基于卷积神经网络的苹果叶片病虫害识别与防治系统，vgg16，resnet，swintransformer，模型融合（pytorch框架，python代码）
更多图像分类、图像识别、目标检测等项目可从主页查看
功能演示：
苹果病虫害识别与防治系统，vgg16，resnet，swintransformer，模型融合，卷积神经网络（pytorch框架，python代码）_哔哩哔哩_bilibili
（一）简介基于卷积神经网络的苹果叶片病虫害识别与防治系统是在pytorch框架下实现的，项目中有4个模型，前3个为VGG16、ResNet50、SwinTransformer，最后一个为前面3个模型的融合（预测结果的融合，提高系统预测结果的可靠性），各个模型之间可对比分析，工作量充足。
界面可实现各个模型的切换，并且可以保存每次识别结果，生成识别报告
该系统涉及的技术栈：python + pyqt5 + opencv
该项目是在pycharm和anaconda搭建的虚拟环境执行，pycharm和anaconda安装和配置可观看教程：
超详细的pycharm+anaconda搭建python虚拟环境_pycharm配置anaconda虚拟环境-CSDN博客
pycharm+anaconda搭建python虚拟环境_哔哩哔哩_bilibili
（二）项目介绍 1. 项目结构
部分数据展示：
class MainProcess:
def __init__(self, train_path, test_path, model_name):
self.train_path = train_path
self.test_path = test_path
self.model_name = model_name
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def main(self, epochs):
log_file_name = './results/vgg16训练和验证过程.txt'
sys.stdout = Logger(log_file_name)
print("using {} device.".format(self.device))
begin_time = time()
train_loader, validate_loader, class_names, train_num, val_num = self.data_load()
print("class_names: ", class_names)
train_steps = len(train_loader)
val_steps = len(validate_loader)
model = self.model_load()
x = torch.randn(16, 3, 224, 224)
model_visual_path = 'results/vgg16_visual.onnx'
torch.onnx.export(model, x, model_visual_path)
model_weight_path = "models/vgg16-pre.pth"
assert os.path.exists(model_weight_path), "file {} does not exist.".format(model_weight_path)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu'))
model.classifier[-1] = nn.Linear(4096, len(class_names), bias=True)
model.to(self.device)
loss_function = nn.CrossEntropyLoss()
params = [p for p in model.parameters() if p.requires_grad]
optimizer = optim.Adam(params=params, lr=0.0001)
train_loss_history, train_acc_history = [], []
test_loss_history, test_acc_history = [], []
best_acc = 0.0
for epoch in range(0, epochs):
model.train()
running_loss = 0.0
train_acc = 0.0
train_bar = tqdm(train_loader, file=sys.stdout)
for step, data in enumerate(train_bar):
images, labels = data
optimizer.zero_grad()
outputs = model(images.to(self.device))
train_loss = loss_function(outputs, labels.to(self.device))
train_loss.backward()
optimizer.step()
running_loss += train_loss.item()
predict_y = torch.max(outputs, dim=1)[1]
train_acc += torch.eq(predict_y, labels.to(self.device)).sum().item()
train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(epoch + 1,
epochs,
train_loss)
model.eval()
val_acc = 0.0
testing_loss = 0.0
with torch.no_grad():
val_bar = tqdm(validate_loader, file=sys.stdout)
for val_data in val_bar:
val_images, val_labels = val_data
outputs = model(val_images.to(self.device))
val_loss = loss_function(outputs, val_labels.to(self.device))
testing_loss += val_loss.item()
predict_y = torch.max(outputs, dim=1)[1]
val_acc += torch.eq(predict_y, val_labels.to(self.device)).sum().item()
train_loss = running_loss / train_steps
train_accurate = train_acc / train_num
test_loss = testing_loss / val_steps
val_accurate = val_acc / val_num
train_loss_history.append(train_loss)
train_acc_history.append(train_accurate)
test_loss_history.append(test_loss)
test_acc_history.append(val_accurate)
print('[epoch %d] train_loss: %.3f val_accuracy: %.3f' %
(epoch + 1, train_loss, val_accurate))
if val_accurate > best_acc:
best_acc = val_accurate
torch.save(model.state_dict(), self.model_name)
end_time = time()
run_time = end_time - begin_time
print('该循环程序运行时间：', run_time, "s")
self.show_loss_acc(train_loss_history, train_acc_history,
test_loss_history, test_acc_history)
self.heatmaps(model, validate_loader, class_names)
该系统可以训练自己的数据集，训练过程也比较简单，只需指定自己数据集中训练集和测试集的路径，训练后模型名称和指定训练的轮数即可
以上即为整个项目的介绍，整个项目主要包括以下内容：完整的程序代码文件、训练好的模型、数据集、UI界面和各种模型指标图表等。
项目运行过程如出现问题，请及时交流！
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