If an image contains two distinct objects, should I create a copy of this image with distinct labels for each copy? Suppose we want to detect whether an object is one of the following classes: $\text{Object}_1, \text{Object}_2, \text{Object}_3$ and $\text{Person}$. Should the annotated images only contain bounding boxes for either a person or an object? In other words, suppose an image has both $\text{Object}_1$ and $\text{Person}$. Should you create a copy of this image where the first version only has a bounding box on the object and the second copy only has a bounding box on the person?
You should use both classes together. Let's say you use the method you proposed. Then they will be contradicting each other as one teaches the network to recognize people, not objects and the other teaches the network to recognizes objects not person. There is no need for seperation of the two classes, unless you are making two seperate classifier. Hope I can help you.