AI人工智能与计算机视觉：打造智能视觉系统

    import torch
    import torch.nn as nn
    import torch.nn.functional as F
    
    class SimpleCNN(nn.Module):
    def __init__(self, num_classes=10):
        super(SimpleCNN, self).__init__()
        # 卷积层1: 输入1通道，输出32通道，卷积核3x3
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
        # 卷积层2: 输入32通道，输出64通道，卷积核3x3
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        # 最大池化层
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        # 全连接层1: 输入64*7*7，输出128
        self.fc1 = nn.Linear(64 * 7 * 7, 128)
        # 全连接层2: 输入128，输出类别数
        self.fc2 = nn.Linear(128, num_classes)
        
    def forward(self, x):
        # 第一层卷积 + ReLU + 池化
        x = self.pool(F.relu(self.conv1(x)))
        # 第二层卷积 + ReLU + 池化
        x = self.pool(F.relu(self.conv2(x)))
        # 展平特征图
        x = x.view(-1, 64 * 7 * 7)
        # 全连接层 + ReLU
        x = F.relu(self.fc1(x))
        # 输出层
        x = self.fc2(x)
        return x
    
    # 创建模型实例
    model = SimpleCNN(num_classes=10)
    print(model)
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

    conda create -n cv_env python=3.8
    conda activate cv_env
    pip install torch torchvision opencv-python matplotlib
    
    
      
      
      
    

    import torch
    import torchvision
    import torchvision.transforms as transforms
    import torch.nn as nn
    import torch.optim as optim
    import matplotlib.pyplot as plt
    import numpy as np
    
    # 数据预处理
    transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
    ])
    
    # 加载CIFAR-10数据集
    trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
    trainloader = torch.utils.data.DataLoader(trainset, batch_size=32,
                                         shuffle=True, num_workers=2)
    
    testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
    testloader = torch.utils.data.DataLoader(testset, batch_size=32,
                                        shuffle=False, num_workers=2)
    
    classes = ('plane', 'car', 'bird', 'cat', 'deer', 
           'dog', 'frog', 'horse', 'ship', 'truck')
    
    # 定义CNN模型
    class CIFAR10CNN(nn.Module):
    def __init__(self):
        super(CIFAR10CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(64 * 8 * 8, 512)
        self.fc2 = nn.Linear(512, 10)
        self.dropout = nn.Dropout(0.25)
        
    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 64 * 8 * 8)
        x = self.dropout(x)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x
    
    # 初始化模型、损失函数和优化器
    model = CIFAR10CNN()
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)
    
    # 训练模型
    for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        
        optimizer.zero_grad()
        
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        
        running_loss += loss.item()
        if i % 500 == 499:
            print(f'Epoch {epoch + 1}, Batch {i + 1}, Loss: {running_loss / 500:.3f}')
            running_loss = 0.0
    
    print('Finished Training')
    
    # 测试模型
    correct = 0
    total = 0
    with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
    
    print(f'Accuracy on test set: {100 * correct / total:.2f}%')
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

 * 使用`transforms`对图像进行归一化处理
 * 将像素值从[0,1]范围归一化到[-1,1]范围

 * 两个卷积层，分别使用32和64个3x3卷积核
 * 每个卷积层后接ReLU激活和2x2最大池化
 * 全连接层前添加Dropout防止过拟合

 * 使用Adam优化器，学习率设为0.001
 * 交叉熵损失函数
 * 训练10个epoch，每个batch包含32张图像

 * 在测试集上计算分类准确率
 * 使用`torch.max`获取预测类别

 * X光、CT、MRI图像的自动分析
 * 疾病检测和诊断辅助
 * 手术导航系统

 * 道路和障碍物检测
 * 交通标志识别
 * 行人检测

 * 产品质量自动检测
 * 生产线监控
 * 缺陷识别

 * 人脸识别门禁系统
 * 异常行为检测
 * 人群密度分析

 * 顾客行为分析
 * 自动结账系统
 * 货架商品识别

 * 作物健康监测
 * 病虫害识别
 * 自动收割机器人

 * Vision Transformer (ViT)等模型在计算机视觉领域取得突破
 * 逐步挑战CNN的传统统治地位

 * 视觉与语言、语音等多模态数据的联合学习
 * CLIP等模型展示的强大泛化能力

 * 减少对标注数据的依赖
 * 通过对比学习等方法学习通用视觉表示

 * 模型压缩和量化技术
 * 适用于移动设备和IoT设备的轻量级模型

 * 点云处理和理解
 * 三维场景重建和分析

 * 训练数据中的偏见可能导致模型歧视
 * 如何确保AI系统的公平性和包容性

 * 精心设计的扰动可能欺骗视觉系统
 * 提高模型的鲁棒性和安全性

 * 深度学习模型的"黑箱"特性
 * 开发可解释的AI技术

 * 大型模型训练的高能耗问题
 * 寻找更高效的训练和推理方法

 * 视觉数据中的敏感信息保护
 * 联邦学习等隐私保护技术的应用

 * PyTorch官方文档: https://pytorch.org/docs/stable/index.html
 * OpenCV官方文档: https://docs.opencv.org/
 * TensorFlow官方文档: https://www.tensorflow.org/api_docs

 * Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. NIPS.
 * Vaswani, A., et al. (2017). Attention is all you need. NIPS.
 * Dosovitskiy, A., et al. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. ICLR.

 * Detectron2: https://github.com/facebookresearch/detectron2
 * MMDetection: https://github.com/open-mmlab/mmdetection
 * Transformers: https://github.com/huggingface/transformers

 * ImageNet: http://www.image-net.org/
 * COCO: https://cocodataset.org/
 * Kaggle: https://www.kaggle.com/datasets

 * Stack Overflow: https://stackoverflow.com/
 * PyTorch论坛: https://discuss.pytorch.org/
 * Reddit计算机视觉板块: https://www.reddit.com/r/computervision/