CESPED: a new benchmark for supervised particle pose estimation in Cryo-EM

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 evaluate the performance of a baseline model, Image2Sphere, on CESPED, showing promising results but also highlighting the need for further advancements in this area.