yolov5m_bdam.yaml

nc: 16  # number of classes
depth_multiple: 0.67  # model depth multiple
width_multiple: 0.75  # layer channel multiple

# anchors coco:24,9, 37,12, 52,15 64,23, 81,19, 98,29 137,27, 199,41, 342,65
anchors:
  - [10,5, 21,10, 38,16]  # P3/8
  - [65,23, 75,53, 167,33]  # P4/16
  - [178,93, 493,80, 486,271]  # P5/32

backbone:
  # [from, number, module, args]
  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
   [-1, 3, C3, [128]],
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 6, C3, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
   [-1, 9, C3, [512]],
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   [-1, 3, C3, [1024]],
   [-1, 1, SPPF, [1024, 5]],  # 9
  ]

# YOLOv5 head
head:
  [[-1, 1, BDAM, [32,32]],
   [-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [6, 1, BDAM, [64, 64]],
   [[-1, -2], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, BottleneckCSP, [512, False]],  # 15

   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [4, 1, BDAM, [128, 128]],
   [[-1, -2], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, BottleneckCSP, [256, False]],  # 20 (P3/8-small) ,output to detect layer

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

   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 11], 1, Concat, [1]],  # cat head P5
   [-1, 3, BottleneckCSP, [1024, False]],  # 26 (P5/32-large)

   [[20, 23, 26], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]