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卷积神经网络实现图像识别

10月前 作者：是aaaa阿腾阿 分类：Toy博客 阅读(35) 违法举报

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项目简介

目的： 实现昆虫的图像分类，同时该模型也可以用于其他图像的分类识别，只需传入相应的训练集进行训练，保存为另一个模型即可，进行调用使用。
配置环境： pycharm（python3.7），导入pytotch库
知识预备： 需要了解卷积神经网络的基本原理与结构，熟悉pytorch的使用，csdn有很多介绍卷积神经网络的文章，可查阅。
例如：

https://blog.csdn.net/yunpiao123456/article/details/52437794
https://blog.csdn.net/weipf8/article/details/103917202

算法设计思路：
（1） 收集数据集，利用 python 的 requests 库和 bs4 进行网络爬虫，下载数据集
（2） 搭建卷积神经网络
（3）对卷积神经网络进行训练
（4） 改进训练集与测试集，并扩大数据集
（5） 保存模型
（6） 调用模型进行测试

项目效果展示

卷积神经网络实现图像识别
卷积神经网络实现图像识别
注，模型我达到的最高正确率在85%，最后稳定在79%，中间出现了过拟合，可减少训练次数进行优化，数据集较少的情况下，建议训练10次就可。

程序运行流程图

卷积神经网络实现图像识别

代码使用说明

先训练模型，进行模型保存之后可对模型进行调用，不用每使用一次模型就要进行训练。文末有项目的完整代码：修改自己的数据集src位置，一般情况下能正常运行，如果不能，请检查自己的第三方库是否成功安装，以及是否成功导入。若有问题可以私信交流学习。

数据集准备

注：由于爬虫，会有一些干扰数据，所以我这里展示的是进行数据清洗之后的数据。
注：训练集：测试集=7：3（可自己修改）
注：若正确率不理想，可扩大数据集，数据清洗，图片处理等方面进行改进

训练集

卷积神经网络实现图像识别
部分数据展示
卷积神经网络实现图像识别
卷积神经网络实现图像识别

测试集

文件格式与训练集一样。

搭建神经网络

框架：
卷积神经网络实现图像识别
结构：
卷积神经网络实现图像识别
代码实现：


# 定义网络
class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3)
        self.max_pool1 = nn.MaxPool2d(2)
        self.conv2 = nn.Conv2d(32, 64, 3)
        self.max_pool2 = nn.MaxPool2d(2)
        self.conv3 = nn.Conv2d(64, 64, 3)
        self.conv4 = nn.Conv2d(64, 64, 3)
        self.max_pool3 = nn.MaxPool2d(2)
        self.conv5 = nn.Conv2d(64, 128, 3)
        self.conv6 = nn.Conv2d(128, 128, 3)
        self.max_pool4 = nn.MaxPool2d(2)
        self.fc1 = nn.Linear(4608, 512)
        self.fc2 = nn.Linear(512, 1)

    def forward(self, x):
        in_size = x.size(0)
        x = self.conv1(x)
        x = F.relu(x)
        x = self.max_pool1(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = self.max_pool2(x)
        x = self.conv3(x)
        x = F.relu(x)
        x = self.conv4(x)
        x = F.relu(x)
        x = self.max_pool3(x)
        x = self.conv5(x)
        x = F.relu(x)
        x = self.conv6(x)
        x = F.relu(x)
        x = self.max_pool4(x)
        # 展开
        x = x.view(in_size, -1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.fc2(x)
        x = torch.sigmoid(x)
        return x

训练函数

def train(model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):

        data, target = data.to(device), target.to(device).float().unsqueeze(1)

        optimizer.zero_grad()

        output = model(data)

        # print(output)

        loss = F.binary_cross_entropy(output, target)

        loss.backward()

        optimizer.step()

        if (batch_idx + 1) % 1 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(

                epoch, (batch_idx + 1) * len(data), len(train_loader.dataset),

                       100. * (batch_idx + 1) / len(train_loader), loss.item()))

