yolov8-mobileone.yaml

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect

# Parameters
nc: 80  # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.33, 0.25, 1024]  # YOLOv8n summary: 225 layers,  3157200 parameters,  3157184 gradients,   8.9 GFLOPs
  s: [0.33, 0.50, 1024]  # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPs
  m: [0.67, 0.75, 768]   # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPs
  l: [1.00, 1.00, 512]   # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
  x: [1.00, 1.25, 512]   # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs

# YOLOv8.0n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, MobileOneBackbone, ['s0']]
  - [0, 1, FeatureLayerExtractor, [0, 48]]
  - [0, 1, FeatureLayerExtractor, [1, 48]]
  - [0, 1, FeatureLayerExtractor, [2, 128]]
  - [0, 1, FeatureLayerExtractor, [3, 256]]
  - [0, 1, FeatureLayerExtractor, [4, 1024]]

# YOLOv8.0n head
head:
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 4], 1, Concat, [1]]  # cat backbone P4
  - [-1, 3, C2f, [1280, 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)

  # - [-1, 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)

  - [[3, 8], 1, Detect, [nc]]  # Detect(P3, P4, P5)