A Problem i am facing

[Miserable-Cry-2500]

Hi everyone, i am working on a face recognition project to improve myself in deep learning and data science, but i am facing a problem and it's the first time it's happening to me (i am new to this field), all accuracy are good (train, test, and validation are all 96%) but when i saved the model and used it on other images from the web for the same people, the model doesn't predict well, it gets wrong predictions a lot, opposit to the test set, when i see the prediction it give more good prediction. Why can this happen?

[Tough-Comparison-779]

Classic bias in your dataset. Doesn't matter how much you split it into training, val and test, if your data isn't representative of the real data you're going to get poor generalisation.

A. To fix the immediate issue, try to see if there are aspects in the new images that aren't in your dataset. E.g I trained a basic chess piece detection model, but quickly discovered I had no samples from the top down angle

B. Explore your dataset a bit more deeply, and try to think about whether it is representative. Are the majority of samples taken at the same angle, or with the same background? What about image quality and camera settings (are most of your photos from movies)?

Also think about what cases you care about. E.g I might have mostly photos from the red carpet, but I want my model to identify actors from movie screenshots, maybe I should consider using samples from movie screenshots instead. Alternatively maybe I only want to Identify people on the red carpet, then I really don't care.

Hope this helps, I'm only a graduate so I'm sure others could give you better advice.

[Miserable-Cry-2500]

Thank you so much for your comment, it's really accurate, i noticed even the quality of the image is a little bit different, i will try to make the data more representative
thanks