ok, got it. however, in my experience the number of labels is far less obvious in real world datasets than one might expect. consider an example with images of bottles, cups and glasses, so three labels.

a model trained on these three labels will need revision if further down after the deployment process ‚bottles‘ need to be split in ‚plastic bottles‘ and ‚glass bottles‘. both label sets are perfectly valid, due to the hierarchical nature of things.

anyway, my point is actually another one: afaik this will require dataset relabeling and fully iterate the training process on the newly labeled dataset.

or is there a faster way to make the model aware of the more finegrained bottle labels?

i mean, without access to data of cups and glasses, basically inform it of more finegrained bottle types but let it still keep its knowledge of cups and glasses.

but finetuning requires the relabeling of the original dataset to include both old and the new label, which op specifically does not want to do.

i don’t think what op wants is doable. or is there some approach i’m missing? i think what op basically wants to do is retrain, but with only data from the new class, and still avoid catastrophic forgetting of the other labels.

is there a way to do this?

also: data augmentation will make the training set more complex.