All feature maps extracted from the network have their output projected down to 256 channels by the FPN block as this greatly improves the speed of the network. These feature maps provided by the FPN backbone are used by the FasterRCNN detector to provide box and class predictions at different scales.
Training & Tuning process
We currently offer two pre-trained models capable of doing object detection at different resolutions. Both models were trained on the COCO dataset using the same hyper-parameters and scripts which can be found in our references folder.