The most recent version of this page is a draft.This version (2020/02/13 13:59) was approved by twagner.
This is an old revision of the document!
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.
Issue 27: Filament mode is not working with micrographs motion corrected by unblur. Will be fixed in the next release.
Issue 1: crYOLO sometimes not exit properly after training finished. Has to be killed manually.
Issue 2: If you use automatic filtering with .tif files, you get an error like “OSError: cannot identify image file 'filtered_folder/another_folder/my_image.tif'”. It will be fixed in the next release.
Issue 3: (Boxmanager) The visualization only shows the first filament when loading eman1 helical box files (start end coordinates). Will be fixed in the next release.
Issue 4: The filament mode will crash if crYOLO cannot identify a single particle in the image. Will be fixed in 1.2.2
Issue 5: If movies were aligned with cisTEM and picked with crYOLO, the box position are vertically flipped. Will be fixed in 1.2.2
Issue 6: crYOLO does overwrite the environmental variable “CUDA_VISIBLE_DEVICES” with 0 if no gpu is specified by the -g parameter. This leads to the behavior that crYOLO ignores previous settings in CUDA_VISIBLE_DEVICES. Will be fixed in 1.2.2
Issue 7: On K3 images crYOLO seems to add a offset toward the longer axis of the input image.
Issue 8: There is a logical error in filament tracing, which sometimes connects two parallel filaments.
Issue 9: Some people report an error when running cryolo prediction/training: “ImportError: numpy.core.multiarray failed to import”. It will be fixed in 1.2.3.
Issue 10: On machines with many cores (e.g 64) an error during filtering might pop up: “[ERROR:0] 53: Can't spawn new thread”
Issue 11: If the -g parameter is not provided, crYOLO will use the memory of all GPUs. Will be fixed in 1.2.3.
Issue 12: The LineEnhancer depdenceny of crYOLO is still dependent from opencv. Workaround: In the crYOLO environment: conda install opencv
Issue 13: After picking it can happen that some of the boxes are not fully immersed in the image. Will be fixed in 1.2.4.
Issue 14: Parallelization in filament mode is broken. Will be fixed in 1.2.4.
Issue 15: If the --gpu_fraction is used, crYOLO always uses GPU 0. Will be fixed in 1.3.1.
Issue 16: --gpu_fraction only works for prediction, not for training. Will be fixed in 1.3.2.
Issue 18: Prediction is broken in 1.3.2. It removes all particles as it claim they are not fully immersed in the image.
Issue 19: Filtering does not work if target image directory is absolute path.
Issue 20: crYOLO 1.3.4 has a normalization bug. During training the images are normalized seperately, but during prediction is done batch wise. Workaround: Use -pbs 1 during prediction. It will be fixed in 1.3.5.
Issue 21: The search range for filament tracing is too low for many datasets. To check if you are affected: Use your trained model and pick without the filament options. Check if your filaments a nicely picked (many consecutive boxes on a filament). In the next version, the search range will be increased and added as an optional parameter.
Issue 22: If absolute paths are used in the field “train_image” in your configuration file, filtering is skipped.
Issue 23: Since crYOLO 1.4.0 it sometimes take long until it starts picking. The reason seems to be the tensorflow update.<del>
* <del>Issue 24: Fine-tune mode does not start (cannot find layer model_3). Will be fixed in 1.4.1.<del>
* <del>Issue 25: When using GUI, prediction behaves differently than using command line. The reason is, that it uses a different multiprocessing start method. Will be fixed with 1.5.1
Issue 26: If you select filtering “None” crYOLO does not train properly.
/web/sphire/www/sphire/wiki/data/pages/pipeline/window/cryolo/issues.txt · Last modified: 2020/03/18 07:43 by twagner