License

  • Author: Thorsten Wagner
  • License: EULA
  • Last Update: 2018-06-20

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crYOLO

Version: 1.0.4

Uploaded: 13. July 2018

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crYOLO boxmanager

Version: 1.0.2

Uploaded: 09. July 2018

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General crYOLO network model

Number of real handpicked datasets included: 10

Uploaded: 28. June 2018

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Info: Trained on cryo images, therefore negative stain will not work.

General YOLO network in patch mode (BETA)

Number of real handpicked datasets included: 14

Uploaded: 04. July 2018

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Info: Trained on cryo images, therefore negative stain will not work.

ARCHIVE

Previous versions of crYOLO, the boxmanager and the general models can be found here: Archive.

Change log

crYOLO

crYOLO Version 1.0.4:

  • Fix a problem reading backend weights from read-only filesystem (Thanks to Michael Cianfrocco and Jason Key)
  • Make sure that tensorflow version is >= 1.5.0 and < 1.9.0
  • Add support for subfolders in training and validation directories
  • More clear error message when the trained model does not fit to the architecture specified in the config file.

crYOLO Version 1.0.3:

  • Ignore non-image files during training and predction (Thanks to Kellie Woll)
  • Fixed misleading error when non existing folder is used as input for prediction (Thanks to Kellie Woll)
  • Add distance threshold during prediction by adding -d distanceInPixel parameter to prediction command (Thanks to Lifei Fu)
  • Add “–write_empty” parameter to prediction command if an empty box file should be written if no particle is picked.

crYOLO Version 1.0.2:

  • Fix problem when mrc image has dimensions (1,width,height) (Thanks to Reza Behrouzi)

crYOLO Version 1.0.1:

  • Normalization technique is now the same for 8-bit and 32 bit images.
  • Unify image augmentation

crYOLO Boxmanager:

crYOLO Boxmanager Version 1.0.2:

  • Fix problem with invisible (start with .) files. Now they are ignored.

crYOLO Boxmanager Version 1.0.1:

  • Fix crash when cancel import boxfiles
  • Fix crash with qt4

General YOLO network model in patch mode

Version 20180704:

  • Added three more handpicked datasets: spliceosome (EMPIAR 10160), picornavirus (EMPIAR 10033) and an internal dataset.

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.

Installation

crYOLO depends on CUDA Toolkit 9.0 and the cuDNN 7.0.4 library:

The CUDA Toolkit can be downloaded here:

https://developer.nvidia.com/cuda-90-download-archive

cuDNN 7.0.4 for CUDA 9.0 are available here:

https://developer.nvidia.com/rdp/cudnn-download

Instructions for installing cuDNN can be found here:

https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html

Please make sure that CUDA and cuDNN are in your LD_LIBRARY_PATH. Otherwise GPU acceleration will not run. You could check it with

ldconfig -v

You should find entries similar to

libcuda.so.1 -> libcuda.so.390.48
libcuda.so.1 -> libcuda.so.390.48
libcudart.so.9.1 -> libcudart.so.9.1.85
libicudata.so.60 -> libicudata.so.60.2

Install crYOLO!

The following instructions assume that pip and anaconda are available.

Create a new virtual environment:

conda create -n cryolo python=2 pyqt=5

Activate the environment:

source activate cryolo

Install crYOLO:

pip install numpy
pip install cryolo-X.Y.Z.tar.gz
pip install cryoloBM-X.Y.Z.tar.gz

That's it!

Start picking!

Use the step-by-step tutorial to get started!