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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, 2, C3k3, [256, True, 1, 0.25]] |
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- [-1, 1, ADown, [256]] # 3-P3/8 |
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- [-1, 2, C3k3, [512, True, 1, 0.25]] |
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- [-1, 1, ADown, [512]] # 5-P4/16 |
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- [-1, 2, C3k3, [512, True]] |
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- [-1, 1, ADown, [512]] # 7-P5/32 |
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- [-1, 2, C3k3, [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, 2, C3k3, [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, 2, C3k3, [256]] # 15 (P3/8-small) |
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- [-1, 1, ADown, [256]] |
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- [[-1, 12], 1, Concat, [1]] # cat head P4 |
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- [-1, 2, C3k3, [512]] # 18 (P4/16-medium) |
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- [-1, 1, ADown, [512]] |
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- [[-1, 9], 1, Concat, [1]] # cat head P5 |
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- [-1, 2, C3k3, [1024]] # 21 (P5/32-large) |
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- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5) |
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