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i'm trying to compose a dataset for the detection of soccer players, ball etc.. in a soccer game, i'm using alexeyAB Darknet framework,

So in the labeling phase in each image there are at least 8 players a ball and other stuff, at some point it is logical to think about the fact that i will have enough instances per player, but not enough for the ball and the goalkeeper for example,

so can i only marking bounded boxes the ball and other things avoiding to do it with the players to not waste time?

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If you are training the model on your own dataset, I would recommend limiting the number of labels/classes in your data to what you seek. For example if you only want your model to see balls, goal-posts and Not players, simply keep the classes as balls and goal-posts. (This reminds me of a classification problem where 0 stands for balls and 1 stands for goal-post). P.S you mentioned object detection and Not Localization, which is the purpose of the YOLO models.

Pe Dro
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  • Yes I have appointed object detection to specify the category of computer vision, my intent is to locate all the figures present in a football game, but the problem is that I find myself with many instances per player and few for the rest of the figures, for example, the goal or the ball, for this reason Yolo struggles to locate them –  May 08 '20 at 09:20
  • You should be getting one bounding box per object. If your are not, then in case of YOLO, you are missing to use IoU (Intersection over Union) and maximal suppression, which are common techniques to suppress duplicate boxes in YOLO ouputs. – Pe Dro May 08 '20 at 10:43