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YoloV5+ECVBlock:基于YoloV5-ECVBlock的小目标检测训练

10月前 作者：Mrs.Gril 分类：Toy博客 阅读(51) 违法举报

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目录

1、前言

2、数据集

3、添加ECVBlock

 4、BackBone+ECVBlock

5、Head+ECVBlock

6、训练结果

6.1 Backbone

6.2 Head

1、前言

YoloV5+ECVBlock:基于YoloV5-ECVBlock的小目标检测训练

 YoloV5+ECVBlock:基于YoloV5-ECVBlock的小目标检测训练

视觉特征金字塔在广泛的应用中显示出其有效性和效率的优越性。然而，现有的方法过分地集中于层间特征交互，而忽略了层内特征规则，这是经验证明是有益的。尽管一些方法试图借助注意机制或视觉变换器学习紧凑的层内特征表示，但它们忽略了对密集预测任务很重要的被忽略的角点区域。为了解决这一问题，本文提出了一种基于全局显式集中式特征规则的集中式特征金字塔（CFP）对象检测方法。具体而言，我们首先提出了一种空间显式视觉中心方案，其中使用轻量级MLP来捕捉全局长距离依赖关系，并使用并行可学习视觉中心机制来捕捉输入图像的局部角区域。在此基础上，我们以自顶向下的方式对常用的特征金字塔提出了一个全局集中的规则，其中使用从最深层内特征获得的显式视觉中心信息

以调节额叶浅部特征。与现有的特征金字塔相比，CFP不仅具有捕获全局长距离依赖关系的能力，而且能够有效地获得全面但有区别的特征表示。在具有挑战性的MS-COCO上的实验结果验证了我们提出的CFP能够在最先进的YOLOv5和YOLOX目标检测基线上实现一致的性能增益。

YoloV5+ECVBlock:基于YoloV5-ECVBlock的小目标检测训练

开源得github网址：

https://github.com/QY1994-0919/CFPNethttps://github.com/QY1994-0919/CFPNet

里面得issuse，有提到有人在自己得数据有所增强，告诉了怎么添加得位置

YoloV5+ECVBlock:基于YoloV5-ECVBlock的小目标检测训练

YoloV5+ECVBlock:基于YoloV5-ECVBlock的小目标检测训练

2、数据集

训练集：930张  验证集：265张 测试集：130张

可以看到数据集中需要检测的是图像的边缘突出的小毛刺，一方面特备的小，一方面分辨率又不是很高。所以存在极大的难度，目前基于YOLOV5s模型最佳的map也只是0.904

3、添加ECVBlock

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.

import torch
import torch.nn as nn
from torch.nn import functional as F
from .Functions import Encoding, Mean, DropPath, Mlp, GroupNorm, LayerNormChannel, ConvBlock


class SiLU(nn.Module):
    """export-friendly version of nn.SiLU()"""

    @staticmethod
    def forward(x):
        return x * torch.sigmoid(x)


def get_activation(name="silu", inplace=True):
    if name == "silu":
        module = nn.SiLU(inplace=inplace)
    elif name == "relu":
        module = nn.ReLU(inplace=inplace)
    elif name == "lrelu":
        module = nn.LeakyReLU(0.1, inplace=inplace)
    else:
        raise AttributeError("Unsupported act type: {}".format(name))
    return module


class BaseConv(nn.Module):
    """A Conv2d -> Batchnorm -> silu/leaky relu block""" # CBL

    def __init__(
        self, in_channels, out_channels, ksize, stride, groups=1, bias=False, act="silu"
    ):
        super().__init__()
        # same padding
        pad = (ksize - 1) // 2
        self.conv = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size=ksize,
            stride=stride,
            padding=pad,
            groups=groups,
            bias=bias,
        )
        self.bn = nn.BatchNorm2d(out_channels)
        self.act = get_activation(act, inplace=True)

    def forward(self, x):
        return self.act(self.bn(self.conv(x)))

    def fuseforward(self, x):
        return self.act(self.conv(x))


class DWConv(nn.Module):
    """Depthwise Conv + Conv"""
    def __init__(self, in_channels, out_channels, ksize, stride=1, act="silu"):
        super().__init__()
        self.dconv = BaseConv(
            in_channels,
            in_channels,
            ksize=ksize,
            stride=stride,
            groups=in_channels,
            act=act,
        )
        self.pconv = BaseConv(
            in_channels, out_channels, ksize=1, stride=1, groups=1, act=act
        )

    def forward(self, x):
        x = self.dconv(x)
        return self.pconv(x)


class Bottleneck(nn.Module):
    # Standard bottleneck
    def __init__(
        self,
        in_channels,
        out_channels,
        shortcut=True,
        expansion=0.5,
        depthwise=False,
        act="silu",
    ):
        super().__init__()
        hidden_channels = int(out_channels * expansion)
        Conv = DWConv if depthwise else BaseConv
        self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)
        self.conv2 = Conv(hidden_channels, out_channels, 3, stride=1, act=act)
        self.use_add = shortcut and in_channels == out_channels

    def forward(self, x):
        y = self.conv2(self.conv1(x))
        if self.use_add:
            y = y + x
        return y


class ResLayer(nn.Module):
    "Residual layer with `in_channels` inputs."

