architecture: PicoDet
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ESNet_x1_0_pretrained.pdparams
export_post_process: False    # Whether post-processing is included in the network when export model.

PicoDet:
  backbone: ESNet
  neck: CSPPAN
  head: PicoHead

ESNet:
  scale: 1.0
  feature_maps: [4, 11, 14]
  act: hard_swish
  channel_ratio: [0.875, 0.5, 1.0, 0.625, 0.5, 0.75, 0.625, 0.625, 0.5, 0.625, 1.0, 0.625, 0.75]

CSPPAN:
  out_channels: 128
  use_depthwise: True
  num_csp_blocks: 1
  num_features: 4

PicoHead:
  conv_feat:
    name: PicoFeat
    feat_in: 128
    feat_out: 128
    num_convs: 4
    num_fpn_stride: 4
    norm_type: bn
    share_cls_reg: True
  fpn_stride: [8, 16, 32, 64]
  feat_in_chan: 128
  prior_prob: 0.01
  reg_max: 7
  cell_offset: 0.5
  loss_class:
    name: VarifocalLoss
    use_sigmoid: True
    iou_weighted: True
    loss_weight: 1.0
  loss_dfl:
    name: DistributionFocalLoss
    loss_weight: 0.25
  loss_bbox:
    name: GIoULoss
    loss_weight: 2.0
  assigner:
    name: SimOTAAssigner
    candidate_topk: 10
    iou_weight: 6
  nms:
    name: MultiClassNMS
    nms_top_k: 1000
    keep_top_k: 100
    score_threshold: 0.025
    nms_threshold: 0.6