深度学习基础--将yolov5的backbone模块用于目标识别会出现怎么效果呢？？

🍨 本文为🔗365天深度学习训练营中的学习记录博客
🍖 原作者：K同学啊

前言

yolov5网络结构比较复杂，上次我们简要介绍了yolov5网络模块，并且复现了C3模块，深度学习基础–yolov5网络结构简介，C3模块构建;
这一次我们将复现backbone模块，将目标检测网络结构用到目标识别上，会是怎样的效果呢？？？；
这周是考试周，周一到周四一直都在准备考试和去考试，昨天开始又发高烧，更新较慢；
欢迎收藏加关注，本人将会持续更新。
文章目录
- 案例
- - 1、数据处理
  - - 1、导入库
    - 2、查看数据类别
    - 3、导入数据
    - 4、数据集划分
    - 5、展示一批数据
  - 2、模型构建
  - 3、模型训练
  - - 1、构建训练集
    - 2、构建测试集
    - 3、设置超参数
  - 4、模型正式训练
  - 5、结果显示和评估
  - - 1、结果显示
    - 2、评估

案例

将backbone模块用于识别天气分类

1、数据处理

1、导入库

import torch 
import torchvision
import torch.nn as nn 
import torchvision.transforms as transforms
from torchvision import datasets, transformsdevice = "cuda" if torch.cuda.is_available() else "cpu"device

'cuda'

2、查看数据类别

import os, pathlib data_dir = './data/'
data_dir = pathlib.Path(data_dir)classnames = [str(path).split("\\")[0] for path in os.listdir(data_dir)]
classnames

['cloudy', 'rain', 'shine', 'sunrise']

3、导入数据

data_transforms = transforms.Compose([transforms.Resize([224, 224]),transforms.ToTensor(),transforms.Normalize(              # 数据标准化处理---> 转化为 标准状态分布，使模型更容易收敛mean=[0.485, 0.456, 0.406],  # rgb，均值std=[0.229, 0.224, 0.225]    # rgb，标准差，这两个从数据集中随机抽样得到的)
])total_data = datasets.ImageFolder("./data/", data_transforms)
total_data

Dataset ImageFolderNumber of datapoints: 1125Root location: ./data/StandardTransform
Transform: Compose(Resize(size=[224, 224], interpolation=bilinear, max_size=None, antialias=True)ToTensor()Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]))

4、数据集划分

训练集：测试集 = 8 ：2

train_size = int(len(total_data) * 0.8)
test_size = len(total_data) - train_size 
train_data, test_data = torch.utils.data.random_split(total_data, [train_size, test_size])
print("train_size", len(train_data))
print("test_size", len(test_data))

train_size 900
test_size 225

# 动态加载数据集
batch_size = 4train_dl = torch.utils.data.DataLoader(train_data,batch_size=batch_size,shuffle=True
)test_dl = torch.utils.data.DataLoader(test_data,batch_size=batch_size,shuffle=True
)

# 查看数据格式
temp_data, temp_label = next(iter(train_dl))print("data: ", temp_data.shape)
print("data_labels: ", temp_label)

data:  torch.Size([4, 3, 224, 224])
data_labels:  tensor([3, 2, 0, 0])

5、展示一批数据

这里一批次大小：4

import matplotlib.pyplot as plt temp_images, temp_labels = next(iter(test_dl))plt.figure(figsize=(20, 10))for i in range(4):plt.subplot(5, 5, i + 1)plt.imshow(temp_images[i].cpu().numpy().transpose(1, 2, 0))  # （C， H， W)  ==>  （H， W， C）plt.title(classnames[temp_labels[i]])plt.axis('off')plt.show()

