Fine tuning Object Keypoint Similarity (OKS)

I have been trying to figure out how to compute the object similarity (OKS), In my **custom dataset** I have 4 keypoints per image, I'd like during training to improve the Object Keypoint Similarity's score properly, therefore I need to initial the simgas values (**cfg.TEST.KEYPOINT_OKS_SIGMAS** ) with a list of values (Each simga for each keypoint). I have read thoroughly the [Object Keypoint Similarity](https://cocodataset.org/#keypoints-eval), BUT unfortunately it's still unclear for me how to find those values:

For each keypoint type i we measured the per-keypoint standard deviation σi with respect to object scale s.
That is we compute σi^2=E[di^2/s^2].

- s we define as the square root of the object segment area

- di are the Euclidean distances between each corresponding ground truth and detected keypoint

it's not clear for me how can I calculate a head of time the Sigmas (Standard deviation) values if the Expected value **depends** on the Euclidean distances between each corresponding ground truth and detected keypoint, which has not been calculated yet?

Can someone PLEASE give a simple example or explanation so it will be a more clear, Thank you.

No repository file or test is named. Start by reading the linked COCO OKS evaluation description and searching for cfg.TEST.KEYPOINT_OKS_SIGMAS in the Detectron2 configuration and evaluation code. Done means documenting a simple four-keypoint example that explains how the sigma values are obtained and whether they affect training or evaluation.

Written by the indexing model from the issue text.