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Cryo-EM is a powerful tool for understanding macromolecular structures, yetcurrent methods for structure reconstruction are slow and computationallydemanding. To accelerate research on pose estimation, we present CESPED, a newdataset specifically designed for Supervised Pose Estimation in Cryo-EM.Alongside CESPED, we provide a PyTorch package to simplify Cryo-EM datahandling and model evaluation. We evaluate the performance of a baseline model,Image2Sphere, on CESPED, showing promising results but also highlighting theneed for further advancements in this area.

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