| --- |
| library_name: EMber |
| tags: |
| - cryo-em |
| - image-feature-extraction |
| - model_hub_mixin |
| - pytorch_model_hub_mixin |
| --- |
| |
| <h1 align="center"><em>EMber</em>: Learning the Shared Latent Structure of Cryo-EM Inverse Problems</h1> |
|
|
| <!-- Replace each href="#" with the author's website URL. --> |
| <p align="center"> |
| <a href="https://nithinvc.github.io/">Nithin Chalapathi</a> 路 |
| <a href="https://www.theosternlieb.com/">Theo Sternlieb</a> 路 |
| <a href="#">Ethan Han</a> 路 |
| <a href="https://sonyahanson.github.io/">Sonya M. Hanson</a> 路 |
| <a href="https://pilarcossio.org/">Pilar Cossio</a> 路 |
| <a href="https://a1k12.github.io/">Aditi S. Krishnapriyan</a> 路 |
| <a href="https://ritwikgupta.me/">Ritwik Gupta</a> |
| </p> |
|
|
| EMber is a family of pretrained vision transformers for extracting representations from single-particle cryo-EM particle images. Given a particle image, EMber produces an image-level embedding and spatial patch features for use in downstream analysis. The models were trained using self-distillation on over 50 million experimental particle images from 214 EMPIAR datasets, together with simulated projections from 268,000 molecular assemblies. |
|
|
| Check out our preprint [here](https://arxiv.org/abs/2610.22523) and code [here](https://github.com/nithinvc/EMber). |
|
|
| EMber-Base has 85.3M parameters and uses a 16x16 patch size. |
|
|
