1.4：YOLOv8的知识蒸馏：学习将大型模型的知识迁移到小型模型（课程共6100字，4段代码举例，带详细操作步骤）

代码例子1: YOLOv8模型训练

代码例子2: YOLOv8模型知识蒸馏

代码例子3: YOLOv8模型部署

代码例子4: YOLOv8模型性能评估

import torch
from torchvision.models.detection import yolo_v3

# 加载训练好的YOLOv8模型
model = yolo_v3()
model.load_state_dict(torch.load('path/to/model_weights.pth'))
model.eval()

# 加载测试集数据进行测试
test_dataset = load_dataset('path/to/test_data')
total_correct = 0
total_samples = len(test_dataset)

# 逐个样本进行预测并计算准确率
for image, target in test_dataset:
    output = model([image])
    predicted = process_output(output)
    correct = calculate_correct(predicted, target)
    total_correct += correct

accuracy = total_correct / total_samples
print("模型准确率：", accuracy)

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