File size: 1,337 Bytes
5143678 7e18eef | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | ---
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.
|