师生网上办事大厅与大模型训练中的商标智能识别技术探索

import torch

from torchvision import datasets, transforms

from torchvision.models import resnet18

# 数据预处理

transform = transforms.Compose([

transforms.Resize((224, 224)),

transforms.ToTensor(),

transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

])

# 加载数据集

train_dataset = datasets.ImageFolder(root='path/to/train', transform=transform)

train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)

# 定义模型

model = resnet18(pretrained=True)

num_ftrs = model.fc.in_features

model.fc = torch.nn.Linear(num_ftrs, len(train_dataset.classes))

# 定义损失函数和优化器

criterion = torch.nn.CrossEntropyLoss()

optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 训练循环

for epoch in range(10):

for inputs, labels in train_loader:

outputs = model(inputs)

loss = criterion(outputs, labels)

optimizer.zero_grad()

loss.backward()

optimizer.step()

print(f'Epoch {epoch+1} completed.')

print(f'Loss: {loss.item():.4f}')

print('-' * 10)

from flask import Flask, request, jsonify

import torch

from torchvision import transforms

from PIL import Image

import io

app = Flask(__name__)

# 加载训练好的模型

model = torch.load('model.pth')

model.eval()

# 图像预处理

transform = transforms.Compose([

transforms.Resize((224, 224)),

transforms.ToTensor(),

transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

])

@app.route('/predict', methods=['POST'])

def predict():

# 接收图片数据

file = request.files['image']

img_bytes = file.read()

img = Image.open(io.BytesIO(img_bytes))

img_tensor = transform(img).unsqueeze(0)

# 进行预测

with torch.no_grad():

outputs = model(img_tensor)

predicted_idx = outputs.argmax(dim=1).item()

# 获取类别名称

class_names = ['商标A', '商标B', '商标C'] # 假设我们有三个商标类别

result = {

'predicted_class': class_names[predicted_idx]

}

return jsonify(result)

if __name__ == '__main__':

app.run(host='0.0.0.0', port=5000)