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Currently, to determine the active learning step, we look at the number of jobs of a particular type run on a folder. This means that if a user deletes the contents of the folder, the current step detected will not reset to zero. We should devise a more sophisticated step-checking method.
We should also try to better communicate the current active learning step to the end user.
The text was updated successfully, but these errors were encountered:
The superpixel algorithm generates an epoch number based on annotation names in all of the images. It find the annotations with a particular name and from that finds the highest previous epoch number. Switching to this method would be consistent, but can still be odd if things are manually altered.
Currently, to determine the active learning step, we look at the number of jobs of a particular type run on a folder. This means that if a user deletes the contents of the folder, the current step detected will not reset to zero. We should devise a more sophisticated step-checking method.
We should also try to better communicate the current active learning step to the end user.
The text was updated successfully, but these errors were encountered: