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downloads:cryolo_1 [2021/02/19 09:34]
twagner
downloads:cryolo_1 [2021/02/19 09:40]
twagner
Line 22: Line 22:
 Preprint: [[https://www.biorxiv.org/content/10.1101/356584v2|SPHIRE-crYOLO: A fast and accurate fully automated particle picker for cryo-EM]] Preprint: [[https://www.biorxiv.org/content/10.1101/356584v2|SPHIRE-crYOLO: A fast and accurate fully automated particle picker for cryo-EM]]
  
-<html> <a href="https://f1000.com/prime/733517098?bd=1" target=_ckgedit_QUOT__blank"><img src="https://s3.amazonaws.com/cdn.f1000.com/images/badges/badgef1000.gif" alt="Access the recommendation on F1000Prime" id="bg" /></a> </html> ==  ==== Download ====== <note important> Before downloading or using this product, make sure you **understand and accept the terms of the license **. </note> ====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 Phosauraus**Net'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 <note> The performance of the general model based on JANNI denoised data compared to low-pass filtered data did not improve. The average AUC on the validation data was in both cases the same (0.85). But this might be because of the data selected for the general model. I assume that especially on very noisy micrographs JANNI will improve the results. </note> === 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 == ==== <note important> **DOCUMENTATION OUTDATED**  The documentation has moved to  https://cryolo.readthedocs.io/en/latest/  </note> **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]' ''  <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 </note> <note tip> In case you want to integrate crYOLO in your module system, [[:pipeline:window:cryolo:integration_into_module_system|here is a brief explanation]]. </note>//__ //+{{:badgef1000.gif?nolink&120x28}} 
 + 
 +[[https://f1000.com/prime/733517098?bd=1//======|https://f1000.com/prime/733517098?bd=1//======]] Download 
 + 
 +======   ====== 
 + 
 +<note important> Before downloading or using this product, make sure you **understand and accept the terms of the license **. 
 + 
 +</note> 
 + 
 +//__//==  ==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 Phosauraus**Net'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 <note> The performance of the general model based on JANNI denoised data compared to low-pass filtered data did not improve. The average AUC on the validation data was in both cases the same (0.85). But this might be because of the data selected for the general model. I assume that especially on very noisy micrographs JANNI will improve the results. </note> === 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 == ==== <note important> **DOCUMENTATION OUTDATED**  The documentation has moved to  https://cryolo.readthedocs.io/en/latest/  </note> **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]' ''  <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 </note> <note tip> In case you want to integrate crYOLO in your module system, [[:pipeline:window:cryolo:integration_into_module_system|here is a brief explanation]]. </note>//__ //
  
 //__//{{page>pipeline:window:cryolo:issues}} //__// //__//{{page>pipeline:window:cryolo:issues}} //__//
downloads/cryolo_1.txt · Last modified: 2021/02/19 09:43 by twagner