基于深度学习的13种通用图像分类模型及其实现

基于深度学习的13种通⽤图像分类模型及其实现

image classification with deep learning model： VGG16、VGG19、InceptionV3、Xception、MobileNet、AlexNet、LeNet、ZF_Net、ResNet18、ResNet34、ResNet50、ResNet_101、ResNet_152

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无线图像传输项⽬集成了 VGG16、VGG19、InceptionV3、Xception、MobileNet、AlexNet、LeNet、ZF_Net、ResNet18、ResNet34、ResNet50、ResNet_101、ResNet_152等13种图像分类模型作图像分类，依据测试结果来看，残差⽹络的分类准确率最⾼，分类效果最好

项⽬地址

雨水弃流井the project apply the following models:

VGG16

VGG19

InceptionV3

Xception

MobileNet

AlexNet

LeNet

ZF_Net

ResNet18

ResNet34

ResNet50

ResNet_101

ResNet_152

your train or test datasets folder should be:

/dataset/train/

cat4.jpg,

cat5.jpg,

cat100.jpg

cat1000.jpg

cat.jpg,

cat2.jpg,

cat3.jpg,

cat4.jpg,

cat5.jpg,

cat100.jpg

cat1000.jpg

cat.jpg,

cat2.jpg,

cat3.jpg,

cat4.jpg,

cat5.jpg,

cat100.jpg

cat1000.jpg

cat.jpg,

cat2.jpg,

cat3.jpg,

cat4.jpg,通用模型

cat5.jpg,

cat100.jpg热力井

cat1000.jpg /dataset/test/

cat4.jpg,

cat5.jpg,

cat100.jpg

cat1000.jpg

cat.jpg,

cat2.jpg,

cat3.jpg,

cat4.jpg,

cat5.jpg,

cat100.jpg

cat1000.jpg

cat.jpg,

cat2.jpg,

cat3.jpg,

cat4.jpg,

cat5.jpg,

cat100.jpg

cat1000.jpg

cat.jpg,

cat2.jpg,

cat3.jpg,课堂教学模式

cat4.jpg,

cat5.jpg,

cat100.jpg

cat1000.jpg

1,2,3,4 is classes name or folder name,whose path is

"training data set folder is:"

/dataset/train/1/cat*.jpg,

/dataset/train/2/cat*.jpg,

再生胶生产设备/dataset/train/3/cat*.jpg,

/dataset/train/4/cat*.jpg,

"testing data set folder is:"

/dataset/test/1/cat*.jpg,

dataset/test/2/cat*.jpg,

/dataset/test/3/cat*.jpg,

/dataset/test/4/cat*.jpg,

Attention: classes name ‘1,2,3,4’ or folder name must be number, not string

environment

my environment is based on ubuntu16、cuda8、tensorflow_gpu1.4, all package needed can be installed with ‘pip3 install package_name’, and you can test which package is missed by run ‘python ’,then pip install the missed package

train and predict your model

train model: python

predict model: python predict

Any Questions

Author email:

本文发布于:2024-09-24 01:19:29，感谢您对本站的认可！

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