# image_class

**Repository Path**: chde222/image_class

## Basic Information

- **Project Name**: image_class
- **Description**: 基于keras集成多种图像分类模型： VGG16、VGG19、InceptionV3、Xception、MobileNet、AlexNet、LeNet、ZF_Net、ResNet18、ResNet34、ResNet50、ResNet_101、ResNet_152、DenseNet
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 2
- **Forks**: 1
- **Created**: 2019-12-11
- **Last Updated**: 2024-01-10

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 图像分类集成以下模型：ResNet18、ResNet34、ResNet50、ResNet101、ResNet152、 VGG16、VGG19、InceptionV3、Xception、MobileNet、AlexNet、LeNet、ZF_Net、DenseNet，在config.py里面选择使用哪种模型，目前本人亲测，残差网络resnet的效果比较好。

## the project apply the following models:


* VGG16
* VGG19
* InceptionV3
* Xception
* MobileNet
* AlexNet
* LeNet
* ZF_Net
* ResNet18
* ResNet34
* ResNet50
* ResNet101
* ResNet152
* DenseNet(dismissed this time)


## your train or test datasets folder should be:


####  classes name contained in folder name
__"train and test data set folder is:"__

/path/classes1/cat*.jpg,

/path/classes2/dog*.jpg,

/path/classes3/people*.jpg,

/path/classes4/*.jpg,


* Attentions ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
* classes name must be contained in folder name 

## environment
My environment is based on 
* __ubuntu16__ 
* __cuda8__ (__cuda9.0__)
* __tensorflow_gpu1.4__ (__tensorflow_gpu1.10__ )
* __keras2.0.8__
* __numpy__
* __tqdm__
* __opencv-python__
* __scikit-learn__
### Install packages
* pip3 install tensorflow_gpu==1.4
* pip3 install keras==2.0.8
* pip3 install numpy
* pip3 install tqdm
* pip3 install opencv-python
* pip3 install scikit-learn

# step1: train or test dataset prepare
* python3 mk_class_idx.py
# step2: train your model
* train model: python train.py
# step3: predict with model 
* predict model: python predict.py model_name classes_name

### Any Questions???
Author email: mymailwith163@163.com