EvalMetric for SSD training

Hi,
I have been trying to use train_ssd.py (link below) with a custom dataset following a similar approach than the finetuning tutorial for object detection with SSD network.
https://gluon-cv.mxnet.io/build/examples_detection/finetune_detection.html

However the method train takes an eval metric, which is not existing for SSD network with custom dataset. (as far as I understood).

So based on the tutorial I started trying to put up an eval metric for SSD, see

I tried to model it against existing VOC and COCO detection metrics but I am a bit lost.
Does it actually make sense ? :sweat_smile:

Also the metric should boil down to one figure like the sum_loss or it could also include the class and bouding box regression loss ?
Because I have seen that the get method could also return lists for names and values.

Thanks for the help !
Btw, this is related to the issue