yolov8训练测试程序

以下是一个简单的 Yolov8 训练和测试程序的示例代码：

import torch
from torchvision import datasets
from torch.utils.data import DataLoader
from torchvision import transforms
from models import Yolov8  # 导入 Yolov8 模型
from utils import load_classes, non_max_suppression, plot_boxes

# 设置训练和测试数据集路径
train_data_path = "path/to/train/data"
test_data_path = "path/to/test/data"

# 设置训练超参数
batch_size = 16
num_epochs = 10
learning_rate = 0.001

# 加载数据集并进行预处理
transform = transforms.Compose([
    transforms.Resize((416, 416)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

train_dataset = datasets.ImageFolder(train_data_path, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

test_dataset = datasets.ImageFolder(test_data_path, transform=transform)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 加载类别标签
class_names = load_classes("path/to/class/labels")

# 初始化模型并定义损失函数和优化器
model = Yolov8(num_classes=len(class_names))
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    for images, targets in train_loader:
        outputs = model(images)
        loss = criterion(outputs, targets)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}")

# 在测试集上评估模型性能
model.eval()
with torch.no_grad():
    for images, targets in test_loader:
        outputs = model(images)
        boxes = non_max_suppression(outputs)

        plot_boxes(images, boxes, class_names)

请注意，这只是一个简单的示例代码，实际使用 Yolov8 进行训练和测试需要更多的配置和调整。你需要根据自己的数据集和需求进行相应的修改和优化。同时，你还需要下载 Yolov8 模型文件、定义模型结构和辅助函数等。这个示例代码提供了一个基本的框架，供你参考和扩展。