在这里插入图片描述

2、模型构建

整体网络：

在这里插入图片描述

C3网络参考：深度学习基础–yolov5网络结构简介，C3模块构建

SPPF网络模块图结构如下：

在这里插入图片描述

import torch.nn.functional as F 
import warnings  # 确保导入 warnings 模块# 自动计算p(填充)
def autop(k, p=None):if p is None:p = k // 2 if isinstance(k, int) else [i // 2 for i in k]return p # Conv模块搭建
'''
卷积层 + 标准化 + 激活函数
'''
class Conv(nn.Module):def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):super().__init__()'''groups: 1: 标准卷积c1:  深度卷积1 ~ c1: 分组卷积bias:false: 不使用偏置'''self.conv = nn.Conv2d(c1, c2, kernel_size=k, stride=s, padding=autop(k, p), groups=g, bias=False)  self.bn = nn.BatchNorm2d(c2)'''act:true: silu激活函数否则: 如果是nn.Mudule(如: nn.Relu), 则调用本身否则: Identity, 什么都不做'''self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module()) else nn.Identity())def forward(self,x):return self.act(self.bn(self.conv(x)))# Bottleneck模块, 用于特征提取和用于防止梯度消失、梯度爆炸问题
class Bottleneck(nn.Module):def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):    # shortcut: 是否需要残差连接， e: 模型深度super().__init__()c_ = int(c1 * 2)self.cv1 = Conv(c1, c_, 1, 1)self.cv2 = Conv(c_, c2, 3, 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))# 搭建C3模块
class C3(nn.Module):def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):super().__init__()'''刚开始: 卷积层2层后面: n层bottlenck后 concat后 conv'''c_ = int(c1 * e)self.cv1 = Conv(c1, c_, 1, 1)self.cv2 = Conv(c1, c_, 1, 1)   # 用于拼接self.cv3 = Conv(2 * c_, c2, 1)self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))  # * 解包def forward(self, x):# 拼接，按照 dim=1维进行拼接，故列要相同return self.cv3(torch.cat([self.m(self.cv1(x)), self.cv2(x)], dim=1))   # 结合图就知道了结构# 搭建SPPF模块，用于特征融合
class SPPF(nn.Module):def __init__(self, c1, c2, k=5):super().__init__()c_ = c1 // 2 self.cv1 = Conv(c1, c_, 1, 1)self.cv2 = Conv(c_ * 4, c2, 1, 1)   # 模型融合，这个时候模型通道扩大4倍，套用池化层公式，发现通过.m 通道数数不变self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)   # 套用卷积层、池化层公式，发现输出通道不变def forward(self, x):x = self.cv1(x)with warnings.catch_warnings():warnings.simplefilter('ignore')y1 = self.m(x)y2 = self.m(y1)y3 = self.m(y2)return self.cv2(torch.cat([x, y1, y2, y3], 1))   # 结合图# 搭建backbone模块
class Yolov5_backbone(nn.Module):def __init__(self):super(Yolov5_backbone, self).__init__()# 采用常规卷积, kernel_size, stride 与 yolov5.yaml配置文件一致self.conv_1 = Conv(3, 64, 3, 2, 2) self.conv_2 = Conv(64, 128, 3, 2)self.c3_3 = C3(128, 128)self.conv_4 = Conv(128, 256, 3, 2)self.c3_5 = C3(256, 256)self.conv_6 = Conv(256, 512, 3, 2)self.c3_7 = C3(512, 512)self.conv_8 = Conv(512, 1024, 3, 2)self.c3_9 = C3(1024, 1024)self.SPPF_10 = SPPF(1024, 1024, 5)self.classifiler = nn.Sequential(nn.Linear(in_features=65536, out_features=100),nn.ReLU(),nn.Linear(in_features=100, out_features=len(classnames)))def forward(self, x):x = self.conv_1(x)x = self.conv_2(x)x = self.c3_3(x)x = self.conv_4(x)x = self.c3_5(x)x = self.conv_6(x)x = self.c3_7(x)x = self.conv_8(x)x = self.c3_9(x)x = self.SPPF_10(x)x = torch.flatten(x, start_dim=1)x = self.classifiler(x)return x

# 输出参数
model = Yolov5_backbone().to(device)
model

Yolov5_backbone((conv_1): Conv((conv): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2), bias=False)(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(conv_2): Conv((conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(c3_3): C3((cv1): Conv((conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(cv2): Conv((conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(cv3): Conv((conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(m): Sequential((0): Bottleneck((cv1): Conv((conv): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(cv2): Conv((conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU()))))(conv_4): Conv((conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(c3_5): C3((cv1): Conv((conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(cv2): Conv((conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(cv3): Conv((conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(m): Sequential((0): Bottleneck((cv1): Conv((conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(cv2): Conv((conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU()))))(conv_6): Conv((conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(c3_7): C3((cv1): Conv((conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(cv2): Conv((conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(cv3): Conv((conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(m): Sequential((0): Bottleneck((cv1): Conv((conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(cv2): Conv((conv): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU()))))(conv_8): Conv((conv): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(c3_9): C3((cv1): Conv((conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(cv2): Conv((conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(cv3): Conv((conv): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(m): Sequential((0): Bottleneck((cv1): Conv((conv): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(cv2): Conv((conv): Conv2d(1024, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU()))))(SPPF_10): SPPF((cv1): Conv((conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(cv2): Conv((conv): Conv2d(2048, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(m): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False))(classifiler): Sequential((0): Linear(in_features=65536, out_features=100, bias=True)(1): ReLU()(2): Linear(in_features=100, out_features=4, bias=True))
)

