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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
- nc: 2 # 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 1.3G
- 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, Conv, [64, 3, 2]] # 0-P1/2
- - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
- - [-1, 3, C2f, [128, True]]
- - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- - [-1, 6, C2f, [256, True]]
- - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- - [-1, 6, C2f, [512, True]]
- - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- - [-1, 3, C2f, [1024, True]]
- - [-1, 1, SPPF, [1024, 5]] # 9
- # YOLOv8.0n head
- head:
- - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- - [[-1, 6], 1, Concat, [1]] # cat backbone P4
- - [-1, 3, C2f, [512]] # 12
- - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- - [[-1, 4], 1, Concat, [1]] # cat backbone P3
- - [-1, 3, C2f, [256]] # 15 (P3/8-small)
- - [-1, 1, Conv, [256, 3, 2]]
- - [[-1, 12], 1, Concat, [1]] # cat head P4
- - [-1, 3, C2f, [512]] # 18 (P4/16-medium)
- - [-1, 1, Conv, [512, 3, 2]]
- - [[-1, 9], 1, Concat, [1]] # cat head P5
- - [-1, 3, C2f, [1024]] # 21 (P5/32-large)
- - [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)
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