downloads:cryolo_1
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downloads:cryolo_1 [2019/05/17 08:35] twagner [crYOLO] |
downloads:cryolo_1 [2019/07/11 11:39] twagner [crYOLO boxmanager] |
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| You can find more technical details in our paper: | You can find more technical details in our paper: | ||
| + | Nature Communications Biology: | ||
| + | [[https:// | ||
| + | |||
| + | Preprint: | ||
| [[https:// | [[https:// | ||
| Line 30: | Line 34: | ||
| ====crYOLO==== | ====crYOLO==== | ||
| - | Version: 1.3.6 | + | Version: 1.4.0 |
| - | Uploaded: | + | Uploaded: |
| - | [[ftp:// | + | [[ftp:// |
| - | [[ftp:// | + | Please see [[downloads:cryolo_1# |
| ====crYOLO boxmanager==== | ====crYOLO boxmanager==== | ||
| - | Version: 1.2.2 | + | Version: 1.2.3 |
| - | Uploaded: | + | Uploaded: |
| - | [[ftp:// | + | [[ftp:// |
| [{{ : | [{{ : | ||
| Line 51: | Line 55: | ||
| - | === For cryo images === | + | === For cryo images |
| - | Number of datasets: 38 real, 10 simulated, 10 particle free datasets on various grids with contaminations | + | Number of datasets: 38 real, 10 simulated, 10 particle free datasets on various grids with contamination |
| Uploaded: 17. May 2019 | Uploaded: 17. May 2019 | ||
| [[ftp:// | [[ftp:// | ||
| + | |DOWNLOAD]] | ||
| + | |||
| + | [[pipeline: | ||
| + | |||
| + | === For cryo images (neural network denoised with JANNI) === | ||
| + | Number of datasets: 38 real, 10 simulated, 10 particle free datasets on various grids with contamination | ||
| + | |||
| + | Uploaded: 11. July 2019 | ||
| + | |||
| + | [[ftp:// | ||
| |DOWNLOAD]] | |DOWNLOAD]] | ||
| [[pipeline: | [[pipeline: | ||
| - | Info: Trained | + | < |
| + | 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. | ||
| + | </ | ||
| + | |||
| === For negative stain images === | === For negative stain images === | ||
| Line 78: | Line 96: | ||
| ====== Known issues ===== | ====== Known issues ===== | ||
| * Issue 0: Training on multiple GPUs sometimes lead to worse performance (higher loss). We currently recommend to train on single gpus. | * Issue 0: Training on multiple GPUs sometimes lead to worse performance (higher loss). We currently recommend to train on single gpus. | ||
| + | * Issue 17: On the fly filtering (%%--%%otf) is slower than using it not, as the filtering is not parallelized in this case. | ||
| + | <hidden Closed issues> | ||
| * < | * < | ||
| * < | * < | ||
| Line 94: | Line 114: | ||
| * < | * < | ||
| * < | * < | ||
| - | * Issue 17: On the fly filtering (%%--%%otf) is slower than using it not, as the filtering is not parallelized in this case. | ||
| * < | * < | ||
| * < | * < | ||
| Line 100: | Line 119: | ||
| * < | * < | ||
| * < | * < | ||
| + | </ | ||
| Line 125: | Line 145: | ||
| After that, create a new virtual environment: | After that, create a new virtual environment: | ||
| < | < | ||
| - | conda create -n cryolo -c anaconda python=3.6 pyqt=5 cudnn=7.1.2 | + | conda create -n cryolo -c anaconda python=3.6 pyqt=5 cudnn=7.1.2 |
| </ | </ | ||
| Line 133: | Line 153: | ||
| </ | </ | ||
| - | Install crYOLO: | + | Install |
| + | < | ||
| + | conda install numpy==1.15.4 | ||
| + | </ | ||
| + | |||
| + | In case you run **crYOLO | ||
| + | < | ||
| + | pip install cryolo-X.Y.Z.tar.gz[gpu] | ||
| + | </ | ||
| + | |||
