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Copy path后端融合（双路）.yaml
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后端融合（双路）.yaml
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# 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
ch: 6
nc: 1 # 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, IN, []] # 0
- [-1, 1, Multiin, [1]] # 1
- [-2, 1, Multiin, [2]] # 2
- [-2, 1, Conv, [32, 3, 2]] # 3-P1/2
- [-2, 1, Conv, [32, 3, 2]]
- [-2, 1, Conv, [64, 3, 2]] # 5-P2/4
- [-2, 1, Conv, [64, 3, 2]]
- [-2, 3, C2f, [64, True]] # 7
- [-2, 3, C2f, [64, True]]
- [-2, 1, Conv, [128, 3, 2]] # 9-P3/8
- [-2, 1, Conv, [128, 3, 2]]
- [-2, 6, C2f, [128, True]] # 11
- [-2, 6, C2f, [128, True]]
- [ -2, 1, Conv, [ 256, 3, 2 ] ] # 13-P4/16
- [ -2, 1, Conv, [ 256, 3, 2 ] ]
- [ -2, 6, C2f, [ 256, True ] ] # 15
- [ -2, 6, C2f, [ 256, True ] ]
- [ -2, 1, Conv, [ 512, 3, 2 ] ] # 17-P5/32
- [ -2, 1, Conv, [ 512, 3, 2 ] ]
- [ -2, 3, C2f, [ 512, True ] ] # 19
- [ -2, 3, C2f, [ 512, True ] ]
- [-2, 1, SPPF, [512, 5]] # 21
- [-2, 1, SPPF, [512, 5]] # 22
# YOLOv8.0n head
head:
- [-2, 1, nn.Upsample, [None, 2, 'nearest']]
- [-2, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-2, 15], 1, Concat, [1]] # cat backbone P4
- [[-2, 16], 1, Concat, [1]] # cat backbone P4
- [-2, 3, C2f, [512]] # 27
- [-2, 3, C2f, [512]] # 28
- [-2, 1, nn.Upsample, [None, 2, 'nearest']]
- [-2, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-2, 9], 1, Concat, [1]] # cat backbone P3
- [[-2, 10], 1, Concat, [1]] # cat backbone P3
- [-2, 3, C2f, [256]] # 33 (P3/8-small)
- [-2, 3, C2f, [256]] # 34 (P3/8-small)
- [-2, 1, Conv, [256, 3, 2]]
- [-2, 1, Conv, [256, 3, 2]]
- [[-2, 27], 1, Concat, [1]] # cat head P4
- [[-2, 28], 1, Concat, [1]] # cat head P4
- [-2, 3, C2f, [512]] # 39 (P4/16-medium)
- [-2, 3, C2f, [512]] # 40 (P4/16-medium)
- [-2, 1, Conv, [512, 3, 2]]
- [-2, 1, Conv, [512, 3, 2]]
- [[-2, 21], 1, Concat, [1]] # cat backbone P5
- [[-2, 22], 1, Concat, [1]] # cat backbone P5
- [-2, 3, C2f, [1024]] # 45 (P5/32-large)
- [-2, 3, C2f, [1024]] # 46 (P5/32-large)
- [[33, 34, 39, 40, 45, 46], 1, Detect, [nc]] # Detect(P3, P4, P5)