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‎README.md

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@@ -183,11 +183,11 @@ ______________________________________________________________________
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### 技术交流 <img title="" src="https://user-images.githubusercontent.com/48054808/157800467-2a9946ad-30d1-49a9-b9db-ba33413d9c90.png" alt="" width="20">
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|FightingCV公众号(论文分享)|YOLOAir目标检测交流群( 答案: yoloair )|
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|FightingCV公众号|YOLOAir目标检测交流群( 答案: yoloair )|
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:-------------------------:|:-------------------------:
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<img src='https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/b6f5057da9a8410fa22dcc7566548193~tplv-k3u1fbpfcp-watermark.image?' width='200px'> | <img src='https://p1-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/1882e8cf3f804918a043d70de1b70e31~tplv-k3u1fbpfcp-watermark.image' width='200px'>
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- FightingCV公众号每天分享最新论文和科技前沿动态
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- FightingCV每天分享前沿论文动态(公众号回复加群, 添加小助手, 加入微信交流群)
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- YOLOAir目标检测交流群
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______________________________________________________________________
@@ -234,23 +234,23 @@ $ python wbf.py
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______________________________________________________________________
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### Performance
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| Model | size<br><sup>(pixels) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Speed<br><sup>CPU b1<br>(ms) | Speed<br><sup>V100 b1<br>(ms) | Speed<br><sup>V100 b32<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@640 (B) |
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|------------------------------------------------------------------------------------------------------|-----------------------|-------------------------|--------------------|------------------------------|-------------------------------|--------------------------------|--------------------|------------------------|
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| YOLOv5n | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |
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| YOLOv5s | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
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| YOLOv5m | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
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| YOLOv5l | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
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| YOLOv5x | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
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| Model | size<br><sup>(pixels) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Speed<br><sup>CPU b1<br>(ms) | Speed<br><sup>V100 b1<br>(ms) | Speed<br><sup>V100 b32<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@640 (B) | Weights
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|------------------------------------------------------------------------------------------------------|-----------------------|-------------------------|--------------------|------------------------------|-------------------------------|--------------------------------|--------------------|------------------------|------------------------|
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| YOLOv5n | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** | [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n.pt)
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| YOLOv5s | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 | [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt)
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| YOLOv5m | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 | [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m.pt)
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| YOLOv5l | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 | [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l.pt)
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| YOLOv5x | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 | [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x.pt)
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| | | | | | | | | |
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| YOLOv5n6 | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
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| YOLOv5s6 | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
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| YOLOv5m6 | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
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| YOLOv5l6 | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
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| YOLOv5x6<br>+ TTA | 1280<br>1536 | 55.0<br>**55.8** | 72.7<br>**72.7** | 3136<br>- | 26.2<br>- | 19.4<br>- | 140.7<br>- | 209.8<br>- |
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| YOLOv5n6 | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |[YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n6.pt)
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| YOLOv5s6 | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |[YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s6.pt)
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| YOLOv5m6 | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |[YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m6.pt)
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| YOLOv5l6 | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |[YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l6.pt)
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| YOLOv5x6<br>+ TTA | 1280<br>1536 | 55.0<br>**55.8** | 72.7<br>**72.7** | 3136<br>- | 26.2<br>- | 19.4<br>- | 140.7<br>- | 209.8<br>- |[YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x6.pt)
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<details><summary> <b>Expand</b> </summary>
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* The original version was created based on YOLOv5
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* The original version was created based on YOLOv5(6.1)
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</details>
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‎configs/attention/yolov5_SOCA.yaml

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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# YOLOv5 🚀 by YOLOAir, GPL-3.0 license
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# Parameters
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nc: 80 # number of classes
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[-1, 1, Conv, [512, 3, 2]],
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[[-1, 10], 1, Concat, [1]], # cat head P5
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[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
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[-1, 1, SOCA, [512]],
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[-1, 1, SOCA, [1024]],
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[[17, 20, 24], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
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]

