Keras model performs well in training, validation and also in evaluating but performs poor when predicting a new image
You write that you trained an image classification model. Let's say that you want to write an app which lets people photograph their dinner and it returns a prediction on which food it is.
If you train your model with "perfect" images, i.e. high-quality images that are sharp, but now you let your model predict a "real-life" which was taken in a dark restaurant, then you have the problem: You let model predict sth it basically didn't learn. That might be the issue.
I think it would be great if you could provide us with some typical images of your training-dataset and the dataset you are predicting on. Maybe the ones you took for predictions are not typical for the whole training dataset.
I have trained a image classification model using keras which gives training acc of 98%, validation acc of 98% testing acc of 90%, but performs very poor on new input images. I don't know the why??