# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8-pose keypoints/pose estimation model. For Usage examples see https://docs.ultralytics.com/tasks/pose

# Parameters
nc: 1  # number of classes
kpt_shape: [17, 3]  # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.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]

# YOLOv8.0x6 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, 3, C2f, [128, True]]
  - [-1, 1, Conv, [256, 3, 2]]  # 3-P3/8
  - [-1, 6, C2f, [256, True]]
  - [-1, 1, Conv, [512, 3, 2]]  # 5-P4/16
  - [-1, 6, C2f, [512, True]]
  - [-1, 1, Conv, [768, 3, 2]]  # 7-P5/32
  - [-1, 3, C2f, [768, True]]
  - [-1, 1, Conv, [1024, 3, 2]]  # 9-P6/64
  - [-1, 3, C2f, [1024, True]]
  - [-1, 1, SPPF, [1024, 5]]  # 11

# YOLOv8.0x6 head
head:
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 8], 1, Concat, [1]]  # cat backbone P5
  - [-1, 3, C2, [768, False]]  # 14

  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 6], 1, Concat, [1]]  # cat backbone P4
  - [-1, 3, C2, [512, False]]  # 17

  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 4], 1, Concat, [1]]  # cat backbone P3
  - [-1, 3, C2, [256, False]]  # 20 (P3/8-small)

  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 17], 1, Concat, [1]]  # cat head P4
  - [-1, 3, C2, [512, False]]  # 23 (P4/16-medium)

  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 14], 1, Concat, [1]]  # cat head P5
  - [-1, 3, C2, [768, False]]  # 26 (P5/32-large)

  - [-1, 1, Conv, [768, 3, 2]]
  - [[-1, 11], 1, Concat, [1]]  # cat head P6
  - [-1, 3, C2, [1024, False]]  # 29 (P6/64-xlarge)

  - [[20, 23, 26, 29], 1, Pose, [nc, kpt_shape]]  # Pose(P3, P4, P5, P6)