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Saeed Khosravi
2025-11-08 19:15:39 +01:00
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ultralytics/cfg/models/11/yolo11-cls-resnet18.yaml Normal file
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLO11-cls image classification model with ResNet18 backbone
# Model docs: https://docs.ultralytics.com/models/yolo11
# Task docs: https://docs.ultralytics.com/tasks/classify
# Parameters
nc: 1000 # number of classes
# ResNet18 backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, TorchVision, [512, resnet18, DEFAULT, True, 2]] # truncate two layers from the end
# YOLO11n head
head:
- [-1, 1, Classify, [nc]] # Classify

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ultralytics/cfg/models/11/yolo11-cls.yaml Normal file
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLO11-cls image classification model
# Model docs: https://docs.ultralytics.com/models/yolo11
# Task docs: https://docs.ultralytics.com/tasks/classify
# Parameters
nc: 1000 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo11n-cls.yaml' will call yolo11-cls.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.50, 0.25, 1024] # summary: 86 layers, 1633584 parameters, 1633584 gradients, 0.5 GFLOPs
s: [0.50, 0.50, 1024] # summary: 86 layers, 5545488 parameters, 5545488 gradients, 1.6 GFLOPs
m: [0.50, 1.00, 512] # summary: 106 layers, 10455696 parameters, 10455696 gradients, 5.0 GFLOPs
l: [1.00, 1.00, 512] # summary: 176 layers, 12937104 parameters, 12937104 gradients, 6.2 GFLOPs
x: [1.00, 1.50, 512] # summary: 176 layers, 28458544 parameters, 28458544 gradients, 13.7 GFLOPs
# YOLO11n 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, 2, C3k2, [256, False, 0.25]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 2, C3k2, [512, False, 0.25]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 2, C3k2, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 2, C3k2, [1024, True]]
- [-1, 2, C2PSA, [1024]] # 9
# YOLO11n head
head:
- [-1, 1, Classify, [nc]] # Classify

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ultralytics/cfg/models/11/yolo11-obb.yaml Normal file
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLO11-obb Oriented Bounding Boxes (OBB) model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolo11
# Task docs: https://docs.ultralytics.com/tasks/obb
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo11n-obb.yaml' will call yolo11-obb.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.50, 0.25, 1024] # summary: 196 layers, 2695747 parameters, 2695731 gradients, 6.9 GFLOPs
s: [0.50, 0.50, 1024] # summary: 196 layers, 9744931 parameters, 9744915 gradients, 22.7 GFLOPs
m: [0.50, 1.00, 512] # summary: 246 layers, 20963523 parameters, 20963507 gradients, 72.2 GFLOPs
l: [1.00, 1.00, 512] # summary: 372 layers, 26220995 parameters, 26220979 gradients, 91.3 GFLOPs
x: [1.00, 1.50, 512] # summary: 372 layers, 58875331 parameters, 58875315 gradients, 204.3 GFLOPs
# YOLO11n 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, 2, C3k2, [256, False, 0.25]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 2, C3k2, [512, False, 0.25]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 2, C3k2, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 2, C3k2, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 9
- [-1, 2, C2PSA, [1024]] # 10
# YOLO11n head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 2, C3k2, [512, False]] # 13
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 13], 1, Concat, [1]] # cat head P4
- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 10], 1, Concat, [1]] # cat head P5
- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
- [[16, 19, 22], 1, OBB, [nc, 1]] # Detect(P3, P4, P5)

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ultralytics/cfg/models/11/yolo11-pose.yaml Normal file
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLO11-pose keypoints/pose estimation model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolo11
# Task docs: https://docs.ultralytics.com/tasks/pose
# Parameters
nc: 80 # 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=yolo11n-pose.yaml' will call yolo11.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.50, 0.25, 1024] # summary: 196 layers, 2908507 parameters, 2908491 gradients, 7.7 GFLOPs
s: [0.50, 0.50, 1024] # summary: 196 layers, 9948811 parameters, 9948795 gradients, 23.5 GFLOPs
m: [0.50, 1.00, 512] # summary: 246 layers, 20973273 parameters, 20973257 gradients, 72.3 GFLOPs
l: [1.00, 1.00, 512] # summary: 372 layers, 26230745 parameters, 26230729 gradients, 91.4 GFLOPs
x: [1.00, 1.50, 512] # summary: 372 layers, 58889881 parameters, 58889865 gradients, 204.3 GFLOPs
# YOLO11n 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, 2, C3k2, [256, False, 0.25]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 2, C3k2, [512, False, 0.25]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 2, C3k2, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 2, C3k2, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 9
- [-1, 2, C2PSA, [1024]] # 10
# YOLO11n head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 2, C3k2, [512, False]] # 13
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 13], 1, Concat, [1]] # cat head P4
- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 10], 1, Concat, [1]] # cat head P5
- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
- [[16, 19, 22], 1, Pose, [nc, kpt_shape]] # Detect(P3, P4, P5)

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ultralytics/cfg/models/11/yolo11-seg.yaml Normal file
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLO11-seg instance segmentation model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolo11
# Task docs: https://docs.ultralytics.com/tasks/segment
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo11n-seg.yaml' will call yolo11-seg.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.50, 0.25, 1024] # summary: 203 layers, 2876848 parameters, 2876832 gradients, 10.5 GFLOPs
s: [0.50, 0.50, 1024] # summary: 203 layers, 10113248 parameters, 10113232 gradients, 35.8 GFLOPs
m: [0.50, 1.00, 512] # summary: 253 layers, 22420896 parameters, 22420880 gradients, 123.9 GFLOPs
l: [1.00, 1.00, 512] # summary: 379 layers, 27678368 parameters, 27678352 gradients, 143.0 GFLOPs
x: [1.00, 1.50, 512] # summary: 379 layers, 62142656 parameters, 62142640 gradients, 320.2 GFLOPs
# YOLO11n 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, 2, C3k2, [256, False, 0.25]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 2, C3k2, [512, False, 0.25]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 2, C3k2, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 2, C3k2, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 9
- [-1, 2, C2PSA, [1024]] # 10
# YOLO11n head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 2, C3k2, [512, False]] # 13
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 13], 1, Concat, [1]] # cat head P4
- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 10], 1, Concat, [1]] # cat head P5
- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
- [[16, 19, 22], 1, Segment, [nc, 32, 256]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLO11 object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolo11
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.50, 0.25, 1024] # summary: 181 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
s: [0.50, 0.50, 1024] # summary: 181 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
m: [0.50, 1.00, 512] # summary: 231 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
l: [1.00, 1.00, 512] # summary: 357 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