import torchsummary as summary 
summary.summary(model, (3, 224, 224))

----------------------------------------------------------------Layer (type)               Output Shape         Param #
================================================================Conv2d-1         [-1, 64, 113, 113]           1,728BatchNorm2d-2         [-1, 64, 113, 113]             128SiLU-3         [-1, 64, 113, 113]               0Conv-4         [-1, 64, 113, 113]               0Conv2d-5          [-1, 128, 57, 57]          73,728BatchNorm2d-6          [-1, 128, 57, 57]             256SiLU-7          [-1, 128, 57, 57]               0Conv-8          [-1, 128, 57, 57]               0Conv2d-9           [-1, 64, 57, 57]           8,192BatchNorm2d-10           [-1, 64, 57, 57]             128SiLU-11           [-1, 64, 57, 57]               0Conv-12           [-1, 64, 57, 57]               0Conv2d-13          [-1, 128, 57, 57]           8,192BatchNorm2d-14          [-1, 128, 57, 57]             256SiLU-15          [-1, 128, 57, 57]               0Conv-16          [-1, 128, 57, 57]               0Conv2d-17           [-1, 64, 57, 57]          73,728BatchNorm2d-18           [-1, 64, 57, 57]             128SiLU-19           [-1, 64, 57, 57]               0Conv-20           [-1, 64, 57, 57]               0Bottleneck-21           [-1, 64, 57, 57]               0Conv2d-22           [-1, 64, 57, 57]           8,192BatchNorm2d-23           [-1, 64, 57, 57]             128SiLU-24           [-1, 64, 57, 57]               0Conv-25           [-1, 64, 57, 57]               0Conv2d-26          [-1, 128, 57, 57]          16,384BatchNorm2d-27          [-1, 128, 57, 57]             256SiLU-28          [-1, 128, 57, 57]               0Conv-29          [-1, 128, 57, 57]               0C3-30          [-1, 128, 57, 57]               0Conv2d-31          [-1, 256, 29, 29]         294,912BatchNorm2d-32          [-1, 256, 29, 29]             512SiLU-33          [-1, 256, 29, 29]               0Conv-34          [-1, 256, 29, 29]               0Conv2d-35          [-1, 128, 29, 29]          32,768BatchNorm2d-36          [-1, 128, 29, 29]             256SiLU-37          [-1, 128, 29, 29]               0Conv-38          [-1, 128, 29, 29]               0Conv2d-39          [-1, 256, 29, 29]          32,768BatchNorm2d-40          [-1, 256, 29, 29]             512SiLU-41          [-1, 256, 29, 29]               0Conv-42          [-1, 256, 29, 29]               0Conv2d-43          [-1, 128, 29, 29]         294,912BatchNorm2d-44          [-1, 128, 29, 29]             256SiLU-45          [-1, 128, 29, 29]               0Conv-46          [-1, 128, 29, 29]               0Bottleneck-47          [-1, 128, 29, 29]               0Conv2d-48          [-1, 128, 29, 29]          32,768BatchNorm2d-49          [-1, 128, 29, 29]             256SiLU-50          [-1, 128, 29, 29]               0Conv-51          [-1, 128, 29, 29]               0Conv2d-52          [-1, 256, 29, 29]          65,536BatchNorm2d-53          [-1, 256, 29, 29]             512SiLU-54          [-1, 256, 29, 29]               0Conv-55          [-1, 256, 29, 29]               0C3-56          [-1, 256, 29, 29]               0Conv2d-57          [-1, 512, 15, 15]       1,179,648BatchNorm2d-58          [-1, 512, 15, 15]           1,024SiLU-59          [-1, 512, 15, 15]               0Conv-60          [-1, 512, 15, 15]               0Conv2d-61          [-1, 256, 15, 15]         131,072BatchNorm2d-62          [-1, 256, 15, 15]             512SiLU-63          [-1, 256, 15, 15]               0Conv-64          [-1, 256, 15, 15]               0Conv2d-65          [-1, 512, 15, 15]         131,072BatchNorm2d-66          [-1, 512, 15, 15]           1,024SiLU-67          [-1, 512, 15, 15]               0Conv-68          [-1, 512, 15, 15]               0Conv2d-69          [-1, 256, 15, 15]       1,179,648BatchNorm2d-70          [-1, 256, 15, 15]             512SiLU-71          [-1, 256, 15, 15]               0Conv-72          [-1, 256, 15, 15]               0Bottleneck-73          [-1, 256, 15, 15]               0Conv2d-74          [-1, 256, 15, 15]         131,072BatchNorm2d-75          [-1, 256, 15, 15]             512SiLU-76          [-1, 256, 15, 15]               0Conv-77          [-1, 256, 15, 15]               0Conv2d-78          [-1, 512, 15, 15]         262,144BatchNorm2d-79          [-1, 512, 15, 15]           1,024SiLU-80          [-1, 512, 15, 15]               0Conv-81          [-1, 512, 15, 15]               0C3-82          [-1, 512, 15, 15]               0Conv2d-83           [-1, 1024, 8, 8]       4,718,592BatchNorm2d-84           [-1, 1024, 8, 8]           2,048SiLU-85           [-1, 1024, 8, 8]               0Conv-86           [-1, 1024, 8, 8]               0Conv2d-87            [-1, 512, 8, 8]         524,288BatchNorm2d-88            [-1, 512, 8, 8]           1,024SiLU-89            [-1, 512, 8, 8]               0Conv-90            [-1, 512, 8, 8]               0Conv2d-91           [-1, 1024, 8, 8]         524,288BatchNorm2d-92           [-1, 1024, 8, 8]           2,048SiLU-93           [-1, 1024, 8, 8]               0Conv-94           [-1, 1024, 8, 8]               0Conv2d-95            [-1, 512, 8, 8]       4,718,592BatchNorm2d-96            [-1, 512, 8, 8]           1,024SiLU-97            [-1, 512, 8, 8]               0Conv-98            [-1, 512, 8, 8]               0Bottleneck-99            [-1, 512, 8, 8]               0Conv2d-100            [-1, 512, 8, 8]         524,288BatchNorm2d-101            [-1, 512, 8, 8]           1,024SiLU-102            [-1, 512, 8, 8]               0Conv-103            [-1, 512, 8, 8]               0Conv2d-104           [-1, 1024, 8, 8]       1,048,576BatchNorm2d-105           [-1, 1024, 8, 8]           2,048SiLU-106           [-1, 1024, 8, 8]               0Conv-107           [-1, 1024, 8, 8]               0C3-108           [-1, 1024, 8, 8]               0Conv2d-109            [-1, 512, 8, 8]         524,288BatchNorm2d-110            [-1, 512, 8, 8]           1,024SiLU-111            [-1, 512, 8, 8]               0Conv-112            [-1, 512, 8, 8]               0MaxPool2d-113            [-1, 512, 8, 8]               0MaxPool2d-114            [-1, 512, 8, 8]               0MaxPool2d-115            [-1, 512, 8, 8]               0Conv2d-116           [-1, 1024, 8, 8]       2,097,152BatchNorm2d-117           [-1, 1024, 8, 8]           2,048SiLU-118           [-1, 1024, 8, 8]               0Conv-119           [-1, 1024, 8, 8]               0SPPF-120           [-1, 1024, 8, 8]               0Linear-121                  [-1, 100]       6,553,700ReLU-122                  [-1, 100]               0Linear-123                    [-1, 4]             404
================================================================
Total params: 25,213,112
Trainable params: 25,213,112
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 149.98
Params size (MB): 96.18
Estimated Total Size (MB): 246.74
----------------------------------------------------------------

