I want to create a model to solve a multi-class classification problem.
Here are more details about my problem.
Every picture contains only one object
The background is very simple
All objects belong to the same family of objects (for example, all objects are knives), but there are different specific subtypes
the model will learn and predict the name of the object (example: the model learn all types of knives, and when it get an image it will tell us the name of the knife)
To be clear, let's say I have 50 types of knives, and the output of the model has to recognize the correct name of the knife. Knife name could be:
- Chef's Knife,
- Heavy Duty Utility Knife,
- Boning Knife, etc.
As a first step, I got a prediction, but I really don't know if I'm on the right way.
For this problem, Mask R-CNN could be the solution, or it is impossible to recognize a tiny difference between two objects from the same class (for example Chef's Knife, Heavy Duty Utility Knife)?