gfl_slim_ld_r18vd_1x_coco.yml

_BASE_: [
  '../datasets/coco_detection.yml',
  '../runtime.yml',
  '_base_/optimizer_1x.yml',
  '_base_/gfl_reader.yml',
]

weights: output/gfl_r18vd_1x_coco/model_final
find_unused_parameters: True

architecture: GFL
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet18_vd_pretrained.pdparams

GFL:
  backbone: ResNet
  neck: FPN
  head: LDGFLHead

ResNet:
  depth: 18
  variant: d
  norm_type: bn
  freeze_at: 0
  return_idx: [1,2,3]
  num_stages: 4

FPN:
  out_channel: 256
  spatial_scales: [0.125, 0.0625, 0.03125]
  extra_stage: 2
  has_extra_convs: true
  use_c5: false

LDGFLHead:   # new head
  conv_feat:
    name: FCOSFeat
    feat_in: 256
    feat_out: 256
    num_convs: 4
    norm_type: "gn"
    use_dcn: false
  fpn_stride: [8, 16, 32, 64, 128]
  prior_prob: 0.01
  reg_max: 16
  loss_class:
    name: QualityFocalLoss
    use_sigmoid: True
    beta: 2.0
    loss_weight: 1.0
  loss_dfl:
    name: DistributionFocalLoss
    loss_weight: 0.25
  loss_bbox:
    name: GIoULoss
    loss_weight: 2.0
  loss_ld:
    name: KnowledgeDistillationKLDivLoss
    loss_weight: 0.25
    T: 10
  loss_ld_vlr:
    name: KnowledgeDistillationKLDivLoss
    loss_weight: 0.25
    T: 10
  loss_kd:
    name: KnowledgeDistillationKLDivLoss
    loss_weight: 10
    T: 2
  nms:
    name: MultiClassNMS
    nms_top_k: 1000
    keep_top_k: 100
    score_threshold: 0.025
    nms_threshold: 0.6