x: [1.00, 1.50, 512] # summary: 357 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs
# YOLO11n 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, 2, C3k2, [256, False, 0.25]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 2, C3k2, [512, False, 0.25]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 2, C3k2, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 2, C3k2, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 9
- [-1, 2, C2PSA, [1024]] # 10
# YOLO11n head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 2, C3k2, [512, False]] # 13
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 13], 1, Concat, [1]] # cat head P4
- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 10], 1, Concat, [1]] # cat head P5
- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
- [[16, 19, 22], 1, Detect, [nc]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# YOLO11-seg instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo11n-seg.yaml' will call yolo11-seg.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.50, 0.25, 1024] # summary: 355 layers, 2876848 parameters, 2876832 gradients, 10.5 GFLOPs
s: [0.50, 0.50, 1024] # summary: 355 layers, 10113248 parameters, 10113232 gradients, 35.8 GFLOPs
m: [0.50, 1.00, 512] # summary: 445 layers, 22420896 parameters, 22420880 gradients, 123.9 GFLOPs
l: [1.00, 1.00, 512] # summary: 667 layers, 27678368 parameters, 27678352 gradients, 143.0 GFLOPs
x: [1.00, 1.50, 512] # summary: 667 layers, 62142656 parameters, 62142640 gradients, 320.2 GFLOPs
# YOLO11n 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, 2, C3k2, [256, False, 0.25]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 2, C3k2, [512, False, 0.25]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 2, C3k2, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 2, C3k2, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 9
- [-1, 2, C2PSA, [1024]] # 10
# YOLO11n head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 2, C3k2, [512, False]] # 13
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 13], 1, Concat, [1]] # cat head P4
- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 10], 1, Concat, [1]] # cat head P5
- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
- [[16, 19, 22], 1, YOLOESegment, [nc, 32, 256, 512, True]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
x: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs
# YOLO11n 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, 2, C3k2, [256, False, 0.25]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 2, C3k2, [512, False, 0.25]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 2, C3k2, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 2, C3k2, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 9
- [-1, 2, C2PSA, [1024]] # 10
# YOLO11n head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 2, C3k2, [512, False]] # 13
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 13], 1, Concat, [1]] # cat head P4
- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 10], 1, Concat, [1]] # cat head P5
- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
- [[16, 19, 22], 1, YOLOEDetect, [nc, 512, True]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# YOLO12-cls image classification model
# Model docs: https://docs.ultralytics.com/models/yolo12
# Task docs: https://docs.ultralytics.com/tasks/classify
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo12n-cls.yaml' will call yolo12-cls.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.50, 0.25, 1024] # summary: 152 layers, 1,820,976 parameters, 1,820,976 gradients, 3.7 GFLOPs
s: [0.50, 0.50, 1024] # summary: 152 layers, 6,206,992 parameters, 6,206,992 gradients, 13.6 GFLOPs
m: [0.50, 1.00, 512] # summary: 172 layers, 12,083,088 parameters, 12,083,088 gradients, 44.2 GFLOPs
l: [1.00, 1.00, 512] # summary: 312 layers, 15,558,640 parameters, 15,558,640 gradients, 56.9 GFLOPs
x: [1.00, 1.50, 512] # summary: 312 layers, 34,172,592 parameters, 34,172,592 gradients, 126.5 GFLOPs
# YOLO12n 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, 2, C3k2, [256, False, 0.25]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 2, C3k2, [512, False, 0.25]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 4, A2C2f, [512, True, 4]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 4, A2C2f, [1024, True, 1]] # 8
# YOLO12n head
head:
- [-1, 1, Classify, [nc]] # Classify

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# YOLO12-obb Oriented Bounding Boxes (OBB) model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolo12
# Task docs: https://docs.ultralytics.com/tasks/obb
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo12n-obb.yaml' will call yolo12-obb.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.50, 0.25, 1024] # summary: 287 layers, 2,673,955 parameters, 2,673,939 gradients, 6.9 GFLOPs
s: [0.50, 0.50, 1024] # summary: 287 layers, 9,570,275 parameters, 9,570,259 gradients, 22.7 GFLOPs
m: [0.50, 1.00, 512] # summary: 307 layers, 21,048,003 parameters, 21,047,987 gradients, 71.8 GFLOPs
l: [1.00, 1.00, 512] # summary: 503 layers, 27,299,619 parameters, 27,299,603 gradients, 93.4 GFLOPs
x: [1.00, 1.50, 512] # summary: 503 layers, 61,119,939 parameters, 61,119,923 gradients, 208.6 GFLOPs
# YOLO12n 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, 2, C3k2, [256, False, 0.25]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 2, C3k2, [512, False, 0.25]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 4, A2C2f, [512, True, 4]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 4, A2C2f, [1024, True, 1]] # 8
# YOLO12n head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 2, A2C2f, [512, False, -1]] # 11
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 2, A2C2f, [256, False, -1]] # 14
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 11], 1, Concat, [1]] # cat head P4
- [-1, 2, A2C2f, [512, False, -1]] # 17
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 8], 1, Concat, [1]] # cat head P5
- [-1, 2, C3k2, [1024, True]] # 20 (P5/32-large)
- [[14, 17, 20], 1, OBB, [nc, 1]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# YOLO12-pose keypoints/pose estimation model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolo12
# Task docs: https://docs.ultralytics.com/tasks/pose
# Parameters
nc: 80 # 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=yolo12n-pose.yaml' will call yolo12-pose.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.50, 0.25, 1024] # summary: 287 layers, 2,886,715 parameters, 2,886,699 gradients, 7.8 GFLOPs
s: [0.50, 0.50, 1024] # summary: 287 layers, 9,774,155 parameters, 9,774,139 gradients, 23.5 GFLOPs
m: [0.50, 1.00, 512] # summary: 307 layers, 21,057,753 parameters, 21,057,737 gradients, 71.8 GFLOPs
l: [1.00, 1.00, 512] # summary: 503 layers, 27,309,369 parameters, 27,309,353 gradients, 93.5 GFLOPs
x: [1.00, 1.50, 512] # summary: 503 layers, 61,134,489 parameters, 61,134,473 gradients, 208.7 GFLOPs
# YOLO12n 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, 2, C3k2, [256, False, 0.25]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 2, C3k2, [512, False, 0.25]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 4, A2C2f, [512, True, 4]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 4, A2C2f, [1024, True, 1]] # 8
# YOLO12n head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 2, A2C2f, [512, False, -1]] # 11
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 2, A2C2f, [256, False, -1]] # 14
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 11], 1, Concat, [1]] # cat head P4
- [-1, 2, A2C2f, [512, False, -1]] # 17
