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Another reason for that is due the fact that Machine Learning is applied initially to the Trainning Images set, therefore, while performing deep training, the Error Loss Function graph is displayed, in relation to the Traning Images set.
If training is not interrupted when the graph is not decreasing anymore, the model will stop learning, meaning that you will not achieve better results. However, this will not cause weakness to the model, in other words, this will not increase the presence of false positives - It will just not provide better results.
Therefore, if the graph is not deacreasing anymore, it means the model stopped from improving.
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