Yolov5/Yolov7加入Yolov8 c2f模块，涨点

1.Yolov8简介

        Ultralytics YOLOv8 是由 Ultralytics 开发的一个前沿的 SOTA 模型。它在以前成功的 YOLO 版本基础上，引入了新的功能和改进，进一步提升了其性能和灵活性。YOLOv8 基于快速、准确和易于使用的设计理念，使其成为广泛的目标检测、图像分割和图像分类任务的绝佳选择。

Yolov5/Yolov7加入Yolov8 c2f模块，涨点

下表为官方在 COCO Val 2017 数据集上测试的 mAP、参数量和 FLOPs 结果。可以看出 YOLOv8 相比 YOLOv5 精度提升非常多，但是 N/S/M 模型相应的参数量和 FLOPs 都增加了不少；

模型 尺寸
（像素）		 mAPval
50-95		 推理速度
CPU ONNX
(ms)		 推理速度
A100 TensorRT
(ms)		 参数量
(M)		 FLOPs
(B)
YOLOv8n 640 37.3 80.4 0.99 3.2 8.7
YOLOv8s 640 44.9 128.4 1.20 11.2 28.6
YOLOv8m 640 50.2 234.7 1.83 25.9 78.9
YOLOv8l 640 52.9 375.2 2.39 43.7 165.2
YOLOv8x 640 53.9 479.1 3.53 68.2 257.8

1.1 Yolov8优化点：

      将 YOLOv5 的C3结构换成了梯度流更丰富的 C2f结构，并对不同尺度模型调整了不同的通道数

C3模块的结构图，然后再对比与C2f的具体的区别。针对C3模块，其主要是借助CSPNet提取分流的思想，同时结合残差结构的思想，设计了C3 Block，CSP主分支梯度模块为BottleNeck模块。同时堆叠的个数由参数n来进行控制，也就是说不同规模的模型，n的值是有变化的。

Yolov5/Yolov7加入Yolov8 c2f模块，涨点

C3模块的Pytorch的实现如下：

class C3(nn.Module):
    # CSP Bottleneck with 3 convolutions  
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion  
        super().__init__()  
        c_ = int(c2 * e)  # hidden channels  
        self.cv1 = Conv(c1, c_, 1, 1)  
        self.cv2 = Conv(c1, c_, 1, 1)  
        self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)  
        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))  
    def forward(self, x):  
        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))

C2f模块的结构图如下：

       C2f模块就是参考了C3模块以及ELAN的思想进行的设计，让YOLOv8可以在保证轻量化的同时获得更加丰富的梯度流信息。

Yolov5/Yolov7加入Yolov8 c2f模块，涨点

class C2f(nn.Module):
    # CSP Bottleneck with 2 convolutions  
    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion  
        super().__init__()  
        self.c = int(c2 * e)  # hidden channels  
        self.cv1 = Conv(c1, 2 * self.c, 1, 1)  
        self.cv2 = Conv((2 + n) * self.c, c2, 1)  # optional act=FReLU(c2)  
        self.m = nn.ModuleList(Bottleneck(self.c, self.c, shortcut, g, k=((3, 3), (3, 3)), e=1.0) for _ in range(n))  
    def forward(self, x):  
        y = list(self.cv1(x).split((self.c, self.c), 1))  
        y.extend(m(y[-1]) for m in self.m)  
        return self.cv2(torch.cat(y, 1))

 2.涨点技巧：Yolov5加入C2F提升小目标检测精度

2.1 Yolov5网络结构图

Yolov5/Yolov7加入Yolov8 c2f模块，涨点

2.2 加入C2f代码修改位置

1)将如下代码添加到common.py中：

class v8_C2fBottleneck(nn.Module):
    # Standard bottleneck
    def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):  # ch_in, ch_out, shortcut, groups, kernels, expand
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, k[0], 1)
        self.cv2 = Conv(c_, c2, k[1], 1, g=g)
        self.add = shortcut and c1 == c2
    def forward(self, x):
        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
class C2f(nn.Module):
    # CSP Bottleneck with 2 convolutions
    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
        super().__init__()
        self.c = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, 2 * self.c, 1, 1)
        self.cv2 = Conv((2 + n) * self.c, c2, 1)  # optional act=FReLU(c2)
        self.m = nn.ModuleList(v8_C2fBottleneck(self.c, self.c, shortcut, g, k=((3, 3), (3, 3)), e=1.0) for _ in range(n))
    def forward(self, x):
        y = list(self.cv1(x).split((self.c, self.c), 1))
        y.extend(m(y[-1]) for m in self.m)
        return self.cv2(torch.cat(y, 1))

 2)在yolo.py中添加C2f（PS:快速搜索C3对应位置）

Yolov5/Yolov7加入Yolov8 c2f模块，涨点

 2.3 修改配置文件yolov8s.yaml

1）加入backbone

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Parameters
nc: 80  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple
anchors:
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32
# YOLOv5 v6.0 backbone
backbone:
  # [from, number, 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]],
   [-1, 1, SPPF, [1024]]
  ]
# 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, anchors]],  # Detect(P3, P4, P5)
  ]

改进后的网络图

Yolov5/Yolov7加入Yolov8 c2f模块，涨点

2) 加入head

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Parameters
nc: 80  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple
anchors:
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32
# 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, C2f, [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, C2f, [256, False]],  # 17 (P3/8-small)
   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 14], 1, Concat, [1]],  # cat head P4
   [-1, 3, C2f, [512, False]],  # 20 (P4/16-medium)
   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 10], 1, Concat, [1]],  # cat head P5
   [-1, 3, C2f, [1024, False]],  # 23 (P5/32-large)
   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

3.总结

针对小目标等提升精度较显著

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