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47 lines
1.9 KiB
47 lines
1.9 KiB
2 years ago
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
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# Parameters
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nc: 80 # number of classes
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scales: # model compound scaling constants, i.e. 'model=yolov8n.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] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
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s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
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m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
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l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
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x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
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# YOLOv8.0n 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, 3, C2f, [128, True]]
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- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
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- [-1, 6, C2f, [256, True]]
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- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
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- [-1, 6, C2f, [512, True]]
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- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
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- [-1, 3, C2f, [1024, True]]
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- [-1, 1, SPPF, [1024, 5]] # 9
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# YOLOv8.0n head
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head:
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- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
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- [[-1, 6], 1, Concat, [1]] # cat backbone P4
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- [-1, 3, C2f, [512]] # 12
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- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
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- [[-1, 4], 1, Concat, [1]] # cat backbone P3
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- [-1, 3, C2f, [256]] # 15 (P3/8-small)
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- [-1, 1, Conv, [256, 3, 2]]
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- [[-1, 12], 1, Concat, [1]] # cat head P4
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- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
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- [-1, 1, Conv, [512, 3, 2]]
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- [[-1, 9], 1, Concat, [1]] # cat head P5
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- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
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- [[15, 18, 21], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
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