3、模型训练

1、构建训练集

def train(dataloader, model, loss_fn, optimizer):size = len(dataloader.dataset)  # 总数目num_batch = len(dataloader)      # 批次数目train_acc, train_loss = 0, 0for X, y in dataloader:X, y = X.to(device), y.to(device) predict = model(X)loss = loss_fn(predict, y)# 梯度清0、求导、重新设置参数optimizer.zero_grad()loss.backward()optimizer.step()train_acc += (predict.argmax(1) == y).type(torch.float).sum().item()train_loss += loss.item()train_acc /= sizetrain_loss /= num_batchreturn train_acc, train_loss

2、构建测试集

def test(dataloader, model, loss_fn):size = len(dataloader.dataset)num_batch = len(dataloader)test_acc, test_loss = 0, 0with torch.no_grad():for X, y in dataloader:X, y = X.to(device), y.to(device)predict = model(X)loss = loss_fn(predict, y)test_acc += (predict.argmax(1) == y).type(torch.float).sum().item()test_loss += loss.item()test_acc /= size test_loss /= num_batchreturn test_acc, test_loss

3、设置超参数

learn_rate = 1e-4 
optimizer = torch.optim.Adam(model.parameters(), lr=learn_rate)
loss_fn = nn.CrossEntropyLoss()

4、模型正式训练

import copy epochs = 60train_acc, train_loss, test_acc, test_loss = [], [], [], []best_acc = 0for epoch in range(epochs):model.train()epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer)model.eval()epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)# 保存最佳模型到 best_modelif epoch_test_acc > best_acc:best_acc   = epoch_test_accbest_model = copy.deepcopy(model)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)# 获取当前的学习率lr = optimizer.state_dict()['param_groups'][0]['lr']template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, Lr:{:.2E}')print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss, lr))# 保存最佳模型到文件中
PATH = './best_model.pth'  # 保存的参数文件名
torch.save(model.state_dict(), PATH)print('Done')

