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downloads:cryolo_1 [2021/02/19 09:33]
downloads:cryolo_1 [2021/02/19 09:43]
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 Preprint: [[|SPHIRE-crYOLO: A fast and accurate fully automated particle picker for cryo-EM]] Preprint: [[|SPHIRE-crYOLO: A fast and accurate fully automated particle picker for cryo-EM]]
-<html> <a href="" target=_ckgedit_QUOT__blank"><img src="" 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 **DOCUMENTATION OUTDATED**  The documentation has moved to  </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?}}
-//__//{{page>pipeline:window:cryolo:issues}} //__//+
-//__//**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==== //__ //+<note warning> 
-//__// {{page>pipeline:window:cryolo:changelog}} //__//+**DOCUMENTATION OUTDATED**  
-//__//====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. <hidden **Old crYOLO Boxmanager change logs**> **crYOLO Boxmanager Version 1.2.9:**  * Fixed a problem that only one filament is shown **crYOLO Boxmanager Version 1.2.8:**  * Add a low pass filter **crYOLO Boxmanager Version 1.2.6:**  * Make it compatible with current new environment **crYOLO Boxmanager Version 1.2.3:**  * Make it compatible with current new environment **crYOLO Boxmanager Version 1.2.2:**  * Makes sure that the correct version of MatplotLib is used. **crYOLO Boxmanager Version 1.2.1:**  * Press "h" for hiding the boxes * Fix for loading different box sets with different colors for the case that on of the box sets are cbox files. **crYOLO Boxmanager Version 1.2:**  * Add interactive threshold selection using cbox files **crYOLO Boxmanager Version 1.1.1:**  * Fix Issue 3 * Now supports STAR Start-End filament format **crYOLO Boxmanager Version 1.1.0:**  * Switch to Python3 * Minor bug fixes **crYOLO Boxmanager Version 1.0.4:**  * Support of visualization of EMAN1 filament coordinates * Make compatible with crYOLO 1.1.3 **crYOLO Boxmanager Version 1.0.3:**  * Support of visualization of EMAN2 helical coordinates (particle coordinates) * New boxes could be loaded with a new color while keeping the old. * Fix problem with makes loading images very long. * Several bug fixes **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 </hidden> ==== 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) <hidden Old General PhosaurusNet model change logs> **Version 20190315:**: * Added KLH * Added one inhouse dataset **Version 20190218:**  * Added K3 apoferritin (Thanks to Shaun Rawson) * Added two more inhouse datasets **Version 20181221:**  * Same datasets as the general YOLO network model version 20181120 but with trained with PhosaurusNet. </hidden> ==== General YOLO network model in patch mode ==== <hidden Old general YOLO network model in patch mode> **Version 20181120:**  Added multiple simulated datasets, where each micrograph contains hundreds of particles with different defocus: * PDB 1SA0 * PDB 5LNK * PDB 5XNL * PDB 6B7N * PDB 6BHU * PDB 6DMR * PDB 6DS5 * PDB 6GDG * PDB 6H3N * PDB 6MPU Besides these simulated datasets we added handpicked * ATP Synthase * DNA Origami * Two more particle-free only-contamination datasetsIt total 45 datasets are now included**Version 20180823:**  Increase the number of hand picked datasets to 25 by adding: * Add EMPIAR 10154 (Thanks to Daniel Prumbaum) * Add EMPIAR 10186 (Thanks to Sebastian Tacke) * Add EMPIAR 10097 Hemagglutinin (Thanks to Birte Siebolds) * Add EMPIAR 10081 HCN1 (Thanks to Pascel Lill) * Add internal dataset (Thanks to Daniel Roderer) * Furthermore we added 8 datasets of protein-free grids (Thanks to Tobias Raisch and Daniel Prumbaum) **Version 20180720:**  Added micrographs of 7 new handpicked datasets: * EMPIAR 10181 (Thanks to Dennis Quentin) * EMPIAR 10017 * EMPIAR 10028 (Thanks to Oleg Sitsel) * User contributed dataset (Thanks to Lifei Fu) * EMPIAR 10089 * EMPIAR 10004 (Thanks to Daniel Roderer) * EMPIAR 10072 (Thanks to Tobias Raisch) Furthermore I had to remove one internal dataset, as it turned out that it is unsuitable for training the general model. **Version 20180704:**  Added three more handpicked datasets: * spliceosome (EMPIAR 10160) * picornavirus (EMPIAR 10033) and * an internal dataset. </hidden>//__ //+The documentation has moved to 
-//__// //__ //+</note> 
downloads/cryolo_1.txt · Last modified: 2021/02/19 09:43 by twagner