YOLOv5、YOLOv8改进-BIFPN

加入BIFPN加权双向金字塔结构，提升不同尺度的检测效果。

第一步：common.py构建Concat_BIFPN模块

class Concat_bifpn(nn.Module):
    # Concatenate a list of tensors along dimension
    def __init__(self, c1, c2):
        super(Concat_bifpn, self).__init__()
        self.w1 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True)
        self.w2 = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True)
       # self.w3 = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True)
        self.epsilon = 0.0001
        self.conv = Conv(c1, c2, 1 ,1 ,0 )
        self.act= nn.ReLU()
 
    def forward(self, x): # mutil-layer 1-3 layers #ADD or Concat 
        #print("bifpn:",x.shape)
        if len(x) == 2:
            w = self.w1
            weight = w / (torch.sum(w, dim=0) + self.epsilon)
            x = self.conv(self.act(weight[0] * x[0] + weight[1] * x[1]))
        elif len(x) == 3: 
            w = self.w2
            weight = w / (torch.sum(w, dim=0) + self.epsilon)
            x = self.conv(self.act (weight[0] * x[0] + weight[1] * x[1] + weight[2] * x[2]))
        # elif len(x) == 4:    
        #     w = self.w3
        #     weight = w / (torch.sum(w, dim=0) + self.epsilon)
        #     x = self.conv(self.act(weight[0] * x[0] + weight[1] * x[1] + weight[2] *x[2] + weight[3]*x[3] ))
        return x

第二步：yolo.py中注册Concat_BIFPNt模块

        elif m is Concat_bifpn:
            c2 = max([ch[x] for x in f])

第三步：修改yaml文件（以修改官方YOLOv5s.yaml为例），需要修改head（特征融合网络）

# parameters
nc: 80  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple
 
# anchors
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 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]],
  ]
 
# YOLOv5 head
head:
  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1,6], 1, Concat_bifpn, [256,256]],  # 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_bifpn, [128,128]],  # cat backbone P3
   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)
 
 
   [-1, 1, Conv, [512, 3, 2]],   # 320, 640 # 
   [[-1, 6, 13], 1, Concat_bifpn, [256,256]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)
 
   [-1, 1, Conv, [1024, 3, 2]], # 640, 1280 # 
   [[-1, 9], 1, Concat_bifpn, [512, 512]],  # cat head P5  cat 20,20 #22
   [-1, 3, C3, [1024, False]],  # 25 (P5/32-large) # 1280, 1280  #23
 
 
   [[17, 20, 23], 1, Detect, [nc, anchors]] # Detect(P3, P4, P5)
  ]

Model Summary: 290 layers, 8114651 parameters, 8114651 gradients, 17.4 GFLOPs

如果需要在YOLOv5l.yaml等网络结构进行修改的话，不可直接用以上的yaml文件或者就简单修改depth_multiple为1.0，而是需要修改Concat_bifpn, [256,256]中的通道数为对应网络实际通道数。具体如下所示：

# parameters
nc: 80  # number of classes
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # layer channel multiple
 
# anchors
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 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]],
  ]
 
# YOLOv5 head
head:
  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1,6], 1, Concat_bifpn, [512,512]],  # 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_bifpn, [256,256]],  # cat backbone P3
   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)
 
 
   [-1, 1, Conv, [512, 3, 2]],   # 320, 640 # 
   [[-1, 6, 13], 1, Concat_bifpn, [512,512]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)
 
   [-1, 1, Conv, [1024, 3, 2]], # 640, 1280 # 
   [[-1, 9], 1, Concat_bifpn, [1024, 1024]],  # cat head P5  cat 20,20 #22
   [-1, 3, C3, [1024, False]],  # 25 (P5/32-large) # 1280, 1280  #23
 
 
   [[17, 20, 23], 1, Detect, [nc, anchors]] # Detect(P3, P4, P5)
  ]

YOLOv5、YOLOv8改进-BIFPN

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