‎configs/attention/yolov5_ShuffleAttention.yaml

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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# YOLOv5 🚀 by YOLOAir, GPL-3.0 license
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# Parameters
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nc: 80 # number of classes
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[-1, 1, Conv, [512, 3, 2]],
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[[-1, 10], 1, Concat, [1]], # cat head P5
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[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
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[-1, 1, ShuffleAttention, [512]],
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[-1, 1, ShuffleAttention, [1024]],
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[[17, 20, 24], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
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]

‎configs/attention/yolov5s_CA.yaml

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# parameters
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nc: 20 # number of classes
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# YOLOv5 🚀 by YOLOAir, GPL-3.0 license
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# Parameters
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nc: 80 # number of classes
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depth_multiple: 0.33 # model depth multiple
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width_multiple: 0.50 # layer channel multiple
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# anchors
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anchors:
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#- [5,6, 7,9, 12,10] # P2/4
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- [10,13, 16,30, 33,23] # P3/8
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- [30,61, 62,45, 59,119] # P4/16
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- [116,90, 156,198, 373,326] # P5/32
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# YOLOv5 backbone
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# YOLOv5 v6.0 backbone
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backbone:
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# [from, number, module, args] # [c=channels,module,kernlsize,strides]
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[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [c=3,64*0.5=32,3]
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[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
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[-1, 3, C3, [128]], #2
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[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
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[-1, 6, C3, [256]], #4
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[-1, 3, CA, [256]] , #5
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[-1, 1, Conv, [512, 3, 2]], # 6-P4/16
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[-1, 9, C3, [512]], #7
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[-1, 3, CA, [512]],
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[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
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[-1, 3, C3, [1024]], # 10
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[-1, 3, CA, [1024]], #11
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[-1, 1, SPPF, [1024, 5]], #12
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# [from, number, module, args]
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[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
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[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
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[-1, 3, C3, [128]],
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[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
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[-1, 6, C3, [256]],
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[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
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[-1, 9, C3, [512]],
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[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
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[-1, 3, C3, [1024]],
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[-1, 1, SPPF, [1024, 5]], # 9
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]
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# YOLOv5 head
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# YOLOv5 v6.0 head
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head:
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[[-1, 1, Conv, [512, 1, 1]], #13
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[[-1, 1, Conv, [512, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 8], 1, Concat, [1]], # cat backbone P4
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[-1, 3, C3, [512, False]], # 16
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[[-1, 6], 1, Concat, [1]], # cat backbone P4
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[-1, 3, C3, [512, False]], # 13
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[-1, 1, Conv, [256, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 5], 1, Concat, [1]], # cat backbone P3
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[-1, 3, C3, [256, False]], # 20 (P3/8-small)
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[[-1, 4], 1, Concat, [1]], # cat backbone P3
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[-1, 3, C3, [256, False]], # 17 (P3/8-small)
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[-1, 1, Conv, [256, 3, 2]],
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[[-1, 17], 1, Concat, [1]], # cat head P4
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[-1, 3, C3, [512, False]], # 23 (P4/16-medium) [256, 256, 1, False]
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#[-1, 3, CBAM, [512]], #26
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[-1, 1, Conv, [256, 3, 2]],
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[[-1, 14], 1, Concat, [1]], # cat head P4
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[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
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[-1, 1, Conv, [512, 3, 2]], #24 #[256, 256, 3, 2]
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[[-1, 13], 1, Concat, [1]], # cat head P5
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[-1, 3, C3, [1024, False]], # 26 (P5/32-large) [512, 512, 1, False]
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[-1, 1, Conv, [512, 3, 2]],
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[[-1, 10], 1, Concat, [1]], # cat head P5
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[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
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[-1, 1, CA, [1024]],
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[[20, 23, 26], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
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[[17, 20, 24], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
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]

