pipeline:window:cryolo:configuration
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| * During training, crYOLO also needs validation data((Micrographs that are selected as validation data are not used to train crYOLO. These micrographs are used to calculate how well the model performs and whether it still improves.)). Typically, 20% of the training data are randomly chosen as validation data. If you want to use specific images as validation data, you can move the images and the corresponding box files to separate folders. __Make sure that they are removed from the original training folder!__ You can then specify the new validation folders in //" | * During training, crYOLO also needs validation data((Micrographs that are selected as validation data are not used to train crYOLO. These micrographs are used to calculate how well the model performs and whether it still improves.)). Typically, 20% of the training data are randomly chosen as validation data. If you want to use specific images as validation data, you can move the images and the corresponding box files to separate folders. __Make sure that they are removed from the original training folder!__ You can then specify the new validation folders in //" | ||
| * By default, your micrographs are low pass filtered to an absolute frequency of 0.1 and saved to disk. You can change the cutoff threshold and the directory for filtered micrographs in the //" | * By default, your micrographs are low pass filtered to an absolute frequency of 0.1 and saved to disk. You can change the cutoff threshold and the directory for filtered micrographs in the //" | ||
| - | * When training from scratch, crYOLO is initialized with weights learned on the ImageNet training data (transfer learning((From Wikipedia: Transfer learning (TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.))). | + | * When training from scratch, crYOLO is initialized with weights learned on the ImageNet training data (transfer learning((From Wikipedia: Transfer learning (TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.))). |
| <note tip> | <note tip> | ||
pipeline/window/cryolo/configuration.txt ยท Last modified: 2020/03/16 15:20 by twagner
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