CESPED: a new benchmark for supervised particle pose estimation in Cryo-EM
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866910430771806208 |
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| author | Sanchez-Garcia, Ruben Saur, Michael Vargas, Javier Poelking, Carl Deane, Charlotte M |
| author_facet | Sanchez-Garcia, Ruben Saur, Michael Vargas, Javier Poelking, Carl Deane, Charlotte M |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_06194 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CESPED: a new benchmark for supervised particle pose estimation in Cryo-EM Sanchez-Garcia, Ruben Saur, Michael Vargas, Javier Poelking, Carl Deane, Charlotte M Quantitative Methods 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. |
| title | CESPED: a new benchmark for supervised particle pose estimation in Cryo-EM |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2311.06194 |