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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

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CosimoFang/image_class

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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

  • python3 train.py

step3: predict with model

  • python3 predict.py classes_name

Any Questions???

Author email: mymailwith163@163.com

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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

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