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Uploaded: 12. February 2019
Uploaded: 21. December 2018
Number of datasets: 27 (real), 10 simulated, 10 particle free datasets on various grids with contaminations
Uploaded: 21. December 2018
Info: Trained on cryo images, therefore negative stain will not work.
Previous versions of crYOLO, the boxmanager and the general models can be found here: Archive.
crYOLO Version 1.2.3:
crYOLO Version 1.2.2:
crYOLO Version 1.2.1:
crYOLO Version 1.2.0:
crYOLO Version 1.1.4:
crYOLO Version 1.1.3:
crYOLO Version 1.1.2:
crYOLO Version 1.1.1:
crYOLO Version 1.1.0:
crYOLO Version 1.0.4:
crYOLO Version 1.0.3:
crYOLO Version 1.0.2:
crYOLO Version 1.0.1:
crYOLO Boxmanager Version 1.1.1:
crYOLO Boxmanager Version 1.1.0:
crYOLO Boxmanager Version 1.0.4:
crYOLO Boxmanager Version 1.0.3:
crYOLO Boxmanager Version 1.0.2:
crYOLO Boxmanager Version 1.0.1:
Same datasets as the general YOLO network model version 20181120 but with trained with PhosaurusNet.
Added multiple simulated datasets, where each micrograph contains hundreds of particles with different defocus:
Besides these simulated datasets we added handpicked
It total 45 datasets are now included.
Increase the number of hand picked datasets to 25 by adding:
Added micrographs of 7 new handpicked datasets:
Furthermore I had to remove one internal dataset, as it turned out that it is unsuitable for training the general model.
Added three more handpicked datasets:
crYOLO was tested on Ubuntu 16.04.4 LTS and Ubuntu 18.04 with an NVIDIA Geforce 1080 / Geforce 1080Ti.
However, it should run on Windows as well.
As the GPU accelerated version of tensorflow does not support MacOS, crYOLO does not support it either.
crYOLO depends on CUDA Toolkit 9.0 and the cuDNN 7.1.2 library. It will be automatically installed during crYOLO installation.
conda env remove --name cryolo
After that, create a new virtual environment:
conda create -n cryolo -c anaconda python=3.6 pyqt=5 cudnn=7.1.2
Activate the environment:
source activate cryolo
pip install numpy pip install cryolo-X.Y.Z.tar.gz pip install cryoloBM-X.Y.Z.tar.gz
There is also a way to run crYOLO on CPU. This is especially usefull when you would like to apply the generalized model and don't have a NVIDIA GPU. Picking with crYOLO is also quite fast on the CPU. On my local machine (Intel i9) it takes roughly 1 second per micrograph and on our low-performance notebooks (Intel i3) 4 seconds.
After you followed the crYOLO installation instructions just replace tensorflow-gpu by tensorflow:
pip uninstall tensorflow-gpu pip install tensorflow==1.10.1
Now crYOLO should work on the CPU as well!
step-by-step tutorial to get started!