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

Cryo-EM is a powerful tool for understanding macromolecular structures, yet cur-rent methods for structure reconstruction are slow and computationally demanding.To accelerate research on pose estimation, we present CESPED, a new datasetspecifically designed for Supervised Pose Estimation in Cryo-EM. Alongside CE-SPED, we provide a PyTorch package to simplify Cryo-EM data handling andmodel evaluation. We evaluate the performance of a baseline model, Image2Sphere,on CESPED, showing promising results but also highlighting the need for furtheradvancements in this area.