Hi,
Is there an option or a way to keep for example 5 best checkpoints in the training process by using train_model.py and not just the best model checkpoint?
As far as I understood, there isn't any option to keep multiple checkpoints in train_model.py, and by any option group, the new checkpoint will be overwritten on the only last checkpoint that has been saved.
Should I add this feature and if it's required create a pull request or is there a logic behind the way train_model.py keeps the checkpoints?