Alex Lech, Mirja Granfors, Jiacheng Huang, Benjamin Midtvedt, Jesús Pineda, Harshith Bachimanchi, Carlo Manzo, Giovanni Volpe
Date: 24 August 2026
Time: 11:45 AM – 12:00 PM PDT
Place: Conv. Ctr. Room 2
DeepTrack2 is a flexible and scalable Python library designed to generate physics-based synthetic microscopy datasets for training deep learning models. It supports a wide range of imaging modalities, including brightfield, fluorescence, darkfield, and holography, enabling the creation of synthetic samples that accurately replicate real experimental conditions. Its modular architecture empowers users to customize optical systems, incorporate optical aberrations and noise, simulate diverse objects across various imaging scenarios, and apply image augmentations. Simulations can be executed using a PyTorch backend, enabling GPU acceleration and backpropagation through the optical system, and allowing seamless integration with deep learning workflows. DeepTrack2 is accompanied by a dedicated GitHub page, providing extensive documentation, examples, and an active community for support and collaboration: https://github.com/DeepTrackAI/DeepTrack2.