arxiv link:

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


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