VGG16网络英文全程
时间: 2024-04-15 20:22:59 浏览: 26
VGG16 is a convolutional neural network architecture that was proposed by the Visual Geometry Group (VGG) at the University of Oxford. It is named after the group and the number of layers it has, which is 16. VGG16 is widely used for image classification tasks.
The full name of VGG16 is "Very Deep Convolutional Networks for Large-Scale Image Recognition". It was introduced in the paper titled "Very Deep Convolutional Networks for Large-Scale Image Recognition" by Karen Simonyan and Andrew Zisserman in 2014.
VGG16 consists of 16 layers, including 13 convolutional layers and 3 fully connected layers. The convolutional layers are responsible for extracting features from the input image, while the fully connected layers are used for classification. VGG16 has a fixed input size of 224x224 pixels.
The architecture of VGG16 is characterized by its simplicity and uniformity. It uses small 3x3 convolutional filters throughout the network, which helps in capturing local patterns effectively. The network also uses max poo
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