yolov5s_shufflenet.yaml

# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license

# Parameters
nc: 6  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.25  # 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

backbone:
  # [from, number, module, args]
  [[-1, 1, ShuffleNetV2, []],  # 0-4-P1/
   [-1, 1, SPPF, [1024, 5]],  # 5
    #[-1,1,deformable_LKA,[]],
  # [-1,1,LKA,[1024]],
  ]

# YOLOv5 v6.0 head
head:
  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 3], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, C3, [512, False]],  # 9
   #[-1, 3, VoVGSCSP, [512, False]],  # 9

   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 2], 1, Concat, [1]],  # cat backbone P3
   [-1,1,split_DLKA,[]],
   [-1, 3, C3, [256, False]],  # 13 (P3/8-small)
   #[-1,1,DLKA_1,[]],
     #14
   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 9], 1, Concat, [1]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 16 (P4/16-medium)
   #[-1,1,DLKA_1,[]],
   [-1, 1, Conv, [512, 3, 2]],
    #[-1,1,DLKA_1,[]],
   [[-1, 5], 1, Concat, [1]],  # cat head P5
   
   [-1, 3, C3, [1024, False]],  # 19 (P5/32-large)
   #[-1,1,DLKA_1,[]],


   
    #[-1,1,res2DF_LKA,[]],
   [[14, 17, 20], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]