测试函数

def test(model, device, test_loader):
    model.eval()

    test_loss = 0

    correct = 0

    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device).float().unsqueeze(1)
            # print(target)
            output = model(data)
            # print(output)
            test_loss += F.binary_cross_entropy(output, target, reduction='mean').item()
            pred = torch.tensor([[1] if num[0] >= 0.5 else [0] for num in output]).to(device)
            correct += pred.eq(target.long()).sum().item()

        print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
            test_loss, correct, len(test_loader.dataset),
            100. * correct / len(test_loader.dataset)))

模型-训练过程完整代码

模型保存使用的是torch.save(model，src),model即须保存的模型，src即模型保存的位置，后缀为pth

import torch.nn.functional as F
import torch.optim as optim
import torch
import torch.nn as nn
import torch.nn.parallel
from PIL import Image
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets


# 设置超参数
#每次的个数
BATCH_SIZE = 20
#迭代次数
EPOCHS = 10
#采用cpu还是gpu进行计算
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# 数据预处理

transform = transforms.Compose([
    transforms.Resize(100),
    transforms.RandomVerticalFlip(),
    transforms.RandomCrop(50),
    transforms.RandomResizedCrop(150),
    transforms.ColorJitter(brightness=0.5, contrast=0.5, hue=0.5),
    transforms.ToTensor(),
    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
#导入训练数据
dataset_train = datasets.ImageFolder('D:\\cnn_net\\train\\insects', transform)

#导入测试数据
dataset_test = datasets.ImageFolder('D:\\cnn_net\\train\\test', transform)

test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=True)

# print(dataset_train.imgs)
# print(dataset_train[0])
# print(dataset_train.classes)
classess=dataset_train.classes #标签
class_to_idxes=dataset_train.class_to_idx #对应关系
print(class_to_idxes)
# print(dataset_train.class_to_idx)

train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True)
# for batch_idx, (data, target) in enumerate(train_loader):
#     # print(data)
#     print(target)
#     data, target = data.to(device), target.to(device).float().unsqueeze(1)
#     # print(data)
#     print(target)

# 定义网络
class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3)
        self.max_pool1 = nn.MaxPool2d(2)
        self.conv2 = nn.Conv2d(32, 64, 3)
        self.max_pool2 = nn.MaxPool2d(2)
        self.conv3 = nn.Conv2d(64, 64, 3)
        self.conv4 = nn.Conv2d(64, 64, 3)
        self.max_pool3 = nn.MaxPool2d(2)
        self.conv5 = nn.Conv2d(64, 128, 3)
        self.conv6 = nn.Conv2d(128, 128, 3)
        self.max_pool4 = nn.MaxPool2d(2)
        self.fc1 = nn.Linear(4608, 512)
        self.fc2 = nn.Linear(512, 1)

    def forward(self, x):
        in_size = x.size(0)
        x = self.conv1(x)
        x = F.relu(x)
        x = self.max_pool1(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = self.max_pool2(x)
        x = self.conv3(x)
        x = F.relu(x)
        x = self.conv4(x)
        x = F.relu(x)
        x = self.max_pool3(x)
        x = self.conv5(x)
        x = F.relu(x)
        x = self.conv6(x)
        x = F.relu(x)
        x = self.max_pool4(x)
        # 展开
        x = x.view(in_size, -1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.fc2(x)
        x = torch.sigmoid(x)
        return x

modellr = 1e-4

# 实例化模型并且移动到GPU

model = ConvNet().to(device)
print(model)
# 选择简单暴力的Adam优化器，学习率调低

optimizer = optim.Adam(model.parameters(), lr=modellr)
#调整学习率
def adjust_learning_rate(optimizer, epoch):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    modellrnew = modellr * (0.1 ** (epoch // 5))
    print("lr:", modellrnew)
    for param_group in optimizer.param_groups:
        param_group['lr'] = modellrnew

# 定义训练过程
def train(model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):

        data, target = data.to(device), target.to(device).float().unsqueeze(1)

        optimizer.zero_grad()

        output = model(data)

