图像分类集成以下模型： VGG16、VGG19、InceptionV3、Xception、MobileNet、AlexNet、LeNet、ZF_Net、ResNet18、ResNet34、ResNet50、ResNet101、ResNet152、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:

0,1,2,3 is classes name or folder name,whose path is,and must start with '0'

"training data set folder is:"

/dataset/train/0/cat*.jpg,

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

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

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

"testing data set folder is:"

/dataset/test/0/cat*.jpg,

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

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

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

• Attentions ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
• classes name ‘0,1,2,3’ or folder name must be number,and must start with '0'

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

train and predict your model

• train model: python train.py model_name

• predict model: python predict.py model_name classes_name

• (Atttention:classes_name should be 0,1,2,3,........)

Any Questions???

Author email: [email protected]