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 8], 1, Concat, [1]] # cat head P5
- [-1, 2, C3k2, [1024, True]] # 20 (P5/32-large)
- [[14, 17, 20], 1, Pose, [nc, kpt_shape]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# YOLO12-seg instance segmentation model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolo12
# Task docs: https://docs.ultralytics.com/tasks/segment
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo12n-seg.yaml' will call yolo12-seg.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.50, 0.25, 1024] # summary: 294 layers, 2,855,056 parameters, 2,855,040 gradients, 10.6 GFLOPs
s: [0.50, 0.50, 1024] # summary: 294 layers, 9,938,592 parameters, 9,938,576 gradients, 35.7 GFLOPs
m: [0.50, 1.00, 512] # summary: 314 layers, 22,505,376 parameters, 22,505,360 gradients, 123.5 GFLOPs
l: [1.00, 1.00, 512] # summary: 510 layers, 28,756,992 parameters, 28,756,976 gradients, 145.1 GFLOPs
x: [1.00, 1.50, 512] # summary: 510 layers, 64,387,264 parameters, 64,387,248 gradients, 324.6 GFLOPs
# YOLO12n 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, 2, C3k2, [256, False, 0.25]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 2, C3k2, [512, False, 0.25]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 4, A2C2f, [512, True, 4]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 4, A2C2f, [1024, True, 1]] # 8
# YOLO12n head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 2, A2C2f, [512, False, -1]] # 11
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 2, A2C2f, [256, False, -1]] # 14
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 11], 1, Concat, [1]] # cat head P4
- [-1, 2, A2C2f, [512, False, -1]] # 17
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 8], 1, Concat, [1]] # cat head P5
- [-1, 2, C3k2, [1024, True]] # 20 (P5/32-large)
- [[14, 17, 20], 1, Segment, [nc, 32, 256]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# YOLO12 object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolo12
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo12n.yaml' will call yolo12.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.50, 0.25, 1024] # summary: 272 layers, 2,602,288 parameters, 2,602,272 gradients, 6.7 GFLOPs
s: [0.50, 0.50, 1024] # summary: 272 layers, 9,284,096 parameters, 9,284,080 gradients, 21.7 GFLOPs
m: [0.50, 1.00, 512] # summary: 292 layers, 20,199,168 parameters, 20,199,152 gradients, 68.1 GFLOPs
l: [1.00, 1.00, 512] # summary: 488 layers, 26,450,784 parameters, 26,450,768 gradients, 89.7 GFLOPs
x: [1.00, 1.50, 512] # summary: 488 layers, 59,210,784 parameters, 59,210,768 gradients, 200.3 GFLOPs
# YOLO12n 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, 2, C3k2, [256, False, 0.25]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 2, C3k2, [512, False, 0.25]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 4, A2C2f, [512, True, 4]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 4, A2C2f, [1024, True, 1]] # 8
# YOLO12n head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 2, A2C2f, [512, False, -1]] # 11
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 2, A2C2f, [256, False, -1]] # 14
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 11], 1, Concat, [1]] # cat head P4
- [-1, 2, A2C2f, [512, False, -1]] # 17
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 8], 1, Concat, [1]] # cat head P5
- [-1, 2, C3k2, [1024, True]] # 20 (P5/32-large)
- [[14, 17, 20], 1, Detect, [nc]] # Detect(P3, P4, P5)

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<a href="https://www.ultralytics.com/" target="_blank"><img src="https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Original.svg" width="320" alt="Ultralytics logo"></a>
# Ultralytics Model Configurations
Welcome to the [Ultralytics](https://www.ultralytics.com/) Models configuration directory! This directory contains a comprehensive collection of pre-configured model configuration files (`*.yaml`). These files serve as blueprints for creating custom [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo11/) models, meticulously crafted and fine-tuned by the Ultralytics team. Our goal is to provide optimal performance across a diverse range of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks, including [object detection](https://docs.ultralytics.com/tasks/detect/), [image segmentation](https://docs.ultralytics.com/tasks/segment/), pose estimation, and [object tracking](https://docs.ultralytics.com/modes/track/).
These configurations cater to various scenarios and are engineered for efficiency, running smoothly on different hardware platforms, from standard [CPUs](https://en.wikipedia.org/wiki/Central_processing_unit) to powerful [GPUs](https://www.ultralytics.com/glossary/gpu-graphics-processing-unit). Whether you're an experienced [machine learning](https://en.wikipedia.org/wiki/Machine_learning) practitioner or new to the YOLO ecosystem, this directory offers an excellent starting point for your custom model development journey.
To begin, explore the models within this directory and select one that aligns with your project requirements. You can then use the corresponding `*.yaml` file (learn more about the [YAML format](https://www.ultralytics.com/glossary/yaml)) to [train](https://docs.ultralytics.com/modes/train/) and deploy your custom YOLO model effortlessly. For detailed guidance, refer to the Ultralytics [Documentation](https://docs.ultralytics.com/), and don't hesitate to reach out to the community via [GitHub Issues](https://github.com/ultralytics/ultralytics/issues) if you need support. Start building your custom YOLO model today!
## 🚀 Usage
Model `*.yaml` configuration files can be directly utilized in the [Command Line Interface (CLI)](https://docs.ultralytics.com/usage/cli/) using the `yolo` command:
```bash
# Train a YOLO11n detection model using the coco8 dataset for 100 epochs
yolo task=detect mode=train model=yolo11n.yaml data=coco8.yaml epochs=100 imgsz=640
```
These files are [Python](https://www.python.org/)-compatible, accepting the same [configuration arguments](https://docs.ultralytics.com/usage/cfg/) as shown in the CLI example:
```python
from ultralytics import YOLO
# Initialize a YOLO11n model from a YAML configuration file
# This creates a model architecture without loading pre-trained weights
model = YOLO("yolo11n.yaml")
# Alternatively, load a pre-trained YOLO11n model directly
# This loads both the architecture and the weights trained on COCO
# model = YOLO("yolo11n.pt")
# Display model information (architecture, layers, parameters, etc.)
model.info()
# Train the model using the COCO8 dataset (a small subset of COCO) for 100 epochs
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
# Run inference with the trained model on an image
results = model("path/to/image.jpg")
```
## 🏗️ Pre-trained Model Architectures
Ultralytics supports a variety of cutting-edge model architectures. Visit the [Ultralytics Models](https://docs.ultralytics.com/models/) documentation page for in-depth information and usage examples for each model, including:
- [YOLO12](https://docs.ultralytics.com/models/yolo12/)
- [YOLO11](https://docs.ultralytics.com/models/yolo11/)
- [YOLOv10](https://docs.ultralytics.com/models/yolov10/)
- [YOLOv9](https://docs.ultralytics.com/models/yolov9/)
- [YOLOv8](https://docs.ultralytics.com/models/yolov8/)
- [YOLOv5](https://docs.ultralytics.com/models/yolov5/)
- [And more...](https://docs.ultralytics.com/models/)
You can easily use any of these models by loading their configuration files (`.yaml`) or their [pre-trained](https://docs.pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) checkpoints (`.pt`).
