中国绅学我术大草 实验框架：Resnet+FPN 1 class+box subnets class subnet WxH class+box ×256 x4 WxH WxH ×256 XKA subnets class+box subnets WxH WxH WxH ×256 ×256 x4A box subnet (a)ResNet (b)feature pyramid net (c)class subnet (top) (d)box subnet (bottom) Figure 3.The one-stage RetinaNet network architecture uses a Feature Pyramid Network(FPN)[20]backbone on top of a feedforward ResNet architecture [16](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).The network design is intentionally simple,which enables this work to focus on a novel focal loss function that eliminates the accuracy gap between our one-stage detector and state-of-the-art two-stage detectors like Faster R-CNN with FPN [20]while running at faster speeds.实验框架：Resnet + FPN