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ultralytics 8.3.78 new YOLO12 models (#19325)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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ultralytics/cfg/models/12/yolo12-cls.yaml Normal file
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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/cfg/models/12/yolo12-obb.yaml Normal file
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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/cfg/models/12/yolo12-pose.yaml Normal file
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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/cfg/models/12/yolo12-seg.yaml Normal file
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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/cfg/models/12/yolo12.yaml Normal file
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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)