Seitenhistorie

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1. Patch size: Together with "Batch size", will change how much data is passed to the GPU per batch. A patch of 256x256 with a batch size of 16 works best for our workstation (the one next to the spinning disk microscope)
2. Number of training epochs: Increase this if your loss is still decreasing after 100 epochs.
3. → Jump to 5. if you already have a trained model
4. Choose a name for the model
5. Select the path where to save the model
6. "Number of training epochs" and "Batch size" can be left as default (if there is a memory error, for the training, the batch size must be reduced).
7. Click on "Start training" and go for a break. This should last 10-15 min.

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4. Look at the result with "Preview Result" and use the navigation pane to observe the before/after

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1. Steps per epochs: You can increase this parameter if you want your network to train on a larger portion of data for each epoch.
2. Train fraction: Indicates which portion of the dataset is used for training, the rest being used for validation. Increase this to 0.99 if you have a large dataset and you want to avoid the validation step to take too long (there is one validation step per epoch)
3. Neighborhood radius: N2V parameter. Read more about it in their paper https://arxiv.org/abs/1811.10980

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4. Apply a trained model to a dataset by going to "Apply N2V"

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1. By default, the program will upload the images in the same dataset by adding the suffix "_N2V" to the image name
2. Images can be uploaded during the processing. Otherwise, they can be uploaded when the processing is done with "Upload to omero"
3. Result display can be toggled off to make the process faster.

5. Apply the model to the dataset and upload results to OMERO → Start with this step if you already have a trained model for your images