**Known issues** * 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 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. * Issue 24: Fine-tune mode does not start (cannot find layer model_3). Will be fixed in 1.4.1. * 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. * Issue 27: Filament mode is not working with micrographs motion corrected by unblur. Will be fixed in the next release. * Issue 28: Minimum distance filter is not applied to cbox files.