v1.0

ultralytics/cfg/models/rt-detr/rtdetr-resnet101.yaml

+# 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)

ultralytics/cfg/models/rt-detr/rtdetr-resnet50.yaml

+# 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)

ultralytics/cfg/models/rt-detr/rtdetr-x.yaml

+# 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)

ultralytics/cfg/models/v10/yolov10b.yaml

+# 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)

ultralytics/cfg/models/v10/yolov10l.yaml

+# 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)

ultralytics/cfg/models/v10/yolov10m.yaml

+# 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)

ultralytics/cfg/models/v10/yolov10n.yaml

+# 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)

ultralytics/cfg/models/v10/yolov10s.yaml

+# 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)

ultralytics/cfg/models/v10/yolov10x.yaml

+# 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)

ultralytics/cfg/models/v12/yolov12.yaml

+# YOLOv12 🚀, AGPL-3.0 license
+# YOLOv12 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
+# CFG file for YOLOv12-turbo
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov12n.yaml' will call yolov12.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 497 layers, 2,553,904 parameters, 2,553,888 gradients, 6.2 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 497 layers, 9,127,424 parameters, 9,127,408 gradients, 19.7 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 533 layers, 19,670,784 parameters, 19,670,768 gradients, 60.4 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 895 layers, 26,506,496 parameters, 26,506,480 gradients, 83.3 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 895 layers, 59,414,176 parameters, 59,414,160 gradients, 185.9 GFLOPs
+
+
+# YOLO12-turbo backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv,  [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv,  [128, 3, 2, 1, 2]] # 1-P2/4
+  - [-1, 2, C3k2,  [256, False, 0.25]]
+  - [-1, 1, Conv,  [256, 3, 2, 1, 4]] # 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
+
+# YOLO12-turbo 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)

ultralytics/cfg/models/v13/yolov13.yaml

+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov13n.yaml' will call yolov13.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024]   # Nano
+  s: [0.50, 0.50, 1024]   # Small
+  l: [1.00, 1.00, 512]    # Large
+  x: [1.00, 1.50, 512]    # Extra Large
+
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv,  [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv,  [128, 3, 2, 1, 2]] # 1-P2/4
+  - [-1, 2, DSC3k2,  [256, False, 0.25]]
+  - [-1, 1, Conv,  [256, 3, 2, 1, 4]] # 3-P3/8
+  - [-1, 2, DSC3k2,  [512, False, 0.25]]
+  - [-1, 1, DSConv,  [512, 3, 2]] # 5-P4/16
+  - [-1, 4, A2C2f, [512, True, 4]]
+  - [-1, 1, DSConv,  [1024, 3, 2]] # 7-P5/32
+  - [-1, 4, A2C2f, [1024, True, 1]] # 8
+
+head:
+  - [[4, 6, 8], 2, HyperACE, [512, 8, True, True, 0.5, 1, "both"]]
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [ 9, 1, DownsampleConv, []]
+  - [[6, 9], 1, FullPAD_Tunnel, []]  #12     
+  - [[4, 10], 1, FullPAD_Tunnel, []]  #13    
+  - [[8, 11], 1, FullPAD_Tunnel, []] #14 
+  
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 12], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, DSC3k2, [512, True]] # 17
+  - [[-1, 9], 1, FullPAD_Tunnel, []]  #18
+
+  - [17, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 13], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, DSC3k2, [256, True]] # 21
+  - [10, 1, Conv, [256, 1, 1]]
+  - [[21, 22], 1, FullPAD_Tunnel, []]  #23
+  
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 18], 1, Concat, [1]] # cat head P4
+  - [-1, 2, DSC3k2, [512, True]] # 26
+  - [[-1, 9], 1, FullPAD_Tunnel, []]  
+
+  - [26, 1, Conv, [512, 3, 2]]
+  - [[-1, 14], 1, Concat, [1]] # cat head P5
+  - [-1, 2, DSC3k2, [1024,True]] # 30 (P5/32-large)
+  - [[-1, 11], 1, FullPAD_Tunnel, []]  
+  
+  - [[23, 27, 31], 1, Detect, [nc]] # Detect(P3, P4, P5)

ultralytics/cfg/models/v3/yolov3-spp.yaml

+# 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)

ultralytics/cfg/models/v3/yolov3-tiny.yaml

+# 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)

ultralytics/cfg/models/v3/yolov3.yaml

+# 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)

ultralytics/cfg/models/v5/yolov5-p6.yaml

+# 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)

ultralytics/cfg/models/v5/yolov5.yaml

+# 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)

ultralytics/cfg/models/v6/yolov6.yaml

+# 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: 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)

ultralytics/cfg/models/v8/yolov8-cls-resnet101.yaml

+# 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

ultralytics/cfg/models/v8/yolov8-cls-resnet50.yaml

+# 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

ultralytics/cfg/models/v8/yolov8-cls.yaml

+# 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