๐งช DECIMER Image Transformer ๐ผ๏ธ
December 2, 2025 ยท View on GitHub
๐งช DECIMER Image Transformer ๐ผ๏ธ
Deep Learning for Chemical Image Recognition using Efficient-Net V2 + Transformer
๐ Table of Contents
- ๐ Abstract
- ๐ก Method and Model Changes
- โ๏ธ Installation
- ๐ Usage
- โ๏ธ Hand-drawn Model
- ๐ Citation
- ๐ Acknowledgements
- ๐จโ๐ฌ Author
- ๐ Project Website
- ๐๏ธ Research Group
๐ 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!
๐ก Method and Model Changes
๐ผ๏ธ Image Feature ExtractionNow utilizing EfficientNet-V2 for superior image analysis |
๐ฎ SMILES PredictionEmploying a state-of-the-art transformer model |
๐ Training Enhancements
- ๐ฆ TFRecord Files - Lightning-fast data reading
- โ๏ธ Google Cloud Buckets - Efficient cloud storage solution
- ๐ TensorFlow Data Pipeline - Optimized data loading
- โก TPU Strategy - Harnessing the power of Google's TPUs
โ๏ธ Installation
# Create a conda wonderland
conda create --name DECIMER python=3.10.0 -y
conda activate DECIMER
# Equip yourself with DECIMER
pip install decimer
๐ 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"๐ Decoded SMILES: {SMILES}")
โ๏ธ DECIMER - Hand-drawn Model
๐ New Feature Alert! ๐
Our latest model brings the magic of AI to hand-drawn chemical structures!
๐ Citation
If DECIMER helps your research, please cite:
- 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).
- Rajan, K., et al. "DECIMER 1.0: deep learning for chemical image recognition using transformers." J Cheminform 13, 61 (2021).
- Rajan, K., et al. "Advancements in hand-drawn chemical structure recognition through an enhanced DECIMER architecture," J Cheminform 16, 78 (2024).
๐ Acknowledgements
- A big thank you to Charles Tapley Hoyt for his invaluable contributions!
- Powered by Google's TPU Research Cloud (TRC)
๐จโ๐ฌ Author: Kohulan
๐ Project Website
Experience DECIMER in action at decimer.ai, brilliantly implemented by Otto Brinkhaus!
๐ Maintained by the Kohulan @ Steinbeck Group
Natural Products Cheminformatics Research Group
Institute for Inorganic and Analytical Chemistry
Friedrich Schiller University Jena, Germany
โญ Star History
Made with โค๏ธ and โ for the global chemistry community