yolov8用数据集训练权重代码

YOLOv8是一种目标检测算法，它是YOLO（You Only Look Once）系列的最新版本。要使用YOLOv8进行目标检测，需要先准备好训练数据集，并使用该数据集训练权重。下面是一个示例代码，展示了如何使用YOLOv8和数据集进行训练：

# 导入必要的库
import torch
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision import transforms
from models import Darknet
from utils import load_classes, weights_init_normal, parse_data_config
from utils import non_max_suppression, compute_ap

# 设置训练参数
epochs = 100
batch_size = 16
learning_rate = 0.001
momentum = 0.9
weight_decay = 0.0005

# 加载数据集
data_config = parse_data_config("data.data")
train_path = data_config["train"]
valid_path = data_config["valid"]
class_names = load_classes(data_config["names"])

# 定义数据转换
transform = transforms.Compose([
    transforms.Resize((416, 416)),
    transforms.ToTensor(),
])

# 加载训练集和验证集
train_dataset = datasets.ImageFolder(train_path, transform=transform)
valid_dataset = datasets.ImageFolder(valid_path, transform=transform)

# 创建数据加载器
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
valid_loader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False)

# 创建模型
model = Darknet("cfg/yolov3.cfg")
model.apply(weights_init_normal)

# 定义损失函数和优化器
criterion = torch.nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=momentum, weight_decay=weight_decay)

# 开始训练
for epoch in range(epochs):
    model.train()
    for batch_i, (imgs, targets) in enumerate(train_loader):
        imgs = imgs.to(device)
        targets = targets.to(device)

        # 前向传播
        outputs = model(imgs)
        loss = criterion(outputs, targets)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    # 在验证集上评估模型
    model.eval()
    with torch.no_grad():
        # 进行预测
        detections = []
        for batch_i, (imgs, targets) in enumerate(valid_loader):
            imgs = imgs.to(device)
            targets = targets.to(device)

            outputs = model(imgs)
            detections.extend(non_max_suppression(outputs, conf_thres=0.5, nms_thres=0.4))

        # 计算平均精度（AP）
        ap = compute_ap(detections, targets)

    # 打印训练进度
    print(f"Epoch {epoch+1}/{epochs}, Loss: {loss.item()}, AP: {ap}")

# 保存训练好的权重
torch.save(model.state_dict(), "yolov8_weights.pth")

这段代码假设你已经准备好了数据集，并且将其按照YOLOv3的数据集格式进行了组织。你需要根据自己的数据集路径和配置文件进行相应的修改。此外，你还需要确保已经安装了必要的库和模型文件。