I want to build a model using a neural network that will be able to extract some features from landscape pictures.
In order to improve the efficiency of my model, I first want to extract the "vertical orientation" of the picture. This "vertical orientation" would be next a feature for my neural network.
Now, to compute this feature I see two solutions.
- Build a Regression model that would return a result in degrees (0 to 180)
- Build a Classification model that would return the class of orientation (ex: High, Medium High, Medium, Medium Low, Low)
Is there a way to decide which solution to use, or should I test both solutions to find the better one ?
- The dataset is not a problem. I can have pictures labeled in degrees or in class easily