‎configs/attention/yolov5s_CrissCrossAttention.yaml

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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# YOLOv5 🚀 by YOLOAir, GPL-3.0 license
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# Parameters
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nc: 80 # number of classes
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[-1, 1, Conv, [256, 3, 2]],
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[[-1, 14], 1, Concat, [1]], # cat head P4
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[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
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# [-1, 1, SKAttention, [256]],
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[-1, 1, Conv, [512, 3, 2]],
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[[-1, 10], 1, Concat, [1]], # cat head P5
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[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
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[-1, 1, CrissCrossAttention, [512]],
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[-1, 1, CrissCrossAttention, [1024]],
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[[17, 20, 24], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
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]

‎configs/attention/yolov5s_GAMAttention.yaml

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# YOLOv5 🚀 by YOLOAir, GPL-3.0 license
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# parameters
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nc: 10 # number of classes
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[-1, 9, C3, [512]],
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[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
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[-1, 3, C3, [1024]],
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[-1, 1, GAMAttention, [512,512]], #9
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[-1, 1, GAMAttention, [1024,1024]], #9
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[-1, 1, SPPF, [1024,5]], #10
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]
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‎configs/attention/yolov5s_NAMAttention.yaml

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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# YOLOv5 🚀 by YOLOAir, GPL-3.0 license
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# Parameters
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nc: 80 # number of classes
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[-1, 1, Conv, [256, 3, 2]],
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[[-1, 14], 1, Concat, [1]], # cat head P4
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[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
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# [-1, 1, SKAttention, [256]],
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[-1, 1, Conv, [512, 3, 2]],
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[[-1, 10], 1, Concat, [1]], # cat head P5
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[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
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[-1, 1, NAMAttention, [512]],
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[-1, 1, NAMAttention, [1024]],
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[[17, 20, 24], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
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]

‎configs/attention/yolov5s_S2Attention.yaml

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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# YOLOv5 🚀 by YOLOAir, GPL-3.0 license
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# Parameters
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nc: 80 # number of classes
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[-1, 1, Conv, [512, 3, 2]],
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[[-1, 10], 1, Concat, [1]], # cat head P5
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[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
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[-1, 1, S2Attention, [512]],
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[-1, 1, S2Attention, [1024]],
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[[17, 20, 24], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
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]

‎configs/attention/yolov5s_SEAttention.yaml

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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# YOLOv5 🚀 by YOLOAir, GPL-3.0 license
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# Parameters
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nc: 80 # number of classes
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[-1, 1, Conv, [512, 3, 2]],
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[[-1, 10], 1, Concat, [1]], # cat head P5
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[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
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[-1, 1, SEAttention, [512]],
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[-1, 1, SEAttention, [1024]],
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[[17, 20, 24], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
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]

‎configs/attention/yolov5s_SKAttention.yaml

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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# YOLOv5 🚀 by YOLOAir, GPL-3.0 license
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# Parameters
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nc: 80 # number of classes
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[-1, 1, Conv, [256, 3, 2]],
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[[-1, 14], 1, Concat, [1]], # cat head P4
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[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
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# [-1, 1, SKAttention, [256]],
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[-1, 1, Conv, [512, 3, 2]],
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[[-1, 10], 1, Concat, [1]], # cat head P5
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[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
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[-1, 1, SKAttention, [512]],
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[-1, 1, SKAttention, [1024]],
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[[17, 20, 24], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
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]

‎configs/attention/yolov5s_SimAM.yaml

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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# YOLOv5 🚀 by YOLOAir, GPL-3.0 license
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# Parameters
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nc: 80 # number of classes
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[-1, 1, Conv, [256, 3, 2]],
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[[-1, 14], 1, Concat, [1]], # cat head P4
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[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
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# [-1, 1, SKAttention, [256]],
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[-1, 1, Conv, [512, 3, 2]],
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[[-1, 10], 1, Concat, [1]], # cat head P5
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[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
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[-1, 1, SimAM, [512]],
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[-1, 1, SimAM, [1024]],
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[[17, 20, 24], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
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‎configs/attention/yolov5s_acmix.yaml

