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Saeed Khosravi
2025-11-08 19:15:39 +01:00
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ultralytics/cfg/models/v8/yoloe-v8-seg.yaml Normal file
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # 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-world summary: 161 layers, 4204111 parameters, 4204095 gradients, 39.6 GFLOPs
s: [0.33, 0.50, 1024] # YOLOv8s-world summary: 161 layers, 13383496 parameters, 13383480 gradients, 71.5 GFLOPs
m: [0.67, 0.75, 768] # YOLOv8m-world summary: 201 layers, 29065310 parameters, 29065294 gradients, 131.4 GFLOPs
l: [1.00, 1.00, 512] # YOLOv8l-world summary: 241 layers, 47553970 parameters, 47553954 gradients, 225.6 GFLOPs
x: [1.00, 1.25, 512] # YOLOv8x-world summary: 241 layers, 73690217 parameters, 73690201 gradients, 330.8 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)
- [15, 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, YOLOESegment, [nc, 32, 256, 512, True]] # Segment(P3, P4, P5)

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ultralytics/cfg/models/v8/yoloe-v8.yaml Normal file
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # 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-worldv2 summary: 148 layers, 3695183 parameters, 3695167 gradients, 19.5 GFLOPS
s: [0.33, 0.50, 1024] # YOLOv8s-worldv2 summary: 148 layers, 12759880 parameters, 12759864 gradients, 51.0 GFLOPS
m: [0.67, 0.75, 768] # YOLOv8m-worldv2 summary: 188 layers, 28376158 parameters, 28376142 gradients, 110.5 GFLOPS
l: [1.00, 1.00, 512] # YOLOv8l-worldv2 summary: 228 layers, 46832050 parameters, 46832034 gradients, 204.5 GFLOPS
x: [1.00, 1.25, 512] # YOLOv8x-worldv2 summary: 228 layers, 72886377 parameters, 72886361 gradients, 309.3 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)
- [15, 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, YOLOEDetect, [nc, 512, True]] # Detect(P3, P4, P5)

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ultralytics/cfg/models/v8/yolov8-cls-resnet101.yaml Normal file
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv8-cls image classification model with ResNet101 backbone
# Model docs: https://docs.ultralytics.com/models/yolov8
# Task docs: https://docs.ultralytics.com/tasks/classify
# Parameters
nc: 1000 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.33, 0.25, 1024]
s: [0.33, 0.50, 1024]
m: [0.67, 0.75, 1024]
l: [1.00, 1.00, 1024]
x: [1.00, 1.25, 1024]
# YOLOv8.0n backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0-P1/2
- [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1-P2/4
- [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2-P3/8
- [-1, 1, ResNetLayer, [512, 256, 2, False, 23]] # 3-P4/16
- [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4-P5/32
# YOLOv8.0n head
head:
- [-1, 1, Classify, [nc]] # Classify

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ultralytics/cfg/models/v8/yolov8-cls-resnet50.yaml Normal file
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv8-cls image classification model with ResNet50 backbone
# Model docs: https://docs.ultralytics.com/models/yolov8
# Task docs: https://docs.ultralytics.com/tasks/classify
# Parameters
nc: 1000 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.33, 0.25, 1024]
s: [0.33, 0.50, 1024]
m: [0.67, 0.75, 1024]
l: [1.00, 1.00, 1024]
x: [1.00, 1.25, 1024]
# YOLOv8.0n backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0-P1/2
- [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1-P2/4
- [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2-P3/8
- [-1, 1, ResNetLayer, [512, 256, 2, False, 6]] # 3-P4/16
- [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4-P5/32
