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54 lines
1.7 KiB
54 lines
1.7 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license
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# YOLOv6 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/models/yolov6
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# Parameters
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nc: 80 # number of classes
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activation: nn.ReLU() # (optional) model default activation function
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scales: # model compound scaling constants, i.e. 'model=yolov6n.yaml' will call yolov8.yaml with scale 'n'
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# [depth, width, max_channels]
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n: [0.33, 0.25, 1024]
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s: [0.33, 0.50, 1024]
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m: [0.67, 0.75, 768]
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l: [1.00, 1.00, 512]
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x: [1.00, 1.25, 512]
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# YOLOv6-3.0s backbone
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backbone:
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# [from, repeats, module, args]
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- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
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- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
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- [-1, 6, Conv, [128, 3, 1]]
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- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
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- [-1, 12, Conv, [256, 3, 1]]
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- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
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- [-1, 18, Conv, [512, 3, 1]]
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- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
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- [-1, 6, Conv, [1024, 3, 1]]
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- [-1, 1, SPPF, [1024, 5]] # 9
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# YOLOv6-3.0s head
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head:
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- [-1, 1, Conv, [256, 1, 1]]
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- [-1, 1, nn.ConvTranspose2d, [256, 2, 2, 0]]
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- [[-1, 6], 1, Concat, [1]] # cat backbone P4
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- [-1, 1, Conv, [256, 3, 1]]
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- [-1, 9, Conv, [256, 3, 1]] # 14
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- [-1, 1, Conv, [128, 1, 1]]
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- [-1, 1, nn.ConvTranspose2d, [128, 2, 2, 0]]
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- [[-1, 4], 1, Concat, [1]] # cat backbone P3
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- [-1, 1, Conv, [128, 3, 1]]
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- [-1, 9, Conv, [128, 3, 1]] # 19
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- [-1, 1, Conv, [128, 3, 2]]
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- [[-1, 15], 1, Concat, [1]] # cat head P4
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- [-1, 1, Conv, [256, 3, 1]]
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- [-1, 9, Conv, [256, 3, 1]] # 23
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- [-1, 1, Conv, [256, 3, 2]]
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- [[-1, 10], 1, Concat, [1]] # cat head P5
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- [-1, 1, Conv, [512, 3, 1]]
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- [-1, 9, Conv, [512, 3, 1]] # 27
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- [[19, 23, 27], 1, Detect, [nc]] # Detect(P3, P4, P5)
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