IgoshinLab/StyleAutoEncoder: Initial public release (metadata update)

🚀 StyleAutoEncoder v1.0.1 This release provides the code and resources for a deep learning framework for analyzing self-organization in Myxococcus xanthus. ✨ Key Features Training pipeline for style-based autoencoders on biological image data Reproducibility: Jupyter notebooks for figure generation 📦 Assets & Pre-trained Models Pre-trained assets are hosted on BioStudies (S-BIAD2328), including: Model checkpoints Extracted feature datasets Training and validation datasets 🛠 Quick Start Create environment: conda env create -f styleautoencoder.yaml Run inference: see publication_figures/model_forward.ipynb 📝 Citation If you use this code, please cite the associated study. Full changelog: https://github.com/IgoshinLab/StyleAutoEncoder/commits/v1.0.1