multi teacher KD cannot achieve better performance than seperate models about centerx HOT 3 CLOSED

Good question！
There may be many reasons for this：

1. The imbalance instances of different datasets：
   1).crowd human has 352978 human instance，and the other has 1/3 or 1/8 of it.
   2). the crowd human model is well trained and the other model is not trained well for lack of annotations.
   3). So multi-teacher KD helps model to training more dataset, and the lack of training dataset well increase mAP.
   4). Theoretically，a multi-class model is worse than one-class model, so the well trained crowd human model is worse than baseline.
2. My super parameters is not the best one.

from centerx.

I didn't understand 1.3), multi-teacher will feed more dataset to the baseline model beyond either isolated dataset, theoretically it should perform better than either baseline model. I would like to try to explain that crowd_human dataset covers a wider domain than wider face or coco car, so I guess that a wider domain task will benefit a narrow one but hurt itself if put them together. So we still need to carefully merge mutli datasets with different labels before we find a method can definitively promote both.

from centerx.

What you said might be one of the reasons. Domain Has a great effect.

“theoretically it should perform better than either baseline model”: this is right when both KD model and baseline are one-class detector, but i think a multi-class KD model might be worse than a single-class baseline in some specific datasets.

from centerx.