## key points

• multi scale with weighted bi-directional fpn.
• model scaling. compound scaling method, which jointly scales up resolution/depth/width for all backbone, feature network, box/class prediction network.
• use efficientnet backbone

## Bi-directional FPN

Here are the key points of bi-directional FPN

• enhancement from PANet with some modifications
• remove nodes with only one input. reason: no feature fusing will happen so it would be okay to eliminate this and also save computational cost
• use original scale feature during upward path same level aggregation(more residual connections)
• do this top-down, bottom-up path multiple times
• weighted: let the network learn the weight. there are a few candidates, but the paper adopts fast normalized fusion

here is the formula for fast normalization, where all w values are outputs of relu, so its value is guaranteed to be >=0:

## Network Structure

Categories: deep learning