VGG16-From-Scratch-and-Built_in
所属分类：工具库
开发工具：Jupyter Notebook
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上传日期：2024-01-25 20:47:22
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sh-1993
说明： 该项目实现了用于图像分类的强大VGG-16卷积神经网络，通过3x3滤波器、相同填充、步幅为1和2x2最大池来展示其效率，以在不同图像中实现卓越的模式识别。
(This project implements the powerful VGG-16 convolutional neural network for image classification, showcasing its efficiency with 3x3 filters, same padding, stride of 1, and 2x2 max-pooling for superior pattern recognition in diverse images.)
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Applying VGG16 (From Scratch and Built_in).ipynb
## VGG-16 Image Classification
## Overview
This repository implements the VGG-16 convolutional neural network for image classification. Developed by the Visual Graphics Group at Oxford, VGG-16 is renowned for its deep architecture using small 3x3 convolutional filters. The project aims to showcase the model's prowess in recognizing and classifying intricate patterns in diverse images.
## VGG-16 Architecture
VGG-16 boasts 16 layers, including 13 convolutional and 3 fully connected layers. Notably, the architecture utilizes 3x3 convolutional filters with same padding and a stride of 1. Max-pooling layers with a 2x2 filter and a stride of 2 enable the model to capture fine-grained details in images.
## Model Summary
The VGG-16 model delivers robust performance with high accuracy in classifying various patterns within images. The simplicity and effectiveness of smaller filters contribute to their ability to discern intricate details. Notably, when compared to AlexNet, VGG-16 has a lower number of parameters, making it more computationally efficient.
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