Let's say I'm looking for any item that has a certain shape (outline) in a photo. but I can further classify it only according to particular features, that most of them are expected to be shown only in a smaller area of the the object itself.
How may I give more weight, in the model, to that particular area, in order to avoid wrong classification issues?
What is the flow, and are there specific tools that should be used for that purpose?
I want to detect all triangles in the image, and try to classify them like this: If triangle has 3 lines in its corner, it's A type. if only two lines, it's B type.
So the triangles outline composes 100% of the object, but we can see that the area where the red lines are present, is only about 10% of the object area. How may I give more weight and tell the model to carefully look for the details in that area, so it doesn't confuse A with B or vice versa, just because the other 90% of the shape is similar.
And Of course, I want the certainty level to be as close as possible to 100%, for both A and B, and to be distinguished from the other option.
So my goal is the get this output:
Purple Triangle ==> Type A, certainty, 99%. Type B: certainty: 50%
Green Triangle ==> Type B, certainty, 99%. Type A: certainty: 50%