Hi Junhyeon,
“trainCellpose” does not directly support for real-time augmentation. Having a pre-augmented dataset is essential.
- To apply augmentations on-the-fly during training, you might consider implementing a custom datastore by subclassing “matlab.io.Datastore”.
“trainCellpose” does not support early stopping in a way that “trainnet” supports. To achieve early stopping using “trainCellpose”, here’s a workaround:
- Ensure validation dataset is separate from training dataset.
- Train the model for a small number of epochs at a time, manually checking the performance on the validation set after each iteration.
- If the validation performance does not improve over successive iterations, stop the training process.
For more information, please refer to the following MathWorks documentation links:
