yolov8微调代码

以下是一个基于PyTorch的YOLOv8微调代码的示例：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import transforms
from dataset import CustomDataset
from model import YOLOv8

# 定义超参数
batch_size = 16
num_epochs = 10
learning_rate = 0.001

# 加载数据集
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 = CustomDataset('train.txt', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

# 加载模型
model = YOLOv8(num_classes=80)
model.load_state_dict(torch.load('yolov8n-seg.pt'))

# 冻结模型的前几层
for param in model.parameters():
    param.requires_grad = False
for param in model.backbone.parameters():
    param.requires_grad = False

# 修改模型的最后一层
model.head.conv[-1] = nn.Conv2d(256, 5 * 3, kernel_size=1, stride=1, padding=0)

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

# 训练模型
for epoch in range(num_epochs):
    for i, (images, targets) in enumerate(train_loader):
        # 前向传播
        outputs = model(images)

        # 计算损失
        loss = 0
        for j in range(3):
            loss += criterion(outputs[j][:, :5, :, :], targets[j][:, :5, :, :])
        loss /= 3

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

        # 打印日志
        if (i + 1) % 10 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch + 1, num_epochs, i + 1, len(train_loader), loss.item()))

# 保存模型
torch.save(model.state_dict(), 'yolov8n-finetune.pt')

其中，`CustomDataset`和`YOLOv8`分别是自定义的数据集和模型，需要根据实际情况进行修改。在微调过程中，我们首先加载预训练模型`yolov8n-seg.pt`，然后冻结模型的前几层，修改模型的最后一层，定义损失函数和优化器，最后进行训练并保存微调后的模型`yolov8n-finetune.pt`。