## 🤝 Contribute New Models
Have you developed a novel YOLO variant, experimented with a unique architecture, or achieved state-of-the-art results through specific tuning? We encourage you to share your innovations with the community by contributing to our Models section! Contributions like new model configurations, architectural improvements, or performance optimizations are highly valuable and help enrich the Ultralytics ecosystem.
Sharing your work here allows others to benefit from your insights and expands the range of available model choices. It's an excellent way to showcase your expertise and make the Ultralytics YOLO platform even more versatile and powerful.
To contribute, please review our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) for detailed instructions on submitting a [Pull Request (PR)](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests) 🛠️. We eagerly await your contributions!
Let's collaborate to enhance the capabilities and diversity of the Ultralytics YOLO models 🙏!

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics RT-DETR-l 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-cls.yaml' will call yolov8-cls.yaml with scale 'n'
# [depth, width, max_channels]
l: [1.00, 1.00, 1024]
backbone:
# [from, repeats, module, args]
- [-1, 1, HGStem, [32, 48]] # 0-P2/4
- [-1, 6, HGBlock, [48, 128, 3]] # stage 1
- [-1, 1, DWConv, [128, 3, 2, 1, False]] # 2-P3/8
- [-1, 6, HGBlock, [96, 512, 3]] # stage 2
- [-1, 1, DWConv, [512, 3, 2, 1, False]] # 4-P3/16
- [-1, 6, HGBlock, [192, 1024, 5, True, False]] # cm, c2, k, light, shortcut
- [-1, 6, HGBlock, [192, 1024, 5, True, True]]
- [-1, 6, HGBlock, [192, 1024, 5, True, True]] # stage 3
- [-1, 1, DWConv, [1024, 3, 2, 1, False]] # 8-P4/32
- [-1, 6, HGBlock, [384, 2048, 5, True, False]] # stage 4
head:
- [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 10 input_proj.2
- [-1, 1, AIFI, [1024, 8]]
- [-1, 1, Conv, [256, 1, 1]] # 12, Y5, lateral_convs.0
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [7, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14 input_proj.1
- [[-2, -1], 1, Concat, [1]]
- [-1, 3, RepC3, [256]] # 16, fpn_blocks.0
- [-1, 1, Conv, [256, 1, 1]] # 17, Y4, lateral_convs.1
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 19 input_proj.0
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
- [-1, 3, RepC3, [256]] # X3 (21), fpn_blocks.1
- [-1, 1, Conv, [256, 3, 2]] # 22, downsample_convs.0
- [[-1, 17], 1, Concat, [1]] # cat Y4
- [-1, 3, RepC3, [256]] # F4 (24), pan_blocks.0
- [-1, 1, Conv, [256, 3, 2]] # 25, downsample_convs.1
- [[-1, 12], 1, Concat, [1]] # cat Y5
- [-1, 3, RepC3, [256]] # F5 (27), pan_blocks.1
- [[21, 24, 27], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics RT-DETR-ResNet101 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-cls.yaml' will call yolov8-cls.yaml with scale 'n'
# [depth, width, max_channels]
l: [1.00, 1.00, 1024]
backbone:
# [from, repeats, module, args]
- [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0
- [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1
- [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2
- [-1, 1, ResNetLayer, [512, 256, 2, False, 23]] # 3
- [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4
head:
- [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 5
- [-1, 1, AIFI, [1024, 8]]
- [-1, 1, Conv, [256, 1, 1]] # 7
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 9
- [[-2, -1], 1, Concat, [1]]
- [-1, 3, RepC3, [256]] # 11
- [-1, 1, Conv, [256, 1, 1]] # 12
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [2, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
- [-1, 3, RepC3, [256]] # X3 (16), fpn_blocks.1
- [-1, 1, Conv, [256, 3, 2]] # 17, downsample_convs.0
- [[-1, 12], 1, Concat, [1]] # cat Y4
- [-1, 3, RepC3, [256]] # F4 (19), pan_blocks.0
- [-1, 1, Conv, [256, 3, 2]] # 20, downsample_convs.1
- [[-1, 7], 1, Concat, [1]] # cat Y5
- [-1, 3, RepC3, [256]] # F5 (22), pan_blocks.1
- [[16, 19, 22], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics RT-DETR-ResNet50 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-cls.yaml' will call yolov8-cls.yaml with scale 'n'
# [depth, width, max_channels]
l: [1.00, 1.00, 1024]
backbone:
# [from, repeats, module, args]
- [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0
- [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1
- [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2
- [-1, 1, ResNetLayer, [512, 256, 2, False, 6]] # 3
- [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4
head:
- [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 5
- [-1, 1, AIFI, [1024, 8]]
- [-1, 1, Conv, [256, 1, 1]] # 7
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 9
- [[-2, -1], 1, Concat, [1]]
- [-1, 3, RepC3, [256]] # 11
- [-1, 1, Conv, [256, 1, 1]] # 12
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [2, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
- [-1, 3, RepC3, [256]] # X3 (16), fpn_blocks.1
- [-1, 1, Conv, [256, 3, 2]] # 17, downsample_convs.0
- [[-1, 12], 1, Concat, [1]] # cat Y4
- [-1, 3, RepC3, [256]] # F4 (19), pan_blocks.0
- [-1, 1, Conv, [256, 3, 2]] # 20, downsample_convs.1
- [[-1, 7], 1, Concat, [1]] # cat Y5
- [-1, 3, RepC3, [256]] # F5 (22), pan_blocks.1
- [[16, 19, 22], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics RT-DETR-x 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-cls.yaml' will call yolov8-cls.yaml with scale 'n'
# [depth, width, max_channels]
x: [1.00, 1.00, 2048]
backbone:
# [from, repeats, module, args]
- [-1, 1, HGStem, [32, 64]] # 0-P2/4
- [-1, 6, HGBlock, [64, 128, 3]] # stage 1
- [-1, 1, DWConv, [128, 3, 2, 1, False]] # 2-P3/8
- [-1, 6, HGBlock, [128, 512, 3]]
- [-1, 6, HGBlock, [128, 512, 3, False, True]] # 4-stage 2
- [-1, 1, DWConv, [512, 3, 2, 1, False]] # 5-P3/16
- [-1, 6, HGBlock, [256, 1024, 5, True, False]] # cm, c2, k, light, shortcut
- [-1, 6, HGBlock, [256, 1024, 5, True, True]]
- [-1, 6, HGBlock, [256, 1024, 5, True, True]]
- [-1, 6, HGBlock, [256, 1024, 5, True, True]]
- [-1, 6, HGBlock, [256, 1024, 5, True, True]] # 10-stage 3
- [-1, 1, DWConv, [1024, 3, 2, 1, False]] # 11-P4/32
- [-1, 6, HGBlock, [512, 2048, 5, True, False]]
- [-1, 6, HGBlock, [512, 2048, 5, True, True]] # 13-stage 4
head:
