gengxin

README.md

@@ -1,3 +1,28 @@
+这是张伟的本科毕业设计。基于yolov5s6模型，对其output进行了改进，增强yolov5s6的分类能力，使得这个模型能同时完成“目标检测+分类”这两个任务。
+
+M0_yolov5_raw：
+原生yolov5s6的output
+![YOLOv5 Baseline](docs/images/1、YOLOv5-baseline.png)
+
+G8_decoupled_head_light分支：
+解耦output，将目标检测和分类任务的output拆开，便于后续单独增强分类的能力
+![YOLOv5s6 Decoupled Head](docs/images/2、YOLOv5s6-DecoupledHead.png)
+
+G11_cls_head_enhance_Lightweight分支:
+在分类支添加两层卷积
+![YOLOv5s6 Head Variant](docs/images/3、YOLOv5s6-HeadVariant.png)
+
+G13_decoupled_head_ECA分支：
+在分类支添加ECA通道注意力机制
+![YOLOv5s6 Decoupled Head ECA](docs/images/4、YOLOv5s6-DecoupledHead-ECA.png)
+
+G12_cls_head_enhance_and_ECA分支：
+在分类支同时添加两层卷积和ECA
+![YOLOv5s6 Head Variant ECA](docs/images/5、YOLOv5s6-HeadVariant-ECA.png)
+
+
+以下是yolov5原git仓的readme
+
 <div align="center">
   <p>
     <a href="https://www.ultralytics.com/blog/all-you-need-to-know-about-ultralytics-yolo11-and-its-applications" target="_blank">

models/yolov5s6.yaml

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 0.33 # model depth multiple
+width_multiple: 0.50 # layer channel multiple
+anchors:
+  - [19, 27, 44, 40, 38, 94] # P3/8
+  - [96, 68, 86, 152, 180, 137] # P4/16
+  - [140, 301, 303, 264, 238, 542] # P5/32
+  - [436, 615, 739, 380, 925, 792] # P6/64
+
+# 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, anchors]], # Detect(P3, P4, P5, P6)
+  ]