    def __init__(self, in_channels: int):
        super().__init__()
        mid_channels = in_channels // 2
        self.layer1 = BaseConv(
            in_channels, mid_channels, ksize=1, stride=1, act="lrelu"
        )
        self.layer2 = BaseConv(
            mid_channels, in_channels, ksize=3, stride=1, act="lrelu"
        )

    def forward(self, x):
        out = self.layer2(self.layer1(x))
        return x + out


class SPPBottleneck(nn.Module):
    """Spatial pyramid pooling layer used in YOLOv3-SPP"""

    def __init__(
        self, in_channels, out_channels, kernel_sizes=(5, 9, 13), activation="silu"
    ):
        super().__init__()
        hidden_channels = in_channels // 2
        self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=activation)
        self.m = nn.ModuleList(
            [
                nn.MaxPool2d(kernel_size=ks, stride=1, padding=ks // 2)
                for ks in kernel_sizes
            ]
        )
        conv2_channels = hidden_channels * (len(kernel_sizes) + 1)
        self.conv2 = BaseConv(conv2_channels, out_channels, 1, stride=1, act=activation)

    def forward(self, x):
        x = self.conv1(x)
        x = torch.cat([x] + [m(x) for m in self.m], dim=1)
        x = self.conv2(x)
        return x


class CSPLayer(nn.Module):
    """C3 in yolov5, CSP Bottleneck with 3 convolutions"""

    def __init__(
        self,
        in_channels,
        out_channels,
        n=1,
        shortcut=True,
        expansion=0.5,
        depthwise=False,
        act="silu",
    ):
        """
        Args:
            in_channels (int): input channels.
            out_channels (int): output channels.
            n (int): number of Bottlenecks. Default value: 1.
        """
        # ch_in, ch_out, number, shortcut, groups, expansion
        super().__init__()
        hidden_channels = int(out_channels * expansion)  # hidden channels
        self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)
        self.conv2 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)
        self.conv3 = BaseConv(2 * hidden_channels, out_channels, 1, stride=1, act=act)
        module_list = [
            Bottleneck(
                hidden_channels, hidden_channels, shortcut, 1.0, depthwise, act=act
            )
            for _ in range(n)
        ]
        self.m = nn.Sequential(*module_list)

    def forward(self, x):
        x_1 = self.conv1(x)
        x_2 = self.conv2(x)
        x_1 = self.m(x_1)
        x = torch.cat((x_1, x_2), dim=1)
        return self.conv3(x)


class Focus(nn.Module):
    """Focus width and height information into channel space."""

    def __init__(self, in_channels, out_channels, ksize=1, stride=1, act="silu"):
        super().__init__()
        self.conv = BaseConv(in_channels * 4, out_channels, ksize, stride, act=act)

    def forward(self, x):
        # shape of x (b,c,w,h) -> y(b,4c,w/2,h/2)
        patch_top_left = x[..., ::2, ::2]
        patch_top_right = x[..., ::2, 1::2]
        patch_bot_left = x[..., 1::2, ::2]
        patch_bot_right = x[..., 1::2, 1::2]
        x = torch.cat(
            (
                patch_top_left,
                patch_bot_left,
                patch_top_right,
                patch_bot_right,
            ),
            dim=1,
        )
        return self.conv(x)


class LVCBlock(nn.Module):
    def __init__(self, in_channels, out_channels, num_codes, channel_ratio=0.25, base_channel=64):
        super(LVCBlock, self).__init__()
        self.out_channels = out_channels
        self.num_codes = num_codes
        num_codes = 64

        self.conv_1 = ConvBlock(in_channels=in_channels, out_channels=in_channels, res_conv=True, stride=1)

        self.LVC = nn.Sequential(
            nn.Conv2d(in_channels, in_channels, 1, bias=False),
            nn.BatchNorm2d(in_channels),
            nn.ReLU(inplace=True),
            Encoding(in_channels=in_channels, num_codes=num_codes),
            nn.BatchNorm1d(num_codes),
            nn.ReLU(inplace=True),
            Mean(dim=1))
        self.fc = nn.Sequential(nn.Linear(in_channels, in_channels), nn.Sigmoid())

    def forward(self, x):
        x = self.conv_1(x, return_x_2=False)
        en = self.LVC(x)
        gam = self.fc(en)
        b, in_channels, _, _ = x.size()
        y = gam.view(b, in_channels, 1, 1)
        x = F.relu_(x + x * y)
        return x


