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Is there some kind of rule of thumb for a minimum of how much data is needed for various types of problems?


Retraining and existing model does not need many (fast.ai lesson 1 example is retraining a net to distinguish cricketers and baseball players with 30 images). For a full net, it's on the order of thousands per category.




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