[代码实战和详解]VGG16

VGG16 详解

我的github代码实现：vgg16 我们在vgg16神经网络上训练了SIGNS数据集，这是一个分类的数据集，在我的github上有介绍怎么下载数据集以及如何训练。

VGG16是一个卷积神经网络（CNN）架构，它在2014年的ILSVR（Imagenet）比赛中获胜。它被认为是迄今为止最优秀的视觉模型之一。VGG16最独特的地方在于，它不是使用大量的超参数，而是专注于使用3x3过滤器的卷积层，步幅为1，并始终使用相同的填充和2x2过滤器的最大池层。它始终在整个架构中一致地遵循这种卷积和最大池层的排列方式。最后，它有2个全连接层，后跟一个softmax输出。

VGG16网络使用pytorch实现

class VGG16(nn.Module): def __init__(self, num_classes=6): super(VGG16, self).__init__() self.features = nn.Sequential( nn.Conv2d(3, 64, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(64, 64, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(64, 128, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(128, 128, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(128, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(256, 512, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(512, 512, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(512, 512, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(512, 512, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(512, 512, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(512, 512, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2) ) self.avgpool = nn.AdaptiveAvgPool2d((7, 7)) self.classifier = nn.Sequential( nn.Linear(512 * 7 * 7, 4096), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(4096, 1000), nn.Linear(1000, num_classes), ) def forward(self, x): x = self.features(x) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.classifier(x) return x