- [-1, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 14 input_proj.2
- [-1, 1, AIFI, [2048, 8]]
- [-1, 1, Conv, [384, 1, 1]] # 16, Y5, lateral_convs.0
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [10, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 18 input_proj.1
- [[-2, -1], 1, Concat, [1]]
- [-1, 3, RepC3, [384]] # 20, fpn_blocks.0
- [-1, 1, Conv, [384, 1, 1]] # 21, Y4, lateral_convs.1
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [4, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 23 input_proj.0
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
- [-1, 3, RepC3, [384]] # X3 (25), fpn_blocks.1
- [-1, 1, Conv, [384, 3, 2]] # 26, downsample_convs.0
- [[-1, 21], 1, Concat, [1]] # cat Y4
- [-1, 3, RepC3, [384]] # F4 (28), pan_blocks.0
- [-1, 1, Conv, [384, 3, 2]] # 29, downsample_convs.1
- [[-1, 16], 1, Concat, [1]] # cat Y5
- [-1, 3, RepC3, [384]] # F5 (31), pan_blocks.1
- [[25, 28, 31], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# YOLOv10b object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov10
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov10n.yaml' will call yolov10.yaml with scale 'n'
# [depth, width, max_channels]
b: [0.67, 1.00, 512]
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, SCDown, [512, 3, 2]] # 5-P4/16
- [-1, 6, C2f, [512, True]]
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
- [-1, 3, C2fCIB, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 9
- [-1, 1, PSA, [1024]] # 10
# YOLOv10.0n head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C2fCIB, [512, True]] # 13
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 13], 1, Concat, [1]] # cat head P4
- [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
- [-1, 1, SCDown, [512, 3, 2]]
- [[-1, 10], 1, Concat, [1]] # cat head P5
- [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# YOLOv10l object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov10
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov10n.yaml' will call yolov10.yaml with scale 'n'
# [depth, width, max_channels]
l: [1.00, 1.00, 512]
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, SCDown, [512, 3, 2]] # 5-P4/16
- [-1, 6, C2f, [512, True]]
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
- [-1, 3, C2fCIB, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 9
- [-1, 1, PSA, [1024]] # 10
# YOLOv10.0n head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C2fCIB, [512, True]] # 13
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 13], 1, Concat, [1]] # cat head P4
- [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
- [-1, 1, SCDown, [512, 3, 2]]
- [[-1, 10], 1, Concat, [1]] # cat head P5
- [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# YOLOv10m object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov10
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov10n.yaml' will call yolov10.yaml with scale 'n'
# [depth, width, max_channels]
m: [0.67, 0.75, 768]
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, SCDown, [512, 3, 2]] # 5-P4/16
- [-1, 6, C2f, [512, True]]
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
- [-1, 3, C2fCIB, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 9
- [-1, 1, PSA, [1024]] # 10
# YOLOv10.0n head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C2f, [512]] # 13
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 13], 1, Concat, [1]] # cat head P4
- [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
- [-1, 1, SCDown, [512, 3, 2]]
- [[-1, 10], 1, Concat, [1]] # cat head P5
- [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# YOLOv10n object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov10
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov10n.yaml' will call yolov10.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.33, 0.25, 1024]
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, SCDown, [512, 3, 2]] # 5-P4/16
- [-1, 6, C2f, [512, True]]
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
- [-1, 3, C2f, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 9
- [-1, 1, PSA, [1024]] # 10
# YOLOv10.0n head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C2f, [512]] # 13
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 13], 1, Concat, [1]] # cat head P4
- [-1, 3, C2f, [512]] # 19 (P4/16-medium)
- [-1, 1, SCDown, [512, 3, 2]]
- [[-1, 10], 1, Concat, [1]] # cat head P5
- [-1, 3, C2fCIB, [1024, True, True]] # 22 (P5/32-large)
- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# YOLOv10s object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov10
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov10n.yaml' will call yolov10.yaml with scale 'n'
# [depth, width, max_channels]
s: [0.33, 0.50, 1024]
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, SCDown, [512, 3, 2]] # 5-P4/16
- [-1, 6, C2f, [512, True]]
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
- [-1, 3, C2fCIB, [1024, True, True]]
- [-1, 1, SPPF, [1024, 5]] # 9
- [-1, 1, PSA, [1024]] # 10
# YOLOv10.0n head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C2f, [512]] # 13
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 13], 1, Concat, [1]] # cat head P4
- [-1, 3, C2f, [512]] # 19 (P4/16-medium)
- [-1, 1, SCDown, [512, 3, 2]]
- [[-1, 10], 1, Concat, [1]] # cat head P5
- [-1, 3, C2fCIB, [1024, True, True]] # 22 (P5/32-large)
- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# YOLOv10x object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov10
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov10n.yaml' will call yolov10.yaml with scale 'n'
# [depth, width, max_channels]
x: [1.00, 1.25, 512]
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, SCDown, [512, 3, 2]] # 5-P4/16
- [-1, 6, C2fCIB, [512, True]]
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
- [-1, 3, C2fCIB, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 9
- [-1, 1, PSA, [1024]] # 10
# YOLOv10.0n head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C2fCIB, [512, True]] # 13
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 13], 1, Concat, [1]] # cat head P4
- [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
- [-1, 1, SCDown, [512, 3, 2]]
- [[-1, 10], 1, Concat, [1]] # cat head P5
