前段时间写了一篇damoYolo的训练教程，同时也对自己的数据集进行了训练，虽然效果确实不是很好，但是damoyolo的一些思想和网络结构啥的还是可以借鉴使用的，此次将damoyolo的RepGFPN结构掏出来放到v5的NECK中，测试一下对本人的数据集(小目标)效果比v5要好，大概提升2个点左右。
放一下damoyolo的github网址：
https://github.com/tinyvision/DAMO-YOLO
damoyolo的整体结构我们是无法看到的因为他的主干网络是nas_backbones 里面是txt文件，RepGFPN是可以看到的。
-
import torch
-
import torch.nn
as nn
-
-
from ..core.ops
import ConvBNAct, CSPStage
-
-
-
class
GiraffeNeckV2(nn.Module):
-
def
__init__(
-
self,
-
depth=1.0,
-
hidden_ratio=1.0,
-
in_features=[2, 3, 4],
-
in_channels=[256, 512, 1024],
-
out_channels=[256, 512, 1024],
-
act='silu',
-
spp=False,
-
block_name='BasicBlock',
-
):
-
super().__init__()
-
self.in_features = in_features
-
self.in_channels = in_channels
-
self.out_channels = out_channels
-
Conv = ConvBNAct
-
-
self.upsample = nn.Upsample(scale_factor=
2, mode=
'nearest')
-
-
# node x3: input x0, x1
-
self.bu_conv13 = Conv(in_channels[
1], in_channels[
1],
3,
2, act=act)
-
self.merge_3 = CSPStage(block_name,
-
in_channels[
1] + in_channels[
2],
-
hidden_ratio,
-
in_channels[
2],
-
round(
3 * depth),
-
act=act,
-
spp=spp)
-
-
# node x4: input x1, x2, x3
-
self.bu_conv24 = Conv(in_channels[
0], in_channels[
0],
3,
2, act=act)
-
self.merge_4 = CSPStage(block_name,
-
in_channels[
0] + in_channels[
1] +
-
in_channels[
2],
-
hidden_ratio,
-
in_channels[
1],
-
round(
3 * depth),
-
act=act,
-
spp=spp)
-
-
# node x5: input x2, x4
-
self.merge_5 = CSPStage(block_name,
-
in_channels[
1] + in_channels[
0],
-
hidden_ratio,
-
out_channels[
0],
-
round(
3 * depth),
-
act=act,
-
spp=spp)
-
-
# node x7: input x4, x5
-
self.bu_conv57 = Conv(out_channels[
0], out_channels[
0],
3,
2, act=act)
-
self.merge_7 = CSPStage(block_name,
-
out_channels[
0] + in_channels[
1],
-
hidden_ratio,
-
out_channels[
1],
-
round(
3 * depth),
-
act=act,
-
spp=spp)
-
-
# node x6: input x3, x4, x7
-
self.bu_conv46 = Conv(in_channels[
1], in_channels[
1],
3,
2, act=act)
-
self.bu_conv76 = Conv(out_channels[
1], out_channels[
1],
3,
2, act=act)
-
self.merge_6 = CSPStage(block_name,
-
in_channels[
1] + out_channels[
1] +
-
in_channels[
2],
-
hidden_ratio,
-
out_channels[
2],
-
round(
3 * depth),
-
act=act,
-
spp=spp)
-
-
def
init_weights(
self):
-
pass
-
-
def
forward(
self, out_features):
-
"""
-
Args:
-
inputs: input images.
-
-
Returns:
-
Tuple[Tensor]: FPN feature.
-
"""
-
-
# backbone
-
[x2, x1, x0] = out_features
-
-
# node x3
-
x13 = self.bu_conv13(x1)
-
x3 = torch.cat([x0, x13],
1)
-
x3 = self.merge_3(x3)
-
-
# node x4
-
x34 = self.upsample(x3)
-
x24 = self.bu_conv24(x2)
-
x4 = torch.cat([x1, x24, x34],
1)
-
x4 = self.merge_4(x4)
-
-
# node x5
-
x45 = self.upsample(x4)
-
x5 = torch.cat([x2, x45],
1)
-
x5 = self.merge_5(x5)
-
-
# node x8
-
# x8 = x5
-
-
# node x7
-
x57 = self.bu_conv57(x5)
-
x7 = torch.cat([x4, x57],
1)
-
x7 = self.merge_7(x7)
-
-
# node x6
-
x46 = self.bu_conv46(x4)
-
x76 = self.bu_conv76(x7)
-
x6 = torch.cat([x3, x46, x76],
1)
-
x6 = self.merge_6(x6)
-
-
outputs = (x5, x7, x6)
-
return outputs
我根据ONNX结构图和上述代码画了简易的展示图：画的相对简单了，可能有些错误，后续我都没在看了，大家还是主要看代码吧
训练自己的数据集：
YoloV5+GFPN（我没用Rep）
yolov5：
map@0.5 相比之下提升了1.7个百分点。。。。还是阔以的
再看下参数量对比：(imgsize,map@50,mAP50-95,参数量(M),FLOPs)
对比之下参数量和FLOPs确实有增加，这种的增加不大，还是可以接受的，但同时map也相应地增加了。具体的话看大家自己抉择了。
转载：https://blog.csdn.net/zhangdaoliang1/article/details/128558733
查看评论