fcos_r50_fpn.yml

architecture: FCOS
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
load_static_weights: True

FCOS:
  backbone: ResNet
  neck: FPN
  fcos_head: FCOSHead
  fcos_post_process: FCOSPostProcess

ResNet:
  # index 0 stands for res2
  depth: 50
  norm_type: bn
  freeze_at: 0
  return_idx: [1,2,3]
  num_stages: 4

FPN:
  in_channels: [256, 512, 1024, 2048]
  out_channel: 256
  min_level: 1
  max_level: 5
  spatial_scale: [0.125, 0.0625, 0.03125]
  has_extra_convs: true
  use_c5: false

FCOSHead:
  fcos_feat:
    name: FCOSFeat
    feat_in: 256
    feat_out: 256
    num_convs: 4
    norm_type: "gn"
    use_dcn: false
  num_classes: 80
  fpn_stride: [8, 16, 32, 64, 128]
  prior_prob: 0.01
  fcos_loss: FCOSLoss
  norm_reg_targets: true
  centerness_on_reg: true

FCOSLoss:
  loss_alpha: 0.25
  loss_gamma: 2.0
  iou_loss_type: "giou"
  reg_weights: 1.0

FCOSPostProcess:
  decode:
    name: FCOSBox
    num_classes: 80
    batch_size: 1
  nms:
    name: MultiClassNMS
    nms_top_k: 1000
    keep_top_k: 100
    score_threshold: 0.025
    nms_threshold: 0.6