- 📝 Abstract
- 💡 Method and Model Changes
- ⚙️ Installation
- 🚀 Usage
- ✍️ Hand-drawn Model
- 📄 Citation
- 🙏 Acknowledgements
- 👨🔬 Author
- 🌐 Project Website
- 🏛️ Research Group
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!
|
Now utilizing EfficientNet-V2 for superior image analysis |
Employing a state-of-the-art transformer model |
- 📦 TFRecord Files - Lightning-fast data reading
- ☁️ Google Cloud Buckets - Efficient cloud storage solution
- 🔄 TensorFlow Data Pipeline - Optimized data loading
- ⚡ TPU Strategy - Harnessing the power of Google's TPUs
# Create a conda wonderland
conda create --name DECIMER python=3.10.0 -y
conda activate DECIMER
# Equip yourself with DECIMER
pip install decimerfrom 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}")import DECIMER does not download or load anything. The model weights are not part of the pip package; they are
downloaded from Zenodo the first time they are needed and cached afterwards:
- The first
predict_SMILES(...)call downloads the printed-structure model (about 300 MB) if it is missing, loads it, and keeps it in memory. Later calls reuse it. - The hand-drawn model is only downloaded and loaded by the first
predict_SMILES(..., hand_drawn=True)call. (Up to version 2.8.0 both models were downloaded and loaded when DECIMER was imported.) - To pay for the download and the loading at a time of your choosing (at the start of a server, or when building an
image), call
load_models()once.load_models(hand_drawn=True)loads both models, asimport DECIMERdid before.
import DECIMER
DECIMER.load_models() # optional: download if missing and load now
SMILES = DECIMER.predict_SMILES("path/to/your/chemical/masterpiece.jpg")The models are stored in $PYSTOW_HOME/DECIMER-V2/ (~/.data/DECIMER-V2/ by default), where PYSTOW_HOME is
read when the models are first needed. A directory that already holds DECIMER_model/ (and, for hand-drawn
structures, DECIMER_HandDrawn_model/) is used as it is, without any network access, so an image with
pre-installed models works offline:
export PYSTOW_HOME=/opt/decimer-models # contains DECIMER-V2/DECIMER_model/saved_model.pb, ...🌟 New Feature Alert! 🌟
Our latest model brings the magic of AI to hand-drawn chemical structures!
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).
- A big thank you to Charles Tapley Hoyt for his invaluable contributions!
- Powered by Google's TPU Research Cloud (TRC)
👨🔬 Author: Kohulan
Experience DECIMER in action at decimer.ai, brilliantly implemented by Otto Brinkhaus!
🎓 Maintained by the Kohulan @ Steinbeck Group
Natural Products Cheminformatics Research Group
Institute for Inorganic and Analytical Chemistry
Friedrich Schiller University Jena, Germany