- [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv3-SPP object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov3
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# darknet53 backbone
backbone:
# [from, number, module, args]
- [-1, 1, Conv, [32, 3, 1]] # 0
- [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
- [-1, 1, Bottleneck, [64]]
- [-1, 1, Conv, [128, 3, 2]] # 3-P2/4
- [-1, 2, Bottleneck, [128]]
- [-1, 1, Conv, [256, 3, 2]] # 5-P3/8
- [-1, 8, Bottleneck, [256]]
- [-1, 1, Conv, [512, 3, 2]] # 7-P4/16
- [-1, 8, Bottleneck, [512]]
- [-1, 1, Conv, [1024, 3, 2]] # 9-P5/32
- [-1, 4, Bottleneck, [1024]] # 10
# YOLOv3-SPP head
head:
- [-1, 1, Bottleneck, [1024, False]]
- [-1, 1, SPP, [512, [5, 9, 13]]]
- [-1, 1, Conv, [1024, 3, 1]]
- [-1, 1, Conv, [512, 1, 1]]
- [-1, 1, Conv, [1024, 3, 1]] # 15 (P5/32-large)
- [-2, 1, Conv, [256, 1, 1]]
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 8], 1, Concat, [1]] # cat backbone P4
- [-1, 1, Bottleneck, [512, False]]
- [-1, 1, Bottleneck, [512, False]]
- [-1, 1, Conv, [256, 1, 1]]
- [-1, 1, Conv, [512, 3, 1]] # 22 (P4/16-medium)
- [-2, 1, Conv, [128, 1, 1]]
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P3
- [-1, 1, Bottleneck, [256, False]]
- [-1, 2, Bottleneck, [256, False]] # 27 (P3/8-small)
- [[27, 22, 15], 1, Detect, [nc]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv3-tiiny object detection model with P4/16 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov3
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# YOLOv3-tiny backbone
backbone:
# [from, number, module, args]
- [-1, 1, Conv, [16, 3, 1]] # 0
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 1-P1/2
- [-1, 1, Conv, [32, 3, 1]]
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 3-P2/4
- [-1, 1, Conv, [64, 3, 1]]
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 5-P3/8
- [-1, 1, Conv, [128, 3, 1]]
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 7-P4/16
- [-1, 1, Conv, [256, 3, 1]]
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 9-P5/32
- [-1, 1, Conv, [512, 3, 1]]
- [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]] # 11
- [-1, 1, nn.MaxPool2d, [2, 1, 0]] # 12
# YOLOv3-tiny head
head:
- [-1, 1, Conv, [1024, 3, 1]]
- [-1, 1, Conv, [256, 1, 1]]
- [-1, 1, Conv, [512, 3, 1]] # 15 (P5/32-large)
- [-2, 1, Conv, [128, 1, 1]]
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 8], 1, Concat, [1]] # cat backbone P4
- [-1, 1, Conv, [256, 3, 1]] # 19 (P4/16-medium)
- [[19, 15], 1, Detect, [nc]] # Detect(P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv3 object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov3
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# darknet53 backbone
backbone:
# [from, number, module, args]
- [-1, 1, Conv, [32, 3, 1]] # 0
- [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
- [-1, 1, Bottleneck, [64]]
- [-1, 1, Conv, [128, 3, 2]] # 3-P2/4
- [-1, 2, Bottleneck, [128]]
- [-1, 1, Conv, [256, 3, 2]] # 5-P3/8
- [-1, 8, Bottleneck, [256]]
- [-1, 1, Conv, [512, 3, 2]] # 7-P4/16
- [-1, 8, Bottleneck, [512]]
- [-1, 1, Conv, [1024, 3, 2]] # 9-P5/32
- [-1, 4, Bottleneck, [1024]] # 10
# YOLOv3 head
head:
- [-1, 1, Bottleneck, [1024, False]]
- [-1, 1, Conv, [512, 1, 1]]
- [-1, 1, Conv, [1024, 3, 1]]
- [-1, 1, Conv, [512, 1, 1]]
- [-1, 1, Conv, [1024, 3, 1]] # 15 (P5/32-large)
- [-2, 1, Conv, [256, 1, 1]]
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 8], 1, Concat, [1]] # cat backbone P4
- [-1, 1, Bottleneck, [512, False]]
- [-1, 1, Bottleneck, [512, False]]
- [-1, 1, Conv, [256, 1, 1]]
- [-1, 1, Conv, [512, 3, 1]] # 22 (P4/16-medium)
- [-2, 1, Conv, [128, 1, 1]]
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P3
- [-1, 1, Bottleneck, [256, False]]
- [-1, 2, Bottleneck, [256, False]] # 27 (P3/8-small)
- [[27, 22, 15], 1, Detect, [nc]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv5 object detection model with P3/8 - P6/64 outputs
# Model docs: https://docs.ultralytics.com/models/yolov5
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov5n-p6.yaml' will call yolov5-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, 1024]
l: [1.00, 1.00, 1024]
x: [1.33, 1.25, 1024]
# YOLOv5 v6.0 backbone
backbone:
# [from, number, module, args]
- [-1, 1, Conv, [64, 6, 2, 2]] # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
- [-1, 3, C3, [128]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 6, C3, [256]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 9, C3, [512]]
- [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
- [-1, 3, C3, [768]]
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
- [-1, 3, C3, [1024]]
- [-1, 1, SPPF, [1024, 5]] # 11
# YOLOv5 v6.0 head
head:
- [-1, 1, Conv, [768, 1, 1]]
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
- [-1, 3, C3, [768, False]] # 15
- [-1, 1, Conv, [512, 1, 1]]
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C3, [512, False]] # 19
- [-1, 1, Conv, [256, 1, 1]]
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C3, [256, False]] # 23 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 20], 1, Concat, [1]] # cat head P4
- [-1, 3, C3, [512, False]] # 26 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 16], 1, Concat, [1]] # cat head P5
- [-1, 3, C3, [768, False]] # 29 (P5/32-large)
- [-1, 1, Conv, [768, 3, 2]]
- [[-1, 12], 1, Concat, [1]] # cat head P6
- [-1, 3, C3, [1024, False]] # 32 (P6/64-xlarge)
- [[23, 26, 29, 32], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLOv5 object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov5
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov5n.yaml' will call yolov5.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: [1.33, 1.25, 1024]
# YOLOv5 v6.0 backbone
backbone:
# [from, number, module, args]
- [-1, 1, Conv, [64, 6, 2, 2]] # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
- [-1, 3, C3, [128]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 6, C3, [256]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 9, C3, [512]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 3, C3, [1024]]
- [-1, 1, SPPF, [1024, 5]] # 9
# YOLOv5 v6.0 head
head:
- [-1, 1, Conv, [512, 1, 1]]
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C3, [512, False]] # 13
- [-1, 1, Conv, [256, 1, 1]]
