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🧪 DECIMER Image Transformer 🖼️

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

<p align="center"> <img src="https://github.com/Kohulan/DECIMER-Image_Transformer/blob/master/DECIMER_V2.png?raw=true" alt="DECIMER Logo" width="600"> </p>

License Maintenance GitHub issues GitHub contributors tensorflow DOI Documentation Status GitHub release PyPI version fury.io

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📚 Table of Contents

🔬 Abstract

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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!

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🧠 Method and Model Changes

<table> <tr> <td width="50%"> <h3>🖼️ Image Feature Extraction</h3> <p>Now utilizing EfficientNet-V2 for superior image analysis</p> </td> <td width="50%"> <h3>🔮 SMILES Prediction</h3> <p>Employing a state-of-the-art transformer model</p> </td> </tr> </table>

🚀 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

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🌟 New Feature Alert! 🌟

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

DOI

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📜 Citation

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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).
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🙏 Acknowledgements

<p align="center"> <img src="https://user-images.githubusercontent.com/30716951/220350828-913e6645-6a0a-403c-bcb8-160d061d4606.png" width="300"> </p>

👨‍🔬 Author: Kohulan

<p align="center"> <img src="https://github.com/Kohulan/DECIMER-Image-to-SMILES/raw/master/assets/DECIMER.gif" width="300"> </p>

🌐 Project Website

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

🏫 Research Group

<p align="center"> <a href="https://cheminf.uni-jena.de"> <img src="https://github.com/Kohulan/DECIMER-Image-to-SMILES/blob/master/assets/CheminfGit.png" width="300"> </a> </p>
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📊 Project Analytics

Repobeats

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