yolov3.yaml

# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license

# Parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
anchors:
  - [10, 13, 16, 30, 33, 23] # P3/8
  - [30, 61, 62, 45, 59, 119] # P4/16
  - [116, 90, 156, 198, 373, 326] # P5/32

# darknet53 backbone
backbone:
  # [from, number, module, args]
  [
    [-1, 1, Conv, [32, 3, 1]], # 0
    [-1, 1, Conv, [64, 3, 2]], # 1-P1/2
    [-1, 1, Bottleneck, [64]],
    [-1, 1, Conv, [128, 3, 2]], # 3-P2/4
    [-1, 2, Bottleneck, [128]],
    [-1, 1, Conv, [256, 3, 2]], # 5-P3/8
    [-1, 8, Bottleneck, [256]],
    [-1, 1, Conv, [512, 3, 2]], # 7-P4/16
    [-1, 8, Bottleneck, [512]],
    [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
    [-1, 4, Bottleneck, [1024]], # 10
  ]

# YOLOv3 head
head: [
    [-1, 1, Bottleneck, [1024, False]],
    [-1, 1, Conv, [512, 1, 1]],
    [-1, 1, Conv, [1024, 3, 1]],
    [-1, 1, Conv, [512, 1, 1]],
    [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)

    [-2, 1, Conv, [256, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
    [[-1, 8], 1, Concat, [1]], # cat backbone P4
    [-1, 1, Bottleneck, [512, False]],
    [-1, 1, Bottleneck, [512, False]],
    [-1, 1, Conv, [256, 1, 1]],
    [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)

    [-2, 1, Conv, [128, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, "nearest"]],
    [[-1, 6], 1, Concat, [1]], # cat backbone P3
    [-1, 1, Bottleneck, [256, False]],
    [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)

    [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
  ]