# YOLOv8.0n head
head:
- [-1, 1, Classify, [nc]] # Classify

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ultralytics/cfg/models/v8/yolov8-cls.yaml Normal file
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv8-cls image classification model with YOLO backbone
# Model docs: https://docs.ultralytics.com/models/yolov8
# Task docs: https://docs.ultralytics.com/tasks/classify
# Parameters
nc: 1000 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.33, 0.25, 1024]
s: [0.33, 0.50, 1024]
m: [0.67, 0.75, 1024]
l: [1.00, 1.00, 1024]
x: [1.00, 1.25, 1024]
# 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]]
# YOLOv8.0n head
head:
- [-1, 1, Classify, [nc]] # Classify

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ultralytics/cfg/models/v8/yolov8-ghost-p2.yaml Normal file
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv8 object detection model with P2/4 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov8
# Task docs: https://docs.ultralytics.com/tasks/detect
# Employs Ghost convolutions and modules proposed in Huawei's GhostNet in https://arxiv.org/abs/1911.11907v2
# Parameters
nc: 80 # 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-ghost-p2 summary: 290 layers, 2033944 parameters, 2033928 gradients, 13.8 GFLOPs
s: [0.33, 0.50, 1024] # YOLOv8s-ghost-p2 summary: 290 layers, 5562080 parameters, 5562064 gradients, 25.1 GFLOPs
m: [0.67, 0.75, 768] # YOLOv8m-ghost-p2 summary: 434 layers, 9031728 parameters, 9031712 gradients, 42.8 GFLOPs
l: [1.00, 1.00, 512] # YOLOv8l-ghost-p2 summary: 578 layers, 12214448 parameters, 12214432 gradients, 69.1 GFLOPs
x: [1.00, 1.25, 512] # YOLOv8x-ghost-p2 summary: 578 layers, 18664776 parameters, 18664760 gradients, 103.3 GFLOPs
# YOLOv8.0-ghost backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
- [-1, 1, GhostConv, [128, 3, 2]] # 1-P2/4
- [-1, 3, C3Ghost, [128, True]]
- [-1, 1, GhostConv, [256, 3, 2]] # 3-P3/8
- [-1, 6, C3Ghost, [256, True]]
- [-1, 1, GhostConv, [512, 3, 2]] # 5-P4/16
- [-1, 6, C3Ghost, [512, True]]
- [-1, 1, GhostConv, [1024, 3, 2]] # 7-P5/32
- [-1, 3, C3Ghost, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 9
# YOLOv8.0-ghost-p2 head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C3Ghost, [512]] # 12
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C3Ghost, [256]] # 15 (P3/8-small)
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 2], 1, Concat, [1]] # cat backbone P2
- [-1, 3, C3Ghost, [128]] # 18 (P2/4-xsmall)
- [-1, 1, GhostConv, [128, 3, 2]]
- [[-1, 15], 1, Concat, [1]] # cat head P3
- [-1, 3, C3Ghost, [256]] # 21 (P3/8-small)
- [-1, 1, GhostConv, [256, 3, 2]]
- [[-1, 12], 1, Concat, [1]] # cat head P4
- [-1, 3, C3Ghost, [512]] # 24 (P4/16-medium)
- [-1, 1, GhostConv, [512, 3, 2]]
- [[-1, 9], 1, Concat, [1]] # cat head P5
- [-1, 3, C3Ghost, [1024]] # 27 (P5/32-large)
- [[18, 21, 24, 27], 1, Detect, [nc]] # Detect(P2, P3, P4, P5)

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ultralytics/cfg/models/v8/yolov8-ghost-p6.yaml Normal file
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv8 object detection model with P3/8 - P6/64 outputs
# Model docs: https://docs.ultralytics.com/models/yolov8
# Task docs: https://docs.ultralytics.com/tasks/detect
# Employs Ghost convolutions and modules proposed in Huawei's GhostNet in https://arxiv.org/abs/1911.11907v2
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.33, 0.25, 1024] # YOLOv8n-ghost-p6 summary: 312 layers, 2901100 parameters, 2901084 gradients, 5.8 GFLOPs
s: [0.33, 0.50, 1024] # YOLOv8s-ghost-p6 summary: 312 layers, 9520008 parameters, 9519992 gradients, 16.4 GFLOPs