Epoch: 1, Train_acc:59.0%, Train_loss:1.106, Test_acc:71.1%, Test_loss:0.740, Lr:1.00E-04
Epoch: 2, Train_acc:70.0%, Train_loss:0.776, Test_acc:83.1%, Test_loss:0.468, Lr:1.00E-04
Epoch: 3, Train_acc:75.2%, Train_loss:0.661, Test_acc:84.0%, Test_loss:0.461, Lr:1.00E-04
Epoch: 4, Train_acc:77.4%, Train_loss:0.605, Test_acc:88.4%, Test_loss:0.396, Lr:1.00E-04
Epoch: 5, Train_acc:80.3%, Train_loss:0.529, Test_acc:82.7%, Test_loss:0.415, Lr:1.00E-04
Epoch: 6, Train_acc:83.8%, Train_loss:0.422, Test_acc:83.6%, Test_loss:0.416, Lr:1.00E-04
Epoch: 7, Train_acc:85.8%, Train_loss:0.423, Test_acc:87.1%, Test_loss:0.343, Lr:1.00E-04
Epoch: 8, Train_acc:85.6%, Train_loss:0.393, Test_acc:87.6%, Test_loss:0.306, Lr:1.00E-04
Epoch: 9, Train_acc:86.3%, Train_loss:0.354, Test_acc:89.3%, Test_loss:0.338, Lr:1.00E-04
Epoch:10, Train_acc:86.6%, Train_loss:0.340, Test_acc:92.9%, Test_loss:0.276, Lr:1.00E-04
Epoch:11, Train_acc:88.8%, Train_loss:0.317, Test_acc:90.2%, Test_loss:0.290, Lr:1.00E-04
Epoch:12, Train_acc:87.9%, Train_loss:0.327, Test_acc:88.0%, Test_loss:0.338, Lr:1.00E-04
Epoch:13, Train_acc:89.2%, Train_loss:0.315, Test_acc:92.0%, Test_loss:0.337, Lr:1.00E-04
Epoch:14, Train_acc:91.1%, Train_loss:0.230, Test_acc:92.0%, Test_loss:0.369, Lr:1.00E-04
Epoch:15, Train_acc:93.3%, Train_loss:0.182, Test_acc:89.8%, Test_loss:0.278, Lr:1.00E-04
Epoch:16, Train_acc:91.6%, Train_loss:0.229, Test_acc:90.2%, Test_loss:0.290, Lr:1.00E-04
Epoch:17, Train_acc:90.9%, Train_loss:0.230, Test_acc:91.6%, Test_loss:0.272, Lr:1.00E-04
Epoch:18, Train_acc:93.9%, Train_loss:0.152, Test_acc:92.0%, Test_loss:0.280, Lr:1.00E-04
Epoch:19, Train_acc:94.7%, Train_loss:0.159, Test_acc:92.0%, Test_loss:0.262, Lr:1.00E-04
Epoch:20, Train_acc:95.9%, Train_loss:0.124, Test_acc:91.1%, Test_loss:0.260, Lr:1.00E-04
Epoch:21, Train_acc:95.7%, Train_loss:0.102, Test_acc:88.9%, Test_loss:0.342, Lr:1.00E-04
Epoch:22, Train_acc:95.9%, Train_loss:0.113, Test_acc:92.4%, Test_loss:0.275, Lr:1.00E-04
Epoch:23, Train_acc:96.1%, Train_loss:0.130, Test_acc:92.9%, Test_loss:0.308, Lr:1.00E-04
Epoch:24, Train_acc:94.8%, Train_loss:0.161, Test_acc:86.7%, Test_loss:0.456, Lr:1.00E-04
Epoch:25, Train_acc:95.2%, Train_loss:0.139, Test_acc:89.3%, Test_loss:0.428, Lr:1.00E-04
Epoch:26, Train_acc:96.0%, Train_loss:0.103, Test_acc:92.9%, Test_loss:0.313, Lr:1.00E-04
Epoch:27, Train_acc:96.0%, Train_loss:0.098, Test_acc:88.9%, Test_loss:0.520, Lr:1.00E-04
Epoch:28, Train_acc:97.2%, Train_loss:0.079, Test_acc:91.1%, Test_loss:0.404, Lr:1.00E-04
Epoch:29, Train_acc:98.6%, Train_loss:0.037, Test_acc:92.0%, Test_loss:0.270, Lr:1.00E-04
Epoch:30, Train_acc:98.8%, Train_loss:0.033, Test_acc:88.4%, Test_loss:0.520, Lr:1.00E-04
