  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.