m: [0.67, 0.75, 768] # YOLOv8m-ghost-p6 summary: 468 layers, 18002904 parameters, 18002888 gradients, 34.4 GFLOPs
l: [1.00, 1.00, 512] # YOLOv8l-ghost-p6 summary: 624 layers, 21227584 parameters, 21227568 gradients, 55.3 GFLOPs
x: [1.00, 1.25, 512] # YOLOv8x-ghost-p6 summary: 624 layers, 33057852 parameters, 33057836 gradients, 85.7 GFLOPs
# YOLOv8.0-ghost backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
- [-1, 1, GhostConv, [128, 3, 2]] # 1-P2/4
- [-1, 3, C3Ghost, [128, True]]
- [-1, 1, GhostConv, [256, 3, 2]] # 3-P3/8
- [-1, 6, C3Ghost, [256, True]]
- [-1, 1, GhostConv, [512, 3, 2]] # 5-P4/16
- [-1, 6, C3Ghost, [512, True]]
- [-1, 1, GhostConv, [768, 3, 2]] # 7-P5/32
- [-1, 3, C3Ghost, [768, True]]
- [-1, 1, GhostConv, [1024, 3, 2]] # 9-P6/64
- [-1, 3, C3Ghost, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 11
# YOLOv8.0-ghost-p6 head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
- [-1, 3, C3Ghost, [768]] # 14
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C3Ghost, [512]] # 17
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C3Ghost, [256]] # 20 (P3/8-small)
- [-1, 1, GhostConv, [256, 3, 2]]
- [[-1, 17], 1, Concat, [1]] # cat head P4
- [-1, 3, C3Ghost, [512]] # 23 (P4/16-medium)
- [-1, 1, GhostConv, [512, 3, 2]]
- [[-1, 14], 1, Concat, [1]] # cat head P5
- [-1, 3, C3Ghost, [768]] # 26 (P5/32-large)
- [-1, 1, GhostConv, [768, 3, 2]]
- [[-1, 11], 1, Concat, [1]] # cat head P6
- [-1, 3, C3Ghost, [1024]] # 29 (P6/64-xlarge)
- [[20, 23, 26, 29], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv8 object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov8
# Task docs: https://docs.ultralytics.com/tasks/detect
# Employs Ghost convolutions and modules proposed in Huawei's GhostNet in https://arxiv.org/abs/1911.11907v2
# Parameters
nc: 80 # 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-ghost summary: 237 layers, 1865316 parameters, 1865300 gradients, 5.8 GFLOPs
s: [0.33, 0.50, 1024] # YOLOv8s-ghost summary: 237 layers, 5960072 parameters, 5960056 gradients, 16.4 GFLOPs
m: [0.67, 0.75, 768] # YOLOv8m-ghost summary: 357 layers, 10336312 parameters, 10336296 gradients, 32.7 GFLOPs
l: [1.00, 1.00, 512] # YOLOv8l-ghost summary: 477 layers, 14277872 parameters, 14277856 gradients, 53.7 GFLOPs
x: [1.00, 1.25, 512] # YOLOv8x-ghost summary: 477 layers, 22229308 parameters, 22229292 gradients, 83.3 GFLOPs
# YOLOv8.0n-ghost backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
- [-1, 1, GhostConv, [128, 3, 2]] # 1-P2/4
- [-1, 3, C3Ghost, [128, True]]
- [-1, 1, GhostConv, [256, 3, 2]] # 3-P3/8
- [-1, 6, C3Ghost, [256, True]]
- [-1, 1, GhostConv, [512, 3, 2]] # 5-P4/16
- [-1, 6, C3Ghost, [512, True]]
- [-1, 1, GhostConv, [1024, 3, 2]] # 7-P5/32
- [-1, 3, C3Ghost, [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, C3Ghost, [512]] # 12
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C3Ghost, [256]] # 15 (P3/8-small)
- [-1, 1, GhostConv, [256, 3, 2]]
- [[-1, 12], 1, Concat, [1]] # cat head P4
- [-1, 3, C3Ghost, [512]] # 18 (P4/16-medium)
- [-1, 1, GhostConv, [512, 3, 2]]
- [[-1, 9], 1, Concat, [1]] # cat head P5
- [-1, 3, C3Ghost, [1024]] # 21 (P5/32-large)
- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv8-obb Oriented Bounding Boxes (OBB) model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov8
# Task docs: https://docs.ultralytics.com/tasks/obb
# Parameters
nc: 80 # 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-obb summary: 144 layers, 3228867 parameters, 3228851 gradients, 9.1 GFLOPs
s: [0.33, 0.50, 1024] # YOLOv8s-obb summary: 144 layers, 11452739 parameters, 11452723 gradients, 29.8 GFLOPs