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C3, [256, False]] # 17 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 14], 1, Concat, [1]] # cat head P4
- [-1, 3, C3, [512, False]] # 20 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 10], 1, Concat, [1]] # cat head P5
- [-1, 3, C3, [1024, False]] # 23 (P5/32-large)
- [[17, 20, 23], 1, Detect, [nc]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Meituan YOLOv6 object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov6
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
activation: torch.nn.ReLU() # (optional) model default activation function
scales: # model compound scaling constants, i.e. 'model=yolov6n.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]
# YOLOv6-3.0s 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, 6, Conv, [128, 3, 1]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 12, Conv, [256, 3, 1]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 18, Conv, [512, 3, 1]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 6, Conv, [1024, 3, 1]]
- [-1, 1, SPPF, [1024, 5]] # 9
# YOLOv6-3.0s head
head:
- [-1, 1, Conv, [256, 1, 1]]
- [-1, 1, nn.ConvTranspose2d, [256, 2, 2, 0]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 1, Conv, [256, 3, 1]]
- [-1, 9, Conv, [256, 3, 1]] # 14
- [-1, 1, Conv, [128, 1, 1]]
- [-1, 1, nn.ConvTranspose2d, [128, 2, 2, 0]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 1, Conv, [128, 3, 1]]
- [-1, 9, Conv, [128, 3, 1]] # 19
- [-1, 1, Conv, [128, 3, 2]]
- [[-1, 15], 1, Concat, [1]] # cat head P4
- [-1, 1, Conv, [256, 3, 1]]
- [-1, 9, Conv, [256, 3, 1]] # 23
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 10], 1, Concat, [1]] # cat head P5
- [-1, 1, Conv, [512, 3, 1]]
- [-1, 9, Conv, [512, 3, 1]] # 27
- [[19, 23, 27], 1, Detect, [nc]] # Detect(P3, P4, P5)

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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 🚀 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 🚀 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 🚀 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 🚀 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 🚀 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 🚀 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)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# YOLOv9c-seg instance segmentation model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov9
# Task docs: https://docs.ultralytics.com/tasks/segment
# 380 layers, 27897120 parameters, 159.4 GFLOPs
# Parameters
nc: 80 # number of classes
# GELAN backbone
backbone:
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
- [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]] # 2
- [-1, 1, ADown, [256]] # 3-P3/8
- [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]] # 4
- [-1, 1, ADown, [512]] # 5-P4/16
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 6
- [-1, 1, ADown, [512]] # 7-P5/32
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 8
- [-1, 1, SPPELAN, [512, 256]] # 9
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 12
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]] # 15 (P3/8-small)
- [-1, 1, ADown, [256]]
- [[-1, 12], 1, Concat, [1]] # cat head P4
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 18 (P4/16-medium)
- [-1, 1, ADown, [512]]
- [[-1, 9], 1, Concat, [1]] # cat head P5
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 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
# YOLOv9c object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov9
# Task docs: https://docs.ultralytics.com/tasks/detect
# 358 layers, 25590912 parameters, 104.0 GFLOPs
# Parameters
nc: 80 # number of classes
# GELAN backbone
backbone:
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
- [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]] # 2
- [-1, 1, ADown, [256]] # 3-P3/8
- [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]] # 4
- [-1, 1, ADown, [512]] # 5-P4/16
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 6
- [-1, 1, ADown, [512]] # 7-P5/32
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 8
- [-1, 1, SPPELAN, [512, 256]] # 9
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 12
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]] # 15 (P3/8-small)
- [-1, 1, ADown, [256]]
- [[-1, 12], 1, Concat, [1]] # cat head P4
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 18 (P4/16-medium)
- [-1, 1, ADown, [512]]
- [[-1, 9], 1, Concat, [1]] # cat head P5
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 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
# YOLOv9e-seg instance segmentation model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov9
# Task docs: https://docs.ultralytics.com/tasks/segment
# 743 layers, 60512800 parameters, 248.4 GFLOPs
# Parameters
nc: 80 # number of classes
# GELAN backbone
backbone:
- [-1, 1, nn.Identity, []]
- [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
- [-1, 1, Conv, [128, 3, 2]] # 2-P2/4
- [-1, 1, RepNCSPELAN4, [256, 128, 64, 2]] # 3
- [-1, 1, ADown, [256]] # 4-P3/8
- [-1, 1, RepNCSPELAN4, [512, 256, 128, 2]] # 5
- [-1, 1, ADown, [512]] # 6-P4/16
- [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 7
- [-1, 1, ADown, [1024]] # 8-P5/32
- [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 9
- [1, 1, CBLinear, [[64]]] # 10
- [3, 1, CBLinear, [[64, 128]]] # 11
- [5, 1, CBLinear, [[64, 128, 256]]] # 12
- [7, 1, CBLinear, [[64, 128, 256, 512]]] # 13
- [9, 1, CBLinear, [[64, 128, 256, 512, 1024]]] # 14
- [0, 1, Conv, [64, 3, 2]] # 15-P1/2
- [[10, 11, 12, 13, 14, -1], 1, CBFuse, [[0, 0, 0, 0, 0]]] # 16
- [-1, 1, Conv, [128, 3, 2]] # 17-P2/4
- [[11, 12, 13, 14, -1], 1, CBFuse, [[1, 1, 1, 1]]] # 18
- [-1, 1, RepNCSPELAN4, [256, 128, 64, 2]] # 19
- [-1, 1, ADown, [256]] # 20-P3/8
- [[12, 13, 14, -1], 1, CBFuse, [[2, 2, 2]]] # 21
- [-1, 1, RepNCSPELAN4, [512, 256, 128, 2]] # 22
- [-1, 1, ADown, [512]] # 23-P4/16
- [[13, 14, -1], 1, CBFuse, [[3, 3]]] # 24
- [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 25
- [-1, 1, ADown, [1024]] # 26-P5/32
- [[14, -1], 1, CBFuse, [[4]]] # 27
- [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 28
- [-1, 1, SPPELAN, [512, 256]] # 29
# GELAN head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 25], 1, Concat, [1]] # cat backbone P4
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 2]] # 32
