GitHub - Gratia2533/cryyy: This research employs an iterative active learning approach for cryo-EM particle picking that selects informative samples based on uncertainty metrics, progressively enhancing model performance while reducing labeling requirements.

Branches Tags

Name		Name	Last commit message	Last commit date
Latest commit History 54 Commits
AutoALTidy		AutoALTidy
base_mrc		base_mrc
base_particle		base_particle
main_pts		main_pts
tool		tool
.gitattributes		.gitattributes
eval.py		eval.py
gen_mrc.py		gen_mrc.py

About

This research employs an iterative active learning approach for cryo-EM particle picking that selects informative samples based on uncertainty metrics, progressively enhancing model performance while reducing labeling requirements.

ethesys.lis.nsysu.edu.tw/ETD-db/ETD-search-c/view_etd?URN=etd-0025125-151053

computer-vision deep-learning object-detection cryo-em active-learning