        # print(output)

        loss = F.binary_cross_entropy(output, target)

        loss.backward()

        optimizer.step()

        if (batch_idx + 1) % 1 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(

                epoch, (batch_idx + 1) * len(data), len(train_loader.dataset),

                       100. * (batch_idx + 1) / len(train_loader), loss.item()))

def test(model, device, test_loader):
    model.eval()

    test_loss = 0

    correct = 0

    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device).float().unsqueeze(1)
            # print(target)
            output = model(data)
            # print(output)
            test_loss += F.binary_cross_entropy(output, target, reduction='mean').item()
            pred = torch.tensor([[1] if num[0] >= 0.5 else [0] for num in output]).to(device)
            correct += pred.eq(target.long()).sum().item()

        print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
            test_loss, correct, len(test_loader.dataset),
            100. * correct / len(test_loader.dataset)))




# 训练
for epoch in range(1, EPOCHS + 1):
    adjust_learning_rate(optimizer, epoch)
    train(model, device, train_loader, optimizer, epoch)
    test(model, device, test_loader)

torch.save(model, 'D:\\cnn_net\\datas\\model_insects.pth')

模型-调用完整代码

模型调用使用，torch.load(src)


from PIL import Image

from torchvision import transforms
import torch.nn.functional as F

import torch
import torch.nn as nn
import torch.nn.parallel


# 定义网络
class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3)
        self.max_pool1 = nn.MaxPool2d(2)
        self.conv2 = nn.Conv2d(32, 64, 3)
        self.max_pool2 = nn.MaxPool2d(2)
        self.conv3 = nn.Conv2d(64, 64, 3)
        self.conv4 = nn.Conv2d(64, 64, 3)
        self.max_pool3 = nn.MaxPool2d(2)
        self.conv5 = nn.Conv2d(64, 128, 3)
        self.conv6 = nn.Conv2d(128, 128, 3)
        self.max_pool4 = nn.MaxPool2d(2)
        self.fc1 = nn.Linear(4608, 512)
        self.fc2 = nn.Linear(512, 1)

    def forward(self, x):
        in_size = x.size(0)
        x = self.conv1(x)
        x = F.relu(x)
        x = self.max_pool1(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = self.max_pool2(x)
        x = self.conv3(x)
        x = F.relu(x)
        x = self.conv4(x)
        x = F.relu(x)
        x = self.max_pool3(x)
        x = self.conv5(x)
        x = F.relu(x)
        x = self.conv6(x)
        x = F.relu(x)
        x = self.max_pool4(x)
        # 展开
        x = x.view(in_size, -1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.fc2(x)
        x = torch.sigmoid(x)
        return x


# 模型存储路径
# model_save_path = 'E:\\Cat_And_Dog\\kaggle\\model_insects.pth'

# ------------------------ 加载数据 --------------------------- #
# Data augmentation and normalization for training
# Just normalization for validation
# 定义预训练变换
# 数据预处理


class_names = ['瓢虫','螳螂',]  # 这个顺序很重要，要和训练时候的类名顺序一致

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# ------------------------ 载入模型并且训练 --------------------------- #
model = torch.load('D:\\cnn_net\\datas\\model_insects.pth')
model.eval()
# print(model)38，49

# image_PIL = Image.open('D:\\cnn_net\\train\\insects\\螳螂\\t28.jpg')
image_PIL = Image.open('D:\\cnn_net\\train\\insects\\瓢虫\\p49.jpg')
# image_PIL = Image.open('D:\\cnn_net\\train\\test\\01.jpg')
transform_test = transforms.Compose([
    transforms.Resize(100),
    transforms.RandomVerticalFlip(),
    transforms.RandomCrop(50),
    transforms.RandomResizedCrop(150),
    transforms.ColorJitter(brightness=0.5, contrast=0.5, hue=0.5),
    transforms.ToTensor(),
    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
    ])

image_tensor = transform_test(image_PIL)
    # 以下语句等效于 image_tensor = torch.unsqueeze(image_tensor, 0)
image_tensor.unsqueeze_(0)
    # 没有这句话会报错
image_tensor = image_tensor.to(device)

out = model(image_tensor)
# print(out)
pred = torch.tensor([[1] if num[0] >= 0.5 else [0] for num in out]).to(device)
print(class_names[pred])

注

有错误的地方欢迎大家交流学习，进行指正，一起学习进步。文章来源地址https://www.toymoban.com/news/detail-405625.html

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