizing EfficientNet-V2 for superior image analysis

🔮 SMILES Prediction

Employing a state-of-the-art transformer model

🚀 Training Enhancements

  1. 📦 TFRecord Files - Lightning-fast data reading
  2. ☁️ Google Cloud Buckets - Efficient cloud storage solution
  3. 🔄 TensorFlow Data Pipeline - Optimized data loading
  4. ⚡ TPU Strategy - Harnessing the power of Google's TPUs

⚙️ Installation

# Create a conda wonderland
conda create --name DECIMER python=3.10.0 -y
conda activate DECIMER

# Equip yourself with DECIMER
pip install decimer

🚀 Usage

from DECIMER import predict_SMILES

# Unleash the power of DECIMER
image_path = "path/to/your/chemical/masterpiece.jpg"
SMILES = predict_SMILES(image_path)
print(f"🎉 Decoded SMILES: {SMILES}")

✍️ DECIMER - Hand-drawn Model

🌟 New Feature Alert! 🌟

Our latest model brings the magic of AI to hand-drawn chemical structures!

DOI


📄 Citation

If DECIMER helps your research, please cite:

  1. Rajan K, et al. "DECIMER.ai - An open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications." Nat. Commun. 14, 5045 (2023).
  2. Rajan, K., et al. "DECIMER 1.0: deep learning for chemical image recognition using transformers." J Cheminform 13, 61 (2021).
  3. Rajan, K., et al. "Advancements in hand-drawn chemical structure recognition through an enhanced DECIMER architecture," J Cheminform 16, 78 (2024).

🙏 Acknowledgements

  • A big thank you to Charles Tapley Hoyt for his invaluable contributions!
  • Powered by Google's TPU Research Cloud (TRC)


👨‍🔬 Author: Kohulan


🌐 Project Website

Experience DECIMER in action at decimer.ai, brilliantly implemented by Otto Brinkhaus!


🎓 Maintained by the Kohulan @ Steinbeck Group

Cheminformatics Group

Natural Products Cheminformatics Research Group
Institute for Inorganic and Analytical Chemistry
Friedrich Schiller University Jena, Germany


⭐ Star History

Star History Chart


📊 Project Analytics

Repobeats

Made with ❤️ and ☕ for the global chemistry community

© 2025 Kohulan @ Steinbeck Lab, Friedrich Schiller University Jena

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