about the accuracy with pure segmentation branch and no further finetuning

We've trained the model with provided code without segmentation branch and have acquired similar results to your paper. It's mentioned in the paper that using the whole network with segmentation branch and the parameters trained without segmentation branch, an increase of mAP could be seen without further finetuning. However, we didn't observe such phenomenon in our experiments conducted, with models trained on different image sizes. We could not easily find the reason. Is there any key points when doing this? Could you kindly shared something that we may have ignored? Sincerely thank you.