feat: initial HSAP platform

Huaxu Sentinel Active Safety Platform with embedded algorithm code, Docker Compose setup, and vendored dataset scaffolds for clone-and-run. Co-authored-by: Cursor <cursoragent@cursor.com>
2026-05-25 16:59:59 +08:00
commit 7c43b44c57
1619 changed files with 373355 additions and 0 deletions
--- a/algorithms/dms_yolo/code/ultralytics/cfg/models/26/yolo26-cls.yaml
+++ b/algorithms/dms_yolo/code/ultralytics/cfg/models/26/yolo26-cls.yaml
@@ -0,0 +1,33 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO26-cls image classification model
+# Model docs: https://docs.ultralytics.com/models/yolo26
+# Task docs: https://docs.ultralytics.com/tasks/classify
+
+# Parameters
+nc: 1000 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolo26n-cls.yaml' will call yolo26-cls.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 86 layers, 2,812,104 parameters, 2,812,104 gradients, 0.5 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 86 layers, 6,724,008 parameters, 6,724,008 gradients, 1.6 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 106 layers, 11,634,216 parameters, 11,634,216 gradients, 5.0 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 176 layers, 14,115,624 parameters, 14,115,624 gradients, 6.2 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 176 layers, 29,637,064 parameters, 29,637,064 gradients, 13.7 GFLOPs
+
+# YOLO26n 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
+
+# YOLO26n head
+head:
+  - [-1, 1, Classify, [nc]] # Classify
--- a/algorithms/dms_yolo/code/ultralytics/cfg/models/26/yolo26-obb.yaml
+++ b/algorithms/dms_yolo/code/ultralytics/cfg/models/26/yolo26-obb.yaml
@@ -0,0 +1,52 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO26-obb Oriented Bounding Boxes (OBB) model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo26
+# Task docs: https://docs.ultralytics.com/tasks/obb
+
+# Parameters
+nc: 80 # number of classes
+end2end: True # whether to use end-to-end mode
+reg_max: 1 # DFL bins
+scales: # model compound scaling constants, i.e. 'model=yolo26n-obb.yaml' will call yolo26-obb.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 291 layers, 2,715,614 parameters, 2,715,614 gradients, 16.9 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 291 layers, 10,582,142 parameters, 10,582,142 gradients, 63.5 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 311 layers, 23,593,918 parameters, 23,593,918 gradients, 211.9 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 423 layers, 27,997,374 parameters, 27,997,374 gradients, 259.0 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 423 layers, 62,811,678 parameters, 62,811,678 gradients, 578.9 GFLOPs
+
+# YOLO26n 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, 3, True]] # 9
+  - [-1, 2, C2PSA, [1024]] # 10
+
+# YOLO26n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, C3k2, [512, True]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, C3k2, [256, True]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 2, C3k2, [512, True]] # 19 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 1, C3k2, [1024, True, 0.5, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, OBB26, [nc, 1]] # Detect(P3, P4, P5)
--- a/algorithms/dms_yolo/code/ultralytics/cfg/models/26/yolo26-p2.yaml
+++ b/algorithms/dms_yolo/code/ultralytics/cfg/models/26/yolo26-p2.yaml
@@ -0,0 +1,60 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO26 object detection model with P2/4 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo26
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+end2end: True # whether to use end-to-end mode
+reg_max: 1 # DFL bins
+scales: # model compound scaling constants, i.e. 'model=yolo26n-p2.yaml' will call yolo26-p2.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 329 layers, 2,662,400 parameters, 2,662,400 gradients, 9.5 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 329 layers, 9,765,856 parameters, 9,765,856 gradients, 27.8 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 349 layers, 21,144,288 parameters, 21,144,288 gradients, 91.4 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 489 layers, 25,815,520 parameters, 25,815,520 gradients, 115.3 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 489 layers, 57,935,232 parameters, 57,935,232 gradients, 256.9 GFLOPs
+
+# YOLO26n 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, 3, True]] # 9
+  - [-1, 2, C2PSA, [1024]] # 10
+
+# YOLO26n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, C3k2, [512, True]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, C3k2, [256, True]] # 16 (P3/8-small)
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 2], 1, Concat, [1]] # cat backbone P2
+  - [-1, 2, C3k2, [128, True]] # 19 (P2/4-xsmall)
+
+  - [-1, 1, Conv, [128, 3, 2]]
+  - [[-1, 16], 1, Concat, [1]] # cat head P3
+  - [-1, 2, C3k2, [256, True]] # 22 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 2, C3k2, [512, True]] # 25 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 1, C3k2, [1024, True, 0.5, True]] # 28 (P5/32-large)
+
+  - [[19, 22, 25, 28], 1, Detect, [nc]] # Detect(P2, P3, P4, P5)
--- a/algorithms/dms_yolo/code/ultralytics/cfg/models/26/yolo26-p6.yaml
+++ b/algorithms/dms_yolo/code/ultralytics/cfg/models/26/yolo26-p6.yaml