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 22], 1, Concat, [1]] # cat backbone P3
- [-1, 1, RepNCSPELAN4, [256, 256, 128, 2]] # 35 (P3/8-small)
- [-1, 1, ADown, [256]]
- [[-1, 32], 1, Concat, [1]] # cat head P4
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 2]] # 38 (P4/16-medium)
- [-1, 1, ADown, [512]]
- [[-1, 29], 1, Concat, [1]] # cat head P5
- [-1, 1, RepNCSPELAN4, [512, 1024, 512, 2]] # 41 (P5/32-large)
- [[35, 38, 41], 1, Segment, [nc, 32, 256]] # Segment (P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# YOLOv9e object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov9
# Task docs: https://docs.ultralytics.com/tasks/detect
# 721 layers, 58206592 parameters, 193.0 GFLOPs
# Parameters
nc: 80 # number of classes
# GELAN backbone
backbone:
- [-1, 1, nn.Identity, []]
- [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
- [-1, 1, Conv, [128, 3, 2]] # 2-P2/4
- [-1, 1, RepNCSPELAN4, [256, 128, 64, 2]] # 3
- [-1, 1, ADown, [256]] # 4-P3/8
- [-1, 1, RepNCSPELAN4, [512, 256, 128, 2]] # 5
- [-1, 1, ADown, [512]] # 6-P4/16
- [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 7
- [-1, 1, ADown, [1024]] # 8-P5/32
- [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 9
- [1, 1, CBLinear, [[64]]] # 10
- [3, 1, CBLinear, [[64, 128]]] # 11
- [5, 1, CBLinear, [[64, 128, 256]]] # 12
- [7, 1, CBLinear, [[64, 128, 256, 512]]] # 13
- [9, 1, CBLinear, [[64, 128, 256, 512, 1024]]] # 14
- [0, 1, Conv, [64, 3, 2]] # 15-P1/2
- [[10, 11, 12, 13, 14, -1], 1, CBFuse, [[0, 0, 0, 0, 0]]] # 16
- [-1, 1, Conv, [128, 3, 2]] # 17-P2/4
- [[11, 12, 13, 14, -1], 1, CBFuse, [[1, 1, 1, 1]]] # 18
- [-1, 1, RepNCSPELAN4, [256, 128, 64, 2]] # 19
- [-1, 1, ADown, [256]] # 20-P3/8
- [[12, 13, 14, -1], 1, CBFuse, [[2, 2, 2]]] # 21
- [-1, 1, RepNCSPELAN4, [512, 256, 128, 2]] # 22
- [-1, 1, ADown, [512]] # 23-P4/16
- [[13, 14, -1], 1, CBFuse, [[3, 3]]] # 24
- [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 25
- [-1, 1, ADown, [1024]] # 26-P5/32
- [[14, -1], 1, CBFuse, [[4]]] # 27
- [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 28
- [-1, 1, SPPELAN, [512, 256]] # 29
# GELAN head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 25], 1, Concat, [1]] # cat backbone P4
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 2]] # 32
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 22], 1, Concat, [1]] # cat backbone P3
- [-1, 1, RepNCSPELAN4, [256, 256, 128, 2]] # 35 (P3/8-small)
- [-1, 1, ADown, [256]]
- [[-1, 32], 1, Concat, [1]] # cat head P4
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 2]] # 38 (P4/16-medium)
- [-1, 1, ADown, [512]]
- [[-1, 29], 1, Concat, [1]] # cat head P5
- [-1, 1, RepNCSPELAN4, [512, 1024, 512, 2]] # 41 (P5/32-large)
- [[35, 38, 41], 1, Detect, [nc]] # Detect(P3, P4, P5)

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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# YOLOv9m object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov9
# Task docs: https://docs.ultralytics.com/tasks/detect
# 348 layers, 20216160 parameters, 77.9 GFLOPs
# Parameters
nc: 80 # number of classes
# GELAN backbone
backbone:
- [-1, 1, Conv, [32, 3, 2]] # 0-P1/2
- [-1, 1, Conv, [64, 3, 2]] # 1-P2/4
- [-1, 1, RepNCSPELAN4, [128, 128, 64, 1]] # 2
- [-1, 1, AConv, [240]] # 3-P3/8
- [-1, 1, RepNCSPELAN4, [240, 240, 120, 1]] # 4
- [-1, 1, AConv, [360]] # 5-P4/16
- [-1, 1, RepNCSPELAN4, [360, 360, 180, 1]] # 6
- [-1, 1, AConv, [480]] # 7-P5/32
- [-1, 1, RepNCSPELAN4, [480, 480, 240, 1]] # 8
- [-1, 1, SPPELAN, [480, 240]] # 9
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 1, RepNCSPELAN4, [360, 360, 180, 1]] # 12
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 1, RepNCSPELAN4, [240, 240, 120, 1]] # 15
- [-1, 1, AConv, [180]]
- [[-1, 12], 1, Concat, [1]] # cat head P4
- [-1, 1, RepNCSPELAN4, [360, 360, 180, 1]] # 18 (P4/16-medium)
- [-1, 1, AConv, [240]]
- [[-1, 9], 1, Concat, [1]] # cat head P5
- [-1, 1, RepNCSPELAN4, [480, 480, 240, 1]] # 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
# YOLOv9s object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov9
# Task docs: https://docs.ultralytics.com/tasks/detect
# 544 layers, 7318368 parameters, 27.6 GFLOPs
# Parameters
nc: 80 # number of classes
# GELAN backbone
backbone:
- [-1, 1, Conv, [32, 3, 2]] # 0-P1/2
- [-1, 1, Conv, [64, 3, 2]] # 1-P2/4
- [-1, 1, ELAN1, [64, 64, 32]] # 2
- [-1, 1, AConv, [128]] # 3-P3/8
- [-1, 1, RepNCSPELAN4, [128, 128, 64, 3]] # 4
- [-1, 1, AConv, [192]] # 5-P4/16
- [-1, 1, RepNCSPELAN4, [192, 192, 96, 3]] # 6
- [-1, 1, AConv, [256]] # 7-P5/32
- [-1, 1, RepNCSPELAN4, [256, 256, 128, 3]] # 8
- [-1, 1, SPPELAN, [256, 128]] # 9
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 1, RepNCSPELAN4, [192, 192, 96, 3]] # 12
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 1, RepNCSPELAN4, [128, 128, 64, 3]] # 15
- [-1, 1, AConv, [96]]
- [[-1, 12], 1, Concat, [1]] # cat head P4
- [-1, 1, RepNCSPELAN4, [192, 192, 96, 3]] # 18 (P4/16-medium)
- [-1, 1, AConv, [128]]
- [[-1, 9], 1, Concat, [1]] # cat head P5
- [-1, 1, RepNCSPELAN4, [256, 256, 128, 3]] # 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
# YOLOv9t object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolov9
# Task docs: https://docs.ultralytics.com/tasks/detect
# 544 layers, 2128720 parameters, 8.5 GFLOPs
# Parameters
nc: 80 # number of classes
# GELAN backbone
backbone:
- [-1, 1, Conv, [16, 3, 2]] # 0-P1/2
- [-1, 1, Conv, [32, 3, 2]] # 1-P2/4
- [-1, 1, ELAN1, [32, 32, 16]] # 2
- [-1, 1, AConv, [64]] # 3-P3/8
- [-1, 1, RepNCSPELAN4, [64, 64, 32, 3]] # 4
- [-1, 1, AConv, [96]] # 5-P4/16
- [-1, 1, RepNCSPELAN4, [96, 96, 48, 3]] # 6
- [-1, 1, AConv, [128]] # 7-P5/32
- [-1, 1, RepNCSPELAN4, [128, 128, 64, 3]] # 8
- [-1, 1, SPPELAN, [128, 64]] # 9
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 1, RepNCSPELAN4, [96, 96, 48, 3]] # 12
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 1, RepNCSPELAN4, [64, 64, 32, 3]] # 15
- [-1, 1, AConv, [48]]
- [[-1, 12], 1, Concat, [1]] # cat head P4
- [-1, 1, RepNCSPELAN4, [96, 96, 48, 3]] # 18 (P4/16-medium)
- [-1, 1, AConv, [64]]
- [[-1, 9], 1, Concat, [1]] # cat head P5
- [-1, 1, RepNCSPELAN4, [128, 128, 64, 3]] # 21 (P5/32-large)
- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)