Model Center Variance and Size Variance

[prerakgupta]

I'm trying to run this DSSD implementation on BDD Dataset which has images of 720x1280.

I first started with input size 320 because a lot model parameters were defined for it. I'm trying to understand the center variance and size variance used in defaults.py. I could identify them being used in box_utils.py but can you please help me understand them?

1. Are they model parameters or dependant on the choice of input? Given my choice of dataset running with input size 320 do I need to change them?

2. Also if I provide my ground truth box co-ordinates (x1, y1, x2, y2) relative to the actual input image in the dataset (720x1280), they (gt_boxes) will be normalised as per the model input size (320) right?