Hi everyone,
I am trying to set up a single class detection model. My goal is to detect drones. The only annotated images I have are images where a drone appears. Otherwise I have images without drone, but without annotations either.
I tried to train my network with two classes, 0 → background and 1-> drone.
But the pytorch model can’t take empty bbox in the case of background class, i can set the bbox as the size of the image but i don’t think that is a good solution.
My question is, is it good to only train my model with drone images and test/ evaluate with background/
no drone images ?
Can my model learn good features of it if I only train with one class ? I’m afraid that  in inference my model confuse a drone with any flying object (bird,helicopter…)
It’s more a theorical question but i’m just a beginner i’m trying to understand what is best for this case.
Thanks a lot