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downloads:cryolo_1 [2019/05/17 08:29] twagner [crYOLO] |
downloads:cryolo_1 [2019/07/11 09:55] 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:// | ||
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====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:// |
[{{ : | [{{ : | ||
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- | === 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) === | ||
+ | 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 on cryo images, therefore negative stain will not work. | + | |
=== For negative stain images === | === For negative stain images === | ||
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====== 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> | ||
* < | * < | ||
* < | * < | ||
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* < | * < | ||
* < | * < | ||
- | * Issue 17: On the fly filtering (%%--%%otf) is slower than using it not, as the filtering is not parallelized in this case. | ||
* < | * < | ||
* < | * < | ||
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* < | * < | ||
* < | * < | ||
+ | </ | ||
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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 |
</ | </ | ||
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</ | </ | ||
- | 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 | ||
</ | </ | ||
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===== 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. | ||
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====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) | ||
- | * Add [[pipeline: | + | * Add search radius factor as [[pipeline: |
* Improved error message in case of corrupted config file | * Improved error message in case of corrupted config file | ||
* Fixed issue 22: If absolute paths are used in the field “train_image” in your configuration file, filtering is skipped. | * Fixed issue 22: If absolute paths are used in the field “train_image” in your configuration file, filtering is skipped. | ||
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* 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. | ||
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* 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: | ||
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* 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 | ||
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**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:** | ||
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* picornavirus (EMPIAR 10033) and | * picornavirus (EMPIAR 10033) and | ||
* an internal dataset. | * an internal dataset. | ||
+ | </ | ||