第一步：Win7 配置 Python 虚拟环境
1. 安装 Python 3.8.10
- 下载地址：https://www.python.org/downloads/release/python-3810/
- 选择
Windows x86-64 executable installer，安装时必须勾选Add Python 3.8 to PATH。
2. 创建并激活虚拟环境
-
打开 cmd 命令行，执行以下命令创建项目文件夹和虚拟环境： bash
运行
# 新建项目根目录 mkdir fruit_ncnn_project && cd fruit_ncnn_project # 创建虚拟环境（venv_fruit 为环境名） python -m venv venv_fruit -
激活虚拟环境： bash
运行
# Win7 cmd 命令 venv_fruit\Scripts\activate.bat激活后命令行前缀会出现
(venv_fruit)，表示进入隔离环境。
3. 安装 Win7 兼容的依赖库
激活虚拟环境后，执行以下命令安装指定版本依赖，避免兼容性问题：
bash
运行
# 升级 pip 到兼容版本
python -m pip install --upgrade pip==21.3.1
# 安装 PyTorch + TorchVision CPU 版（Win7 最高兼容版本）
pip install torch==1.12.1+cpu torchvision==0.13.1+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
# 安装其他必备库
pip install pillow==9.5.0 numpy==1.23.5 opencv-python==4.5.5.644. 退出虚拟环境（训练完成后执行）
bash
运行
deactivate第二步：训练模型并生成 .pth 文件
注意：运行前需将 Fruits-360 数据集放在
./datasets目录下，确保Training和Test子文件夹存在。全程在激活的虚拟环境中执行。
训练代码（fruit_train.py）
python
运行
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader
# ==================== 配置项 ====================
TRAIN_DIR = "./datasets/Training" # 数据集训练集路径
TEST_DIR = "./datasets/Test" # 数据集测试集路径
MODEL_SAVE_PATH = "fruit_mobilenetv2.pth" # 模型保存路径
NUM_EPOCHS = 15 # 训练轮数
BATCH_SIZE = 32 # 批次大小
# ================================================
# 1. 数据预处理（适配 Fruits-360 图像尺寸）
transform = transforms.Compose([
transforms.Resize((100, 100)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# 2. 加载数据集（Win7 必须设 num_workers=0）
train_dataset = ImageFolder(TRAIN_DIR, transform=transform)
test_dataset = ImageFolder(TEST_DIR, transform=transform)
num_classes = len(train_dataset.classes) # 自动适配 208 类
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=0)
test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=0)
# 3. 加载预训练 MobileNetV2 并修改分类头（迁移学习）
from torchvision import models
model = models.mobilenet_v2(pretrained=True)
model.classifier[1] = nn.Linear(model.last_channel, num_classes)
model = model.to("cpu") # 强制 CPU 训练
# 4. 训练配置
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 5. 训练+验证循环
print(f"开始训练 {num_classes} 类水果分类模型...")
for epoch in range(NUM_EPOCHS):
# 训练阶段
model.train()
train_loss = 0.0
for imgs, labels in train_loader:
optimizer.zero_grad()
outputs = model(imgs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
train_loss += loss.item() * imgs.size(0)
# 验证阶段
model.eval()
test_acc = 0.0
with torch.no_grad():
for imgs, labels in test_loader:
outputs = model(imgs)
_, preds = torch.max(outputs, 1)
test_acc += torch.sum(preds == labels.data)
# 打印日志
avg_loss = train_loss / len(train_dataset)
avg_acc = test_acc.double() / len(test_dataset)
print(f"Epoch [{epoch+1}/{NUM_EPOCHS}] | Loss: {avg_loss:.4f} | Test Acc: {avg_acc:.4f}")
# 6. 保存训练好的模型
torch.save(model.state_dict(), MODEL_SAVE_PATH)
print(f"\n模型训练完成，已保存至 {MODEL_SAVE_PATH}")阿雪技术观
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