I am trying to train a neural network to detect objects within a tattoo. I couldn't find any existing labeled dataset so I need to manually create and label my own. I only understand the basics of machine learning and was going to start by using TensorFlow's Object Detection (https://github.com/tensorflow/models/tree/master/research/object_detection)
For my labeling I am stuck between two options. Using https://scontent-ort2-1.cdninstagram.com/vp/85794872ffd8b8166b1dd559fcf718b0/5C90FA77/t51.2885-15/e15/11326797_987161747982140_1156040219_n.jpg as an example:
Approach 1: Create a simple bounding box around each object in the tattoo: Multiple boxes: 1 around the rose, the clock and the words. The box for the clock however may bleed into the roses, if the tattoo was more complicated and compact it would bleed a lot (Bleed as in parts of the rose are in the clock's bounding box).
Approach 2: Cut out each object exactly so there is no bleeding and every object is exactly what it is and fill the negative space with black.
I understand that background noise is important. When the NN is training against many images it detects that the background isn't significant and can pick up the features of just object being detected, however, since all my images are tattoos which are on people's bodies the background for everything is just skin tone. I am afraid if I don't have an exact depiction of the tattoo object it will not train accurately especially since every tattoo is different.
I am not sure which approach to take or if I should go a different directions. Thanks