@@ -0,0 +1,62 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO26 object detection model with P3/8 - P6/64 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo26
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+end2end: True # whether to use end-to-end mode
+reg_max: 1 # DFL bins
+scales: # model compound scaling constants, i.e. 'model=yolo26n-p6.yaml' will call yolo26-p6.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 349 layers, 4,063,872 parameters, 4,063,872 gradients, 6.0 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 349 layers, 15,876,448 parameters, 15,876,448 gradients, 22.3 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 369 layers, 32,400,096 parameters, 32,400,096 gradients, 77.3 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 523 layers, 39,365,600 parameters, 39,365,600 gradients, 97.0 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 523 layers, 88,330,368 parameters, 88,330,368 gradients, 216.6 GFLOPs
+
+# YOLO26n 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, [768, 3, 2]] # 7-P5/32
+  - [-1, 2, C3k2, [768, True]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
+  - [-1, 2, C3k2, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 11
+  - [-1, 2, C2PSA, [1024]] # 12
+
+# YOLO26n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 8], 1, Concat, [1]] # cat backbone P5
+  - [-1, 2, C3k2, [768, True]] # 15
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, C3k2, [512, True]] # 18
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, C3k2, [256, True]] # 21 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 18], 1, Concat, [1]] # cat head P4
+  - [-1, 2, C3k2, [512, True]] # 24 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 15], 1, Concat, [1]] # cat head P5
+  - [-1, 2, C3k2, [768, True]] # 27 (P5/32-large)
+
+  - [-1, 1, Conv, [768, 3, 2]]
+  - [[-1, 12], 1, Concat, [1]] # cat head P6
+  - [-1, 1, C3k2, [1024, True, 0.5, True]] # 30 (P6/64-large)
+
+  - [[21, 24, 27, 30], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)
--- a/algorithms/dms_yolo/code/ultralytics/cfg/models/26/yolo26-pose.yaml
+++ b/algorithms/dms_yolo/code/ultralytics/cfg/models/26/yolo26-pose.yaml
@@ -0,0 +1,53 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO26-pose keypoints/pose estimation model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo26
+# Task docs: https://docs.ultralytics.com/tasks/pose
+
+# Parameters
+nc: 80 # number of classes
+end2end: True # whether to use end-to-end mode
+reg_max: 1 # DFL bins
+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=yolo26n-pose.yaml' will call yolo26-pose.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 363 layers, 3,747,554 parameters, 3,747,554 gradients, 10.7 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 363 layers, 11,870,498 parameters, 11,870,498 gradients, 29.6 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 383 layers, 24,344,482 parameters, 24,344,482 gradients, 85.9 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 495 layers, 28,747,938 parameters, 28,747,938 gradients, 104.3 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 495 layers, 62,914,350 parameters, 62,914,350 gradients, 226.3 GFLOPs
+
+# YOLO26n 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, 3, True]] # 9
+  - [-1, 2, C2PSA, [1024]] # 10
+
+# YOLO26n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, C3k2, [512, True]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, C3k2, [256, True]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 2, C3k2, [512, True]] # 19 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 1, C3k2, [1024, True, 0.5, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, Pose26, [nc, kpt_shape]] # Detect(P3, P4, P5)
--- a/algorithms/dms_yolo/code/ultralytics/cfg/models/26/yolo26-seg.yaml
+++ b/algorithms/dms_yolo/code/ultralytics/cfg/models/26/yolo26-seg.yaml
@@ -0,0 +1,52 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO26-seg instance segmentation model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo26
+# Task docs: https://docs.ultralytics.com/tasks/segment
+
+# Parameters
+nc: 80 # number of classes
+end2end: True # whether to use end-to-end mode
+reg_max: 1 # DFL bins
+scales: # model compound scaling constants, i.e. 'model=yolo26n-seg.yaml' will call yolo26-seg.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 309 layers, 3,126,280 parameters, 3,126,280 gradients, 10.5 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 309 layers, 11,505,800 parameters, 11,505,800 gradients, 37.4 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 329 layers, 27,112,072 parameters, 27,112,072 gradients, 132.5 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 441 layers, 31,515,528 parameters, 31,515,528 gradients, 150.9 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 441 layers, 70,693,800 parameters, 70,693,800 gradients, 337.7 GFLOPs
+
+# YOLO26n 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, 3, True]] # 9
+  - [-1, 2, C2PSA, [1024]] # 10
+
