training command for crYOLO.
Tags
templatejavaimagej2scijava
Citation
Wagner, T., Merino, F., Stabrin, M., Moriya, T., Antoni, C., Apelbaum, A., Hagel, P., Sitsel, O., Raisch, T., Prumbaum, D. and Quentin, D., 2019. SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM. Communications biology, 2(1), p.218..https://cryolo.readthedocs.io/en/stable/index.html
Solution written by
Kyle Harrington
License of solution
MIT
Arguments
--conf
Path to configuration file for crYOLO. (default value: PARAMETER_VALUE)
--warmup
Number of warmup epochs. (default value: PARAMETER_VALUE)
--gpu
GPU(s) to be used, separated by whitespace. (default value: PARAMETER_VALUE)
--num_cpu
Number of CPUs used during training. (default value: PARAMETER_VALUE)
--gpu_fraction
Fraction of memory per GPU used during training. (default value: PARAMETER_VALUE)
--early
Early stop patience. (default value: PARAMETER_VALUE)
--fine_tune
Set to true for fine-tuning mode. (default value: PARAMETER_VALUE)
--layers_fine_tune
Layers to be trained when using fine tuning. (default value: PARAMETER_VALUE)
--cleanup
Delete filtered images after training. (default value: PARAMETER_VALUE)
--ignore_directions
Skip directional learning for filament training data. (default value: PARAMETER_VALUE)
--seed
Seed for random number generator. (default value: PARAMETER_VALUE)
--warm_restarts
Use warm restarts and cosine annealing during training. (default value: PARAMETER_VALUE)
--skip_augmentation
Deactivate data augmentation during training. (default value: PARAMETER_VALUE)