metadata
library_name: EMber
tags:
- cryo-em
- image-feature-extraction
- model_hub_mixin
- pytorch_model_hub_mixin
EMber: Learning the Shared Latent Structure of Cryo-EM Inverse Problems
Nithin Chalapathi 路 Theo Sternlieb 路 Ethan Han 路 Sonya M. Hanson 路 Pilar Cossio 路 Aditi S. Krishnapriyan 路 Ritwik Gupta
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 and code here.
EMber-Base has 85.3M parameters and uses a 16x16 patch size.