Epoch:31, Train_acc:95.6%, Train_loss:0.139, Test_acc:91.6%, Test_loss:0.370, Lr:1.00E-04
Epoch:32, Train_acc:96.7%, Train_loss:0.116, Test_acc:89.3%, Test_loss:0.376, Lr:1.00E-04
Epoch:33, Train_acc:96.4%, Train_loss:0.102, Test_acc:91.6%, Test_loss:0.342, Lr:1.00E-04
Epoch:34, Train_acc:98.6%, Train_loss:0.049, Test_acc:87.1%, Test_loss:0.417, Lr:1.00E-04
Epoch:35, Train_acc:97.9%, Train_loss:0.068, Test_acc:90.7%, Test_loss:0.423, Lr:1.00E-04
Epoch:36, Train_acc:98.3%, Train_loss:0.048, Test_acc:89.3%, Test_loss:0.492, Lr:1.00E-04
Epoch:37, Train_acc:98.0%, Train_loss:0.054, Test_acc:91.1%, Test_loss:0.355, Lr:1.00E-04
Epoch:38, Train_acc:98.6%, Train_loss:0.060, Test_acc:92.4%, Test_loss:0.402, Lr:1.00E-04
Epoch:39, Train_acc:97.9%, Train_loss:0.065, Test_acc:86.7%, Test_loss:0.498, Lr:1.00E-04
Epoch:40, Train_acc:98.0%, Train_loss:0.055, Test_acc:88.4%, Test_loss:0.514, Lr:1.00E-04
Epoch:41, Train_acc:99.1%, Train_loss:0.029, Test_acc:90.7%, Test_loss:0.381, Lr:1.00E-04
Epoch:42, Train_acc:98.0%, Train_loss:0.069, Test_acc:92.4%, Test_loss:0.377, Lr:1.00E-04
Epoch:43, Train_acc:99.4%, Train_loss:0.021, Test_acc:90.2%, Test_loss:0.403, Lr:1.00E-04
Epoch:44, Train_acc:98.0%, Train_loss:0.055, Test_acc:85.3%, Test_loss:0.686, Lr:1.00E-04
Epoch:45, Train_acc:98.0%, Train_loss:0.074, Test_acc:91.1%, Test_loss:0.321, Lr:1.00E-04
Epoch:46, Train_acc:98.6%, Train_loss:0.038, Test_acc:91.6%, Test_loss:0.426, Lr:1.00E-04
Epoch:47, Train_acc:97.4%, Train_loss:0.075, Test_acc:87.1%, Test_loss:0.604, Lr:1.00E-04
Epoch:48, Train_acc:99.6%, Train_loss:0.027, Test_acc:91.6%, Test_loss:0.379, Lr:1.00E-04
Epoch:49, Train_acc:99.8%, Train_loss:0.007, Test_acc:92.4%, Test_loss:0.381, Lr:1.00E-04
Epoch:50, Train_acc:100.0%, Train_loss:0.007, Test_acc:92.9%, Test_loss:0.361, Lr:1.00E-04
Epoch:51, Train_acc:99.8%, Train_loss:0.018, Test_acc:90.7%, Test_loss:0.446, Lr:1.00E-04
Epoch:52, Train_acc:99.0%, Train_loss:0.032, Test_acc:89.8%, Test_loss:0.588, Lr:1.00E-04
Epoch:53, Train_acc:97.7%, Train_loss:0.060, Test_acc:90.7%, Test_loss:0.456, Lr:1.00E-04
Epoch:54, Train_acc:97.7%, Train_loss:0.059, Test_acc:89.8%, Test_loss:0.506, Lr:1.00E-04
Epoch:55, Train_acc:98.6%, Train_loss:0.046, Test_acc:90.7%, Test_loss:0.350, Lr:1.00E-04
Epoch:56, Train_acc:99.7%, Train_loss:0.010, Test_acc:91.6%, Test_loss:0.349, Lr:1.00E-04
Epoch:57, Train_acc:99.6%, Train_loss:0.012, Test_acc:91.6%, Test_loss:0.369, Lr:1.00E-04
Epoch:58, Train_acc:98.9%, Train_loss:0.053, Test_acc:88.9%, Test_loss:0.666, Lr:1.00E-04
Epoch:59, Train_acc:98.2%, Train_loss:0.054, Test_acc:87.1%, Test_loss:0.509, Lr:1.00E-04
Epoch:60, Train_acc:98.7%, Train_loss:0.037, Test_acc:90.2%, Test_loss:0.513, Lr:1.00E-04
Done