# LightMLPBlock
class LightMLPBlock(nn.Module):
    def __init__(self, in_channels, out_channels, ksize=1, stride=1, act="silu",
    mlp_ratio=4., drop=0., act_layer=nn.GELU, 
    use_layer_scale=True, layer_scale_init_value=1e-5, drop_path=0., norm_layer=GroupNorm):  # act_layer=nn.GELU,
        super().__init__()
        self.dw = DWConv(in_channels, out_channels, ksize=1, stride=1, act="silu")
        self.linear = nn.Linear(out_channels, out_channels)  # learnable position embedding
        self.out_channels = out_channels

        self.norm1 = norm_layer(in_channels)
        self.norm2 = norm_layer(in_channels)

        mlp_hidden_dim = int(in_channels * mlp_ratio)
        self.mlp = Mlp(in_features=in_channels, hidden_features=mlp_hidden_dim, act_layer=nn.GELU,
                       drop=drop)

        self.drop_path = DropPath(drop_path) if drop_path > 0. \
            else nn.Identity()

        self.use_layer_scale = use_layer_scale
        if use_layer_scale:
            self.layer_scale_1 = nn.Parameter(
                layer_scale_init_value * torch.ones((out_channels)), requires_grad=True)
            self.layer_scale_2 = nn.Parameter(
                layer_scale_init_value * torch.ones((out_channels)), requires_grad=True)

    def forward(self, x):
        if self.use_layer_scale:
            x = x + self.drop_path(self.layer_scale_1.unsqueeze(-1).unsqueeze(-1) * self.dw(self.norm1(x)))
            x = x + self.drop_path(self.layer_scale_2.unsqueeze(-1).unsqueeze(-1) * self.mlp(self.norm2(x)))
        else:
            x = x + self.drop_path(self.dw(self.norm1(x)))
            x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


# EVCBlock
class EVCBlock(nn.Module):
    def __init__(self, in_channels, out_channels, channel_ratio=4, base_channel=16):
        super().__init__()
        expansion = 2
        ch = out_channels * expansion
        # Stem stage: get the feature maps by conv block (copied form resnet.py) 进入conformer框架之前的处理
        self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size=7, stride=1, padding=3, bias=False)  # 1 / 2 [112, 112]
        self.bn1 = nn.BatchNorm2d(in_channels)
        self.act1 = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)  # 1 / 4 [56, 56]

        # LVC
        self.lvc = LVCBlock(in_channels=in_channels, out_channels=out_channels, num_codes=64)  # c1值暂时未定
        # LightMLPBlock
        self.l_MLP = LightMLPBlock(in_channels, out_channels, ksize=1, stride=1, act="silu", act_layer=nn.GELU, mlp_ratio=4., drop=0.,
                                     use_layer_scale=True, layer_scale_init_value=1e-5, drop_path=0., norm_layer=GroupNorm)
        self.cnv1 = nn.Conv2d(ch, out_channels, kernel_size=1, stride=1, padding=0)

    def forward(self, x):
        x1 = self.maxpool(self.act1(self.bn1(self.conv1(x))))
        # LVCBlock
        x_lvc = self.lvc(x1)
        # LightMLPBlock
        x_lmlp = self.l_MLP(x1)
        # concat
        x = torch.cat((x_lvc, x_lmlp), dim=1)
        x = self.cnv1(x)
        return x

 4、BackBone+ECVBlock

Yaml文件：


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]],     # 4
   [-1, 1, EVCBlock, [256, 256]], # update
   [-2, 1, Conv, [512, 3, 2]],  # 6-P4/16
   [-1, 9, C3, [512]],
   [-1, 1, Conv, [1024, 3, 2]],  # 8-P5/32
   [-1, 3, C3, [1024]],
   [-1, 1, SPPF, [1024, 5]],  # 10
  ]

# YOLOv5 v6.0 head
head:
  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 7], 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, 5], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 15], 1, Concat, [1]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 11], 1, Concat, [1]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)

   [[18, 21, 24], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

5、Head+ECVBlock

# YOLOv5 v6.0 head
head:
  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [-1, 1, EVCBlock, [512, 512]], # update
   [[-1, 6], 1, Concat, [1]],  # cat backbone P4 -2 输出
   [-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 # 512
   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 15], 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)

   [[18, 21, 24], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

6、训练结果

6.1 Backbone

Yolov5s+ECVBlock

YoloV5+ECVBlock:基于YoloV5-ECVBlock的小目标检测训练

YOLOV5S:

YoloV5+ECVBlock:基于YoloV5-ECVBlock的小目标检测训练

6.2 Head

YoloV5+ECVBlock:基于YoloV5-ECVBlock的小目标检测训练

。。。。。。。。。。。。。。。怎么说呢  就是感觉可能在自己的数据上不是很好吧。文章来源地址https://www.toymoban.com/news/detail-445727.html

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