Question: Image classification neural network performs well on python generated data but poorly on real data

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Hi. Im a student trying to make a neural network that can classify light diffraction images created with python. The problem is that the model achieves very high scores on accurasy and the classification report/confusion matrix when tested on new pythong generated images but it performs very poorly when i give it real life images i find from the internet. I suspect it has to do with the less than ideal noise the real images have and now im trying to introduce noise to my training dataset. Any tips? Bellow is an single slit diffraction image generated with python (images like that are on the training set) real images of diffraction will have different colors and more noise in the background. I try to augment my train dataset but i dont know if it will work.


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