5、结果显示和评估

1、结果显示

import matplotlib.pyplot as plt #隐藏警告和显示中文
import warnings
warnings.filterwarnings("ignore")               #忽略警告信息
plt.rcParams['font.sans-serif']    = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False      # 用来正常显示负号
plt.rcParams['figure.dpi']         = 100        #分辨率x = range(epochs)
# 创建画板
plt.figure(figsize=(12, 3))
# 子图一
plt.subplot(1, 2, 1)
plt.plot(x, train_acc, label='Train Accurary')
plt.plot(x, test_acc, label='Test Accurary')
plt.legend(loc='lower right')
plt.title("Train and test Accurary")
# 子图二
plt.subplot(1, 2, 2)
plt.plot(x, train_loss, label='Train loss')
plt.plot(x, test_loss, label='Test loss')
plt.legend(loc='upper right')
plt.title("Train and test Loss")plt.show()

在这里插入图片描述

👀 解释：

总体效果还是不错的，损失率低于1，但是测试集的损失率有点小小不稳定；
准确率：刚开始出现了欠拟合的现象，但是后面好了，训练准确率稳定在100%附件(98%、99%等)，测试集稳定在90%附件；
整体：yolov5这个用于目标检测的网络用语目标识别也是有不错的效果。

2、评估

best_model.load_state_dict(torch.load(PATH, map_location=device))
epoch_test_acc, epoch_test_loss = test(test_dl, best_model, loss_fn)epoch_test_acc, epoch_test_loss

(0.9022222222222223, 0.5129974257313852)

准确率在0.9左右，效果良好，且损失率为0.5，低于1.0。

深度学习基础--将yolov5的backbone模块用于目标识别会出现怎么效果呢？？

文章目录

案例

1、数据处理

1、导入库

2、查看数据类别

3、导入数据

4、数据集划分

5、展示一批数据

2、模型构建

3、模型训练

1、构建训练集

2、构建测试集

3、设置超参数

4、模型正式训练

5、结果显示和评估

1、结果显示

2、评估

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