m: [0.67, 0.75, 768] # YOLOv8m-obb summary: 184 layers, 26463235 parameters, 26463219 gradients, 81.5 GFLOPs
l: [1.00, 1.00, 512] # YOLOv8l-obb summary: 224 layers, 44540355 parameters, 44540339 gradients, 169.4 GFLOPs
x: [1.00, 1.25, 512] # YOLOv8x-obb summary: 224 layers, 69555651 parameters, 69555635 gradients, 264.3 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, OBB, [nc, 1]] # OBB(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv8 object detection model with P2/4 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov8
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # 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]
s: [0.33, 0.50, 1024]
m: [0.67, 0.75, 768]
l: [1.00, 1.00, 512]
x: [1.00, 1.25, 512]
# YOLOv8.0 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
# YOLOv8.0-p2 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, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 2], 1, Concat, [1]] # cat backbone P2
- [-1, 3, C2f, [128]] # 18 (P2/4-xsmall)
- [-1, 1, Conv, [128, 3, 2]]
- [[-1, 15], 1, Concat, [1]] # cat head P3
- [-1, 3, C2f, [256]] # 21 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 12], 1, Concat, [1]] # cat head P4
- [-1, 3, C2f, [512]] # 24 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 9], 1, Concat, [1]] # cat head P5
- [-1, 3, C2f, [1024]] # 27 (P5/32-large)
- [[18, 21, 24, 27], 1, Detect, [nc]] # Detect(P2, P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv8 object detection model with P3/8 - P6/64 outputs
# Model docs: https://docs.ultralytics.com/models/yolov8
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.33, 0.25, 1024] # YOLOv8n-p6 summary: 170 layers, 4984352 parameters, 4984336 gradients, 8.8 GFLOPs
s: [0.33, 0.50, 1024] # YOLOv8s-p6 summary: 170 layers, 17911792 parameters, 17911776 gradients, 28.7 GFLOPs
m: [0.67, 0.75, 768] # YOLOv8m-p6 summary: 222 layers, 44887488 parameters, 44887472 gradients, 83.5 GFLOPs
l: [1.00, 1.00, 512] # YOLOv8l-p6 summary: 274 layers, 62384016 parameters, 62384000 gradients, 167.9 GFLOPs
x: [1.00, 1.25, 512] # YOLOv8x-p6 summary: 274 layers, 97423072 parameters, 97423056 gradients, 261.8 GFLOPs
# YOLOv8.0x6 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, [768, 3, 2]] # 7-P5/32
- [-1, 3, C2f, [768, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
- [-1, 3, C2f, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 11
# YOLOv8.0x6 head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
- [-1, 3, C2, [768, False]] # 14
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C2, [512, False]] # 17
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2, [256, False]] # 20 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 17], 1, Concat, [1]] # cat head P4
- [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 14], 1, Concat, [1]] # cat head P5
- [-1, 3, C2, [768, False]] # 26 (P5/32-large)
- [-1, 1, Conv, [768, 3, 2]]
- [[-1, 11], 1, Concat, [1]] # cat head P6
- [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
- [[20, 23, 26, 29], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv8-pose keypoints/pose estimation model with P3/8 - P6/64 outputs
# Model docs: https://docs.ultralytics.com/models/yolov8
# Task docs: https://docs.ultralytics.com/tasks/pose
# Parameters
nc: 1 # number of classes
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.33, 0.25, 1024]
s: [0.33, 0.50, 1024]
m: [0.67, 0.75, 768]
l: [1.00, 1.00, 512]
x: [1.00, 1.25, 512]
# YOLOv8.0x6 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, [768, 3, 2]] # 7-P5/32
- [-1, 3, C2f, [768, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
- [-1, 3, C2f, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 11
