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downloads:cryolo_1 [2019/03/18 08:06] twagner [crYOLO] |
downloads:cryolo_1 [2019/07/09 20:13] twagner [Run it on the CPU] |
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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: 1.3.1 | + | Version: 1.3.6 |
+ | |||
+ | Uploaded: | ||
- | Uploaded: 18. March 2019 | + | [[ftp://ftp.gwdg.de/ |
- | [[ftp://ftp.gwdg.de/ | + | Please see install instruction how to get it running on the CPU. |
====crYOLO boxmanager==== | ====crYOLO boxmanager==== | ||
- | Version: 1.2.0 | + | Version: 1.2.2 |
- | Uploaded: | + | Uploaded: |
- | [[ftp:// | + | [[ftp:// |
[{{ : | [{{ : | ||
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=== For cryo images === | === For cryo images === | ||
- | Number of datasets: | + | Number of datasets: |
- | Uploaded: | + | Uploaded: |
- | [[ftp:// | + | [[ftp:// |
|DOWNLOAD]] | |DOWNLOAD]] | ||
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* < | * < | ||
* < | * < | ||
- | * Issue 15: If the --gpu_fraction is used, crYOLO always uses GPU 0. Will be fixed in 1.3.1. | + | * <del>Issue 15: If the %%--%%gpu_fraction is used, crYOLO always uses GPU 0. Will be fixed in 1.3.1.</ |
+ | * < | ||
+ | * 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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**Install crYOLO!** | **Install crYOLO!** | ||
- | The following instructions assume that pip and [[https:// | + | The following instructions assume that pip and [[https:// |
In case you have a old cryolo environment installed, first remove the old one with: | In case you have a old cryolo environment installed, first remove the old one with: | ||
< | < | ||
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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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< | < | ||
source activate cryolo | source activate cryolo | ||
+ | </ | ||
+ | |||
+ | Install fast numpy from conda: | ||
+ | < | ||
+ | conda install numpy==1.15.4 | ||
</ | </ | ||
Install crYOLO: | Install crYOLO: | ||
< | < | ||
- | pip install | + | #IN CASE YOU WANT TO INSTALL THE GPU VERSION: |
- | pip install cryolo-X.Y.Z.tar.gz | + | pip install |
+ | #IN CASE YOU WANT TO INSTALL THE CPU VERSION: | ||
+ | pip install cryolo-X.Y.Z.tar.gz[cpu] | ||
pip install cryoloBM-X.Y.Z.tar.gz | pip install cryoloBM-X.Y.Z.tar.gz | ||
</ | </ | ||
- | That's it! | + | **That's it!** |
+ | |||
+ | You might want to check if everything is running as expected. Here is a reference example: | ||
+ | |||
+ | [[http:// | ||
===== Run it on the CPU ==== | ===== Run it on the CPU ==== | ||
- | There is also a way to run crYOLO on CPU. This is especially | + | There is also a way to run crYOLO on CPU. To use it, just follow the instruction in the [[downloads: |
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. | ||
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 [[pipeline: | 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 [[pipeline: | ||
- | |||
- | **Here is how you prepare your crYOLO setup for using it on the CPU:** | ||
- | |||
- | After you followed the crYOLO installation instructions just replace tensorflow-gpu by tensorflow: | ||
- | < | ||
- | pip uninstall tensorflow-gpu | ||
- | pip install tensorflow==1.10.1 | ||
- | </ | ||
- | |||
- | Now crYOLO should work on the CPU as well! | ||
====== Start picking! ====== | ====== Start picking! ====== | ||
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Use the **__'' | Use the **__'' | ||
- | ====== Change log ===== | + | ====== Change log ====== |
====crYOLO==== | ====crYOLO==== | ||
+ | |||
+ | **crYOLO 1.3.6:** | ||
+ | * Changed filament search radius factor from 0.8 to 1.41 (this fixed issue 21) | ||
+ | * Add search radius factor as [[pipeline: | ||
+ | * 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. | ||
+ | |||
+ | **crYOLO 1.3.5:** | ||
+ | * Fixed issue 20: During training the images are normalized separately, but during prediction is done batch wise. The lead to confusing results: some micrographs were perfectly picked, some totally unreasonable, | ||
+ | * Remove unnecessary dependencies | ||
+ | * Add %%__%%version%%__%% to %%__%%init%%__%%.py for easy access to package version. | ||
+ | |||
+ | **crYOLO 1.3.4:** | ||
+ | * Support for SPHIRE 1.2 | ||
+ | * Changed the minimum threshold for cbox files from 0.01 to 0.1. Much faster in many cases but still low enough. If -t is lower than 0.1, the new threshold is used as minimum. | ||
+ | * Installation now checks if python 3 is used. | ||
+ | * Fix issue 19: Filtering does not work if target image directory is absolute path. | ||
+ | * Fix crash when %%--%%otf was specified but filtering was not specified in the config file | ||
+ | |||
+ | **crYOLO 1.3.3:** | ||
+ | * Fix issue 18: Prediction is broken in 1.3.2. It removes all particles as it claim they are not fully immersed in the image. | ||
+ | |||
+ | **crYOLO 1.3.2:** | ||
+ | * Speedup prediction: Vectorized some parts of the code and optimized the creation of the cbox files. 30% speed up picking / 15% faster training compared to 1.3.1/ | ||
+ | * Bug fix in merging of filaments that sometimes throw " | ||
+ | * Fix in cryolo_evaluation: | ||
+ | * Change library requirement to PILLOW version 6.0.0 | ||
+ | * Fix issue 16: %%--%%gpu_fraction only works for prediction, not for training. | ||
+ | |||
+ | **crYOLO 1.3.1:** | ||
+ | * Fix Issue 15: -g was ignored when --gpu_fraction was used. | ||
**crYOLO 1.3.0:** | **crYOLO 1.3.0:** | ||
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====crYOLO Boxmanager==== | ====crYOLO Boxmanager==== | ||
+ | **crYOLO Boxmanager Version 1.2.2:** | ||
+ | * Makes sure that the correct version of MatplotLib is used. | ||
+ | |||
+ | **crYOLO Boxmanager Version 1.2.1:** | ||
+ | * Press " | ||
+ | * 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: | **crYOLO Boxmanager Version 1.2: | ||
* Add interactive threshold selection using cbox files | * Add interactive threshold selection using cbox files | ||
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==== General PhosaurusNet model ==== | ==== General PhosaurusNet model ==== | ||
+ | **Version 20190516: | ||
+ | * Added four more inhouse datasets | ||
+ | * Added SNRNP (Thanks to Clement Charenton) | ||
+ | |||
**Version 20190315: | **Version 20190315: | ||
* Added KLH | * Added KLH |