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I am working on this project in which I have to detect objects only when they are complete(not when objects are incomplete or partially visible).I have more than 20 classes of objects.
I tried with yolo by annotating only complete objects, but that didn't work.

How can I approach this problem, any suggestion is useful.

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    $\begingroup$ This is an interesting question, as the usual goal is to generalise to include incomplete images. I don't have very much experience with YOLO, so not writing a full answer. But you could try this: Treating complete and incomplete objects of same type as different classes. This would allow you to continue using YOLO architecture, although I don't know how well it will cope with differentiating between such similar classes in practice - it should be step up from removing class labels altogether from training data. $\endgroup$ Commented Oct 11, 2019 at 13:04

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