# YOLOv8.0x6 head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
- [-1, 3, C2, [768, False]] # 14
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C2, [512, False]] # 17
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2, [256, False]] # 20 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 17], 1, Concat, [1]] # cat head P4
- [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 14], 1, Concat, [1]] # cat head P5
- [-1, 3, C2, [768, False]] # 26 (P5/32-large)
- [-1, 1, Conv, [768, 3, 2]]
- [[-1, 11], 1, Concat, [1]] # cat head P6
- [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
- [[20, 23, 26, 29], 1, Pose, [nc, kpt_shape]] # Pose(P3, P4, P5, P6)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv8-pose keypoints/pose estimation model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov8
# Task docs: https://docs.ultralytics.com/tasks/pose
# Parameters
nc: 1 # number of classes
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
scales: # model compound scaling constants, i.e. 'model=yolov8n-pose.yaml' will call yolov8-pose.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.33, 0.25, 1024]
s: [0.33, 0.50, 1024]
m: [0.67, 0.75, 768]
l: [1.00, 1.00, 512]
x: [1.00, 1.25, 512]
# 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, Pose, [nc, kpt_shape]] # Pose(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv8-RTDETR hybrid object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/rtdetr
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # 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-rtdetr summary: 235 layers, 9643868 parameters, 9643868 gradients, 17.1 GFLOPs
s: [0.33, 0.50, 1024] # YOLOv8s-rtdetr summary: 235 layers, 16518572 parameters, 16518572 gradients, 32.8 GFLOPs
m: [0.67, 0.75, 768] # YOLOv8m-rtdetr summary: 275 layers, 29645180 parameters, 29645180 gradients, 75.8 GFLOPs
l: [1.00, 1.00, 512] # YOLOv8l-rtdetr summary: 315 layers, 45644364 parameters, 45644364 gradients, 152.3 GFLOPs
x: [1.00, 1.25, 512] # YOLOv8x-rtdetr summary: 315 layers, 67113884 parameters, 67113884 gradients, 230.8 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, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv8-seg instance segmentation model with P3/8 - P6/64 outputs
# Model docs: https://docs.ultralytics.com/models/yolov8
# Task docs: https://docs.ultralytics.com/tasks/segment
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n-seg-p6.yaml' will call yolov8-seg-p6.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.33, 0.25, 1024]
s: [0.33, 0.50, 1024]
m: [0.67, 0.75, 768]
l: [1.00, 1.00, 512]
x: [1.00, 1.25, 512]
# YOLOv8.0x6 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, [768, 3, 2]] # 7-P5/32
- [-1, 3, C2f, [768, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
- [-1, 3, C2f, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 11
# YOLOv8.0x6 head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
- [-1, 3, C2, [768, False]] # 14
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C2, [512, False]] # 17
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2, [256, False]] # 20 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 17], 1, Concat, [1]] # cat head P4
- [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 14], 1, Concat, [1]] # cat head P5
- [-1, 3, C2, [768, False]] # 26 (P5/32-large)
- [-1, 1, Conv, [768, 3, 2]]
- [[-1, 11], 1, Concat, [1]] # cat head P6
- [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
- [[20, 23, 26, 29], 1, Segment, [nc, 32, 256]] # Pose(P3, P4, P5, P6)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv8-seg instance segmentation model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov8
