Welcome to the community, @Adilbek_Salimgereyev: Good question!
In addition to @gent.spah‘s great reply:
How much data is needed to solve a specific problem can be quite challenging to determine in the conflict of interest between data acquisition cost and technical excellence. One way to quantity the (expected) information e.g. via (Shannon) entropy approaches can be active learning (AL) where model uncertainty can be utilized, see also: How much data does a CNN need to learn? - #2 by Christian_Simonis
So, Active learning can help to quantify:
- which label is expected to provide a valuable benefit and also
- when a sufficient amount of data has been used to train your model.
This thread on the batch could be interesting for you if you are interested in AL.
Many Thanks, @saifkhanengr, for the hint!
Best regards
Christian