I also agree. Even in cases where it is not super critical (say Coke vs Pepsi vs RC cola), different models will give a top score based on confidence. 3/4 models will say Pepsi is Pepsi, but 1/4 says it’s Coke. I guess as the one developing the model we should just reject the one that says Pepsi is Coke, but if you are developing a complex model with more than 3 samples, it might not be so simple.
And if you use the wrong model and scan somebody’s skin, it will tell you they are made of licorice…
Also, I’m developing models for candy (fat, sugar, etc) but when I tried to use the model on a butter curry chicken to impress my friends, it gave a value of -10% fat… so yeah, models need to be material-specific. Pretty sure butter chicken has more than -10% fat.
But overall, very impressed with the accuracy of application specific models! 