YOLOv8-16bit/ultralytics/models/v6/yolov6.yaml

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv6 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect

# Parameters
act: nn.ReLU()
nc: 80  # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov6n.yaml' will call yolov8.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [ 0.33, 0.25, 1024 ]
  s: [ 0.33, 0.50, 1024 ]
  m: [ 0.67, 0.75, 768 ]
  l: [ 1.00, 1.00, 512 ]
  x: [ 1.00, 1.25, 512 ]

# YOLOv6-3.0s backbone
backbone:
  # [from, repeats, module, args]
  - [ -1, 1, Conv, [ 64, 3, 2 ] ]  # 0-P1/2
  - [ -1, 1, Conv, [ 128, 3, 2 ] ]  # 1-P2/4
  - [ -1, 6, Conv, [ 128, 3, 1 ] ]
  - [ -1, 1, Conv, [ 256, 3, 2 ] ]  # 3-P3/8
  - [ -1, 12, Conv, [ 256, 3, 1 ] ]
  - [ -1, 1, Conv, [ 512, 3, 2 ] ]  # 5-P4/16
  - [ -1, 18, Conv, [ 512, 3, 1 ] ]
  - [ -1, 1, Conv, [ 1024, 3, 2 ] ]  # 7-P5/32
  - [ -1, 9, Conv, [ 1024, 3, 1 ] ]
  - [ -1, 1, SPPF, [ 1024, 5 ] ]  # 9

# YOLOv6-3.0s head
head:
  - [ -1, 1, nn.ConvTranspose2d, [ 256, 2, 2, 0 ] ]
  - [ [ -1, 6 ], 1, Concat, [ 1 ] ]  # cat backbone P4
  - [ -1, 1, Conv, [ 256, 3, 1 ] ]
  - [ -1, 9, Conv, [ 256, 3, 1 ] ]  # 13

  - [ -1, 1, nn.ConvTranspose2d, [ 128, 2, 2, 0 ] ]
  - [ [ -1, 4 ], 1, Concat, [ 1 ] ]  # cat backbone P3
  - [ -1, 1, Conv, [ 128, 3, 1 ] ]
  - [ -1, 9, Conv, [ 128, 3, 1 ] ]  # 17

  - [ -1, 1, Conv, [ 128, 3, 2 ] ]
  - [ [ -1, 12 ], 1, Concat, [ 1 ] ]  # cat head P4
  - [ -1, 1, Conv, [ 256, 3, 1 ] ]
  - [ -1, 9, Conv, [ 256, 3, 1 ] ]  # 21

  - [ -1, 1, Conv, [ 256, 3, 2 ] ]
  - [ [ -1, 9 ], 1, Concat, [ 1 ] ]  # cat head P5
  - [ -1, 1, Conv, [ 512, 3, 1 ] ]
  - [ -1, 9, Conv, [ 512, 3, 1 ] ]  # 25

  - [ [ 17, 21, 25 ], 1, Detect, [ nc ] ]  # Detect(P3, P4, P5)