Suppose that we have different animals that we have 4 types of dogs that we want to detect (Golden Retriever, Black Labrador, Cocker Spaniel, and Pit Bull). The training data consists of png images of a data set of dogs along with their annotations. We want to train a model using YOLOv3. Does the choice of optimizer really matter in terms of training the model? Would the Adam optimizer be better than the Adadelta optimizer? Or would they all basically be the same?
Would some optimizers be better because they allow most of the weights to achieve their "global" minima?