solov2_r50_fpn.yml

architecture: SOLOv2
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_cos_pretrained.pdparams

SOLOv2:
  backbone: ResNet
  neck: FPN
  solov2_head: SOLOv2Head
  mask_head: SOLOv2MaskHead

ResNet:
  depth: 50
  norm_type: bn
  freeze_at: 0
  return_idx: [0,1,2,3]
  num_stages: 4

FPN:
  out_channel: 256

SOLOv2Head:
  seg_feat_channels: 512
  stacked_convs: 4
  num_grids: [40, 36, 24, 16, 12]
  kernel_out_channels: 256
  solov2_loss: SOLOv2Loss
  mask_nms: MaskMatrixNMS

SOLOv2MaskHead:
  mid_channels: 128
  out_channels: 256
  start_level: 0
  end_level: 3

SOLOv2Loss:
  ins_loss_weight: 3.0
  focal_loss_gamma: 2.0
  focal_loss_alpha: 0.25

MaskMatrixNMS:
  pre_nms_top_n: 500
  post_nms_top_n: 100