# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

# Ultralytics YOLOE-v8 object detection model with P3/8 - P5/32 outputs
# Task docs: https://docs.ultralytics.com/tasks/detect

# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yoloe-v8n.yaml' will call yoloe-v8.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.33, 0.25, 1024] # YOLOE-v8n summary: 148 layers, 3695183 parameters, 3695167 gradients, 19.5 GFLOPs
  s: [0.33, 0.50, 1024] # YOLOE-v8s summary: 148 layers, 12759880 parameters, 12759864 gradients, 51.0 GFLOPs
  m: [0.67, 0.75, 768] # YOLOE-v8m summary: 188 layers, 28376158 parameters, 28376142 gradients, 110.5 GFLOPs
  l: [1.00, 1.00, 512] # YOLOE-v8l summary: 228 layers, 46832050 parameters, 46832034 gradients, 204.5 GFLOPs
  x: [1.00, 1.25, 512] # YOLOE-v8x summary: 228 layers, 72886377 parameters, 72886361 gradients, 309.3 GFLOPs

# YOLOv8.0n 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, [1024, 3, 2]] # 7-P5/32
  - [-1, 3, C2f, [1024, True]]
  - [-1, 1, SPPF, [1024, 5]] # 9

# YOLOv8.0n head
head:
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
  - [-1, 3, C2f, [512]] # 12

  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
  - [-1, 3, C2f, [256]] # 15 (P3/8-small)

  - [15, 1, Conv, [256, 3, 2]]
  - [[-1, 12], 1, Concat, [1]] # cat head P4
  - [-1, 3, C2f, [512]] # 18 (P4/16-medium)

  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 9], 1, Concat, [1]] # cat head P5
  - [-1, 3, C2f, [1024]] # 21 (P5/32-large)

  - [[15, 18, 21], 1, YOLOEDetect, [nc, 512, True]] # YOLOEDetect(P3, P4, P5)