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# YOLOv5 🚀 by YOLOAir, GPL-3.0 license
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# parameters
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nc: 10 # number of classes
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depth_multiple: 0.33 # model depth multiple
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[-1, 9, C3, [512]],
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[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
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[-1, 3, C3, [1024]],
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[-1, 1, ACmix, [512, 512]], #9
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[-1, 1, ACmix, [1024, 1024]], #9
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[-1, 1, SPPF, [1024,5]], #10
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]
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‎configs/attention/yolov5s_cbam.yaml

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# parameters
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nc: 10 # number of classes
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# YOLOv5 🚀 by YOLOAir, GPL-3.0 license
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# Parameters
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nc: 80 # number of classes
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depth_multiple: 0.33 # model depth multiple
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width_multiple: 0.50 # layer channel multiple
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# anchors
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anchors:
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#- [5,6, 7,9, 12,10] # P2/4
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- [10,13, 16,30, 33,23] # P3/8
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- [30,61, 62,45, 59,119] # P4/16
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- [116,90, 156,198, 373,326] # P5/32
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# YOLOv5 backbone
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# YOLOv5 v6.0 backbone
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backbone:
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# [from, number, module, args] # [c=channels,module,kernlsize,strides]
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[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [c=3,64*0.5=32,3]
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[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
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[-1, 3, C3, [128]],
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[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
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[-1, 6, C3, [256]],
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[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
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[-1, 9, C3, [512]],
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# [from, number, module, args]
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[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
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[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
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[-1, 3, C3, [128]],
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[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
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[-1, 6, C3, [256]],
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[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
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[-1, 9, C3, [512]],
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[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
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[-1, 3, C3, [1024]],
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[-1, 1, CBAM, [1024]], #9
26-
[-1, 1, SPPF, [1024,5]], #10
23+
[-1, 3, C3, [1024]],
24+
[-1, 1, SPPF, [1024, 5]], # 9
2725
]
2826

29-
# YOLOv5 head
27+
# YOLOv5 v6.0 head
3028
head:
31-
[[-1, 1, Conv, [512, 1, 1]],
29+
[[-1, 1, Conv, [512, 1, 1]],
3230
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
3331
[[-1, 6], 1, Concat, [1]], # cat backbone P4
34-
[-1, 3, C3, [512, False]], # 14
32+
[-1, 3, C3, [512, False]], # 13
3533

3634
[-1, 1, Conv, [256, 1, 1]],
3735
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
3836
[[-1, 4], 1, Concat, [1]], # cat backbone P3
39-
[-1, 3, C3, [256, False]], # 18 (P3/8-small)
40-
[-1, 1, CBAM, [256]], #19
41-
37+
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
4238

4339
[-1, 1, Conv, [256, 3, 2]],
44-
[[-1, 15], 1, Concat, [1]], # cat head P4
45-
[-1, 3, C3, [512, False]], # 22 (P4/16-medium) [256, 256, 1, False]
46-
[-1, 1, CBAM, [512]],
47-
40+
[[-1, 14], 1, Concat, [1]], # cat head P4
41+
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
4842

49-
[-1, 1, Conv, [512, 3, 2]], #[256, 256, 3, 2]
50-
[[-1, 11], 1, Concat, [1]], # cat head P5
51-
[-1, 3, C3, [1024, False]], # 25 (P5/32-large) [512, 512, 1, False]
52-
[-1, 1, CBAM, [1024]],
53-
43+
[-1, 1, Conv, [512, 3, 2]],
44+
[[-1, 10], 1, Concat, [1]], # cat head P5
45+
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
46+
[-1, 1, CBAM, [1024]],
5447

55-
[[19, 23, 27], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
48+
[[17, 20, 24], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
5649
]

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