DINHR trained model checkpoints for CryoBench datasets and experimental cryo-EM datasets
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| Format: | Recurso digital |
| Sprache: | Englisch |
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2026
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| _version_ | 1866901494768336896 |
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| author | He, Jiahua Cheng, Yifan |
| author_facet | He, Jiahua Cheng, Yifan |
| contents | <p>This record provides trained <strong>DINHR</strong> (<strong>D</strong>enoising <strong>I</strong>mplicit <strong>N</strong>eural <strong>H</strong>igh-order <strong>R</strong>epresentation) model checkpoints and auxiliary files to reproduce the results reported in our manuscript. The release includes checkpoints for <em>CryoBench </em>simulated datasets (<em>IgG-1D </em>at multiple SNRs, <em>IgG-RL</em>, <em>TomoTwin-100</em>, <em>Ribosembly</em>) and experimental datasets (<em>EMPIAR-10073</em>, <em>EMPIAR-10076</em>, <em>INO80–Hexasome</em>).<br>Each subfolder contains the released <code>.pt</code> checkpoints (DINHR and INHR baselines where applicable) and dataset-specific auxiliary files (e.g., <code>list_refs.txt</code>, <code>*.npy</code>) used by our evaluation scripts.<br>Code and usage instructions are available in the DINHR GitHub repository.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18447739 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | DINHR trained model checkpoints for CryoBench datasets and experimental cryo-EM datasets He, Jiahua Cheng, Yifan cryo-EM heterogeneous reconstruction implicit neural representation denoising CryoBench RELION cryoSPARC <p>This record provides trained <strong>DINHR</strong> (<strong>D</strong>enoising <strong>I</strong>mplicit <strong>N</strong>eural <strong>H</strong>igh-order <strong>R</strong>epresentation) model checkpoints and auxiliary files to reproduce the results reported in our manuscript. The release includes checkpoints for <em>CryoBench </em>simulated datasets (<em>IgG-1D </em>at multiple SNRs, <em>IgG-RL</em>, <em>TomoTwin-100</em>, <em>Ribosembly</em>) and experimental datasets (<em>EMPIAR-10073</em>, <em>EMPIAR-10076</em>, <em>INO80–Hexasome</em>).<br>Each subfolder contains the released <code>.pt</code> checkpoints (DINHR and INHR baselines where applicable) and dataset-specific auxiliary files (e.g., <code>list_refs.txt</code>, <code>*.npy</code>) used by our evaluation scripts.<br>Code and usage instructions are available in the DINHR GitHub repository.</p> |
| title | DINHR trained model checkpoints for CryoBench datasets and experimental cryo-EM datasets |
| topic | cryo-EM heterogeneous reconstruction implicit neural representation denoising CryoBench RELION cryoSPARC |
| url | https://doi.org/10.5281/zenodo.18447739 |