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

# Ultralytics YOLO26-cls image classification model
# Model docs: https://docs.ultralytics.com/models/yolo26
# Task docs: https://docs.ultralytics.com/tasks/classify

# Parameters
nc: 1000 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo26n-cls.yaml' will call yolo26-cls.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.50, 0.25, 1024] # summary: 86 layers, 2,812,104 parameters, 2,812,104 gradients, 0.5 GFLOPs
  s: [0.50, 0.50, 1024] # summary: 86 layers, 6,724,008 parameters, 6,724,008 gradients, 1.6 GFLOPs
  m: [0.50, 1.00, 512] # summary: 106 layers, 11,634,216 parameters, 11,634,216 gradients, 5.0 GFLOPs
  l: [1.00, 1.00, 512] # summary: 176 layers, 14,115,624 parameters, 14,115,624 gradients, 6.2 GFLOPs
  x: [1.00, 1.50, 512] # summary: 176 layers, 29,637,064 parameters, 29,637,064 gradients, 13.7 GFLOPs

# YOLO26n 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, 2, C3k2, [256, False, 0.25]]
  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
  - [-1, 2, C3k2, [512, False, 0.25]]
  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
  - [-1, 2, C3k2, [512, True]]
  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
  - [-1, 2, C3k2, [1024, True]]
  - [-1, 2, C2PSA, [1024]] # 9

# YOLO26n head
head:
  - [-1, 1, Classify, [nc]] # Classify