image_classification_keras

• Multi image label classification by multi models in keras.
• Copied from tslgithub/image_class and add new models.

support these models：ResNet18、ResNet34、ResNet50、ResNet101、ResNet152、VGG16、VGG19、InceptionV3、Xception、MobileNet、AlexNet、LeNet、ZF_Net、DenseNet、mnist_net、TSL16、InceptionResNetV2、Shufflenet、NASNet、EfficientNet，choose model in config.py.

the project apply the following models:

• VGG16
• VGG19
• InceptionV3
• Xception
• MobileNet
• AlexNet
• LeNet
• ZF_Net
• ResNet18
• ResNet34
• ResNet50
• ResNet101
• ResNet152
• DenseNet(dismissed this time)
• mnist_net
• TSL16
• InceptionResNetV2
• Shufflenet
• NASNet
• EfficientNet

Train or test dataset

classes name contained in folder name

"training or testing dataset 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
• pip3 install segmentation_models
• pip3 install keras_efficientnets

Conclusion

1.confirm config.py

• choose model and change parameter in config.py

2.train or test dataset prepare

• python3 mk_class_idx.py

3.train your model

• Train sigle model : python3 train.py modelName
• Train All model : run " sh trainAll.sh " to train all model (in ubuntu)
• Tensorboard : take LeNet as example, run " tensorboard --logdir=./checkpoints/LeNet " to watch training with tensorboard

4.predict your model

• predict model: python3 predict.py modelName classesName

5.result of mnist dataset(number 0~9)