+# YOLO26n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, C3k2, [512, True]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, C3k2, [256, True]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 2, C3k2, [512, True]] # 19 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 1, C3k2, [1024, True, 0.5, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, Segment26, [nc, 32, 256]] # Segment(P3, P4, P5)
--- a/algorithms/dms_yolo/code/ultralytics/cfg/models/26/yolo26.yaml
+++ b/algorithms/dms_yolo/code/ultralytics/cfg/models/26/yolo26.yaml
@@ -0,0 +1,52 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO26 object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo26
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+end2end: True # whether to use end-to-end mode
+reg_max: 1 # DFL bins
+scales: # model compound scaling constants, i.e. 'model=yolo26n.yaml' will call yolo26.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 260 layers, 2,572,280 parameters, 2,572,280 gradients, 6.1 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 260 layers, 10,009,784 parameters, 10,009,784 gradients, 22.8 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 280 layers, 21,896,248 parameters, 21,896,248 gradients, 75.4 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 392 layers, 26,299,704 parameters, 26,299,704 gradients, 93.8 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 392 layers, 58,993,368 parameters, 58,993,368 gradients, 209.5 GFLOPs
+
+# YOLO26n 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, 3, True]] # 9
+  - [-1, 2, C2PSA, [1024]] # 10
+
+# YOLO26n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, C3k2, [512, True]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, C3k2, [256, True]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 2, C3k2, [512, True]] # 19 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 1, C3k2, [1024, True, 0.5, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, Detect, [nc]] # Detect(P3, P4, P5)
--- a/algorithms/dms_yolo/code/ultralytics/cfg/models/26/yoloe-26-seg.yaml
+++ b/algorithms/dms_yolo/code/ultralytics/cfg/models/26/yoloe-26-seg.yaml
@@ -0,0 +1,53 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOE-26 open-vocabulary instance segmentation model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo26
+# Task docs: https://docs.ultralytics.com/tasks/segment
+
+# Parameters
+nc: 80 # number of classes
+end2end: True # whether to use end-to-end mode
+reg_max: 1 # DFL bins
+text_model: mobileclip2:b
+scales: # model compound scaling constants, i.e. 'model=yoloe-26n-seg.yaml' will call yoloe-26-seg.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 347 layers, 5,615,540 parameters, 5,615,540 gradients, 11.7 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 347 layers, 15,272,852 parameters, 15,272,852 gradients, 39.3 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 367 layers, 34,922,132 parameters, 34,922,132 gradients, 136.3 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 479 layers, 39,325,588 parameters, 39,325,588 gradients, 154.7 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 479 layers, 85,397,684 parameters, 85,397,684 gradients, 343.3 GFLOPs
+
+# YOLOE26n 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, 3, True]] # 9
+  - [-1, 2, C2PSA, [1024]] # 10
+
+# YOLOE26n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, C3k2, [512, True]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, C3k2, [256, True]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 2, C3k2, [512, True]] # 19 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 1, C3k2, [1024, True, 0.5, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, YOLOESegment26, [nc, 32, 256, 512, True]] # YOLOESegment26(P3, P4, P5)
--- a/algorithms/dms_yolo/code/ultralytics/cfg/models/26/yoloe-26.yaml
+++ b/algorithms/dms_yolo/code/ultralytics/cfg/models/26/yoloe-26.yaml
@@ -0,0 +1,53 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOE-26 open-vocabulary object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo26
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+end2end: True # whether to use end-to-end mode
+reg_max: 1 # DFL bins
+text_model: mobileclip2:b
+scales: # model compound scaling constants, i.e. 'model=yoloe-26n.yaml' will call yoloe-26.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 298 layers, 5,061,540 parameters, 5,061,540 gradients, 7.3 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 298 layers, 13,776,836 parameters, 13,776,836 gradients, 24.8 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 318 layers, 29,706,308 parameters, 29,706,308 gradients, 79.2 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 430 layers, 34,109,764 parameters, 34,109,764 gradients, 97.6 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 430 layers, 73,697,252 parameters, 73,697,252 gradients, 215.2 GFLOPs
+
+# YOLOE26n 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, 3, True]] # 9
+  - [-1, 2, C2PSA, [1024]] # 10
+
+# YOLOE26n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, C3k2, [512, True]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, C3k2, [256, True]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 2, C3k2, [512, True]] # 19 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 1, C3k2, [1024, True, 0.5, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, YOLOEDetect, [nc, 512, True]] # YOLOEDetect(P3, P4, P5)