| + | But if you want to run **crYOLO on a CPU** run: | ||
| + | < | ||
| + | pip install cryolo-X.Y.Z.tar.gz[cpu] | ||
| + | </ | ||
| + | |||
| + | Finally you install the cryolo boxmanager: | ||
| < | < | ||
| - | conda install numpy==1.14.5 | ||
| - | pip install cryolo-X.Y.Z.tar.gz | ||
| pip install cryoloBM-X.Y.Z.tar.gz | pip install cryoloBM-X.Y.Z.tar.gz | ||
| </ | </ | ||
| Line 148: | Line 181: | ||
| ===== Run it on the CPU ==== | ===== Run it on the CPU ==== | ||
| - | There is also a way to run crYOLO on CPU. To use it, just install | + | There is also a way to run crYOLO on CPU. To use it, just follow |
| 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. | 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. | ||
| Line 162: | Line 195: | ||
| ====crYOLO==== | ====crYOLO==== | ||
| + | **crYOLO 1.4.0:** | ||
| + | * Support Just Another Noise 2 Noise Implemnentation ([[: | ||
| + | * Add --mask_width as optional parameter for the filament mode | ||
| + | * Update tensorflow from 1.10.1 to 1.12.3 to make crYOLO compatible with JANNI | ||
| + | * Update numpy from 1.14.5 to 1.15.4 to make crYOLO compatible with JANNI | ||
| + | |||
| + | <hidden **Old crYOLO change logs**> | ||
| **crYOLO 1.3.6:** | **crYOLO 1.3.6:** | ||
| * Changed filament search radius factor from 0.8 to 1.41 (this fixed issue 21) | * Changed filament search radius factor from 0.8 to 1.41 (this fixed issue 21) | ||
| Line 298: | Line 338: | ||
| * Unify image augmentation | * Unify image augmentation | ||
| + | </ | ||
| ====crYOLO Boxmanager==== | ====crYOLO Boxmanager==== | ||
| + | **crYOLO Boxmanager Version 1.2.3:** | ||
| + | * Make it compatible with current new environment | ||
| + | |||
| + | <hidden **Old crYOLO Boxmanager change logs**> | ||
| **crYOLO Boxmanager Version 1.2.2:** | **crYOLO Boxmanager Version 1.2.2:** | ||
| * Makes sure that the correct version of MatplotLib is used. | * Makes sure that the correct version of MatplotLib is used. | ||
| Line 333: | Line 378: | ||
| * Fix crash when cancel import boxfiles | * Fix crash when cancel import boxfiles | ||
| * Fix crash with qt4 | * Fix crash with qt4 | ||
| + | </ | ||
| ==== General PhosaurusNet model ==== | ==== General PhosaurusNet model ==== | ||
| **Version 20190516: | **Version 20190516: | ||
| Line 339: | Line 384: | ||
| * Added SNRNP (Thanks to Clement Charenton) | * Added SNRNP (Thanks to Clement Charenton) | ||
| + | <hidden Old General PhosaurusNet model change logs> | ||
| **Version 20190315: | **Version 20190315: | ||
| * Added KLH | * Added KLH | ||
| Line 348: | Line 394: | ||
| **Version 20181221:** | **Version 20181221:** | ||
| - | + | * Same datasets as the general YOLO network model version 20181120 but with trained with PhosaurusNet. | |
| - | Same datasets as the general YOLO network model version 20181120 but with trained with PhosaurusNet. | + | </ |
| ==== General YOLO network model in patch mode ==== | ==== General YOLO network model in patch mode ==== | ||
| + | <hidden Old general YOLO network model in patch mode> | ||
| **Version 20181120:** | **Version 20181120:** | ||
| Line 402: | Line 447: | ||
| * picornavirus (EMPIAR 10033) and | * picornavirus (EMPIAR 10033) and | ||
| * an internal dataset. | * an internal dataset. | ||
| + | </ | ||
downloads/cryolo_1.txt · Last modified: 2021/02/19 09:43 by twagner
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