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pipeline:window:cryolo:picking_general_refine [2019/09/17 15:42] twagner |
pipeline:window:cryolo:picking_general_refine [2019/09/18 10:36] twagner [Picking particles - Using the general model refined for your data] |
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What does // | What does // | ||
- | The general model was trained on a lot of particles with a variety of shapes and therefore learned a very good set of generic features. The last layers, however, learn a pretty | + | The general model was trained on a lot of particles with a variety of shapes and therefore learned a robust |
Why should I // | Why should I // | ||
- | - From theory, using fine-tuning should reduce the risk of overfitting ((Overfitting means, that the model works good on the training micrographs, | + | - From theory, using fine-tuning should reduce the risk of overfitting ((Overfitting means, that the model works good on the training micrographs, |
- The training is much faster, as not all layers have to be trained. | - 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. | - 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. |