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Yes, this is quite the expected behavior. The main difference between expected and current behavior lies in the amount of data you are using for training VS the amount of data that the pre-trained model was trained with.
Take into account that pre-trained models have been trained over popular datasets, the most common ones are: COCO, ImageNet and Open Images....
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There is no label for such bounding boxes, they are simply "ignored" during training. You can assign any value for their "labels", then multiplying what ever loss these boxes generated with 0. If there is no loss, there is no gradient from these boxes.
You can do that by defining a count_boxes vector with binary values. Object and ...
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