How Single Shot Detectors (SSD) object detection calculates it's class scores and bbx locations? As in the paper I can understand SSD try to predict object locations and their relevant class scores from different feature maps .

So for each layers there can be different predictions with respect to number of anchor(reference) boxes in different scale. 
So if one convolutional feature map has 5 reference boxes there should be class scores and bbx coordinates for each of the reference box . 
We do above predictions by sliding a window(kernel Ex: 3*3) over the feature maps of different layers .  So what I not clear is connection from sliding window at a position to score layer .
1. It just connection of convolution window output to score layer in a fully connected way ? 
2.Or we do some other operation for convolution window output before connecting it to score layer ? 
 A: The class score and bbx predictions are obtained by convolution. It's the difference between YOLO and SSD . SSD doesn't go for a fully connected way. I will explain how the score function is taken . 

Above is a 8 *8 spacial sized feature map in a ssd feature extractor model. For each position in the feature map we gonna predict following 


*

*4 BBX coordinates w.r.t default boxes (showed in dotted lines)

*class scores for each default boxes  (c number of classes)


Let's say if we have k number of default (anchor) boxes we predict *(4+c)K
Now the tricky part . How we get those scores . 


*

*Here we use set of convolutional kernels which have depth of the feature map. (normally 3*3) 

*Since there are (4+C) predictions w.r.t single anchor box it's like we have (4+C) above mentioned kernels which have depth of feature map. So it's more like set of filters .
These set of filters will predict above (4+c) scalars. 
So for a single feature map , if there are K number anchor box which we reference them in prediction , 
We have **K *(4+c)  filters(3*3 in spacial location) are applied around each location of the feature map in a sliding window manner .** 
We train those filter values ! 
.
