TensorFlow Object Detection API: basics of detection (2/2)

My first (at all!) post was devoted to 2 basic questions of training detection models using TensorFlow Object Detection API: how are negative examples mined and how the loss for training is chosen. This time I'd like to cover 3 more questions regarding the following: As before, I totally recommend to recap the SSD architecture features following the same links as were provided in my previous post. In SSD, there is no region-proposal step (in contrast with R-CNN models) and the set of regions to be considered by the model is completely predefined by the configuration. In short, the features from the feature-head of the network are passed to a pipeline of the detection blocks. Every detection block receives a reduced in spatial size tensor (which is still a somewhat representation of input image) and overlays it with a regular grid which nodes are later used as centers for the set of assumed bounding boxes.