# Task docs: https://docs.ultralytics.com/tasks/segment
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n-seg.yaml' will call yolov8-seg.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.33, 0.25, 1024]
s: [0.33, 0.50, 1024]
m: [0.67, 0.75, 768]
l: [1.00, 1.00, 512]
x: [1.00, 1.25, 512]
# 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, Segment, [nc, 32, 256]] # Segment(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv8-World hybrid object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolo-world
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # 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-world summary: 161 layers, 4204111 parameters, 4204095 gradients, 39.6 GFLOPs
s: [0.33, 0.50, 1024] # YOLOv8s-world summary: 161 layers, 13383496 parameters, 13383480 gradients, 71.5 GFLOPs
m: [0.67, 0.75, 768] # YOLOv8m-world summary: 201 layers, 29065310 parameters, 29065294 gradients, 131.4 GFLOPs
l: [1.00, 1.00, 512] # YOLOv8l-world summary: 241 layers, 47553970 parameters, 47553954 gradients, 225.6 GFLOPs
x: [1.00, 1.25, 512] # YOLOv8x-world summary: 241 layers, 73690217 parameters, 73690201 gradients, 330.8 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, C2fAttn, [512, 256, 8]] # 12
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2fAttn, [256, 128, 4]] # 15 (P3/8-small)
- [[15, 12, 9], 1, ImagePoolingAttn, [256]] # 16 (P3/8-small)
- [15, 1, Conv, [256, 3, 2]]
- [[-1, 12], 1, Concat, [1]] # cat head P4
- [-1, 3, C2fAttn, [512, 256, 8]] # 19 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 9], 1, Concat, [1]] # cat head P5
- [-1, 3, C2fAttn, [1024, 512, 16]] # 22 (P5/32-large)
- [[15, 19, 22], 1, WorldDetect, [nc, 512, False]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv8-Worldv2 hybrid object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolo-world
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # 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-worldv2 summary: 148 layers, 3695183 parameters, 3695167 gradients, 19.5 GFLOPS
s: [0.33, 0.50, 1024] # YOLOv8s-worldv2 summary: 148 layers, 12759880 parameters, 12759864 gradients, 51.0 GFLOPS
m: [0.67, 0.75, 768] # YOLOv8m-worldv2 summary: 188 layers, 28376158 parameters, 28376142 gradients, 110.5 GFLOPS
l: [1.00, 1.00, 512] # YOLOv8l-worldv2 summary: 228 layers, 46832050 parameters, 46832034 gradients, 204.5 GFLOPS
x: [1.00, 1.25, 512] # YOLOv8x-worldv2 summary: 228 layers, 72886377 parameters, 72886361 gradients, 309.3 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, C2fAttn, [512, 256, 8]] # 12
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2fAttn, [256, 128, 4]] # 15 (P3/8-small)
- [15, 1, Conv, [256, 3, 2]]
- [[-1, 12], 1, Concat, [1]] # cat head P4
- [-1, 3, C2fAttn, [512, 256, 8]] # 18 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 9], 1, Concat, [1]] # cat head P5
- [-1, 3, C2fAttn, [1024, 512, 16]] # 21 (P5/32-large)
- [[15, 18, 21], 1, WorldDetect, [nc, 512, True]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv8 object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov8
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # 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: 129 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPS
s: [0.33, 0.50, 1024] # YOLOv8s summary: 129 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPS
m: [0.67, 0.75, 768] # YOLOv8m summary: 169 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPS
l: [1.00, 1.00, 512] # YOLOv8l summary: 209 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPS
x: [1.00, 1.25, 512] # YOLOv8x summary: 209 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)