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Scripts used in experiments to develop models for image classification

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kenken-neko/tfkeras_image_classification

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Image classification

This is an experimental script to perform image classification using Tensorflow's Keras and tensorflow_datasets.
The following arguments can be used for learning or evaluation.

Arguments

scripts/train.py

Argument name Description Default Data type
dataset_name Dataset name for tensorflow_datasets. None str
original_dataset_path Original dataset path. None str
dataset_size Number of images in train dataset. If it is set to -1, all train image in tensorflow_datasets will be used. -1 int
augmentation_times Increase the number of image by augmentation_times times by random augmentation. If set to 0, no image augmentation is performed. The augmentation process are horizontal flip and brightness adjustment. 0 int
augmentation_seed Seed value for the random augmentation. 0 int
valid_per_train Ratio of evaluation dataset to train dataset. 0.2 float
model_type Model name for image classification. Specify one of the pre-defined "SimpleCNN", "VGG16", and "Xception". SimpleCNN str
is_fine_tuning Specifies whether or not to perform fine-tuning. Fine-tuning is available only when model VGG16 or Xception is selected. store_true bool
is_dropout Whether dropout layer is included or not. store_true bool
epochs Number of training epochs. 10 int
batch_size Size of training batch. 32 int
optimizer Specify the optimization name in Keras optimizer. adam str

scripts/evaluate.py

Argument name Description Default Data type
dataset_name Dataset name for tensorflow_datasets. None str
original_dataset_path Original dataset path. None str
single_image_path Target single image path. None str
single_image_height Height of target single image. None int
single_image_width Width of target single image. None int
model_type Model name for image classification. Specify one of the pre-defined "SimpleCNN", "VGG16", and "Xception". SimpleCNN str

Sample docker commands

docker build . -t imageclass
docker run \
  -v $(pwd):/app \
  -it \
  --rm \
  imageclass \
  /bin/sh -c "python train.py  --dataset_name mnist"

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