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)
