Yolov5更换上采样方式

Yolov5更换上采样方式

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常用上采样方式介绍

1. 最近邻插值(Nearest neighbor interpolation)

2. 双线性插值(Bi-Linear interpolation)

3. 双立方插值(Bi-Cubic interpolation)

计算效果：最近邻插值算法 < 双线性插值 < 双三次插值
计算速度：最近邻插值算法 > 双线性插值 > 双三次插值

4.反池化

5.转置卷积

yolov5默认采用的就是最近邻插值

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实验结果

这里我将原本的最近邻插值的上采样方式替换为转置卷积；有人通过实验证明了确实涨点，但是我在VOC数据集上测试并没有涨点，mAP0.5大概掉了不到1点

修改方式：

第一步；在models/yolo.py添加nn.ConvTranspose2d

第二步；models/yolo.py添加如下代码

 elif m is nn.ConvTranspose2d:
                if len(args) >= 7:
                    args[6] = make_divisible(args[6] * gw, 8)
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第三步；修改配置文件，以yolov5s.yaml为例

# 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.ConvTranspose2d, [512, 4, 2, 1, 0, 512]],
   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, C3, [512, False]],  # 13

   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.ConvTranspose2d, [256, 4, 2, 1, 0, 256]],
   [[-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)
  ]
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出现下面这样子就是运行成功啦