I wanted to train a model that recognizes sign language. I have found a dataset for this and was able to create a model that would get 94% accuracy on the test set. I have trained models before and my main goal is not to have the best model (I know 94% could easiy be tuned up). However these models where always for class exercises and thus were never used on 'real' new data.
So I took a new picture of my hand that I know I wanted to be a certain letter (let's assume A).
Since my model was trained on 28x28 images, I needed to re-size my own image because it was larger. After that I fed this image to my model only to get a wrong classification.
These are my pictures (upper-left = my own image (expected class A), upper-right = an image of class A (that my model correctly classifies as A), bottom = picture of class Z (the class my image was classified as)).
You can clearly see that my own image looks for more like the image of class A (that I wanted my model to predict), than the model it did predict.
What could be reasons that my model does not work on real-life images? (If code is wanted I can provide it ofcourse but since I don't know where I go wrong, it seemed out of line to copy all the code).