This is an old revision of the document!
The documentation has moved to https://cryolo.readthedocs.io/en/latest/
If you are interested in using crYOLO in a commercial context please contact firstname.lastname@example.org
You can find more technical details in our paper:
Communications Biology: SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM
====crYOLO==== Version: 1.6.1 Uploaded: 30. March 2020 DOWNLOAD Please see install instructions how to get it running on the CPU. ====crYOLO boxmanager==== Version: 1.3.5 Uploaded: 22. March 2020 DOWNLOAD [crYOLO PhosaurausNet's eponym] ==== General PhosaurusNet models ==== Please see the tutorial how to use the general models === For cryo images (low-pass filtered) === Number of datasets: 43 real, 10 simulated, 10 particle free datasets on various grids with contamination Uploaded: 16. March 2020 DOWNLOAD === For cryo images (neural network denoised with JANNI) === Number of datasets: 43 real, 10 simulated, 10 particle free datasets on various grids with contamination Uploaded: 17. March 2020 DOWNLOAD
=== For negative stain images === Number of datasets: 10 real datasets Uploaded: 26. February 2019 DOWNLOAD ====ARCHIVE==== Previous versions of crYOLO, the boxmanager and the general models can be found here: Archive. ====== Installation == ====
System requirements: 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. Install crYOLO! The following instructions assume that pip and anaconda or miniconda are available. In case you have a old cryolo environment installed, first remove the old one with:
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 numpy ==1.14.5 cython wxPython==4.0.4 intel-openmp==2019.4 Activate the environment:
source activate cryolo In case you run crYOLO on a GPU run:
pip install 'cryolo[gpu]' But if you want to run crYOLO on a CPU run:
pip install 'cryolo[cpu]'
ERROR: imagecodecs-lite 2019.2.22 has requirement numpy>=1.15.4, but you'll have numpy 1.14.5 which is incompatible.However, you can ignore it. It is actually also working with numpy==1.14.5
That's it! You might want to check if everything is running as expected. Here is a reference example: Reference example with TcdA1 ===== Run it on the CPU ==== There is also a way to run crYOLO on CPU. To use it, just follow the instruction in the install section . This is especially useful 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. Training crYOLO is much more computational expensive. Training a model with 14 micrographs from scratch on my local machine take 34 minutes per epoch on the CPU. Given that you often need 25 epochs until convergence it is a task to do overnight (~ 12 hours). However, you might want to try refining the general model, which takes 12 minutes per epoch (~ 5 hours). ====== Tutorials ====== Use our
step-by-step tutorials to get started! ====== Change log ====== ====crYOLO====
Find new versions here: https://cryolo.readthedocs.io
====crYOLO Boxmanager==== crYOLO Boxmanager 1.3.5 * Fixed a bug when placing, moving or deleting a box * Fixed bug of nun closing progress dialog when writing boxfiles crYOLO Boxmanager 1.3.1 * Speed up boxfile import is now 2x faster compared to 1.3.0. * Big speed-up for live-preview during filtering. Should now even work with very big datasets. crYOLO Boxmanager 1.3.0 * Added option to plot size- and confidence distribution for cbox files. * Added slider to filter particles according their estimated size. * Added addition field for the number of boxes with live update. * Added wildcard commandline option. * Show progress-bar when reading and writing box-files. * Various speed-ups.
==== General PhosaurusNet model ==== Version 202002: * Adjusted the training data that the box sizes better reflect the particle diameter. Version 201912: * Added two more datasets. Version 20190516: * Added four more inhouse datasets * Added SNRNP (Thanks to Clement Charenton)
==== General YOLO network model in patch mode ====