A big question you need to answer when approaching an image classification problem: how many samples are you going to need to get to your required test metrics? Solution: use a starter dataset to model how accuracy increases with data size. Find out how:https://keras.io/examples/keras_recipes/sample_size_estimate/ …
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Very nice article! I saw that the author chose an exponential function to fit the learning curve. Is this always considered the best choice or it depends on the problem (e.g. dataset, architecture)?
There are diminishing returns to adding more data so this type of function is a good fit. Though many different variants could be valid depending on the data regime
