Cryo-EM is a powerful tool for understanding macromolecular structures, yet current methods for structure reconstruction are slow and computationally demanding. To accelerate research on pose estimation, we present CESPED, a new dataset specifically designed for Supervised Pose Estimation in Cryo-EM. Alongside CESPED, we provide a PyTorch package to simplify Cryo-EM data handling and model evaluation. We evaluated the performance of a baseline model, Image2Sphere, on CESPED, which showed promising results but also highlighted the need for further improvements. Additionally, we illustrate the potential of deep learning-based pose estimators to generalise across different samples, suggesting a promising path toward more efficient processing strategies. CESPED is available at https://github.com/oxpig/cesped.
@article{arxiv.2311.06194,
title = {CESPED: a new benchmark for supervised particle pose estimation in Cryo-EM},
author = {Ruben Sanchez-Garcia and Michael Saur and Javier Vargas and Carl Poelking and Charlotte M Deane},
journal= {arXiv preprint arXiv:2311.06194},
year = {2024}
}