# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv6 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/models/yolov6

# 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, 6, Conv, [1024, 3, 1]]
  - [-1, 1, SPPF, [1024, 5]]  # 9

# YOLOv6-3.0s head
head:
  - [-1, 1, Conv, [256, 1, 1]]
  - [-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]]  # 14

  - [-1, 1, Conv, [128, 1, 1]]
  - [-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]]  # 19

  - [-1, 1, Conv, [128, 3, 2]]
  - [[-1, 15], 1, Concat, [1]]  # cat head P4
  - [-1, 1, Conv, [256, 3, 1]]
  - [-1, 9, Conv, [256, 3, 1]]  # 23

  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 10], 1, Concat, [1]]  # cat head P5
  - [-1, 1, Conv, [512, 3, 1]]
  - [-1, 9, Conv, [512, 3, 1]]  # 27

  - [[19, 23, 27], 1, Detect, [nc]]  # Detect(P3, P4, P5)