How can I apply CBAM (C3TR Layer) at YOLOv5x6.yaml ?

[star4s]

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Question

How can I apply CBAM (C3TR Layer) at YOLOv5x6.yaml ?

Can you make YOLOv5x6_CBAM.yaml ?

YOLOv5x6.yaml

parameters

nc: 80 # number of classes depth_multiple: 1.33 # model depth multiple width_multiple: 1.25 # layer channel multiple

anchors

anchors:

• [ 19,27, 44,40, 38,94 ] # P3/8
• [ 96,68, 86,152, 180,137 ] # P4/16
• [ 140,301, 303,264, 238,542 ] # P5/32
• [ 436,615, 739,380, 925,792 ] # P6/64

YOLOv5 backbone

backbone:

[from, number, module, args]

[ [ -1, 1, Focus, [ 64, 3 ] ], # 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, 9, C3, [ 256 ] ], [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 [ -1, 9, C3, [ 512 ] ], [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 [ -1, 3, C3, [ 768 ] ], [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ], [ -1, 3, C3, [ 1024, False ] ], # 11 ]

YOLOv5 head

head: [ [ -1, 1, Conv, [ 768, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 [ -1, 3, C3, [ 768, False ] ], # 15

[ -1, 1, Conv, [ 512, 1, 1 ] ],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
[ -1, 3, C3, [ 512, False ] ],  # 19

[ -1, 1, Conv, [ 256, 1, 1 ] ],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
[ -1, 3, C3, [ 256, False ] ],  # 23 (P3/8-small)

[ -1, 1, Conv, [ 256, 3, 2 ] ],
[ [ -1, 20 ], 1, Concat, [ 1 ] ],  # cat head P4
[ -1, 3, C3, [ 512, False ] ],  # 26 (P4/16-medium)

[ -1, 1, Conv, [ 512, 3, 2 ] ],
[ [ -1, 16 ], 1, Concat, [ 1 ] ],  # cat head P5
[ -1, 3, C3, [ 768, False ] ],  # 29 (P5/32-large)

[ -1, 1, Conv, [ 768, 3, 2 ] ],
[ [ -1, 12 ], 1, Concat, [ 1 ] ],  # cat head P6
[ -1, 3, C3, [ 1024, False ] ],  # 32 (P6/64-xlarge)

[ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5, P6)

]

Additional

No response

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[glenn-jocher]

@star4s you can apply the CBAM layer to the YOLOv5x6.yaml file by updating the backbone and head sections with the CBAM layer. You may need to modify the network architecture and parameters to incorporate the CBAM layer effectively. Creating a YOLOv5x6_CBAM.yaml file requires custom implementation and is not currently available out-of-the-box. For detailed guidance on integrating the CBAM layer, please refer to the YOLOv5 docs at https://docs.ultralytics.com/yolov5/ and consider reaching out to the YOLO community for additional support. Best of luck with your implementation!