yolov6_cspbep.yml

architecture: YOLOv6
norm_type: sync_bn
use_ema: True
ema_decay: 0.9999
ema_decay_type: "exponential"
find_unused_parameters: True

act: 'relu' # 'silu' in L model
training_mode: "repvgg"
self_distill: False

depth_mult: 1.0 # default: L model
width_mult: 1.0

YOLOv6:
  backbone: CSPBepBackbone
  neck: CSPRepBiFPAN
  yolo_head: EffiDeHead_fuseab
  post_process: ~

CSPBepBackbone:
  arch: 'P5'
  return_idx: [2, 3, 4]
  csp_e: 0.5
  fuse_P2: True # add P2 and return 4 layers
  cspsppf: False

CSPRepBiFPAN:
  csp_e: 0.5

EffiDeHead_fuseab:
  fpn_strides: [8, 16, 32]
  grid_cell_scale: 5.0
  grid_cell_offset: 0.5
  reg_max: 16
  use_dfl: True # in m/l version
  static_assigner_epoch: 3 # warmup_epoch
  loss_weight: {cls: 1.0, iou: 2.5, dfl: 0.5, cwd: 10.0}
  iou_type: 'giou'  # 'siou' in n/t version, 'giou' in s/m/l version
  distill_weight: {cls: 1.0, dfl: 1.0}
  static_assigner:
    name: ATSSAssigner
    topk: 9
  assigner:
    name: TaskAlignedAssigner
    topk: 13
    alpha: 1.0
    beta: 6.0
  nms:
    name: MultiClassNMS
    nms_top_k: 3000
    keep_top_k: 300
    score_threshold: 0.03
    nms_threshold: 0.65

EffiDeHead:
  fpn_strides: [8, 16, 32]
  grid_cell_scale: 5.0
  grid_cell_offset: 0.5
  reg_max: 16
  use_dfl: True # in m/l version
  static_assigner_epoch: 3 # warmup_epoch
  loss_weight: {cls: 1.0, iou: 2.5, dfl: 0.5, cwd: 10.0}
  iou_type: 'giou'  # 'siou' in n/t version, 'giou' in s/m/l version
  distill_weight: {cls: 1.0, dfl: 1.0}
  static_assigner:
    name: ATSSAssigner
    topk: 9
  assigner:
    name: TaskAlignedAssigner
    topk: 13
    alpha: 1.0
    beta: 6.0
  nms:
    name: MultiClassNMS
    nms_top_k: 3000
    keep_top_k: 300
    score_threshold: 0.03
    nms_threshold: 0.65