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The RetinaNet architecture uses a Feature Pyramid Network [21] backbone on top of a feedforward ResNet architecture [22] (a) to generate a rich, multi-scale convolutional feature pyramid (b). To this backbone RetinaNet attaches two subnetworks: one for classifying anchor boxes (c) and one for regressing from anchor boxes to ground-truth object boxes (d). (Reprinted and adapted with permission from ICCV 2017 [20].)
