https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2024Sanchez_Cesped2024Sanchez Cesped - Revision history2026-05-24T21:07:08ZRevision history for this page on the wikiMediaWiki 1.44.2https://3demmethods.i2pc.es/index.php?title=2024Sanchez_Cesped&diff=4915&oldid=prevWikiSysop: Created page with "== Citation == R. Sanchez-Garcia, M. Saur, J. Vargas, C. Poelking, and C. M. Deane, “CESPED: A benchmark for supervised particle pose estimation in cryo-EM,” Physical Review Research, vol. 6, no. 2, p. 23245, 2024. == Abstract == 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 data set spec..."2025-01-14T09:47:02Z<p>Created page with "== Citation == R. Sanchez-Garcia, M. Saur, J. Vargas, C. Poelking, and C. M. Deane, “CESPED: A benchmark for supervised particle pose estimation in cryo-EM,” Physical Review Research, vol. 6, no. 2, p. 23245, 2024. == Abstract == 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 data set spec..."</p>
<p><b>New page</b></p><div>== Citation ==<br />
<br />
R. Sanchez-Garcia, M. Saur, J. Vargas, C. Poelking, and C. M. Deane, “CESPED: A benchmark for supervised particle pose estimation in cryo-EM,” Physical Review Research, vol. 6, no. 2, p. 23245, 2024.<br />
<br />
== Abstract ==<br />
<br />
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 data set 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, which shows promising results but also highlights the need for further improvements. Additionally, we illustrate the potential of deep learning-based pose estimators to generalize across different samples, suggesting a promising path toward more efficient processing strategies.<br />
<br />
== Keywords ==<br />
<br />
== Links ==<br />
<br />
https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.023245<br />
<br />
== Related software ==<br />
<br />
== Related methods ==<br />
<br />
== Comments ==</div>WikiSysop