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--- pipeline:window:cryolo:picking_general_refine [2019/09/18 10:35]
twagner [Picking particles - Using the general model refined for your data]
+++ pipeline:window:cryolo:picking_general_refine [2019/09/18 10:37]
twagner [Picking particles - Using the general model refined for your data]
@@ Line 9: / Line 9: @@
 Why should I //fine-tune// my model instead of training from scratch?
-  -  From theory, using fine-tuning should reduce the risk of overfitting ((Overfitting means, that the model works good on the training micrographs, but not on new unseen micrographs. The model just memorized what it saw instead of learning generic features.)) and the amount of training data.
+  -  From theory, using fine-tuning should reduce the risk of overfitting ((Overfitting means, that the model works good on the training micrographs, but not on new unseen micrographs. The model just memorized what it saw instead of learning generic features.)) and the amount of the required training data.
   - The training is much faster, as not all layers have to be trained.
   - The training will need less GPU memory ((We are testing crYOLO with its default configuration on graphic cards with >= 8 GB memory. Using the fine tune mode, it should also work with GPUs with 4 GB memory)) and therefore is usable with NVIDIA cards with less memory.
-However, the fine tune mode is still somewhat experimental and we will update this section if see more advantages or disadvantages.
+However, the fine tune mode is still somewhat experimental and we will update this section as crYOLO develops over time.
 If you followed the installation instructions, you now have to activate the cryolo virtual environment with

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