Installation and Download

System requirements

At our institute in Dortmund, crYOLO is running on the following operation systems:

  • Ubuntu 16.04.4 LTS
  • Ubuntu 18.04 LTS
  • CentOS 7

We don’t test it but it should run on Windows as well.

Moreover the following GPUs are used:

  • NVIDIA Titan V
  • NVIDIA GTX 1080
  • NVIDIA GTX 1080Ti
  • NVIDIA RTX 2080 TI
  • NVIDIA GV 100

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. These will be automatically installed during crYOLO installation.

Install crYOLO

Note

Please read the Complimentary Science Software License before using crYOLO. If you are interested in using crYOLO in a commercial context please contact stefan.raunser@mpi-dortmund.mpg.de

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 conda-forge -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]'

Note

During the installation of crYOLO you will see the following error message:

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

Hint

You can also integrate crYOLO as Environment Module

That’s it!

You might want to check if everything is running as expected. Here is a reference example:

Reference example with TcdA1

Download the general models

We provide three general models. One for cryo-EM images which was trained on low-pass filtered images, another one for cryo-EM images but trained for images denoised by JANNI and one for negative stain images.

For cryo images (low-pass filtered)

Datasets:43 real, 10 simulated, 10 particle free datasets on various grids with contamination
Uploaded:27 May 2020
Download:Link
Config:Commands to create the config file can be found here.

For cryo images (neural network denoised with JANNI)

Datasets:43 real, 10 simulated, 10 particle free data sets on various grids with contamination
Uploaded:27 May 2020
Download:Link
Config:Commands to create the config file can be found here.

For negative stain images

Datasets:10 real data sets
Uploaded:26 February 2019
Download:Link
Config:Commands to create the config file can be found here.