Skip to content

A study and evaluation of state-of-the-art universal deep-learning–based detectors for macromolecule localization in cryo-electron tomography (cryo-ET)

Notifications You must be signed in to change notification settings

C-HernanG/cryoet-particle-picking

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

5 Commits
 
 
 
 
 
 

Repository files navigation

Universal Deep Learning Detectors for Macromolecule Localization in Cryo-ET

Note: This repository contains experimental implementations and benchmarks. A comprehensive paper documenting these methods and results is currently in development (docs/CryoET_Particle_Picking.pdf).

ProPicker - Promptable Segmentation

ProPicker Repository

Documentation

Documentation and examples are located in the docs/ProPicker/ directory of this repository:

  • ProPicker.pdf: Original paper
  • PROPICKER_empiar10988_prompt_based_picking.ipynb: Notebook with prompt-based picking example using the EMPIAR-10988 dataset, including a brief overview of the model architecture and functionality

About

A study and evaluation of state-of-the-art universal deep-learning–based detectors for macromolecule localization in cryo-electron tomography (cryo-ET)

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published